Repository: Yzmblog/SurfD
Branch: main
Commit: 50826b077c11
Files: 112
Total size: 2.0 MB

Directory structure:
gitextract_su3bycqo/

├── AutoEncoder/
│   ├── cfg/
│   │   ├── deepfashion3d/
│   │   │   └── deepfashion3d.yaml
│   │   ├── pix3d/
│   │   │   └── pix3d.yaml
│   │   └── shapenet/
│   │       └── text2shape.yaml
│   ├── data/
│   │   ├── __init__.py
│   │   └── dataset.py
│   ├── dataset_info_files/
│   │   ├── Deepfashion3d/
│   │   │   ├── deepfashion3d_test.txt
│   │   │   └── deepfashion3d_train.txt
│   │   ├── Pix3d/
│   │   │   ├── test.txt
│   │   │   └── train.txt
│   │   ├── ShapeNet_filelists/
│   │   │   ├── 02691156_test.lst
│   │   │   ├── 02691156_train.lst
│   │   │   ├── 02828884_test.lst
│   │   │   ├── 02828884_train.lst
│   │   │   ├── 02933112_test.lst
│   │   │   ├── 02933112_train.lst
│   │   │   ├── 02958343_test.lst
│   │   │   ├── 02958343_train.lst
│   │   │   ├── 03001627_test.lst
│   │   │   ├── 03001627_train.lst
│   │   │   ├── 03211117_test.lst
│   │   │   ├── 03211117_train.lst
│   │   │   ├── 03636649_test.lst
│   │   │   ├── 03636649_train.lst
│   │   │   ├── 03691459_test.lst
│   │   │   ├── 03691459_train.lst
│   │   │   ├── 04090263_test.lst
│   │   │   ├── 04090263_train.lst
│   │   │   ├── 04256520_test.lst
│   │   │   ├── 04256520_train.lst
│   │   │   ├── 04379243_test.lst
│   │   │   ├── 04379243_train.lst
│   │   │   ├── 04401088_test.lst
│   │   │   ├── 04401088_train.lst
│   │   │   ├── 04530566_test.lst
│   │   │   └── 04530566_train.lst
│   │   ├── info-pix3d.json
│   │   └── info-shapenet.json
│   ├── encdec/
│   │   ├── DynamicSampler.py
│   │   ├── __init__.py
│   │   ├── export_meshes.py
│   │   ├── normalized_obj.py
│   │   ├── preprocess_udfs.py
│   │   └── train_encdec.py
│   ├── models/
│   │   ├── __init__.py
│   │   ├── cbndec.py
│   │   ├── coordsenc.py
│   │   └── dgcnn.py
│   ├── trainers/
│   │   ├── __init__.py
│   │   ├── encdec.py
│   │   └── test.py
│   └── utils.py
├── CLIP/
│   ├── .gitignore
│   ├── LICENSE
│   ├── MANIFEST.in
│   ├── README.md
│   ├── __init__.py
│   ├── clip/
│   │   ├── __init__.py
│   │   ├── clip.py
│   │   ├── model.py
│   │   └── simple_tokenizer.py
│   ├── clip.py
│   ├── data/
│   │   ├── country211.md
│   │   ├── prompts.md
│   │   ├── rendered-sst2.md
│   │   └── yfcc100m.md
│   ├── hubconf.py
│   ├── model-card.md
│   ├── model.py
│   ├── requirements.txt
│   ├── setup.py
│   ├── simple_tokenizer.py
│   └── tests/
│       └── test_consistency.py
├── README.md
├── data_loaders/
│   └── dataset.py
├── diffusion/
│   ├── fp16_util.py
│   ├── gaussian_diffusion.py
│   ├── logger.py
│   ├── losses.py
│   ├── nn.py
│   ├── resample.py
│   └── respace.py
├── environment.yaml
├── meshudf/
│   ├── _marching_cubes_lewiner.py
│   ├── _marching_cubes_lewiner_cy.pyx
│   ├── _marching_cubes_lewiner_luts.py
│   ├── meshudf.py
│   ├── setup.py
│   └── setup.sh
├── models/
│   ├── cfg_sampler.py
│   ├── mdm.py
│   ├── models.py
│   └── openaimodel.py
├── modules/
│   └── attention.py
├── sample/
│   ├── generate_cat.py
│   ├── generate_image.py
│   ├── generate_sketch.py
│   ├── generate_text.py
│   └── generate_uncond.py
├── train_diffcloth.py
├── training_loop_single.py
└── utils/
    ├── PYTORCH3D_LICENSE
    ├── __init__.py
    ├── comm.py
    ├── dist_util.py
    ├── fixseed.py
    ├── ldm_utils.py
    ├── logger.py
    ├── misc.py
    ├── miscellaneous.py
    ├── model_util.py
    ├── parser_util.py
    └── utils.py

================================================
FILE CONTENTS
================================================

================================================
FILE: AutoEncoder/cfg/deepfashion3d/deepfashion3d.yaml
================================================
dset:
  train_ids_file: none
  test_ids_file: none
  root: ./dataset/Deepfashion3D/udfs
  split: "train"
  name: "deepfashion3d"
  exp_name: "deepfashion3d"

num_points_pcd: 10_000
udf_max_dist: 0.1
latent_size: 32
num_points_forward: 20_000

decoder:
  hidden_dim: 512
  num_hidden_layers: 5

train_bs: 8
val_bs: 8
lr: 1e-4
num_epochs: 6_000

watertight: False
resolution: 512

log_dir: ./output/deepfashion3d

================================================
FILE: AutoEncoder/cfg/pix3d/pix3d.yaml
================================================
dset:
  train_ids_file: none
  test_ids_file: none
  root: ./Dataset/pix3d/udfs
  split: "train"
  name: "pix3d"
  exp_name: "pix3d"

num_points_pcd: 10_000
udf_max_dist: 0.1
latent_size: 64
num_points_forward: 20_000

decoder:
  hidden_dim: 512
  num_hidden_layers: 5

train_bs: 2
val_bs: 8
lr: 1e-4
num_epochs: 20000

watertight: False
resolution: 512

log_dir: ./outputs/pix3d



================================================
FILE: AutoEncoder/cfg/shapenet/text2shape.yaml
================================================
dset:
  train_ids_file: none
  test_ids_file: none
  root: ./dataset/ShapeNet/udfs
  name: text2shape
  split: train
  exp_name: text2shape

num_points_pcd: 10_000
udf_max_dist: 0.1
latent_size: 64
num_points_forward: 20_000

decoder:
  hidden_dim: 512
  num_hidden_layers: 5


train_bs: 6
val_bs: 8
lr: 1e-4
num_epochs: 10000

watertight: True
resolution: 512

log_dir: ./outputs/shapenet

================================================
FILE: AutoEncoder/data/__init__.py
================================================


================================================
FILE: AutoEncoder/data/dataset.py
================================================
import pickle
from pathlib import Path
from typing import Tuple

import numpy as np
import torch
from torch import Tensor
from torch.utils.data import Dataset
import os

T_ITEM = Tuple[int, str, Tensor, Tensor, Tensor, Tensor]


class UdfsDataset(Dataset):
    def __init__(self, name: str, root: Path, split: str) -> None:
        super().__init__()

        self.root = str(root)
        self.ids = []
        self.npz_list = []
        self.id2category = {}
        self.training_idxes = []
        self.name = name
        

        if name in ['shapenet', 'deepfashion3d']:
            self.data_root = os.path.join(self.root, 'train')
            ids = os.listdir(self.data_root)
            print(split, len(ids))
            for id in ids:
                #print(id)
                assert id.endswith(".npz")
                self.ids.append(id[:-4])
                self.npz_list.append(os.path.join(self.data_root, id))
        elif 'text2shape' in name:
            data_root_chair = os.path.join(self.root, '03001627', 'train')
            ids_chair = os.listdir(data_root_chair)

            data_root_table = os.path.join(self.root, '04379243', 'train')
            ids_table = os.listdir(data_root_table)

            for id in ids_chair:
                self.ids.append(id[:-4])
                self.npz_list.append(os.path.join(data_root_chair, id))

            for id in ids_table:
                self.ids.append(id[:-4])
                self.npz_list.append(os.path.join(data_root_table, id))

            self.ids = sorted(self.ids)
            self.npz_list = sorted(self.npz_list)


        elif name == 'pix3d':
            cats = os.listdir(os.path.join(self.root, split))
            for cat in cats:
                ids = os.listdir(os.path.join(self.root, split, cat))
                for id in ids:
                    self.ids.append(id[:-4])
                    self.npz_list.append(os.path.join(self.root, split, cat, id))


    def __len__(self) -> int:
        return len(self.ids)

    def get_training_idxes(self):
        return self.training_idxes

    def update_training_idxes(self, new_idxes):
        self.training_idxes = self.training_idxes + new_idxes
        with open('./training_idxes.txt', 'w') as f:
            for info in self.training_idxes:
                f.write(f'{info}\n')
    
    def val_del_idxes(self):
        self.ids
        self.npz_list

    def __getitem__(self, index: int) -> T_ITEM:

        item_id = self.npz_list[index].split('/')[-1][:-4]
        npz = np.load(self.npz_list[index])
        pcd = torch.from_numpy(npz["pcd"])
        coords = torch.from_numpy(npz["coords"])
        labels = torch.from_numpy(npz["labels"])
        gradients = torch.from_numpy(npz["gradients"])

        return index, item_id, pcd, coords, labels, gradients

    def get_mesh(self, index: int) -> Tuple[Tensor, Tensor]:
        npz = np.load(self.root / f"{self.ids[index]}.npz")
        v = torch.from_numpy(npz["vertices"])
        t = torch.from_numpy(npz["triangles"])

        return v, t


================================================
FILE: AutoEncoder/dataset_info_files/Deepfashion3d/deepfashion3d_test.txt
================================================
234
396
221
21
569
377-4
518
76
20
79
112
329
3
324
224
64
48
71
37-2
146
19
187
36
253
333
40
568
334
596
150
322
390
60
213-2
29
182
90
93
563-2
203
328
196
195
117
216
97
294
94
278
546
495
552
543
448
415
553
423
581
308
457
579
178
418
160
436
276
268
443
430
142
529
425
548
490
460
174
379
467
483
162
400
177


================================================
FILE: AutoEncoder/dataset_info_files/Deepfashion3d/deepfashion3d_train.txt
================================================
10
15-19
83
223
297
364
47
115
346
247-5
231
505
2
110
68
521
129
4-18
516
58
51
354
374
522
6-15
30-2
513
519-4
438
157
242
13
194
251
520
357
14
23
33
207
358
109
349-4
230
193
368
86
225
360
67
62
102
18
1
78
114
389
45
373
375
53
66
43
49
136
376
184
55
235
507
46
523-5
597
170
81
147
246
332
121
292-5
369
305
500
85
70-2
348
25
301
337-2
502
561-2
123
34
256-2
290
220
343
291
39
499
249-6
84
186
42
387
105
137
504
395-2
138
228
152
103
141
594
498-2
148
38
503
592
155
171
145
218
144-2
394
149
238-2
595
288
35
287
134
61
392
213-2
183
509
393
120
122
26
345
363
119
118
212
28-5
232
299
133
351
298
289
113
340
304
24
303
222
302
168
198
140
63
353
169
355
172
567
321
197
92
565
314
89
100
564
202
571
99
27
107
215
326
128
254
108
338
320
335
342
127
116
319
217
166
91
341
88
17
156
80
327
527
538
539
535
257
537
515
514
547
453
413
419
549
411
432
417
452
550
530-2
261
442
454
449
406
262
551
450
444
435
416
456
540
408
494
403
414
578
577
421
459
468
179
175
462
380
584
458
590
473
266
165
434
472
476
465
585
440
599
463
164
398
589
447
480
307
159
583
422
576
433
386
426
176
455
559
429
464
575
316
487
586
378
161
477
296
591
382
427
280
282
285
424
582
402
479
469
485
281
277
347
405
412
573
587
441
420
542
295
317
471
491
481
558
437
486
318
404
330
312
272
475
284
310
283
167
401
163
484
135
399
311
275
352
428
489
388
574
488
588
431
350


================================================
FILE: AutoEncoder/dataset_info_files/Pix3d/test.txt
================================================
img/bed/0255.jpg
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img/bed/0257.jpg
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/02691156_test.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/02691156_train.lst
================================================
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/02828884_test.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/02828884_train.lst
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/02933112_test.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/02933112_train.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/02958343_test.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/02958343_train.lst
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/03001627_test.lst
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/03001627_train.lst
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/03211117_test.lst
================================================
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/03211117_train.lst
================================================
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/03636649_test.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/03636649_train.lst
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/03691459_test.lst
================================================
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/03691459_train.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04090263_test.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04090263_train.lst
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04256520_test.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04256520_train.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04379243_test.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04379243_train.lst
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04401088_test.lst
================================================
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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04401088_train.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04530566_test.lst
================================================
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FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/04530566_train.lst
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================================================
FILE: AutoEncoder/dataset_info_files/info-pix3d.json
================================================
{
    "lst_dir": "./dataset_info_files/pix3d_filelists",
    "cats": {
        "bed": 0,
        "bookcase": 1,
        "chair": 2,
        "desk": 3,
        "misc": 4,
        "sofa": 5,
        "table": 6,
        "tool": 7,
        "wardrobe": 8
    },
    "all_cats": [
        "bed",
        "bookcase",
        "chair",
        "desk",
        "misc",
        "sofa",
        "table",
        "tool",
        "wardrobe"
    ],
    "raw_dirs_v1": {
        "mesh_dir": "pix3d/model_align",
        "norm_mesh_dir": "pix3d/norm_mesh_dir/",
        "sdf_dir": "pix3d/SDF_v1_64/"
    }
}


================================================
FILE: AutoEncoder/dataset_info_files/info-shapenet.json
================================================
{
    "lst_dir": "../dataset_info_files/ShapeNet_filelists",
    "cats": {
        "watercraft": "04530566",
        "rifle": "04090263",
        "display": "03211117",
        "lamp": "03636649",
        "speaker": "03691459",
        "cabinet": "02933112",
        "chair": "03001627",
        "bench": "02828884",
        "car": "02958343",
        "airplane": "02691156",
        "sofa": "04256520",
        "table": "04379243",
        "phone": "04401088"
    },
    "all_cats": [
        "airplane",
        "bench",
        "cabinet",
        "car",
        "chair",
        "display",
        "lamp",
        "speaker",
        "rifle",
        "sofa",
        "table",
        "phone",
        "watercraft"
    ],
    "raw_dirs_v1": {
        "mesh_dir": "/raid/zybak/data/ShapeNetCore.v2/",
        "norm_mesh_dir": "/raid/zybak/data/ShapeNetCar100SDF/norm_mesh_dir_v2/",
        "sdf_dir": "/raid/zybak/data/ShapeNetCar100SDF/SDF_v2"
    }
}

================================================
FILE: AutoEncoder/encdec/DynamicSampler.py
================================================

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data.sampler import Sampler, WeightedRandomSampler, BatchSampler
from typing import Iterator, Sequence
from torch.utils.data import Dataset, DataLoader
import random


class DummyDataset(Dataset):
    def __init__(self):
        self.training_idxes = list(range(1))

    def __getitem__(self, index):
        return index

    def __len__(self):
        return 10000

    def get_training_idxes(self):
        return self.training_idxes

    def update_training_idxes(self, new_idxes):
        self.training_idxes = self.training_idxes + new_idxes

class SequenceSampler(Sampler):
    def __init__(self, training_idxes, N):
        self.training_idxes = training_idxes
        self.N = N

    def __iter__(self) -> Iterator[int]:
        
        yield from iter(list(set(range(self.N)) - set(self.training_idxes)))

    def __len__(self) -> int:
        return self.N - len(self.training_idxes)

    def update_training_idxes(self, new_add_idxes):
        self.training_idxes = self.training_idxes+new_add_idxes

class SequenceSampler_Train(Sampler):
    def __init__(self, training_idxes):
        self.training_idxes = training_idxes


    def __iter__(self) -> Iterator[int]:
        random.shuffle(self.training_idxes)
        yield from iter(self.training_idxes)

    def __len__(self) -> int:
        return len(self.training_idxes)

    def update_training_idxes(self, new_add_idxes):
        self.training_idxes = self.training_idxes+new_add_idxes

class WeightedDynamicSampler(WeightedRandomSampler):

    def __init__(self, weights: Sequence[float], num_samples: int,
                 replacement: bool = True, generator=None) -> None:
        if not isinstance(num_samples, int) or isinstance(num_samples, bool) or \
                num_samples <= 0:
            raise ValueError(f"num_samples should be a positive integer value, but got num_samples={num_samples}")
        if not isinstance(replacement, bool):
            raise ValueError(f"replacement should be a boolean value, but got replacement={replacement}")

        weights_tensor = torch.as_tensor(weights, dtype=torch.double)
        if len(weights_tensor.shape) != 1:
            raise ValueError("weights should be a 1d sequence but given "
                             f"weights have shape {tuple(weights_tensor.shape)}")

        self.weights = weights_tensor
        self.num_samples = num_samples
        self.replacement = replacement
        self.generator = generator

    def __iter__(self) -> Iterator[int]:
        rand_tensor = torch.multinomial(self.weights, (self.weights > 0).sum().int(), self.replacement, generator=self.generator)
        yield from iter(rand_tensor.tolist())

    def __len__(self) -> int:
        return ((self.weights > 0).sum().int())

    def update_weights(self, weights):
        self.weights = weights


class DynamicBatchSampler(BatchSampler):

    def update_weights(self, weights):
        self.sampler.update_weights(weights)

    def update_training_idxes(self, training_idxes):
        self.sampler.update_training_idxes(training_idxes)


if __name__ == '__main__':

    dataset = DummyDataset()

    training_idxes = dataset.get_training_idxes()

    weights = [1 if i in training_idxes else 0 for i in range(len(dataset))]

    sampler = WeightedDynamicSampler(weights, len(dataset))
    batch_sampler = DynamicBatchSampler(sampler = sampler, batch_size=8, drop_last=False)

    loader = DataLoader(dataset, sampler=batch_sampler)

    count = 1
    for epoch in range(10):
        for batch in loader:
            print(batch)
        
        curr_training_idxes = dataset.get_training_idxes().copy()
        total_samples = len(dataset)
        if len(curr_training_idxes) < total_samples:
            ##testing here
            new_add_idxes = [count, count+1]
            dataset.update_training_idxes(new_add_idxes)
            count += 2
            easy_p = 0.5/len(curr_training_idxes)
            hard_p = 0.5 / len(new_add_idxes)
            new_weights = torch.zeros(total_samples)

            
            new_weights.index_fill_(0, torch.LongTensor(curr_training_idxes), easy_p)
            new_weights.index_fill_(0, torch.LongTensor(new_add_idxes), hard_p)

            new_weights[curr_training_idxes] = easy_p
            new_weights[new_add_idxes] = hard_p

            print(curr_training_idxes, new_add_idxes, new_weights[:22])
            batch_sampler.update_weights(new_weights)




================================================
FILE: AutoEncoder/encdec/__init__.py
================================================


================================================
FILE: AutoEncoder/encdec/export_meshes.py
================================================
import sys

sys.path.append("..")

from pathlib import Path
from typing import Any, Dict


import torch
from hesiod import hcfg, hmain
from torch import Tensor
from torch.utils.data import DataLoader

from AutoEncoder.data.dataset import UdfsDataset
from meshudf.meshudf import get_mesh_from_udf
from models.cbndec import CbnDecoder
from models.coordsenc import CoordsEncoder
from models.dgcnn import Dgcnn
from utils import get_o3d_mesh_from_tensors, progress_bar, random_point_sampling

import open3d as o3d 
import os
import numpy as np


import trimesh
from utils.utils import GridFiller

if len(sys.argv) != 2:
    print("Usage: python export_meshes.py <run_cfg_file>")
    exit(1)

@hmain(
    base_cfg_dir="../cfg/bases",
    run_cfg_file=sys.argv[1],
    parse_cmd_line=False,
    out_dir_root="../logs",
)
def main() -> None:
    seed = 10
    import random
    torch.backends.cudnn.benchmark = False
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.random.manual_seed(seed)


    ckpt_path = hcfg("ckpt_path", str)

    ckpt = torch.load(ckpt_path)

    latent_size = hcfg("latent_size", int)
    num_points_pcd = hcfg("num_points_pcd", int)
    udf_max_dist = hcfg("udf_max_dist", float)

    watertight = hcfg("watertight", bool)
    resolution = hcfg("resolution", int)

    encoder = Dgcnn(latent_size)
    encoder.load_state_dict(ckpt["encoder"], strict=True)
    encoder = encoder.cuda()
    encoder.eval()

    coords_encoder = CoordsEncoder()

    decoder_cfg = hcfg("decoder", Dict[str, Any])
    decoder = CbnDecoder(
        coords_encoder.out_dim,
        latent_size,
        decoder_cfg["hidden_dim"],
        decoder_cfg["num_hidden_layers"],
    )
    decoder.load_state_dict(ckpt["decoder"], strict=True)
    decoder = decoder.cuda()
    decoder.eval()


    dset_root = Path(hcfg("dset.root", str))
    name = hcfg("dset.name", str)

    bs = 1
    test_dset = UdfsDataset(name, dset_root, 'train') #deepfasion3d
    test_loader = DataLoader(test_dset, bs, num_workers=2, shuffle=False)


    for batch in progress_bar(test_loader, "Exporting"):

        _, item_ids, pcds, _, _, _ = batch
        
        bs = pcds.shape[0]
        pcds = pcds.cuda()
        
        pcds = random_point_sampling(pcds, num_points_pcd)
        

        with torch.no_grad():

            latent_codes = encoder(pcds)

        for i in progress_bar(range(bs), "Meshing"):

            lat = latent_codes[i].unsqueeze(0)

            def udf_func(c: Tensor) -> Tensor:

                c_ = coords_encoder.encode(c.unsqueeze(0))
                p = decoder(c_, lat).squeeze(0)
                p = torch.sigmoid(p)
                p = (1 - p) * udf_max_dist

                return p

            if watertight:
                size = resolution #256 faster
                fast_grid_filler = GridFiller(size)
                udf, gradients = fast_grid_filler.fill_grid(udf_func, max_batch=2**16)
                udf[udf < 0] = 0

                import mcubes
                vertices, faces = mcubes.marching_cubes(udf.detach().cpu().numpy(), 0.01)
                mesh = trimesh.Trimesh(vertices, faces)
                components = mesh.split(only_watertight=True)
                bbox = []
                for k, c in enumerate(components):
                    bbmin = c.vertices.min(0)
                    bbmax = c.vertices.max(0)
                    bbox.append((bbmax - bbmin).max())
                max_component = np.argmax(bbox)
                mesh = components[max_component]
                mesh.vertices = mesh.vertices * (2.0 / size) - 1.0  # normalize it to [-1, 1]
                mesh_path = f"./outputs/AE/{hcfg('dset.exp_name', str)}/meshes_test/{item_ids[i]}mc.obj"
                os.makedirs(os.path.dirname(mesh_path), exist_ok=True)
                trimesh.Trimesh(vertices=vertices, faces=faces).export(mesh_path)

            else:
                v, t, udf, gradients = get_mesh_from_udf(
                udf_func,
                coords_range=(-1, 1),
                max_dist=udf_max_dist,
                N=resolution,
                max_batch=2**16,
                differentiable=False,
                use_fast_grid_filler=True
                )
                pred_mesh_o3d = get_o3d_mesh_from_tensors(v, t)
                mesh_path = f"./outputs/AE/{hcfg('dset.exp_name', str)}/meshes_test/{item_ids[i]}_meshudf.obj"
                os.makedirs(os.path.dirname(mesh_path), exist_ok=True)

                o3d.io.write_triangle_mesh(str(mesh_path), pred_mesh_o3d)
    

if __name__ == "__main__":
    main()


================================================
FILE: AutoEncoder/encdec/normalized_obj.py
================================================
import trimesh
import os

def obj_normalization(input_mesh_file, output_file_path):
    '''
    Normalize the obj file based on the information from the first frame (T-pose).
    :param path: the path of original obj file
    :return:
        normalized obj file [-1,1] at the origin (0,0,0).
    '''
    obj = trimesh.load(input_mesh_file, force='mesh')
    v = obj.vertices
    v = v - v.mean(0)
    # v = v*2
    print(v.mean(0), v.max(0), v.min(0))

    trimesh.Trimesh(vertices=v, faces=obj.faces).export(output_file_path)
    return True


def box_center_normalization(input_mesh_file, output_file_path):
    '''
    Normalize the obj file based on the information from the first frame (T-pose).
    :param path: the path of original obj file
    :return:
        normalized obj file [-1,1] at the origin (0,0,0).
    '''
    obj = trimesh.load(input_mesh_file, force='mesh')
    v = obj.vertices
    max_v = v.max(0)
    min_v = v.min(0)
    lhw = max_v = min_v
    box_center = min_v + (lhw / 2)

    print(v.mean(0), v.max(0), v.min(0))
    v = v - box_center

    trimesh.Trimesh(vertices=v, faces=obj.faces).export(output_file_path)
    return True


data_root = './dataset/Deepfashion3D/filtered_registered_mesh'
ids = os.listdir(data_root)
output_root = './dataset/Deepfashion3D/norm_objs'
os.makedirs(output_root, exist_ok=True)

for id in ids:
    input_mesh_file = os.path.join(data_root, id, 'model_cleaned.obj')
    output_file_path = os.path.join(output_root, id+'.obj')
    obj_normalization(input_mesh_file, output_file_path)



================================================
FILE: AutoEncoder/encdec/preprocess_udfs.py
================================================
import sys

sys.path.append("..")

from pathlib import Path
import os

import numpy as np
from utils import (
    compute_udf_from_mesh,
    get_o3d_mesh_from_tensors,
    get_tensor_pcd_from_o3d,
    progress_bar,
    read_mesh,
)

np.random.seed(1024)

cat2id = {
    "chair": "03001627",
    "bench": "02828884",
    "cabinet": "02933112",
    "car": "02958343",
    "airplane": "02691156",
    "display": "03211117",
    "lamp": "03636649",
    "speaker": "03691459",
    "rifle": "04090263",
    "sofa": "04256520",
    "table": "04379243",
    "phone": "04401088",
    "watercraft": "04530566"
}

data_root = sys.argv[1]
out_dir = sys.argv[2]
name = sys.argv[3]
assert name in ['shapenet', 'deepfashion3d', 'pix3d']
if name == 'shapenet':
    if len(sys.argv) != 5:
        print("Usage: python preprocess_udfs.py </path/to/meshes> </out/path> <name of dataset> <category>")
        exit(1)
    category = sys.argv[4]

    assert category in cat2id.keys()
    id = cat2id[category]
    out_dir = os.path.join(out_dir, id)
    sub_ids = os.listdir(os.path.join(data_root, id))
else:
    if len(sys.argv) != 4:
        print("Usage: python preprocess_udfs.py </path/to/meshes> </out/path> <name of dataset> <category>")
        exit(1)
    

os.makedirs(out_dir, exist_ok=True)


if name == 'shapenet':
    lst_dir = '../dataset_info_files/ShapeNet_filelists'

    with open(lst_dir+"/"+str(id)+"_test.lst", "r") as f:
        list_obj_test = f.readlines()

    with open(lst_dir+"/"+str(id)+"_train.lst", "r") as f:
        list_obj_train = f.readlines()

    list_obj_test = [f.rstrip() for f in list_obj_test]
    list_obj_train = [f.rstrip() for f in list_obj_train]

elif name == 'deepfashion3d':
    lst_dir = '../dataset_info_files/Deepfashion3d'

    with open(lst_dir+"/"+"deepfashion3d_test.txt", "r") as f:
        list_obj_test = f.readlines()

    with open(lst_dir+"/"+"deepfashion3d_train.txt", "r") as f:
        list_obj_train = f.readlines()

    list_obj_test = [f.rstrip('\n') for f in list_obj_test]
    list_obj_train = [f.rstrip('\n') for f in list_obj_train]
    print(len(list_obj_train), len(list_obj_train))
    check_dict = {}
    for id in list_obj_train:
        if check_dict.__contains__(id):
            print(id)
        else:
            check_dict[id] = 1

elif name == 'pix3d':
    list_obj_train = []
    list_obj_test = []

    data_root = './dataset/pix3d/models'

    cats_train = os.listdir(os.path.join(data_root, 'train'))
    for cat in cats_train:
        ids = os.listdir(os.path.join(data_root, 'train', cat))
        for id in ids:
            model_path = os.path.join(data_root, 'train', cat, id, 'model.obj')
            list_obj_train.append(model_path)

    cats_test = os.listdir(os.path.join(data_root, 'test'))
    for cat in cats_test:
        ids = os.listdir(os.path.join(data_root, 'test', cat))
        for id in ids:
            model_path = os.path.join(data_root, 'test', cat, id, 'model.obj')
            list_obj_test.append(model_path)


def PrepareOneUDF(sub_id, split):
    if name == 'shapenet':
        mesh_path = os.path.join(data_root, id, sub_id, 'model.obj')

    elif name == 'deepfashion3d':
        mesh_path = os.path.join(data_root, f'{sub_id}.obj')

    elif name == 'pix3d':
        mesh_path = os.path.join(data_root, f'{sub_id}.obj')


    out_dir_split = os.path.join(out_dir, split)

    os.makedirs(out_dir_split, exist_ok=True)

    v, t = read_mesh(mesh_path)
    mesh_o3d = get_o3d_mesh_from_tensors(v, t)

    pcd_o3d = mesh_o3d.sample_points_uniformly(number_of_points=100_000)
    pcd = get_tensor_pcd_from_o3d(pcd_o3d)[:, :3]

    coords, labels, gradients = compute_udf_from_mesh(
        mesh_o3d,
        num_queries_on_surface=250_000,
        num_queries_per_std=[250_000, 200_000, 25_000, 25_000],

    )

    if name == 'pix3d':
        out_file = os.path.join(out_dir_split, sub_id.split('/')[-3], f"{sub_id.split('/')[-2]}.npz")
        os.makedirs(os.path.dirname(out_file), exist_ok=True)
    else:
        out_file = os.path.join(out_dir_split, f"{sub_id}.npz")


    print('train: ', len(list_obj_train), 'test: ', len(list_obj_test))
    print(out_file)
    np.savez(
        out_file,
        vertices=v.numpy(),
        triangles=t.numpy(),
        pcd=pcd.numpy(),
        coords=coords.numpy(),
        labels=labels.numpy(),
        gradients=gradients.numpy(),
    )


for sub_id in progress_bar(list_obj_train):
    PrepareOneUDF(sub_id, 'train')

for sub_id in progress_bar(list_obj_test):
    PrepareOneUDF(sub_id, 'test')


================================================
FILE: AutoEncoder/encdec/train_encdec.py
================================================
import sys

sys.path.append("..")

from hesiod import hmain

from trainers.encdec import EncoderDecoderTrainer



if len(sys.argv) > 1:
    run_cfg_file = sys.argv[1]
    del sys.argv[1]
else:
    run_cfg_file = None


@hmain(
    base_cfg_dir="../cfg/bases",
    template_cfg_file="../cfg/encdec.yaml",
    run_cfg_file=run_cfg_file,
    out_dir_root="../logs",
)
def main() -> None:
    trainer = EncoderDecoderTrainer()
    trainer.train()


if __name__ == "__main__":
    main()


================================================
FILE: AutoEncoder/models/__init__.py
================================================


================================================
FILE: AutoEncoder/models/cbndec.py
================================================
from einops import repeat
from torch import Tensor, nn

class DecoderConditionalBatchNorm(nn.Module):
    def __init__(
        self,
        input_dim: int,
        dim_condition_embedding: int,
        dim_hidden_layers: int,
        num_hidden_layers: int,
        dim_out: int,
        refine = False
    ):
        super().__init__()

        self.fc_p = nn.Conv1d(input_dim, dim_hidden_layers, 1)

        self.num_blocks = num_hidden_layers
        self.blocks = nn.ModuleList()
        for _ in range(num_hidden_layers):
            self.blocks.append(
                ConditionalResnetBlock1d(
                    dim_condition_embedding,
                    dim_hidden_layers,
                )
            )

        self.bn = ConditionalBatchNorm1d(dim_condition_embedding, dim_hidden_layers)

        self.fc_out = nn.Conv1d(dim_hidden_layers, dim_out, 1)
        if refine:
            nn.init.zeros_(self.fc_out.weight)
        self.actvn = nn.ReLU()

    def forward(self, points: Tensor, conditions: Tensor) -> Tensor:
        p = points.transpose(1, 2)
        c = conditions.transpose(1, 2)
        net = self.fc_p(p)

        for i in range(self.num_blocks):
            net = self.blocks[i](net, c)

        out = self.fc_out(self.actvn(self.bn(net, c)))

        out = out.squeeze(1)

        return out


class ConditionalBatchNorm1d(nn.Module):
    def __init__(self, c_dim: int, f_dim: int) -> None:
        super().__init__()
        self.c_dim = c_dim
        self.f_dim = f_dim

        self.conv_gamma = nn.Conv1d(c_dim, f_dim, 1)
        self.conv_beta = nn.Conv1d(c_dim, f_dim, 1)
        self.bn = nn.BatchNorm1d(f_dim, affine=False)

        self.reset_parameters()

    def reset_parameters(self) -> None:
        nn.init.zeros_(self.conv_gamma.weight)
        nn.init.zeros_(self.conv_beta.weight)
        nn.init.ones_(self.conv_gamma.bias)  # type: ignore
        nn.init.zeros_(self.conv_beta.bias)  # type: ignore

    def forward(self, x: Tensor, c: Tensor) -> Tensor:
        assert x.shape[0] == c.shape[0]  # batch size
        assert c.shape[1] == self.c_dim  # embedding dim
        assert x.shape[2] == c.shape[2]  # num points

        # Affine mapping
        gamma = self.conv_gamma(c)
        beta = self.conv_beta(c)

        # Batchnorm
        net = self.bn(x)
        out = gamma * net + beta


        return out


class ConditionalResnetBlock1d(nn.Module):
    def __init__(self, c_dim: int, size_in: int) -> None:
        super().__init__()
        self.size_in = size_in
        self.bn_0 = ConditionalBatchNorm1d(c_dim, size_in)
        self.bn_1 = ConditionalBatchNorm1d(c_dim, size_in)

        self.fc_0 = nn.Conv1d(size_in, size_in, 1)
        self.fc_1 = nn.Conv1d(size_in, size_in, 1)

        self.actvn = nn.ReLU()

        nn.init.zeros_(self.fc_1.weight)

    def forward(self, x: Tensor, c: Tensor) -> Tensor:
        net = self.fc_0(self.actvn(self.bn_0(x, c)))
        dx = self.fc_1(self.actvn(self.bn_1(net, c)))

        return x + dx


class CbnDecoder(nn.Module):
    def __init__(
        self,
        input_dim: int,
        latent_dim: int,
        hidden_dim: int,
        num_hidden_layers: int,
        out_dim: int = 1,
        refine = False
    ) -> None:
        super().__init__()
        self.decoder = DecoderConditionalBatchNorm(
            input_dim,
            latent_dim,
            hidden_dim,
            num_hidden_layers,
            out_dim,
            refine
        )


    def forward(self, coords_emb, latent_codes) -> Tensor:
        # coords -> (B, N, 3)
        # latent_codes -> (B, D) or (B, N, D)

        if len(latent_codes.shape) == 2:
            latent_codes = repeat(latent_codes, "b d -> b r d", r=coords_emb.shape[1])
        out = self.decoder(coords_emb, latent_codes)
        return out




================================================
FILE: AutoEncoder/models/coordsenc.py
================================================
from typing import Callable, Tuple

import torch
from torch import Tensor


class CoordsEncoder:
    def __init__(
        self,
        input_dims: int = 3,
        include_input: bool = True,
        max_freq_log2: int = 9,
        num_freqs: int = 10,
        log_sampling: bool = True,
        periodic_fns: Tuple[Callable, Callable] = (torch.sin, torch.cos),
    ) -> None:
        self.input_dims = input_dims
        self.include_input = include_input
        self.max_freq_log2 = max_freq_log2
        self.num_freqs = num_freqs
        self.log_sampling = log_sampling
        self.periodic_fns = periodic_fns
        self.create_encoding_fn()

    def create_encoding_fn(self) -> None:
        encoding_fns = []
        d = self.input_dims
        out_dim = 0
        if self.include_input:
            encoding_fns.append(lambda x: x)
            out_dim += d

        if self.log_sampling:
            freq_bands = 2.0 ** torch.linspace(
                0.0, self.max_freq_log2, steps=self.num_freqs
            )
        else:
            freq_bands = torch.linspace(
                2.0**0.0, 2.0**self.max_freq_log2, steps=self.num_freqs
            )

        for freq in freq_bands:
            for p_fn in self.periodic_fns:
                encoding_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
                out_dim += d

        self.encoding_fns = encoding_fns
        self.out_dim = out_dim

    def encode(self, inputs: Tensor) -> Tensor:
        return torch.cat([fn(inputs) for fn in self.encoding_fns], -1)


================================================
FILE: AutoEncoder/models/dgcnn.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
from pytorch3d.ops import knn_gather, knn_points
from torch import Tensor


def get_graph_feature(x: Tensor, indices: Tensor) -> Tensor:
    """Select features from neighbors.

    Args:
        x: the input features with shape (B, PTS, D).
        indices: the indices indicating the K neighbors for each input point
            with shape (B, PTS, K).

    Returns:
        The selected features with shape # (B, PTS, K, 2*D)
    """
    features = knn_gather(x, indices)
    x = repeat(x, "b n d -> b n r d", r=features.shape[2])
    features = torch.cat((features - x, x), dim=3)

    return features


class Dgcnn(nn.Module):
    def __init__(
        self,
        size_latent: int,
        k: int = 20,
        aggregate_ops_local: str = "max",
        aggregate_ops_global: str = "max",
    ):
        super().__init__()

        self.k = k
        self.size_latent = size_latent
        self.aggreate_ops_local = aggregate_ops_local
        self.aggreate_ops_global = aggregate_ops_global

        self.bn_1 = nn.BatchNorm1d(64)
        self.bn_2 = nn.BatchNorm1d(64)
        self.bn_3 = nn.BatchNorm1d(128)
        self.bn_4 = nn.BatchNorm1d(256)
        self.bn_5 = nn.BatchNorm1d(self.size_latent)

        self.slope = 0.2
        self.conv_1 = nn.Linear(3 * 2, 64, bias=False)
        self.conv_2 = nn.Linear(64 * 2, 64, bias=False)
        self.conv_3 = nn.Linear(64 * 2, 128, bias=False)
        self.conv_4 = nn.Linear(128 * 2, 256, bias=False)
        self.conv_5 = nn.Linear(512, self.size_latent, bias=False)

    def block_forward(
        self,
        features: Tensor,
        conv: nn.Linear,
        bn,
        indices: Tensor,
        agggreate_ops: str,
    ) -> Tensor:
        x = get_graph_feature(features, indices)
        x = conv(x)
        x = rearrange(x, "b n k d -> b d (n k)")
        x = bn(x)
        x = F.leaky_relu(x, negative_slope=self.slope)
        features_out = rearrange(x, "b d (n k) -> b n d k", k=self.k)

        if agggreate_ops == "max":
            features_out = features_out.max(dim=-1)[0]
        elif agggreate_ops == "avg":
            features_out = features_out.mean(dim=-1)

        return features_out

    def forward(self, x: Tensor, latent_index=None) -> Tensor:
        """Forward pass.

        Args:
            x: the input point cloud with shape (B, N, 3).

        Returns:
           The global embeddings with shape (B, size_latent).
        """
        _, indices, _ = knn_points(x, x, K=self.k)

        x1 = self.block_forward(
            x, self.conv_1, self.bn_1, indices, self.aggreate_ops_local
        )
        x2 = self.block_forward(
            x1, self.conv_2, self.bn_2, indices, self.aggreate_ops_local
        )
        x3 = self.block_forward(
            x2, self.conv_3, self.bn_3, indices, self.aggreate_ops_local
        )
        x4 = self.block_forward(
            x3, self.conv_4, self.bn_4, indices, self.aggreate_ops_local
        )
        x5 = self.conv_5(torch.cat((x1, x2, x3, x4), dim=-1))
        x5 = rearrange(x5, "b n d -> b d n")
        x5 = self.bn_5(x5)
        feat = F.leaky_relu(x5, negative_slope=self.slope)

        if self.aggreate_ops_global == "max":
            feat = feat.max(dim=-1)[0]
        elif self.aggreate_ops_global == "avg":
            feat = feat.mean(dim=-1)
        else:
            feat = rearrange(feat, "b d n -> b n d")

        if latent_index is not None:
            feat = torch.cat((feat, latent_index.unsqueeze(-1)), dim=1)

        return feat


================================================
FILE: AutoEncoder/trainers/__init__.py
================================================


================================================
FILE: AutoEncoder/trainers/encdec.py
================================================
from pathlib import Path
from typing import Any, Dict

import torch
import torch.nn.functional as F
from hesiod import get_out_dir, hcfg
from torch.optim import Adam
from torch.utils.data import DataLoader
from torch.utils.tensorboard.writer import SummaryWriter

from AutoEncoder.data.dataset import UdfsDataset
from models.cbndec import CbnDecoder
from models.coordsenc import CoordsEncoder
from models.dgcnn import Dgcnn
from utils import compute_gradients, progress_bar, random_point_sampling
import numpy as np
import os
from encdec.DynamicSampler import WeightedDynamicSampler, DynamicBatchSampler, SequenceSampler, SequenceSampler_Train

import wandb

use_wandb = True
if use_wandb:
    # start a new wandb run to track this script
    wandb.init(
        # set the wandb project where this run will be logged
        project="AutoEncoder",
        name="training",
        config={
        "learning_rate": 1e-4,
        "epochs": 20000,
        }
    )

class EncoderDecoderTrainer:
    def __init__(self) -> None:
        train_ids_file = Path(hcfg("dset.train_ids_file", str))
        dset_split = hcfg("dset.split", str)
        dset_root = Path(hcfg("dset.root", str))
        name = hcfg("dset.name", str)

        self.evaluation = False

        out_dir = Path(hcfg("log_dir", str))

        self.train_dset = UdfsDataset(name, dset_root, dset_split)
        train_bs = hcfg("train_bs", int)

        val_bs = hcfg("val_bs", int)
        
        if 'curriculum' in name:
            training_idxes = self.train_dset.get_training_idxes()
            print("first sample nums: ", len(training_idxes))
            weights = [1 if i in training_idxes else 0 for i in range(len(self.train_dset))]
        
            #self.sampler = WeightedDynamicSampler(weights, len(self.train_dset))
            self.sampler = SequenceSampler_Train(training_idxes)
            self.batch_sampler = DynamicBatchSampler(sampler = self.sampler, batch_size=train_bs, drop_last=False)
            self.train_loader = DataLoader(self.train_dset, batch_sampler=self.batch_sampler)

            self.val_sampler = SequenceSampler(training_idxes, len(self.train_dset))
            self.val_batch_sampler = DynamicBatchSampler(sampler = self.val_sampler, batch_size=val_bs, drop_last=False)
            self.val_loader = DataLoader(self.train_dset, batch_sampler=self.val_batch_sampler)

        else:
            self.train_loader = DataLoader(
                self.train_dset,
                train_bs,
                shuffle=True,
                num_workers=2,
                pin_memory=True
            )

        self.num_points_pcd = hcfg("num_points_pcd", int)
        latent_size = hcfg("latent_size", int)
        self.max_dist = hcfg("udf_max_dist", float)
        self.num_points_forward = hcfg("num_points_forward", int)

        encoder = Dgcnn(latent_size)
        self.encoder = encoder.cuda()

        self.coords_encoder = CoordsEncoder()

        decoder_cfg = hcfg("decoder", Dict[str, Any])
        decoder = CbnDecoder(
            self.coords_encoder.out_dim,
            latent_size,
            decoder_cfg["hidden_dim"],
            decoder_cfg["num_hidden_layers"],
        )
        self.decoder = decoder.cuda()

        params = list(encoder.parameters())
        params.extend(decoder.parameters())
        lr = hcfg("lr", float)
        self.optimizer = Adam(params, lr)

        self.epoch = 0
        self.global_step = 0

        self.ckpts_path = out_dir / "ckpts"

        tune = False
        if tune:
            self.restore_from_last_ckpt()

        else:
            if self.ckpts_path.exists():
                self.restore_from_last_ckpt()

        os.makedirs(str(self.ckpts_path), exist_ok=True)

        self.logger = SummaryWriter(out_dir / "logs")

    def train(self) -> None:
        name = hcfg("dset.name", str)
        num_epochs = hcfg("num_epochs", int)
        start_epoch = self.epoch
        best_val_loss = 1e9
        
        for epoch in range(start_epoch, num_epochs):
            self.epoch = epoch

            epoch_losses = []
            epoch_loss = 0

            self.encoder.train()
            self.decoder.train()

            desc = f"Epoch {epoch}/{num_epochs}"
            for batch in progress_bar(self.train_loader, desc=desc):
                indexes, _, pcds, coords, gt_udf, gt_grad = batch
                pcds = pcds.cuda()
                coords = coords.cuda()
                gt_udf = gt_udf.cuda()
                gt_grad = gt_grad.cuda()


                pcds = random_point_sampling(pcds, self.num_points_pcd)

                gt_udf = gt_udf / self.max_dist
                gt_udf = 1 - gt_udf
                c_u_g = torch.cat([coords, gt_udf.unsqueeze(-1), gt_grad], dim=-1)

                selected_c_u_g = random_point_sampling(c_u_g, self.num_points_forward)
                selected_coords = selected_c_u_g[:, :, :3]
                selected_coords.requires_grad = True
                selected_gt_udf = selected_c_u_g[:, :, 3]
                selected_gt_grad = selected_c_u_g[:, :, 4:]

                latent_codes = self.encoder(pcds)

                coords_encoded = self.coords_encoder.encode(selected_coords)
                pred = self.decoder(coords_encoded, latent_codes)


                udf_loss = F.binary_cross_entropy_with_logits(pred, selected_gt_udf)

                udf_pred = torch.sigmoid(pred)
                

                udf_pred = 1 - udf_pred
                udf_pred = udf_pred * self.max_dist

                gradients = compute_gradients(selected_coords, udf_pred)

                grad_loss = F.mse_loss(gradients, selected_gt_grad, reduction="none")


                mask = (selected_gt_udf > 0) & (selected_gt_udf < 1)
                grad_loss = grad_loss[mask].mean()


                self.optimizer.zero_grad()

                loss = udf_loss + 0.1*grad_loss

                epoch_losses.append(udf_loss.detach().cpu().item())

                loss.backward()
                self.optimizer.step()

                if self.global_step % 10 == 0:
                    self.logger.add_scalar(
                        "train/udf_loss",
                        udf_loss.item(),
                        self.global_step,
                    )
                    self.logger.add_scalar(
                        "train/grad_loss",
                        grad_loss.item(),
                        self.global_step,
                    )
                    if use_wandb:
                        wandb.log({"train/udf_loss": udf_loss.item(), "train/grad_loss": grad_loss.item()}, step=self.global_step) #"train/grad_loss": grad_loss.item()}, step=self.global_step)

                    print(f'steps: {self.global_step}; loss: {loss.item()}; udf_loss: {udf_loss.item()}; grad_loss: {grad_loss.item()}') #eikonal_loss: {gradient_error.item()}

                self.global_step += 1

            epoch_loss = np.mean(epoch_losses)
            print(f'epoch_udf_loss: {epoch_loss}')

            switch_epoch = 64
            curr_training_idxes = self.train_dset.get_training_idxes().copy()
            total_samples = len(self.train_dset)
            if epoch % switch_epoch == 63 and 'curriculum' in name and len(curr_training_idxes) < total_samples:

                _, _, _, new_add_idxes = self.val()

                self.train_dset.update_training_idxes(new_add_idxes)

                #self.batch_sampler.update_weights(new_weights)
                self.batch_sampler.update_training_idxes(new_add_idxes)
                self.val_batch_sampler.update_training_idxes(new_add_idxes)
                print(f'training samples now: {len(curr_training_idxes)+len(new_add_idxes)}')

                assert(len(set(curr_training_idxes)&set(new_add_idxes)) == 0)
                print(curr_training_idxes, new_add_idxes)

            if epoch % 1000 == 0:
                self.save_ckpt(all=True)

            self.save_ckpt()
        if use_wandb:
            wandb.finish()

    def val(self) -> float:
        self.encoder.eval()
        self.decoder.eval()
        print('evaluation now')

        val_losses = []
        udf_losses = []
        grad_losses = []
        remain_indexes = []

        for batch in progress_bar(self.val_loader):
            indexes, _, pcds, coords, gt_udf, gt_grad = batch
            pcds = pcds.cuda()
            coords = coords.cuda()
            gt_udf = gt_udf.cuda()
            gt_grad = gt_grad.cuda()
            indexes = list(indexes.cpu().numpy())

            pcds = random_point_sampling(pcds, self.num_points_pcd)

            gt_udf = gt_udf / self.max_dist
            gt_udf = 1 - gt_udf
            c_u_g = torch.cat([coords, gt_udf.unsqueeze(-1), gt_grad], dim=-1)

            selected_c_u_g = random_point_sampling(c_u_g, self.num_points_forward)
            selected_coords = selected_c_u_g[:, :, :3]
            selected_coords.requires_grad = True
            selected_gt_udf = selected_c_u_g[:, :, 3]
            selected_gt_grad = selected_c_u_g[:, :, 4:]

            with torch.no_grad():
                latent_codes = self.encoder(pcds)

            selected_coords.requires_grad = True
            coords_encoded = self.coords_encoder.encode(selected_coords)
            pred = self.decoder(coords_encoded, latent_codes)

            udf_loss = F.binary_cross_entropy_with_logits(pred, selected_gt_udf)


            udf_pred = torch.sigmoid(pred)
            udf_pred = 1 - udf_pred
            udf_pred *= self.max_dist

            udf_pred.sum().backward()
            gradients = selected_coords.grad
            
            selected_coords.grad.zero_()
            grad_loss = F.mse_loss(gradients, selected_gt_grad, reduction="none")
            
            mask = (selected_gt_udf > 0) & (selected_gt_udf < 1)
            grad_loss = grad_loss[mask].mean()

            loss = udf_loss + 0.1*grad_loss

            val_losses.append(loss.detach().cpu().item())
            udf_losses.append(udf_loss.detach().cpu().item())
            grad_losses.append(grad_loss.detach().cpu().item())
            remain_indexes.append(indexes)
        

        val_losses = np.array(val_losses)
        val_losses = val_losses.reshape(-1)
        print('val_losses: ', val_losses)
        remain_indexes = np.array(remain_indexes).reshape(-1)
        new_can_idxes = list(np.argsort(val_losses)[:100])

        new_add_idxes = [remain_indexes[i] for i in new_can_idxes]
        return np.mean(val_losses), np.mean(udf_losses), np.mean(grad_losses), new_add_idxes


    def save_ckpt(self, all: bool = False, best=False) -> None:
        ckpt = {
            "epoch": self.epoch,
            "encoder": self.encoder.state_dict(),
            "decoder": self.decoder.state_dict(),
            "optimizer": self.optimizer.state_dict(),
        }

        if best:
            for previous_ckpt_path in self.ckpts_path.glob("*.pt"):
                if "best" in previous_ckpt_path.name:
                    previous_ckpt_path.unlink()
            ckpt_path = self.ckpts_path / f"best_{self.epoch}.pt"
            torch.save(ckpt, ckpt_path)

        elif all:
            ckpt_path = self.ckpts_path / f"{self.epoch}.pt"
            torch.save(ckpt, ckpt_path)
        else:
            for previous_ckpt_path in self.ckpts_path.glob("*.pt"):
                if "last" in previous_ckpt_path.name:
                    previous_ckpt_path.unlink()

            ckpt_path = self.ckpts_path / f"last_{self.epoch}.pt"
            torch.save(ckpt, ckpt_path)

    def restore_from_last_ckpt(self) -> None:

        if self.ckpts_path.exists():

            ckpt_paths = [p for p in self.ckpts_path.glob("*.pt") if "last" in p.name]
            error_msg = "Expected only one last ckpt, found none or too many."
            assert len(ckpt_paths) == 1, error_msg

            ckpt_path = ckpt_paths[0]
            ckpt = torch.load(ckpt_path)

            self.epoch = ckpt["epoch"] + 1
            self.global_step = self.epoch * len(self.train_loader)

            self.encoder.load_state_dict(ckpt["encoder"])
            self.decoder.load_state_dict(ckpt["decoder"])
            self.optimizer.load_state_dict(ckpt["optimizer"])


================================================
FILE: AutoEncoder/trainers/test.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data.sampler import Sampler, WeightedRandomSampler



class DynamicWeightedRandomSampler(WeightedRandomSampler):
    r"""Samples elements randomly, without replacement.
    Arguments:
        data_source (Dataset): dataset to sample from
    """

    def update_distribution(self, weights):
        self.weights = weights

    def __len__(self):
        return (self.weights>0).sum().int()

class BatchSampler(Sampler):
    def __init__(self, sampler, batch_size, drop_last):
        self.sampler = sampler
        self.batch_size = batch_size
        self.drop_last = drop_last

    def __iter__(self):
        batch = []
        for _, idx in enumerate(iter(self.sampler)):
            batch = idx
            yield batch

        if len(batch) > 0 and not self.drop_last:
            yield batch

    def __len__(self):
        return len(self.sampler) // self.batch_size

================================================
FILE: AutoEncoder/utils.py
================================================
from pathlib import Path
from typing import Iterable, List, Tuple, Union

import numpy as np
import open3d as o3d
import torch
import torch.nn.functional as F
from einops import repeat
from torch import Tensor
from tqdm import tqdm


def read_mesh(
    mesh_path: Union[str, Path],
    dtype: torch.dtype = torch.float,
) -> Tuple[Tensor, Tensor]:
    """Read a mesh from a given file.
    The mesh is returned as a tuple of torch tensors, containing:
    - The vertices with shape (N, D). D can be 3 if only coordinates are available,
      6 if also normals or colors are available, 9 if both normals
      and colors are available.
    - The trianges with shape (M, D). D can be 3 if normals are not available,
      6 otherwise.
    Args:
        mesh_path: The path of the mesh file.
        dtype: The data type for the output tensors.
    Raises:
        ValueError: If the given file doesn't exist.
    Returns:
        - The tensor with the mesh vertices with shape (N, D).
        - The tensor with the mesh triangles with shape (M, D).
    """
    mesh_path = Path(mesh_path)
    if not mesh_path.exists():
        raise ValueError(f"The mesh file {str(mesh_path)} does not exists.")

    mesh_o3d = o3d.io.read_triangle_mesh(str(mesh_path))
    return get_tensor_mesh_from_o3d(mesh_o3d, dtype)


def get_tensor_mesh_from_o3d(
    mesh_o3d: o3d.geometry.TriangleMesh,
    dtype: torch.dtype = torch.float,
) -> Tuple[Tensor, Tensor]:
    """Convert an open3d mesh to a tuple of torch tensors.
    The mesh is returned as a tuple of torch tensors, containing:
    - The vertices with shape (N, D). D can be 3 if only coordinates are available,
      6 if also normals or colors are available, 9 if both normals
      and colors are available.
    - The trianges with shape (M, D). D can be 3 if normals are not available,
      6 otherwise.
    Args:
        mesh_o3d: The open3d mesh.
    Returns:
        - The tensor with the mesh vertices with shape (N, D).
        - The tensor with the mesh triangles with shape (M, D).
    """
    vertices = torch.tensor(np.asarray(mesh_o3d.vertices), dtype=dtype)

    if len(mesh_o3d.vertex_normals) > 0:
        vertices_normals = torch.tensor(
            np.asarray(mesh_o3d.vertex_normals), dtype=dtype
        )
        vertices = torch.cat((vertices, vertices_normals), dim=-1)

    if len(mesh_o3d.vertex_colors) > 0:
        vertices_colors = torch.tensor(np.asarray(mesh_o3d.vertex_colors), dtype=dtype)
        vertices = torch.cat((vertices, vertices_colors), dim=-1)

    triangles = torch.tensor(np.asarray(mesh_o3d.triangles), dtype=dtype)

    if len(mesh_o3d.triangle_normals) > 0:
        triangles_normals = torch.tensor(
            np.asarray(mesh_o3d.triangle_normals), dtype=dtype
        )
        triangles = torch.cat((triangles, triangles_normals), dim=-1)

    return vertices, triangles


def get_o3d_mesh_from_tensors(
    vertices: Union[Tensor, np.ndarray],
    triangles: Union[Tensor, np.ndarray],
) -> o3d.geometry.TriangleMesh:
    """Get open3d mesh from either numpy arrays or torch tensors.
    The input vertices must have shape (NUM_VERTICES, D), where D
    can be 3 (only X,Y,Z), 6 (X,Y,Z and normals) or 9 (X,Y,Z, normals and colors).
    The input triangles must have shape (NUM_TRIANGLES, D), where D can be 3
    (only vertex indices) or 6 (vertex indices and normals).
    Args:
        vertices: The numpy array or torch tensor with vertices
            with shape (NUM_VERTICES, D).
        triangles: The numpy array or torch tensor with triangles
            with shape (NUM_TRIANGLES, D).
    Returns:
        The open3d mesh.
    """
    mesh_o3d = o3d.geometry.TriangleMesh()

    if isinstance(vertices, Tensor):
        v = vertices.clone().detach().cpu().numpy()
    else:
        v = np.copy(vertices)

    if isinstance(triangles, Tensor):
        t = triangles.clone().detach().cpu().numpy()
    else:
        t = np.copy(triangles)

    mesh_o3d.vertices = o3d.utility.Vector3dVector(v[:, :3])

    if v.shape[1] == 6:
        mesh_o3d.vertex_normals = o3d.utility.Vector3dVector(v[:, 3:6])

    if v.shape[1] == 9:
        mesh_o3d.vertex_colors = o3d.utility.Vector3dVector(v[:, 6:9])

    mesh_o3d.triangles = o3d.utility.Vector3iVector(t[:, :3])

    if t.shape[1] == 6:
        mesh_o3d.triangle_normals = o3d.utility.Vector3dVector(t[:, 3:6])

    return mesh_o3d


def progress_bar(iterable: Iterable, desc: str = "", num_cols: int = 60) -> Iterable:
    """Decorate an iterable object using a progress bar.
    Args:
        iterable: the iterable to decorate.
        desc: the description to print. Defaults to "".
        num_cols: The width of the entire output message. Defaults to 60.
    Returns:
        The decorated iterable.
    """
    bar_format = "{percentage:.0f}%|{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}]"
    if len(desc) > 0:
        bar_format = "{desc}: " + bar_format
    return tqdm(iterable, desc=desc, bar_format=bar_format, ncols=num_cols, leave=False)


def get_tensor_pcd_from_o3d(
    pcd_o3d: o3d.geometry.PointCloud,
    dtype: torch.dtype = torch.float,
) -> Tensor:
    """Convert an open3d point cloud to a torch tensor.
    The point cloud is returned as a torch tensor with shape (NUM_POINTS, D).
    D can be 3 (only XYZ coordinates), 6 (XYZ coordinates and
    normals/colors) or 9 (XYZ coordinates, normals and colors).
    Args:
        pcd_o3d: The open3d point cloud.
    Returns:
        A torch tensor with the loaded point cloud with shape (NUM_POINTS, D).
    """
    pcd_torch = torch.tensor(np.asarray(pcd_o3d.points), dtype=dtype)

    if len(pcd_o3d.normals) > 0:
        normals_torch = torch.tensor(np.asarray(pcd_o3d.normals), dtype=dtype)
        pcd_torch = torch.cat((pcd_torch, normals_torch), dim=-1)

    if len(pcd_o3d.colors) > 0:
        colors_torch = torch.tensor(np.asarray(pcd_o3d.colors), dtype=dtype)
        pcd_torch = torch.cat((pcd_torch, colors_torch), dim=-1)

    return pcd_torch


def sample_points_around_pcd(
    pcd: Tensor,
    stds: List[float],
    num_points_per_std: List[int],
    coords_range: Tuple[float, float],
    device: str = "cpu",
) -> Tensor:
    """Sample points around the given point cloud.
    Points are sampled by adding gaussian noise to the input cloud,
    according to the given standard deviations. Additionally, points
    are also sampled uniformly in the given range.
    Args:
        pcd: The point cloud tensor with shape (N, 3).
        stds: A list of standard deviations to compute the gaussian noise
            to obtain the points.
        num_points_per_std: A list with the number of points to sample for each
            standard deviation. The last number refers to points sampled uniformly
            in the given range (i.e., len(num_points_per_std) = len(stds) + 1).
        coords_range: The range for the points coordinates.
        device: The device for the sampled points. Defaults to "cpu".
    Returns:
        The sampled points with shape (M, 3).
    """
    coords = torch.empty(0, 3).to(device)
    num_points_pcd = pcd.shape[0]

    for sigma, num_points in zip(stds, num_points_per_std[:-1]):
        mul = num_points // num_points_pcd

        if mul > 0:
            coords_for_sampling = repeat(pcd, "n d -> (n r) d", r=mul).to(device)
        else:
            coords_for_sampling = torch.empty(0, 3).to(device)

        still_needed = num_points % num_points_pcd
        if still_needed > 0:
            weights = torch.ones(num_points_pcd, dtype=torch.float).to(device)
            indices_random = torch.multinomial(weights, still_needed, replacement=False)
            pcd_random = pcd[indices_random].to(device)
            coords_for_sampling = torch.cat((coords_for_sampling, pcd_random), dim=0)

        offsets = torch.randn(num_points, 3).to(device) * sigma
        coords_i = coords_for_sampling + offsets

        coords = torch.cat((coords, coords_i), dim=0)

    random_coords = torch.rand(num_points_per_std[-1], 3).to(device)
    random_coords *= coords_range[1] - coords_range[0]
    random_coords += coords_range[0]
    coords = torch.cat((coords, random_coords), dim=0)

    coords = torch.clip(coords, min=coords_range[0], max=coords_range[1])

    return coords


def compute_udf_and_gradients(
    mesh_o3d: o3d.geometry.TriangleMesh,
    queries: Tensor,
) -> Tuple[Tensor, Tensor]:
    scene = o3d.t.geometry.RaycastingScene()
    vertices = np.asarray(mesh_o3d.vertices, dtype=np.float32)
    triangles = np.asarray(mesh_o3d.triangles, dtype=np.uint32)
    _ = scene.add_triangles(vertices, triangles)

    #signed_distance = scene.compute_signed_distance(query_point)
    closest_points = scene.compute_closest_points(queries.numpy())["points"]
    closest_points = torch.tensor(closest_points.numpy())

    q2p = queries - closest_points
    udf = torch.linalg.vector_norm(q2p, dim=-1)
    gradients = F.normalize(q2p, dim=-1)

    return udf, gradients

def compute_sdf_and_gradients(
    mesh_o3d: o3d.geometry.TriangleMesh,
    queries: Tensor,
) -> Tuple[Tensor, Tensor]:
    scene = o3d.t.geometry.RaycastingScene()
    vertices = np.asarray(mesh_o3d.vertices, dtype=np.float32)
    triangles = np.asarray(mesh_o3d.triangles, dtype=np.uint32)
    _ = scene.add_triangles(vertices, triangles)

    signed_distance = scene.compute_signed_distance(queries.numpy())
    closest_points = scene.compute_closest_points(queries.numpy())["points"]
    closest_points = torch.tensor(closest_points.numpy())
    signed_distance = torch.tensor(signed_distance.numpy())

    #print(signed_distance.shape)

    #gradients = np.zeros((signed_distance.shape[0], 3))
    q2p = queries - closest_points
    gradients = F.normalize(q2p, dim=-1)
    gradients = np.sign(signed_distance)[:, None] * gradients
    #print(gradients.shape)

    return signed_distance, gradients



def compute_udf_from_mesh(
    mesh_o3d: o3d.geometry.TriangleMesh,
    num_surface_points: int = 100_000,
    num_queries_on_surface: int = 10_000,
    queries_stds: List[float] = [0.003, 0.01, 0.1],
    num_queries_per_std: List[int] = [5_000, 4_000, 500, 500],
    coords_range: Tuple[float, float] = (-1.0, 1.0),
    max_dist: float = 0.1,
    convert_to_bce_labels: bool = False,
    use_cuda: bool = True,
    input_queries = None
) -> Tuple[Tensor, Tensor, Tensor]:
    pcd_o3d = mesh_o3d.sample_points_uniformly(number_of_points=num_surface_points)
    pcd = get_tensor_pcd_from_o3d(pcd_o3d)[:, :3]

    device = "cuda" if use_cuda else "cpu"
    if input_queries is not None:
        queries = input_queries
    else:
        queries = sample_points_around_pcd(
            pcd,
            queries_stds,
            num_queries_per_std,
            coords_range,
            device,
        )
    queries = queries.cpu()

    udf, gradients = compute_udf_and_gradients(mesh_o3d, queries)
    values = torch.clip(udf, min=0, max=max_dist)

    # q_on_surf_o3d = mesh_o3d.sample_points_uniformly(
    #     number_of_points=num_queries_on_surface
    # )
    # queries_on_surface = get_tensor_pcd_from_o3d(q_on_surf_o3d)[:, :3]
    # values_on_surface = torch.zeros(num_queries_on_surface)
    # gradients_on_surface = torch.zeros(num_queries_on_surface, 3)

    # queries = torch.cat([queries_on_surface, queries], dim=0)
    # values = torch.cat([values_on_surface, values], dim=0)
    # gradients = torch.cat([gradients_on_surface, gradients], dim=0)

    # if convert_to_bce_labels:
    #     values /= max_dist
    #     values = 1 - values

    return queries, values, gradients


def compute_sdf_from_mesh(
    mesh_o3d: o3d.geometry.TriangleMesh,
    num_surface_points: int = 100_000,
    num_queries_on_surface: int = 10_000,
    queries_stds: List[float] = [0.003, 0.01, 0.1],
    num_queries_per_std: List[int] = [5_000, 4_000, 500, 500],
    coords_range: Tuple[float, float] = (-1.0, 1.0),
    max_dist: float = 0.1,
    convert_to_bce_labels: bool = False,
    use_cuda: bool = True,
    input_queries = None
) -> Tuple[Tensor, Tensor, Tensor]:
    pcd_o3d = mesh_o3d.sample_points_uniformly(number_of_points=num_surface_points)
    pcd = get_tensor_pcd_from_o3d(pcd_o3d)[:, :3]

    device = "cuda" if use_cuda else "cpu"
    if input_queries is not None:
        queries = input_queries
    else:
        queries = sample_points_around_pcd(
            pcd,
            queries_stds,
            num_queries_per_std,
            coords_range,
            device,
        )
    queries = queries.cpu()

    sdf, gradients = compute_sdf_and_gradients(mesh_o3d, queries)
    values = torch.clip(sdf, min=-max_dist, max=max_dist)

    q_on_surf_o3d = mesh_o3d.sample_points_uniformly(
        number_of_points=num_queries_on_surface
    )
    queries_on_surface = get_tensor_pcd_from_o3d(q_on_surf_o3d)[:, :3]
    values_on_surface = torch.zeros(num_queries_on_surface)
    gradients_on_surface = torch.zeros(num_queries_on_surface, 3)

    queries = torch.cat([queries_on_surface, queries], dim=0)
    values = torch.cat([values_on_surface, values], dim=0)
    gradients = torch.cat([gradients_on_surface, gradients], dim=0)

    if convert_to_bce_labels:
        values /= max_dist
        values = 1 - values

    return queries, values, gradients

def compute_gradients(x: Tensor, y: Tensor) -> Tensor:
    grad_outputs = torch.ones_like(y)
    grads = torch.autograd.grad(y, x, grad_outputs=grad_outputs, create_graph=True)[0]
    return grads


def batchify(inputs: List[Tensor], required_dim: int) -> Tuple[bool, List[Tensor]]:
    """Batchify input tensors if needed.
    All the input tensors with a number of dimensions smaller than
    required_dim will be expanded with a leading batch dimension.
    Args:
        inputs: The tensors to batchify.
        required_dim: The required number of dimensions.
    Returns:
        - A flag that indicates wether one of the inputs has been batchified.
        - The batchified tensors.
    """
    results: List[Tensor] = []
    has_changed = False

    for t in inputs:
        has_changed = len(t.shape) < required_dim or has_changed
        batched_t = torch.unsqueeze(t, dim=0) if has_changed else t
        results.append(batched_t)

    return has_changed, results


def unbatchify(inputs: List[Tensor]) -> List[Tensor]:
    """Remove batch dimension from input tensors.
    Args:
        inputs: The tensors to unbatchify.
    Returns:
        The unbatchified tensors.
    """
    results: List[Tensor] = []
    for t in inputs:
        unbatched_t = torch.squeeze(t, dim=0)
        results.append(unbatched_t)

    return results


def random_point_sampling(pcd: Tensor, num_points: int) -> Tensor:
    """Sample the requested number of points from the given point cloud(s).
    Points are sampled randomly. If num_points is greater than NUM_POINTS,
    then points are sampled with replacement.
    Args:
        pcd: The input point cloud(s) with shape ([B,] NUM_POINTS, D).
        num_points: The number of points to sample.
    Returns:
        The sampled points with shape ([B,] NUM_SAMPLED_POINTS, D).
    """
    batched, [pcd] = batchify([pcd], 3)

    batch_size, original_num_points, _ = pcd.shape

    weights = torch.ones((batch_size, original_num_points), dtype=torch.float)
    weights = weights.to(pcd.device)

    replacement = original_num_points < num_points

    indices_to_sample = torch.multinomial(weights, num_points, replacement=replacement)
    
    batch_indices = torch.arange(batch_size).reshape(batch_size, 1)
    sampled_points = pcd[batch_indices, indices_to_sample]

    if batched:
        [sampled_points] = unbatchify([sampled_points])

    return sampled_points


================================================
FILE: CLIP/.gitignore
================================================
__pycache__/
*.py[cod]
*$py.class
*.egg-info
.pytest_cache
.ipynb_checkpoints

thumbs.db
.DS_Store
.idea


================================================
FILE: CLIP/LICENSE
================================================
MIT License

Copyright (c) 2021 OpenAI

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



================================================
FILE: CLIP/MANIFEST.in
================================================
include clip/bpe_simple_vocab_16e6.txt.gz


================================================
FILE: CLIP/README.md
================================================
# CLIP

[[Blog]](https://openai.com/blog/clip/) [[Paper]](https://arxiv.org/abs/2103.00020) [[Model Card]](model-card.md) [[Colab]](https://colab.research.google.com/github/openai/clip/blob/master/notebooks/Interacting_with_CLIP.ipynb)

CLIP (Contrastive Language-Image Pre-Training) is a neural network trained on a variety of (image, text) pairs. It can be instructed in natural language to predict the most relevant text snippet, given an image, without directly optimizing for the task, similarly to the zero-shot capabilities of GPT-2 and 3. We found CLIP matches the performance of the original ResNet50 on ImageNet “zero-shot” without using any of the original 1.28M labeled examples, overcoming several major challenges in computer vision.



## Approach

![CLIP](CLIP.png)



## Usage

First, [install PyTorch 1.7.1](https://pytorch.org/get-started/locally/) (or later) and torchvision, as well as small additional dependencies, and then install this repo as a Python package. On a CUDA GPU machine, the following will do the trick:

```bash
$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
$ pip install ftfy regex tqdm
$ pip install git+https://github.com/openai/CLIP.git
```

Replace `cudatoolkit=11.0` above with the appropriate CUDA version on your machine or `cpuonly` when installing on a machine without a GPU.

```python
import torch
import clip
from PIL import Image

device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)

image = preprocess(Image.open("CLIP.png")).unsqueeze(0).to(device)
text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)

with torch.no_grad():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    
    logits_per_image, logits_per_text = model(image, text)
    probs = logits_per_image.softmax(dim=-1).cpu().numpy()

print("Label probs:", probs)  # prints: [[0.9927937  0.00421068 0.00299572]]
```


## API

The CLIP module `clip` provides the following methods:

#### `clip.available_models()`

Returns the names of the available CLIP models.

#### `clip.load(name, device=..., jit=False)`

Returns the model and the TorchVision transform needed by the model, specified by the model name returned by `clip.available_models()`. It will download the model as necessary. The `name` argument can also be a path to a local checkpoint.

The device to run the model can be optionally specified, and the default is to use the first CUDA device if there is any, otherwise the CPU. When `jit` is `False`, a non-JIT version of the model will be loaded.

#### `clip.tokenize(text: Union[str, List[str]], context_length=77)`

Returns a LongTensor containing tokenized sequences of given text input(s). This can be used as the input to the model

---

The model returned by `clip.load()` supports the following methods:

#### `model.encode_image(image: Tensor)`

Given a batch of images, returns the image features encoded by the vision portion of the CLIP model.

#### `model.encode_text(text: Tensor)`

Given a batch of text tokens, returns the text features encoded by the language portion of the CLIP model.

#### `model(image: Tensor, text: Tensor)`

Given a batch of images and a batch of text tokens, returns two Tensors, containing the logit scores corresponding to each image and text input. The values are cosine similarities between the corresponding image and text features, times 100.



## More Examples

### Zero-Shot Prediction

The code below performs zero-shot prediction using CLIP, as shown in Appendix B in the paper. This example takes an image from the [CIFAR-100 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), and predicts the most likely labels among the 100 textual labels from the dataset.

```python
import os
import clip
import torch
from torchvision.datasets import CIFAR100

# Load the model
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load('ViT-B/32', device)

# Download the dataset
cifar100 = CIFAR100(root=os.path.expanduser("~/.cache"), download=True, train=False)

# Prepare the inputs
image, class_id = cifar100[3637]
image_input = preprocess(image).unsqueeze(0).to(device)
text_inputs = torch.cat([clip.tokenize(f"a photo of a {c}") for c in cifar100.classes]).to(device)

# Calculate features
with torch.no_grad():
    image_features = model.encode_image(image_input)
    text_features = model.encode_text(text_inputs)

# Pick the top 5 most similar labels for the image
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarity = (100.0 * image_features @ text_features.T).softmax(dim=-1)
values, indices = similarity[0].topk(5)

# Print the result
print("\nTop predictions:\n")
for value, index in zip(values, indices):
    print(f"{cifar100.classes[index]:>16s}: {100 * value.item():.2f}%")
```

The output will look like the following (the exact numbers may be slightly different depending on the compute device):

```
Top predictions:

           snake: 65.31%
          turtle: 12.29%
    sweet_pepper: 3.83%
          lizard: 1.88%
       crocodile: 1.75%
```

Note that this example uses the `encode_image()` and `encode_text()` methods that return the encoded features of given inputs.


### Linear-probe evaluation

The example below uses [scikit-learn](https://scikit-learn.org/) to perform logistic regression on image features.

```python
import os
import clip
import torch

import numpy as np
from sklearn.linear_model import LogisticRegression
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR100
from tqdm import tqdm

# Load the model
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load('ViT-B/32', device)

# Load the dataset
root = os.path.expanduser("~/.cache")
train = CIFAR100(root, download=True, train=True, transform=preprocess)
test = CIFAR100(root, download=True, train=False, transform=preprocess)


def get_features(dataset):
    all_features = []
    all_labels = []
    
    with torch.no_grad():
        for images, labels in tqdm(DataLoader(dataset, batch_size=100)):
            features = model.encode_image(images.to(device))

            all_features.append(features)
            all_labels.append(labels)

    return torch.cat(all_features).cpu().numpy(), torch.cat(all_labels).cpu().numpy()

# Calculate the image features
train_features, train_labels = get_features(train)
test_features, test_labels = get_features(test)

# Perform logistic regression
classifier = LogisticRegression(random_state=0, C=0.316, max_iter=1000, verbose=1)
classifier.fit(train_features, train_labels)

# Evaluate using the logistic regression classifier
predictions = classifier.predict(test_features)
accuracy = np.mean((test_labels == predictions).astype(float)) * 100.
print(f"Accuracy = {accuracy:.3f}")
```

Note that the `C` value should be determined via a hyperparameter sweep using a validation split.


## See Also

* [OpenCLIP](https://github.com/mlfoundations/open_clip): includes larger and independently trained CLIP models up to ViT-G/14
* [Hugging Face implementation of CLIP](https://huggingface.co/docs/transformers/model_doc/clip): for easier integration with the HF ecosystem


================================================
FILE: CLIP/__init__.py
================================================
from .clip import *


================================================
FILE: CLIP/clip/__init__.py
================================================
from .clip import *


================================================
FILE: CLIP/clip/clip.py
================================================
import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging

import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm

from .model import build_model
from .simple_tokenizer import SimpleTokenizer as _Tokenizer

try:
    from torchvision.transforms import InterpolationMode
    BICUBIC = InterpolationMode.BICUBIC
except ImportError:
    BICUBIC = Image.BICUBIC


if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
    warnings.warn("PyTorch version 1.7.1 or higher is recommended")


__all__ = ["available_models", "load", "tokenize"]
_tokenizer = _Tokenizer()

_MODELS = {
    "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
    "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
    "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
    "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
    "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
    "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
}


def _download(url: str, root: str):
    os.makedirs(root, exist_ok=True)
    filename = os.path.basename(url)

    expected_sha256 = url.split("/")[-2]
    download_target = os.path.join(root, filename)

    if os.path.exists(download_target) and not os.path.isfile(download_target):
        raise RuntimeError(f"{download_target} exists and is not a regular file")

    if os.path.isfile(download_target):
        if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
            return download_target
        else:
            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")

    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
            while True:
                buffer = source.read(8192)
                if not buffer:
                    break

                output.write(buffer)
                loop.update(len(buffer))

    if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")

    return download_target


def _convert_image_to_rgb(image):
    return image.convert("RGB")


def _transform(n_px):
    return Compose([
        Resize(n_px, interpolation=BICUBIC),
        CenterCrop(n_px),
        _convert_image_to_rgb,
        ToTensor(),
        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
    ])


def available_models() -> List[str]:
    """Returns the names of available CLIP models"""
    return list(_MODELS.keys())


def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
    """Load a CLIP model

    Parameters
    ----------
    name : str
        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict

    device : Union[str, torch.device]
        The device to put the loaded model

    jit : bool
        Whether to load the optimized JIT model or more hackable non-JIT model (default).

    download_root: str
        path to download the model files; by default, it uses "~/.cache/clip"

    Returns
    -------
    model : torch.nn.Module
        The CLIP model

    preprocess : Callable[[PIL.Image], torch.Tensor]
        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
    """
    if name in _MODELS:
        model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
    elif os.path.isfile(name):
        model_path = name
    else:
        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")

    with open(model_path, 'rb') as opened_file:
        try:
            # loading JIT archive
            model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
            state_dict = None
        except RuntimeError:
            # loading saved state dict
            if jit:
                warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
                jit = False
            state_dict = torch.load(opened_file, map_location="cpu")

    if not jit:
        model = build_model(state_dict or model.state_dict()).to(device)
        if str(device) == "cpu":
            model.float()
        return model, _transform(model.visual.input_resolution)

    # patch the device names
    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]

    def patch_device(module):
        try:
            graphs = [module.graph] if hasattr(module, "graph") else []
        except RuntimeError:
            graphs = []

        if hasattr(module, "forward1"):
            graphs.append(module.forward1.graph)

        for graph in graphs:
            for node in graph.findAllNodes("prim::Constant"):
                if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
                    node.copyAttributes(device_node)

    model.apply(patch_device)
    patch_device(model.encode_image)
    patch_device(model.encode_text)

    # patch dtype to float32 on CPU
    if str(device) == "cpu":
        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
        float_node = float_input.node()

        def patch_float(module):
            try:
                graphs = [module.graph] if hasattr(module, "graph") else []
            except RuntimeError:
                graphs = []

            if hasattr(module, "forward1"):
                graphs.append(module.forward1.graph)

            for graph in graphs:
                for node in graph.findAllNodes("aten::to"):
                    inputs = list(node.inputs())
                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
                        if inputs[i].node()["value"] == 5:
                            inputs[i].node().copyAttributes(float_node)

        model.apply(patch_float)
        patch_float(model.encode_image)
        patch_float(model.encode_text)

        model.float()

    return model, _transform(model.input_resolution.item())


def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
    """
    Returns the tokenized representation of given input string(s)

    Parameters
    ----------
    texts : Union[str, List[str]]
        An input string or a list of input strings to tokenize

    context_length : int
        The context length to use; all CLIP models use 77 as the context length

    truncate: bool
        Whether to truncate the text in case its encoding is longer than the context length

    Returns
    -------
    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
    """
    if isinstance(texts, str):
        texts = [texts]

    sot_token = _tokenizer.encoder["<|startoftext|>"]
    eot_token = _tokenizer.encoder["<|endoftext|>"]
    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
    if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
    else:
        result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)

    for i, tokens in enumerate(all_tokens):
        if len(tokens) > context_length:
            if truncate:
                tokens = tokens[:context_length]
                tokens[-1] = eot_token
            else:
                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
        result[i, :len(tokens)] = torch.tensor(tokens)

    return result


================================================
FILE: CLIP/clip/model.py
================================================
from collections import OrderedDict
from typing import Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1):
        super().__init__()

        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu2 = nn.ReLU(inplace=True)

        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()

        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu3 = nn.ReLU(inplace=True)

        self.downsample = None
        self.stride = stride

        if stride > 1 or inplanes != planes * Bottleneck.expansion:
            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
            self.downsample = nn.Sequential(OrderedDict([
                ("-1", nn.AvgPool2d(stride)),
                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
                ("1", nn.BatchNorm2d(planes * self.expansion))
            ]))

    def forward(self, x: torch.Tensor):
        identity = x

        out = self.relu1(self.bn1(self.conv1(x)))
        out = self.relu2(self.bn2(self.conv2(out)))
        out = self.avgpool(out)
        out = self.bn3(self.conv3(out))

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu3(out)
        return out


class AttentionPool2d(nn.Module):
    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
        super().__init__()
        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    def forward(self, x):
        x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
        x, _ = F.multi_head_attention_forward(
            query=x[:1], key=x, value=x,
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False
        )
        return x.squeeze(0)


class ModifiedResNet(nn.Module):
    """
    A ResNet class that is similar to torchvision's but contains the following changes:
    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
    - The final pooling layer is a QKV attention instead of an average pool
    """

    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
        super().__init__()
        self.output_dim = output_dim
        self.input_resolution = input_resolution

        # the 3-layer stem
        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width // 2)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(width // 2)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(width)
        self.relu3 = nn.ReLU(inplace=True)
        self.avgpool = nn.AvgPool2d(2)

        # residual layers
        self._inplanes = width  # this is a *mutable* variable used during construction
        self.layer1 = self._make_layer(width, layers[0])
        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)

        embed_dim = width * 32  # the ResNet feature dimension
        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)

    def _make_layer(self, planes, blocks, stride=1):
        layers = [Bottleneck(self._inplanes, planes, stride)]

        self._inplanes = planes * Bottleneck.expansion
        for _ in range(1, blocks):
            layers.append(Bottleneck(self._inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        def stem(x):
            x = self.relu1(self.bn1(self.conv1(x)))
            x = self.relu2(self.bn2(self.conv2(x)))
            x = self.relu3(self.bn3(self.conv3(x)))
            x = self.avgpool(x)
            return x

        x = x.type(self.conv1.weight.dtype)
        x = stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.attnpool(x)

        return x


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        ret = super().forward(x.type(torch.float32))
        return ret.type(orig_type)


class QuickGELU(nn.Module):
    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlock(nn.Module):
    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
        super().__init__()

        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(OrderedDict([
            ("c_fc", nn.Linear(d_model, d_model * 4)),
            ("gelu", QuickGELU()),
            ("c_proj", nn.Linear(d_model * 4, d_model))
        ]))
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask

    def attention(self, x: torch.Tensor):
        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]

    def forward(self, x: torch.Tensor):
        x = x + self.attention(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class Transformer(nn.Module):
    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
        super().__init__()
        self.width = width
        self.layers = layers
        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])

    def forward(self, x: torch.Tensor):
        return self.resblocks(x)


class VisionTransformer(nn.Module):
    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
        super().__init__()
        self.input_resolution = input_resolution
        self.output_dim = output_dim
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)

        scale = width ** -0.5
        self.class_embedding = nn.Parameter(scale * torch.randn(width))
        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
        self.ln_pre = LayerNorm(width)

        self.transformer = Transformer(width, layers, heads)

        self.ln_post = LayerNorm(width)
        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))

    def forward(self, x: torch.Tensor):
        x = self.conv1(x)  # shape = [*, width, grid, grid]
        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
        x = x + self.positional_embedding.to(x.dtype)
        x = self.ln_pre(x)

        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD

        x = self.ln_post(x[:, 0, :])

        if self.proj is not None:
            x = x @ self.proj

        return x


class CLIP(nn.Module):
    def __init__(self,
                 embed_dim: int,
                 # vision
                 image_resolution: int,
                 vision_layers: Union[Tuple[int, int, int, int], int],
                 vision_width: int,
                 vision_patch_size: int,
                 # text
                 context_length: int,
                 vocab_size: int,
                 transformer_width: int,
                 transformer_heads: int,
                 transformer_layers: int
                 ):
        super().__init__()

        self.context_length = context_length

        if isinstance(vision_layers, (tuple, list)):
            vision_heads = vision_width * 32 // 64
            self.visual = ModifiedResNet(
                layers=vision_layers,
                output_dim=embed_dim,
                heads=vision_heads,
                input_resolution=image_resolution,
                width=vision_width
            )
        else:
            vision_heads = vision_width // 64
            self.visual = VisionTransformer(
                input_resolution=image_resolution,
                patch_size=vision_patch_size,
                width=vision_width,
                layers=vision_layers,
                heads=vision_heads,
                output_dim=embed_dim
            )

        self.transformer = Transformer(
            width=transformer_width,
            layers=transformer_layers,
            heads=transformer_heads,
            attn_mask=self.build_attention_mask()
        )

        self.vocab_size = vocab_size
        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
        self.ln_final = LayerNorm(transformer_width)

        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))

        self.initialize_parameters()

    def initialize_parameters(self):
        nn.init.normal_(self.token_embedding.weight, std=0.02)
        nn.init.normal_(self.positional_embedding, std=0.01)

        if isinstance(self.visual, ModifiedResNet):
            if self.visual.attnpool is not None:
                std = self.visual.attnpool.c_proj.in_features ** -0.5
                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)

            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
                for name, param in resnet_block.named_parameters():
                    if name.endswith("bn3.weight"):
                        nn.init.zeros_(param)

        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
        attn_std = self.transformer.width ** -0.5
        fc_std = (2 * self.transformer.width) ** -0.5
        for block in self.transformer.resblocks:
            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)

        if self.text_projection is not None:
            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)

    def build_attention_mask(self):
        # lazily create causal attention mask, with full attention between the vision tokens
        # pytorch uses additive attention mask; fill with -inf
        mask = torch.empty(self.context_length, self.context_length)
        mask.fill_(float("-inf"))
        mask.triu_(1)  # zero out the lower diagonal
        return mask

    @property
    def dtype(self):
        return self.visual.conv1.weight.dtype

    def encode_image(self, image):
        return self.visual(image.type(self.dtype))

    def encode_text(self, text):
        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]

        x = x + self.positional_embedding.type(self.dtype)
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD
        x = self.ln_final(x).type(self.dtype)

        # x.shape = [batch_size, n_ctx, transformer.width]
        # take features from the eot embedding (eot_token is the highest number in each sequence)
        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection

        return x

    def forward(self, image, text):
        image_features = self.encode_image(image)
        text_features = self.encode_text(text)

        # normalized features
        image_features = image_features / image_features.norm(dim=1, keepdim=True)
        text_features = text_features / text_features.norm(dim=1, keepdim=True)

        # cosine similarity as logits
        logit_scale = self.logit_scale.exp()
        logits_per_image = logit_scale * image_features @ text_features.t()
        logits_per_text = logits_per_image.t()

        # shape = [global_batch_size, global_batch_size]
        return logits_per_image, logits_per_text


def convert_weights(model: nn.Module):
    """Convert applicable model parameters to fp16"""

    def _convert_weights_to_fp16(l):
        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
            l.weight.data = l.weight.data.half()
            if l.bias is not None:
                l.bias.data = l.bias.data.half()

        if isinstance(l, nn.MultiheadAttention):
            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
                tensor = getattr(l, attr)
                if tensor is not None:
                    tensor.data = tensor.data.half()

        for name in ["text_projection", "proj"]:
            if hasattr(l, name):
                attr = getattr(l, name)
                if attr is not None:
                    attr.data = attr.data.half()

    model.apply(_convert_weights_to_fp16)


def build_model(state_dict: dict):
    vit = "visual.proj" in state_dict

    if vit:
        vision_width = state_dict["visual.conv1.weight"].shape[0]
        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
        image_resolution = vision_patch_size * grid_size
    else:
        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
        vision_layers = tuple(counts)
        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
        vision_patch_size = None
        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
        image_resolution = output_width * 32

    embed_dim = state_dict["text_projection"].shape[1]
    context_length = state_dict["positional_embedding"].shape[0]
    vocab_size = state_dict["token_embedding.weight"].shape[0]
    transformer_width = state_dict["ln_final.weight"].shape[0]
    transformer_heads = transformer_width // 64
    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))

    model = CLIP(
        embed_dim,
        image_resolution, vision_layers, vision_width, vision_patch_size,
        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
    )

    for key in ["input_resolution", "context_length", "vocab_size"]:
        if key in state_dict:
            del state_dict[key]

    convert_weights(model)
    model.load_state_dict(state_dict)
    return model.eval()


================================================
FILE: CLIP/clip/simple_tokenizer.py
================================================
import gzip
import html
import os
from functools import lru_cache

import ftfy
import regex as re


@lru_cache()
def default_bpe():
    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")


@lru_cache()
def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a signficant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8+n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


def get_pairs(word):
    """Return set of symbol pairs in a word.
    Word is represented as tuple of symbols (symbols being variable-length strings).
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


def basic_clean(text):
    text = ftfy.fix_text(text)
    text = html.unescape(html.unescape(text))
    return text.strip()


def whitespace_clean(text):
    text = re.sub(r'\s+', ' ', text)
    text = text.strip()
    return text


class SimpleTokenizer(object):
    def __init__(self, bpe_path: str = default_bpe()):
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
        merges = merges[1:49152-256-2+1]
        merges = [tuple(merge.split()) for merge in merges]
        vocab = list(bytes_to_unicode().values())
        vocab = vocab + [v+'</w>' for v in vocab]
        for merge in merges:
            vocab.append(''.join(merge))
        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
        self.encoder = dict(zip(vocab, range(len(vocab))))
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)

    def bpe(self, token):
        if token in self.cache:
            return self.cache[token]
        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
        pairs = get_pairs(word)

        if not pairs:
            return token+'</w>'

        while True:
            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                    new_word.extend(word[i:j])
                    i = j
                except:
                    new_word.extend(word[i:])
                    break

                if word[i] == first and i < len(word)-1 and word[i+1] == second:
                    new_word.append(first+second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = ' '.join(word)
        self.cache[token] = word
        return word

    def encode(self, text):
        bpe_tokens = []
        text = whitespace_clean(basic_clean(text)).lower()
        for token in re.findall(self.pat, text):
            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
        return bpe_tokens

    def decode(self, tokens):
        text = ''.join([self.decoder[token] for token in tokens])
        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
        return text


================================================
FILE: CLIP/clip.py
================================================
import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging

import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm

from .model import build_model
from .simple_tokenizer import SimpleTokenizer as _Tokenizer

try:
    from torchvision.transforms import InterpolationMode
    BICUBIC = InterpolationMode.BICUBIC
except ImportError:
    BICUBIC = Image.BICUBIC


if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
    warnings.warn("PyTorch version 1.7.1 or higher is recommended")


__all__ = ["available_models", "load", "tokenize"]
_tokenizer = _Tokenizer()

_MODELS = {
    "RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
    "RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
    "RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
    "RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
    "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
    "ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
    "ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
    "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
    "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
}


def _download(url: str, root: str):
    os.makedirs(root, exist_ok=True)
    filename = os.path.basename(url)

    expected_sha256 = url.split("/")[-2]
    download_target = os.path.join(root, filename)

    if os.path.exists(download_target) and not os.path.isfile(download_target):
        raise RuntimeError(f"{download_target} exists and is not a regular file")

    if os.path.isfile(download_target):
        if hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
            return download_target
        else:
            warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")

    with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
        with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:
            while True:
                buffer = source.read(8192)
                if not buffer:
                    break

                output.write(buffer)
                loop.update(len(buffer))

    if hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
        raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match")

    return download_target


def _convert_image_to_rgb(image):
    return image.convert("RGB")


def _transform(n_px):
    return Compose([
        Resize(n_px, interpolation=BICUBIC),
        CenterCrop(n_px),
        _convert_image_to_rgb,
        ToTensor(),
        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
    ])


def available_models() -> List[str]:
    """Returns the names of available CLIP models"""
    return list(_MODELS.keys())


def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
    """Load a CLIP model

    Parameters
    ----------
    name : str
        A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict

    device : Union[str, torch.device]
        The device to put the loaded model

    jit : bool
        Whether to load the optimized JIT model or more hackable non-JIT model (default).

    download_root: str
        path to download the model files; by default, it uses "~/.cache/clip"

    Returns
    -------
    model : torch.nn.Module
        The CLIP model

    preprocess : Callable[[PIL.Image], torch.Tensor]
        A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
    """
    if name in _MODELS:
        model_path = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
    elif os.path.isfile(name):
        model_path = name
    else:
        raise RuntimeError(f"Model {name} not found; available models = {available_models()}")

    with open(model_path, 'rb') as opened_file:
        try:
            # loading JIT archive
            model = torch.jit.load(opened_file, map_location=device if jit else "cpu").eval()
            state_dict = None
        except RuntimeError:
            # loading saved state dict
            if jit:
                warnings.warn(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
                jit = False
            state_dict = torch.load(opened_file, map_location="cpu")

    if not jit:
        model = build_model(state_dict or model.state_dict()).to(device)
        if str(device) == "cpu":
            model.float()
        return model, _transform(model.visual.input_resolution)

    # patch the device names
    device_holder = torch.jit.trace(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
    device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-1]

    def patch_device(module):
        try:
            graphs = [module.graph] if hasattr(module, "graph") else []
        except RuntimeError:
            graphs = []

        if hasattr(module, "forward1"):
            graphs.append(module.forward1.graph)

        for graph in graphs:
            for node in graph.findAllNodes("prim::Constant"):
                if "value" in node.attributeNames() and str(node["value"]).startswith("cuda"):
                    node.copyAttributes(device_node)

    model.apply(patch_device)
    patch_device(model.encode_image)
    patch_device(model.encode_text)

    # patch dtype to float32 on CPU
    if str(device) == "cpu":
        float_holder = torch.jit.trace(lambda: torch.ones([]).float(), example_inputs=[])
        float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
        float_node = float_input.node()

        def patch_float(module):
            try:
                graphs = [module.graph] if hasattr(module, "graph") else []
            except RuntimeError:
                graphs = []

            if hasattr(module, "forward1"):
                graphs.append(module.forward1.graph)

            for graph in graphs:
                for node in graph.findAllNodes("aten::to"):
                    inputs = list(node.inputs())
                    for i in [1, 2]:  # dtype can be the second or third argument to aten::to()
                        if inputs[i].node()["value"] == 5:
                            inputs[i].node().copyAttributes(float_node)

        model.apply(patch_float)
        patch_float(model.encode_image)
        patch_float(model.encode_text)

        model.float()

    return model, _transform(model.input_resolution.item())


def tokenize(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> Union[torch.IntTensor, torch.LongTensor]:
    """
    Returns the tokenized representation of given input string(s)

    Parameters
    ----------
    texts : Union[str, List[str]]
        An input string or a list of input strings to tokenize

    context_length : int
        The context length to use; all CLIP models use 77 as the context length

    truncate: bool
        Whether to truncate the text in case its encoding is longer than the context length

    Returns
    -------
    A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
    We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
    """
    if isinstance(texts, str):
        texts = [texts]

    sot_token = _tokenizer.encoder["<|startoftext|>"]
    eot_token = _tokenizer.encoder["<|endoftext|>"]
    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
    if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
        result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
    else:
        result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)

    for i, tokens in enumerate(all_tokens):
        if len(tokens) > context_length:
            if truncate:
                tokens = tokens[:context_length]
                tokens[-1] = eot_token
            else:
                raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
        result[i, :len(tokens)] = torch.tensor(tokens)

    return result


================================================
FILE: CLIP/data/country211.md
================================================
# The Country211 Dataset

In the paper, we used an image classification dataset called Country211, to evaluate the model's capability on geolocation. To do so, we filtered the YFCC100m dataset that have GPS coordinate corresponding to a [ISO-3166 country code](https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes) and created a balanced dataset by sampling 150 train images, 50 validation images, and 100 test images images for each country.

The following command will download an 11GB archive countaining the images and extract into a subdirectory `country211`:

```bash
wget https://openaipublic.azureedge.net/clip/data/country211.tgz
tar zxvf country211.tgz
```

These images are a subset of the YFCC100m dataset. Use of the underlying media files is subject to the Creative Commons licenses chosen by their creators/uploaders. For more information about the YFCC100M dataset, visit [the official website](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/).

================================================
FILE: CLIP/data/prompts.md
================================================
# Prompts for Image Classification

Below are the class names and templates that are used for collecting the zero-shot classification scores in the paper. Each dataset has two lists `classes` and `templates`, where the string `{}` in the template is to be replaced with the corresponding class names. For the Facial Emotion Recognition 2013 dataset specifically, we used multiple class names for certain classes.

This file contains prompt data for 26 of the 27 datasets shown in Table 9 of the paper; the text prompts for ImageNet (as well as other [ImageNet Testbed](https://modestyachts.github.io/imagenet-testbed/) datasets in Figure 13) can be found in [this notebook](https://github.com/openai/CLIP/blob/main/notebooks/Prompt_Engineering_for_ImageNet.ipynb), as well as how to ensemble predictions from multiple prompts using these templates.

If you are viewing this document on GitHub, use the table of contents icon at the upper left to browse the datasets.


## Birdsnap

```bash
classes = [
    'Acadian Flycatcher',
    'Acorn Woodpecker',
    'Alder Flycatcher',
    'Allens Hummingbird',
    'Altamira Oriole',
    'American Avocet',
    'American Bittern',
    'American Black Duck',
    'American Coot',
    'American Crow',
    'American Dipper',
    'American Golden Plover',
    'American Goldfinch',
    'American Kestrel',
    'American Oystercatcher',
    'American Pipit',
    'American Redstart',
    'American Robin',
    'American Three toed Woodpecker',
    'American Tree Sparrow',
    'American White Pelican',
    'American Wigeon',
    'American Woodcock',
    'Anhinga',
    'Annas Hummingbird',
    'Arctic Tern',
    'Ash throated Flycatcher',
    'Audubons Oriole',
    'Bairds Sandpiper',
    'Bald Eagle',
    'Baltimore Oriole',
    'Band tailed Pigeon',
    'Barn Swallow',
    'Barred Owl',
    'Barrows Goldeneye',
    'Bay breasted Warbler',
    'Bells Vireo',
    'Belted Kingfisher',
    'Bewicks Wren',
    'Black Guillemot',
    'Black Oystercatcher',
    'Black Phoebe',
    'Black Rosy Finch',
    'Black Scoter',
    'Black Skimmer',
    'Black Tern',
    'Black Turnstone',
    'Black Vulture',
    'Black and white Warbler',
    'Black backed Woodpecker',
    'Black bellied Plover',
    'Black billed Cuckoo',
    'Black billed Magpie',
    'Black capped Chickadee',
    'Black chinned Hummingbird',
    'Black chinned Sparrow',
    'Black crested Titmouse',
    'Black crowned Night Heron',
    'Black headed Grosbeak',
    'Black legged Kittiwake',
    'Black necked Stilt',
    'Black throated Blue Warbler',
    'Black throated Gray Warbler',
    'Black throated Green Warbler',
    'Black throated Sparrow',
    'Blackburnian Warbler',
    'Blackpoll Warbler',
    'Blue Grosbeak',
    'Blue Jay',
    'Blue gray Gnatcatcher',
    'Blue headed Vireo',
    'Blue winged Teal',
    'Blue winged Warbler',
    'Boat tailed Grackle',
    'Bobolink',
    'Bohemian Waxwing',
    'Bonapartes Gull',
    'Boreal Chickadee',
    'Brandts Cormorant',
    'Brant',
    'Brewers Blackbird',
    'Brewers Sparrow',
    'Bridled Titmouse',
    'Broad billed Hummingbird',
    'Broad tailed Hummingbird',
    'Broad winged Hawk',
    'Bronzed Cowbird',
    'Brown Creeper',
    'Brown Pelican',
    'Brown Thrasher',
    'Brown capped Rosy Finch',
    'Brown crested Flycatcher',
    'Brown headed Cowbird',
    'Brown headed Nuthatch',
    'Bufflehead',
    'Bullocks Oriole',
    'Burrowing Owl',
    'Bushtit',
    'Cackling Goose',
    'Cactus Wren',
    'California Gull',
    'California Quail',
    'California Thrasher',
    'California Towhee',
    'Calliope Hummingbird',
    'Canada Goose',
    'Canada Warbler',
    'Canvasback',
    'Canyon Towhee',
    'Canyon Wren',
    'Cape May Warbler',
    'Carolina Chickadee',
    'Carolina Wren',
    'Caspian Tern',
    'Cassins Finch',
    'Cassins Kingbird',
    'Cassins Sparrow',
    'Cassins Vireo',
    'Cattle Egret',
    'Cave Swallow',
    'Cedar Waxwing',
    'Cerulean Warbler',
    'Chestnut backed Chickadee',
    'Chestnut collared Longspur',
    'Chestnut sided Warbler',
    'Chihuahuan Raven',
    'Chimney Swift',
    'Chipping Sparrow',
    'Cinnamon Teal',
    'Clapper Rail',
    'Clarks Grebe',
    'Clarks Nutcracker',
    'Clay colored Sparrow',
    'Cliff Swallow',
    'Common Black Hawk',
    'Common Eider',
    'Common Gallinule',
    'Common Goldeneye',
    'Common Grackle',
    'Common Ground Dove',
    'Common Loon',
    'Common Merganser',
    'Common Murre',
    'Common Nighthawk',
    'Common Raven',
    'Common Redpoll',
    'Common Tern',
    'Common Yellowthroat',
    'Connecticut Warbler',
    'Coopers Hawk',
    'Cordilleran Flycatcher',
    'Costas Hummingbird',
    'Couchs Kingbird',
    'Crested Caracara',
    'Curve billed Thrasher',
    'Dark eyed Junco',
    'Dickcissel',
    'Double crested Cormorant',
    'Downy Woodpecker',
    'Dunlin',
    'Dusky Flycatcher',
    'Dusky Grouse',
    'Eared Grebe',
    'Eastern Bluebird',
    'Eastern Kingbird',
    'Eastern Meadowlark',
    'Eastern Phoebe',
    'Eastern Screech Owl',
    'Eastern Towhee',
    'Eastern Wood Pewee',
    'Elegant Trogon',
    'Elf Owl',
    'Eurasian Collared Dove',
    'Eurasian Wigeon',
    'European Starling',
    'Evening Grosbeak',
    'Ferruginous Hawk',
    'Ferruginous Pygmy Owl',
    'Field Sparrow',
    'Fish Crow',
    'Florida Scrub Jay',
    'Forsters Tern',
    'Fox Sparrow',
    'Franklins Gull',
    'Fulvous Whistling Duck',
    'Gadwall',
    'Gambels Quail',
    'Gila Woodpecker',
    'Glaucous Gull',
    'Glaucous winged Gull',
    'Glossy Ibis',
    'Golden Eagle',
    'Golden crowned Kinglet',
    'Golden crowned Sparrow',
    'Golden fronted Woodpecker',
    'Golden winged Warbler',
    'Grasshopper Sparrow',
    'Gray Catbird',
    'Gray Flycatcher',
    'Gray Jay',
    'Gray Kingbird',
    'Gray cheeked Thrush',
    'Gray crowned Rosy Finch',
    'Great Black backed Gull',
    'Great Blue Heron',
    'Great Cormorant',
    'Great Crested Flycatcher',
    'Great Egret',
    'Great Gray Owl',
    'Great Horned Owl',
    'Great Kiskadee',
    'Great tailed Grackle',
    'Greater Prairie Chicken',
    'Greater Roadrunner',
    'Greater Sage Grouse',
    'Greater Scaup',
    'Greater White fronted Goose',
    'Greater Yellowlegs',
    'Green Jay',
    'Green tailed Towhee',
    'Green winged Teal',
    'Groove billed Ani',
    'Gull billed Tern',
    'Hairy Woodpecker',
    'Hammonds Flycatcher',
    'Harlequin Duck',
    'Harriss Hawk',
    'Harriss Sparrow',
    'Heermanns Gull',
    'Henslows Sparrow',
    'Hepatic Tanager',
    'Hermit Thrush',
    'Herring Gull',
    'Hoary Redpoll',
    'Hooded Merganser',
    'Hooded Oriole',
    'Hooded Warbler',
    'Horned Grebe',
    'Horned Lark',
    'House Finch',
    'House Sparrow',
    'House Wren',
    'Huttons Vireo',
    'Iceland Gull',
    'Inca Dove',
    'Indigo Bunting',
    'Killdeer',
    'King Rail',
    'Ladder backed Woodpecker',
    'Lapland Longspur',
    'Lark Bunting',
    'Lark Sparrow',
    'Laughing Gull',
    'Lazuli Bunting',
    'Le Contes Sparrow',
    'Least Bittern',
    'Least Flycatcher',
    'Least Grebe',
    'Least Sandpiper',
    'Least Tern',
    'Lesser Goldfinch',
    'Lesser Nighthawk',
    'Lesser Scaup',
    'Lesser Yellowlegs',
    'Lewiss Woodpecker',
    'Limpkin',
    'Lincolns Sparrow',
    'Little Blue Heron',
    'Loggerhead Shrike',
    'Long billed Curlew',
    'Long billed Dowitcher',
    'Long billed Thrasher',
    'Long eared Owl',
    'Long tailed Duck',
    'Louisiana Waterthrush',
    'Magnificent Frigatebird',
    'Magnolia Warbler',
    'Mallard',
    'Marbled Godwit',
    'Marsh Wren',
    'Merlin',
    'Mew Gull',
    'Mexican Jay',
    'Mississippi Kite',
    'Monk Parakeet',
    'Mottled Duck',
    'Mountain Bluebird',
    'Mountain Chickadee',
    'Mountain Plover',
    'Mourning Dove',
    'Mourning Warbler',
    'Muscovy Duck',
    'Mute Swan',
    'Nashville Warbler',
    'Nelsons Sparrow',
    'Neotropic Cormorant',
    'Northern Bobwhite',
    'Northern Cardinal',
    'Northern Flicker',
    'Northern Gannet',
    'Northern Goshawk',
    'Northern Harrier',
    'Northern Hawk Owl',
    'Northern Mockingbird',
    'Northern Parula',
    'Northern Pintail',
    'Northern Rough winged Swallow',
    'Northern Saw whet Owl',
    'Northern Shrike',
    'Northern Waterthrush',
    'Nuttalls Woodpecker',
    'Oak Titmouse',
    'Olive Sparrow',
    'Olive sided Flycatcher',
    'Orange crowned Warbler',
    'Orchard Oriole',
    'Osprey',
    'Ovenbird',
    'Pacific Golden Plover',
    'Pacific Loon',
    'Pacific Wren',
    'Pacific slope Flycatcher',
    'Painted Bunting',
    'Painted Redstart',
    'Palm Warbler',
    'Pectoral Sandpiper',
    'Peregrine Falcon',
    'Phainopepla',
    'Philadelphia Vireo',
    'Pied billed Grebe',
    'Pigeon Guillemot',
    'Pileated Woodpecker',
    'Pine Grosbeak',
    'Pine Siskin',
    'Pine Warbler',
    'Piping Plover',
    'Plumbeous Vireo',
    'Prairie Falcon',
    'Prairie Warbler',
    'Prothonotary Warbler',
    'Purple Finch',
    'Purple Gallinule',
    'Purple Martin',
    'Purple Sandpiper',
    'Pygmy Nuthatch',
    'Pyrrhuloxia',
    'Red Crossbill',
    'Red Knot',
    'Red Phalarope',
    'Red bellied Woodpecker',
    'Red breasted Merganser',
    'Red breasted Nuthatch',
    'Red breasted Sapsucker',
    'Red cockaded Woodpecker',
    'Red eyed Vireo',
    'Red headed Woodpecker',
    'Red naped Sapsucker',
    'Red necked Grebe',
    'Red necked Phalarope',
    'Red shouldered Hawk',
    'Red tailed Hawk',
    'Red throated Loon',
    'Red winged Blackbird',
    'Reddish Egret',
    'Redhead',
    'Ring billed Gull',
    'Ring necked Duck',
    'Ring necked Pheasant',
    'Rock Pigeon',
    'Rock Ptarmigan',
    'Rock Sandpiper',
    'Rock Wren',
    'Rose breasted Grosbeak',
    'Roseate Tern',
    'Rosss Goose',
    'Rough legged Hawk',
    'Royal Tern',
    'Ruby crowned Kinglet',
    'Ruby throated Hummingbird',
    'Ruddy Duck',
    'Ruddy Turnstone',
    'Ruffed Grouse',
    'Rufous Hummingbird',
    'Rufous crowned Sparrow',
    'Rusty Blackbird',
    'Sage Thrasher',
    'Saltmarsh Sparrow',
    'Sanderling',
    'Sandhill Crane',
    'Sandwich Tern',
    'Says Phoebe',
    'Scaled Quail',
    'Scarlet Tanager',
    'Scissor tailed Flycatcher',
    'Scotts Oriole',
    'Seaside Sparrow',
    'Sedge Wren',
    'Semipalmated Plover',
    'Semipalmated Sandpiper',
    'Sharp shinned Hawk',
    'Sharp tailed Grouse',
    'Short billed Dowitcher',
    'Short eared Owl',
    'Snail Kite',
    'Snow Bunting',
    'Snow Goose',
    'Snowy Egret',
    'Snowy Owl',
    'Snowy Plover',
    'Solitary Sandpiper',
    'Song Sparrow',
    'Sooty Grouse',
    'Sora',
    'Spotted Owl',
    'Spotted Sandpiper',
    'Spotted Towhee',
    'Spruce Grouse',
    'Stellers Jay',
    'Stilt Sandpiper',
    'Summer Tanager',
    'Surf Scoter',
    'Surfbird',
    'Swainsons Hawk',
    'Swainsons Thrush',
    'Swallow tailed Kite',
    'Swamp Sparrow',
    'Tennessee Warbler',
    'Thayers Gull',
    'Townsends Solitaire',
    'Townsends Warbler',
    'Tree Swallow',
    'Tricolored Heron',
    'Tropical Kingbird',
    'Trumpeter Swan',
    'Tufted Titmouse',
    'Tundra Swan',
    'Turkey Vulture',
    'Upland Sandpiper',
    'Varied Thrush',
    'Veery',
    'Verdin',
    'Vermilion Flycatcher',
    'Vesper Sparrow',
    'Violet green Swallow',
    'Virginia Rail',
    'Wandering Tattler',
    'Warbling Vireo',
    'Western Bluebird',
    'Western Grebe',
    'Western Gull',
    'Western Kingbird',
    'Western Meadowlark',
    'Western Sandpiper',
    'Western Screech Owl',
    'Western Scrub Jay',
    'Western Tanager',
    'Western Wood Pewee',
    'Whimbrel',
    'White Ibis',
    'White breasted Nuthatch',
    'White crowned Sparrow',
    'White eyed Vireo',
    'White faced Ibis',
    'White headed Woodpecker',
    'White rumped Sandpiper',
    'White tailed Hawk',
    'White tailed Kite',
    'White tailed Ptarmigan',
    'White throated Sparrow',
    'White throated Swift',
    'White winged Crossbill',
    'White winged Dove',
    'White winged Scoter',
    'Wild Turkey',
    'Willet',
    'Williamsons Sapsucker',
    'Willow Flycatcher',
    'Willow Ptarmigan',
    'Wilsons Phalarope',
    'Wilsons Plover',
    'Wilsons Snipe',
    'Wilsons Warbler',
    'Winter Wren',
    'Wood Stork',
    'Wood Thrush',
    'Worm eating Warbler',
    'Wrentit',
    'Yellow Warbler',
    'Yellow bellied Flycatcher',
    'Yellow bellied Sapsucker',
    'Yellow billed Cuckoo',
    'Yellow billed Magpie',
    'Yellow breasted Chat',
    'Yellow crowned Night Heron',
    'Yellow eyed Junco',
    'Yellow headed Blackbird',
    'Yellow rumped Warbler',
    'Yellow throated Vireo',
    'Yellow throated Warbler',
    'Zone tailed Hawk',
]

templates = [
    'a photo of a {}, a type of bird.',
]
```



## CIFAR10

```bash
classes = [
    'airplane',
    'automobile',
    'bird',
    'cat',
    'deer',
    'dog',
    'frog',
    'horse',
    'ship',
    'truck',
]

templates = [
    'a photo of a {}.',
    'a blurry photo of a {}.',
    'a black and white photo of a {}.',
    'a low contrast photo of a {}.',
    'a high contrast photo of a {}.',
    'a bad photo of a {}.',
    'a good photo of a {}.',
    'a photo of a small {}.',
    'a photo of a big {}.',
    'a photo of the {}.',
    'a blurry photo of the {}.',
    'a black and white photo of the {}.',
    'a low contrast photo of the {}.',
    'a high contrast photo of the {}.',
    'a bad photo of the {}.',
    'a good photo of the {}.',
    'a photo of the small {}.',
    'a photo of the big {}.',
]
```



## CIFAR100

```bash
classes = [
    'apple',
    'aquarium fish',
    'baby',
    'bear',
    'beaver',
    'bed',
    'bee',
    'beetle',
    'bicycle',
    'bottle',
    'bowl',
    'boy',
    'bridge',
    'bus',
    'butterfly',
    'camel',
    'can',
    'castle',
    'caterpillar',
    'cattle',
    'chair',
    'chimpanzee',
    'clock',
    'cloud',
    'cockroach',
    'couch',
    'crab',
    'crocodile',
    'cup',
    'dinosaur',
    'dolphin',
    'elephant',
    'flatfish',
    'forest',
    'fox',
    'girl',
    'hamster',
    'house',
    'kangaroo',
    'keyboard',
    'lamp',
    'lawn mower',
    'leopard',
    'lion',
    'lizard',
    'lobster',
    'man',
    'maple tree',
    'motorcycle',
    'mountain',
    'mouse',
    'mushroom',
    'oak tree',
    'orange',
    'orchid',
    'otter',
    'palm tree',
    'pear',
    'pickup truck',
    'pine tree',
    'plain',
    'plate',
    'poppy',
    'porcupine',
    'possum',
    'rabbit',
    'raccoon',
    'ray',
    'road',
    'rocket',
    'rose',
    'sea',
    'seal',
    'shark',
    'shrew',
    'skunk',
    'skyscraper',
    'snail',
    'snake',
    'spider',
    'squirrel',
    'streetcar',
    'sunflower',
    'sweet pepper',
    'table',
    'tank',
    'telephone',
    'television',
    'tiger',
    'tractor',
    'train',
    'trout',
    'tulip',
    'turtle',
    'wardrobe',
    'whale',
    'willow tree',
    'wolf',
    'woman',
    'worm',
]

templates = [
    'a photo of a {}.',
    'a blurry photo of a {}.',
    'a black and white photo of a {}.',
    'a low contrast photo of a {}.',
    'a high contrast photo of a {}.',
    'a bad photo of a {}.',
    'a good photo of a {}.',
    'a photo of a small {}.',
    'a photo of a big {}.',
    'a photo of the {}.',
    'a blurry photo of the {}.',
    'a black and white photo of the {}.',
    'a low contrast photo of the {}.',
    'a high contrast photo of the {}.',
    'a bad photo of the {}.',
    'a good photo of the {}.',
    'a photo of the small {}.',
    'a photo of the big {}.',
]
```



## CLEVRCounts

```bash
classes = [
    '10',
    '3',
    '4',
    '5',
    '6',
    '7',
    '8',
    '9',
]

templates = [
    'a photo of {} objects.',
]
```



## Caltech101

```bash
classes = [
    'background',
    'off-center face',
    'centered face',
    'leopard',
    'motorbike',
    'accordion',
    'airplane',
    'anchor',
    'ant',
    'barrel',
    'bass',
    'beaver',
    'binocular',
    'bonsai',
    'brain',
    'brontosaurus',
    'buddha',
    'butterfly',
    'camera',
    'cannon',
    'side of a car',
    'ceiling fan',
    'cellphone',
    'chair',
    'chandelier',
    'body of a cougar cat',
    'face of a cougar cat',
    'crab',
    'crayfish',
    'crocodile',
    'head of a  crocodile',
    'cup',
    'dalmatian',
    'dollar bill',
    'dolphin',
    'dragonfly',
    'electric guitar',
    'elephant',
    'emu',
    'euphonium',
    'ewer',
    'ferry',
    'flamingo',
    'head of a flamingo',
    'garfield',
    'gerenuk',
    'gramophone',
    'grand piano',
    'hawksbill',
    'headphone',
    'hedgehog',
    'helicopter',
    'ibis',
    'inline skate',
    'joshua tree',
    'kangaroo',
    'ketch',
    'lamp',
    'laptop',
    'llama',
    'lobster',
    'lotus',
    'mandolin',
    'mayfly',
    'menorah',
    'metronome',
    'minaret',
    'nautilus',
    'octopus',
    'okapi',
    'pagoda',
    'panda',
    'pigeon',
    'pizza',
    'platypus',
    'pyramid',
    'revolver',
    'rhino',
    'rooster',
    'saxophone',
    'schooner',
    'scissors',
    'scorpion',
    'sea horse',
    'snoopy (cartoon beagle)',
    'soccer ball',
    'stapler',
    'starfish',
    'stegosaurus',
    'stop sign',
    'strawberry',
    'sunflower',
    'tick',
    'trilobite',
    'umbrella',
    'watch',
    'water lilly',
    'wheelchair',
    'wild cat',
    'windsor chair',
    'wrench',
    'yin and yang symbol',
]

templates = [
    'a photo of a {}.',
    'a painting of a {}.',
    'a plastic {}.',
    'a sculpture of a {}.',
    'a sketch of a {}.',
    'a tattoo of a {}.',
    'a toy {}.',
    'a rendition of a {}.',
    'a embroidered {}.',
    'a cartoon {}.',
    'a {} in a video game.',
    'a plushie {}.',
    'a origami {}.',
    'art of a {}.',
    'graffiti of a {}.',
    'a drawing of a {}.',
    'a doodle of a {}.',
    'a photo of the {}.',
    'a painting of the {}.',
    'the plastic {}.',
    'a sculpture of the {}.',
    'a sketch of the {}.',
    'a tattoo of the {}.',
    'the toy {}.',
    'a rendition of the {}.',
    'the embroidered {}.',
    'the cartoon {}.',
    'the {} in a video game.',
    'the plushie {}.',
    'the origami {}.',
    'art of the {}.',
    'graffiti of the {}.',
    'a drawing of the {}.',
    'a doodle of the {}.',
]
```



## Country211

```bash
classes = [
    'Andorra',
    'United Arab Emirates',
    'Afghanistan',
    'Antigua and Barbuda',
    'Anguilla',
    'Albania',
    'Armenia',
    'Angola',
    'Antarctica',
    'Argentina',
    'Austria',
    'Australia',
    'Aruba',
    'Aland Islands',
    'Azerbaijan',
    'Bosnia and Herzegovina',
    'Barbados',
    'Bangladesh',
    'Belgium',
    'Burkina Faso',
    'Bulgaria',
    'Bahrain',
    'Benin',
    'Bermuda',
    'Brunei Darussalam',
    'Bolivia',
    'Bonaire, Saint Eustatius and Saba',
    'Brazil',
    'Bahamas',
    'Bhutan',
    'Botswana',
    'Belarus',
    'Belize',
    'Canada',
    'DR Congo',
    'Central African Republic',
    'Switzerland',
    "Cote d'Ivoire",
    'Cook Islands',
    'Chile',
    'Cameroon',
    'China',
    'Colombia',
    'Costa Rica',
    'Cuba',
    'Cabo Verde',
    'Curacao',
    'Cyprus',
    'Czech Republic',
    'Germany',
    'Denmark',
    'Dominica',
    'Dominican Republic',
    'Algeria',
    'Ecuador',
    'Estonia',
    'Egypt',
    'Spain',
    'Ethiopia',
    'Finland',
    'Fiji',
    'Falkland Islands',
    'Faeroe Islands',
    'France',
    'Gabon',
    'United Kingdom',
    'Grenada',
    'Georgia',
    'French Guiana',
    'Guernsey',
    'Ghana',
    'Gibraltar',
    'Greenland',
    'Gambia',
    'Guadeloupe',
    'Greece',
    'South Georgia and South Sandwich Is.',
    'Guatemala',
    'Guam',
    'Guyana',
    'Hong Kong',
    'Honduras',
    'Croatia',
    'Haiti',
    'Hungary',
    'Indonesia',
    'Ireland',
    'Israel',
    'Isle of Man',
    'India',
    'Iraq',
    'Iran',
    'Iceland',
    'Italy',
    'Jersey',
    'Jamaica',
    'Jordan',
    'Japan',
    'Kenya',
    'Kyrgyz Republic',
    'Cambodia',
    'St. Kitts and Nevis',
    'North Korea',
    'South Korea',
    'Kuwait',
    'Cayman Islands',
    'Kazakhstan',
    'Laos',
    'Lebanon',
    'St. Lucia',
    'Liechtenstein',
    'Sri Lanka',
    'Liberia',
    'Lithuania',
    'Luxembourg',
    'Latvia',
    'Libya',
    'Morocco',
    'Monaco',
    'Moldova',
    'Montenegro',
    'Saint-Martin',
    'Madagascar',
    'Macedonia',
    'Mali',
    'Myanmar',
    'Mongolia',
    'Macau',
    'Martinique',
    'Mauritania',
    'Malta',
    'Mauritius',
    'Maldives',
    'Malawi',
    'Mexico',
    'Malaysia',
    'Mozambique',
    'Namibia',
    'New Caledonia',
    'Nigeria',
    'Nicaragua',
    'Netherlands',
    'Norway',
    'Nepal',
    'New Zealand',
    'Oman',
    'Panama',
    'Peru',
    'French Polynesia',
    'Papua New Guinea',
    'Philippines',
    'Pakistan',
    'Poland',
    'Puerto Rico',
    'Palestine',
    'Portugal',
    'Palau',
    'Paraguay',
    'Qatar',
    'Reunion',
    'Romania',
    'Serbia',
    'Russia',
    'Rwanda',
    'Saudi Arabia',
    'Solomon Islands',
    'Seychelles',
    'Sudan',
    'Sweden',
    'Singapore',
    'St. Helena',
    'Slovenia',
    'Svalbard and Jan Mayen Islands',
    'Slovakia',
    'Sierra Leone',
    'San Marino',
    'Senegal',
    'Somalia',
    'South Sudan',
    'El Salvador',
    'Sint Maarten',
    'Syria',
    'Eswatini',
    'Togo',
    'Thailand',
    'Tajikistan',
    'Timor-Leste',
    'Turkmenistan',
    'Tunisia',
    'Tonga',
    'Turkey',
    'Trinidad and Tobago',
    'Taiwan',
    'Tanzania',
    'Ukraine',
    'Uganda',
    'United States',
    'Uruguay',
    'Uzbekistan',
    'Vatican',
    'Venezuela',
    'British Virgin Islands',
    'United States Virgin Islands',
    'Vietnam',
    'Vanuatu',
    'Samoa',
    'Kosovo',
    'Yemen',
    'South Africa',
    'Zambia',
    'Zimbabwe',
]

templates = [
    'a photo i took in {}.',
    'a photo i took while visiting {}.',
    'a photo from my home country of {}.',
    'a photo from my visit to {}.',
    'a photo showing the country of {}.',
]
```



## DescribableTextures

```bash
classes = [
    'banded',
    'blotchy',
    'braided',
    'bubbly',
    'bumpy',
    'chequered',
    'cobwebbed',
    'cracked',
    'crosshatched',
    'crystalline',
    'dotted',
    'fibrous',
    'flecked',
    'freckled',
    'frilly',
    'gauzy',
    'grid',
    'grooved',
    'honeycombed',
    'interlaced',
    'knitted',
    'lacelike',
    'lined',
    'marbled',
    'matted',
    'meshed',
    'paisley',
    'perforated',
    'pitted',
    'pleated',
    'polka-dotted',
    'porous',
    'potholed',
    'scaly',
    'smeared',
    'spiralled',
    'sprinkled',
    'stained',
    'stratified',
    'striped',
    'studded',
    'swirly',
    'veined',
    'waffled',
    'woven',
    'wrinkled',
    'zigzagged',
]

templates = [
    'a photo of a {} texture.',
    'a photo of a {} pattern.',
    'a photo of a {} thing.',
    'a photo of a {} object.',
    'a photo of the {} texture.',
    'a photo of the {} pattern.',
    'a photo of the {} thing.',
    'a photo of the {} object.',
]
```



## EuroSAT

```bash
classes = [
    'forest',
    'permanent crop land',
    'residential buildings or homes or apartments',
    'river',
    'pasture land',
    'lake or sea',
    'brushland or shrubland',
    'annual crop land',
    'industrial buildings or commercial buildings',
    'highway or road',
]

templates = [
    'a centered satellite photo of {}.',
    'a centered satellite photo of a {}.',
    'a centered satellite photo of the {}.',
]
```



## FGVCAircraft

```bash
classes = [
    '707-320',
    '727-200',
    '737-200',
    '737-300',
    '737-400',
    '737-500',
    '737-600',
    '737-700',
    '737-800',
    '737-900',
    '747-100',
    '747-200',
    '747-300',
    '747-400',
    '757-200',
    '757-300',
    '767-200',
    '767-300',
    '767-400',
    '777-200',
    '777-300',
    'A300B4',
    'A310',
    'A318',
    'A319',
    'A320',
    'A321',
    'A330-200',
    'A330-300',
    'A340-200',
    'A340-300',
    'A340-500',
    'A340-600',
    'A380',
    'ATR-42',
    'ATR-72',
    'An-12',
    'BAE 146-200',
    'BAE 146-300',
    'BAE-125',
    'Beechcraft 1900',
    'Boeing 717',
    'C-130',
    'C-47',
    'CRJ-200',
    'CRJ-700',
    'CRJ-900',
    'Cessna 172',
    'Cessna 208',
    'Cessna 525',
    'Cessna 560',
    'Challenger 600',
    'DC-10',
    'DC-3',
    'DC-6',
    'DC-8',
    'DC-9-30',
    'DH-82',
    'DHC-1',
    'DHC-6',
    'DHC-8-100',
    'DHC-8-300',
    'DR-400',
    'Dornier 328',
    'E-170',
    'E-190',
    'E-195',
    'EMB-120',
    'ERJ 135',
    'ERJ 145',
    'Embraer Legacy 600',
    'Eurofighter Typhoon',
    'F-16A/B',
    'F/A-18',
    'Falcon 2000',
    'Falcon 900',
    'Fokker 100',
    'Fokker 50',
    'Fokker 70',
    'Global Express',
    'Gulfstream IV',
    'Gulfstream V',
    'Hawk T1',
    'Il-76',
    'L-1011',
    'MD-11',
    'MD-80',
    'MD-87',
    'MD-90',
    'Metroliner',
    'Model B200',
    'PA-28',
    'SR-20',
    'Saab 2000',
    'Saab 340',
    'Spitfire',
    'Tornado',
    'Tu-134',
    'Tu-154',
    'Yak-42',
]

templates = [
    'a photo of a {}, a type of aircraft.',
    'a photo of the {}, a type of aircraft.',
]
```



## FacialEmotionRecognition2013

```bash
classes = [
    ['angry'],
    ['disgusted'],
    ['fearful'],
    ['happy', 'smiling'],
    ['sad', 'depressed'],
    ['surprised', 'shocked', 'spooked'],
    ['neutral', 'bored'],
]

templates = [
    'a photo of a {} looking face.',
    'a photo of a face showing the emotion: {}.',
    'a photo of a face looking {}.',
    'a face that looks {}.',
    'they look {}.',
    'look at how {} they are.',
]
```



## Flowers102

```bash
classes = [
    'pink primrose',
    'hard-leaved pocket orchid',
    'canterbury bells',
    'sweet pea',
    'english marigold',
    'tiger lily',
    'moon orchid',
    'bird of paradise',
    'monkshood',
    'globe thistle',
    'snapdragon',
    "colt's foot",
    'king protea',
    'spear thistle',
    'yellow iris',
    'globe flower',
    'purple coneflower',
    'peruvian lily',
    'balloon flower',
    'giant white arum lily',
    'fire lily',
    'pincushion flower',
    'fritillary',
    'red ginger',
    'grape hyacinth',
    'corn poppy',
    'prince of wales feathers',
    'stemless gentian',
    'artichoke',
    'sweet william',
    'carnation',
    'garden phlox',
    'love in the mist',
    'mexican aster',
    'alpine sea holly',
    'ruby-lipped cattleya',
    'cape flower',
    'great masterwort',
    'siam tulip',
    'lenten rose',
    'barbeton daisy',
    'daffodil',
    'sword lily',
    'poinsettia',
    'bolero deep blue',
    'wallflower',
    'marigold',
    'buttercup',
    'oxeye daisy',
    'common dandelion',
    'petunia',
    'wild pansy',
    'primula',
    'sunflower',
    'pelargonium',
    'bishop of llandaff',
    'gaura',
    'geranium',
    'orange dahlia',
    'pink and yellow dahlia',
    'cautleya spicata',
    'japanese anemone',
    'black-eyed susan',
    'silverbush',
    'californian poppy',
    'osteospermum',
    'spring crocus',
    'bearded iris',
    'windflower',
    'tree poppy',
    'gazania',
    'azalea',
    'water lily',
    'rose',
    'thorn apple',
    'morning glory',
    'passion flower',
    'lotus',
    'toad lily',
    'anthurium',
    'frangipani',
    'clematis',
    'hibiscus',
    'columbine',
    'desert-rose',
    'tree mallow',
    'magnolia',
    'cyclamen',
    'watercress',
    'canna lily',
    'hippeastrum',
    'bee balm',
    'air plant',
    'foxglove',
    'bougainvillea',
    'camellia',
    'mallow',
    'mexican petunia',
    'bromelia',
    'blanket flower',
    'trumpet creeper',
    'blackberry lily',
]

templates = [
    'a photo of a {}, a type of flower.',
]
```



## Food101

```bash
classes = [
    'apple pie',
    'baby back ribs',
    'baklava',
    'beef carpaccio',
    'beef tartare',
    'beet salad',
    'beignets',
    'bibimbap',
    'bread pudding',
    'breakfast burrito',
    'bruschetta',
    'caesar salad',
    'cannoli',
    'caprese salad',
    'carrot cake',
    'ceviche',
    'cheese plate',
    'cheesecake',
    'chicken curry',
    'chicken quesadilla',
    'chicken wings',
    'chocolate cake',
    'chocolate mousse',
    'churros',
    'clam chowder',
    'club sandwich',
    'crab cakes',
    'creme brulee',
    'croque madame',
    'cup cakes',
    'deviled eggs',
    'donuts',
    'dumplings',
    'edamame',
    'eggs benedict',
    'escargots',
    'falafel',
    'filet mignon',
    'fish and chips',
    'foie gras',
    'french fries',
    'french onion soup',
    'french toast',
    'fried calamari',
    'fried rice',
    'frozen yogurt',
    'garlic bread',
    'gnocchi',
    'greek salad',
    'grilled cheese sandwich',
    'grilled salmon',
    'guacamole',
    'gyoza',
    'hamburger',
    'hot and sour soup',
    'hot dog',
    'huevos rancheros',
    'hummus',
    'ice cream',
    'lasagna',
    'lobster bisque',
    'lobster roll sandwich',
    'macaroni and cheese',
    'macarons',
    'miso soup',
    'mussels',
    'nachos',
    'omelette',
    'onion rings',
    'oysters',
    'pad thai',
    'paella',
    'pancakes',
    'panna cotta',
    'peking duck',
    'pho',
    'pizza',
    'pork chop',
    'poutine',
    'prime rib',
    'pulled pork sandwich',
    'ramen',
    'ravioli',
    'red velvet cake',
    'risotto',
    'samosa',
    'sashimi',
    'scallops',
    'seaweed salad',
    'shrimp and grits',
    'spaghetti bolognese',
    'spaghetti carbonara',
    'spring rolls',
    'steak',
    'strawberry shortcake',
    'sushi',
    'tacos',
    'takoyaki',
    'tiramisu',
    'tuna tartare',
    'waffles',
]

templates = [
    'a photo of {}, a type of food.',
]
```



## GTSRB

```bash
classes = [
    'red and white circle 20 kph speed limit',
    'red and white circle 30 kph speed limit',
    'red and white circle 50 kph speed limit',
    'red and white circle 60 kph speed limit',
    'red and white circle 70 kph speed limit',
    'red and white circle 80 kph speed limit',
    'end / de-restriction of 80 kph speed limit',
    'red and white circle 100 kph speed limit',
    'red and white circle 120 kph speed limit',
    'red and white circle red car and black car no passing',
    'red and white circle red truck and black car no passing',
    'red and white triangle road intersection warning',
    'white and yellow diamond priority road',
    'red and white upside down triangle yield right-of-way',
    'stop',
    'empty red and white circle',
    'red and white circle no truck entry',
    'red circle with white horizonal stripe no entry',
    'red and white triangle with exclamation mark warning',
    'red and white triangle with black left curve approaching warning',
    'red and white triangle with black right curve approaching warning',
    'red and white triangle with black double curve approaching warning',
    'red and white triangle rough / bumpy road warning',
    'red and white triangle car skidding / slipping warning',
    'red and white triangle with merging / narrow lanes warning',
    'red and white triangle with person digging / construction / road work warning',
    'red and white triangle with traffic light approaching warning',
    'red and white triangle with person walking warning',
    'red and white triangle with child and person walking warning',
    'red and white triangle with bicyle warning',
    'red and white triangle with snowflake / ice warning',
    'red and white triangle with deer warning',
    'white circle with gray strike bar no speed limit',
    'blue circle with white right turn arrow mandatory',
    'blue circle with white left turn arrow mandatory',
    'blue circle with white forward arrow mandatory',
    'blue circle with white forward or right turn arrow mandatory',
    'blue circle with white forward or left turn arrow mandatory',
    'blue circle with white keep right arrow mandatory',
    'blue circle with white keep left arrow mandatory',
    'blue circle with white arrows indicating a traffic circle',
    'white circle with gray strike bar indicating no passing for cars has ended',
    'white circle with gray strike bar indicating no passing for trucks has ended',
]

templates = [
    'a zoomed in photo of a "{}" traffic sign.',
    'a centered photo of a "{}" traffic sign.',
    'a close up photo of a "{}" traffic sign.',
]
```



## HatefulMemes

```bash
classes = [
    'meme',
    'hatespeech meme',
]

templates = [
    'a {}.',
]
```



## KITTI

```bash
classes = [
    'a photo i took of a car on my left or right side.',
    'a photo i took with a car nearby.',
    'a photo i took with a car in the distance.',
    'a photo i took with no car.',
]

templates = [
    '{}',
]
```



## Kinetics700

```bash
classes = [
    'abseiling',
    'acting in play',
    'adjusting glasses',
    'air drumming',
    'alligator wrestling',
    'answering questions',
    'applauding',
    'applying cream',
    'archaeological excavation',
    'archery',
    'arguing',
    'arm wrestling',
    'arranging flowers',
    'arresting',
    'assembling bicycle',
    'assembling computer',
    'attending conference',
    'auctioning',
    'baby waking up',
    'backflip (human)',
    'baking cookies',
    'bandaging',
    'barbequing',
    'bartending',
    'base jumping',
    'bathing dog',
    'battle rope training',
    'beatboxing',
    'bee keeping',
    'being excited',
    'being in zero gravity',
    'belly dancing',
    'bench pressing',
    'bending back',
    'bending metal',
    'biking through snow',
    'blasting sand',
    'blending fruit',
    'blowdrying hair',
    'blowing bubble gum',
    'blowing glass',
    'blowing leaves',
    'blowing nose',
    'blowing out candles',
    'bobsledding',
    'bodysurfing',
    'bookbinding',
    'bottling',
    'bouncing ball (not juggling)',
    'bouncing on bouncy castle',
    'bouncing on trampoline',
    'bowling',
    'braiding hair',
    'breading or breadcrumbing',
    'breakdancing',
    'breaking boards',
    'breaking glass',
    'breathing fire',
    'brush painting',
    'brushing floor',
    'brushing hair',
    'brushing teeth',
    'building cabinet',
    'building lego',
    'building sandcastle',
    'building shed',
    'bulldozing',
    'bungee jumping',
    'burping',
    'busking',
    'calculating',
    'calligraphy',
    'canoeing or kayaking',
    'capoeira',
    'capsizing',
    'card stacking',
    'card throwing',
    'carrying baby',
    'carrying weight',
    'cartwheeling',
    'carving ice',
    'carving marble',
    'carving pumpkin',
    'carving wood with a knife',
    'casting fishing line',
    'catching fish',
    'catching or throwing baseball',
    'catching or throwing frisbee',
    'catching or throwing softball',
    'celebrating',
    'changing gear in car',
    'changing oil',
    'changing wheel (not on bike)',
    'chasing',
    'checking tires',
    'checking watch',
    'cheerleading',
    'chewing gum',
    'chiseling stone',
    'chiseling wood',
    'chopping meat',
    'chopping wood',
    'clam digging',
    'clapping',
    'clay pottery making',
    'clean and jerk',
    'cleaning gutters',
    'cleaning pool',
    'cleaning shoes',
    'cleaning toilet',
    'cleaning windows',
    'climbing a rope',
    'climbing ladder',
    'climbing tree',
    'closing door',
    'coloring in',
    'combing hair',
    'contact juggling',
    'contorting',
    'cooking chicken',
    'cooking egg',
    'cooking on campfire',
    'cooking sausages (not on barbeque)',
    'cooking scallops',
    'cosplaying',
    'coughing',
    'counting money',
    'country line dancing',
    'cracking back',
    'cracking knuckles',
    'cracking neck',
    'crawling baby',
    'crocheting',
    'crossing eyes',
    'crossing river',
    'crying',
    'cumbia',
    'curling (sport)',
    'curling eyelashes',
    'curling hair',
    'cutting apple',
    'cutting cake',
    'cutting nails',
    'cutting orange',
    'cutting pineapple',
    'cutting watermelon',
    'dancing ballet',
    'dancing charleston',
    'dancing gangnam style',
    'dancing macarena',
    'deadlifting',
    'dealing cards',
    'decorating the christmas tree',
    'decoupage',
    'delivering mail',
    'digging',
    'dining',
    'directing traffic',
    'disc golfing',
    'diving cliff',
    'docking boat',
    'dodgeball',
    'doing aerobics',
    'doing jigsaw puzzle',
    'doing laundry',
    'doing nails',
    'doing sudoku',
    'drawing',
    'dribbling basketball',
    'drinking shots',
    'driving car',
    'driving tractor',
    'drooling',
    'drop kicking',
    'drumming fingers',
    'dumpster diving',
    'dunking basketball',
    'dyeing eyebrows',
    'dyeing hair',
    'eating burger',
    'eating cake',
    'eating carrots',
    'eating chips',
    'eating doughnuts',
    'eating hotdog',
    'eating ice cream',
    'eating nachos',
    'eating spaghetti',
    'eating watermelon',
    'egg hunting',
    'embroidering',
    'entering church',
    'exercising arm',
    'exercising with an exercise ball',
    'extinguishing fire',
    'faceplanting',
    'falling off bike',
    'falling off chair',
    'feeding birds',
    'feeding fish',
    'feeding goats',
    'fencing (sport)',
    'fidgeting',
    'filling cake',
    'filling eyebrows',
    'finger snapping',
    'fixing bicycle',
    'fixing hair',
    'flint knapping',
    'flipping bottle',
    'flipping pancake',
    'fly tying',
    'flying kite',
    'folding clothes',
    'folding napkins',
    'folding paper',
    'front raises',
    'frying vegetables',
    'gargling',
    'geocaching',
    'getting a haircut',
    'getting a piercing',
    'getting a tattoo',
    'giving or receiving award',
    'gold panning',
    'golf chipping',
    'golf driving',
    'golf putting',
    'gospel singing in church',
    'grinding meat',
    'grooming cat',
    'grooming dog',
    'grooming horse',
    'gymnastics tumbling',
    'hammer throw',
    'hand washing clothes',
    'head stand',
    'headbanging',
    'headbutting',
    'helmet diving',
    'herding cattle',
    'high fiving',
    'high jump',
    'high kick',
    'historical reenactment',
    'hitting baseball',
    'hockey stop',
    'holding snake',
    'home roasting coffee',
    'hopscotch',
    'hoverboarding',
    'huddling',
    'hugging (not baby)',
    'hugging baby',
    'hula hooping',
    'hurdling',
    'hurling (sport)',
    'ice climbing',
    'ice fishing',
    'ice skating',
    'ice swimming',
    'inflating balloons',
    'installing carpet',
    'ironing',
    'ironing hair',
    'javelin throw',
    'jaywalking',
    'jetskiing',
    'jogging',
    'juggling balls',
    'juggling fire',
    'juggling soccer ball',
    'jumping bicycle',
    'jumping into pool',
    'jumping jacks',
    'jumping sofa',
    'jumpstyle dancing',
    'karaoke',
    'kicking field goal',
    'kicking soccer ball',
    'kissing',
    'kitesurfing',
    'knitting',
    'krumping',
    'land sailing',
    'laughing',
    'lawn mower racing',
    'laying bricks',
    'laying concrete',
    'laying decking',
    'laying stone',
    'laying tiles',
    'leatherworking',
    'letting go of balloon',
    'licking',
    'lifting hat',
    'lighting candle',
    'lighting fire',
    'listening with headphones',
    'lock picking',
    'long jump',
    'longboarding',
    'looking at phone',
    'looking in mirror',
    'luge',
    'lunge',
    'making a cake',
    'making a sandwich',
    'making balloon shapes',
    'making bubbles',
    'making cheese',
    'making horseshoes',
    'making jewelry',
    'making latte art',
    'making paper aeroplanes',
    'making pizza',
    'making slime',
    'making snowman',
    'making sushi',
    'making tea',
    'making the bed',
    'marching',
    'marriage proposal',
    'massaging back',
    'massaging feet',
    'massaging legs',
    'massaging neck',
    "massaging person's head",
    'metal detecting',
    'milking cow',
    'milking goat',
    'mixing colours',
    'moon walking',
    'mopping floor',
    'mosh pit dancing',
    'motorcycling',
    'mountain climber (exercise)',
    'moving baby',
    'moving child',
    'moving furniture',
    'mowing lawn',
    'mushroom foraging',
    'needle felting',
    'news anchoring',
    'opening bottle (not wine)',
    'opening coconuts',
    'opening door',
    'opening present',
    'opening refrigerator',
    'opening wine bottle',
    'packing',
    'paragliding',
    'parasailing',
    'parkour',
    'passing American football (in game)',
    'passing American football (not in game)',
    'passing soccer ball',
    'peeling apples',
    'peeling banana',
    'peeling potatoes',
    'person collecting garbage',
    'petting animal (not cat)',
    'petting cat',
    'petting horse',
    'photobombing',
    'photocopying',
    'picking apples',
    'picking blueberries',
    'pillow fight',
    'pinching',
    'pirouetting',
    'planing wood',
    'planting trees',
    'plastering',
    'playing accordion',
    'playing american football',
    'playing badminton',
    'playing bagpipes',
    'playing basketball',
    'playing bass guitar',
    'playing beer pong',
    'playing billiards',
    'playing blackjack',
    'playing cards',
    'playing cello',
    'playing checkers',
    'playing chess',
    'playing clarinet',
    'playing controller',
    'playing cricket',
    'playing cymbals',
    'playing darts',
    'playing didgeridoo',
    'playing dominoes',
    'playing drums',
    'playing field hockey',
    'playing flute',
    'playing gong',
    'playing guitar',
    'playing hand clapping games',
    'playing harmonica',
    'playing harp',
    'playing ice hockey',
    'playing keyboard',
    'playing kickball',
    'playing laser tag',
    'playing lute',
    'playing mahjong',
    'playing maracas',
    'playing marbles',
    'playing monopoly',
    'playing netball',
    'playing nose flute',
    'playing oboe',
    'playing ocarina',
    'playing organ',
    'playing paintball',
    'playing pan pipes',
    'playing piano',
    'playing piccolo',
    'playing pinball',
    'playing ping pong',
    'playing poker',
    'playing polo',
    'playing recorder',
    'playing road hockey',
    'playing rounders',
    'playing rubiks cube',
    'playing saxophone',
    'playing scrabble',
    'playing shuffleboard',
    'playing slot machine',
    'playing squash or racquetball',
    'playing tennis',
    'playing trombone',
    'playing trumpet',
    'playing ukulele',
    'playing violin',
    'playing volleyball',
    'playing with trains',
    'playing xylophone',
    'poaching eggs',
    'poking bellybutton',
    'pole vault',
    'polishing furniture',
    'polishing metal',
    'popping balloons',
    'pouring beer',
    'pouring milk',
    'pouring wine',
    'preparing salad',
    'presenting weather forecast',
    'pretending to be a statue',
    'pull ups',
    'pulling espresso shot',
    'pulling rope (game)',
    'pumping fist',
    'pumping gas',
    'punching bag',
    'punching person (boxing)',
    'push up',
    'pushing car',
    'pushing cart',
    'pushing wheelbarrow',
    'pushing wheelchair',
    'putting in contact lenses',
    'putting on eyeliner',
    'putting on foundation',
    'putting on lipstick',
    'putting on mascara',
    'putting on sari',
    'putting on shoes',
    'putting wallpaper on wall',
    'raising eyebrows',
    'reading book',
    'reading newspaper',
    'recording music',
    'repairing puncture',
    'riding a bike',
    'riding camel',
    'riding elephant',
    'riding mechanical bull',
    'riding mule',
    'riding or walking with horse',
    'riding scooter',
    'riding snow blower',
    'riding unicycle',
    'ripping paper',
    'roasting marshmallows',
    'roasting pig',
    'robot dancing',
    'rock climbing',
    'rock scissors paper',
    'roller skating',
    'rolling eyes',
    'rolling pastry',
    'rope pushdown',
    'running on treadmill',
    'sailing',
    'salsa dancing',
    'saluting',
    'sanding floor',
    'sanding wood',
    'sausage making',
    'sawing wood',
    'scrambling eggs',
    'scrapbooking',
    'scrubbing face',
    'scuba diving',
    'seasoning food',
    'separating eggs',
    'setting table',
    'sewing',
    'shaking hands',
    'shaking head',
    'shaping bread dough',
    'sharpening knives',
    'sharpening pencil',
    'shaving head',
    'shaving legs',
    'shearing sheep',
    'shining flashlight',
    'shining shoes',
    'shoot dance',
    'shooting basketball',
    'shooting goal (soccer)',
    'shooting off fireworks',
    'shopping',
    'shot put',
    'shouting',
    'shoveling snow',
    'shredding paper',
    'shucking oysters',
    'shuffling cards',
    'shuffling feet',
    'side kick',
    'sieving',
    'sign language interpreting',
    'silent disco',
    'singing',
    'sipping cup',
    'situp',
    'skateboarding',
    'ski ballet',
    'ski jumping',
    'skiing crosscountry',
    'skiing mono',
    'skiing slalom',
    'skipping rope',
    'skipping stone',
    'skydiving',
    'slacklining',
    'slapping',
    'sled dog racing',
    'sleeping',
    'slicing onion',
    'smashing',
    'smelling feet',
    'smoking',
    'smoking hookah',
    'smoking pipe',
    'snatch weight lifting',
    'sneezing',
    'snorkeling',
    'snowboarding',
    'snowkiting',
    'snowmobiling',
    'somersaulting',
    'spelunking',
    'spinning plates',
    'spinning poi',
    'splashing water',
    'spray painting',
    'spraying',
    'springboard diving',
    'square dancing',
    'squat',
    'squeezing orange',
    'stacking cups',
    'stacking dice',
    'standing on hands',
    'staring',
    'steer roping',
    'steering car',
    'sticking tongue out',
    'stomping grapes',
    'stretching arm',
    'stretching leg',
    'sucking lolly',
    'surfing crowd',
    'surfing water',
    'surveying',
    'sweeping floor',
    'swimming backstroke',
    'swimming breast stroke',
    'swimming butterfly stroke',
    'swimming front crawl',
    'swimming with dolphins',
    'swimming with sharks',
    'swing dancing',
    'swinging baseball bat',
    'swinging on something',
    'sword fighting',
    'sword swallowing',
    'tackling',
    'tagging graffiti',
    'tai chi',
    'taking photo',
    'talking on cell phone',
    'tango dancing',
    'tap dancing',
    'tapping guitar',
    'tapping pen',
    'tasting beer',
    'tasting food',
    'tasting wine',
    'testifying',
    'texting',
    'threading needle',
    'throwing axe',
    'throwing ball (not baseball or American football)',
    'throwing discus',
    'throwing knife',
    'throwing snowballs',
    'throwing tantrum',
    'throwing water balloon',
    'tickling',
    'tie dying',
    'tightrope walking',
    'tiptoeing',
    'tobogganing',
    'tossing coin',
    'tossing salad',
    'training dog',
    'trapezing',
    'treating wood',
    'trimming or shaving beard',
    'trimming shrubs',
    'trimming trees',
    'triple jump',
    'twiddling fingers',
    'tying bow tie',
    'tying knot (not on a tie)',
    'tying necktie',
    'tying shoe laces',
    'unboxing',
    'uncorking champagne',
    'unloading truck',
    'using a microscope',
    'using a paint roller',
    'using a power drill',
    'using a sledge hammer',
    'using a wrench',
    'using atm',
    'using bagging machine',
    'using circular saw',
    'using inhaler',
    'using megaphone',
    'using puppets',
    'using remote controller (not gaming)',
    'using segway',
    'vacuuming car',
    'vacuuming floor',
    'visiting the zoo',
    'wading through mud',
    'wading through water',
    'waiting in line',
    'waking up',
    'walking on stilts',
    'walking the dog',
    'walking through snow',
    'walking with crutches',
    'washing dishes',
    'washing feet',
    'washing hair',
    'washing hands',
    'watching tv',
    'water skiing',
    'water sliding',
    'watering plants',
    'waving hand',
    'waxing armpits',
    'waxing back',
    'waxing chest',
    'waxing eyebrows',
    'waxing legs',
    'weaving basket',
    'weaving fabric',
    'welding',
    'whistling',
    'windsurfing',
    'winking',
    'wood burning (art)',
    'wrapping present',
    'wrestling',
    'writing',
    'yarn spinning',
    'yawning',
    'yoga',
    'zumba'
]

templates = [
    'a photo of {}.',
    'a photo of a person {}.',
    'a photo of a person using {}.',
    'a photo of a person doing {}.',
    'a photo of a person during {}.',
    'a photo of a person performing {}.',
    'a photo of a person practicing {}.',
    'a video of {}.',
    'a video of a person {}.',
    'a video of a person using {}.',
    'a video of a person doing {}.',
    'a video of a person during {}.',
    'a video of a person performing {}.',
    'a video of a person practicing {}.',
    'a example of {}.',
    'a example of a person {}.',
    'a example of a person using {}.',
    'a example of a person doing {}.',
    'a example of a person during {}.',
    'a example of a person performing {}.',
    'a example of a person practicing {}.',
    'a demonstration of {}.',
    'a demonstration of a person {}.',
    'a demonstration of a person using {}.',
    'a demonstration of a person doing {}.',
    'a demonstration of a person during {}.',
    'a demonstration of a person performing {}.',
    'a demonstration of a person practicing {}.',
]
```



## MNIST

```bash
classes = [
    '0',
    '1',
    '2',
    '3',
    '4',
    '5',
    '6',
    '7',
    '8',
    '9',
]

templates = [
    'a photo of the number: "{}".',
]
```



## OxfordPets

```bash
classes = [
    'Abyssinian',
    'Bengal',
    'Birman',
    'Bombay',
    'British Shorthair',
    'Egyptian Mau',
    'Maine Coon',
    'Persian',
    'Ragdoll',
    'Russian Blue',
    'Siamese',
    'Sphynx',
    'american bulldog',
    'american pit bull terrier',
    'basset hound',
    'beagle',
    'boxer',
    'chihuahua',
    'english cocker spaniel',
    'english setter',
    'german shorthaired',
    'great pyrenees',
    'havanese',
    'japanese chin',
    'keeshond',
    'leonberger',
    'miniature pinscher',
    'newfoundland',
    'pomeranian',
    'pug',
    'saint bernard',
    'samoyed',
    'scottish terrier',
    'shiba inu',
    'staffordshire bull terrier',
    'wheaten terrier',
    'yorkshire terrier',
]

templates = [
    'a photo of a {}, a type of pet.',
]
```



## PascalVOC2007

```bash
classes = [
    'aeroplane',
    'bicycle',
    'bird',
    'boat',
    'bottle',
    'bus',
    'car',
    'cat',
    'chair',
    'cow',
    'dog',
    'horse',
    'motorbike',
    'person',
    'sheep',
    'sofa',
    'diningtable',
    'pottedplant',
    'train',
    'tvmonitor',
]

templates = [
    'a photo of a {}.',
]
```



## PatchCamelyon

```bash
classes = [
    'lymph node',
    'lymph node containing metastatic tumor tissue',
]

templates = [
    'this is a photo of {}',
]
```



## RESISC45

```bash
classes = [
    'airplane',
    'airport',
    'baseball diamond',
    'basketball court',
    'beach',
    'bridge',
    'chaparral',
    'church',
    'circular farmland',
    'cloud',
    'commercial area',
    'dense residential',
    'desert',
    'forest',
    'freeway',
    'golf course',
    'ground track field',
    'harbor',
    'industrial area',
    'intersection',
    'island',
    'lake',
    'meadow',
    'medium residential',
    'mobile home park',
    'mountain',
    'overpass',
    'palace',
    'parking lot',
    'railway',
    'railway station',
    'rectangular farmland',
    'river',
    'roundabout',
    'runway',
    'sea ice',
    'ship',
    'snowberg',
    'sparse residential',
    'stadium',
    'storage tank',
    'tennis court',
    'terrace',
    'thermal power station',
    'wetland',
]

templates = [
    'satellite imagery of {}.',
    'aerial imagery of {}.',
    'satellite photo of {}.',
    'aerial photo of {}.',
    'satellite view of {}.',
    'aerial view of {}.',
    'satellite imagery of a {}.',
    'aerial imagery of a {}.',
    'satellite photo of a {}.',
    'aerial photo of a {}.',
    'satellite view of a {}.',
    'aerial view of a {}.',
    'satellite imagery of the {}.',
    'aerial imagery of the {}.',
    'satellite photo of the {}.',
    'aerial photo of the {}.',
    'satellite view of the {}.',
    'aerial view of the {}.',
]
```



## SST2

```bash
classes = [
    'negative',
    'positive',
]

templates = [
    'a {} review of a movie.',
]
```



## STL10

```bash
classes = [
    'airplane',
    'bird',
    'car',
    'cat',
    'deer',
    'dog',
    'horse',
    'monkey',
    'ship',
    'truck',
]

templates = [
    'a photo of a {}.',
    'a photo of the {}.',
]
```



## SUN397

```bash
classes = [
    'abbey',
    'airplane cabin',
    'airport terminal',
    'alley',
    'amphitheater',
    'amusement arcade',
    'amusement park',
    'anechoic chamber',
    'apartment building outdoor',
    'apse indoor',
    'aquarium',
    'aqueduct',
    'arch',
    'archive',
    'arrival gate outdoor',
    'art gallery',
    'art school',
    'art studio',
    'assembly line',
    'athletic field outdoor',
    'atrium public',
    'attic',
    'auditorium',
    'auto factory',
    'badlands',
    'badminton court indoor',
    'baggage claim',
    'bakery shop',
    'balcony exterior',
    'balcony interior',
    'ball pit',
    'ballroom',
    'bamboo forest',
    'banquet hall',
    'bar',
    'barn',
    'barndoor',
    'baseball field',
    'basement',
    'basilica',
    'basketball court outdoor',
    'bathroom',
    'batters box',
    'bayou',
    'bazaar indoor',
    'bazaar outdoor',
    'beach',
    'beauty salon',
    'bedroom',
    'berth',
    'biology laboratory',
    'bistro indoor',
    'boardwalk',
    'boat deck',
    'boathouse',
    'bookstore',
    'booth indoor',
    'botanical garden',
    'bow window indoor',
    'bow window outdoor',
    'bowling alley',
    'boxing ring',
    'brewery indoor',
    'bridge',
    'building facade',
    'bullring',
    'burial chamber',
    'bus interior',
    'butchers shop',
    'butte',
    'cabin outdoor',
    'cafeteria',
    'campsite',
    'campus',
    'canal natural',
    'canal urban',
    'candy store',
    'canyon',
    'car interior backseat',
    'car interior frontseat',
    'carrousel',
    'casino indoor',
    'castle',
    'catacomb',
    'cathedral indoor',
    'cathedral outdoor',
    'cavern indoor',
    'cemetery',
    'chalet',
    'cheese factory',
    'chemistry lab',
    'chicken coop indoor',
    'chicken coop outdoor',
    'childs room',
    'church indoor',
    'church outdoor',
    'classroom',
    'clean room',
    'cliff',
    'cloister indoor',
    'closet',
    'clothing store',
    'coast',
    'cockpit',
    'coffee shop',
    'computer room',
    'conference center',
    'conference room',
    'construction site',
    'control room',
    'control tower outdoor',
    'corn field',
    'corral',
    'corridor',
    'cottage garden',
    'courthouse',
    'courtroom',
    'courtyard',
    'covered bridge exterior',
    'creek',
    'crevasse',
    'crosswalk',
    'cubicle office',
    'dam',
    'delicatessen',
    'dentists office',
    'desert sand',
    'desert vegetation',
    'diner indoor',
    'diner outdoor',
    'dinette home',
    'dinette vehicle',
    'dining car',
    'dining room',
    'discotheque',
    'dock',
    'doorway outdoor',
    'dorm room',
    'driveway',
    'driving range outdoor',
    'drugstore',
    'electrical substation',
    'elevator door',
    'elevator interior',
    'elevator shaft',
    'engine room',
    'escalator indoor',
    'excavation',
    'factory indoor',
    'fairway',
    'fastfood restaurant',
    'field cultivated',
    'field wild',
    'fire escape',
    'fire station',
    'firing range indoor',
    'fishpond',
    'florist shop indoor',
    'food court',
    'forest broadleaf',
    'forest needleleaf',
    'forest path',
    'forest road',
    'formal garden',
    'fountain',
    'galley',
    'game room',
    'garage indoor',
    'garbage dump',
    'gas station',
    'gazebo exterior',
    'general store indoor',
    'general store outdoor',
    'gift shop',
    'golf course',
    'greenhouse indoor',
    'greenhouse outdoor',
    'gymnasium indoor',
    'hangar indoor',
    'hangar outdoor',
    'harbor',
    'hayfield',
    'heliport',
    'herb garden',
    'highway',
    'hill',
    'home office',
    'hospital',
    'hospital room',
    'hot spring',
    'hot tub outdoor',
    'hotel outdoor',
    'hotel room',
    'house',
    'hunting lodge outdoor',
    'ice cream parlor',
    'ice floe',
    'ice shelf',
    'ice skating rink indoor',
    'ice skating rink outdoor',
    'iceberg',
    'igloo',
    'industrial area',
    'inn outdoor',
    'islet',
    'jacuzzi indoor',
    'jail cell',
    'jail indoor',
    'jewelry shop',
    'kasbah',
    'kennel indoor',
    'kennel outdoor',
    'kindergarden classroom',
    'kitchen',
    'kitchenette',
    'labyrinth outdoor',
    'lake natural',
    'landfill',
    'landing deck',
    'laundromat',
    'lecture room',
    'library indoor',
    'library outdoor',
    'lido deck outdoor',
    'lift bridge',
    'lighthouse',
    'limousine interior',
    'living room',
    'lobby',
    'lock chamber',
    'locker room',
    'mansion',
    'manufactured home',
    'market indoor',
    'market outdoor',
    'marsh',
    'martial arts gym',
    'mausoleum',
    'medina',
    'moat water',
    'monastery outdoor',
    'mosque indoor',
    'mosque outdoor',
    'motel',
    'mountain',
    'mountain snowy',
    'movie theater indoor',
    'museum indoor',
    'music store',
    'music studio',
    'nuclear power plant outdoor',
    'nursery',
    'oast house',
    'observatory outdoor',
    'ocean',
    'office',
    'office building',
    'oil refinery outdoor',
    'oilrig',
    'operating room',
    'orchard',
    'outhouse outdoor',
    'pagoda',
    'palace',
    'pantry',
    'park',
    'parking garage indoor',
    'parking garage outdoor',
    'parking lot',
    'parlor',
    'pasture',
    'patio',
    'pavilion',
    'pharmacy',
    'phone booth',
    'physics laboratory',
    'picnic area',
    'pilothouse indoor',
    'planetarium outdoor',
    'playground',
    'playroom',
    'plaza',
    'podium indoor',
    'podium outdoor',
    'pond',
    'poolroom establishment',
    'poolroom home',
    'power plant outdoor',
    'promenade deck',
    'pub indoor',
    'pulpit',
    'putting green',
    'racecourse',
    'raceway',
    'raft',
    'railroad track',
    'rainforest',
    'reception',
    'recreation room',
    'residential neighborhood',
    'restaurant',
    'restaurant kitchen',
    'restaurant patio',
    'rice paddy',
    'riding arena',
    'river',
    'rock arch',
    'rope bridge',
    'ruin',
    'runway',
    'sandbar',
    'sandbox',
    'sauna',
    'schoolhouse',
    'sea cliff',
    'server room',
    'shed',
    'shoe shop',
    'shopfront',
    'shopping mall indoor',
    'shower',
    'skatepark',
    'ski lodge',
    'ski resort',
    'ski slope',
    'sky',
    'skyscraper',
    'slum',
    'snowfield',
    'squash court',
    'stable',
    'stadium baseball',
    'stadium football',
    'stage indoor',
    'staircase',
    'street',
    'subway interior',
    'subway station platform',
    'supermarket',
    'sushi bar',
    'swamp',
    'swimming pool indoor',
    'swimming pool outdoor',
    'synagogue indoor',
    'synagogue outdoor',
    'television studio',
    'temple east asia',
    'temple south asia',
    'tennis court indoor',
    'tennis court outdoor',
    'tent outdoor',
    'theater indoor procenium',
    'theater indoor seats',
    'thriftshop',
    'throne room',
    'ticket booth',
    'toll plaza',
    'topiary garden',
    'tower',
    'toyshop',
    'track outdoor',
    'train railway',
    'train station platform',
    'tree farm',
    'tree house',
    'trench',
    'underwater coral reef',
    'utility room',
    'valley',
    'van interior',
    'vegetable garden',
    'veranda',
    'veterinarians office',
    'viaduct',
    'videostore',
    'village',
    'vineyard',
    'volcano',
    'volleyball court indoor',
    'volleyball court outdoor',
    'waiting room',
    'warehouse indoor',
    'water tower',
    'waterfall block',
    'waterfall fan',
    'waterfall plunge',
    'watering hole',
    'wave',
    'wet bar',
    'wheat field',
    'wind farm',
    'windmill',
    'wine cellar barrel storage',
    'wine cellar bottle storage',
    'wrestling ring indoor',
    'yard',
    'youth hostel',
]

templates = [
    'a photo of a {}.',
    'a photo of the {}.',
]
```



## StanfordCars

```bash
classes = [
    'AM General Hummer SUV 2000',
    'Acura RL Sedan 2012',
    'Acura TL Sedan 2012',
    'Acura TL Type-S 2008',
    'Acura TSX Sedan 2012',
    'Acura Integra Type R 2001',
    'Acura ZDX Hatchback 2012',
    'Aston Martin V8 Vantage Convertible 2012',
    'Aston Martin V8 Vantage Coupe 2012',
    'Aston Martin Virage Convertible 2012',
    'Aston Martin Virage Coupe 2012',
    'Audi RS 4 Convertible 2008',
    'Audi A5 Coupe 2012',
    'Audi TTS Coupe 2012',
    'Audi R8 Coupe 2012',
    'Audi V8 Sedan 1994',
    'Audi 100 Sedan 1994',
    'Audi 100 Wagon 1994',
    'Audi TT Hatchback 2011',
    'Audi S6 Sedan 2011',
    'Audi S5 Convertible 2012',
    'Audi S5 Coupe 2012',
    'Audi S4 Sedan 2012',
    'Audi S4 Sedan 2007',
    'Audi TT RS Coupe 2012',
    'BMW ActiveHybrid 5 Sedan 2012',
    'BMW 1 Series Convertible 2012',
    'BMW 1 Series Coupe 2012',
    'BMW 3 Series Sedan 2012',
    'BMW 3 Series Wagon 2012',
    'BMW 6 Series Convertible 2007',
    'BMW X5 SUV 2007',
    'BMW X6 SUV 2012',
    'BMW M3 Coupe 2012',
    'BMW M5 Sedan 2010',
    'BMW M6 Convertible 2010',
    'BMW X3 SUV 2012',
    'BMW Z4 Convertible 2012',
    'Bentley Continental Supersports Conv. Convertible 2012',
    'Bentley Arnage Sedan 2009',
    'Bentley Mulsanne Sedan 2011',
    'Bentley Continental GT Coupe 2012',
    'Bentley Continental GT Coupe 2007',
    'Bentley Continental Flying Spur Sedan 2007',
    'Bugatti Veyron 16.4 Convertible 2009',
    'Bugatti Veyron 16.4 Coupe 2009',
    'Buick Regal GS 2012',
    'Buick Rainier SUV 2007',
    'Buick Verano Sedan 2012',
    'Buick Enclave SUV 2012',
    'Cadillac CTS-V Sedan 2012',
    'Cadillac SRX SUV 2012',
    'Cadillac Escalade EXT Crew Cab 2007',
    'Chevrolet Silverado 1500 Hybrid Crew Cab 2012',
    'Chevrolet Corvette Convertible 2012',
    'Chevrolet Corvette ZR1 2012',
    'Chevrolet Corvette Ron Fellows Edition Z06 2007',
    'Chevrolet Traverse SUV 2012',
    'Chevrolet Camaro Convertible 2012',
    'Chevrolet HHR SS 2010',
    'Chevrolet Impala Sedan 2007',
    'Chevrolet Tahoe Hybrid SUV 2012',
    'Chevrolet Sonic Sedan 2012',
    'Chevrolet Express Cargo Van 2007',
    'Chevrolet Avalanche Crew Cab 2012',
    'Chevrolet Cobalt SS 2010',
    'Chevrolet Malibu Hybrid Sedan 2010',
    'Chevrolet TrailBlazer SS 2009',
    'Chevrolet Silverado 2500HD Regular Cab 2012',
    'Chevrolet Silverado 1500 Classic Extended Cab 2007',
    'Chevrolet Express Van 2007',
    'Chevrolet Monte Carlo Coupe 2007',
    'Chevrolet Malibu Sedan 2007',
    'Chevrolet Silverado 1500 Extended Cab 2012',
    'Chevrolet Silverado 1500 Regular Cab 2012',
    'Chrysler Aspen SUV 2009',
    'Chrysler Sebring Convertible 2010',
    'Chrysler Town and Country Minivan 2012',
    'Chrysler 300 SRT-8 2010',
    'Chrysler Crossfire Convertible 2008',
    'Chrysler PT Cruiser Convertible 2008',
    'Daewoo Nubira Wagon 2002',
    'Dodge Caliber Wagon 2012',
    'Dodge Caliber Wagon 2007',
    'Dodge Caravan Minivan 1997',
    'Dodge Ram Pickup 3500 Crew Cab 2010',
    'Dodge Ram Pickup 3500 Quad Cab 2009',
    'Dodge Sprinter Cargo Van 2009',
    'Dodge Journey SUV 2012',
    'Dodge Dakota Crew Cab 2010',
    'Dodge Dakota Club Cab 2007',
    'Dodge Magnum Wagon 2008',
    'Dodge Challenger SRT8 2011',
    'Dodge Durango SUV 2012',
    'Dodge Durango SUV 2007',
    'Dodge Charger Sedan 2012',
    'Dodge Charger SRT-8 2009',
    'Eagle Talon Hatchback 1998',
    'FIAT 500 Abarth 2012',
    'FIAT 500 Convertible 2012',
    'Ferrari FF Coupe 2012',
    'Ferrari California Convertible 2012',
    'Ferrari 458 Italia Convertible 2012',
    'Ferrari 458 Italia Coupe 2012',
    'Fisker Karma Sedan 2012',
    'Ford F-450 Super Duty Crew Cab 2012',
    'Ford Mustang Convertible 2007',
    'Ford Freestar Minivan 2007',
    'Ford Expedition EL SUV 2009',
    'Ford Edge SUV 2012',
    'Ford Ranger SuperCab 2011',
    'Ford GT Coupe 2006',
    'Ford F-150 Regular Cab 2012',
    'Ford F-150 Regular Cab 2007',
    'Ford Focus Sedan 2007',
    'Ford E-Series Wagon Van 2012',
    'Ford Fiesta Sedan 2012',
    'GMC Terrain SUV 2012',
    'GMC Savana Van 2012',
    'GMC Yukon Hybrid SUV 2012',
    'GMC Acadia SUV 2012',
    'GMC Canyon Extended Cab 2012',
    'Geo Metro Convertible 1993',
    'HUMMER H3T Crew Cab 2010',
    'HUMMER H2 SUT Crew Cab 2009',
    'Honda Odyssey Minivan 2012',
    'Honda Odyssey Minivan 2007',
    'Honda Accord Coupe 2012',
    'Honda Accord Sedan 2012',
    'Hyundai Veloster Hatchback 2012',
    'Hyundai Santa Fe SUV 2012',
    'Hyundai Tucson SUV 2012',
    'Hyundai Veracruz SUV 2012',
    'Hyundai Sonata Hybrid Sedan 2012',
    'Hyundai Elantra Sedan 2007',
    'Hyundai Accent Sedan 2012',
    'Hyundai Genesis Sedan 2012',
    'Hyundai Sonata Sedan 2012',
    'Hyundai Elantra Touring Hatchback 2012',
    'Hyundai Azera Sedan 2012',
    'Infiniti G Coupe IPL 2012',
    'Infiniti QX56 SUV 2011',
    'Isuzu Ascender SUV 2008',
    'Jaguar XK XKR 2012',
    'Jeep Patriot SUV 2012',
    'Jeep Wrangler SUV 2012',
    'Jeep Liberty SUV 2012',
    'Jeep Grand Cherokee SUV 2012',
    'Jeep Compass SUV 2012',
    'Lamborghini Reventon Coupe 2008',
    'Lamborghini Aventador Coupe 2012',
    'Lamborghini Gallardo LP 570-4 Superleggera 2012',
    'Lamborghini Diablo Coupe 2001',
    'Land Rover Range Rover SUV 2012',
    'Land Rover LR2 SUV 2012',
    'Lincoln Town Car Sedan 2011',
    'MINI Cooper Roadster Convertible 2012',
    'Maybach Landaulet Convertible 2012',
    'Mazda Tribute SUV 2011',
    'McLaren MP4-12C Coupe 2012',
    'Mercedes-Benz 300-Class Convertible 1993',
    'Mercedes-Benz C-Class Sedan 2012',
    'Mercedes-Benz SL-Class Coupe 2009',
    'Mercedes-Benz E-Class Sedan 2012',
    'Mercedes-Benz S-Class Sedan 2012',
    'Mercedes-Benz Sprinter Van 2012',
    'Mitsubishi Lancer Sedan 2012',
    'Nissan Leaf Hatchback 2012',
    'Nissan NV Passenger Van 2012',
    'Nissan Juke Hatchback 2012',
    'Nissan 240SX Coupe 1998',
    'Plymouth Neon Coupe 1999',
    'Porsche Panamera Sedan 2012',
    'Ram C/V Cargo Van Minivan 2012',
    'Rolls-Royce Phantom Drophead Coupe Convertible 2012',
    'Rolls-Royce Ghost Sedan 2012',
    'Rolls-Royce Phantom Sedan 2012',
    'Scion xD Hatchback 2012',
    'Spyker C8 Convertible 2009',
    'Spyker C8 Coupe 2009',
    'Suzuki Aerio Sedan 2007',
    'Suzuki Kizashi Sedan 2012',
    'Suzuki SX4 Hatchback 2012',
    'Suzuki SX4 Sedan 2012',
    'Tesla Model S Sedan 2012',
    'Toyota Sequoia SUV 2012',
    'Toyota Camry Sedan 2012',
    'Toyota Corolla Sedan 2012',
    'Toyota 4Runner SUV 2012',
    'Volkswagen Golf Hatchback 2012',
    'Volkswagen Golf Hatchback 1991',
    'Volkswagen Beetle Hatchback 2012',
    'Volvo C30 Hatchback 2012',
    'Volvo 240 Sedan 1993',
    'Volvo XC90 SUV 2007',
    'smart fortwo Convertible 2012',
]

templates = [
    'a photo of a {}.',
    'a photo of the {}.',
    'a photo of my {}.',
    'i love my {}!',
    'a photo of my dirty {}.',
    'a photo of my clean {}.',
    'a photo of my new {}.',
    'a photo of my old {}.',
]
```



## UCF101

```bash
classes = [
    'Apply Eye Makeup',
    'Apply Lipstick',
    'Archery',
    'Baby Crawling',
    'Balance Beam',
    'Band Marching',
    'Baseball Pitch',
    'Basketball',
    'Basketball Dunk',
    'Bench Press',
    'Biking',
    'Billiards',
    'Blow Dry Hair',
    'Blowing Candles',
    'Body Weight Squats',
    'Bowling',
    'Boxing Punching Bag',
    'Boxing Speed Bag',
    'Breast Stroke',
    'Brushing Teeth',
    'Clean And Jerk',
    'Cliff Diving',
    'Cricket Bowling',
    'Cricket Shot',
    'Cutting In Kitchen',
    'Diving',
    'Drumming',
    'Fencing',
    'Field Hockey Penalty',
    'Floor Gymnastics',
    'Frisbee Catch',
    'Front Crawl',
    'Golf Swing',
    'Haircut',
    'Hammer Throw',
    'Hammering',
    'Hand Stand Pushups',
    'Handstand Walking',
    'Head Massage',
    'High Jump',
    'Horse Race',
    'Horse Riding',
    'Hula Hoop',
    'Ice Dancing',
    'Javelin Throw',
    'Juggling Balls',
    'Jump Rope',
    'Jumping Jack',
    'Kayaking',
    'Knitting',
    'Long Jump',
    'Lunges',
    'Military Parade',
    'Mixing',
    'Mopping Floor',
    'Nunchucks',
    'Parallel Bars',
    'Pizza Tossing',
    'Playing Cello',
    'Playing Daf',
    'Playing Dhol',
    'Playing Flute',
    'Playing Guitar',
    'Playing Piano',
    'Playing Sitar',
    'Playing Tabla',
    'Playing Violin',
    'Pole Vault',
    'Pommel Horse',
    'Pull Ups',
    'Punch',
    'Push Ups',
    'Rafting',
    'Rock Climbing Indoor',
    'Rope Climbing',
    'Rowing',
    'Salsa Spin',
    'Shaving Beard',
    'Shotput',
    'Skate Boarding',
    'Skiing',
    'Skijet',
    'Sky Diving',
    'Soccer Juggling',
    'Soccer Penalty',
    'Still Rings',
    'Sumo Wrestling',
    'Surfing',
    'Swing',
    'Table Tennis Shot',
    'Tai Chi',
    'Tennis Swing',
    'Throw Discus',
    'Trampoline Jumping',
    'Typing',
    'Uneven Bars',
    'Volleyball Spiking',
    'Walking With Dog',
    'Wall Pushups',
    'Writing On Board',
    'Yo Yo',
]

templates = [
    'a photo of a person {}.',
    'a video of a person {}.',
    'a example of a person {}.',
    'a demonstration of a person {}.',
    'a photo of the person {}.',
    'a video of the person {}.',
    'a example of the person {}.',
    'a demonstration of the person {}.',
    'a photo of a person using {}.',
    'a video of a person using {}.',
    'a example of a person using {}.',
    'a demonstration of a person using {}.',
    'a photo of the person using {}.',
    'a video of the person using {}.',
    'a example of the person using {}.',
    'a demonstration of the person using {}.',
    'a photo of a person doing {}.',
    'a video of a person doing {}.',
    'a example of a person doing {}.',
    'a demonstration of a person doing {}.',
    'a photo of the person doing {}.',
    'a video of the person doing {}.',
    'a example of the person doing {}.',
    'a demonstration of the person doing {}.',
    'a photo of a person during {}.',
    'a video of a person during {}.',
    'a example of a person during {}.',
    'a demonstration of a person during {}.',
    'a photo of the person during {}.',
    'a video of the person during {}.',
    'a example of the person during {}.',
    'a demonstration of the person during {}.',
    'a photo of a person performing {}.',
    'a video of a person performing {}.',
    'a example of a person performing {}.',
    'a demonstration of a person performing {}.',
    'a photo of the person performing {}.',
    'a video of the person performing {}.',
    'a example of the person performing {}.',
    'a demonstration of the person performing {}.',
    'a photo of a person practicing {}.',
    'a video of a person practicing {}.',
    'a example of a person practicing {}.',
    'a demonstration of a person practicing {}.',
    'a photo of the person practicing {}.',
    'a video of the person practicing {}.',
    'a example of the person practicing {}.',
    'a demonstration of the person practicing {}.',
]
```




================================================
FILE: CLIP/data/rendered-sst2.md
================================================
# The Rendered SST2 Dataset

In the paper, we used an image classification dataset called Rendered SST2, to evaluate the model's capability on optical character recognition. To do so, we rendered the sentences in the [Standford Sentiment Treebank v2](https://nlp.stanford.edu/sentiment/treebank.html) dataset and used those as the input to the CLIP image encoder.

The following command will download a 131MB archive countaining the images and extract into a subdirectory `rendered-sst2`:

```bash
wget https://openaipublic.azureedge.net/clip/data/rendered-sst2.tgz
tar zxvf rendered-sst2.tgz
```



================================================
FILE: CLIP/data/yfcc100m.md
================================================
# The YFCC100M Subset

In the paper, we performed a dataset ablation using a subset of the YFCC100M dataset and showed that the performance remained largely similar. 

The subset contains 14,829,396 images, about 15% of the full dataset, which have been filtered to only keep those with natural languag titles and/or descriptions in English.

We provide the list of (line number, photo identifier, photo hash) of each image contained in this subset. These correspond to the first three columns in the dataset's metadata TSV file.

```bash
wget https://openaipublic.azureedge.net/clip/data/yfcc100m_subset_data.tsv.bz2
bunzip2 yfcc100m_subset_data.tsv.bz2
```

Use of the underlying media files is subject to the Creative Commons licenses chosen by their creators/uploaders. For more information about the YFCC100M dataset, visit [the official website](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/).

================================================
FILE: CLIP/hubconf.py
================================================
from clip.clip import tokenize as _tokenize, load as _load, available_models as _available_models
import re
import string

dependencies = ["torch", "torchvision", "ftfy", "regex", "tqdm"]

# For compatibility (cannot include special characters in function name)
model_functions = { model: re.sub(f'[{string.punctuation}]', '_', model) for model in _available_models()}

def _create_hub_entrypoint(model):
    def entrypoint(**kwargs):      
        return _load(model, **kwargs)
    
    entrypoint.__doc__ = f"""Loads the {model} CLIP model

        Parameters
        ----------
        device : Union[str, torch.device]
            The device to put the loaded model

        jit : bool
            Whether to load the optimized JIT model or more hackable non-JIT model (default).

        download_root: str
            path to download the model files; by default, it uses "~/.cache/clip"

        Returns
        -------
        model : torch.nn.Module
            The {model} CLIP model

        preprocess : Callable[[PIL.Image], torch.Tensor]
            A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
        """
    return entrypoint

def tokenize():
    return _tokenize

_entrypoints = {model_functions[model]: _create_hub_entrypoint(model) for model in _available_models()}

globals().update(_entrypoints)

================================================
FILE: CLIP/model-card.md
================================================
# Model Card: CLIP

Inspired by [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993) and [Lessons from Archives (Jo & Gebru)](https://arxiv.org/pdf/1912.10389.pdf), we’re providing some accompanying information about the multimodal model.

## Model Details

The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.

### Model Date

January 2021

### Model Type

The base model uses a ResNet50 with several modifications as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss. There is also a variant of the model where the ResNet image encoder is replaced with a Vision Transformer.

### Model Versions

Initially, we’ve released one CLIP model based on the Vision Transformer architecture equivalent to ViT-B/32, along with the RN50 model, using the architecture equivalent to ResNet-50.

As part of the staged release process, we have also released the RN101 model, as well as RN50x4, a RN50 scaled up 4x according to the [EfficientNet](https://arxiv.org/abs/1905.11946) scaling rule. In July 2021, we additionally released the RN50x16 and ViT-B/16 models, and in January 2022, the RN50x64 and ViT-L/14 models were released. Lastly, the ViT-L/14@336px model was released in April 2022.

Please see the paper linked below for further details about their specification.

### Documents

- [Blog Post](https://openai.com/blog/clip/)
- [CLIP Paper](https://arxiv.org/abs/2103.00020)



## Model Use

### Intended Use

The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.

#### Primary intended uses

The primary intended users of these models are AI researchers.

We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.

### Out-of-Scope Use Cases

**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful. 

Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.

Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.



## Data

The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.

### Data Mission Statement

Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.



## Performance and Limitations

### Performance

We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:

- Food101
- CIFAR10   
- CIFAR100   
- Birdsnap
- SUN397
- Stanford Cars
- FGVC Aircraft
- VOC2007
- DTD
- Oxford-IIIT Pet dataset
- Caltech101
- Flowers102
- MNIST   
- SVHN 
- IIIT5K   
- Hateful Memes   
- SST-2
- UCF101
- Kinetics700
- Country211
- CLEVR Counting
- KITTI Distance
- STL-10
- RareAct
- Flickr30
- MSCOCO
- ImageNet
- ImageNet-A
- ImageNet-R
- ImageNet Sketch
- ObjectNet (ImageNet Overlap)
- Youtube-BB
- ImageNet-Vid

## Limitations

CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.

### Bias and Fairness

We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).

We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.



## Feedback

### Where to send questions or comments about the model

Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9)


================================================
FILE: CLIP/model.py
================================================
from collections import OrderedDict
from typing import Tuple, Union

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1):
        super().__init__()

        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu2 = nn.ReLU(inplace=True)

        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()

        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu3 = nn.ReLU(inplace=True)

        self.downsample = None
        self.stride = stride

        if stride > 1 or inplanes != planes * Bottleneck.expansion:
            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
            self.downsample = nn.Sequential(OrderedDict([
                ("-1", nn.AvgPool2d(stride)),
                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
                ("1", nn.BatchNorm2d(planes * self.expansion))
            ]))

    def forward(self, x: torch.Tensor):
        identity = x

        out = self.relu1(self.bn1(self.conv1(x)))
        out = self.relu2(self.bn2(self.conv2(out)))
        out = self.avgpool(out)
        out = self.bn3(self.conv3(out))

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu3(out)
        return out


class AttentionPool2d(nn.Module):
    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
        super().__init__()
        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    def forward(self, x):
        x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
        x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
        x, _ = F.multi_head_attention_forward(
            query=x[:1], key=x, value=x,
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False
        )
        return x.squeeze(0)


class ModifiedResNet(nn.Module):
    """
    A ResNet class that is similar to torchvision's but contains the following changes:
    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
    - The final pooling layer is a QKV attention instead of an average pool
    """

    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
        super().__init__()
        self.output_dim = output_dim
        self.input_resolution = input_resolution

        # the 3-layer stem
        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width // 2)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(width // 2)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(width)
        self.relu3 = nn.ReLU(inplace=True)
        self.avgpool = nn.AvgPool2d(2)

        # residual layers
        self._inplanes = width  # this is a *mutable* variable used during construction
        self.layer1 = self._make_layer(width, layers[0])
        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)

        embed_dim = width * 32  # the ResNet feature dimension
        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)

    def _make_layer(self, planes, blocks, stride=1):
        layers = [Bottleneck(self._inplanes, planes, stride)]

        self._inplanes = planes * Bottleneck.expansion
        for _ in range(1, blocks):
            layers.append(Bottleneck(self._inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        def stem(x):
            x = self.relu1(self.bn1(self.conv1(x)))
            x = self.relu2(self.bn2(self.conv2(x)))
            x = self.relu3(self.bn3(self.conv3(x)))
            x = self.avgpool(x)
            return x

        x = x.type(self.conv1.weight.dtype)
        x = stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        x = self.attnpool(x)

        return x


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        ret = super().forward(x.type(torch.float32))
        return ret.type(orig_type)


class QuickGELU(nn.Module):
    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlock(nn.Module):
    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
        super().__init__()

        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(OrderedDict([
            ("c_fc", nn.Linear(d_model, d_model * 4)),
            ("gelu", QuickGELU()),
            ("c_proj", nn.Linear(d_model * 4, d_model))
        ]))
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask

    def attention(self, x: torch.Tensor):
        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]

    def forward(self, x: torch.Tensor):
        x = x + self.attention(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class Transformer(nn.Module):
    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
        super().__init__()
        self.width = width
        self.layers = layers
        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])

    def forward(self, x: torch.Tensor):
        return self.resblocks(x)


class VisionTransformer(nn.Module):
    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
        super().__init__()
        self.input_resolution = input_resolution
        self.output_dim = output_dim
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)

        scale = width ** -0.5
        self.class_embedding = nn.Parameter(scale * torch.randn(width))
        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
        self.ln_pre = LayerNorm(width)

        self.transformer = Transformer(width, layers, heads)

        self.ln_post = LayerNorm(width)
        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))

    def forward(self, x: torch.Tensor):
        x = self.conv1(x)  # shape = [*, width, grid, grid]
        x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
        x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
        x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
        x = x + self.positional_embedding.to(x.dtype)
        x = self.ln_pre(x)

        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD

        x = self.ln_post(x[:, 0, :])

        if self.proj is not None:
            x = x @ self.proj

        return x


class CLIP(nn.Module):
    def __init__(self,
                 embed_dim: int,
                 # vision
                 image_resolution: int,
                 vision_layers: Union[Tuple[int, int, int, int], int],
                 vision_width: int,
                 vision_patch_size: int,
                 # text
                 context_length: int,
                 vocab_size: int,
                 transformer_width: int,
                 transformer_heads: int,
                 transformer_layers: int
                 ):
        super().__init__()

        self.context_length = context_length

        if isinstance(vision_layers, (tuple, list)):
            vision_heads = vision_width * 32 // 64
            self.visual = ModifiedResNet(
                layers=vision_layers,
                output_dim=embed_dim,
                heads=vision_heads,
                input_resolution=image_resolution,
                width=vision_width
            )
        else:
            vision_heads = vision_width // 64
            self.visual = VisionTransformer(
                input_resolution=image_resolution,
                patch_size=vision_patch_size,
                width=vision_width,
                layers=vision_layers,
                heads=vision_heads,
                output_dim=embed_dim
            )

        self.transformer = Transformer(
            width=transformer_width,
            layers=transformer_layers,
            heads=transformer_heads,
            attn_mask=self.build_attention_mask()
        )

        self.vocab_size = vocab_size
        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
        self.ln_final = LayerNorm(transformer_width)

        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))

        self.initialize_parameters()

    def initialize_parameters(self):
        nn.init.normal_(self.token_embedding.weight, std=0.02)
        nn.init.normal_(self.positional_embedding, std=0.01)

        if isinstance(self.visual, ModifiedResNet):
            if self.visual.attnpool is not None:
                std = self.visual.attnpool.c_proj.in_features ** -0.5
                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)

            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
                for name, param in resnet_block.named_parameters():
                    if name.endswith("bn3.weight"):
                        nn.init.zeros_(param)

        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
        attn_std = self.transformer.width ** -0.5
        fc_std = (2 * self.transformer.width) ** -0.5
        for block in self.transformer.resblocks:
            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)

        if self.text_projection is not None:
            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)

    def build_attention_mask(self):
        # lazily create causal attention mask, with full attention between the vision tokens
        # pytorch uses additive attention mask; fill with -inf
        mask = torch.empty(self.context_length, self.context_length)
        mask.fill_(float("-inf"))
        mask.triu_(1)  # zero out the lower diagonal
        return mask

    @property
    def dtype(self):
        return self.visual.conv1.weight.dtype

    def encode_image(self, image):
        return self.visual(image.type(self.dtype))

    def encode_text(self, text):
        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]

        x = x + self.positional_embedding.type(self.dtype)
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD
        x = self.ln_final(x).type(self.dtype)

        # x.shape = [batch_size, n_ctx, transformer.width]
        # take features from the eot embedding (eot_token is the highest number in each sequence)
        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection

        return x

    def forward(self, image, text):
        image_features = self.encode_image(image)
        text_features = self.encode_text(text)

        # normalized features
        image_features = image_features / image_features.norm(dim=1, keepdim=True)
        text_features = text_features / text_features.norm(dim=1, keepdim=True)

        # cosine similarity as logits
        logit_scale = self.logit_scale.exp()
        logits_per_image = logit_scale * image_features @ text_features.t()
        logits_per_text = logits_per_image.t()

        # shape = [global_batch_size, global_batch_size]
        return logits_per_image, logits_per_text


def convert_weights(model: nn.Module):
    """Convert applicable model parameters to fp16"""

    def _convert_weights_to_fp16(l):
        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
            l.weight.data = l.weight.data.half()
            if l.bias is not None:
                l.bias.data = l.bias.data.half()

        if isinstance(l, nn.MultiheadAttention):
            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
                tensor = getattr(l, attr)
                if tensor is not None:
                    tensor.data = tensor.data.half()

        for name in ["text_projection", "proj"]:
            if hasattr(l, name):
                attr = getattr(l, name)
                if attr is not None:
                    attr.data = attr.data.half()

    model.apply(_convert_weights_to_fp16)


def build_model(state_dict: dict):
    vit = "visual.proj" in state_dict

    if vit:
        vision_width = state_dict["visual.conv1.weight"].shape[0]
        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
        image_resolution = vision_patch_size * grid_size
    else:
        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
        vision_layers = tuple(counts)
        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
        vision_patch_size = None
        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
        image_resolution = output_width * 32

    embed_dim = state_dict["text_projection"].shape[1]
    context_length = state_dict["positional_embedding"].shape[0]
    vocab_size = state_dict["token_embedding.weight"].shape[0]
    transformer_width = state_dict["ln_final.weight"].shape[0]
    transformer_heads = transformer_width // 64
    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))

    model = CLIP(
        embed_dim,
        image_resolution, vision_layers, vision_width, vision_patch_size,
        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
    )

    for key in ["input_resolution", "context_length", "vocab_size"]:
        if key in state_dict:
            del state_dict[key]

    convert_weights(model)
    model.load_state_dict(state_dict)
    return model.eval()


================================================
FILE: CLIP/requirements.txt
================================================
ftfy
regex
tqdm
torch
torchvision


================================================
FILE: CLIP/setup.py
================================================
import os

import pkg_resources
from setuptools import setup, find_packages

setup(
    name="clip",
    py_modules=["clip"],
    version="1.0",
    description="",
    author="OpenAI",
    packages=find_packages(exclude=["tests*"]),
    install_requires=[
        str(r)
        for r in pkg_resources.parse_requirements(
            open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
        )
    ],
    include_package_data=True,
    extras_require={'dev': ['pytest']},
)


================================================
FILE: CLIP/simple_tokenizer.py
================================================
import gzip
import html
import os
from functools import lru_cache

import ftfy
import regex as re


@lru_cache()
def default_bpe():
    return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz")


@lru_cache()
def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a corresponding list of unicode strings.
    The reversible bpe codes work on unicode strings.
    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
    This is a signficant percentage of your normal, say, 32K bpe vocab.
    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    And avoids mapping to whitespace/control characters the bpe code barfs on.
    """
    bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1))
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8+n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


def get_pairs(word):
    """Return set of symbol pairs in a word.
    Word is represented as tuple of symbols (symbols being variable-length strings).
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


def basic_clean(text):
    text = ftfy.fix_text(text)
    text = html.unescape(html.unescape(text))
    return text.strip()


def whitespace_clean(text):
    text = re.sub(r'\s+', ' ', text)
    text = text.strip()
    return text


class SimpleTokenizer(object):
    def __init__(self, bpe_path: str = default_bpe()):
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        merges = gzip.open(bpe_path).read().decode("utf-8").split('\n')
        merges = merges[1:49152-256-2+1]
        merges = [tuple(merge.split()) for merge in merges]
        vocab = list(bytes_to_unicode().values())
        vocab = vocab + [v+'</w>' for v in vocab]
        for merge in merges:
            vocab.append(''.join(merge))
        vocab.extend(['<|startoftext|>', '<|endoftext|>'])
        self.encoder = dict(zip(vocab, range(len(vocab))))
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
        self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE)

    def bpe(self, token):
        if token in self.cache:
            return self.cache[token]
        word = tuple(token[:-1]) + ( token[-1] + '</w>',)
        pairs = get_pairs(word)

        if not pairs:
            return token+'</w>'

        while True:
            bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf')))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                    new_word.extend(word[i:j])
                    i = j
                except:
                    new_word.extend(word[i:])
                    break

                if word[i] == first and i < len(word)-1 and word[i+1] == second:
                    new_word.append(first+second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = ' '.join(word)
        self.cache[token] = word
        return word

    def encode(self, text):
        bpe_tokens = []
        text = whitespace_clean(basic_clean(text)).lower()
        for token in re.findall(self.pat, text):
            token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
            bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
        return bpe_tokens

    def decode(self, tokens):
        text = ''.join([self.decoder[token] for token in tokens])
        text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ')
        return text


================================================
FILE: CLIP/tests/test_consistency.py
================================================
import numpy as np
import pytest
import torch
from PIL import Image

import clip


@pytest.mark.parametrize('model_name', clip.available_models())
def test_consistency(model_name):
    device = "cpu"
    jit_model, transform = clip.load(model_name, device=device, jit=True)
    py_model, _ = clip.load(model_name, device=device, jit=False)

    image = transform(Image.open("CLIP.png")).unsqueeze(0).to(device)
    text = clip.tokenize(["a diagram", "a dog", "a cat"]).to(device)

    with torch.no_grad():
        logits_per_image, _ = jit_model(image, text)
        jit_probs = logits_per_image.softmax(dim=-1).cpu().numpy()

        logits_per_image, _ = py_model(image, text)
        py_probs = logits_per_image.softmax(dim=-1).cpu().numpy()

    assert np.allclose(jit_probs, py_probs, atol=0.01, rtol=0.1)


================================================
FILE: README.md
================================================
# Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models (ECCV 2024)

<img src="./assets/fig_virtual_try_on.gif">

**Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models**<br>
**[Project Page](https://yzmblog.github.io/projects/SurfD)**
| **[Paper](https://arxiv.org/abs/2311.17050)**

This is an official implementation of Surf-D using [PyTorch](https://pytorch.org/)

>We present **Surf-D**, a novel method for generating high-quality 3D shapes as **Surfaces** with arbitrary topologies using **Diffusion** models. Previous methods explored shape generation with different representations and they suffer from limited topologies and poor geometry details. To generate high-quality surfaces of arbitrary topologies, we use the Unsigned Distance Field (UDF) as our surface representation to accommodate arbitrary topologies. Furthermore, we propose a new pipeline that employs a point-based AutoEncoder to learn a compact and continuous latent space for accurately encoding UDF and support high-resolution mesh extraction. We further show that our new pipeline significantly outperforms the prior approaches to learning the distance fields, such as the grid-based AutoEncoder, which is not scalable and incapable of learning accurate UDF. In addition, we adopt a curriculum learning strategy to efficiently embed various surfaces. With the pretrained shape latent space, we employ a latent diffusion model to acquire the distribution of various shapes. Extensive experiments are presented on using Surf-D for unconditional generation, category conditional generation, image conditional generation, and text-to-shape tasks. The experiments demonstrate the superior performance of Surf-D in shape generation across multiple modalities as conditions.

## Installation

We recommend to use [Anaconda](https://www.anaconda.com/).

Create and activate a virtual environment.

    conda env create -f environment.yaml
    conda activate SurfD

    cd meshudf
    python3 setup.py build_ext --inplace

## Download pretrained models
Download our pretrained models at [google drive](https://drive.google.com/drive/folders/19Wdbg-zOB48IZ3KSxayRK3q1v1HfWRUP?usp=sharing).

## Generate from Diffusion:
### Extract a 512 resolution mesh will take some time, if you want to get a mesh quickly, feel free to modify the following command to --resolution 256

Unconditional generation:

    python -m sample.generate_uncond \
        --model_path pretrained_models/diffusion_uncond.pt \
        --output_dir ./outputs/uncond/ \
        --cond_mode no_cond \
        --ae_dir pretrained_models/ae_deepfashion3d.pt  \
        --num_samples 10 \
        --resolution 512

Sketch conditional generation:

    python -m sample.generate_sketch \
        --model_path pretrained_models/diffusion_sketch.pt \
        --output_dir ./outputs/sketch_cond/ \
        --cond_mode sketch \
        --ae_dir pretrained_models/ae_deepfashion3d.pt \
        --sketch_path demo_images/sketch.png \
        --resolution 512

Image conditional generation:

    python -m sample.generate_image \
        --model_path pretrained_models/diffusion_image.pt \
        --output_dir ./outputs/image_cond/ \
        --cond_mode img \
        --ae_dir pretrained_models/ae_pix3d.pt \
        --image_path demo_images/0049.jpg \
        --mask_path demo_images/0049.png \
        --resolution 512

Text conditional generation:

    python -m sample.generate_text \
        --model_path pretrained_models/diffusion_text.pt \
        --output_dir ./outputs/text_cond/ \
        --cond_mode text \
        --ae_dir pretrained_models/ae_text.pt  \
        --prompt "a dining chair" \
        --watertight --num_samples 10 \
        --resolution 512

# Training

## Prepare dataset
Down load DeepFashion3D dataset at [DeepFashion3D](https://github.com/GAP-LAB-CUHK-SZ/deepFashion3D), Pix3D dataset at [Pix3D](http://pix3d.csail.mit.edu/) and ShapeNet dataset at [ShapeNet](https://shapenet.org/).

## Preprocess the dataset
    cd AutoEncoder/encdc
    python preprocess_udfs.py /path/to/data_root /path/to/output dataset_name

## AutoEncoder Training:
```
cd AutoEncoder/encdc
```

    python train_encdec.py ../cfg/xxx/xxx.yaml


## Diffusion Training:

    python train_diffcloth.py --cond_mode no_cond --save_dir xxx --overwrite --data_dir xxx --ae_dir xxx --log_interval 25 --save_interval 10000 --dataset deepfashion3d

## Acknowledgement

Our code took reference from [MDM](https://github.com/GuyTevet/motion-diffusion-model), [SDFusion](https://github.com/yccyenchicheng/SDFusion), [DrapeNet](https://github.com/liren2515/DrapeNet), [MeshUDF](https://github.com/cvlab-epfl/MeshUDF), [Stable Diffusion](https://github.com/CompVis/stable-diffusion). We thank these authors for their great works and open-source contribution.

<a name="citation"></a>
## Citation
If you find this work useful for your research, please consider citing our paper: 

```bibtex
@article{yu2023surf,
  title={Surf-D: High-Quality Surface Generation for Arbitrary Topologies using Diffusion Models},
  author={Yu, Zhengming and Dou, Zhiyang and Long, Xiaoxiao and Lin, Cheng and Li, Zekun and Liu, Yuan and M{\"u}ller, Norman and Komura, Taku and Habermann, Marc and Theobalt, Christian and others},
  journal={arXiv preprint arXiv:2311.17050},
  year={2023}
}
```
or
```bibtex
@inproceedings{yu2025surf,
  title={Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models},
  author={Yu, Zhengming and Dou, Zhiyang and Long, Xiaoxiao and Lin, Cheng and Li, Zekun and Liu, Yuan and M{\"u}ller, Norman and Komura, Taku and Habermann, Marc and Theobalt, Christian and others},
  booktitle={European Conference on Computer Vision},
  pages={419--438},
  year={2025},
  organization={Springer}
}
```


================================================
FILE: data_loaders/dataset.py
================================================
import pickle
from pathlib import Path
from typing import Tuple

import numpy as np
import torch
from torch import Tensor
from torch.utils.data import Dataset

from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from PIL import Image
import os
import csv

T_ITEM = Tuple[int, str, Tensor, Tensor, Tensor, Tensor]

# for img2shape
# https://stackoverflow.com/questions/31400769/bounding-box-of-numpy-array
def mask2bbox(mask):
    # mask: w x h
    rows = np.any(mask, axis=1)
    cols = np.any(mask, axis=0)
    rmin, rmax = np.where(rows)[0][[0, -1]]
    cmin, cmax = np.where(cols)[0][[0, -1]]
    # return rmin, rmax, cmin, cmax
    return cmin, rmin, cmax, rmax

# ref: pix2vox: https://github.com/hzxie/Pix2Vox/blob/f1b82823e79d4afeedddfadb3da0940bcf1c536d/utils/data_transforms.py
def crop_square(img, bbox, img_size_h=256, img_size_w=256):
    # from pix2vox
    img_height, img_width, c = img.shape

    x0, y0, x1, y1 = bbox

    # Calculate the size of bounding boxes
    bbox_width = x1 - x0
    bbox_height = y1 - y0
    bbox_x_mid = (x0 + x1) * .5
    bbox_y_mid = (y0 + y1) * .5

    # Make the crop area as a square
    square_object_size = max(bbox_width, bbox_height)
    x_left = int(bbox_x_mid - square_object_size * .5)
    x_right = int(bbox_x_mid + square_object_size * .5)
    y_top = int(bbox_y_mid - square_object_size * .5)
    y_bottom = int(bbox_y_mid + square_object_size * .5)

    # If the crop position is out of the image, fix it with padding
    pad_x_left = 0
    if x_left < 0:
        pad_x_left = -x_left
        x_left = 0
    pad_x_right = 0
    if x_right >= img_width:
        pad_x_right = x_right - img_width + 1
        x_right = img_width - 1
    pad_y_top = 0
    if y_top < 0:
        pad_y_top = -y_top
        y_top = 0
    pad_y_bottom = 0
    if y_bottom >= img_height:
        pad_y_bottom = y_bottom - img_height + 1
        y_bottom = img_height - 1

    # Padding the image and resize the image
    processed_image = np.pad(img[y_top:y_bottom + 1, x_left:x_right + 1],
                                ((pad_y_top, pad_y_bottom), (pad_x_left, pad_x_right), (0, 0)),
                                mode='edge')
    
    pil_img = Image.fromarray(processed_image)
    pil_img = pil_img.resize((img_size_w, img_size_h))
    # processed_image = cv2.resize(processed_image, (img_size_w, img_size_h))

    return pil_img

def _convert_image_to_rgb(image):
    return image.convert("RGB")

def _transform(n_px):
    return Compose([
        CenterCrop(n_px),
        _convert_image_to_rgb,
        ToTensor(),
        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
    ])

def _transform_rgb(n_px):
    return Compose([
        ToTensor(),
        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
        Resize((n_px, n_px)),
    ])


class UDFs3d(Dataset):
    def __init__(self, name: str, root: Path, split: str, cond: str) -> None:
        super().__init__()

        self.root = str(root)
        self.ids = []
        self.npz_list = []
        self.name2text = {}
        self.text2name_all = {}
        self.text2name = {}
        self.cond = cond
        self.id2cat = {}
        self.cat2garment_type = {}
        self.name2npz = {}
        self.split = split
        self.name = name
        if 'sketch' in self.cond:
            self.clip_preprocess = _transform(224)
        elif 'img' in self.cond:
            print('here')
            self.clip_preprocess = _transform_rgb(224)

        if 'text' in self.cond:
            with open('./dataset/ShapeNet/text2shape/captions.tablechair_train.csv') as f:
                
                reader = csv.reader(f, delimiter=',')
                self.header = next(reader, None)
                data = [row for row in reader]
            for d in data:
                _, model_id, text, cat_i, synset, subSynsetId = d
                self.name2text[model_id] = text
                self.text2name_all[text] = model_id

        if 'category' in self.cond:
            with open('./dataset/Deepfashion3D/garment_type_list.txt', 'r') as f:
                data = f.readlines()
            for i, line in enumerate(data):
                line = line.rstrip()
                line = line.split(' ')
                for l in line[1:]:
                    self.id2cat[l] = i
                self.cat2garment_type[i] = line[0]
            print(self.cat2garment_type)

        if name in ['shapenet', 'deepfashion3d']:

            if name == 'deepfashion3d':
                self.data_root = os.path.join(self.root, 'udfs', 'train')
                self.sketch_root = os.path.join(self.root, 'images', 'train', 'sketch')
            else:
                self.data_root = os.path.join(self.root, 'train')
            ids = os.listdir(self.data_root)

            for id in ids:

                assert id.endswith(".npz")
                self.ids.append(id[:-4])
                self.npz_list.append(os.path.join(self.data_root, id))
        elif name == 'text2shape':
            data_root_chair = os.path.join(self.root, '03001627', 'train')
            ids_chair = os.listdir(data_root_chair)
            for id in ids_chair:
                #print(id)
                assert id.endswith(".npz")
                self.ids.append(id[:-4])
                self.npz_list.append(os.path.join(data_root_chair, id))

            data_root_table = os.path.join(self.root, '04379243', 'train')
            ids_table = os.listdir(data_root_table)
            for id in ids_table:
                #print(id)
                assert id.endswith(".npz")
                self.ids.append(id[:-4])
                self.npz_list.append(os.path.join(data_root_table, id))

            for npz_ in self.npz_list:
                self.name2npz[npz_.split('/')[-1][:-4]] = npz_

            for t in self.text2name_all.keys():
                if self.text2name_all[t] in self.ids:
                    self.text2name[t] = self.text2name_all[t]

            self.info_text = list(self.text2name.keys())


        elif name == 'pix3d':
            cats = os.listdir(os.path.join(self.root, 'udfs', split))
            for cat in cats:
                ids = os.listdir(os.path.join(self.root, 'udfs', split, cat))
                for id in ids:
                    self.ids.append(id[:-4])
                    self.npz_list.append(os.path.join(self.root, 'udfs', split, cat, id))

            self.img_root = os.path.join(self.root, 'images', 'train')
        


    def __len__(self) -> int:
        if 'text' in self.name:
            return (len(self.text2name.keys()))
        else:
            return len(self.ids)

    def __getitem__(self, index: int) -> T_ITEM:
        if not 'text' in self.name:
            item_id = self.ids[index]
        if 'sketch' in self.cond:            
            sketch_view_index = 0

            sketch_path = os.path.join(self.sketch_root, item_id, f'sketch_{sketch_view_index}.png')
            img = Image.open(sketch_path)
            img = self.clip_preprocess(img)
        elif 'img' in self.cond:
            cat = self.npz_list[index].split('/')[-2]
            all_imgs = os.listdir(os.path.join(self.img_root, cat, item_id))
            select_img = np.random.choice(all_imgs, 1)[0]
    
            img_path = os.path.join(self.img_root, cat, item_id, select_img)
            img_np = np.array(Image.open(img_path).convert('RGB'))
            mask_path = os.path.join(self.root, 'mask', cat, select_img.split('.')[0]+'.png')
            mask_np = np.array(Image.open(mask_path).convert('1'))
            
            x0, y0, x1, y1 = mask2bbox(mask_np)
            bbox = [x0, y0, x1, y1]
                
            try:
                img_clean = img_np * mask_np[:, :, None]
            except:
                print(img_path, mask_path)
            img_clean = crop_square(img_clean.astype(np.uint8), bbox)

            img = self.clip_preprocess(img_clean)


        if 'text' in self.name:
            text = self.info_text[index]
            item_id = self.text2name[text]
            npz = np.load(self.name2npz[item_id])
        else:
            npz = np.load(self.npz_list[index])
        pcd = torch.from_numpy(npz["pcd"])
        coords = torch.from_numpy(npz["coords"])
        labels = torch.from_numpy(npz["labels"])
        gradients = torch.from_numpy(npz["gradients"])

        if 'text' in self.cond:
            return index, item_id, pcd, coords, labels, gradients, text

        if 'sketch' in self.cond or 'img' in self.cond:
            return index, item_id, pcd, coords, labels, gradients, img

        if 'category' in self.cond:
            cat = self.id2cat[item_id.split('-')[0]]
            return index, item_id, pcd, coords, labels, gradients, cat

        return index, item_id, pcd, coords, labels, gradients

    def get_mesh(self, index: int) -> Tuple[Tensor, Tensor]:
        npz = np.load(self.root / f"{self.ids[index]}.npz")
        v = torch.from_numpy(npz["vertices"])
        t = torch.from_numpy(npz["triangles"])
        return v, t
    


================================================
FILE: diffusion/fp16_util.py
================================================
"""
Helpers to train with 16-bit precision.
"""

import numpy as np
import torch as th
import torch.nn as nn
from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors

from diffusion import logger

INITIAL_LOG_LOSS_SCALE = 20.0


def convert_module_to_f16(l):
    """
    Convert primitive modules to float16.
    """
    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
        l.weight.data = l.weight.data.half()
        if l.bias is not None:
            l.bias.data = l.bias.data.half()


def convert_module_to_f32(l):
    """
    Convert primitive modules to float32, undoing convert_module_to_f16().
    """
    if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
        l.weight.data = l.weight.data.float()
        if l.bias is not None:
            l.bias.data = l.bias.data.float()


def make_master_params(param_groups_and_shapes):
    """
    Copy model parameters into a (differently-shaped) list of full-precision
    parameters.
    """
    master_params = []
    for param_group, shape in param_groups_and_shapes:
        master_param = nn.Parameter(
            _flatten_dense_tensors(
                [param.detach().float() for (_, param) in param_group]
            ).view(shape)
        )
        master_param.requires_grad = True
        master_params.append(master_param)
    return master_params


def model_grads_to_master_grads(param_groups_and_shapes, master_params):
    """
    Copy the gradients from the model parameters into the master parameters
    from make_master_params().
    """
    for master_param, (param_group, shape) in zip(
        master_params, param_groups_and_shapes
    ):
        master_param.grad = _flatten_dense_tensors(
            [param_grad_or_zeros(param) for (_, param) in param_group]
        ).view(shape)


def master_params_to_model_params(param_groups_and_shapes, master_params):
    """
    Copy the master parameter data back into the model parameters.
    """
    # Without copying to a list, if a generator is passed, this will
    # silently not copy any parameters.
    for master_param, (param_group, _) in zip(master_params, param_groups_and_shapes):
        for (_, param), unflat_master_param in zip(
            param_group, unflatten_master_params(param_group, master_param.view(-1))
        ):
            param.detach().copy_(unflat_master_param)


def unflatten_master_params(param_group, master_param):
    return _unflatten_dense_tensors(master_param, [param for (_, param) in param_group])


def get_param_groups_and_shapes(named_model_params):
    named_model_params = list(named_model_params)
    scalar_vector_named_params = (
        [(n, p) for (n, p) in named_model_params if p.ndim <= 1],
        (-1),
    )
    matrix_named_params = (
        [(n, p) for (n, p) in named_model_params if p.ndim > 1],
        (1, -1),
    )
    return [scalar_vector_named_params, matrix_named_params]


def master_params_to_state_dict(
    model, param_groups_and_shapes, master_params, use_fp16
):
    if use_fp16:
        state_dict = model.state_dict()
        for master_param, (param_group, _) in zip(
            master_params, param_groups_and_shapes
        ):
            for (name, _), unflat_master_param in zip(
                param_group, unflatten_master_params(param_group, master_param.view(-1))
            ):
                assert name in state_dict
                state_dict[name] = unflat_master_param
    else:
        state_dict = model.state_dict()
        for i, (name, _value) in enumerate(model.named_parameters()):
            assert name in state_dict
            state_dict[name] = master_params[i]
    return state_dict


def state_dict_to_master_params(model, state_dict, use_fp16):
    if use_fp16:
        named_model_params = [
            (name, state_dict[name]) for name, _ in model.named_parameters()
        ]
        param_groups_and_shapes = get_param_groups_and_shapes(named_model_params)
        master_params = make_master_params(param_groups_and_shapes)
    else:
        master_params = [state_dict[name] for name, _ in model.named_parameters()]
    return master_params


def zero_master_grads(master_params):
    for param in master_params:
        param.grad = None


def zero_grad(model_params):
    for param in model_params:
        # Taken from https://pytorch.org/docs/stable/_modules/torch/optim/optimizer.html#Optimizer.add_param_group
        if param.grad is not None:
            param.grad.detach_()
            param.grad.zero_()


def param_grad_or_zeros(param):
    if param.grad is not None:
        return param.grad.data.detach()
    else:
        return th.zeros_like(param)


class MixedPrecisionTrainer:
    def __init__(
        self,
        *,
        model,
        use_fp16=False,
        fp16_scale_growth=1e-3,
        initial_lg_loss_scale=INITIAL_LOG_LOSS_SCALE,
    ):
        self.model = model
        self.use_fp16 = use_fp16
        self.fp16_scale_growth = fp16_scale_growth

        self.model_params = list(self.model.parameters())
        self.master_params = self.model_params
        self.param_groups_and_shapes = None
        self.lg_loss_scale = initial_lg_loss_scale

        if self.use_fp16:
            self.param_groups_and_shapes = get_param_groups_and_shapes(
                self.model.named_parameters()
            )
            self.master_params = make_master_params(self.param_groups_and_shapes)
            self.model.convert_to_fp16()

    def zero_grad(self):
        zero_grad(self.model_params)

    def backward(self, loss: th.Tensor):
        if self.use_fp16:
            loss_scale = 2 ** self.lg_loss_scale
            (loss * loss_scale).backward()
        else:
            loss.backward()

    def optimize(self, opt: th.optim.Optimizer):
        if self.use_fp16:
            return self._optimize_fp16(opt)
        else:
            return self._optimize_normal(opt)

    def _optimize_fp16(self, opt: th.optim.Optimizer):
        logger.logkv_mean("lg_loss_scale", self.lg_loss_scale)
        model_grads_to_master_grads(self.param_groups_and_shapes, self.master_params)
        grad_norm, param_norm = self._compute_norms(grad_scale=2 ** self.lg_loss_scale)
        if check_overflow(grad_norm):
            self.lg_loss_scale -= 1
            logger.log(f"Found NaN, decreased lg_loss_scale to {self.lg_loss_scale}")
            zero_master_grads(self.master_params)
            return False

        logger.logkv_mean("grad_norm", grad_norm)
        logger.logkv_mean("param_norm", param_norm)

        self.master_params[0].grad.mul_(1.0 / (2 ** self.lg_loss_scale))
        opt.step()
        zero_master_grads(self.master_params)
        master_params_to_model_params(self.param_groups_and_shapes, self.master_params)
        self.lg_loss_scale += self.fp16_scale_growth
        return True

    def _optimize_normal(self, opt: th.optim.Optimizer):
        grad_norm, param_norm = self._compute_norms()
        logger.logkv_mean("grad_norm", grad_norm)
        logger.logkv_mean("param_norm", param_norm)
        opt.step()
        return True

    def _compute_norms(self, grad_scale=1.0):
        grad_norm = 0.0
        param_norm = 0.0
        for p in self.master_params:
            with th.no_grad():
                param_norm += th.norm(p, p=2, dtype=th.float32).item() ** 2
                if p.grad is not None:
                    grad_norm += th.norm(p.grad, p=2, dtype=th.float32).item() ** 2
        return np.sqrt(grad_norm) / grad_scale, np.sqrt(param_norm)

    def master_params_to_state_dict(self, master_params):
        return master_params_to_state_dict(
            self.model, self.param_groups_and_shapes, master_params, self.use_fp16
        )

    def state_dict_to_master_params(self, state_dict):
        return state_dict_to_master_params(self.model, state_dict, self.use_fp16)


def check_overflow(value):
    return (value == float("inf")) or (value == -float("inf")) or (value != value)


================================================
FILE: diffusion/gaussian_diffusion.py
================================================
# This code is based on https://github.com/openai/guided-diffusion
"""
This code started out as a PyTorch port of Ho et al's diffusion models:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py

Docstrings have been added, as well as DDIM sampling and a new collection of beta schedules.
"""

import enum
import math
import time
import numpy as np
import torch
import torch as th
from copy import deepcopy
from diffusion.nn import mean_flat, sum_flat
from diffusion.losses import normal_kl, discretized_gaussian_log_likelihood

import torch.nn.functional as F
from utils.utils import compute_gradients


def get_named_beta_schedule(schedule_name, num_diffusion_timesteps, scale_betas=1.):
    """
    Get a pre-defined beta schedule for the given name.

    The beta schedule library consists of beta schedules which remain similar
    in the limit of num_diffusion_timesteps.
    Beta schedules may be added, but should not be removed or changed once
    they are committed to maintain backwards compatibility.
    """
    if schedule_name == "linear":
        # Linear schedule from Ho et al, extended to work for any number of
        # diffusion steps.
        scale = scale_betas * 1000 / num_diffusion_timesteps
        beta_start = scale * 0.0001
        beta_end = scale * 0.02
        return np.linspace(
            beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64
        )
    elif schedule_name == "cosine":
        return betas_for_alpha_bar(
            num_diffusion_timesteps,
            lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
        )
    else:
        raise NotImplementedError(f"unknown beta schedule: {schedule_name}")


def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
    """
    Create a beta schedule that discretizes the given alpha_t_bar function,
    which defines the cumulative product of (1-beta) over time from t = [0,1].

    :param num_diffusion_timesteps: the number of betas to produce.
    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
                      produces the cumulative product of (1-beta) up to that
                      part of the diffusion process.
    :param max_beta: the maximum beta to use; use values lower than 1 to
                     prevent singularities.
    """
    betas = []
    for i in range(num_diffusion_timesteps):
        t1 = i / num_diffusion_timesteps
        t2 = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
    return np.array(betas)


class ModelMeanType(enum.Enum):
    """
    Which type of output the model predicts.
    """

    PREVIOUS_X = enum.auto()  # the model predicts x_{t-1}
    START_X = enum.auto()  # the model predicts x_0
    EPSILON = enum.auto()  # the model predicts epsilon


class ModelVarType(enum.Enum):
    """
    What is used as the model's output variance.

    The LEARNED_RANGE option has been added to allow the model to predict
    values between FIXED_SMALL and FIXED_LARGE, making its job easier.
    """

    LEARNED = enum.auto()
    FIXED_SMALL = enum.auto()
    FIXED_LARGE = enum.auto()
    LEARNED_RANGE = enum.auto()


class LossType(enum.Enum):
    MSE = enum.auto()  # use raw MSE loss (and KL when learning variances)
    RESCALED_MSE = (
        enum.auto()
    )  # use raw MSE loss (with RESCALED_KL when learning variances)
    KL = enum.auto()  # use the variational lower-bound
    RESCALED_KL = enum.auto()  # like KL, but rescale to estimate the full VLB

    def is_vb(self):
        return self == LossType.KL or self == LossType.RESCALED_KL


class GaussianDiffusion:
    """
    Utilities for training and sampling diffusion models.

    Ported directly from here, and then adapted over time to further experimentation.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42

    :param betas: a 1-D numpy array of betas for each diffusion timestep,
                  starting at T and going to 1.
    :param model_mean_type: a ModelMeanType determining what the model outputs.
    :param model_var_type: a ModelVarType determining how variance is output.
    :param loss_type: a LossType determining the loss function to use.
    :param rescale_timesteps: if True, pass floating point timesteps into the
                              model so that they are always scaled like in the
                              original paper (0 to 1000).
    """

    def __init__(
        self,
        *,
        betas,
        model_mean_type,
        model_var_type,
        loss_type,
        rescale_timesteps=False,
        args,
    ):
        ############## final value clipping...

        self.clip_value = args.clip_value


        self.model_mean_type = model_mean_type
        self.model_var_type = model_var_type
        self.loss_type = loss_type
        self.rescale_timesteps = rescale_timesteps

        # Use float64 for accuracy.
        betas = np.array(betas, dtype=np.float64)
        self.betas = betas
        assert len(betas.shape) == 1, "betas must be 1-D"
        assert (betas > 0).all() and (betas <= 1).all()

        self.num_timesteps = int(betas.shape[0])

        alphas = 1.0 - betas
        self.alphas_cumprod = np.cumprod(alphas, axis=0)
        self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
        self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
        assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)

        # calculations for diffusion q(x_t | x_{t-1}) and others
        self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
        self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
        self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
        self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
        self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)

        # calculations for posterior q(x_{t-1} | x_t, x_0)
        self.posterior_variance = (
            betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
        )
        # log calculation clipped because the posterior variance is 0 at the
        # beginning of the diffusion chain.
        self.posterior_log_variance_clipped = np.log(
            np.append(self.posterior_variance[1], self.posterior_variance[1:])
        )
        self.posterior_mean_coef1 = (
            betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
        )
        self.posterior_mean_coef2 = (
            (1.0 - self.alphas_cumprod_prev)
            * np.sqrt(alphas)
            / (1.0 - self.alphas_cumprod)
        )

        self.l2_loss = lambda a, b: (a - b) ** 2  # th.nn.MSELoss(reduction='none')  # must be None for handling mask later on.
        self.time_con = []
    def masked_l2(self, a, b, mask):
        # assuming a.shape == b.shape == bs, J, Jdim, seqlen
        # assuming mask.shape == bs, 1, 1, seqlen
        loss = self.l2_loss(a, b)
        loss = sum_flat(loss * mask.float())  # gives \sigma_euclidean over unmasked elements
        n_entries = a.shape[1] * a.shape[2]
        non_zero_elements = sum_flat(mask) * n_entries
        mse_loss_val = loss / non_zero_elements
        return mse_loss_val


    def q_mean_variance(self, x_start, t):
        """
        Get the distribution q(x_t | x_0).

        :param x_start: the [N x C x ...] tensor of noiseless inputs.
        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
        :return: A tuple (mean, variance, log_variance), all of x_start's shape.
        """
        mean = (
            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
        )
        variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
        log_variance = _extract_into_tensor(
            self.log_one_minus_alphas_cumprod, t, x_start.shape
        )
        return mean, variance, log_variance

    def q_sample(self, x_start, t, noise=None):
        """
        Diffuse the dataset for a given number of diffusion steps.

        In other words, sample from q(x_t | x_0).

        :param x_start: the initial dataset batch.
        :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
        :param noise: if specified, the split-out normal noise.
        :return: A noisy version of x_start.
        """
        if noise is None:
            noise = th.randn_like(x_start)
        assert noise.shape == x_start.shape
        #print(t.device,x_start.device)
        #print(noise.shape, noise)
        return (
            _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
            + _extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
            * noise
        )

    def q_posterior_mean_variance(self, x_start, x_t, t):
        """
        Compute the mean and variance of the diffusion posterior:

            q(x_{t-1} | x_t, x_0)

        """
        assert x_start.shape == x_t.shape
        posterior_mean = (
            _extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
            + _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
        )
        posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
        posterior_log_variance_clipped = _extract_into_tensor(
            self.posterior_log_variance_clipped, t, x_t.shape
        )
        assert (
            posterior_mean.shape[0]
            == posterior_variance.shape[0]
            == posterior_log_variance_clipped.shape[0]
            == x_start.shape[0]
        )
        return posterior_mean, posterior_variance, posterior_log_variance_clipped

    def p_mean_variance(
        self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None
    ):
        """
        Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
        the initial x, x_0.

        :param model: the model, which takes a signal and a batch of timesteps
                      as input.
        :param x: the [N x C x ...] tensor at time t.
        :param t: a 1-D Tensor of timesteps.
        :param clip_denoised: if True, clip the denoised signal into [-1, 1].
        :param denoised_fn: if not None, a function which applies to the
            x_start prediction before it is used to sample. Applies before
            clip_denoised.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :return: a dict with the following keys:
                 - 'mean': the model mean output.
                 - 'variance': the model variance output.
                 - 'log_variance': the log of 'variance'.
                 - 'pred_xstart': the prediction for x_0.
        """
        if model_kwargs is None:
            model_kwargs = {}

        B, C = x.shape[:2]
        assert t.shape == (B,)

        model_output = model(x, self._scale_timesteps(t), **model_kwargs)
        if 'inpainting_mask' in model_kwargs['y'].keys() and 'inpainted_motion' in model_kwargs['y'].keys():
            inpainting_mask, inpainted_motion = model_kwargs['y']['inpainting_mask'], model_kwargs['y']['inpainted_motion']
            assert self.model_mean_type == ModelMeanType.START_X, 'This feature supports only X_start pred for mow!'
            assert model_output.shape == inpainting_mask.shape == inpainted_motion.shape
            model_output = (model_output * ~inpainting_mask) + (inpainted_motion * inpainting_mask)
            input("type1")

        if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
            input("type2")
            assert model_output.shape == (B, C * 2, *x.shape[2:])
            model_output, model_var_values = th.split(model_output, C, dim=1)
            if self.model_var_type == ModelVarType.LEARNED:
                model_log_variance = model_var_values
                model_variance = th.exp(model_log_variance)
            else:
                min_log = _extract_into_tensor(
                    self.posterior_log_variance_clipped, t, x.shape
                )
                max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
                # The model_var_values is [-1, 1] for [min_var, max_var].
                frac = (model_var_values + 1) / 2
                model_log_variance = frac * max_log + (1 - frac) * min_log
                model_variance = th.exp(model_log_variance)
        else:
            # here...
            model_variance, model_log_variance = {
                # for fixedlarge, we set the initial (log-)variance like so
                # to get a better decoder log likelihood.
                ModelVarType.FIXED_LARGE: (
                    np.append(self.posterior_variance[1], self.betas[1:]),
                    np.log(np.append(self.posterior_variance[1], self.betas[1:])),
                ),
                ModelVarType.FIXED_SMALL: (
                    self.posterior_variance,
                    self.posterior_log_variance_clipped,
                ),
            }[self.model_var_type]

            model_variance = _extract_into_tensor(model_variance, t, x.shape)
            model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)

        def process_xstart(x):
            if denoised_fn is not None:
                x = denoised_fn(x)
            if clip_denoised:

                return x.clamp(-1, 1)
            return x

        if self.model_mean_type == ModelMeanType.PREVIOUS_X:
            pred_xstart = process_xstart(
                self._predict_xstart_from_xprev(x_t=x, t=t, xprev=model_output)
            )
            model_mean = model_output
        elif self.model_mean_type in [ModelMeanType.START_X, ModelMeanType.EPSILON]:  # THIS IS US!
            if self.model_mean_type == ModelMeanType.START_X:
                pred_xstart = process_xstart(model_output)
            else:
                pred_xstart = process_xstart(
                    self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
                )
            model_mean, _, _ = self.q_posterior_mean_variance(
                x_start=pred_xstart, x_t=x, t=t
            )
        else:
            raise NotImplementedError(self.model_mean_type)

        assert (
            model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
        )
        return {
            "mean": model_mean,
            "variance": model_variance,
            "log_variance": model_log_variance,
            "pred_xstart": pred_xstart,
        }

    def _predict_xstart_from_eps(self, x_t, t, eps):
        assert x_t.shape == eps.shape
        return (
            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
            - _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
        )

    def _predict_xstart_from_xprev(self, x_t, t, xprev):
        assert x_t.shape == xprev.shape
        return (  # (xprev - coef2*x_t) / coef1
            _extract_into_tensor(1.0 / self.posterior_mean_coef1, t, x_t.shape) * xprev
            - _extract_into_tensor(
                self.posterior_mean_coef2 / self.posterior_mean_coef1, t, x_t.shape
            )
            * x_t
        )

    def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
        return (
            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
            - pred_xstart
        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)

    def _scale_timesteps(self, t):
        if self.rescale_timesteps:
            return t.float() * (1000.0 / self.num_timesteps)
        return t

    def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
        """
        Compute the mean for the previous step, given a function cond_fn that
        computes the gradient of a conditional log probability with respect to
        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
        condition on y.

        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
        """
        gradient = cond_fn(x, self._scale_timesteps(t), **model_kwargs)
        new_mean = (
            p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
        )
        return new_mean

    def condition_mean_with_grad(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
        """
        Compute the mean for the previous step, given a function cond_fn that
        computes the gradient of a conditional log probability with respect to
        x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
        condition on y.

        This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
        """
        gradient = cond_fn(x, t, p_mean_var, **model_kwargs)
        new_mean = (
            p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
        )
        return new_mean

    def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
        """
        Compute what the p_mean_variance output would have been, should the
        model's score function be conditioned by cond_fn.

        See condition_mean() for details on cond_fn.

        Unlike condition_mean(), this instead uses the conditioning strategy
        from Song et al (2020).
        """
        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)

        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(
            x, self._scale_timesteps(t), **model_kwargs
        )

        out = p_mean_var.copy()
        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
        out["mean"], _, _ = self.q_posterior_mean_variance(
            x_start=out["pred_xstart"], x_t=x, t=t
        )
        return out

    def condition_score_with_grad(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
        """
        Compute what the p_mean_variance output would have been, should the
        model's score function be conditioned by cond_fn.

        See condition_mean() for details on cond_fn.

        Unlike condition_mean(), this instead uses the conditioning strategy
        from Song et al (2020).
        """
        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)

        eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
        eps = eps - (1 - alpha_bar).sqrt() * cond_fn(
            x, t, p_mean_var, **model_kwargs
        )

        out = p_mean_var.copy()
        out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
        out["mean"], _, _ = self.q_posterior_mean_variance(
            x_start=out["pred_xstart"], x_t=x, t=t
        )
        return out

    def p_sample(
        self,
        model,
        x,
        t,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        const_noise=False,
    ):
        """
        Sample x_{t-1} from the model at the given timestep.

        :param model: the model to sample from.
        :param x: the current tensor at x_{t-1}.
        :param t: the value of t, starting at 0 for the first diffusion step.
        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
        :param denoised_fn: if not None, a function which applies to the
            x_start prediction before it is used to sample.
        :param cond_fn: if not None, this is a gradient function that acts
                        similarly to the model.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :return: a dict containing the following keys:
                 - 'sample': a random sample from the model.
                 - 'pred_xstart': a prediction of x_0.
        """
        out = self.p_mean_variance(
            model,
            x,
            t,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
        )
        noise = th.randn_like(x)
        # print('const_noise', const_noise)
        if const_noise:
            noise = noise[[0]].repeat(x.shape[0], 1, 1, 1)

        nonzero_mask = (
            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
        )  # no noise when t == 0
        if cond_fn is not None:
            out["mean"] = self.condition_mean(
                cond_fn, out, x, t, model_kwargs=model_kwargs
            )
        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
        return {"sample": sample, "pred_xstart": out["pred_xstart"]}

    def p_sample_with_grad(
        self,
        model,
        x,
        t,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
    ):
        """
        Sample x_{t-1} from the model at the given timestep.

        :param model: the model to sample from.
        :param x: the current tensor at x_{t-1}.
        :param t: the value of t, starting at 0 for the first diffusion step.
        :param clip_denoised: if True, clip the x_start prediction to [-1, 1].
        :param denoised_fn: if not None, a function which applies to the
            x_start prediction before it is used to sample.
        :param cond_fn: if not None, this is a gradient function that acts
                        similarly to the model.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :return: a dict containing the following keys:
                 - 'sample': a random sample from the model.
                 - 'pred_xstart': a prediction of x_0.
        """
        with th.enable_grad():
            x = x.detach().requires_grad_()
            out = self.p_mean_variance(
                model,
                x,
                t,
                clip_denoised=clip_denoised,
                denoised_fn=denoised_fn,
                model_kwargs=model_kwargs,
            )
            noise = th.randn_like(x)
            nonzero_mask = (
                (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
            )  # no noise when t == 0
            if cond_fn is not None:
                out["mean"] = self.condition_mean_with_grad(
                    cond_fn, out, x, t, model_kwargs=model_kwargs
                )
        sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
        return {"sample": sample, "pred_xstart": out["pred_xstart"].detach()}

    def p_sample_loop(
        self,
        model,
        shape,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
        skip_timesteps=0,
        init_image=None,
        randomize_class=False,
        cond_fn_with_grad=False,
        dump_steps=None,
        const_noise=False,
    ):
        """
        Generate samples from the model.

        :param model: the model module.
        :param shape: the shape of the samples, (N, C, H, W).
        :param noise: if specified, the noise from the encoder to sample.
                      Should be of the same shape as `shape`.
        :param clip_denoised: if True, clip x_start predictions to [-1, 1].
        :param denoised_fn: if not None, a function which applies to the
            x_start prediction before it is used to sample.
        :param cond_fn: if not None, this is a gradient function that acts
                        similarly to the model.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :param device: if specified, the device to create the samples on.
                       If not specified, use a model parameter's device.
        :param progress: if True, show a tqdm progress bar.
        :param const_noise: If True, will noise all samples with the same noise throughout sampling
        :return: a non-differentiable batch of samples.
        """
        final = None
        if dump_steps is not None:
            dump = []

        for i, sample in enumerate(self.p_sample_loop_progressive(
            model,
            shape,
            noise=noise,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            cond_fn=cond_fn,
            model_kwargs=model_kwargs,
            device=device,
            progress=progress,
            skip_timesteps=skip_timesteps,
            init_image=init_image,
            randomize_class=randomize_class,
            cond_fn_with_grad=cond_fn_with_grad,
            const_noise=const_noise,
        )):
            if dump_steps is not None and i in dump_steps:
                dump.append(deepcopy(sample["sample"]))
            final = sample
        if dump_steps is not None:
            return dump
        return final["sample"]

    def p_sample_loop_progressive(
        self,
        model,
        shape,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
        skip_timesteps=0,
        init_image=None,
        randomize_class=False,
        cond_fn_with_grad=False,
        const_noise=False,
    ):
        """
        Generate samples from the model and yield intermediate samples from
        each timestep of diffusion.

        Arguments are the same as p_sample_loop().
        Returns a generator over dicts, where each dict is the return value of
        p_sample().
        """
        if device is None:
            device = next(model.parameters()).device
        assert isinstance(shape, (tuple, list))
        if noise is not None:
            img = noise
        else:
            img = th.randn(*shape, device=device)

        if skip_timesteps and init_image is None:
            init_image = th.zeros_like(img)

        indices = list(range(self.num_timesteps - skip_timesteps))[::-1]

        if init_image is not None:
            my_t = th.ones([shape[0]], device=device, dtype=th.long) * indices[0]
            img = self.q_sample(init_image, my_t, img)

        if progress:
            # Lazy import so that we don't depend on tqdm.
            from tqdm.auto import tqdm

            indices = tqdm(indices)

        for i in indices:
            time_st = time.time()


            t = th.tensor([i] * shape[0], device=device)

            if randomize_class and 'y' in model_kwargs:
                model_kwargs['y'] = th.randint(low=0, high=model.num_classes,
                                               size=model_kwargs['y'].shape,
                                               device=model_kwargs['y'].device)
            with th.no_grad():
                sample_fn = self.p_sample_with_grad if cond_fn_with_grad else self.p_sample
                out = sample_fn(
                    model,
                    img,
                    t,
                    clip_denoised=clip_denoised,
                    denoised_fn=denoised_fn,
                    cond_fn=cond_fn,
                    model_kwargs=model_kwargs,
                    const_noise=const_noise,
                )

                yield out
                img = out["sample"]
            self.time_con.append(time.time() - time_st)


    def ddim_sample(
        self,
        model,
        x,
        t,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        eta=0.0,
    ):
        """
        Sample x_{t-1} from the model using DDIM.

        Same usage as p_sample().
        """
        out_orig = self.p_mean_variance(
            model,
            x,
            t,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
        )
        if cond_fn is not None:
            out = self.condition_score(cond_fn, out_orig, x, t, model_kwargs=model_kwargs)
        else:
            out = out_orig

        # Usually our model outputs epsilon, but we re-derive it
        # in case we used x_start or x_prev prediction.
        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])

        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
        sigma = (
            eta
            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
        )
        # Equation 12.
        noise = th.randn_like(x)
        mean_pred = (
            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
        )
        nonzero_mask = (
            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
        )  # no noise when t == 0
        sample = mean_pred + nonzero_mask * sigma * noise
        return {"sample": sample, "pred_xstart": out_orig["pred_xstart"]}

    def ddim_sample_with_grad(
        self,
        model,
        x,
        t,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        eta=0.0,
    ):
        """
        Sample x_{t-1} from the model using DDIM.

        Same usage as p_sample().
        """
        with th.enable_grad():
            x = x.detach().requires_grad_()
            out_orig = self.p_mean_variance(
                model,
                x,
                t,
                clip_denoised=clip_denoised,
                denoised_fn=denoised_fn,
                model_kwargs=model_kwargs,
            )
            if cond_fn is not None:
                out = self.condition_score_with_grad(cond_fn, out_orig, x, t,
                                                     model_kwargs=model_kwargs)
            else:
                out = out_orig

        out["pred_xstart"] = out["pred_xstart"].detach()

        # Usually our model outputs epsilon, but we re-derive it
        # in case we used x_start or x_prev prediction.
        eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])

        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
        sigma = (
            eta
            * th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
            * th.sqrt(1 - alpha_bar / alpha_bar_prev)
        )
        # Equation 12.
        noise = th.randn_like(x)
        mean_pred = (
            out["pred_xstart"] * th.sqrt(alpha_bar_prev)
            + th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
        )
        nonzero_mask = (
            (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
        )  # no noise when t == 0
        sample = mean_pred + nonzero_mask * sigma * noise
        return {"sample": sample, "pred_xstart": out_orig["pred_xstart"].detach()}

    def ddim_reverse_sample(
        self,
        model,
        x,
        t,
        clip_denoised=True,
        denoised_fn=None,
        model_kwargs=None,
        eta=0.0,
    ):
        """
        Sample x_{t+1} from the model using DDIM reverse ODE.
        """
        assert eta == 0.0, "Reverse ODE only for deterministic path"
        out = self.p_mean_variance(
            model,
            x,
            t,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            model_kwargs=model_kwargs,
        )
        # Usually our model outputs epsilon, but we re-derive it
        # in case we used x_start or x_prev prediction.
        eps = (
            _extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
            - out["pred_xstart"]
        ) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
        alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)

        # Equation 12. reversed
        mean_pred = (
            out["pred_xstart"] * th.sqrt(alpha_bar_next)
            + th.sqrt(1 - alpha_bar_next) * eps
        )

        return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}

    def ddim_sample_loop(
        self,
        model,
        shape,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
        eta=0.0,
        skip_timesteps=0,
        init_image=None,
        randomize_class=False,
        cond_fn_with_grad=False,
        dump_steps=None,
        const_noise=False,
    ):
        """
        Generate samples from the model using DDIM.

        Same usage as p_sample_loop().
        """
        if dump_steps is not None:
            raise NotImplementedError()
        if const_noise == True:
            raise NotImplementedError()

        final = None
        for sample in self.ddim_sample_loop_progressive(
            model,
            shape,
            noise=noise,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            cond_fn=cond_fn,
            model_kwargs=model_kwargs,
            device=device,
            progress=progress,
            eta=eta,
            skip_timesteps=skip_timesteps,
            init_image=init_image,
            randomize_class=randomize_class,
            cond_fn_with_grad=cond_fn_with_grad,
        ):
            final = sample
        return final["sample"]

    def ddim_sample_loop_progressive(
        self,
        model,
        shape,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
        eta=0.0,
        skip_timesteps=0,
        init_image=None,
        randomize_class=False,
        cond_fn_with_grad=False,
    ):
        """
        Use DDIM to sample from the model and yield intermediate samples from
        each timestep of DDIM.

        Same usage as p_sample_loop_progressive().
        """
        if device is None:
            device = next(model.parameters()).device
        assert isinstance(shape, (tuple, list))
        if noise is not None:
            img = noise
        else:
            img = th.randn(*shape, device=device)

        if skip_timesteps and init_image is None:
            init_image = th.zeros_like(img)

        indices = list(range(self.num_timesteps - skip_timesteps))[::-1]

        if init_image is not None:
            my_t = th.ones([shape[0]], device=device, dtype=th.long) * indices[0]
            img = self.q_sample(init_image, my_t, img)

        if progress:
            # Lazy import so that we don't depend on tqdm.
            from tqdm.auto import tqdm

            indices = tqdm(indices)

        for i in indices:
            t = th.tensor([i] * shape[0], device=device)
            if randomize_class and 'y' in model_kwargs:
                model_kwargs['y'] = th.randint(low=0, high=model.num_classes,
                                               size=model_kwargs['y'].shape,
                                               device=model_kwargs['y'].device)
            with th.no_grad():
                sample_fn = self.ddim_sample_with_grad if cond_fn_with_grad else self.ddim_sample
                out = sample_fn(
                    model,
                    img,
                    t,
                    clip_denoised=clip_denoised,
                    denoised_fn=denoised_fn,
                    cond_fn=cond_fn,
                    model_kwargs=model_kwargs,
                    eta=eta,
                )
                yield out
                img = out["sample"]

    def plms_sample(
        self,
        model,
        x,
        t,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        cond_fn_with_grad=False,
        order=2,
        old_out=None,
    ):
        """
        Sample x_{t-1} from the model using Pseudo Linear Multistep.

        Same usage as p_sample().
        """
        if not int(order) or not 1 <= order <= 4:
            raise ValueError('order is invalid (should be int from 1-4).')

        def get_model_output(x, t):
            with th.set_grad_enabled(cond_fn_with_grad and cond_fn is not None):
                x = x.detach().requires_grad_() if cond_fn_with_grad else x
                out_orig = self.p_mean_variance(
                    model,
                    x,
                    t,
                    clip_denoised=clip_denoised,
                    denoised_fn=denoised_fn,
                    model_kwargs=model_kwargs,
                )
                if cond_fn is not None:
                    if cond_fn_with_grad:
                        out = self.condition_score_with_grad(cond_fn, out_orig, x, t, model_kwargs=model_kwargs)
                        x = x.detach()
                    else:
                        out = self.condition_score(cond_fn, out_orig, x, t, model_kwargs=model_kwargs)
                else:
                    out = out_orig

            # Usually our model outputs epsilon, but we re-derive it
            # in case we used x_start or x_prev prediction.
            eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
            return eps, out, out_orig

        alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
        alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
        eps, out, out_orig = get_model_output(x, t)

        if order > 1 and old_out is None:
            # Pseudo Improved Euler
            old_eps = [eps]
            mean_pred = out["pred_xstart"] * th.sqrt(alpha_bar_prev) + th.sqrt(1 - alpha_bar_prev) * eps
            eps_2, _, _ = get_model_output(mean_pred, t - 1)
            eps_prime = (eps + eps_2) / 2
            pred_prime = self._predict_xstart_from_eps(x, t, eps_prime)
            mean_pred = pred_prime * th.sqrt(alpha_bar_prev) + th.sqrt(1 - alpha_bar_prev) * eps_prime
        else:
            # Pseudo Linear Multistep (Adams-Bashforth)
            old_eps = old_out["old_eps"]
            old_eps.append(eps)
            cur_order = min(order, len(old_eps))
            if cur_order == 1:
                eps_prime = old_eps[-1]
            elif cur_order == 2:
                eps_prime = (3 * old_eps[-1] - old_eps[-2]) / 2
            elif cur_order == 3:
                eps_prime = (23 * old_eps[-1] - 16 * old_eps[-2] + 5 * old_eps[-3]) / 12
            elif cur_order == 4:
                eps_prime = (55 * old_eps[-1] - 59 * old_eps[-2] + 37 * old_eps[-3] - 9 * old_eps[-4]) / 24
            else:
                raise RuntimeError('cur_order is invalid.')
            pred_prime = self._predict_xstart_from_eps(x, t, eps_prime)
            mean_pred = pred_prime * th.sqrt(alpha_bar_prev) + th.sqrt(1 - alpha_bar_prev) * eps_prime

        if len(old_eps) >= order:
            old_eps.pop(0)

        nonzero_mask = (t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
        sample = mean_pred * nonzero_mask + out["pred_xstart"] * (1 - nonzero_mask)

        return {"sample": sample, "pred_xstart": out_orig["pred_xstart"], "old_eps": old_eps}

    def plms_sample_loop(
        self,
        model,
        shape,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
        skip_timesteps=0,
        init_image=None,
        randomize_class=False,
        cond_fn_with_grad=False,
        order=2,
    ):
        """
        Generate samples from the model using Pseudo Linear Multistep.

        Same usage as p_sample_loop().
        """
        final = None
        for sample in self.plms_sample_loop_progressive(
            model,
            shape,
            noise=noise,
            clip_denoised=clip_denoised,
            denoised_fn=denoised_fn,
            cond_fn=cond_fn,
            model_kwargs=model_kwargs,
            device=device,
            progress=progress,
            skip_timesteps=skip_timesteps,
            init_image=init_image,
            randomize_class=randomize_class,
            cond_fn_with_grad=cond_fn_with_grad,
            order=order,
        ):
            final = sample
        return final["sample"]

    def plms_sample_loop_progressive(
        self,
        model,
        shape,
        noise=None,
        clip_denoised=True,
        denoised_fn=None,
        cond_fn=None,
        model_kwargs=None,
        device=None,
        progress=False,
        skip_timesteps=0,
        init_image=None,
        randomize_class=False,
        cond_fn_with_grad=False,
        order=2,
    ):
        """
        Use PLMS to sample from the model and yield intermediate samples from each
        timestep of PLMS.

        Same usage as p_sample_loop_progressive().
        """
        if device is None:
            device = next(model.parameters()).device
        assert isinstance(shape, (tuple, list))
        if noise is not None:
            img = noise
        else:
            img = th.randn(*shape, device=device)

        if skip_timesteps and init_image is None:
            init_image = th.zeros_like(img)

        indices = list(range(self.num_timesteps - skip_timesteps))[::-1]

        if init_image is not None:
            my_t = th.ones([shape[0]], device=device, dtype=th.long) * indices[0]
            img = self.q_sample(init_image, my_t, img)

        if progress:
            # Lazy import so that we don't depend on tqdm.
            from tqdm.auto import tqdm

            indices = tqdm(indices)

        old_out = None

        for i in indices:
            t = th.tensor([i] * shape[0], device=device)
            if randomize_class and 'y' in model_kwargs:
                model_kwargs['y'] = th.randint(low=0, high=model.num_classes,
                                               size=model_kwargs['y'].shape,
                                               device=model_kwargs['y'].device)
            with th.no_grad():
                out = self.plms_sample(
                    model,
                    img,
                    t,
                    clip_denoised=clip_denoised,
                    denoised_fn=denoised_fn,
                    cond_fn=cond_fn,
                    model_kwargs=model_kwargs,
                    cond_fn_with_grad=cond_fn_with_grad,
                    order=order,
                    old_out=old_out,
                )
                yield out
                old_out = out
                img = out["sample"]

    def _vb_terms_bpd(
        self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
    ):
        """
        Get a term for the variational lower-bound.

        The resulting units are bits (rather than nats, as one might expect).
        This allows for comparison to other papers.

        :return: a dict with the following keys:
                 - 'output': a shape [N] tensor of NLLs or KLs.
                 - 'pred_xstart': the x_0 predictions.
        """
        true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
            x_start=x_start, x_t=x_t, t=t
        )
        out = self.p_mean_variance(
            model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
        )
        kl = normal_kl(
            true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
        )
        kl = mean_flat(kl) / np.log(2.0)

        decoder_nll = -discretized_gaussian_log_likelihood(
            x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
        )
        assert decoder_nll.shape == x_start.shape
        decoder_nll = mean_flat(decoder_nll) / np.log(2.0)

        # At the first timestep return the decoder NLL,
        # otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
        output = th.where((t == 0), decoder_nll, kl)
        return {"output": output, "pred_xstart": out["pred_xstart"]}

    def training_losses(self, model, x_start, t, loss_L1, loss_args=None, model_kwargs=None, noise=None, dataset=None):
        """
        Compute training losses for a single timestep.

        :param model: the model to evaluate loss on.
        :param x_start: the [N x C x ...] tensor of inputs.
        :param t: a batch of timestep indices.
        :param Loss_L1: L1 loss function.
        :param model_kwargs: if not None, a dict of extra keyword arguments to
            pass to the model. This can be used for conditioning.
        :param noise: if specified, the specific Gaussian noise to try to remove.
        :return: a dict with the key "loss" containing a tensor of shape [N].
                 Some mean or variance settings may also have other keys.
        """

        if model_kwargs is None:
            model_kwargs = {}
        if noise is None:
            noise = th.randn_like(x_start)


        x_t = self.q_sample(x_start, t, noise=noise)

        terms = {}

        if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
            input("not used.")
            terms["loss"] = self._vb_terms_bpd(
                model=model,
                x_start=x_start,
                x_t=x_t,
                t=t,
                clip_denoised=False,
                model_kwargs=model_kwargs,
            )["output"]
            if self.loss_type == LossType.RESCALED_KL:
                terms["loss"] *= self.num_timesteps
        elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:

            model_output = model(x_t, self._scale_timesteps(t), **model_kwargs)

            decode_loss = False
            if decode_loss:
                selected_coords = loss_args["selected_coords"]
                selected_gt_udf = loss_args["selected_gt_udf"]
                selected_gt_grad = loss_args["selected_gt_grad"]
                coords_encoder = loss_args["coords_encoder"]
                decoder = loss_args["decoder"]
                max_dist = 0.1

                latent_codes = model_output.squeeze(1)

                coords_encoded = coords_encoder.encode(selected_coords)
                pred = decoder(coords_encoded, latent_codes)

                udf_loss = F.binary_cross_entropy_with_logits(pred, selected_gt_udf)

                udf_pred = torch.sigmoid(pred)
                udf_pred = 1 - udf_pred
                udf_pred *= max_dist
                gradients = compute_gradients(selected_coords, udf_pred)

                grad_loss = F.mse_loss(gradients, selected_gt_grad, reduction="none")
                mask = (selected_gt_udf > 0) & (selected_gt_udf < 1)
                grad_loss = grad_loss[mask].mean()


                terms["UDF_L1Loss"] = udf_loss
                terms["Grad_loss"] = 0.1 * grad_loss

            if self.model_var_type in [
                ModelVarType.LEARNED,
                ModelVarType.LEARNED_RANGE,
            ]:
                
                B, C = x_t.shape[:2]
                assert model_output.shape == (B, C * 2, *x_t.shape[2:])
                model_output, model_var_values = th.split(model_output, C, dim=1)
                # Learn the variance using the variational bound, but don't let
                # it affect our mean prediction.
                frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
                terms["vb"] = self._vb_terms_bpd(
                    model=lambda *args, r=frozen_out: r,
                    x_start=x_start,
                    x_t=x_t,
                    t=t,
                    clip_denoised=False,
                )["output"]
                if self.loss_type == LossType.RESCALED_MSE:
                    # Divide by 1000 for equivalence with initial implementation.
                    # Without a factor of 1/1000, the VB term hurts the MSE term.
                    terms["vb"] *= self.num_timesteps / 1000.0
            
            target = {
                ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
                    x_start=x_start, x_t=x_t, t=t
                )[0],
                ModelMeanType.START_X: x_start,
                ModelMeanType.EPSILON: noise,
            }[self.model_mean_type]
            


            target = target.to(model_output.device)
            assert model_output.device == target.device

            target = target[:,:,:]
            x_start = x_start[:,:,:]

            assert model_output.shape == target.shape == x_start.shape  # [bs, njoints, nfeats, nframes]

            terms["Latent_L1Loss"] = 1000*loss_L1(model_output, target) # mean_flat(rot_mse)

            terms["loss"] = terms["Latent_L1Loss"]
            if decode_loss:
                terms["loss"] = terms["loss"] + terms.get('UDF_L1Loss', 0.) + terms.get('Grad_loss', 0.)

        else:
            raise NotImplementedError(self.loss_type)

        return terms

    
def _extract_into_tensor(arr, timesteps, broadcast_shape):
    """
    Extract values from a 1-D numpy array for a batch of indices.

    :param arr: the 1-D numpy array.
    :param timesteps: a tensor of indices into the array to extract.
    :param broadcast_shape: a larger shape of K dimensions with the batch
                            dimension equal to the length of timesteps.
    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
    """
    res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
    while len(res.shape) < len(broadcast_shape):
        res = res[..., None]
    return res.expand(broadcast_shape)


================================================
FILE: diffusion/logger.py
================================================
"""
Logger copied from OpenAI baselines to avoid extra RL-based dependencies:
https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/logger.py
"""

import os
import sys
import shutil
import os.path as osp
import json
import time
import datetime
import tempfile
import warnings
from collections import defaultdict
from contextlib import contextmanager

DEBUG = 10
INFO = 20
WARN = 30
ERROR = 40

DISABLED = 50


class KVWriter(object):
    def writekvs(self, kvs):
        raise NotImplementedError


class SeqWriter(object):
    def writeseq(self, seq):
        raise NotImplementedError


class HumanOutputFormat(KVWriter, SeqWriter):
    def __init__(self, filename_or_file):
        if isinstance(filename_or_file, str):
            self.file = open(filename_or_file, "wt")
            self.own_file = True
        else:
            assert hasattr(filename_or_file, "read"), (
                "expected file or str, got %s" % filename_or_file
            )
            self.file = filename_or_file
            self.own_file = False

    def writekvs(self, kvs):
        # Create strings for printing
        key2str = {}
        for (key, val) in sorted(kvs.items()):
            if hasattr(val, "__float__"):
                valstr = "%-8.3g" % val
            else:
                valstr = str(val)
            key2str[self._truncate(key)] = self._truncate(valstr)

        # Find max widths
        if len(key2str) == 0:
            print("WARNING: tried to write empty key-value dict")
            return
        else:
            keywidth = max(map(len, key2str.keys()))
            valwidth = max(map(len, key2str.values()))

        # Write out the data
        dashes = "-" * (keywidth + valwidth + 7)
        lines = [dashes]
        for (key, val) in sorted(key2str.items(), key=lambda kv: kv[0].lower()):
            lines.append(
                "| %s%s | %s%s |"
                % (key, " " * (keywidth - len(key)), val, " " * (valwidth - len(val)))
            )
        lines.append(dashes)
        self.file.write("\n".join(lines) + "\n")

        # Flush the output to the file
        self.file.flush()

    def _truncate(self, s):
        maxlen = 30
        return s[: maxlen - 3] + "..." if len(s) > maxlen else s

    def writeseq(self, seq):
        seq = list(seq)
        for (i, elem) in enumerate(seq):
            self.file.write(elem)
            if i < len(seq) - 1:  # add space unless this is the last one
                self.file.write(" ")
        self.file.write("\n")
        self.file.flush()

    def close(self):
        if self.own_file:
            self.file.close()


class JSONOutputFormat(KVWriter):
    def __init__(self, filename):
        self.file = open(filename, "wt")

    def writekvs(self, kvs):
        for k, v in sorted(kvs.items()):
            if hasattr(v, "dtype"):
                kvs[k] = float(v)
        self.file.write(json.dumps(kvs) + "\n")
        self.file.flush()

    def close(self):
        self.file.close()


class CSVOutputFormat(KVWriter):
    def __init__(self, filename):
        self.file = open(filename, "w+t")
        self.keys = []
        self.sep = ","

    def writekvs(self, kvs):
        # Add our current row to the history
        extra_keys = list(kvs.keys() - self.keys)
        extra_keys.sort()
        if extra_keys:
            self.keys.extend(extra_keys)
            self.file.seek(0)
            lines = self.file.readlines()
            self.file.seek(0)
            for (i, k) in enumerate(self.keys):
                if i > 0:
                    self.file.write(",")
                self.file.write(k)
            self.file.write("\n")
            for line in lines[1:]:
                self.file.write(line[:-1])
                self.file.write(self.sep * len(extra_keys))
                self.file.write("\n")
        for (i, k) in enumerate(self.keys):
            if i > 0:
                self.file.write(",")
            v = kvs.get(k)
            if v is not None:
                self.file.write(str(v))
        self.file.write("\n")
        self.file.flush()

    def close(self):
        self.file.close()


class TensorBoardOutputFormat(KVWriter):
    """
    Dumps key/value pairs into TensorBoard's numeric format.
    """

    def __init__(self, dir):
        os.makedirs(dir, exist_ok=True)
        self.dir = dir
        self.step = 1
        prefix = "events"
        path = osp.join(osp.abspath(dir), prefix)
        import tensorflow as tf
        from tensorflow.python import pywrap_tensorflow
        from tensorflow.core.util import event_pb2
        from tensorflow.python.util import compat

        self.tf = tf
        self.event_pb2 = event_pb2
        self.pywrap_tensorflow = pywrap_tensorflow
        self.writer = pywrap_tensorflow.EventsWriter(compat.as_bytes(path))

    def writekvs(self, kvs):
        def summary_val(k, v):
            kwargs = {"tag": k, "simple_value": float(v)}
            return self.tf.Summary.Value(**kwargs)

        summary = self.tf.Summary(value=[summary_val(k, v) for k, v in kvs.items()])
        event = self.event_pb2.Event(wall_time=time.time(), summary=summary)
        event.step = (
            self.step
        )  # is there any reason why you'd want to specify the step?
        self.writer.WriteEvent(event)
        self.writer.Flush()
        self.step += 1

    def close(self):
        if self.writer:
            self.writer.Close()
            self.writer = None


def make_output_format(format, ev_dir, log_suffix=""):
    os.makedirs(ev_dir, exist_ok=True)
    if format == "stdout":
        return HumanOutputFormat(sys.stdout)
    elif format == "log":
        return HumanOutputFormat(osp.join(ev_dir, "log%s.txt" % log_suffix))
    elif format == "json":
        return JSONOutputFormat(osp.join(ev_dir, "progress%s.json" % log_suffix))
    elif format == "csv":
        return CSVOutputFormat(osp.join(ev_dir, "progress%s.csv" % log_suffix))
    elif format == "tensorboard":
        return TensorBoardOutputFormat(osp.join(ev_dir, "tb%s" % log_suffix))
    else:
        raise ValueError("Unknown format specified: %s" % (format,))


# ================================================================
# API
# ================================================================


def logkv(key, val):
    """
    Log a value of some diagnostic
    Call this once for each diagnostic quantity, each iteration
    If called many times, last value will be used.
    """
    get_current().logkv(key, val)


def logkv_mean(key, val):
    """
    The same as logkv(), but if called many times, values averaged.
    """
    get_current().logkv_mean(key, val)


def logkvs(d):
    """
    Log a dictionary of key-value pairs
    """
    for (k, v) in d.items():
        logkv(k, v)


def dumpkvs():
    """
    Write all of the diagnostics from the current iteration
    """
    return get_current().dumpkvs()


def getkvs():
    return get_current().name2val


def log(*args, level=INFO):
    """
    Write the sequence of args, with no separators, to the console and output files (if you've configured an output file).
    """
    get_current().log(*args, level=level)


def debug(*args):
    log(*args, level=DEBUG)


def info(*args):
    log(*args, level=INFO)


def warn(*args):
    log(*args, level=WARN)


def error(*args):
    log(*args, level=ERROR)


def set_level(level):
    """
    Set logging threshold on current logger.
    """
    get_current().set_level(level)


def set_comm(comm):
    get_current().set_comm(comm)


def get_dir():
    """
    Get directory that log files are being written to.
    will be None if there is no output directory (i.e., if you didn't call start)
    """
    return get_current().get_dir()


record_tabular = logkv
dump_tabular = dumpkvs


@contextmanager
def profile_kv(scopename):
    logkey = "wait_" + scopename
    tstart = time.time()
    try:
        yield
    finally:
        get_current().name2val[logkey] += time.time() - tstart


def profile(n):
    """
    Usage:
    @profile("my_func")
    def my_func(): code
    """

    def decorator_with_name(func):
        def func_wrapper(*args, **kwargs):
            with profile_kv(n):
                return func(*args, **kwargs)

        return func_wrapper

    return decorator_with_name


# ================================================================
# Backend
# ================================================================


def get_current():
    if Logger.CURRENT is None:
        _configure_default_logger()

    return Logger.CURRENT


class Logger(object):
    DEFAULT = None  # A logger with no output files. (See right below class definition)
    # So that you can still log to the terminal without setting up any output files
    CURRENT = None  # Current logger being used by the free functions above

    def __init__(self, dir, output_formats, comm=None):
        self.name2val = defaultdict(float)  # values this iteration
        self.name2cnt = defaultdict(int)
        self.level = INFO
        self.dir = dir
        self.output_formats = output_formats
        self.comm = comm

    # Logging API, forwarded
    # ----------------------------------------
    def logkv(self, key, val):
        self.name2val[key] = val

    def logkv_mean(self, key, val):
        oldval, cnt = self.name2val[key], self.name2cnt[key]
        self.name2val[key] = oldval * cnt / (cnt + 1) + val / (cnt + 1)
        self.name2cnt[key] = cnt + 1

    def dumpkvs(self):
        if self.comm is None:
            d = self.name2val
        else:
            d = mpi_weighted_mean(
                self.comm,
                {
                    name: (val, self.name2cnt.get(name, 1))
                    for (name, val) in self.name2val.items()
                },
            )
            if self.comm.rank != 0:
                d["dummy"] = 1  # so we don't get a warning about empty dict
        out = d.copy()  # Return the dict for unit testing purposes
        for fmt in self.output_formats:
            if isinstance(fmt, KVWriter):
                fmt.writekvs(d)
        self.name2val.clear()
        self.name2cnt.clear()
        return out

    def log(self, *args, level=INFO):
        if self.level <= level:
            self._do_log(args)

    # Configuration
    # ----------------------------------------
    def set_level(self, level):
        self.level = level

    def set_comm(self, comm):
        self.comm = comm

    def get_dir(self):
        return self.dir

    def close(self):
        for fmt in self.output_formats:
            fmt.close()

    # Misc
    # ----------------------------------------
    def _do_log(self, args):
        for fmt in self.output_formats:
            if isinstance(fmt, SeqWriter):
                fmt.writeseq(map(str, args))


def get_rank_without_mpi_import():
    # check environment variables here instead of importing mpi4py
    # to avoid calling MPI_Init() when this module is imported
    for varname in ["PMI_RANK", "OMPI_COMM_WORLD_RANK"]:
        if varname in os.environ:
            return int(os.environ[varname])
    return 0


def mpi_weighted_mean(comm, local_name2valcount):
    """
    Copied from: https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/common/mpi_util.py#L110
    Perform a weighted average over dicts that are each on a different node
    Input: local_name2valcount: dict mapping key -> (value, count)
    Returns: key -> mean
    """
    all_name2valcount = comm.gather(local_name2valcount)
    if comm.rank == 0:
        name2sum = defaultdict(float)
        name2count = defaultdict(float)
        for n2vc in all_name2valcount:
            for (name, (val, count)) in n2vc.items():
                try:
                    val = float(val)
                except ValueError:
                    if comm.rank == 0:
                        warnings.warn(
                            "WARNING: tried to compute mean on non-float {}={}".format(
                                name, val
                            )
                        )
                else:
                    name2sum[name] += val * count
                    name2count[name] += count
        return {name: name2sum[name] / name2count[name] for name in name2sum}
    else:
        return {}


def configure(dir=None, format_strs=None, comm=None, log_suffix=""):
    """
    If comm is provided, average all numerical stats across that comm
    """
    if dir is None:
        dir = os.getenv("OPENAI_LOGDIR")
    if dir is None:
        dir = osp.join(
            tempfile.gettempdir(),
            datetime.datetime.now().strftime("openai-%Y-%m-%d-%H-%M-%S-%f"),
        )
    assert isinstance(dir, str)
    dir = os.path.expanduser(dir)
    os.makedirs(os.path.expanduser(dir), exist_ok=True)

    rank = get_rank_without_mpi_import()
    if rank > 0:
        log_suffix = log_suffix + "-rank%03i" % rank

    if format_strs is None:
        if rank == 0:
            format_strs = os.getenv("OPENAI_LOG_FORMAT", "stdout,log,csv").split(",")
        else:
            format_strs = os.getenv("OPENAI_LOG_FORMAT_MPI", "log").split(",")
    format_strs = filter(None, format_strs)
    output_formats = [make_output_format(f, dir, log_suffix) for f in format_strs]

    Logger.CURRENT = Logger(dir=dir, output_formats=output_formats, comm=comm)
    if output_formats:
        log("Logging to %s" % dir)


def _configure_default_logger():
    configure()
    Logger.DEFAULT = Logger.CURRENT


def reset():
    if Logger.CURRENT is not Logger.DEFAULT:
        Logger.CURRENT.close()
        Logger.CURRENT = Logger.DEFAULT
        log("Reset logger")


@contextmanager
def scoped_configure(dir=None, format_strs=None, comm=None):
    prevlogger = Logger.CURRENT
    configure(dir=dir, format_strs=format_strs, comm=comm)
    try:
        yield
    finally:
        Logger.CURRENT.close()
        Logger.CURRENT = prevlogger



================================================
FILE: diffusion/losses.py
================================================
# This code is based on https://github.com/openai/guided-diffusion
"""
Helpers for various likelihood-based losses. These are ported from the original
Ho et al. diffusion models codebase:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/utils.py
"""

import numpy as np
import torch as th


def normal_kl(mean1, logvar1, mean2, logvar2):
    """
    Compute the KL divergence between two gaussians.

    Shapes are automatically broadcasted, so batches can be compared to
    scalars, among other use cases.
    """
    tensor = None
    for obj in (mean1, logvar1, mean2, logvar2):
        if isinstance(obj, th.Tensor):
            tensor = obj
            break
    assert tensor is not None, "at least one argument must be a Tensor"

    # Force variances to be Tensors. Broadcasting helps convert scalars to
    # Tensors, but it does not work for th.exp().
    logvar1, logvar2 = [
        x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
        for x in (logvar1, logvar2)
    ]

    return 0.5 * (
        -1.0
        + logvar2
        - logvar1
        + th.exp(logvar1 - logvar2)
        + ((mean1 - mean2) ** 2) * th.exp(-logvar2)
    )


def approx_standard_normal_cdf(x):
    """
    A fast approximation of the cumulative distribution function of the
    standard normal.
    """
    return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))


def discretized_gaussian_log_likelihood(x, *, means, log_scales):
    """
    Compute the log-likelihood of a Gaussian distribution discretizing to a
    given image.

    :param x: the target images. It is assumed that this was uint8 values,
              rescaled to the range [-1, 1].
    :param means: the Gaussian mean Tensor.
    :param log_scales: the Gaussian log stddev Tensor.
    :return: a tensor like x of log probabilities (in nats).
    """
    assert x.shape == means.shape == log_scales.shape
    centered_x = x - means
    inv_stdv = th.exp(-log_scales)
    plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
    cdf_plus = approx_standard_normal_cdf(plus_in)
    min_in = inv_stdv * (centered_x - 1.0 / 255.0)
    cdf_min = approx_standard_normal_cdf(min_in)
    log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
    log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
    cdf_delta = cdf_plus - cdf_min
    log_probs = th.where(
        x < -0.999,
        log_cdf_plus,
        th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
    )
    assert log_probs.shape == x.shape
    return log_probs


================================================
FILE: diffusion/nn.py
================================================
# This code is based on https://github.com/openai/guided-diffusion
"""
Various utilities for neural networks.
"""

import math

import torch as th
import torch.nn as nn


# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
    def forward(self, x):
        return x * th.sigmoid(x)


class GroupNorm32(nn.GroupNorm):
    def forward(self, x):
        return super().forward(x.float()).type(x.dtype)


def conv_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D convolution module.
    """
    if dims == 1:
        return nn.Conv1d(*args, **kwargs)
    elif dims == 2:
        return nn.Conv2d(*args, **kwargs)
    elif dims == 3:
        return nn.Conv3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def linear(*args, **kwargs):
    """
    Create a linear module.
    """
    return nn.Linear(*args, **kwargs)


def avg_pool_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D average pooling module.
    """
    if dims == 1:
        return nn.AvgPool1d(*args, **kwargs)
    elif dims == 2:
        return nn.AvgPool2d(*args, **kwargs)
    elif dims == 3:
        return nn.AvgPool3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def update_ema(target_params, source_params, rate=0.99):
    """
    Update target parameters to be closer to those of source parameters using
    an exponential moving average.

    :param target_params: the target parameter sequence.
    :param source_params: the source parameter sequence.
    :param rate: the EMA rate (closer to 1 means slower).
    """
    for targ, src in zip(target_params, source_params):
        targ.detach().mul_(rate).add_(src, alpha=1 - rate)


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def scale_module(module, scale):
    """
    Scale the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().mul_(scale)
    return module


def mean_flat(tensor):
    """
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))

def sum_flat(tensor):
    """
    Take the sum over all non-batch dimensions.
    """
    return tensor.sum(dim=list(range(1, len(tensor.shape))))


def normalization(channels):
    """
    Make a standard normalization layer.

    :param channels: number of input channels.
    :return: an nn.Module for normalization.
    """
    return GroupNorm32(32, channels)


def timestep_embedding(timesteps, dim, max_period=10000):
    """
    Create sinusoidal timestep embeddings.

    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an [N x dim] Tensor of positional embeddings.
    """
    half = dim // 2
    freqs = th.exp(
        -math.log(max_period) * th.arange(start=0, end=half, dtype=th.float32) / half
    ).to(device=timesteps.device)
    args = timesteps[:, None].float() * freqs[None]
    embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
    if dim % 2:
        embedding = th.cat([embedding, th.zeros_like(embedding[:, :1])], dim=-1)
    return embedding


def checkpoint(func, inputs, params, flag):
    """
    Evaluate a function without caching intermediate activations, allowing for
    reduced memory at the expense of extra compute in the backward pass.
    :param func: the function to evaluate.
    :param inputs: the argument sequence to pass to `func`.
    :param params: a sequence of parameters `func` depends on but does not
                   explicitly take as arguments.
    :param flag: if False, disable gradient checkpointing.
    """
    if flag:
        args = tuple(inputs) + tuple(params)
        return CheckpointFunction.apply(func, len(inputs), *args)
    else:
        return func(*inputs)


class CheckpointFunction(th.autograd.Function):
    @staticmethod
    @th.cuda.amp.custom_fwd
    def forward(ctx, run_function, length, *args):
        ctx.run_function = run_function
        ctx.input_length = length
        ctx.save_for_backward(*args)
        with th.no_grad():
            output_tensors = ctx.run_function(*args[:length])
        return output_tensors

    @staticmethod
    @th.cuda.amp.custom_bwd
    def backward(ctx, *output_grads):
        args = list(ctx.saved_tensors)

        # Filter for inputs that require grad. If none, exit early.
        input_indices = [i for (i, x) in enumerate(args) if x.requires_grad]
        if not input_indices:
            return (None, None) + tuple(None for _ in args)

        with th.enable_grad():
            for i in input_indices:
                if i < ctx.input_length:
                    # Not sure why the OAI code does this little
                    # dance. It might not be necessary.
                    args[i] = args[i].detach().requires_grad_()
                    args[i] = args[i].view_as(args[i])
            output_tensors = ctx.run_function(*args[:ctx.input_length])

        if isinstance(output_tensors, th.Tensor):
            output_tensors = [output_tensors]

        # Filter for outputs that require grad. If none, exit early.
        out_and_grads = [(o, g) for (o, g) in zip(output_tensors, output_grads) if o.requires_grad]
        if not out_and_grads:
            return (None, None) + tuple(None for _ in args)

        # Compute gradients on the filtered tensors.
        computed_grads = th.autograd.grad(
            [o for (o, g) in out_and_grads],
            [args[i] for i in input_indices],
            [g for (o, g) in out_and_grads]
        )

        # Reassemble the complete gradient tuple.
        input_grads = [None for _ in args]
        for (i, g) in zip(input_indices, computed_grads):
            input_grads[i] = g
        return (None, None) + tuple(input_grads)


================================================
FILE: diffusion/resample.py
================================================
from abc import ABC, abstractmethod

import numpy as np
import torch as th
import torch.distributed as dist


def create_named_schedule_sampler(name, diffusion):
    """
    Create a ScheduleSampler from a library of pre-defined samplers.

    :param name: the name of the sampler.
    :param diffusion: the diffusion object to sample for.
    """
    if name == "uniform":
        return UniformSampler(diffusion)
    elif name == "loss-second-moment":
        return LossSecondMomentResampler(diffusion)
    else:
        raise NotImplementedError(f"unknown schedule sampler: {name}")


class ScheduleSampler(ABC):
    """
    A distribution over timesteps in the diffusion process, intended to reduce
    variance of the objective.

    By default, samplers perform unbiased importance sampling, in which the
    objective's mean is unchanged.
    However, subclasses may override sample() to change how the resampled
    terms are reweighted, allowing for actual changes in the objective.
    """

    @abstractmethod
    def weights(self):
        """
        Get a numpy array of weights, one per diffusion step.

        The weights needn't be normalized, but must be positive.
        """

    def sample(self, batch_size, device):
        """
        Importance-sample timesteps for a batch.

        :param batch_size: the number of timesteps.
        :param device: the torch device to save to.
        :return: a tuple (timesteps, weights):
                 - timesteps: a tensor of timestep indices.
                 - weights: a tensor of weights to scale the resulting losses.
        """
        w = self.weights()
        p = w / np.sum(w)
        indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
        indices = th.from_numpy(indices_np).long().to(device)
        weights_np = 1 / (len(p) * p[indices_np])
        weights = th.from_numpy(weights_np).float().to(device)
        return indices, weights


class UniformSampler(ScheduleSampler):
    def __init__(self, diffusion):
        self.diffusion = diffusion
        self._weights = np.ones([diffusion.num_timesteps])

    def weights(self):
        return self._weights


class LossAwareSampler(ScheduleSampler):
    def update_with_local_losses(self, local_ts, local_losses):
        """
        Update the reweighting using losses from a model.

        Call this method from each rank with a batch of timesteps and the
        corresponding losses for each of those timesteps.
        This method will perform synchronization to make sure all of the ranks
        maintain the exact same reweighting.

        :param local_ts: an integer Tensor of timesteps.
        :param local_losses: a 1D Tensor of losses.
        """
        batch_sizes = [
            th.tensor([0], dtype=th.int32, device=local_ts.device)
            for _ in range(dist.get_world_size())
        ]
        dist.all_gather(
            batch_sizes,
            th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
        )

        # Pad all_gather batches to be the maximum batch size.
        batch_sizes = [x.item() for x in batch_sizes]
        max_bs = max(batch_sizes)

        timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
        loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
        dist.all_gather(timestep_batches, local_ts)
        dist.all_gather(loss_batches, local_losses)
        timesteps = [
            x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
        ]
        losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
        self.update_with_all_losses(timesteps, losses)

    @abstractmethod
    def update_with_all_losses(self, ts, losses):
        """
        Update the reweighting using losses from a model.

        Sub-classes should override this method to update the reweighting
        using losses from the model.

        This method directly updates the reweighting without synchronizing
        between workers. It is called by update_with_local_losses from all
        ranks with identical arguments. Thus, it should have deterministic
        behavior to maintain state across workers.

        :param ts: a list of int timesteps.
        :param losses: a list of float losses, one per timestep.
        """


class LossSecondMomentResampler(LossAwareSampler):
    def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
        self.diffusion = diffusion
        self.history_per_term = history_per_term
        self.uniform_prob = uniform_prob
        self._loss_history = np.zeros(
            [diffusion.num_timesteps, history_per_term], dtype=np.float64
        )
        self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)

    def weights(self):
        if not self._warmed_up():
            return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
        weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
        weights /= np.sum(weights)
        weights *= 1 - self.uniform_prob
        weights += self.uniform_prob / len(weights)
        return weights

    def update_with_all_losses(self, ts, losses):
        for t, loss in zip(ts, losses):
            if self._loss_counts[t] == self.history_per_term:
                # Shift out the oldest loss term.
                self._loss_history[t, :-1] = self._loss_history[t, 1:]
                self._loss_history[t, -1] = loss
            else:
                self._loss_history[t, self._loss_counts[t]] = loss
                self._loss_counts[t] += 1

    def _warmed_up(self):
        return (self._loss_counts == self.history_per_term).all()


================================================
FILE: diffusion/respace.py
================================================
# This code is based on https://github.com/openai/guided-diffusion
import numpy as np
import torch as th

from .gaussian_diffusion import GaussianDiffusion

def space_timesteps(num_timesteps, section_counts):
    """
    Create a list of timesteps to use from an original diffusion process,
    given the number of timesteps we want to take from equally-sized portions
    of the original process.

    For example, if there's 300 timesteps and the section counts are [10,15,20]
    then the first 100 timesteps are strided to be 10 timesteps, the second 100
    are strided to be 15 timesteps, and the final 100 are strided to be 20.

    If the stride is a string starting with "ddim", then the fixed striding
    from the DDIM paper is used, and only one section is allowed.

    :param num_timesteps: the number of diffusion steps in the original
                          process to divide up.
    :param section_counts: either a list of numbers, or a string containing
                           comma-separated numbers, indicating the step count
                           per section. As a special case, use "ddimN" where N
                           is a number of steps to use the striding from the
                           DDIM paper.
    :return: a set of diffusion steps from the original process to use.
    """
    if isinstance(section_counts, str):
        if section_counts.startswith("ddim"):
            desired_count = int(section_counts[len("ddim") :])
            for i in range(1, num_timesteps):
                if len(range(0, num_timesteps, i)) == desired_count:
                    return set(range(0, num_timesteps, i))
            raise ValueError(
                f"cannot create exactly {num_timesteps} steps with an integer stride"
            )
        section_counts = [int(x) for x in section_counts.split(",")]
    size_per = num_timesteps // len(section_counts)
    extra = num_timesteps % len(section_counts)
    start_idx = 0
    all_steps = []
    for i, section_count in enumerate(section_counts):
        size = size_per + (1 if i < extra else 0)
        if size < section_count:
            raise ValueError(
                f"cannot divide section of {size} steps into {section_count}"
            )
        if section_count <= 1:
            frac_stride = 1
        else:
            frac_stride = (size - 1) / (section_count - 1)
        cur_idx = 0.0
        taken_steps = []
        for _ in range(section_count):
            taken_steps.append(start_idx + round(cur_idx))
            cur_idx += frac_stride
        all_steps += taken_steps
        start_idx += size
    return set(all_steps)


class SpacedDiffusion(GaussianDiffusion):
    """
    A diffusion process which can skip steps in a base diffusion process.

    :param use_timesteps: a collection (sequence or set) of timesteps from the
                          original diffusion process to retain.
    :param kwargs: the kwargs to create the base diffusion process.
    """

    def __init__(self, use_timesteps, **kwargs):
        self.use_timesteps = set(use_timesteps)
        self.timestep_map = []
        self.original_num_steps = len(kwargs["betas"])

        base_diffusion = GaussianDiffusion(**kwargs)  # pylint: disable=missing-kwoa
        last_alpha_cumprod = 1.0
        new_betas = []
        for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
            if i in self.use_timesteps:
                new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
                last_alpha_cumprod = alpha_cumprod
                self.timestep_map.append(i)
        kwargs["betas"] = np.array(new_betas)
        super().__init__(**kwargs)

    def p_mean_variance(
        self, model, *args, **kwargs
    ):  # pylint: disable=signature-differs
        return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)

    def training_losses(
        self, model, *args, **kwargs
    ):  # pylint: disable=signature-differs
        return super().training_losses(self._wrap_model(model), *args, **kwargs)

    def condition_mean(self, cond_fn, *args, **kwargs):
        return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)

    def condition_score(self, cond_fn, *args, **kwargs):
        return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)

    def _wrap_model(self, model):
        if isinstance(model, _WrappedModel):
            return model
        return _WrappedModel(
            model, self.timestep_map, self.rescale_timesteps, self.original_num_steps
        )

    def _scale_timesteps(self, t):
        # Scaling is done by the wrapped model.
        return t


class _WrappedModel:
    def __init__(self, model, timestep_map, rescale_timesteps, original_num_steps):
        self.model = model
        self.timestep_map = timestep_map
        self.rescale_timesteps = rescale_timesteps
        self.original_num_steps = original_num_steps

    def __call__(self, x, ts, **kwargs):
        map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
        new_ts = map_tensor[ts]
        if self.rescale_timesteps:
            new_ts = new_ts.float() * (1000.0 / self.original_num_steps)

        # print(self.original_num_steps) # 1000.
        # input("stop")

        return self.model(x, new_ts, **kwargs)


================================================
FILE: environment.yaml
================================================
name: SurfD
channels:
  - iopath
  - fvcore
  - pytorch
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - blas=1.0=mkl
  - brotli-python=1.0.9=py38h6a678d5_7
  - bzip2=1.0.8=h7b6447c_0
  - ca-certificates=2023.11.17=hbcca054_0
  - certifi=2023.11.17=pyhd8ed1ab_0
  - cffi=1.16.0=py38h5eee18b_0
  - charset-normalizer=2.0.4=pyhd3eb1b0_0
  - colorama=0.4.6=pyhd8ed1ab_0
  - cryptography=41.0.7=py38hdda0065_0
  - cudatoolkit=11.3.1=h2bc3f7f_2
  - ffmpeg=4.3=hf484d3e_0
  - freetype=2.12.1=h4a9f257_0
  - fvcore=0.1.5.post20210915=py38
  - giflib=5.2.1=h5eee18b_3
  - gmp=6.2.1=h295c915_3
  - gnutls=3.6.15=he1e5248_0
  - idna=3.4=py38h06a4308_0
  - intel-openmp=2023.1.0=hdb19cb5_46306
  - iopath=0.1.9=py38
  - jpeg=9e=h5eee18b_1
  - lame=3.100=h7b6447c_0
  - lcms2=2.12=h3be6417_0
  - ld_impl_linux-64=2.38=h1181459_1
  - lerc=3.0=h295c915_0
  - libdeflate=1.17=h5eee18b_1
  - libffi=3.4.4=h6a678d5_0
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libiconv=1.16=h7f8727e_2
  - libidn2=2.3.4=h5eee18b_0
  - libpng=1.6.39=h5eee18b_0
  - libstdcxx-ng=11.2.0=h1234567_1
  - libtasn1=4.19.0=h5eee18b_0
  - libtiff=4.5.1=h6a678d5_0
  - libunistring=0.9.10=h27cfd23_0
  - libuv=1.44.2=h5eee18b_0
  - libwebp=1.3.2=h11a3e52_0
  - libwebp-base=1.3.2=h5eee18b_0
  - lz4-c=1.9.4=h6a678d5_0
  - mkl=2023.1.0=h213fc3f_46344
  - mkl-service=2.4.0=py38h5eee18b_1
  - mkl_fft=1.3.8=py38h5eee18b_0
  - mkl_random=1.2.4=py38hdb19cb5_0
  - ncurses=6.4=h6a678d5_0
  - nettle=3.7.3=hbbd107a_1
  - numpy=1.24.3=py38hf6e8229_1
  - numpy-base=1.24.3=py38h060ed82_1
  - openh264=2.1.1=h4ff587b_0
  - openjpeg=2.4.0=h3ad879b_0
  - openssl=3.0.12=h7f8727e_0
  - pillow=10.0.1=py38ha6cbd5a_0
  - pip=23.3.1=py38h06a4308_0
  - portalocker=1.4.0=py_0
  - pycparser=2.21=pyhd3eb1b0_0
  - pyopenssl=23.2.0=py38h06a4308_0
  - pysocks=1.7.1=py38h06a4308_0
  - python=3.8.18=h955ad1f_0
  - pytorch=1.11.0=py3.8_cuda11.3_cudnn8.2.0_0
  - pytorch-mutex=1.0=cuda
  - readline=8.2=h5eee18b_0
  - requests=2.31.0=py38h06a4308_0
  - setuptools=68.2.2=py38h06a4308_0
  - sqlite=3.41.2=h5eee18b_0
  - tabulate=0.9.0=pyhd8ed1ab_1
  - tbb=2021.8.0=hdb19cb5_0
  - termcolor=2.4.0=pyhd8ed1ab_0
  - tk=8.6.12=h1ccaba5_0
  - torchaudio=0.11.0=py38_cu113
  - torchvision=0.12.0=py38_cu113
  - tqdm=4.66.1=pyhd8ed1ab_0
  - urllib3=1.26.18=py38h06a4308_0
  - wheel=0.41.2=py38h06a4308_0
  - xz=5.4.5=h5eee18b_0
  - yacs=0.1.8=pyhd8ed1ab_0
  - yaml=0.2.5=h7f98852_2
  - zlib=1.2.13=h5eee18b_0
  - zstd=1.5.5=hc292b87_0
  - pip:
      - absl-py==2.0.0
      - addict==2.4.0
      - aiofiles==23.2.1
      - altair==5.2.0
      - annotated-types==0.6.0
      - ansi2html==1.9.1
      - anyio==4.2.0
      - appdirs==1.4.4
      - asciimatics==1.15.0
      - asttokens==2.4.1
      - attrs==23.2.0
      - backcall==0.2.0
      - blinker==1.7.0
      - blobfile==2.1.1
      - cachetools==5.3.2
      - click==8.1.7
      - clip==1.0
      - comm==0.2.1
      - configargparse==1.7
      - contourpy==1.1.1
      - cycler==0.12.1
      - cython==3.0.8
      - dash==2.14.2
      - dash-core-components==2.0.0
      - dash-html-components==2.0.0
      - dash-table==5.0.0
      - dcor==0.6
      - decorator==5.1.1
      - docker-pycreds==0.4.0
      - einops==0.7.0
      - emd==0.6.2
      - exceptiongroup==1.2.0
      - executing==2.0.1
      - fastapi==0.109.2
      - fastjsonschema==2.19.1
      - ffmpy==0.3.1
      - filelock==3.13.1
      - fire==0.5.0
      - flask==3.0.0
      - fonttools==4.47.0
      - fsspec==2024.2.0
      - ftfy==6.1.3
      - gitdb==4.0.11
      - gitpython==3.1.41
      - google-auth==2.26.1
      - google-auth-oauthlib==1.0.0
      - gradio==4.17.0
      - gradio-client==0.9.0
      - grpcio==1.60.0
      - h11==0.14.0
      - h5py==3.10.0
      - hesiod==1.0.0
      - httpcore==1.0.2
      - httpx==0.26.0
      - huggingface-hub==0.20.3
      - imageio==2.34.0
      - importlib-metadata==7.0.1
      - importlib-resources==6.1.1
      - ipython==8.12.3
      - ipywidgets==8.1.1
      - itsdangerous==2.1.2
      - jedi==0.19.1
      - jinja2==3.1.3
      - joblib==1.3.2
      - jsonpatch==1.33
      - jsonpointer==2.4
      - jsonschema==4.20.0
      - jsonschema-specifications==2023.12.1
      - jupyter-core==5.7.1
      - jupyterlab-widgets==3.0.9
      - kiwisolver==1.4.5
      - llvmlite==0.41.1
      - lxml==4.9.4
      - markdown==3.5.2
      - markdown-it-py==3.0.0
      - markupsafe==2.1.3
      - matplotlib==3.7.4
      - matplotlib-inline==0.1.6
      - mdurl==0.1.2
      - nbformat==5.9.2
      - nest-asyncio==1.5.8
      - networkx==3.1
      - neuralnet-pytorch==0.0.3
      - numba==0.58.1
      - oauthlib==3.2.2
      - open3d==0.18.0
      - orjson==3.9.13
      - packaging==23.2
      - pandas==2.0.3
      - parso==0.8.3
      - pexpect==4.9.0
      - pickleshare==0.7.5
      - pkgutil-resolve-name==1.3.10
      - platformdirs==4.1.0
      - plotly==5.18.0
      - prompt-toolkit==3.0.43
      - protobuf==4.25.2
      - psutil==5.9.7
      - ptyprocess==0.7.0
      - pure-eval==0.2.2
      - pyasn1==0.5.1
      - pyasn1-modules==0.3.0
      - pycryptodomex==3.20.0
      - pydantic==2.6.1
      - pydantic-core==2.16.2
      - pydub==0.25.1
      - pyfiglet==0.8.post1
      - pygments==2.17.2
      - pymcubes==0.1.4
      - pymeshlab==2023.12
      - pyparsing==3.1.1
      - pyquaternion==0.9.9
      - python-dateutil==2.8.2
      - python-multipart==0.0.7
      - pytorch3d==0.7.2
      - pytz==2023.3.post1
      - pyyaml==6.0.1
      - referencing==0.32.1
      - regex==2023.12.25
      - requests-oauthlib==1.3.1
      - retrying==1.3.4
      - rich==13.7.0
      - rpds-py==0.16.2
      - rsa==4.9
      - ruamel-yaml==0.16.13
      - ruamel-yaml-clib==0.2.8
      - ruff==0.2.1
      - scikit-learn==1.3.2
      - scipy==1.10.1
      - seaborn==0.13.2
      - semantic-version==2.10.0
      - sentry-sdk==1.39.2
      - setproctitle==1.3.3
      - shellingham==1.5.4
      - six==1.16.0
      - smmap==5.0.1
      - sniffio==1.3.0
      - sparse==0.15.1
      - stack-data==0.6.3
      - starlette==0.36.3
      - tenacity==8.2.3
      - tensorboard==2.14.0
      - tensorboard-data-server==0.7.2
      - threadpoolctl==3.2.0
      - tomlkit==0.12.0
      - toolz==0.12.1
      - torch-dct==0.1.6
      - torch-scatter==2.1.2
      - tornado==6.4
      - traitlets==5.14.1
      - trimesh==4.0.8
      - typeguard==2.13.3
      - typer==0.9.0
      - typing-extensions==4.9.0
      - tzdata==2023.4
      - uvicorn==0.27.0.post1
      - visdom==0.2.4
      - wandb==0.16.2
      - wcwidth==0.2.13
      - websocket-client==1.7.0
      - websockets==11.0.3
      - werkzeug==3.0.1
      - widgetsnbextension==4.0.9
      - zipp==3.17.0
prefix: /home/yuzhengming/miniconda3/envs/SurfD


================================================
FILE: meshudf/_marching_cubes_lewiner.py
================================================
import base64

import numpy as np

import meshudf._marching_cubes_lewiner_luts as mcluts
from meshudf import _marching_cubes_lewiner_cy


def marching_cubes_lewiner(
    volume,
    level=None,
    spacing=(1.0, 1.0, 1.0),
    gradient_direction="descent",
    step_size=1,
    allow_degenerate=True,
    use_classic=False,
    mask=None,
):
    """Lewiner et al. algorithm for marching cubes. See
    marching_cubes_lewiner for documentation.
    """

    # Check volume and ensure its in the format that the alg needs
    if not isinstance(volume, np.ndarray) or (volume.ndim != 3):
        raise ValueError("Input volume should be a 3D numpy array.")
    if volume.shape[0] < 2 or volume.shape[1] < 2 or volume.shape[2] < 2:
        raise ValueError("Input array must be at least 2x2x2.")
    volume = np.ascontiguousarray(volume, np.float32)  # no copy if not necessary

    # Check/convert other inputs:
    # level
    if level is None:
        level = 0.5 * (volume.min() + volume.max())
    else:
        level = float(level)
        if level < volume.min() or level > volume.max():
            raise ValueError("Surface level must be within volume data range.")
    # spacing
    if len(spacing) != 3:
        raise ValueError("`spacing` must consist of three floats.")
    # step_size
    step_size = int(step_size)
    if step_size < 1:
        raise ValueError("step_size must be at least one.")
    # use_classic
    use_classic = bool(use_classic)

    # Get LutProvider class (reuse if possible)
    L = _get_mc_luts()

    # Check if a mask array is passed
    if mask is not None:
        if not mask.shape == volume.shape:
            raise ValueError("volume and mask must have the same shape.")

    # Apply algorithm
    func = _marching_cubes_lewiner_cy.marching_cubes
    vertices, faces, normals, values = func(volume, level, L, step_size, use_classic, mask)

    if not len(vertices):
        raise RuntimeError("No surface found at the given iso value.")

    # Output in z-y-x order, as is common in skimage
    vertices = np.fliplr(vertices)
    normals = np.fliplr(normals)

    # Finishing touches to output
    faces.shape = -1, 3
    if gradient_direction == "descent":
        # MC implementation is right-handed, but gradient_direction is
        # left-handed
        faces = np.fliplr(faces)
    elif not gradient_direction == "ascent":
        raise ValueError(
            "Incorrect input %s in `gradient_direction`, see " "docstring." % (gradient_direction)
        )
    if not np.array_equal(spacing, (1, 1, 1)):
        vertices = vertices * np.r_[spacing]

    if allow_degenerate:
        return vertices, faces, normals, values
    else:
        fun = _marching_cubes_lewiner_cy.remove_degenerate_faces
        return fun(vertices.astype(np.float32), faces, normals, values)


def udf_mc_lewiner(
    volume,
    grads,
    spacing=(1.0, 1.0, 1.0),
    gradient_direction="descent",
    step_size=1,
    allow_degenerate=True,
    use_classic=False,
    mask=None,
):
    """Lewiner et al. algorithm for marching cubes. See
    marching_cubes_lewiner for documentation.
    """

    # Check volume and ensure its in the format that the alg needs
    if not isinstance(volume, np.ndarray) or (volume.ndim != 3):
        raise ValueError("Input volume should be a 3D numpy array.")
    if volume.shape[0] < 2 or volume.shape[1] < 2 or volume.shape[2] < 2:
        raise ValueError("Input array must be at least 2x2x2.")
    volume = np.ascontiguousarray(volume, np.float32)  # no copy if not necessary

    # spacing
    if len(spacing) != 3:
        raise ValueError("`spacing` must consist of three floats.")
    # step_size
    step_size = int(step_size)
    if step_size < 1:
        raise ValueError("step_size must be at least one.")
    # use_classic
    use_classic = bool(use_classic)

    # Get LutProvider class (reuse if possible)
    L = _get_mc_luts()

    # Check if a mask array is passed
    if mask is not None:
        if not mask.shape == volume.shape:
            raise ValueError("volume and mask must have the same shape.")

    # Apply algorithm
    func = _marching_cubes_lewiner_cy.marching_cubes_udf
    vertices, faces, normals, values = func(volume, grads, L, step_size, use_classic, mask)

    if not len(vertices):
        raise RuntimeError("No surface found at the given iso value.")

    # Output in z-y-x order, as is common in skimage
    vertices = np.fliplr(vertices)
    normals = np.fliplr(normals)

    # Finishing touches to output
    faces.shape = -1, 3
    if gradient_direction == "descent":
        # MC implementation is right-handed, but gradient_direction is
        # left-handed
        faces = np.fliplr(faces)
    elif not gradient_direction == "ascent":
        raise ValueError(
            "Incorrect input %s in `gradient_direction`, see " "docstring." % (gradient_direction)
        )
    if not np.array_equal(spacing, (1, 1, 1)):
        vertices = vertices * np.r_[spacing]

    if allow_degenerate:
        return vertices, faces, normals, values
    else:
        fun = _marching_cubes_lewiner_cy.remove_degenerate_faces
        return fun(vertices.astype(np.float32), faces, normals, values)


def _to_array(args):
    shape, text = args
    byts = base64.decodebytes(text.encode("utf-8"))
    ar = np.frombuffer(byts, dtype="int8")
    ar.shape = shape
    return ar


# Map an edge-index to two relative pixel positions. The ege index
# represents a point that lies somewhere in between these pixels.
# Linear interpolation should be used to determine where it is exactly.
#   0
# 3   1   ->  0x
#   2         xx
EDGETORELATIVEPOSX = np.array(
    [
        [0, 1],
        [1, 1],
        [1, 0],
        [0, 0],
        [0, 1],
        [1, 1],
        [1, 0],
        [0, 0],
        [0, 0],
        [1, 1],
        [1, 1],
        [0, 0],
    ],
    "int8",
)
EDGETORELATIVEPOSY = np.array(
    [
        [0, 0],
        [0, 1],
        [1, 1],
        [1, 0],
        [0, 0],
        [0, 1],
        [1, 1],
        [1, 0],
        [0, 0],
        [0, 0],
        [1, 1],
        [1, 1],
    ],
    "int8",
)
EDGETORELATIVEPOSZ = np.array(
    [
        [0, 0],
        [0, 0],
        [0, 0],
        [0, 0],
        [1, 1],
        [1, 1],
        [1, 1],
        [1, 1],
        [0, 1],
        [0, 1],
        [0, 1],
        [0, 1],
    ],
    "int8",
)


def _get_mc_luts():
    """Kind of lazy obtaining of the luts."""
    if not hasattr(mcluts, "THE_LUTS"):

        mcluts.THE_LUTS = _marching_cubes_lewiner_cy.LutProvider(
            EDGETORELATIVEPOSX,
            EDGETORELATIVEPOSY,
            EDGETORELATIVEPOSZ,
            _to_array(mcluts.CASESCLASSIC),
            _to_array(mcluts.CASES),
            _to_array(mcluts.TILING1),
            _to_array(mcluts.TILING2),
            _to_array(mcluts.TILING3_1),
            _to_array(mcluts.TILING3_2),
            _to_array(mcluts.TILING4_1),
            _to_array(mcluts.TILING4_2),
            _to_array(mcluts.TILING5),
            _to_array(mcluts.TILING6_1_1),
            _to_array(mcluts.TILING6_1_2),
            _to_array(mcluts.TILING6_2),
            _to_array(mcluts.TILING7_1),
            _to_array(mcluts.TILING7_2),
            _to_array(mcluts.TILING7_3),
            _to_array(mcluts.TILING7_4_1),
            _to_array(mcluts.TILING7_4_2),
            _to_array(mcluts.TILING8),
            _to_array(mcluts.TILING9),
            _to_array(mcluts.TILING10_1_1),
            _to_array(mcluts.TILING10_1_1_),
            _to_array(mcluts.TILING10_1_2),
            _to_array(mcluts.TILING10_2),
            _to_array(mcluts.TILING10_2_),
            _to_array(mcluts.TILING11),
            _to_array(mcluts.TILING12_1_1),
            _to_array(mcluts.TILING12_1_1_),
            _to_array(mcluts.TILING12_1_2),
            _to_array(mcluts.TILING12_2),
            _to_array(mcluts.TILING12_2_),
            _to_array(mcluts.TILING13_1),
            _to_array(mcluts.TILING13_1_),
            _to_array(mcluts.TILING13_2),
            _to_array(mcluts.TILING13_2_),
            _to_array(mcluts.TILING13_3),
            _to_array(mcluts.TILING13_3_),
            _to_array(mcluts.TILING13_4),
            _to_array(mcluts.TILING13_5_1),
            _to_array(mcluts.TILING13_5_2),
            _to_array(mcluts.TILING14),
            _to_array(mcluts.TEST3),
            _to_array(mcluts.TEST4),
            _to_array(mcluts.TEST6),
            _to_array(mcluts.TEST7),
            _to_array(mcluts.TEST10),
            _to_array(mcluts.TEST12),
            _to_array(mcluts.TEST13),
            _to_array(mcluts.SUBCONFIG13),
        )

    return mcluts.THE_LUTS


================================================
FILE: meshudf/_marching_cubes_lewiner_cy.pyx
================================================
#cython: cdivision=True
#cython: boundscheck=False
#cython: nonecheck=False
#cython: wraparound=False

"""
This is an implementation of the marching cubes algorithm proposed in:

Efficient implementation of Marching Cubes' cases with topological guarantees.
Thomas Lewiner, Helio Lopes, Antonio Wilson Vieira and Geovan Tavares.
Journal of Graphics Tools 8(2): pp. 1-15 (december 2003)

This algorithm has the advantage that it provides topologically correct
results, and the algorithms implementation is relatively simple. Most
of the magic is in the lookup tables, which are provided as open source.

Originally implemented in C++ by Thomas Lewiner in 2002, ported to Cython
by Almar Klein in 2012. Adapted for scikit-image in 2016.

"""

# Cython specific imports
import numpy as np
cimport numpy as np
import cython
from libcpp.deque cimport deque
np.import_array()
from cpython cimport array
import array

# Enable low level memory management
from libc.stdlib cimport malloc, free

# Define tiny winy number
cdef double FLT_EPSILON = np.spacing(1.0) #0.0000001

# Define abs function for doubles
cdef inline double dabs(double a): return a if a>=0 else -a
cdef inline int imin(int a, int b): return a if a<b else b

# todo: allow dynamic isovalue?
# todo: can we disable Cython from checking for zero division? Sometimes we know that it never happens!

def remove_degenerate_faces(vertices, faces, *arrays):

    vertices_map0 = np.arange(len(vertices), dtype=np.int32)
    vertices_map1 = vertices_map0.copy()
    faces_ok = np.ones(len(faces), dtype=np.int32)

    cdef float [:, :] vertices_ = vertices
    cdef float [:] v1, v2, v3
    cdef int [:, :]  faces_ = faces

    cdef int [:] vertices_map1_ = vertices_map1
    cdef int [:] faces_ok_ = faces_ok

    cdef int j, i1, i2, i3

    # Iterate over all faces. When we encounter a degenerate triangle,
    # we update the vertex map, i.e. we merge the corresponding vertices.
    for j in range(faces_.shape[0]):
        i1, i2, i3 = faces_[j][0], faces_[j][1], faces_[j][2]
        v1, v2, v3 = vertices_[i1], vertices_[i2], vertices_[i3]
        if v1[0] == v2[0] and v1[1] == v2[1] and v1[2] == v2[2]:
            vertices_map1_[i1] = vertices_map1_[i2] = imin(vertices_map1_[i1], vertices_map1_[i2])
            faces_ok_[j] = 0
        if v1[0] == v3[0] and v1[1] == v3[1] and v1[2] == v3[2]:
            vertices_map1_[i1] = vertices_map1_[i3] = imin(vertices_map1_[i1], vertices_map1_[i3])
            faces_ok_[j] = 0
        if v2[0] == v3[0] and v2[1] == v3[1] and v2[2] == v3[2]:
            vertices_map1_[i2] = vertices_map1_[i3] = imin(vertices_map1_[i2], vertices_map1_[i3])
            faces_ok_[j] = 0

    # Create mask and mapping to new vertex indices
    vertices_ok = vertices_map1 == vertices_map0
    vertices_map2 = np.cumsum(vertices_ok) - 1

    # Apply selection and mapping
    faces2 = vertices_map2[vertices_map1[faces[faces_ok>0]]]
    vertices2 = vertices[vertices_ok]
    arrays2 = [arr[vertices_ok] for arr in arrays]

    return (vertices2, faces2) + tuple(arrays2)


cdef class Cell:
    """ Class to keep track of some stuff during the whole cube marching
    procedure.

    This "struct" keeps track of the current cell location, and the values
    of corners of the cube. Gradients for the cube corners are calculated
    when needed.

    Additionally, it keeps track of the array of vertices, faces and normals.

    Notes on vertices
    -----------------
    The vertices are stored in a C-array that is increased in size with
    factors of two if needed. The same applies to the faces and normals.

    Notes on faces
    --------------
    To keep track of the vertices already defined, this class maintains
    two faceLayer arrays. faceLayer1 is of the current layer (z-value)
    and faceLayer2 is of the next. Both face layers have 4 elements per
    cell in that layer, 1 for each unique edge per cell (see
    get_index_in_facelayer). These are initialized as -1, and set to the
    index in the vertex array when a new vertex is created.
    In summary, this allows us to keep track of the already created
    vertices without keeping a very big array.

    Notes on normals
    ----------------
    The normal is simply defined as the gradient. Each time that a face is
    created, we also add the gradient of that vertex position to the
    normals array. The gradients are all calculated from the differences between
    the 8 corners of the current cube, but because the final value of a normal
    was contributed from multiple cells, the normals are quite accurate.

    """

    # Reference to LUTS object
    cdef LutProvider luts

    # Location of cube
    cdef int x
    cdef int y
    cdef int z

    # Stepsize
    cdef int step

    # Values of cube corners (isovalue subtracted)
    cdef double v0
    cdef double v1
    cdef double v2
    cdef double v3
    cdef double v4
    cdef double v5
    cdef double v6
    cdef double v7

    # Small arrays to store the above values in (allowing indexing)
    # and also the gradient at these points
    cdef double *vv
    cdef double *vg

    # Max value of the eight corners
    cdef double vmax

    # Vertex position of center of cube (only calculated if needed)
    cdef double v12_x
    cdef double v12_y
    cdef double v12_z
    # And corresponding gradient
    cdef double v12_xg
    cdef double v12_yg
    cdef double v12_zg
    cdef int v12_calculated # a boolean

    # The index value, our magic 256 bit word
    cdef int index

    # Dimensions of the total volume
    cdef int nx
    cdef int ny
    cdef int nz

    # Arrays with face information
    cdef int *faceLayer # The current facelayer (reference-copy of one of the below)
    # cdef int *faceLayer1 # The actual first face layer
    # cdef int *faceLayer2 # The actual second face layer

    # Stuff to store the output vertices
    cdef float *_vertices
    cdef float *_normals
    cdef float *_values
    cdef int _vertexCount
    cdef int _vertexMaxCount

    # Stuff to store the output faces
    cdef int *_faces
    cdef int _faceCount
    cdef int _faceMaxCount


    def __init__(self, LutProvider luts, int nx, int ny, int nz):
        self.luts = luts
        self.nx, self.ny, self.nz = nx, ny, nz

        # Allocate face layers
        # self.faceLayer1 = <int *>malloc(self.nx*self.ny*4 * sizeof(int))
        # self.faceLayer2 = <int *>malloc(self.nx*self.ny*4 * sizeof(int))
        self.faceLayer = <int *>malloc(self.nx*self.ny*self.nz*4 * sizeof(int))

        # if (self.faceLayer1 is NULL or self.faceLayer2 is NULL or
        #     self.vv is NULL or self.vg is NULL or self._vertices is NULL or
        #     self._normals is NULL or self._values is NULL or
        #     self._faces is NULL):
        #     raise MemoryError()
        if (self.faceLayer is NULL or
            self.vv is NULL or self.vg is NULL or self._vertices is NULL or
            self._normals is NULL or self._values is NULL or
            self._faces is NULL):
            raise MemoryError()

        cdef int i
        # for i in range(self.nx*self.ny*4):
        #     self.faceLayer1[i] = -1
        #     self.faceLayer2[i] = -1
        # self.faceLayer = self.faceLayer1
        for i in range(self.nx*self.ny*self.nz*4):
            self.faceLayer[i] = -1


    def __cinit__(self):

        # Init tiny arrays for vertices and gradients at the vertices
        self.vv = <double *>malloc(8 * sizeof(double))
        self.vg = <double *>malloc(8*3 * sizeof(double))

        # Init face layers
        # self.faceLayer1 = NULL
        # self.faceLayer2 = NULL
        self.faceLayer = NULL

        # Init vertices
        self._vertexCount = 0
        self._vertexMaxCount = 8
        self._vertices = <float *>malloc(self._vertexMaxCount*3 * sizeof(float))
        self._normals = <float *>malloc(self._vertexMaxCount*3 * sizeof(float))
        self._values = <float *>malloc(self._vertexMaxCount * sizeof(float))
        # Clear normals and values
        cdef int i, j
        if self._values is not NULL and self._normals is not NULL:
            for i in range(self._vertexMaxCount):
                self._values[i] = 0.0
                for j in range(3):
                    self._normals[i*3+j] = 0.0

        # Init faces
        self._faceCount = 0
        self._faceMaxCount = 8
        self._faces = <int *>malloc(self._faceMaxCount * sizeof(int))


    def __dealloc__(self):
        free(self.vv)
        free(self.vg)
        # free(self.faceLayer1)
        # free(self.faceLayer2)
        free(self.faceLayer)
        free(self._vertices)
        free(self._normals)
        free(self._values)
        free(self._faces)


    cdef void _increase_size_vertices(self):
        """ Increase the size of the vertices array by a factor two.
        """
        # Allocate new array
        cdef int newMaxCount = self._vertexMaxCount * 2
        cdef float *newVertices = <float *>malloc(newMaxCount*3 * sizeof(float))
        cdef float *newNormals = <float *>malloc(newMaxCount*3 * sizeof(float))
        cdef float *newValues = <float *>malloc(newMaxCount * sizeof(float))
        if newVertices is NULL or newNormals is NULL or newValues is NULL:
            free(newVertices)
            free(newNormals)
            free(newValues)
            raise MemoryError()
        # Clear
        cdef int i, j
        for i in range(self._vertexCount, newMaxCount):
            newValues[i] = 0.0
            for j in range(3):
                newNormals[i*3+j] = 0.0
        # Copy
        for i in range(self._vertexCount):
            newValues[i] = self._values[i]
            for j in range(3):
                newVertices[i*3+j] = self._vertices[i*3+j]
                newNormals[i*3+j] = self._normals[i*3+j]
        # Apply
        free(self._vertices); self._vertices = newVertices
        free(self._normals); self._normals = newNormals
        free(self._values); self._values = newValues
        self._vertexMaxCount = newMaxCount


    cdef void _increase_size_faces(self):
        """ Increase the size of the faces array by a factor two.
        """
        # Allocate new array
        cdef int newMaxCount = self._faceMaxCount * 2
        cdef int *newFaces = <int *>malloc(newMaxCount * sizeof(int))
        if newFaces is NULL:
            raise MemoryError()
        # Copy
        cdef int i
        for i in range(self._faceCount):
            newFaces[i] = self._faces[i]
        # Apply
        free(self._faces)
        self._faces = newFaces
        self._faceMaxCount = newMaxCount


    ## Adding results

    cdef int add_vertex(self, float x, float y, float z):
        """ Add a vertex to the result. Return index in vertex array.
        """
        # Check if array is large enough
        if self._vertexCount >= self._vertexMaxCount:
            self._increase_size_vertices()
        # Add vertex
        self._vertices[self._vertexCount*3+0] = x
        self._vertices[self._vertexCount*3+1] = y
        self._vertices[self._vertexCount*3+2] = z
        self._vertexCount += 1
        return self._vertexCount -1


    cdef void add_gradient(self, int vertexIndex, float gx, float gy, float gz):
        """ Add a gradient value to the vertex corresponding to the given index.
        """
        self._normals[vertexIndex*3+0] += gx
        self._normals[vertexIndex*3+1] += gy
        self._normals[vertexIndex*3+2] += gz


    cdef void add_gradient_from_index(self, int vertexIndex, int i, float strength):
        """ Add a gradient value to the vertex corresponding to the given index.
        vertexIndex is the index in the large array of vertices that is returned.
        i is the index of the array of vertices 0-7 for the current cell.
        """
        self.add_gradient(vertexIndex, self.vg[i*3+0] * strength, self.vg[i*3+1] * strength, self.vg[i*3+2] * strength)


    cdef add_face(self, int index):
        """ Add a face to the result. Also updates the value.
        """
        # Check if array is large enough
        if self._faceCount >= self._faceMaxCount:
            self._increase_size_faces()
        # Add face
        self._faces[self._faceCount] = index
        self._faceCount += 1
        # if (self._faceCount < 18):
        #     print(str(self._faces[self._faceCount-1]))
        #     # print("f: " + str(self._faceCount) + " v: " + str(self._vertexCount))
        # Also update value
        if self.vmax > self._values[index]:
            self._values[index] = self.vmax


    ## Getting results

    def get_vertices(self):
        """ Get the final vertex array.
        """
        vertices = np.empty((self._vertexCount,3), np.float32)
        cdef float [:, :] vertices_ = vertices
        cdef int i, j
        for i in range(self._vertexCount):
            for j in range(3):
                vertices_[i, j] = self._vertices[i*3+j]
        return vertices

    def get_normals(self):
        """ Get the final normals array.
        The normals are normalized to unit length.
        """
        normals = np.empty((self._vertexCount,3), np.float32)
        cdef float [:, :] normals_ = normals

        cdef int i, j
        cdef double length, dtmp
        for i in range(self._vertexCount):
            length = 0.0
            for j in range(3):
                dtmp = self._normals[i*3+j] # Make it double before taking **2!
                length +=  dtmp*dtmp
            if length > 0.0:
                length = 1.0 / length**0.5
            for j in range(3):
                normals_[i,j] = self._normals[i*3+j] * length
        return normals

    def get_faces(self):
        faces = np.empty((self._faceCount,), np.int32)
        cdef int [:] faces_ = faces
        cdef int i, j
        for i in range(self._faceCount):
            faces_[i] = self._faces[i]
        return faces

    def get_values(self):
        values = np.empty((self._vertexCount,), np.float32)
        cdef float [:] values_ = values
        cdef int i, j
        for i in range(self._vertexCount):
            values_[i] = self._values[i]
        return values


    ## Called from marching cube function

    # cdef void new_z_value(self):
    #     """ This method should be called each time a new z layer is entered.
    #     We will swap the layers with face information and empty the second.
    #     """
    #     # Swap layers
    #     self.faceLayer1, self.faceLayer2 = self.faceLayer2, self.faceLayer1
    #     # Empty last
    #     cdef int i
    #     for i in range(self.nx*self.ny*4):
    #         self.faceLayer2[i] = -1


    cdef void set_cube(self,    double isovalue, int x, int y, int z, int step,
                                double v0, double v1, double v2, double v3,
                                double v4, double v5, double v6, double v7):
        """ Set the cube to the new location.

        Set the values of the cube corners. The isovalue is subtracted
        from them, such that in further calculations the isovalue can be
        taken as zero.

        This method also calculated the magic 256 word to identify the
        cases (i.e. cell.index).
        """

        # Set location and step
        self.x = x
        self.y = y
        self.z = z
        self.step = step

        # Set values
        self.v0 = v0 - isovalue
        self.v1 = v1 - isovalue
        self.v2 = v2 - isovalue
        self.v3 = v3 - isovalue
        self.v4 = v4 - isovalue
        self.v5 = v5 - isovalue
        self.v6 = v6 - isovalue
        self.v7 = v7 - isovalue

        # Calculate index
        cdef int index = 0
        if self.v0 > 0.0:   index += 1
        if self.v1 > 0.0:   index += 2
        if self.v2 > 0.0:   index += 4
        if self.v3 > 0.0:   index += 8
        if self.v4 > 0.0:   index += 16
        if self.v5 > 0.0:   index += 32
        if self.v6 > 0.0:   index += 64
        if self.v7 > 0.0:   index += 128
        self.index = index

        # Reset c12
        self.v12_calculated = 0


    cdef bint check_triangles(self, Lut lut, int lutIndex, int nt):
        """ Check triangles.

        The vertices for the triangles are specified in the given
        Lut at the specified index. There are nt triangles.

        The reason that nt should be given is because it is often known
        beforehand.

        """

        cdef int i, j
        cdef int vi
        cdef int result = 0
        cdef list fl_values = []

        self.prepare_for_adding_triangles()

        for i in range(nt):
            for j in range(3):
                # Get two sides for each element in this vertex
                vi = lut.get2(lutIndex, i*3+j)
                index = self.get_index_in_facelayer(vi)
                fl_value = self.faceLayer[index]
                # print(index, fl_value)
                if (not fl_value in fl_values) and fl_value >= 0:
                    # print("True")
                    result += 1
                fl_values.append(fl_value)
        # print(result)
        return result


    cdef bint check_triangles2(self, Lut lut, int lutIndex, int lutIndex2, int nt):
        """ Same as check_triangles, except that now the geometry is in a LUT
        with 3 dimensions, and an extra index is provided.

        """
        cdef int i, j
        cdef int vi
        cdef int result = 0
        cdef list fl_values = []

        self.prepare_for_adding_triangles()

        for i in range(nt):
            for j in range(3):
                # Get two sides for each element in this vertex
                vi = lut.get3(lutIndex, lutIndex2, i*3+j)
                index = self.get_index_in_facelayer(vi)
                fl_value = self.faceLayer[index]
                # print(index, fl_value)
                if (not fl_value in fl_values) and fl_value >= 0:
                    # print("True")
                    result += 1
                fl_values.append(fl_value)
        # print(result)
        return result



    cdef bint add_triangles(self, Lut lut, int lutIndex, int nt):
        """ Add triangles.

        The vertices for the triangles are specified in the given
        Lut at the specified index. There are nt triangles.

        The reason that nt should be given is because it is often known
        beforehand.

        """

        cdef int i, j
        cdef int vi
        cdef bint result = False
        # cdef list indices = []

        self.prepare_for_adding_triangles()

        for i in range(nt):
            for j in range(3):
                # Get two sides for each element in this vertex
                vi = lut.get2(lutIndex, i*3+j)
                self._add_face_from_edge_index(vi)
                # index, fl_value = self._add_face_from_edge_index(vi)
                # print(index, fl_value)
                # if (not index in indices) and fl_value >= 0:
                #     print(index)
                #     print(indices)
                #     result = True
                # indices.append(index)
        return result


    cdef bint add_triangles2(self, Lut lut, int lutIndex, int lutIndex2, int nt):
        """ Same as add_triangles, except that now the geometry is in a LUT
        with 3 dimensions, and an extra index is provided.

        """
        cdef int i, j
        cdef int vi
        cdef bint result = False
        # cdef list indices = []

        self.prepare_for_adding_triangles()

        for i in range(nt):
            for j in range(3):
                # Get two sides for each element in this vertex
                vi = lut.get3(lutIndex, lutIndex2, i*3+j)
                self._add_face_from_edge_index(vi)
                # index, fl_value = self._add_face_from_edge_index(vi)
                # print(index, fl_value)
                # if (not index in indices) and fl_value >= 0:
                #     print(index)
                #     print(indices)
                #     result = True
                # indices.append(index)
        return result

    ## Used internally

    cdef (int, int) _add_face_from_edge_index(self, int vi):
        """ Add one face from an edge index. Only adds a face if the
        vertex already exists. Otherwise also adds a vertex and applies
        interpolation.
        """

        # typedefs
        cdef int indexInVertexArray, indexInFaceLayer
        cdef int dx1, dy1, dz1
        cdef int dx2, dy2, dz2
        cdef int index1, index2
        cdef double tmpf1, tmpf2
        cdef double fx, fy, fz, ff
        cdef double stp = <double>self.step

        # Get index in the face layer and corresponding vertex number
        indexInFaceLayer = self.get_index_in_facelayer(vi)
        indexInVertexArray = self.faceLayer[indexInFaceLayer]
        fl_value = self.faceLayer[indexInFaceLayer]

        # If we have the center vertex, we have things pre-calculated,
        # otherwise we need to interpolate.
        # In both cases we distinguish between having this vertex already
        # or not.

        if vi == 12: # center vertex
            if self.v12_calculated == 0:
                self.calculate_center_vertex()
            if indexInVertexArray >= 0:
                # Vertex already calculated, only need to add face and gradient
                self.add_face(indexInVertexArray)
                self.add_gradient(indexInVertexArray, self.v12_xg, self.v12_yg, self.v12_zg)
            else:
                # Add precalculated center vertex position (is interpolated)
                indexInVertexArray = self.add_vertex( self.v12_x, self.v12_y, self.v12_z)
                # Update face layer
                self.faceLayer[indexInFaceLayer] = indexInVertexArray
                # Add face and gradient
                self.add_face(indexInVertexArray)
                self.add_gradient(indexInVertexArray, self.v12_xg, self.v12_yg, self.v12_zg)

        else:

            # Get relative edge indices for x, y and z
            dx1, dx2 = self.luts.EDGESRELX.get2(vi,0), self.luts.EDGESRELX.get2(vi,1)
            dy1, dy2 = self.luts.EDGESRELY.get2(vi,0), self.luts.EDGESRELY.get2(vi,1)
            dz1, dz2 = self.luts.EDGESRELZ.get2(vi,0), self.luts.EDGESRELZ.get2(vi,1)
            # Make two vertex indices
            index1 = dz1*4 + dy1*2 + dx1
            index2 = dz2*4 + dy2*2 + dx2
            # Define strength of both corners
            tmpf1 = 1.0 / (FLT_EPSILON + dabs(self.vv[index1]))
            tmpf2 = 1.0 / (FLT_EPSILON + dabs(self.vv[index2]))

            # print('indexInVertexArray', self.x, self.y, self.z, '-', vi, indexInVertexArray, indexInFaceLayer)

            if indexInVertexArray >= 0:
                # Vertex already calculated, only need to add face and gradient
                self.add_face(indexInVertexArray)
                self.add_gradient_from_index(indexInVertexArray, index1, tmpf1)
                self.add_gradient_from_index(indexInVertexArray, index2, tmpf2)

            else:
                # Interpolate by applying a kind of center-of-mass method
                fx, fy, fz, ff = 0.0, 0.0, 0.0, 0.0
                fx += <double>dx1 * tmpf1;  fy += <double>dy1 * tmpf1;  fz += <double>dz1 * tmpf1;  ff += tmpf1
                fx += <double>dx2 * tmpf2;  fy += <double>dy2 * tmpf2;  fz += <double>dz2 * tmpf2;  ff += tmpf2

                # Add vertex
                indexInVertexArray = self.add_vertex(
                                <double>self.x + stp*fx/ff,
                                <double>self.y + stp*fy/ff,
                                <double>self.z + stp*fz/ff )
                # Update face layer
                self.faceLayer[indexInFaceLayer] = indexInVertexArray
                # Add face and gradient
                self.add_face(indexInVertexArray)
                self.add_gradient_from_index(indexInVertexArray, index1, tmpf1)
                self.add_gradient_from_index(indexInVertexArray, index2, tmpf2)


#         # Create vertex non-interpolated
#         self.add_vertex( self.x + 0.5* dx1 + 0.5 * dx2,
#                         self.y + 0.5* dy1 + 0.5 * dy2,
#                         self.z + 0.5* dz1 + 0.5 * dz2 )

        return indexInFaceLayer, fl_value

    cdef int get_index_in_facelayer(self, int vi):
        """
        Get the index of a vertex position, given the edge on which it lies.
        We keep a list of faces so we can reuse vertices. This improves
        speed because we need less interpolation, and the result is more
        compact and can be visualized better because normals can be
        interpolated.

        For each cell, we store 4 vertex indices; all other edges can be
        represented as the edge of another cell.  The fourth is the center vertex.

        This method returns -1 if no vertex has been defined yet.


              vertices              edges                edge-indices per cell
        *         7 ________ 6           _____6__             ________
        *         /|       /|         7/|       /|          /|       /|
        *       /  |     /  |        /  |     /5 |        /  |     /  |
        *   4 /_______ /    |      /__4____ /    10     /_______ /    |
        *    |     |  |5    |     |    11  |     |     |     |  |     |
        *    |    3|__|_____|2    |     |__|__2__|     |     |__|_____|
        *    |    /   |    /      8   3/   9    /      2    /   |    /
        *    |  /     |  /        |  /     |  /1       |  1     |  /
        *    |/_______|/          |/___0___|/          |/___0___|/
        *   0          1
        */
        """

        # Init indices, both are corrected below
        # cdef int i = self.nx * self.y + self.x  # Index of cube to get vertex at
        cdef int i = self.ny*self.nx*self.z + self.nx * self.y + self.x  # Index of cube to get vertex at
        cdef int j = 0 # Vertex number for that cell
        cdef int vi_ = vi

        # cdef int *faceLayer
        cdef int k = 0 #Defines whether to go to a higher z or not

        # Select either upper or lower half
        if vi < 8:
            #  8 horizontal edges
            if vi < 4:
                # faceLayer = self.faceLayer1
                k = 0
            else:
                vi -= 4
                # faceLayer = self.faceLayer2
                k = 1

            # Calculate actual index based on edge
            #if vi == 0: pass  # no step
            if vi == 1:  # step in x
                i += self.step
                j = 1
            elif vi == 2:  # step in y
                i += self.nx * self.step
            elif vi == 3:  # no step
                j = 1

        elif vi < 12:
            # 4 vertical edges
            # faceLayer = self.faceLayer1
            k = 0
            j = 2

            #if vi == 8: pass # no step
            if vi == 9:   # step in x
                i += self.step
            elif vi == 10:   # step in x and y
                i += self.nx * self.step + self.step
            elif vi == 11:  # step in y
                i += self.nx * self.step

        else:
            # center vertex
            # faceLayer = self.faceLayer1
            k = 0
            j = 3

        k = self.nx*self.ny * k
        i = i+k

        # Store facelayer and return index
        # self.faceLayer = faceLayer # Dirty way of returning a value
        return 4*i + j


    cdef void prepare_for_adding_triangles(self):
        """ Calculates some things to help adding the triangles:
        array with corner values, max corner value, gradient at each corner.
        """

        cdef int i

        # Copy values in array so we can index them. Note the misalignment
        # because the numbering does not correspond with bitwise OR of xyz.
        self.vv[0] = self.v0
        self.vv[1] = self.v1
        self.vv[2] = self.v3#
        self.vv[3] = self.v2#
        self.vv[4] = self.v4
        self.vv[5] = self.v5
        self.vv[6] = self.v7#
        self.vv[7] = self.v6#

        # Calculate max
        cdef double vmin, vmax
        vmin, vmax = 0.0, 0.0
        for i in range(8):
            if self.vv[i] > vmax:
                vmax = self.vv[i]
            if self.vv[i] < vmin:
                vmin = self.vv[i]
        self.vmax = vmax-vmin

        # Calculate gradients
        # Derivatives, selected to always point in same direction.
        # Note that many corners have the same components as other points,
        # by interpolating  and averaging the normals this is solved.
        # todo: we can potentially reuse these similar to how we store vertex indices in face layers
        self.vg[0*3+0], self.vg[0*3+1], self.vg[0*3+2] = self.v0-self.v1, self.v0-self.v3, self.v0-self.v4
        self.vg[1*3+0], self.vg[1*3+1], self.vg[1*3+2] = self.v0-self.v1, self.v1-self.v2, self.v1-self.v5
        self.vg[2*3+0], self.vg[2*3+1], self.vg[2*3+2] = self.v3-self.v2, self.v1-self.v2, self.v2-self.v6
        self.vg[3*3+0], self.vg[3*3+1], self.vg[3*3+2] = self.v3-self.v2, self.v0-self.v3, self.v3-self.v7
        self.vg[4*3+0], self.vg[4*3+1], self.vg[4*3+2] = self.v4-self.v5, self.v4-self.v7, self.v0-self.v4
        self.vg[5*3+0], self.vg[5*3+1], self.vg[5*3+2] = self.v4-self.v5, self.v5-self.v6, self.v1-self.v5
        self.vg[6*3+0], self.vg[6*3+1], self.vg[6*3+2] = self.v7-self.v6, self.v5-self.v6, self.v2-self.v6
        self.vg[7*3+0], self.vg[7*3+1], self.vg[7*3+2] = self.v7-self.v6, self.v4-self.v7, self.v3-self.v7


    cdef void calculate_center_vertex(self):
        """ Calculate interpolated center vertex and its gradient.
        """
        cdef double v0, v1, v2, v3, v4, v5, v6, v7
        cdef double fx, fy, fz, ff
        fx, fy, fz, ff = 0.0, 0.0, 0.0, 0.0

        # Define "strength" of each corner of the cube that we need
        v0 = 1.0 / (FLT_EPSILON + dabs(self.v0))
        v1 = 1.0 / (FLT_EPSILON + dabs(self.v1))
        v2 = 1.0 / (FLT_EPSILON + dabs(self.v2))
        v3 = 1.0 / (FLT_EPSILON + dabs(self.v3))
        v4 = 1.0 / (FLT_EPSILON + dabs(self.v4))
        v5 = 1.0 / (FLT_EPSILON + dabs(self.v5))
        v6 = 1.0 / (FLT_EPSILON + dabs(self.v6))
        v7 = 1.0 / (FLT_EPSILON + dabs(self.v7))

        # Apply a kind of center-of-mass method
        fx += 0.0*v0;  fy += 0.0*v0;  fz += 0.0*v0;  ff += v0
        fx += 1.0*v1;  fy += 0.0*v1;  fz += 0.0*v1;  ff += v1
        fx += 1.0*v2;  fy += 1.0*v2;  fz += 0.0*v2;  ff += v2
        fx += 0.0*v3;  fy += 1.0*v3;  fz += 0.0*v3;  ff += v3
        fx += 0.0*v4;  fy += 0.0*v4;  fz += 1.0*v4;  ff += v4
        fx += 1.0*v5;  fy += 0.0*v5;  fz += 1.0*v5;  ff += v5
        fx += 1.0*v6;  fy += 1.0*v6;  fz += 1.0*v6;  ff += v6
        fx += 0.0*v7;  fy += 1.0*v7;  fz += 1.0*v7;  ff += v7

        # Store
        cdef double stp = <double>self.step
        self.v12_x = self.x + stp * fx / ff
        self.v12_y = self.y + stp * fy / ff
        self.v12_z = self.z + stp * fz / ff

        # Also pre-calculate gradient of center
        # note that prepare_for_adding_triangles() must have been called for
        # the gradient data to exist.
        self.v12_xg = ( v0*self.vg[0*3+0] + v1*self.vg[1*3+0] + v2*self.vg[2*3+0] + v3*self.vg[3*3+0] +
                        v4*self.vg[4*3+0] + v5*self.vg[5*3+0] + v6*self.vg[6*3+0] + v7*self.vg[7*3+0] )
        self.v12_yg = ( v0*self.vg[0*3+1] + v1*self.vg[1*3+1] + v2*self.vg[2*3+1] + v3*self.vg[3*3+1] +
                        v4*self.vg[4*3+1] + v5*self.vg[5*3+1] + v6*self.vg[6*3+1] + v7*self.vg[7*3+1] )
        self.v12_xg = ( v0*self.vg[0*3+2] + v1*self.vg[1*3+2] + v2*self.vg[2*3+2] + v3*self.vg[3*3+2] +
                        v4*self.vg[4*3+2] + v5*self.vg[5*3+2] + v6*self.vg[6*3+2] + v7*self.vg[7*3+2] )

        # Set flag that this stuff is calculated
        self.v12_calculated = 1



cdef class Lut:
    """ Representation of a lookup table.
    The tables are initially defined as numpy arrays. On initialization,
    this class converts them to a C array for fast access.
    This class defines functions to look up values using 1, 2 or 3 indices.
    """

    cdef signed char* VALUES
    cdef int L0 # Length
    cdef int L1 # size of tuple
    cdef int L2 # size of tuple in tuple (if any)

    def __init__(self, array):

        # Get the shape of the LUT
        self.L1 = 1
        self.L2 = 1
        #
        self.L0 = array.shape[0]
        if array.ndim > 1:
            self.L1 = array.shape[1]
        if array.ndim > 2:
            self.L2 = array.shape[2]

        # Copy the contents
        array = array.ravel()
        cdef int n, N
        N = self.L0 * self.L1 * self.L2
        self.VALUES = <signed char *> malloc(N * sizeof(signed char))
        if self.VALUES is NULL:
            raise MemoryError()
        for n in range(N):
            self.VALUES[n] = array[n]

    def __cinit__(self):
        self.VALUES = NULL

    def __dealloc__(self):
        if self.VALUES is not NULL:
            free(self.VALUES)

    cdef int get1(self, int i0):
        return self.VALUES[i0]

    cdef int get2(self, int i0, int i1):
        return self.VALUES[i0*self.L1 + i1]

    cdef int get3(self, int i0, int i1, int i2):
        return self.VALUES[i0*self.L1*self.L2 + i1*self.L2 + i2]



cdef class LutProvider:
    """ Class that provides a common interface to the many lookup tables
    used by the algorithm.
    All the lists of lut names are autogenerated to prevent human error.
    """

    cdef Lut EDGESRELX # Edges relative X
    cdef Lut EDGESRELY
    cdef Lut EDGESRELZ

    cdef Lut CASESCLASSIC
    cdef Lut CASES

    cdef Lut TILING1
    cdef Lut TILING2
    cdef Lut TILING3_1
    cdef Lut TILING3_2
    cdef Lut TILING4_1
    cdef Lut TILING4_2
    cdef Lut TILING5
    cdef Lut TILING6_1_1
    cdef Lut TILING6_1_2
    cdef Lut TILING6_2
    cdef Lut TILING7_1
    cdef Lut TILING7_2
    cdef Lut TILING7_3
    cdef Lut TILING7_4_1
    cdef Lut TILING7_4_2
    cdef Lut TILING8
    cdef Lut TILING9
    cdef Lut TILING10_1_1
    cdef Lut TILING10_1_1_
    cdef Lut TILING10_1_2
    cdef Lut TILING10_2
    cdef Lut TILING10_2_
    cdef Lut TILING11
    cdef Lut TILING12_1_1
    cdef Lut TILING12_1_1_
    cdef Lut TILING12_1_2
    cdef Lut TILING12_2
    cdef Lut TILING12_2_
    cdef Lut TILING13_1
    cdef Lut TILING13_1_
    cdef Lut TILING13_2
    cdef Lut TILING13_2_
    cdef Lut TILING13_3
    cdef Lut TILING13_3_
    cdef Lut TILING13_4
    cdef Lut TILING13_5_1
    cdef Lut TILING13_5_2
    cdef Lut TILING14

    cdef Lut TEST3
    cdef Lut TEST4
    cdef Lut TEST6
    cdef Lut TEST7
    cdef Lut TEST10
    cdef Lut TEST12
    cdef Lut TEST13

    cdef Lut SUBCONFIG13


    def __init__(self, EDGESRELX, EDGESRELY, EDGESRELZ, CASESCLASSIC, CASES,

            TILING1, TILING2, TILING3_1, TILING3_2, TILING4_1, TILING4_2,
            TILING5, TILING6_1_1, TILING6_1_2, TILING6_2, TILING7_1, TILING7_2,
            TILING7_3, TILING7_4_1, TILING7_4_2, TILING8, TILING9,
            TILING10_1_1, TILING10_1_1_, TILING10_1_2, TILING10_2, TILING10_2_,
            TILING11, TILING12_1_1, TILING12_1_1_, TILING12_1_2, TILING12_2,
            TILING12_2_, TILING13_1, TILING13_1_, TILING13_2, TILING13_2_,
            TILING13_3, TILING13_3_, TILING13_4, TILING13_5_1, TILING13_5_2,
            TILING14,

            TEST3, TEST4, TEST6, TEST7, TEST10, TEST12, TEST13,
            SUBCONFIG13,
            ):

        self.EDGESRELX = Lut(EDGESRELX)
        self.EDGESRELY = Lut(EDGESRELY)
        self.EDGESRELZ = Lut(EDGESRELZ)

        self.CASESCLASSIC = Lut(CASESCLASSIC)
        self.CASES = Lut(CASES)

        self.TILING1 = Lut(TILING1)
        self.TILING2 = Lut(TILING2)
        self.TILING3_1 = Lut(TILING3_1)
        self.TILING3_2 = Lut(TILING3_2)
        self.TILING4_1 = Lut(TILING4_1)
        self.TILING4_2 = Lut(TILING4_2)
        self.TILING5 = Lut(TILING5)
        self.TILING6_1_1 = Lut(TILING6_1_1)
        self.TILING6_1_2 = Lut(TILING6_1_2)
        self.TILING6_2 = Lut(TILING6_2)
        self.TILING7_1 = Lut(TILING7_1)
        self.TILING7_2 = Lut(TILING7_2)
        self.TILING7_3 = Lut(TILING7_3)
        self.TILING7_4_1 = Lut(TILING7_4_1)
        self.TILING7_4_2 = Lut(TILING7_4_2)
        self.TILING8 = Lut(TILING8)
        self.TILING9 = Lut(TILING9)
        self.TILING10_1_1 = Lut(TILING10_1_1)
        self.TILING10_1_1_ = Lut(TILING10_1_1_)
        self.TILING10_1_2 = Lut(TILING10_1_2)
        self.TILING10_2 = Lut(TILING10_2)
        self.TILING10_2_ = Lut(TILING10_2_)
        self.TILING11 = Lut(TILING11)
        self.TILING12_1_1 = Lut(TILING12_1_1)
        self.TILING12_1_1_ = Lut(TILING12_1_1_)
        self.TILING12_1_2 = Lut(TILING12_1_2)
        self.TILING12_2 = Lut(TILING12_2)
        self.TILING12_2_ = Lut(TILING12_2_)
        self.TILING13_1 = Lut(TILING13_1)
        self.TILING13_1_ = Lut(TILING13_1_)
        self.TILING13_2 = Lut(TILING13_2)
        self.TILING13_2_ = Lut(TILING13_2_)
        self.TILING13_3 = Lut(TILING13_3)
        self.TILING13_3_ = Lut(TILING13_3_)
        self.TILING13_4 = Lut(TILING13_4)
        self.TILING13_5_1 = Lut(TILING13_5_1)
        self.TILING13_5_2 = Lut(TILING13_5_2)
        self.TILING14 = Lut(TILING14)

        self.TEST3 = Lut(TEST3)
        self.TEST4 = Lut(TEST4)
        self.TEST6 = Lut(TEST6)
        self.TEST7 = Lut(TEST7)
        self.TEST10 = Lut(TEST10)
        self.TEST12 = Lut(TEST12)
        self.TEST13 = Lut(TEST13)

        self.SUBCONFIG13 = Lut(SUBCONFIG13)

# def marching_cubes(float[:, :, :] im not None, double isovalue,
#                    LutProvider luts, int st=1, int classic=0,
#                    np.ndarray[np.npy_bool, ndim=3, cast=True] mask=None):
#     """ marching_cubes(im, double isovalue, LutProvider luts, int st=1, int classic=0)
#     Main entry to apply marching cubes.

#     Masked version of marching cubes. This function will check a
#     masking array (same size as im) to decide if the algorithm must be
#     computed for a given voxel. This adds a small overhead that
#     rapidly gets compensated by the fewer computed cubes
#     Returns (vertices, faces, normals, values)
#     """
#     # Get dimemsnions
#     cdef int Nx, Ny, Nz
#     Nx, Ny, Nz = im.shape[2], im.shape[1], im.shape[0]

#     # Create cell to use throughout
#     cdef Cell cell = Cell(luts, Nx, Ny, Nz)

#     # Typedef variables
#     cdef int x, y, z, x_st, y_st, z_st
#     cdef int nt
#     cdef int case, config, subconfig
#     cdef bint no_mask = mask is None
#     # Unfortunately specifying a step in range() significantly degrades
#     # performance. Therefore we use a while loop.
#     # we have:  max_x = Nx_bound + st + st - 1
#     #       ->  Nx_bound = max_allowable_x + 1 - 2 * st
#     #       ->  Nx_bound = Nx - 2 * st
#     assert st > 0
#     cdef int Nx_bound, Ny_bound, Nz_bound
#     Nx_bound, Ny_bound, Nz_bound = Nx - 2 * st, Ny - 2 * st, Nz - 2 * st  # precalculated index range

#     z = -st
#     while z < Nz_bound:
#         z += st
#         z_st = z + st

#         cell.new_z_value()  # Indicate that we enter a new layer
#         y = -st
#         while y < Ny_bound:
#             y += st
#             y_st = y + st

#             x = -st
#             while x < Nx_bound:
#                 x += st
#                 x_st = x + st
#                 if no_mask or mask[z_st, y_st, x_st]:
#                     # Initialize cell
#                     cell.set_cube(isovalue, x, y, z, st,
#                         im[z   ,y, x], im[z   ,y, x_st], im[z   ,y_st, x_st], im[z   ,y_st, x],
#                         im[z_st,y, x], im[z_st,y, x_st], im[z_st,y_st, x_st], im[z_st,y_st, x] )

#                     # Do classic!
#                     if classic:
#                         # Determine number of vertices
#                         nt = 0
#                         while luts.CASESCLASSIC.get2(cell.index, 3*nt) != -1:
#                             nt += 1
#                         # Add triangles
#                         if nt > 0:
#                             cell.add_triangles(luts.CASESCLASSIC, cell.index, nt)
#                     else:
#                         # Get case, if non-nul, enter the big switch
#                         case = luts.CASES.get2(cell.index, 0)
#                         if case > 0:
#                             config = luts.CASES.get2(cell.index, 1)
#                             the_big_switch(luts, cell, case, config)

#     # Done
#     return cell.get_vertices(), cell.get_faces(), cell.get_normals(), cell.get_values()



def marching_cubes_udf(float[:, :, :] im not None, float[:, :, :, :] grads not None,
                   LutProvider luts, int st=1, int classic=0,
                   np.ndarray[np.npy_bool, ndim=3, cast=True] mask=None):
    """ marching_cubes(im, double isovalue, LutProvider luts, int st=1, int classic=0)
    Main entry to apply marching cubes.

    Masked version of marching cubes. This function will check a
    masking array (same size as im) to decide if the algorithm must be
    computed for a given voxel. This adds a small overhead that
    rapidly gets compensated by the fewer computed cubes
    Returns (vertices, faces, normals, values)
    """
    # Get dimemsnions
    cdef int Nx, Ny, Nz
    Nx, Ny, Nz = im.shape[2], im.shape[1], im.shape[0]
    # Compute voxel size
    cdef voxel_size = 2.0 / (Nx - 1)

    # Create cell to use throughout
    cdef Cell cell = Cell(luts, Nx, Ny, Nz)

    # Create a 3D matrix to store signed im values
    cdef float[:,:,:] signed_im = np.zeros((Nz, Ny, Nx), dtype=np.float32)
    cdef bint[:,:,:] signed_im_mask = np.zeros((Nz, Ny, Nx), dtype=np.int32)

    # Typedef variables
    cdef int x, y, z, x_st, y_st, z_st, xi, yi, zi, x_i, y_i, z_i
    cdef int dir_x, dir_y, dir_z, cur_x_i, cur_y_i, cur_z_i
    cdef int nt
    cdef int case, config, subconfig
    cdef bint no_mask = mask is None
    cdef double v0, v1, v2, v3, v4, v5, v6, v7
    # Unfortunately specifying a step in range() significantly degrades
    # performance. Therefore we use a while loop.
    # we have:  max_x = Nx_bound + st + st - 1
    #       ->  Nx_bound = max_allowable_x + 1 - 2 * st
    #       ->  Nx_bound = Nx - 2 * st
    assert st > 0
    cdef int Nx_bound, Ny_bound, Nz_bound
    Nx_bound, Ny_bound, Nz_bound = Nx - 2 * st, Ny - 2 * st, Nz - 2 * st  # precalculated index range
    cdef float[:] base_vec = np.empty((3), dtype=np.float32)

    cdef float avg_cube_val_thresh = 1.05 * voxel_size
    cdef float max_cube_val_thresh = 1.74 * voxel_size

    # BFS data structures
    cdef bint[:,:,:] visited = np.zeros((Nz, Ny, Nx), dtype=np.int32)

    # cdef list queue = []
    cdef deque[(int,int,int)] queue
    cdef deque[(int,int,int)] unsure_cases_queue
    cdef deque[(int,int,int)] non_trivial_mc_cases_queue
    cdef (int, int, int) current_tuple

    cdef array.array sign_vs = array.array('f', [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])
    cdef array.array visited_vs = array.array('i', [0, 0, 0, 0, 0, 0, 0, 0])
    cdef int cube_sign
    
    cdef int total_cubes = 0
    cdef int connected_components = 0

    cdef array.array vertex_index_array_z
    cdef array.array vertex_index_array_y
    cdef array.array vertex_index_array_x

    cdef array.array directions_z = array.array('i', [st, -st, 0, 0, 0, 0])
    cdef array.array directions_y = array.array('i', [0, 0, st, -st, 0, 0])
    cdef array.array directions_x = array.array('i', [0, 0, 0, 0, st, -st])

    cdef int max_distance = 1
    cdef int max_distance_temp

    cdef float unsure_cases_thresh = 0.707
    cdef bint change_cube

    cdef int i, v_index, dir_index

    cdef bint unsure_cases_visit_neighbours

    # Raster scan of the whole 3D grid
    zi = -st
    while zi < Nz_bound:
        zi += st

        yi = -st
        while yi < Ny_bound:
            yi += st

            xi = -st
            while xi < Nx_bound:
                xi += st

                z, y, x = zi, yi, xi
                
                z_st = z+st
                y_st = y+st
                x_st = x+st

                if visited[z,y,x] == False and (no_mask or mask[z_st, y_st, x_st]):
                    
                    if (avg_cube( im[z   ,y, x], im[z   ,y, x_st], im[z   ,y_st, x_st], im[z   ,y_st, x],
                                    im[z_st,y, x], im[z_st,y, x_st], im[z_st,y_st, x_st], im[z_st,y_st, x]) < avg_cube_val_thresh and 
                        max_cube( im[z   ,y, x], im[z   ,y, x_st], im[z   ,y_st, x_st], im[z   ,y_st, x],
                                    im[z_st,y, x], im[z_st,y, x_st], im[z_st,y_st, x_st], im[z_st,y_st, x]) <= max_cube_val_thresh):
                        
                        vertex_index_array_z = array.array('i', [z, z,    z,    z,    z_st, z_st, z_st, z_st])
                        vertex_index_array_y = array.array('i', [y, y,    y_st, y_st, y,    y,    y_st, y_st])
                        vertex_index_array_x = array.array('i', [x, x_st, x_st, x,    x,    x_st, x_st, x])

                        # The following block follows this pseudocode and lets 
                        # the nearby vertices vote for the sign of each of the 
                        # vertices of current cube

                        #for each non-computed non-zero vertex v
                            #for each of the 6 directions
                                #for each vertex v_i along that direction, up to max-distance vertices
                                    #if v_i hasn't been computed
                                        #continue
                                    #if v_i is 0 
                                        #if max-distance is not reached
                                            #continue
                                        #else
                                            #while v_i along that direction is not 0
                                            #compute edge vote
                                    #else
                                        #compute edge vote

                        v_index = -1
                        change_cube = False
                        while v_index < 7: #from 0 to 7, included
                            v_index += 1
                            
                            visited_vs[v_index] = 0

                            z_i = vertex_index_array_z[v_index]
                            y_i = vertex_index_array_y[v_index]
                            x_i = vertex_index_array_x[v_index]

                            sign_vs[v_index] = 0.0
                            
                            #If the vertex value has been previously computed or is zero, skip it
                            if signed_im_mask[z_i,y_i,x_i] == True:
                                visited_vs[v_index] = 1
                                sign_vs[v_index] = signed_im[z_i,y_i,x_i]
                                continue

                            if im[z_i,y_i,x_i] == 0.0:
                                visited_vs[v_index] = 1
                                continue
                            
                            dir_index = -1
                            while dir_index < 5: #from 0 to 5, included
                                dir_index += 1

                                dir_z = directions_z[dir_index]
                                dir_y = directions_y[dir_index]
                                dir_x = directions_x[dir_index]

                                i = 0
                                max_distance_temp = max_distance
                                while i < max_distance_temp: #from 1 to max_distance, included
                                    i += 1

                                    cur_z_i = z_i + i*dir_z
                                    cur_y_i = y_i + i*dir_y
                                    cur_x_i = x_i + i*dir_x

                                    #If out of bounds, search on another direction
                                    if (cur_z_i > Nz_bound or cur_z_i < 0 or
                                        cur_y_i > Ny_bound or cur_y_i < 0 or
                                        cur_x_i > Nx_bound or cur_x_i < 0):
                                        break
                                    
                                    # If the vertex value is zero, continue to check what's beyond
                                    if im[cur_z_i, cur_y_i, cur_x_i] == 0.0:
                                        if i < max_distance_temp:
                                            continue
                                        else:
                                            max_distance_temp = max_distance_temp + 1 #Check one vertex further away
                                            continue
                                    
                                    # 1.3: If not computed during previous cubes AND not already computed during this cube, skip it
                                    if signed_im[cur_z_i, cur_y_i, cur_x_i] == 0.0:
                                        continue

                                    # Normal case
                                    visited_vs[v_index] += 1
                                    sign_vs[v_index] += signed_im[cur_z_i, cur_y_i, cur_x_i] * compute_edge_vote(grads[z_i,y_i,x_i], grads[cur_z_i, cur_y_i, cur_x_i], dir_z, dir_y, dir_x)
                                    
                            # Set the vertex. This value is used to compute adjacent vertices, but is not considered as precomputed, so it will be computed again
                            signed_im[z_i, y_i, x_i] = my_sign(sign_vs[v_index])




                        # Compute and use anchor gradient only when needed (when some vertices have no votes)
                        if not (visited_vs[0] >= 1 and visited_vs[1] >= 1 and visited_vs[2] >= 1 and visited_vs[3] >= 1 and visited_vs[4] >= 1 and visited_vs[5] >= 1 and visited_vs[6] >= 1 and visited_vs[7] >= 1):
                            anchor_sign = 1.
                            if signed_im_mask[z,y,x] and not non_zero_norm(grads[z,y,x]) == 0: #1
                                anchor_sign = my_sign(signed_im[z,y,x])
                                base_vec[0], base_vec[1], base_vec[2] = grads[z,y,x][0], grads[z,y,x][1], grads[z,y,x][2]
                            elif signed_im_mask[z,y,x_st] and not non_zero_norm(grads[z,y,x_st]) == 0: #2
                                anchor_sign = my_sign(signed_im[z,y,x_st])
                                base_vec[0], base_vec[1], base_vec[2] = grads[z,y,x_st][0], grads[z,y,x_st][1], grads[z,y,x_st][2]
                            elif signed_im_mask[z,y_st,x] and not non_zero_norm(grads[z,y_st,x]) == 0: #4
                                anchor_sign = my_sign(signed_im[z,y_st,x])
                                base_vec[0], base_vec[1], base_vec[2] = grads[z,y_st,x][0], grads[z,y_st,x][1], grads[z,y_st,x][2]
                            elif signed_im_mask[z,y_st,x_st] and not non_zero_norm(grads[z,y_st,x_st]) == 0: #3
                                anchor_sign = my_sign(signed_im[z,y_st,x_st])
                                base_vec[0], base_vec[1], base_vec[2] = grads[z,y_st,x_st][0], grads[z,y_st,x_st][1], grads[z,y_st,x_st][2]
                            elif signed_im_mask[z_st,y,x] and not non_zero_norm(grads[z_st,y,x]) == 0: #5
                                anchor_sign = my_sign(signed_im[z_st,y,x])
                                base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y,x][0], grads[z_st,y,x][1], grads[z_st,y,x][2]
                            elif signed_im_mask[z_st,y,x_st] and not non_zero_norm(grads[z_st,y,x_st]) == 0: #6
                                anchor_sign = my_sign(signed_im[z_st,y,x_st])
                                base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y,x_st][0], grads[z_st,y,x_st][1], grads[z_st,y,x_st][2]
                            elif signed_im_mask[z_st,y_st,x] and not non_zero_norm(grads[z_st,y_st,x]) == 0: #8
                                anchor_sign = my_sign(signed_im[z_st,y_st,x])
                                base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y_st,x][0], grads[z_st,y_st,x][1], grads[z_st,y_st,x][2]
                            elif signed_im_mask[z_st,y_st,x_st] and not non_zero_norm(grads[z_st,y_st,x_st]) == 0: #7
                                anchor_sign = my_sign(signed_im[z_st,y_st,x_st])
                                base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y_st,x_st][0], grads[z_st,y_st,x_st][1], grads[z_st,y_st,x_st][2]
                        
                            elif not non_zero_norm(grads[z,y,x]) == 0: #1
                                base_vec[0], base_vec[1], base_vec[2] = grads[z,y,x][0], grads[z,y,x][1], grads[z,y,x][2]
                            elif not non_zero_norm(grads[z,y,x_st]) == 0: #2
                                base_vec[0], base_vec[1], base_vec[2] = grads[z,y,x_st][0], grads[z,y,x_st][1], grads[z,y,x_st][2]
                            elif not non_zero_norm(grads[z,y_st,x]) == 0: #4
                                base_vec[0], base_vec[1], base_vec[2] = grads[z,y_st,x][0], grads[z,y_st,x][1], grads[z,y_st,x][2]
                            elif not non_zero_norm(grads[z,y_st,x_st]) == 0: #3
                                base_vec[0], base_vec[1], base_vec[2] = grads[z,y_st,x_st][0], grads[z,y_st,x_st][1], grads[z,y_st,x_st][2]
                            elif not non_zero_norm(grads[z_st,y,x]) == 0: #5
                                base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y,x][0], grads[z_st,y,x][1], grads[z_st,y,x][2]
                            elif not non_zero_norm(grads[z_st,y,x_st]) == 0: #6
                                base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y,x_st][0], grads[z_st,y,x_st][1], grads[z_st,y,x_st][2]
                            elif not non_zero_norm(grads[z_st,y_st,x]) == 0: #8
                                base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y_st,x][0], grads[z_st,y_st,x][1], grads[z_st,y_st,x][2]
                            elif not non_zero_norm(grads[z_st,y_st,x_st]) == 0: #7
                                base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y_st,x_st][0], grads[z_st,y_st,x_st][1], grads[z_st,y_st,x_st][2]
                            else:
                                print('all 0 vec...')

                            base_vec[0], base_vec[1], base_vec[2] = anchor_sign * base_vec[0], anchor_sign * base_vec[1], anchor_sign * base_vec[2]

                            if visited_vs[0] == 0:
                                signed_im[z,y,x] = my_sign(dot3(base_vec, grads[z   ,y, x]))
                            if visited_vs[1] == 0:
                                signed_im[z,y,x_st] = my_sign(dot3(base_vec, grads[z   ,y, x_st]))
                            if visited_vs[2] == 0:
                                signed_im[z,y_st,x_st] = my_sign(dot3(base_vec, grads[z   ,y_st, x_st]))
                            if visited_vs[3] == 0:
                                signed_im[z,y_st,x] = my_sign(dot3(base_vec, grads[z   ,y_st, x]))
                            if visited_vs[4] == 0:
                                signed_im[z_st,y,x] = my_sign(dot3(base_vec, grads[z_st,y, x]))
                            if visited_vs[5] == 0:
                                signed_im[z_st,y,x_st] = my_sign(dot3(base_vec, grads[z_st,y, x_st]))
                            if visited_vs[6] == 0:
                                signed_im[z_st,y_st,x_st] = my_sign(dot3(base_vec, grads[z_st,y_st, x_st]))
                            if visited_vs[7] == 0:
                                signed_im[z_st,y_st,x] = my_sign(dot3(base_vec, grads[z_st,y_st, x]))
                            
                        
                        v0 = signed_im[z,y,x] * im[z   ,y, x]
                        v1 = signed_im[z,y,x_st] * im[z   ,y, x_st]
                        v2 = signed_im[z,y_st,x_st] * im[z   ,y_st, x_st]
                        v3 = signed_im[z,y_st,x] * im[z   ,y_st, x]
                        v4 = signed_im[z_st,y,x] * im[z_st,y, x]
                        v5 = signed_im[z_st,y,x_st] * im[z_st,y, x_st]
                        v6 = signed_im[z_st,y_st,x_st] * im[z_st,y_st, x_st]
                        v7 = signed_im[z_st,y_st,x] * im[z_st,y_st, x]

                        cell.set_cube(0.0, x, y, z, st, v0, v1, v2, v3, v4, v5, v6, v7)

                        signed_im_mask[z,y,x] = True
                        signed_im_mask[z,y,x_st] = True
                        signed_im_mask[z,y_st,x_st] = True
                        signed_im_mask[z,y_st,x] = True
                        signed_im_mask[z_st,y,x] = True
                        signed_im_mask[z_st,y,x_st] = True
                        signed_im_mask[z_st,y_st,x_st] = True
                        signed_im_mask[z_st,y_st,x] = True


                        # Get case, if non-nul, enter the big switch
                        case = luts.CASES.get2(cell.index, 0)
                        if case > 0:
                            
                            config = luts.CASES.get2(cell.index, 1)
                            visited[z,y,x] = True

                            the_big_switch(luts, cell, case, config)

                            if x_st < Nx_bound:
                                queue.push_back((z,y,x_st))
                            if y_st < Ny_bound:
                                queue.push_back((z,y_st,x))
                            if x-st >= 0:
                                queue.push_back((z,y,x-st))
                            if y-st >= 0:
                                queue.push_back((z,y-st,x))
                            if z-st >= 0:
                                queue.push_back((z-st,y,x))
                            if z_st < Nz_bound:
                                queue.push_back((z_st,y,x))


                        else:
                            visited[z,y,x] = True
                            continue
                    else:
                        continue
                else:
                    continue

                
                # If reaching here, it means that a cube has been visited and a face has been produced.
                # We now use this cube as the starting point and start the breadth-first exploration.
                
                unsure_cases_visit_neighbours = True
                while queue.empty() == False or unsure_cases_queue.empty() == False or non_trivial_mc_cases_queue.empty() == False:
                    
                    if queue.empty():
                        if unsure_cases_queue.empty():
                            current_tuple = non_trivial_mc_cases_queue.front()
                            non_trivial_mc_cases_queue.pop_front()
                        else:
                            current_tuple = unsure_cases_queue.front()
                            
                            # If a cube is taken from the queue with low threshold cases, compute first its neighbors then the cube itself
                            # When visiting neighbours we set unsure_cases_visit_neighbours to False, so that such neighbours are treated
                            # differently: no faces is computed from them, and they do not take part to the breadth-first exploration. 
                            # (Note: they could still be part of the search and thus produce faces if they are visited again during the normal exploration)
                            # After visiting such cubes, we re-visit the unsure case that requested them to increase its reliability.
                            if unsure_cases_visit_neighbours:

                                z, y, x = current_tuple[0], current_tuple[1], current_tuple[2]

                                if visited[z,y,x] == True:
                                    unsure_cases_queue.pop_front()
                                    continue
                                
                                z_st = z+st
                                y_st = y+st
                                x_st = x+st
                                
                                if x_st < Nx_bound:
                                    queue.push_back((z,y,x_st))
                                if y_st < Ny_bound:
                                    queue.push_back((z,y_st,x))
                                if x-st >= 0:
                                    queue.push_back((z,y,x-st))
                                if y-st >= 0:
                                    queue.push_back((z,y-st,x))
                                if z-st >= 0:
                                    queue.push_back((z-st,y,x))
                                if z_st < Nz_bound:
                                    queue.push_back((z_st,y,x))

                                unsure_cases_visit_neighbours = False
                                continue
                            
                            else:
                                unsure_cases_queue.pop_front()
                                unsure_cases_visit_neighbours = True

                    else:
                        current_tuple = queue.front()
                        queue.pop_front()


                    z, y, x = current_tuple[0], current_tuple[1], current_tuple[2]
                    
                    z_st = z+st
                    y_st = y+st
                    x_st = x+st

                    if visited[z,y,x] == False and (no_mask or mask[z_st, y_st, x_st]):

                        if (avg_cube( im[z   ,y, x], im[z   ,y, x_st], im[z   ,y_st, x_st], im[z   ,y_st, x],
                                        im[z_st,y, x], im[z_st,y, x_st], im[z_st,y_st, x_st], im[z_st,y_st, x]) < avg_cube_val_thresh and 
                            max_cube( im[z   ,y, x], im[z   ,y, x_st], im[z   ,y_st, x_st], im[z   ,y_st, x],
                                        im[z_st,y, x], im[z_st,y, x_st], im[z_st,y_st, x_st], im[z_st,y_st, x]) <= max_cube_val_thresh):
                            
                            vertex_index_array_z = array.array('i', [z, z,    z,    z,    z_st, z_st, z_st, z_st])
                            vertex_index_array_y = array.array('i', [y, y,    y_st, y_st, y,    y,    y_st, y_st])
                            vertex_index_array_x = array.array('i', [x, x_st, x_st, x,    x,    x_st, x_st, x])


                            # The following block follows this pseudocode and lets 
                            # the nearby vertices vote for the sign of each of the 
                            # vertices of current cube

                            #for each non-computed non-zero vertex v
                                #for each of the 6 directions
                                    #for each vertex v_i along that direction, up to max-distance vertices
                                        #if v_i hasn't been computed
                                            #continue
                                        #if v_i is 0 
                                            #if max-distance is not reached
                                                #continue
                                            #else
                                                #while v_i along that direction is not 0
                                                #compute edge vote
                                        #else
                                            #compute edge vote

                            v_index = -1
                            change_cube = False
                            while v_index < 7: #from 0 to 7, included
                                v_index += 1
                                
                                visited_vs[v_index] = 0

                                z_i = vertex_index_array_z[v_index]
                                y_i = vertex_index_array_y[v_index]
                                x_i = vertex_index_array_x[v_index]

                                sign_vs[v_index] = 0.0
                                
                                #If the vertex value has been previously computed or is zero, skip it
                                if signed_im_mask[z_i,y_i,x_i] == True:
                                    visited_vs[v_index] = 1
                                    sign_vs[v_index] = signed_im[z_i,y_i,x_i]
                                    continue

                                if im[z_i,y_i,x_i] == 0.0:
                                    visited_vs[v_index] = 1
                                    continue
                                
                                dir_index = -1
                                while dir_index < 5: #from 0 to 5, included
                                    dir_index += 1

                                    dir_z = directions_z[dir_index]
                                    dir_y = directions_y[dir_index]
                                    dir_x = directions_x[dir_index]

                                    i = 0
                                    max_distance_temp = max_distance
                                    while i < max_distance_temp: #from 1 to max_distance, included
                                        i += 1

                                        cur_z_i = z_i + i*dir_z
                                        cur_y_i = y_i + i*dir_y
                                        cur_x_i = x_i + i*dir_x

                                        #If out of bounds, search on another direction
                                        if (cur_z_i > Nz_bound or cur_z_i < 0 or
                                            cur_y_i > Ny_bound or cur_y_i < 0 or
                                            cur_x_i > Nx_bound or cur_x_i < 0):
                                            break
                                        
                                        # If the vertex value is zero, continue to check what's beyond
                                        if im[cur_z_i, cur_y_i, cur_x_i] == 0.0:
                                            if i < max_distance_temp:
                                                continue
                                            else:
                                                max_distance_temp = max_distance_temp + 1 #Check one vertex further away
                                                continue
                                        
                                        # 1.3: If not computed during previous cubes AND not already computed during this cube, skip it
                                        if signed_im[cur_z_i, cur_y_i, cur_x_i] == 0.0:
                                            continue

                                        # Normal case
                                        visited_vs[v_index] += 1
                                        sign_vs[v_index] += signed_im[cur_z_i, cur_y_i, cur_x_i] * compute_edge_vote(grads[z_i,y_i,x_i], grads[cur_z_i, cur_y_i, cur_x_i], dir_z, dir_y, dir_x)
                                        
                                # If the sign of one vertex is not sure enough, put the current cube into a lower priority queue to be computed later.
                                if visited_vs[v_index] >= 1 and abs(sign_vs[v_index]) / visited_vs[v_index] < unsure_cases_thresh and (queue.empty() == False):
                                    if unsure_cases_visit_neighbours == True:
                                        unsure_cases_queue.push_back((z,y,x))
                                    change_cube = True
                                    break
                                
                                # Set the vertex. This value is used to compute adjacent vertices, but is not considered as precomputed, so it will be computed again
                                signed_im[z_i, y_i, x_i] = my_sign(sign_vs[v_index])

                            if change_cube:
                                continue


                            # Compute and use anchor gradient only when needed (when some vertices have no votes)
                            if not (visited_vs[0] >= 1 and visited_vs[1] >= 1 and visited_vs[2] >= 1 and visited_vs[3] >= 1 and visited_vs[4] >= 1 and visited_vs[5] >= 1 and visited_vs[6] >= 1 and visited_vs[7] >= 1):
                                anchor_sign = 1.
                                if signed_im_mask[z,y,x] and not non_zero_norm(grads[z,y,x]) == 0: #1
                                    anchor_sign = my_sign(signed_im[z,y,x])
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z,y,x][0], grads[z,y,x][1], grads[z,y,x][2]
                                elif signed_im_mask[z,y,x_st] and not non_zero_norm(grads[z,y,x_st]) == 0: #2
                                    anchor_sign = my_sign(signed_im[z,y,x_st])
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z,y,x_st][0], grads[z,y,x_st][1], grads[z,y,x_st][2]
                                elif signed_im_mask[z,y_st,x] and not non_zero_norm(grads[z,y_st,x]) == 0: #4
                                    anchor_sign = my_sign(signed_im[z,y_st,x])
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z,y_st,x][0], grads[z,y_st,x][1], grads[z,y_st,x][2]
                                elif signed_im_mask[z,y_st,x_st] and not non_zero_norm(grads[z,y_st,x_st]) == 0: #3
                                    anchor_sign = my_sign(signed_im[z,y_st,x_st])
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z,y_st,x_st][0], grads[z,y_st,x_st][1], grads[z,y_st,x_st][2]
                                elif signed_im_mask[z_st,y,x] and not non_zero_norm(grads[z_st,y,x]) == 0: #5
                                    anchor_sign = my_sign(signed_im[z_st,y,x])
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y,x][0], grads[z_st,y,x][1], grads[z_st,y,x][2]
                                elif signed_im_mask[z_st,y,x_st] and not non_zero_norm(grads[z_st,y,x_st]) == 0: #6
                                    anchor_sign = my_sign(signed_im[z_st,y,x_st])
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y,x_st][0], grads[z_st,y,x_st][1], grads[z_st,y,x_st][2]
                                elif signed_im_mask[z_st,y_st,x] and not non_zero_norm(grads[z_st,y_st,x]) == 0: #8
                                    anchor_sign = my_sign(signed_im[z_st,y_st,x])
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y_st,x][0], grads[z_st,y_st,x][1], grads[z_st,y_st,x][2]
                                elif signed_im_mask[z_st,y_st,x_st] and not non_zero_norm(grads[z_st,y_st,x_st]) == 0: #7
                                    anchor_sign = my_sign(signed_im[z_st,y_st,x_st])
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y_st,x_st][0], grads[z_st,y_st,x_st][1], grads[z_st,y_st,x_st][2]
                            
                                elif not non_zero_norm(grads[z,y,x]) == 0: #1
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z,y,x][0], grads[z,y,x][1], grads[z,y,x][2]
                                elif not non_zero_norm(grads[z,y,x_st]) == 0: #2
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z,y,x_st][0], grads[z,y,x_st][1], grads[z,y,x_st][2]
                                elif not non_zero_norm(grads[z,y_st,x]) == 0: #4
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z,y_st,x][0], grads[z,y_st,x][1], grads[z,y_st,x][2]
                                elif not non_zero_norm(grads[z,y_st,x_st]) == 0: #3
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z,y_st,x_st][0], grads[z,y_st,x_st][1], grads[z,y_st,x_st][2]
                                elif not non_zero_norm(grads[z_st,y,x]) == 0: #5
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y,x][0], grads[z_st,y,x][1], grads[z_st,y,x][2]
                                elif not non_zero_norm(grads[z_st,y,x_st]) == 0: #6
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y,x_st][0], grads[z_st,y,x_st][1], grads[z_st,y,x_st][2]
                                elif not non_zero_norm(grads[z_st,y_st,x]) == 0: #8
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y_st,x][0], grads[z_st,y_st,x][1], grads[z_st,y_st,x][2]
                                elif not non_zero_norm(grads[z_st,y_st,x_st]) == 0: #7
                                    base_vec[0], base_vec[1], base_vec[2] = grads[z_st,y_st,x_st][0], grads[z_st,y_st,x_st][1], grads[z_st,y_st,x_st][2]
                                else:
                                    print('all 0 vec...')

                                base_vec[0], base_vec[1], base_vec[2] = anchor_sign * base_vec[0], anchor_sign * base_vec[1], anchor_sign * base_vec[2]

                                # If the sign of one vertex is not sure enough, put the current cube into a lower priority queue of unsure cases to be computed later.
                                # This behaviour is not applied to the neighbours of unsure cases
                                if unsure_cases_visit_neighbours == True and queue.empty() == False:
                                    if visited_vs[0] == 0:
                                        sign_vs[0] = dot3(base_vec, grads[z   ,y, x])
                                        if abs(sign_vs[0]) < unsure_cases_thresh:
                                            unsure_cases_queue.push_back((z,y,x))
                                            continue
                                        signed_im[z,y,x] = my_sign(sign_vs[0])
                                    if visited_vs[1] == 0:
                                        sign_vs[1] = dot3(base_vec, grads[z   ,y, x_st])
                                        if abs(sign_vs[1]) < unsure_cases_thresh:
                                            unsure_cases_queue.push_back((z,y,x))
                                            continue
                                        signed_im[z,y,x_st] = my_sign(sign_vs[1])
                                    if visited_vs[2] == 0:
                                        sign_vs[2] = dot3(base_vec, grads[z   ,y_st, x_st])
                                        if abs(sign_vs[2]) < unsure_cases_thresh:
                                            unsure_cases_queue.push_back((z,y,x))
                                            continue
                                        signed_im[z,y_st,x_st] = my_sign(sign_vs[2])
                                    if visited_vs[3] == 0:
                                        sign_vs[3] = dot3(base_vec, grads[z   ,y_st, x])
                                        if abs(sign_vs[3]) < unsure_cases_thresh:
                                            unsure_cases_queue.push_back((z,y,x))
                                            continue
                                        signed_im[z,y_st,x] = my_sign(sign_vs[3])
                                    if visited_vs[4] == 0:
                                        sign_vs[4] = dot3(base_vec, grads[z_st,y, x])
                                        if abs(sign_vs[4]) < unsure_cases_thresh:
                                            unsure_cases_queue.push_back((z,y,x))
                                            continue
                                        signed_im[z_st,y,x] = my_sign(sign_vs[4])
                                    if visited_vs[5] == 0:
                                        sign_vs[5] = dot3(base_vec, grads[z_st,y, x_st])
                                        if abs(sign_vs[5]) < unsure_cases_thresh:
                                            unsure_cases_queue.push_back((z,y,x))
                                            continue
                                        signed_im[z_st,y,x_st] = my_sign(sign_vs[5])
                                    if visited_vs[6] == 0:
                                        sign_vs[6] = dot3(base_vec, grads[z_st,y_st, x_st])
                                        if abs(sign_vs[6]) < unsure_cases_thresh:
                                            unsure_cases_queue.push_back((z,y,x))
                                            continue
                                        signed_im[z_st,y_st,x_st] = my_sign(sign_vs[6])
                                    if visited_vs[7] == 0:
                                        sign_vs[7] = dot3(base_vec, grads[z_st,y_st, x])
                                        if abs(sign_vs[7]) < unsure_cases_thresh:
                                            unsure_cases_queue.push_back((z,y,x))
                                            continue
                                        signed_im[z_st,y_st,x] = my_sign(sign_vs[7])
                                else:
                                    if visited_vs[0] == 0:
                                        signed_im[z,y,x] = my_sign(dot3(base_vec, grads[z   ,y, x]))
                                    if visited_vs[1] == 0:
                                        signed_im[z,y,x_st] = my_sign(dot3(base_vec, grads[z   ,y, x_st]))
                                    if visited_vs[2] == 0:
                                        signed_im[z,y_st,x_st] = my_sign(dot3(base_vec, grads[z   ,y_st, x_st]))
                                    if visited_vs[3] == 0:
                                        signed_im[z,y_st,x] = my_sign(dot3(base_vec, grads[z   ,y_st, x]))
                                    if visited_vs[4] == 0:
                                        signed_im[z_st,y,x] = my_sign(dot3(base_vec, grads[z_st,y, x]))
                                    if visited_vs[5] == 0:
                                        signed_im[z_st,y,x_st] = my_sign(dot3(base_vec, grads[z_st,y, x_st]))
                                    if visited_vs[6] == 0:
                                        signed_im[z_st,y_st,x_st] = my_sign(dot3(base_vec, grads[z_st,y_st, x_st]))
                                    if visited_vs[7] == 0:
                                        signed_im[z_st,y_st,x] = my_sign(dot3(base_vec, grads[z_st,y_st, x]))
                                
                            
                            
                            if unsure_cases_visit_neighbours == True:
                                v0 = signed_im[z,y,x] * im[z   ,y, x]
                                v1 = signed_im[z,y,x_st] * im[z   ,y, x_st]
                                v2 = signed_im[z,y_st,x_st] * im[z   ,y_st, x_st]
                                v3 = signed_im[z,y_st,x] * im[z   ,y_st, x]
                                v4 = signed_im[z_st,y,x] * im[z_st,y, x]
                                v5 = signed_im[z_st,y,x_st] * im[z_st,y, x_st]
                                v6 = signed_im[z_st,y_st,x_st] * im[z_st,y_st, x_st]
                                v7 = signed_im[z_st,y_st,x] * im[z_st,y_st, x]

                                cell.set_cube(0.0, x, y, z, st, v0, v1, v2, v3, v4, v5, v6, v7)

                                signed_im_mask[z,y,x] = True
                                signed_im_mask[z,y,x_st] = True
                                signed_im_mask[z,y_st,x_st] = True
                                signed_im_mask[z,y_st,x] = True
                                signed_im_mask[z_st,y,x] = True
                                signed_im_mask[z_st,y,x_st] = True
                                signed_im_mask[z_st,y_st,x_st] = True
                                signed_im_mask[z_st,y_st,x] = True


                                # Get case, if non-nul, enter the big switch
                                case = luts.CASES.get2(cell.index, 0)
                                # If the current cube is being visited to increase the reliability of an unsure case,
                                # no faces are created and no further neighbours are visited
                                if case > 0:
                                
                                    # If the current cube is producing a non-trivial Marching Cubes configuration, 
                                    # we leave it for later. It could otherwise produce unwanted inversions in face orientations.
                                    if (not case in [1,2,5,8,9]) and (queue.empty() == False or unsure_cases_queue.empty() == False):
                                        non_trivial_mc_cases_queue.push_back((z,y,x))
                                        continue
                                    
                                    config = luts.CASES.get2(cell.index, 1)
                                    if check_the_big_switch(luts, cell, case, config) >= 2:
                                        visited[z,y,x] = True

                                        the_big_switch(luts, cell, case, config)

                                        if x_st < Nx_bound:
                                            queue.push_back((z,y,x_st))
                                        if y_st < Ny_bound:
                                            queue.push_back((z,y_st,x))
                                        if x-st >= 0:
                                            queue.push_back((z,y,x-st))
                                        if y-st >= 0:
                                            queue.push_back((z,y-st,x))
                                        if z-st >= 0:
                                            queue.push_back((z-st,y,x))
                                        if z_st < Nz_bound:
                                            queue.push_back((z_st,y,x))

                                else:
                                    visited[z,y,x] = True

    return cell.get_vertices(), cell.get_faces(), cell.get_normals(), cell.get_values()


cdef float compute_edge_vote(float[:] g1, float[:] g2, float dir_z, float dir_y, float dir_x):
    """ Computes the edge vote
        Assumes that one and only one among dir_z, dir_y and dir_x is not zero
    """
    cdef float result = 0.0
    cdef float dir_sum = dir_z + dir_y + dir_x
    cdef float proj_g1 
    cdef float proj_g2

    if dir_z != 0:
        proj_g1 = g1[0]
        proj_g2 = g2[0]
    elif dir_y != 0:
        proj_g1 = g1[1]
        proj_g2 = g2[1]
    else:
        proj_g1 = g1[2]
        proj_g2 = g2[2]

    if dir_sum > 0:
        if proj_g2 > 0 and proj_g1 < 0:
            result = 1.0
        else:
            result = dot3(g1, g2)
    else:
        if proj_g2 < 0 and proj_g1 > 0:
            result = 1.0
        else:
            result = dot3(g1, g2)
    
    return result


cdef float my_sign(float a):
    if a > 0:
        return 1.
    if a < 0:
        return -1.
    if a == 0:
        return 0.


cdef int non_zero_norm(float[:] a):
    """ Returns True if the sum of absolute values > 0
    """
    return (abs(a[0]) + abs(a[1]) + abs(a[2])) > 0
    # return abs(a[0]) > 0 or abs(a[1]) > 0 or abs(a[2]) > 0 #faster?


cdef float avg_cube(float v1, float v2, float v3, float v4,
                    float v5, float v6, float v7, float v8):
    """ Return the average value of v_i's
    """
    return 0.125 * (v1 + v2 + v3 + v4 + v5 + v6 + v7 + v8)

cdef float max_cube(float v1, float v2, float v3, float v4,
                    float v5, float v6, float v7, float v8):
    """ Return the max value of v_i's
    """
    return max(v1, max(
                v2, max(
                v3, max(
                v4, max(
                v5, max(
                v6, max(
                v7, v8)))))))

cdef float dot3(float[:] a, float[:] b):
    return a[0]*b[0] + a[1]*b[1] + a[2]*b[2]


cdef bint the_big_switch(LutProvider luts, Cell cell, int case, int config):
    """ The big switch (i.e. if-statement) that I meticulously ported from
    the source code provided by Lewiner et. al.
    Together with all the look-up tables, this is where the magic is ...
    """

    cdef int subconfig = 0
    cdef bint result = False


    # Sinatures for tests
    #test_face(cell, luts.TESTX.get1(config)):
    #test_internal(cell, luts, case, config, subconfig, luts.TESTX.get1(config)):
    #cell.add_triangles(luts.TILINGX, config, N)

    if case == 1:
        result = cell.add_triangles(luts.TILING1, config, 1)

    elif case == 2:
        result = cell.add_triangles(luts.TILING2, config, 2)

    elif case == 3:
        if test_face(cell, luts.TEST3.get1(config)):
            result = cell.add_triangles(luts.TILING3_2, config, 4)
        else:
            result = cell.add_triangles(luts.TILING3_1, config, 2)

    elif case == 4 :
        if test_internal(cell, luts, case, config, subconfig, luts.TEST4.get1(config)):
            result = cell.add_triangles(luts.TILING4_1, config, 2)
        else:
            result = cell.add_triangles(luts.TILING4_2, config, 6)

    elif case == 5 :
        result = cell.add_triangles(luts.TILING5, config, 3)

    elif case == 6 :
        if test_face(cell, luts.TEST6.get2(config,0)):
            result = cell.add_triangles(luts.TILING6_2, config, 5)
        else:
            if test_internal(cell, luts, case, config, subconfig, luts.TEST6.get2(config,1)):
                result = cell.add_triangles(luts.TILING6_1_1, config, 3)
            else:
                #cell.calculate_center_vertex() # v12 needed
                result = cell.add_triangles(luts.TILING6_1_2, config, 9)

    elif case == 7 :
        # Get subconfig
        if test_face(cell, luts.TEST7.get2(config,0)): subconfig += 1
        if test_face(cell, luts.TEST7.get2(config,1)): subconfig += 2
        if test_face(cell, luts.TEST7.get2(config,2)): subconfig += 4
        # Behavior depends on subconfig
        if subconfig == 0: result = cell.add_triangles(luts.TILING7_1, config, 3)
        elif subconfig == 1: result = cell.add_triangles2(luts.TILING7_2, config, 0, 5)
        elif subconfig == 2: result = cell.add_triangles2(luts.TILING7_2, config, 1, 5)
        elif subconfig == 3:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING7_3, config, 0, 9)
        elif subconfig == 4: result = cell.add_triangles2(luts.TILING7_2, config, 2, 5)
        elif subconfig == 5:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING7_3, config, 1, 9)
        elif subconfig == 6:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING7_3, config, 2, 9)
        elif subconfig == 7:
            if test_internal(cell, luts, case, config, subconfig, luts.TEST7.get2(config,3)):
                result = cell.add_triangles(luts.TILING7_4_2, config, 9)
            else:
                result = cell.add_triangles(luts.TILING7_4_1, config, 5)

    elif case == 8 :
        result = cell.add_triangles(luts.TILING8, config, 2)

    elif case == 9 :
        result = cell.add_triangles(luts.TILING9, config, 4)

    elif case == 10 :
        if test_face(cell, luts.TEST10.get2(config,0)):
            if test_face(cell, luts.TEST10.get2(config,1)):
                result = cell.add_triangles(luts.TILING10_1_1_, config, 4)
            else:
                #cell.calculate_center_vertex() # v12 needed
                result = cell.add_triangles(luts.TILING10_2, config, 8)
        else:
            if test_face(cell, luts.TEST10.get2(config,1)):
                #cell.calculate_center_vertex() # v12 needed
                result = cell.add_triangles(luts.TILING10_2_, config, 8)
            else:
                if test_internal(cell, luts, case, config, subconfig, luts.TEST10.get2(config,2)):
                    result = cell.add_triangles(luts.TILING10_1_1, config, 4)
                else:
                    result = cell.add_triangles(luts.TILING10_1_2, config, 8)

    elif case == 11 :
        result = cell.add_triangles(luts.TILING11, config, 4)

    elif case == 12 :
        if test_face(cell, luts.TEST12.get2(config,0)):
            if test_face(cell, luts.TEST12.get2(config,1)):
                result = cell.add_triangles(luts.TILING12_1_1_, config, 4)
            else:
                #cell.calculate_center_vertex() # v12 needed
                result = cell.add_triangles(luts.TILING12_2, config, 8)
        else:
            if test_face(cell, luts.TEST12.get2(config,1)):
                #cell.calculate_center_vertex() # v12 needed
                result = cell.add_triangles(luts.TILING12_2_, config, 8)
            else:
                if test_internal(cell, luts, case, config, subconfig, luts.TEST12.get2(config,2)):
                    result = cell.add_triangles(luts.TILING12_1_1, config, 4)
                else:
                    result = cell.add_triangles(luts.TILING12_1_2, config, 8)

    elif case == 13 :
        # Calculate subconfig
        if test_face(cell, luts.TEST13.get2(config,0)): subconfig += 1
        if test_face(cell, luts.TEST13.get2(config,1)): subconfig += 2
        if test_face(cell, luts.TEST13.get2(config,2)): subconfig += 4
        if test_face(cell, luts.TEST13.get2(config,3)): subconfig += 8
        if test_face(cell, luts.TEST13.get2(config,4)): subconfig += 16
        if test_face(cell, luts.TEST13.get2(config,5)): subconfig += 32

        # Map via LUT
        subconfig = luts.SUBCONFIG13.get1(subconfig)

        # Behavior depends on subconfig
        if subconfig==0:    result = cell.add_triangles(luts.TILING13_1, config, 4)
        elif subconfig==1:  result = cell.add_triangles2(luts.TILING13_2, config, 0, 6)
        elif subconfig==2:  result = cell.add_triangles2(luts.TILING13_2, config, 1, 6)
        elif subconfig==3:  result = cell.add_triangles2(luts.TILING13_2, config, 2, 6)
        elif subconfig==4:  result = cell.add_triangles2(luts.TILING13_2, config, 3, 6)
        elif subconfig==5:  result = cell.add_triangles2(luts.TILING13_2, config, 4, 6)
        elif subconfig==6:  result = cell.add_triangles2(luts.TILING13_2, config, 5, 6)
        #
        elif subconfig==7:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 0, 10)
        elif subconfig==8:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 1, 10)
        elif subconfig==9:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 2, 10)
        elif subconfig==10:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 3, 10)
        elif subconfig==11:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 4, 10)
        elif subconfig==12:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 5, 10)
        elif subconfig==13:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 6, 10)
        elif subconfig==14:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 7, 10)
        elif subconfig==15:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 8, 10)
        elif subconfig==16:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 9, 10)
        elif subconfig==17:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 10, 10)
        elif subconfig==18:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3, config, 11, 10)
        #
        elif subconfig==19:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_4, config, 0, 12)
        elif subconfig==20:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_4, config, 1, 12)
        elif subconfig==21:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_4, config, 2, 12)
        elif subconfig==22:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_4, config, 3, 12)
        #
        elif subconfig==23:
            subconfig = 0 # Note: the original source code sets the subconfig, without apparent reason
            if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)):
                result = cell.add_triangles2(luts.TILING13_5_1, config, 0, 6)
            else:
                result = cell.add_triangles2(luts.TILING13_5_2, config, 0, 10)
        elif subconfig==24:
            subconfig = 1
            if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)):
                result = cell.add_triangles2(luts.TILING13_5_1, config, 1, 6)
            else:
                result = cell.add_triangles2(luts.TILING13_5_2, config, 1, 10)
        elif subconfig==25:
            subconfig = 2 ;
            if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)):
                result = cell.add_triangles2(luts.TILING13_5_1, config, 2, 6)
            else:
                result = cell.add_triangles2(luts.TILING13_5_2, config, 2, 10)
        elif subconfig==26:
            subconfig = 3 ;
            if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)):
                result = cell.add_triangles2(luts.TILING13_5_1, config, 3, 6)
            else:
                result = cell.add_triangles2(luts.TILING13_5_2, config, 3, 10)
        #
        elif subconfig==27:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 0, 10)
        elif subconfig==28:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 1, 10)
        elif subconfig==29:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 2, 10)
        elif subconfig==30:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 3, 10)
        elif subconfig==31:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 4, 10)
        elif subconfig==32:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 5, 10)
        elif subconfig==33:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config,6, 10)
        elif subconfig==34:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 7, 10)
        elif subconfig==35:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 8, 10)
        elif subconfig==36:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 9, 10)
        elif subconfig==37:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 10, 10)
        elif subconfig==38:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.add_triangles2(luts.TILING13_3_, config, 11, 10)
        #
        elif subconfig==39:
            result = cell.add_triangles2(luts.TILING13_2_, config, 0, 6)
        elif subconfig==40:
            result = cell.add_triangles2(luts.TILING13_2_, config, 1, 6)
        elif subconfig==41:
            result = cell.add_triangles2(luts.TILING13_2_, config, 2, 6)
        elif subconfig==42:
            result = cell.add_triangles2(luts.TILING13_2_, config, 3, 6)
        elif subconfig==43:
            result = cell.add_triangles2(luts.TILING13_2_, config, 4, 6)
        elif subconfig==44:
            result = cell.add_triangles2(luts.TILING13_2_, config, 5, 6)
        #
        elif subconfig==45:
            result = cell.add_triangles(luts.TILING13_1_, config, 4)
        #
        else:
            print("Marching Cubes: Impossible case 13?" )

    elif case == 14 :
        result = cell.add_triangles(luts.TILING14, config, 4)
        
    return result







cdef int check_the_big_switch(LutProvider luts, Cell cell, int case, int config):
    """ CHECK The big switch (i.e. if-statement) that I meticulously ported from
    the source code provided by Lewiner et. al.
    Together with all the look-up tables, this is where the magic is ...
    """

    cdef int subconfig = 0
    cdef int result = 0


    # Sinatures for tests
    #test_face(cell, luts.TESTX.get1(config)):
    #test_internal(cell, luts, case, config, subconfig, luts.TESTX.get1(config)):
    #cell.check_triangles(luts.TILINGX, config, N)

    if case == 1:
        result = cell.check_triangles(luts.TILING1, config, 1)

    elif case == 2:
        result = cell.check_triangles(luts.TILING2, config, 2)

    elif case == 3:
        if test_face(cell, luts.TEST3.get1(config)):
            result = cell.check_triangles(luts.TILING3_2, config, 4)
        else:
            result = cell.check_triangles(luts.TILING3_1, config, 2)

    elif case == 4 :
        if test_internal(cell, luts, case, config, subconfig, luts.TEST4.get1(config)):
            result = cell.check_triangles(luts.TILING4_1, config, 2)
        else:
            result = cell.check_triangles(luts.TILING4_2, config, 6)

    elif case == 5 :
        result = cell.check_triangles(luts.TILING5, config, 3)

    elif case == 6 :
        if test_face(cell, luts.TEST6.get2(config,0)):
            result = cell.check_triangles(luts.TILING6_2, config, 5)
        else:
            if test_internal(cell, luts, case, config, subconfig, luts.TEST6.get2(config,1)):
                result = cell.check_triangles(luts.TILING6_1_1, config, 3)
            else:
                #cell.calculate_center_vertex() # v12 needed
                result = cell.check_triangles(luts.TILING6_1_2, config, 9)

    elif case == 7 :
        # Get subconfig
        if test_face(cell, luts.TEST7.get2(config,0)): subconfig += 1
        if test_face(cell, luts.TEST7.get2(config,1)): subconfig += 2
        if test_face(cell, luts.TEST7.get2(config,2)): subconfig += 4
        # Behavior depends on subconfig
        if subconfig == 0: result = cell.check_triangles(luts.TILING7_1, config, 3)
        elif subconfig == 1: result = cell.check_triangles2(luts.TILING7_2, config, 0, 5)
        elif subconfig == 2: result = cell.check_triangles2(luts.TILING7_2, config, 1, 5)
        elif subconfig == 3:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING7_3, config, 0, 9)
        elif subconfig == 4: result = cell.check_triangles2(luts.TILING7_2, config, 2, 5)
        elif subconfig == 5:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING7_3, config, 1, 9)
        elif subconfig == 6:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING7_3, config, 2, 9)
        elif subconfig == 7:
            if test_internal(cell, luts, case, config, subconfig, luts.TEST7.get2(config,3)):
                result = cell.check_triangles(luts.TILING7_4_2, config, 9)
            else:
                result = cell.check_triangles(luts.TILING7_4_1, config, 5)

    elif case == 8 :
        result = cell.check_triangles(luts.TILING8, config, 2)

    elif case == 9 :
        result = cell.check_triangles(luts.TILING9, config, 4)

    elif case == 10 :
        if test_face(cell, luts.TEST10.get2(config,0)):
            if test_face(cell, luts.TEST10.get2(config,1)):
                result = cell.check_triangles(luts.TILING10_1_1_, config, 4)
            else:
                #cell.calculate_center_vertex() # v12 needed
                result = cell.check_triangles(luts.TILING10_2, config, 8)
        else:
            if test_face(cell, luts.TEST10.get2(config,1)):
                #cell.calculate_center_vertex() # v12 needed
                result = cell.check_triangles(luts.TILING10_2_, config, 8)
            else:
                if test_internal(cell, luts, case, config, subconfig, luts.TEST10.get2(config,2)):
                    result = cell.check_triangles(luts.TILING10_1_1, config, 4)
                else:
                    result = cell.check_triangles(luts.TILING10_1_2, config, 8)

    elif case == 11 :
        result = cell.check_triangles(luts.TILING11, config, 4)

    elif case == 12 :
        if test_face(cell, luts.TEST12.get2(config,0)):
            if test_face(cell, luts.TEST12.get2(config,1)):
                result = cell.check_triangles(luts.TILING12_1_1_, config, 4)
            else:
                #cell.calculate_center_vertex() # v12 needed
                result = cell.check_triangles(luts.TILING12_2, config, 8)
        else:
            if test_face(cell, luts.TEST12.get2(config,1)):
                #cell.calculate_center_vertex() # v12 needed
                result = cell.check_triangles(luts.TILING12_2_, config, 8)
            else:
                if test_internal(cell, luts, case, config, subconfig, luts.TEST12.get2(config,2)):
                    result = cell.check_triangles(luts.TILING12_1_1, config, 4)
                else:
                    result = cell.check_triangles(luts.TILING12_1_2, config, 8)

    elif case == 13 :
        # Calculate subconfig
        if test_face(cell, luts.TEST13.get2(config,0)): subconfig += 1
        if test_face(cell, luts.TEST13.get2(config,1)): subconfig += 2
        if test_face(cell, luts.TEST13.get2(config,2)): subconfig += 4
        if test_face(cell, luts.TEST13.get2(config,3)): subconfig += 8
        if test_face(cell, luts.TEST13.get2(config,4)): subconfig += 16
        if test_face(cell, luts.TEST13.get2(config,5)): subconfig += 32

        # Map via LUT
        subconfig = luts.SUBCONFIG13.get1(subconfig)

        # Behavior depends on subconfig
        if subconfig==0:    result = cell.check_triangles(luts.TILING13_1, config, 4)
        elif subconfig==1:  result = cell.check_triangles2(luts.TILING13_2, config, 0, 6)
        elif subconfig==2:  result = cell.check_triangles2(luts.TILING13_2, config, 1, 6)
        elif subconfig==3:  result = cell.check_triangles2(luts.TILING13_2, config, 2, 6)
        elif subconfig==4:  result = cell.check_triangles2(luts.TILING13_2, config, 3, 6)
        elif subconfig==5:  result = cell.check_triangles2(luts.TILING13_2, config, 4, 6)
        elif subconfig==6:  result = cell.check_triangles2(luts.TILING13_2, config, 5, 6)
        #
        elif subconfig==7:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 0, 10)
        elif subconfig==8:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 1, 10)
        elif subconfig==9:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 2, 10)
        elif subconfig==10:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 3, 10)
        elif subconfig==11:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 4, 10)
        elif subconfig==12:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 5, 10)
        elif subconfig==13:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 6, 10)
        elif subconfig==14:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 7, 10)
        elif subconfig==15:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 8, 10)
        elif subconfig==16:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 9, 10)
        elif subconfig==17:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 10, 10)
        elif subconfig==18:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3, config, 11, 10)
        #
        elif subconfig==19:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_4, config, 0, 12)
        elif subconfig==20:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_4, config, 1, 12)
        elif subconfig==21:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_4, config, 2, 12)
        elif subconfig==22:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_4, config, 3, 12)
        #
        elif subconfig==23:
            subconfig = 0 # Note: the original source code sets the subconfig, without apparent reason
            if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)):
                result = cell.check_triangles2(luts.TILING13_5_1, config, 0, 6)
            else:
                result = cell.check_triangles2(luts.TILING13_5_2, config, 0, 10)
        elif subconfig==24:
            subconfig = 1
            if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)):
                result = cell.check_triangles2(luts.TILING13_5_1, config, 1, 6)
            else:
                result = cell.check_triangles2(luts.TILING13_5_2, config, 1, 10)
        elif subconfig==25:
            subconfig = 2 ;
            if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)):
                result = cell.check_triangles2(luts.TILING13_5_1, config, 2, 6)
            else:
                result = cell.check_triangles2(luts.TILING13_5_2, config, 2, 10)
        elif subconfig==26:
            subconfig = 3 ;
            if test_internal(cell, luts, case, config, subconfig, luts.TEST13.get2(config,6)):
                result = cell.check_triangles2(luts.TILING13_5_1, config, 3, 6)
            else:
                result = cell.check_triangles2(luts.TILING13_5_2, config, 3, 10)
        #
        elif subconfig==27:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 0, 10)
        elif subconfig==28:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 1, 10)
        elif subconfig==29:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 2, 10)
        elif subconfig==30:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 3, 10)
        elif subconfig==31:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 4, 10)
        elif subconfig==32:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 5, 10)
        elif subconfig==33:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config,6, 10)
        elif subconfig==34:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 7, 10)
        elif subconfig==35:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 8, 10)
        elif subconfig==36:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 9, 10)
        elif subconfig==37:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 10, 10)
        elif subconfig==38:
            #cell.calculate_center_vertex() # v12 needed
            result = cell.check_triangles2(luts.TILING13_3_, config, 11, 10)
        #
        elif subconfig==39:
            result = cell.check_triangles2(luts.TILING13_2_, config, 0, 6)
        elif subconfig==40:
            result = cell.check_triangles2(luts.TILING13_2_, config, 1, 6)
        elif subconfig==41:
            result = cell.check_triangles2(luts.TILING13_2_, config, 2, 6)
        elif subconfig==42:
            result = cell.check_triangles2(luts.TILING13_2_, config, 3, 6)
        elif subconfig==43:
            result = cell.check_triangles2(luts.TILING13_2_, config, 4, 6)
        elif subconfig==44:
            result = cell.check_triangles2(luts.TILING13_2_, config, 5, 6)
        #
        elif subconfig==45:
            result = cell.check_triangles(luts.TILING13_1_, config, 4)
        #
        else:
            print("Marching Cubes: Impossible case 13?" )

    elif case == 14 :
        result = cell.check_triangles(luts.TILING14, config, 4)
        
    return result









cdef int test_face(Cell cell, int face):
    """ Return True of the face contains part of the surface.
    """

    # Get face absolute value
    cdef int absFace = face
    if face < 0:
        absFace *= -1

    # Get values of corners A B C D
    cdef double A, B, C, D
    if absFace == 1:
        A, B, C, D = cell.v0, cell.v4, cell.v5, cell.v1
    elif absFace == 2:
        A, B, C, D = cell.v1, cell.v5, cell.v6, cell.v2
    elif absFace == 3:
        A, B, C, D = cell.v2, cell.v6, cell.v7, cell.v3
    elif absFace == 4:
        A, B, C, D = cell.v3, cell.v7, cell.v4, cell.v0
    elif absFace == 5:
        A, B, C, D = cell.v0, cell.v3, cell.v2, cell.v1
    elif absFace == 6:
        A, B, C, D = cell.v4, cell.v7, cell.v6, cell.v5

    # Return sign
    cdef double AC_BD = A*C - B*D
    if AC_BD > - FLT_EPSILON and AC_BD < FLT_EPSILON:
        return face >= 0
    else:
        return face * A * AC_BD >= 0;  # face and A invert signs


cdef int test_internal(Cell cell, LutProvider luts, int case, int config, int subconfig, int s):
    """ Return True of the face contains part of the surface.
    """

    # Typedefs
    cdef double t, At, Bt, Ct, Dt, a, b
    cdef int test = 0
    cdef int edge = -1 # reference edge of the triangulation


    # Calculate At Bt Ct Dt a b
    # Select case 4, 10,  7, 12, 13
    At, Bt, Ct, Dt = 0.0, 0.0, 0.0, 0.0

    if case==4 or case==10:
        a = ( cell.v4 - cell.v0 ) * ( cell.v6 - cell.v2 ) - ( cell.v7 - cell.v3 ) * ( cell.v5 - cell.v1 )
        b =  cell.v2 * ( cell.v4 - cell.v0 ) + cell.v0 * ( cell.v6 - cell.v2 ) - cell.v1 * ( cell.v7 - cell.v3 ) - cell.v3 * ( cell.v5 - cell.v1 )
        t = - b / (2*a + FLT_EPSILON)
        if t<0 or t>1:  return s>0 ;

        At = cell.v0 + ( cell.v4 - cell.v0 ) * t
        Bt = cell.v3 + ( cell.v7 - cell.v3 ) * t
        Ct = cell.v2 + ( cell.v6 - cell.v2 ) * t
        Dt = cell.v1 + ( cell.v5 - cell.v1 ) * t

    elif case==6 or case==7 or case==12 or case==13:
        # Define edge
        if case == 6:  edge = luts.TEST6.get2(config, 2)
        elif case == 7: edge = luts.TEST7.get2(config, 4)
        elif case == 12: edge = luts.TEST12.get2(config, 3)
        elif case == 13: edge = luts.TILING13_5_1.get3(config, subconfig, 0)

        if edge==0:
            t  = cell.v0 / ( cell.v0 - cell.v1 + FLT_EPSILON )
            At = 0
            Bt = cell.v3 + ( cell.v2 - cell.v3 ) * t
            Ct = cell.v7 + ( cell.v6 - cell.v7 ) * t
            Dt = cell.v4 + ( cell.v5 - cell.v4 ) * t
        elif edge==1:
            t  = cell.v1 / ( cell.v1 - cell.v2 + FLT_EPSILON )
            At = 0
            Bt = cell.v0 + ( cell.v3 - cell.v0 ) * t
            Ct = cell.v4 + ( cell.v7 - cell.v4 ) * t
            Dt = cell.v5 + ( cell.v6 - cell.v5 ) * t
        elif edge==2:
            t  = cell.v2 / ( cell.v2 - cell.v3 + FLT_EPSILON )
            At = 0
            Bt = cell.v1 + ( cell.v0 - cell.v1 ) * t
            Ct = cell.v5 + ( cell.v4 - cell.v5 ) * t
            Dt = cell.v6 + ( cell.v7 - cell.v6 ) * t
        elif edge==3:
            t  = cell.v3 / ( cell.v3 - cell.v0 + FLT_EPSILON )
            At = 0
            Bt = cell.v2 + ( cell.v1 - cell.v2 ) * t
            Ct = cell.v6 + ( cell.v5 - cell.v6 ) * t
            Dt = cell.v7 + ( cell.v4 - cell.v7 ) * t
        elif edge==4:
            t  = cell.v4 / ( cell.v4 - cell.v5 + FLT_EPSILON )
            At = 0
            Bt = cell.v7 + ( cell.v6 - cell.v7 ) * t
            Ct = cell.v3 + ( cell.v2 - cell.v3 ) * t
            Dt = cell.v0 + ( cell.v1 - cell.v0 ) * t
        elif edge==5:
            t  = cell.v5 / ( cell.v5 - cell.v6 + FLT_EPSILON )
            At = 0
            Bt = cell.v4 + ( cell.v7 - cell.v4 ) * t
            Ct = cell.v0 + ( cell.v3 - cell.v0 ) * t
            Dt = cell.v1 + ( cell.v2 - cell.v1 ) * t
        elif edge==6:
            t  = cell.v6 / ( cell.v6 - cell.v7 + FLT_EPSILON )
            At = 0
            Bt = cell.v5 + ( cell.v4 - cell.v5 ) * t
            Ct = cell.v1 + ( cell.v0 - cell.v1 ) * t
            Dt = cell.v2 + ( cell.v3 - cell.v2 ) * t
        elif edge==7:
            t  = cell.v7 / ( cell.v7 - cell.v4 + FLT_EPSILON )
            At = 0
            Bt = cell.v6 + ( cell.v5 - cell.v6 ) * t
            Ct = cell.v2 + ( cell.v1 - cell.v2 ) * t
            Dt = cell.v3 + ( cell.v0 - cell.v3 ) * t
        elif edge==8:
            t  = cell.v0 / ( cell.v0 - cell.v4 + FLT_EPSILON )
            At = 0
            Bt = cell.v3 + ( cell.v7 - cell.v3 ) * t
            Ct = cell.v2 + ( cell.v6 - cell.v2 ) * t
            Dt = cell.v1 + ( cell.v5 - cell.v1 ) * t
        elif edge==9:
            t  = cell.v1 / ( cell.v1 - cell.v5 + FLT_EPSILON )
            At = 0
            Bt = cell.v0 + ( cell.v4 - cell.v0 ) * t
            Ct = cell.v3 + ( cell.v7 - cell.v3 ) * t
            Dt = cell.v2 + ( cell.v6 - cell.v2 ) * t
        elif edge==10:
            t  = cell.v2 / ( cell.v2 - cell.v6 + FLT_EPSILON )
            At = 0
            Bt = cell.v1 + ( cell.v5 - cell.v1 ) * t
            Ct = cell.v0 + ( cell.v4 - cell.v0 ) * t
            Dt = cell.v3 + ( cell.v7 - cell.v3 ) * t
        elif edge==11:
            t  = cell.v3 / ( cell.v3 - cell.v7 + FLT_EPSILON )
            At = 0
            Bt = cell.v2 + ( cell.v6 - cell.v2 ) * t
            Ct = cell.v1 + ( cell.v5 - cell.v1 ) * t
            Dt = cell.v0 + ( cell.v4 - cell.v0 ) * t
        else:
            print( "Invalid edge %i." % edge )
    else:
        print( "Invalid ambiguous case %i." % case )

    # Process results
    if At >= 0: test += 1
    if Bt >= 0: test += 2
    if Ct >= 0: test += 4
    if Dt >= 0: test += 8

    # Determine what to return
    if test==0: return s>0
    elif test==1: return s>0
    elif test==2: return s>0
    elif test==3: return s>0
    elif test==4: return s>0
    elif test==5:
        if At * Ct - Bt * Dt <  FLT_EPSILON: return s>0
    elif test==6: return s>0
    elif test==7: return s<0
    elif test==8: return s>0
    elif test==9: return s>0
    elif test==10:
        if At * Ct - Bt * Dt >= FLT_EPSILON: return s>0
    elif test==11: return s<0
    elif test==12: return s>0
    elif test==13: return s<0
    elif test==14: return s<0
    elif test==15: return s<0
    else: return s<0


================================================
FILE: meshudf/_marching_cubes_lewiner_luts.py
================================================
# -*- coding: utf-8 -*-
# This file was auto-generated from `mc_meta/LookUpTable.h` by
# `mc_meta/createluts.py`.

#static const char casesClassic[256][16]
CASESCLASSIC = (256, 16), """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==
"""

#static const char cases[256][2]
CASES = (256, 2), """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=
"""

#static const char tiling1[16][3]
TILING1 = (16, 3), """
AAgDAAEJAQIKAwsCBAcICQUECgYFBwYLBwsGCgUGCQQFBAgHAwILAQoCAAkBAAMI
"""

#static const char tiling2[24][6]
TILING2 = (24, 6), """
AQgDCQgBAAsCCAsABAMABwMECQIKAAIJAAUEAQUAAwoBCwoDAQYFAgYBBwIDBgIHCQcIBQcJBggE
CwgGCgQJBgQKCwUKBwULCwoFBwsFCgkEBgoEBgQICwYICQgHBQkHBwMCBgcCAQUGAgEGAwEKCwMK
AAQFAQAFCQoCAAkCBAADBwQDAAILCAALAQMICQEI
"""

#static const char tiling3_1[24][6]
TILING3_1 = (24, 6), """
AAgDAQIKCQUEAAgDAwAICwcGAQkAAgMLAAEJCAQHCQABBQoGAQIKCQUECgECBgsHCAQHAwsCAgML
CgYFBQoGBAcIBAkFBwYLBQkECwYHBgoFCAcECwMCBQYKBwQIAgsDAgEKBwsGCgIBBAUJAQAJBgoF
CQEABwQIAAkBCwMCCAADBgcLBAUJAwgAAwgACgIB
"""

#static const char tiling3_2[24][12]
TILING3_2 = (24, 12), """
CgMCCggDCgEACAoAAwQIAwUEAwAJBQMJBggHBgAIBgsDAAYDCwADCwkACwIBCQsBBwkEBwEJBwgA
AQcABgEKBgABCQAGCQYFBAoFBAIKBAkBAgQBBwILBwECBwYKAQcKAgcLAgQHAgMIBAIIBQsGBQML
BQoCAwUCCAYHCAoGCAQFCggFCwUGCwkFCwcECQsEBgULBQkLBAcLBAsJBwYIBgoIBQQIBQgKBgsF
CwMFAgoFAgUDCwcCBwQCCAMCCAIECwIHAgEHCgYHCgcBBQoECgIEAQkEAQQCCgEGAQAGBgAJBQYJ
BAkHCQEHAAgHAAcBAwALAAkLAQILAQsJBwgGCAAGAwsGAwYACAQDBAUDCQADCQMFAgMKAwgKAAEK
AAoI
"""

#static const char tiling4_1[8][6]
TILING4_1 = (8, 6), """
AAgDBQoGAAEJCwcGAQIKCAQHCQUEAgMLBAUJCwMCCgIBBwQICQEABgcLAwgABgoF
"""

#static const char tiling4_2[8][18]
TILING4_2 = (8, 18), """
CAUABQgGAwYIBgMKAAoDCgAFCQYBBgkHAAcJBwALAQsACwEGCgcCBwoEAQQKBAEIAggBCAIHCwQD
BAsFAgULBQIJAwkCCQMEAwQLBQsECwUCCQIFAgkDBAMJAgcKBAoHCgQBCAEEAQgCBwIIAQYJBwkG
CQcACwAHAAsBBgELAAUIBggFCAYDCgMGAwoABQAK
"""

#static const char tiling5[48][9]
TILING5 = (48, 9), """
AggDAgoICgkIAQsCAQkLCQgLBAEJBAcBBwMBCAUECAMFAwEFAAoBAAgKCAsKCwQHCwIEAgAEBwAI
BwYABgIACQMACQUDBQcDAwYLAwAGAAQGAwkAAwsJCwoJBQIKBQQCBAACCQYFCQAGAAIGAAcIAAEH
AQUHCgABCgYABgQABgMLBgUDBQEDCgcGCgEHAQMHAQQJAQIEAgYECwECCwcBBwUBCAIDCAQCBAYC
AgUKAgMFAwcFBwoGBwgKCAkKBgkFBgsJCwgJBQgEBQoICgsIBAsHBAkLCQoLBAcLBAsJCQsKBQQI
BQgKCggLBgUJBgkLCwkIBwYKBwoICAoJAgoFAgUDAwUHCAMCCAIEBAIGCwIBCwEHBwEFAQkEAQQC
AgQGCgYHCgcBAQcDBgsDBgMFBQMBCgEACgAGBgAEAAgHAAcBAQcFCQUGCQYAAAYCBQoCBQIEBAIA
AwAJAwkLCwkKAwsGAwYAAAYECQADCQMFBQMHBwgABwAGBgACCwcECwQCAgQAAAEKAAoICAoLCAQF
CAUDAwUBBAkBBAEHBwEDAQILAQsJCQsIAgMIAggKCggJ
"""

#static const char tiling6_1_1[48][9]
TILING6_1_1 = (48, 9), """
BgUKAwEICQgBCwcGCQMBAwkIAQIKBwAEAAcDAwAIBQIGAgUBBQQJAgALCAsACgYFCAIAAggLCgYF
AAQDBwMEAwAIBgQKCQoECAMACgcFBwoLCAQHCgACAAoJBwYLAAIJCgkCAgMLBAEFAQQAAAEJBgMH
AwYCCQABCwQGBAsICwcGAQUABAAFAAEJBwULCgsFBAcIAQMKCwoDCQUECwEDAQsKCgECCAUHBQgJ
CAQHAgYBBQEGAQIKBAYICwgGAgMLBQcJCAkHCwIDCQYEBgkKCQUEAwcCBgIHBAUJAgcDBwIGAwIL
BAYJCgkGCwMCCQcFBwkICgIBCAYEBggLBwQIAQYCBgEFAgEKBwUICQgFBAUJAwELCgsBCAcECgMB
AwoLCQEACwUHBQsKBgcLAAUBBQAEAQAJBgQLCAsECQEABwMGAgYDCwMCBQEEAAQBCwYHCQIAAgkK
BwQIAgAKCQoAAAMIBQcKCwoHCAADCgQGBAoJBQYKAwQABAMHBQYKAAIICwgCCQQFCwACAAsICAAD
BgIFAQUCCgIBBAAHAwcABgcLAQMJCAkDCgUGCAEDAQgJ
"""

#static const char tiling6_1_2[48][27]
TILING6_1_2 = (48, 27), """
AQwDDAoDBgMKAwYIBQgGCAUMDAkIAQkMDAUKAQwDAQsMCwEGCQYBBgkHDAcJCQgMDAgDCwcMBAwA
BAEMAQQKBwoECgcCDAIHBwMMDAMAAQIMBgwCBgMMAwYIBQgGCAUADAAFBQEMDAECAwAMAAwCDAkC
BQIJAgULBAsFCwQMDAgLAAgMDAQJAAwCAAoMCgAFCAUABQgGDAYICAsMDAsCCgYMBAwADAUACgAF
AAoDBgMKAwYMDAcDBAcMDAYFBAwGDAgGAwYIBgMKAAoDCgAMDAkKBAkMDAAIBQwHBQgMCAUACgAF
AAoDDAMKCgsMDAsHCAMMAgwAAggMCAIHCgcCBwoEDAQKCgkMDAkACAQMAgwADAsABwALAAcJBgkH
CQYMDAoJAgoMDAYLBQwBBQIMAgULBAsFCwQDDAMEBAAMDAABAgMMBwwDBwAMAAcJBgkHCQYBDAEG
BgIMDAIDAAEMBgwEBgkMCQYBCwEGAQsADAALCwgMDAgECQAMBQwBDAYBCwEGAQsABwALAAcMDAQA
BQQMDAcGBQwHDAkHAAcJBwALAQsACwEMDAoLBQoMDAEJAwwBDAgBBAEIAQQKBwoECgcMDAsKAwsM
DAcIAwwBAwkMCQMECwQDBAsFDAULCwoMDAoBCQUMBwwFBwoMCgcCCAIHAggBDAEICAkMDAkFCgEM
BgwCDAcCCAIHAggBBAEIAQQMDAUBBgUMDAQHBgwEDAoEAQQKBAEIAggBCAIMDAsIBgsMDAIKBwwF
DAsFAgULBQIJAwkCCQMMDAgJBwgMDAMLBAwGBAsMCwQDCQMEAwkCDAIJCQoMDAoGCwIMBwwDDAQD
CQMEAwkCBQIJAgUMDAYCBwYMDAUEAwwHAwQMBAMJAgkDCQIFDAUCAgYMDAYHBAUMBgwEDAsEAwQL
BAMJAgkDCQIMDAoJBgoMDAILBQwHBQsMCwUCCQIFAgkDDAMJCQgMDAgHCwMMBAwGBAoMCgQBCAEE
AQgCDAIICAsMDAsGCgIMAgwGAgcMBwIIAQgCCAEEDAQBAQUMDAUGBwQMBQwHDAoHAgcKBwIIAQgC
CAEMDAkIBQkMDAEKAQwDDAkDBAMJAwQLBQsECwUMDAoLAQoMDAUJAQwDAQgMCAEECgQBBAoHDAcK
CgsMDAsDCAcMBwwFBwkMCQcACwAHAAsBDAELCwoMDAoFCQEMAQwFAQYMBgELAAsBCwAHDAcAAAQM
DAQFBgcMBAwGDAkGAQYJBgELAAsBCwAMDAgLBAgMDAAJAwwHDAAHCQcABwkGAQYJBgEMDAIGAwIM
DAEAAQwFDAIFCwUCBQsEAwQLBAMMDAAEAQAMDAMCAAwCAAsMCwAHCQcABwkGDAYJCQoMDAoCCwYM
AAwCDAgCBwIIAgcKBAoHCgQMDAkKAAkMDAQIBwwFDAgFAAUIBQAKAwoACgMMDAsKBwsMDAMIBgwE
BggMCAYDCgMGAwoADAAKCgkMDAkECAAMAAwEAAUMBQAKAwoACgMGDAYDAwcMDAcEBQYMAgwADAoA
BQAKAAUIBggFCAYMDAsIAgsMDAYKAgwAAgkMCQIFCwUCBQsEDAQLCwgMDAgACQQMAgwGDAMGCAYD
BggFAAUIBQAMDAEFAgEMDAADAAwEDAEECgQBBAoHAgcKBwIMDAMHAAMMDAIBAwwBDAsBBgELAQYJ
BwkGCQcMDAgJAwgMDAcLAwwBAwoMCgMGCAYDBggFDAUICAkMDAkBCgUM
"""

#static const char tiling6_2[48][15]
TILING6_2 = (48, 15), """
AQoDBgMKAwYIBQgGCAUJAQsDCwEGCQYBBgkHCAcJBAEAAQQKBwoECgcCAwIHBgMCAwYIBQgGCAUA
AQAFAAkCBQIJAgULBAsFCwQIAAoCCgAFCAUABQgGCwYIBAUACgAFAAoDBgMKAwYHBAgGAwYIBgMK
AAoDCgAJBQgHCAUACgAFAAoDCwMKAggACAIHCgcCBwoECQQKAgsABwALAAcJBgkHCQYKBQIBAgUL
BAsFCwQDAAMEBwADAAcJBgkHCQYBAgEGBgkECQYBCwEGAQsACAALBQYBCwEGAQsABwALAAcEBQkH
AAcJBwALAQsACwEKAwgBBAEIAQQKBwoECgcLAwkBCQMECwQDBAsFCgULBwoFCgcCCAIHAggBCQEI
BgcCCAIHAggBBAEIAQQFBgoEAQQKBAEIAggBCAILBwsFAgULBQIJAwkCCQMIBAsGCwQDCQMEAwkC
CgIJBwQDCQMEAwkCBQIJAgUGAwQHBAMJAgkDCQIFBgUCBgsEAwQLBAMJAgkDCQIKBQsHCwUCCQIF
AgkDCAMJBAoGCgQBCAEEAQgCCwIIAgcGBwIIAQgCCAEEBQQBBQoHAgcKBwIIAQgCCAEJAQkDBAMJ
AwQLBQsECwUKAQgDCAEECgQBBAoHCwcKBwkFCQcACwAHAAsBCgELAQYFBgELAAsBCwAHBAcABAkG
AQYJBgELAAsBCwAIAwAHCQcABwkGAQYJBgECAQIFCwUCBQsEAwQLBAMAAAsCCwAHCQcABwkGCgYJ
AAgCBwIIAgcKBAoHCgQJBwgFAAUIBQAKAwoACgMLBggECAYDCgMGAwoACQAKAAUEBQAKAwoACgMG
BwYDAgoABQAKAAUIBggFCAYLAgkACQIFCwUCBQsECAQLAgMGCAYDBggFAAUIBQABAAEECgQBBAoH
AgcKBwIDAwsBBgELAQYJBwkGCQcIAwoBCgMGCAYDBggFCQUI
"""

#static const char tiling7_1[16][9]
TILING7_1 = (16, 9), """
CQUECgECCAMACwcGCAMACgECAwAIBQQJBwYLCAQHCQABCwIDCgYFCwIDCQABAAEJBgUKBAcIAQIK
BwYLBQQJAgMLBAcIBgUKCwMCCAcECgUGCgIBCwYHCQQFCQEACgUGCAcEBQYKAwILAQAJBwQIAQAJ
AwILCAADCQQFCwYHBgcLAAMIAgEKBAUJAgEKAAMI
"""

#static const char tiling7_2[16][3][15]
TILING7_2 = (16, 3, 15), """
AQIKAwQIBAMFAAUDBQAJAwAICQEEAgQBBAIFCgUCCQUEAAoBCgAICggCAwIIAwAIAQYKBgEHAgcB
BwILAQIKCwMGAAYDBgAHCAcACwcGAggDCAIKCAoAAQAKCQUECwMGAAYDBgAHCAcACwcGAwQIBAMF
AAUDBQAJAwAIBAkHCwcJBQsJCwUGAAEJAgcLBwIEAwQCBAMIAgMLCAAHAQcABwEECQQBCAQHAwkA
CQMLCQsBAgELAgMLAAUJBQAGAQYABgEKAAEJCgIFAwUCBQMGCwYDBgUKAQsCCwEJCwkDAAMJBgUK
CAAHAQcABwEECQQBCAQHAAUJBQAGAQYABgEKAAEJBQoECAQKBggKCAYHCwcGCQEEAgQBBAIFCgUC
CQUEAQYKBgEHAgcBBwILAQIKBgsFCQULBwkLCQcECAQHCgIFAwUCBQMGCwYDBgUKAgcLBwIEAwQC
BAMIAgMLBwgGCgYIBAoICgQFBwQIBQIKAgUDBgMFAwYLCgUGCwcCBAIHAgQDCAMECwMCBggHCAYK
CAoEBQQKBgcLBAEJAQQCBQIEAgUKBAUJCgYBBwEGAQcCCwIHCgIBBQsGCwUJCwkHBAcJCgUGBwAI
AAcBBAEHAQQJBwQICQUABgAFAAYBCgEGCQEABAoFCgQICggGBwYICwMCCQUABgAFAAYBCgEGCQEA
BQIKAgUDBgMFAwYLCgUGAgsBCQELAwkLCQMACQEACwcCBAIHAgQDCAMECwMCBwAIAAcBBAEHAQQJ
BwQIAAkDCwMJAQsJCwECBAUJBgMLAwYABwAGAAcIBgcLCAQDBQMEAwUACQAFCAADBwkECQcLCQsF
BgULCAADCgYBBwEGAQcCCwIHCgIBBgMLAwYABwAGAAcIBgcLAwgCCgIIAAoICgABCgIBCAQDBQME
AwUACQAFCAADBAEJAQQCBQIEAgUKBAUJAQoACAAKAggKCAID
"""

#static const char tiling7_3[16][3][27]
TILING7_3 = (16, 3, 27), """
DAIKDAoFDAUEDAQIDAgDDAMADAAJDAkBDAECDAUEDAQIDAgDDAMCDAIKDAoBDAEADAAJDAkFBQQM
CgUMAgoMAwIMCAMMAAgMAQAMCQEMBAkMDAAIDAgHDAcGDAYKDAoBDAECDAILDAsDDAMADAcGDAYK
DAoBDAEADAAIDAgDDAMCDAILDAsHBwYMCAcMAAgMAQAMCgEMAgoMAwIMCwMMBgsMCQUMAAkMAwAM
CwMMBgsMBwYMCAcMBAgMBQQMAwAMCwMMBgsMBQYMCQUMBAkMBwQMCAcMAAgMDAMADAAJDAkFDAUG
DAYLDAsHDAcEDAQIDAgDDAEJDAkEDAQHDAcLDAsCDAIDDAMIDAgADAABDAQHDAcLDAsCDAIBDAEJ
DAkADAADDAMIDAgEBAcMCQQMAQkMAgEMCwIMAwsMAAMMCAAMBwgMDAMLDAsGDAYFDAUJDAkADAAB
DAEKDAoCDAIDDAYFDAUJDAkADAADDAMLDAsCDAIBDAEKDAoGBgUMCwYMAwsMAAMMCQAMAQkMAgEM
CgIMBQoMCgYMAQoMAAEMCAAMBwgMBAcMCQQMBQkMBgUMAAEMCAAMBwgMBgcMCgYMBQoMBAUMCQQM
AQkMDAABDAEKDAoGDAYHDAcIDAgEDAQFDAUJDAkACwcMAgsMAQIMCQEMBAkMBQQMCgUMBgoMBwYM
AQIMCQEMBAkMBwQMCwcMBgsMBQYMCgUMAgoMDAECDAILDAsHDAcEDAQJDAkFDAUGDAYKDAoBCAQM
AwgMAgMMCgIMBQoMBgUMCwYMBwsMBAcMAgMMCgIMBQoMBAUMCAQMBwgMBgcMCwYMAwsMDAIDDAMI
DAgEDAQFDAUKDAoGDAYHDAcLDAsCDAQIDAgDDAMCDAIKDAoFDAUGDAYLDAsHDAcEDAMCDAIKDAoF
DAUEDAQIDAgHDAcGDAYLDAsDAwIMCAMMBAgMBQQMCgUMBgoMBwYMCwcMAgsMDAcLDAsCDAIBDAEJ
DAkEDAQFDAUKDAoGDAYHDAIBDAEJDAkEDAQHDAcLDAsGDAYFDAUKDAoCAgEMCwIMBwsMBAcMCQQM
BQkMBgUMCgYMAQoMDAYKDAoBDAEADAAIDAgHDAcEDAQJDAkFDAUGDAEADAAIDAgHDAcGDAYKDAoF
DAUEDAQJDAkBAQAMCgEMBgoMBwYMCAcMBAgMBQQMCQUMAAkMCwMMBgsMBQYMCQUMAAkMAQAMCgEM
AgoMAwIMBQYMCQUMAAkMAwAMCwMMAgsMAQIMCgEMBgoMDAUGDAYLDAsDDAMADAAJDAkBDAECDAIK
DAoFCQEMBAkMBwQMCwcMAgsMAwIMCAMMAAgMAQAMBwQMCwcMAgsMAQIMCQEMAAkMAwAMCAMMBAgM
DAcEDAQJDAkBDAECDAILDAsDDAMADAAIDAgHDAUJDAkADAADDAMLDAsGDAYHDAcIDAgEDAQFDAAD
DAMLDAsGDAYFDAUJDAkEDAQHDAcIDAgAAAMMCQAMBQkMBgUMCwYMBwsMBAcMCAQMAwgMCAAMBwgM
BgcMCgYMAQoMAgEMCwIMAwsMAAMMBgcMCgYMAQoMAAEMCAAMAwgMAgMMCwIMBwsMDAYHDAcIDAgA
DAABDAEKDAoCDAIDDAMLDAsGCgIMBQoMBAUMCAQMAwgMAAMMCQAMAQkMAgEMBAUMCAQMAwgMAgMM
CgIMAQoMAAEMCQAMBQkMDAQFDAUKDAoCDAIDDAMIDAgADAABDAEJDAkE
"""

#static const char tiling7_4_1[16][15]
TILING7_4_1 = (16, 15), """
AwQIBAMKAgoDBAoFCQEAAQYKBgEIAAgBBggHCwMCCwMGCQYDBgkFAAkDBwQIAgcLBwIJAQkCBwkE
CAADAAUJBQALAwsABQsGCgIBCAAHCgcABwoGAQoABAUJCQEECwQBBAsHAgsBBQYKCgIFCAUCBQgE
AwgCBgcLBQIKAgUIBAgFAggDCwcGBAEJAQQLBwsEAQsCCgYFBwAIAAcKBgoHAAoBCQUECQUACwAF
AAsDBgsFAQIKCwcCCQIHAgkBBAkHAwAIBgMLAwYJBQkGAwkACAQHCgYBCAEGAQgABwgGAgMLCAQD
CgMEAwoCBQoEAAEJ
"""

#static const char tiling7_4_2[16][27]
TILING7_4_2 = (16, 27), """
CQQIBAkFCgUJAQoJCgECAAIBAgADCAMACQgACwYKBgsHCAcLAwgLCAMAAgADAAIBCgECCwoCCwMI
AAgDCAAJCAkEBQQJBAUHBgcFBwYLBwsICAcLBwgECQQIAAkICQABAwEAAQMCCwIDCAsDCgUJBQoG
CwYKAgsKCwIDAQMCAwEACQABCgkBCAAJAQkACQEKCQoFBgUKBQYEBwQGBAcIBAgJCQEKAgoBCgIL
CgsGBwYLBgcFBAUHBQQJBQkKCgILAwsCCwMICwgHBAcIBwQGBQYEBgUKBgoLCwIKAgsDCAMLBwgL
CAcEBgQHBAYFCgUGCwoGCgEJAQoCCwIKBgsKCwYHBQcGBwUECQQFCgkFCQAIAAkBCgEJBQoJCgUG
BAYFBgQHCAcECQgECQUKBgoFCgYLCgsCAwILAgMBAAEDAQAJAQkKCwcIBAgHCAQJCAkAAQAJAAED
AgMBAwILAwsICAMLAwgACQAIBAkICQQFBwUEBQcGCwYHCAsHCgYLBwsGCwcICwgDAAMIAwACAQIA
AgEKAgoLCAQJBQkECQUKCQoBAgEKAQIAAwACAAMIAAgJ
"""

#static const char tiling8[6][6]
TILING8 = (6, 6), """
CQgKCggLAQUDAwUHAAQCBAYCAAIEBAIGAQMFAwcFCQoICgsI
"""

#static const char tiling9[8][12]
TILING9 = (8, 12), """
AgoFAwIFAwUEAwQIBAcLCQQLCQsCCQIBCgcGAQcKAQgHAQAIAwYLAAYDAAUGAAkFAwsGAAMGAAYF
AAUJCgYHAQoHAQcIAQgABAsHCQsECQILCQECAgUKAwUCAwQFAwgE
"""

#static const char tiling10_1_1[6][12]
TILING10_1_1 = (6, 12), """
BQoHCwcKCAEJAQgDAQIFBgUCBAMAAwQHCwAIAAsCBAkGCgYJCQAKAgoABggECAYLBwIDAgcGAAEE
BQQBBwkFCQcICgELAwsB
"""

#static const char tiling10_1_1_[6][12]
TILING10_1_1_ = (6, 12), """
BQkHCAcJCwEKAQsDAwIHBgcCBAEAAQQFCgAJAAoCBAgGCwYICAALAgsABgkECQYKBQIBAgUGAAME
BwQDBwoFCgcLCQEIAwgB
"""

#static const char tiling10_1_2[6][24]
TILING10_1_2 = (6, 24), """
AwsHAwcICQgHBQkHCQUKCQoBAwEKCwMKBwYFBwUEAAQFAQAFAAECAAIDBwMCBgcCCwIKBgsKCwYE
CwQIAAgECQAEAAkKAAoCCwIKCwoGBAYKCQQKBAkABAAICwgAAgsABwYFBAcFBwQABwADAgMAAQIA
AgEFAgUGBwgDCwcDBwsKBwoFCQUKAQkKCQEDCQMI
"""

#static const char tiling10_2[6][24]
TILING10_2 = (6, 24), """
DAUJDAkIDAgDDAMBDAEKDAoLDAsHDAcFDAEADAAEDAQHDAcDDAMCDAIGDAYFDAUBBAgMBgQMCgYM
CQoMAAkMAgAMCwIMCAsMDAkEDAQGDAYLDAsIDAgADAACDAIKDAoJAAMMBAAMBQQMAQUMAgEMBgIM
BwYMAwcMCgUMCwoMAwsMAQMMCQEMCAkMBwgMBQcM
"""

#static const char tiling10_2_[6][24]
TILING10_2_ = (6, 24), """
CAcMCQgMAQkMAwEMCwMMCgsMBQoMBwUMBAUMAAQMAwAMBwMMBgcMAgYMAQIMBQEMDAsGDAYEDAQJ
DAkKDAoCDAIADAAIDAgLBgoMBAYMCAQMCwgMAgsMAAIMCQAMCgkMDAcEDAQADAABDAEFDAUGDAYC
DAIDDAMHDAcLDAsKDAoBDAEDDAMIDAgJDAkFDAUH
"""

#static const char tiling11[12][12]
TILING11 = (12, 12), """
AgoJAgkHAgcDBwkEAQYCAQgGAQkICAcGCAMBCAEGCAYEBgEKAAgLAAsFAAUBBQsGCQUHCQcCCQIA
AgcLBQAEBQsABQoLCwMABQQABQALBQsKCwADCQcFCQIHCQACAgsHAAsIAAULAAEFBQYLCAEDCAYB
CAQGBgoBAQIGAQYIAQgJCAYHAgkKAgcJAgMHBwQJ
"""

#static const char tiling12_1_1[24][12]
TILING12_1_1 = (24, 12), """
BwYLCgMCAwoICQgKBgUKCQIBAgkLCAsJCgYFBwkECQcBAwEHBwYLBAgFAwUIBQMBBQQJCAEAAQgK
CwoIAQIKAAkDBQMJAwUHCgECAAsDCwAGBAYACAMAAgkBCQIEBgQCAwAIAgsBBwELAQcFBgUKBwsE
AgQLBAIACQUEBggHCAYAAgAGCAMABwQLCQsECwkKBAcICwADAAsJCgkLBAcIBQkGAAYJBgACCwcG
BAoFCgQCAAIECwIDAQgACAEHBQcBAAEJAwgCBAIIAgQGAgMLAQoABgAKAAYECQABAwoCCgMFBwUD
CQABBAUICggFCAoLCAQHBQsGCwUDAQMFBQQJBgoHAQcKBwEDCgECBQYJCwkGCQsICwIDBgcKCAoH
CggJ
"""

#static const char tiling12_1_1_[24][12]
TILING12_1_1_ = (24, 12), """
AwILCgcGBwoICQgKAgEKCQYFBgkLCAsJCQQFBwoGCgcBAwEHBwQIBgsFAwULBQMBAQAJCAUEBQgK
CwoIAQAJAgoDBQMKAwUHCwMCAAoBCgAGBAYACQEAAggDCAIEBgQCAwILAAgBBwEIAQcFBgcLBQoE
AgQKBAIACAcEBgkFCQYAAgAGCAcEAwALCQsACwkKAAMICwQHBAsJCgkLBAUJBwgGAAYIBgACCgUG
BAsHCwQCAAIECAADAQsCCwEHBQcBAAMIAQkCBAIJAgQGAgEKAwsABgALAAYECgIBAwkACQMFBwUD
CQQFAAEICggBCAoLCwYHBQgECAUDAQMFBQYKBAkHAQcJBwEDCgUGAQIJCwkCCQsICwYHAgMKCAoD
CggJ
"""

#static const char tiling12_1_2[24][24]
TILING12_1_2 = (24, 24), """
BwMLAwcICQgHBgkHCQYKAgoGCwIGAgsDBgIKAgYLCAsGBQgGCAUJAQkFCgEFAQoCCgkFCQoBAwEK
BgMKAwYHBAcGBQQGBAUJBwgLAwsICwMBCwEGBQYBBgUEBgQHCAcEBQEJAQUKCwoFBAsFCwQIAAgE
CQAEAAkBAQkKBQoJCgUHCgcCAwIHAgMAAgABCQEACgsCCwoGBAYKAQQKBAEAAwABAgMBAwILCAkA
CQgEBgQIAwYIBgMCAQIDAAEDAQAJAwsIBwgLCAcFCAUAAQAFAAECAAIDCwMCBgsKAgoLCgIACgAF
BAUABQQHBQcGCwYHCQgECAkAAgAJBQIJAgUGBwYFBAcFBwQICAQACQAEAAkKAAoDCwMKAwsHAwcI
BAgHBAAIAAQJCgkEBwoECgcLAwsHCAMHAwgABAkIAAgJCAACCAIHBgcCBwYFBwUECQQFCwoGCgsC
AAILBwALAAcEBQQHBgUHBQYKCwgDCAsHBQcLAgULBQIBAAECAwACAAMIAAgJBAkICQQGCQYBAgEG
AQIDAQMACAADAgoLBgsKCwYECwQDAAMEAwABAwECCgIBCQoBCgkFBwUJAAcJBwADAgMAAQIAAgEK
CQUBCgEFAQoLAQsACAALAAgEAAQJBQkECAsHCwgDAQMIBAEIAQQFBgUEBwYEBgcLBQoJAQkKCQED
CQMEBwQDBAcGBAYFCgUGCgYCCwIGAgsIAggBCQEIAQkFAQUKBgoFCwcDCAMHAwgJAwkCCgIJAgoG
AgYLBwsG
"""

#static const char tiling12_2[24][24]
TILING12_2 = (24, 24), """
CQgMCgkMAgoMAwIMCwMMBgsMBwYMCAcMCAsMCQgMAQkMAgEMCgIMBQoMBgUMCwYMAwEMBwMMBAcM
CQQMBQkMBgUMCgYMAQoMDAMBDAEFDAUGDAYLDAsHDAcEDAQIDAgDCwoMCAsMAAgMAQAMCQEMBAkM
BQQMCgUMDAUHDAcDDAMCDAIKDAoBDAEADAAJDAkFBAYMAAQMAQAMCgEMAgoMAwIMCwMMBgsMBgQM
AgYMAwIMCAMMAAgMAQAMCQEMBAkMDAcFDAUBDAEADAAIDAgDDAMCDAILDAsHDAIADAAEDAQFDAUK
DAoGDAYHDAcLDAsCAgAMBgIMBwYMCAcMBAgMBQQMCQUMAAkMDAkKDAoLDAsHDAcEDAQIDAgDDAMA
DAAJCgkMCwoMBwsMBAcMCAQMAwgMAAMMCQAMDAACDAIGDAYHDAcIDAgEDAQFDAUJDAkAAAIMBAAM
BQQMCgUMBgoMBwYMCwcMAgsMBQcMAQUMAAEMCAAMAwgMAgMMCwIMBwsMDAQGDAYCDAIDDAMIDAgA
DAABDAEJDAkEDAYEDAQADAABDAEKDAoCDAIDDAMLDAsGBwUMAwcMAgMMCgIMAQoMAAEMCQAMBQkM
DAoLDAsIDAgADAABDAEJDAkEDAQFDAUKAQMMBQEMBgUMCwYMBwsMBAcMCAQMAwgMDAEDDAMHDAcE
DAQJDAkFDAUGDAYKDAoBDAsIDAgJDAkBDAECDAIKDAoFDAUGDAYLDAgJDAkKDAoCDAIDDAMLDAsG
DAYHDAcI
"""

#static const char tiling12_2_[24][24]
TILING12_2_ = (24, 24), """
DAILDAsHDAcGDAYKDAoJDAkIDAgDDAMCDAEKDAoGDAYFDAUJDAkIDAgLDAsCDAIBDAQFDAUKDAoG
DAYHDAcDDAMBDAEJDAkEBwYMCAcMBAgMBQQMAQUMAwEMCwMMBgsMDAAJDAkFDAUEDAQIDAgLDAsK
DAoBDAEAAQIMCQEMAAkMAwAMBwMMBQcMCgUMAgoMDAECDAILDAsDDAMADAAEDAQGDAYKDAoBDAMA
DAAJDAkBDAECDAIGDAYEDAQIDAgDAwAMCwMMAgsMAQIMBQEMBwUMCAcMAAgMBgUMCwYMBwsMBAcM
AAQMAgAMCgIMBQoMDAcEDAQJDAkFDAUGDAYCDAIADAAIDAgHCAcMAAgMAwAMCwMMCgsMCQoMBAkM
BwQMDAcIDAgADAADDAMLDAsKDAoJDAkEDAQHBAcMCQQMBQkMBgUMAgYMAAIMCAAMBwgMDAUGDAYL
DAsHDAcEDAQADAACDAIKDAoFDAADDAMLDAsCDAIBDAEFDAUHDAcIDAgAAAMMCQAMAQkMAgEMBgIM
BAYMCAQMAwgMAgEMCwIMAwsMAAMMBAAMBgQMCgYMAQoMDAIBDAEJDAkADAADDAMHDAcFDAUKDAoC
CQAMBQkMBAUMCAQMCwgMCgsMAQoMAAEMDAYHDAcIDAgEDAQFDAUBDAEDDAMLDAsGBQQMCgUMBgoM
BwYMAwcMAQMMCQEMBAkMCgEMBgoMBQYMCQUMCAkMCwgMAgsMAQIMCwIMBwsMBgcMCgYMCQoMCAkM
AwgMAgMM
"""

#static const char tiling13_1[2][12]
TILING13_1 = (2, 12), """
CwcGAQIKCAMACQUECAQHAgMLCQABCgYF
"""

#static const char tiling13_1_[2][12]
TILING13_1_ = (2, 12), """
BwQICwMCAQAJBQYKBgcLCgIBAAMIBAUJ
"""

#static const char tiling13_2[2][6][18]
TILING13_2 = (2, 6, 18), """
AQIKCwcGAwQIBAMFAAUDBQAJCAMACwcGCQEEAgQBBAIFCgUCCQUECAMAAQYKBgEHAgcBBwILCQUE
AQIKCwMGAAYDBgAHCAcACQUECwcGAAoBCgAICggCAwIIAQIKAwAIBAkHCwcJBQsJCwUGAgMLCAQH
AAUJBQAGAQYABgEKCQABCAQHCgIFAwUCBQMGCwYDBgUKCQABAgcLBwIEAwQCBAMIBgUKAgMLCAAH
AQcABwEECQQBBgUKCAQHAQsCCwEJCwkDAAMJAgMLAAEJBQoECAQKBggKCAYH
"""

#static const char tiling13_2_[2][6][18]
TILING13_2_ = (2, 6, 18), """
CgUGCwMCBwAIAAcBBAEHAQQJCwMCBwQICQUABgAFAAYBCgEGAQAJBwQIBQIKAgUDBgMFAwYLCgUG
AQAJCwcCBAIHAgQDCAMECgUGBwQIAgsBCQELAwkLCQMACwMCCQEABAoFCgQICggGBwYIBgcLCAAD
BAEJAQQCBQIEAgUKCAADBAUJCgYBBwEGAQcCCwIHAgEKBAUJBgMLAwYABwAGAAcIBgcLAgEKCAQD
BQMEAwUACQAFBgcLBAUJAwgCCgIIAAoICgABCAADCgIBBQsGCwUJCwkHBAcJ
"""

#static const char tiling13_3[2][12][30]
TILING13_3 = (2, 12, 30), """
CwcGDAIKDAoFDAUEDAQIDAgDDAMADAAJDAkBDAECAQIKCQUMAAkMAwAMCwMMBgsMBwYMCAcMBAgM
BQQMCwcGDAUEDAQIDAgDDAMCDAIKDAoBDAEADAAJDAkFAQIKDAMADAAJDAkFDAUGDAYLDAsHDAcE
DAQIDAgDCAMACwcMAgsMAQIMCQEMBAkMBQQMCgUMBgoMBwYMCwcGBQQMCgUMAgoMAwIMCAMMAAgM
AQAMCQEMBAkMCAMAAQIMCQEMBAkMBwQMCwcMBgsMBQYMCgUMAgoMCQUEDAAIDAgHDAcGDAYKDAoB
DAECDAILDAsDDAMACQUEDAcGDAYKDAoBDAEADAAIDAgDDAMCDAILDAsHCAMADAECDAILDAsHDAcE
DAQJDAkFDAUGDAYKDAoBCQUEBwYMCAcMAAgMAQAMCgEMAgoMAwIMCwMMBgsMAQIKAwAMCwMMBgsM
BQYMCQUMBAkMBwQMCAcMAAgMCAQHDAMLDAsGDAYFDAUJDAkADAABDAEKDAoCDAIDAgMLCgYMAQoM
AAEMCAAMBwgMBAcMCQQMBQkMBgUMCAQHDAYFDAUJDAkADAADDAMLDAsCDAIBDAEKDAoGAgMLDAAB
DAEKDAoGDAYHDAcIDAgEDAQFDAUJDAkAAAEJCAQMAwgMAgMMCgIMBQoMBgUMCwYMBwsMBAcMCAQH
BgUMCwYMAwsMAAMMCQAMAQkMAgEMCgIMBQoMCQABAgMMCgIMBQoMBAUMCAQMBwgMBgcMCwYMAwsM
BgUKDAEJDAkEDAQHDAcLDAsCDAIDDAMIDAgADAABBgUKDAQHDAcLDAsCDAIBDAEJDAkADAADDAMI
DAgECQABDAIDDAMIDAgEDAQFDAUKDAoGDAYHDAcLDAsCBgUKBAcMCQQMAQkMAgEMCwIMAwsMAAMM
CAAMBwgMAgMLAAEMCAAMBwgMBgcMCgYMBQoMBAUMCQQMAQkM
"""

#static const char tiling13_3_[2][12][30]
TILING13_3_ = (2, 12, 30), """
AwILCAcMAAgMAQAMCgEMBgoMBQYMCQUMBAkMBwQMBQYKDAILDAsHDAcEDAQJDAkBDAEADAAIDAgD
DAMCCgUGDAcEDAQJDAkBDAECDAILDAsDDAMADAAIDAgHCwMCDAEADAAIDAgHDAcGDAYKDAoFDAUE
DAQJDAkBBwQICwMMBgsMBQYMCQUMAAkMAQAMCgEMAgoMAwIMBwQIBQYMCQUMAAkMAwAMCwMMAgsM
AQIMCgEMBgoMCwMCAQAMCgEMBgoMBwYMCAcMBAgMBQQMCQUMAAkMAQAJDAQIDAgDDAMCDAIKDAoF
DAUGDAYLDAsHDAcEBwQIDAUGDAYLDAsDDAMADAAJDAkBDAECDAIKDAoFAQAJDAMCDAIKDAoFDAUE
DAQIDAgHDAcGDAYLDAsDCgUGBwQMCwcMAgsMAQIMCQEMAAkMAwAMCAMMBAgMCQEAAwIMCAMMBAgM
BQQMCgUMBgoMBwYMCwcMAgsMAAMICQQMAQkMAgEMCwIMBwsMBgcMCgYMBQoMBAUMCwYHDAMIDAgE
DAQFDAUKDAoCDAIBDAEJDAkADAADBgcLDAQFDAUKDAoCDAIDDAMIDAgADAABDAEJDAkECAADDAIB
DAEJDAkEDAQHDAcLDAsGDAYFDAUKDAoCBAUJCAAMBwgMBgcMCgYMAQoMAgEMCwIMAwsMAAMMBAUJ
BgcMCgYMAQoMAAEMCAAMAwgMAgMMCwIMBwsMCAADAgEMCwIMBwsMBAcMCQQMBQkMBgUMCgYMAQoM
AgEKDAUJDAkADAADDAMLDAsGDAYHDAcIDAgEDAQFBAUJDAYHDAcIDAgADAABDAEKDAoCDAIDDAML
DAsGAgEKDAADDAMLDAsGDAYFDAUJDAkEDAQHDAcIDAgABgcLBAUMCAQMAwgMAgMMCgIMAQoMAAEM
CQAMBQkMCgIBAAMMCQAMBQkMBgUMCwYMBwsMBAcMCAQMAwgM
"""

#static const char tiling13_4[2][4][36]
TILING13_4 = (2, 4, 36), """
DAIKDAoFDAUGDAYLDAsHDAcEDAQIDAgDDAMADAAJDAkBDAECCwMMBgsMBwYMCAcMBAgMBQQMCQUM
AAkMAQAMCgEMAgoMAwIMCQEMBAkMBQQMCgUMBgoMBwYMCwcMAgsMAwIMCAMMAAgMAQAMDAAIDAgH
DAcEDAQJDAkFDAUGDAYKDAoBDAECDAILDAsDDAMADAMLDAsGDAYHDAcIDAgEDAQFDAUJDAkADAAB
DAEKDAoCDAIDCAAMBwgMBAcMCQQMBQkMBgUMCgYMAQoMAgEMCwIMAwsMAAMMCgIMBQoMBgUMCwYM
BwsMBAcMCAQMAwgMAAMMCQAMAQkMAgEMDAEJDAkEDAQFDAUKDAoGDAYHDAcLDAsCDAIDDAMIDAgA
DAAB
"""

#static const char tiling13_5_1[2][4][18]
TILING13_5_1 = (2, 4, 18), """
BwYLAQAJCgMCAwoFAwUIBAgFAQIKBwQIAwALBgsACQYABgkFAwAIBQYKAQIJBAkCCwQCBAsHBQQJ
AwILCAEAAQgHAQcKBgoHBAcIAgEKCwADAAsGAAYJBQkGAgMLBAUJAAEIBwgBCgcBBwoGAAEJBgcL
AgMKBQoDCAUDBQgEBgUKAAMICQIBAgkEAgQLBwsE
"""

#static const char tiling13_5_2[2][4][30]
TILING13_5_2 = (2, 4, 30), """
AQAJBwQIBwgDBwMLAgsDCwIKCwoGBQYKBgUHBAcFBwQICwMCBgsCCgYCBgoFCQUKAQkKCQEAAgAB
AAIDBQYKCQEABAkACAQABAgHCwcIAwsICwMCAAIDAgABAwILBQYKBQoBBQEJAAkBCQAICQgEBAgH
BAcFBgUHAgEKBAUJBAkABAAIAwgACAMLCAsHBgcLBwYEBQQGBAUJCAADBwgDCwcDBwsGCgYLAgoL
CgIBAwECAQMABgcLCgIBBQoBCQUBBQkECAQJAAgJCAADAQMAAwECAAMIBgcLBgsCBgIKAQoCCgEJ
CgkFBQkEBQQGBwYE
"""

#static const char tiling14[12][12]
TILING14 = (12, 12), """
BQkIBQgCBQIGAwIIAgEFAgUIAggLBAgFCQQGCQYDCQMBCwMGAQsKAQQLAQAEBwsECAIACAUCCAcF
CgIFAAcDAAoHAAkKBgcKAAMHAAcKAAoJBgoHCAACCAIFCAUHCgUCAQoLAQsEAQQABwQLCQYECQMG
CQEDCwYDAgUBAggFAgsIBAUIBQgJBQIIBQYCAwgC
"""

#static const char test3[24]
TEST3 = (24,), """
BQEEBQECAgMEAwYG+vr9/P3+/v/7/P/7
"""

#static const char test4[8]
TEST4 = (8,), """
BwcHB/n5+fk=
"""

#static const char test6[48][3]
TEST6 = (48, 3), """
AgcKBAcLBQcBBQcDAQcJAwcKBgcFAQcIBAcIAQcIAwcLBQcCBQcAAQcJBgcGAgcJBAcIAgcJAgcK
BgcHAwcKBAcLAwcLBgcE+vkE/fkL/PkL/fkK+vkH/vkK/vkJ/PkI/vkJ+vkG//kJ+/kA+/kC/fkL
//kI/PkI//kI+vkF/fkK//kJ+/kD+/kB/PkL/vkK
"""

#static const char test7[16][5]
TEST7 = (16, 5), """
AQIFBwEDBAUHAwQBBgcEBAEFBwACAwUHAgECBgcFAgMGBwYDBAYHB/38+vkH/v36+Qb//vr5Bf79
+/kC/P/7+QD8//r5BP38+/kD//77+QE=
"""

#static const char test10[6][3]
TEST10 = (6, 3), """
AgQHBQYHAQMHAQMHBQYHAgQH
"""

#static const char test12[24][4]
TEST12 = (24, 4), """
BAMHCwMCBwoCBgcFBgQHBwIBBwkFAgcBBQMHAgUBBwAFBAcDBgMHBgEGBwQBBAcIBAEHCAYBBwQD
BgcGBAUHAwEFBwADBQcCAgUHAQECBwkEBgcHBgIHBQIDBwoDBAcL
"""

#static const char test13[2][7]
TEST13 = (2, 7), """
AQIDBAUGBwIDBAEFBgc=
"""

#static const char subconfig13[64]
SUBCONFIG13 = (64,), """
AAECBwP/C/8ECP//Dv///wUJDBcP/xUmERT/JBohHiwGCg0TEP8ZJRIY/yMWIB0r////Iv//HCr/
H/8pGygnLQ==
"""


================================================
FILE: meshudf/meshudf.py
================================================
""" 
Meshing algorithm for UDFs from
"Meshudf: Fast and differentiable meshing of unsigned distance field networks." 
Guillard, Benoit, Federico Stella, and Pascal Fua. ECCV 2022.

Original implementation: https://github.com/cvlab-epfl/MeshUDF
"""

import math
from collections import defaultdict
from typing import Callable, Tuple

import numpy as np
import torch
import torch.nn.functional as F
import trimesh
from scipy.sparse import coo_matrix
from torch import Tensor

from meshudf._marching_cubes_lewiner import udf_mc_lewiner


class GridFiller:
    """
    Coarse to fine method for querying an SDF network, using cached grids.
    #
    We start by evaluating the field on a low resolution grid, and then
    iteratively subdivide each voxel and re-evaluate the field only
    where needed until we reach a desired grid resolution.
    We subdivide voxels if the field absolute value on any of the voxel corners
    is smaller than the voxel diagonal √2∆x, where ∆x denotes voxel size.
    #
    In practice the coarsest level is here hardcoded to be 32**3.
    """

    def __init__(
        self,
        final_resolution: int,
        voxel_origin: Tuple[int, int, int] = (-1, -1, -1),
        cube_side_length: float = 2.0,
    ):
        # Save attributes
        self.N_max = final_resolution
        self.num_samples = final_resolution**3
        self.N_levels = [32 * (2**i) for i in range(int(math.log2(self.N_max) - 4))]
        self.voxel_origin = voxel_origin
        self.cube_side_length = cube_side_length

        # Construct grid, and precompute sparse masks, from 32 (coarsest grid) to final_resolution
        """
        Create one empty grid (N,N,N,7) where the 7 channels are (x,y,z, UDF, +3 for gradients).
        """
        voxel_size = self.cube_side_length / (self.N_max - 1)
        self.voxel_size = voxel_size
        overall_index = torch.arange(0, self.N_max**3, 1, out=torch.LongTensor())
        samples = torch.zeros(self.N_max**3, 7)

        # Transform the first 3 columns to be the x, y, z indices.
        samples[:, 2] = overall_index % self.N_max
        samples[:, 1] = (
            torch.div(overall_index, self.N_max, rounding_mode="floor") % self.N_max
        )
        samples[:, 0] = (
            torch.div(
                torch.div(overall_index, self.N_max, rounding_mode="floor"),
                self.N_max,
                rounding_mode="floor",
            )
            % self.N_max
        )

        # Then transform the first 3 columns to be the x, y, z coordinates.
        samples[:, 0] = (samples[:, 0] * voxel_size) + voxel_origin[2]
        samples[:, 1] = (samples[:, 1] * voxel_size) + voxel_origin[1]
        samples[:, 2] = (samples[:, 2] * voxel_size) + voxel_origin[0]
        samples.requires_grad = False
        #samples.pin_memory()
        self.samples = samples.cuda()

        """
        Precompute binary masks for adressing the above grid at different resolutions.
        """
        mask = torch.zeros(self.N_max**3).bool()
        mask = mask.reshape(self.N_max, self.N_max, self.N_max)

        # Fill dictionaries with precomputed masks.
        self.masks_coarse = {}
        self.masks_coarse_no_recompute = {}
        self.idxs_coarse_neighbors_blocks = {}
        for i, N in enumerate(self.N_levels):
            #### 1: Subsample coarsely.
            mask_coarse = mask.clone()
            mask_coarse[
                :: self.N_max // N, :: self.N_max // N, :: self.N_max // N
            ] = True

            # (N_max**3) array, with True only for indices of the coarse sampling (N**3 locations):
            mask_coarse = mask_coarse.reshape(-1)
            self.masks_coarse[i] = mask_coarse.clone().cuda()

            #### 2: Compute the indices of neighboring blocks.
            neighbors_block_coarse = mask.clone()
            neighbors_block_coarse[
                : self.N_max // N, : self.N_max // N, : self.N_max // N
            ] = True
            neighbors_block_coarse = neighbors_block_coarse.reshape(-1)
            # Shape (N**3 / 64, 64): idxs_coarse_neighbors_blocks[i] represents the (N_max // N)**3 indices covered by coarse point i.
            idxs_coarse_neighbors_blocks = torch.where(mask_coarse)[0].reshape(
                -1, 1
            ) + torch.where(neighbors_block_coarse)[0].reshape(1, -1)
            self.idxs_coarse_neighbors_blocks[
                i
            ] = idxs_coarse_neighbors_blocks.clone().cuda()

            #### 3: For levels finer than the coarsest one, do not recompute already queried SDFs.
            if i > 0:
                mask_coarse_no_recompute = mask_coarse.clone()
                mask_coarse_no_recompute[self.masks_coarse[i - 1]] = False
                self.masks_coarse_no_recompute[
                    i
                ] = mask_coarse_no_recompute.clone().cuda()

    def fill_grid(
        self, udf_func: Callable[[Tensor], Tensor], max_batch: int
    ) -> Tuple[Tensor, Tensor]:
        with torch.no_grad():
            samples = self.samples.clone()
            close_surface_masks = {}
            idxs_coarse_neighbors_blocks_LOCAL = {}

            for level, N in enumerate(self.N_levels):
                """Prepare masks based on previous levels"""
                if level == 0:
                    mask_coarse = self.masks_coarse[level]
                    idxs_coarse_neighbors_blocks = self.idxs_coarse_neighbors_blocks[
                        level
                    ].clone()
                    mask_coarse_no_recompute = self.masks_coarse[level]
                else:
                    # Mask using previous queries: binary mask.
                    mask_coarse = self.masks_coarse[level].clone()
                    for l in range(level):
                        mask_coarse[
                            idxs_coarse_neighbors_blocks_LOCAL[l][
                                ~close_surface_masks[l]
                            ]
                        ] = False

                    # Compute the corresponding indices tensor.
                    if N < self.N_max:
                        idxs_coarse_neighbors_blocks = (
                            self.idxs_coarse_neighbors_blocks[level].clone()
                        )
                        idxs_coarse_neighbors_blocks = idxs_coarse_neighbors_blocks[
                            mask_coarse[self.masks_coarse[level]]
                        ]
                    else:
                        idxs_coarse_neighbors_blocks = (
                            self.idxs_coarse_neighbors_blocks[level]
                        )

                    # The no_recompute version does not query the decoder for nodes that have
                    # already been computed at coarser levels.
                    mask_coarse_no_recompute = self.masks_coarse_no_recompute[
                        level
                    ].clone()
                    for l in range(level):
                        mask_coarse_no_recompute[
                            idxs_coarse_neighbors_blocks_LOCAL[l][
                                ~close_surface_masks[l]
                            ]
                        ] = False
                idxs_coarse_neighbors_blocks_LOCAL[level] = idxs_coarse_neighbors_blocks

                """ Query the network """
                xyz = samples[mask_coarse_no_recompute, 0:3].cuda()
                # Query and fill grid.
                samples[mask_coarse_no_recompute, 3] = sample_udf(
                    udf_func, xyz, max_batch=max_batch
                )

                """ Prepare next levels queries """
                if N < self.N_max:
                    ## Which samples are close to the surface?
                    step_size = 2.0 / N
                    close_surface_mask = (
                        torch.abs(samples[mask_coarse, 3]) < 1.5 * 1.7 * step_size
                    )
                    close_surface_masks[level] = close_surface_mask

                    # For those far of the surface, we can ignore them for the future and copy the high value to their neighbors
                    samples[
                        idxs_coarse_neighbors_blocks[~close_surface_mask], 3
                    ] = samples[mask_coarse, 3][~close_surface_mask].unsqueeze(-1)

            udf_values = samples[:, 3]
            udf_values = udf_values.reshape(self.N_max, self.N_max, self.N_max)

        # Compute gradients only where the predicted udf value is small.
        mask_gradients = samples[:, 3] < (2.5 * self.cube_side_length / self.N_max)
        samples[mask_gradients, 4:] = sample_grads(
            udf_func, samples[mask_gradients, :3], max_batch=max_batch
        )
        gradients = samples[:, 4:]
        gradients = gradients.reshape(self.N_max, self.N_max, self.N_max, 3)
        return udf_values, gradients


def sample_udf(
    udf_func: Callable[[Tensor], Tensor],
    coords: Tensor,
    max_batch: int,
    grad: bool = False,
) -> Tensor:
    udf = torch.zeros(coords.shape[0]).cuda()
    start = 0

    while start < coords.shape[0]:
        end = min(start + max_batch, coords.shape[0])
        p = coords[start:end]
        if grad:
            udf[start:end] = udf_func(p)
        else:
            with torch.no_grad():
                udf[start:end] = udf_func(p)
        start = end

    return udf


def sample_grads(
    udf_func: Callable[[Tensor], Tensor],
    coords: Tensor,
    max_batch: int,
) -> Tensor:
    grads = torch.zeros(coords.shape[0], 3).cuda()
    start = 0

    while start < coords.shape[0]:
        end = min(start + max_batch, coords.shape[0])
        p = coords[start:end].detach().clone()
        p.requires_grad = True
        udf = udf_func(p)
        udf.sum().backward()
        g = p.grad
        # norms = torch.norm(g, dim=-1)
        # g[norms < 0.5] = torch.zeros(1, 3).cuda()
        grads[start:end] = -F.normalize(g, dim=1)
        start = end

    return grads


def get_udf_and_grads(
    udf_func: Callable[[Tensor], Tensor],
    coords_range: Tuple[float, float],
    max_dist: float,
    N: int,
    max_batch: int,
) -> Tuple[Tensor, Tensor]:
    """
    Fills a dense N*N*N regular grid by querying the given function.

    Args:
        udf_func: Function to call to get the udf values.
        coords_range: The udf coordinates range.
        max_dist: The udf clipping distance.
        N: Grid resolution.
        max_batch: The maximum number of points that we can evaluate simultaneously.

    Returns:
        - (N, N, N) tensor representing udf values on the grid.
        - (N, N, N, 3) tensor representing gradients values on the grid.
    """
    # compute origin of the volume and voxel size
    origin = [coords_range[0]] * 3
    spacing = (coords_range[1] - coords_range[0]) / (N - 1)

    # prepare grid coordinates, each axis goes from 0 to (N - 1)
    x = torch.arange(0, N)
    coords_x, coords_y, coords_z = torch.meshgrid(x, x, x, indexing="ij")
    coords = torch.stack((coords_x, coords_y, coords_z), dim=-1).float()

    # scale and shift coordinates so that each axis goes from coords_range[0] to coords_range[1]
    coords *= spacing
    coords += torch.tensor(origin)
    coords = coords.reshape(N**3, 3)

    # comput udf for every corner of the grid
    zeros = torch.zeros(coords.shape[0], 4)
    samples = torch.cat([coords, zeros], dim=-1).cuda()
    samples[:, 3] = sample_udf(udf_func, samples[:, :3], max_batch)

    # compute gradients only where the predicted udf value is small
    mask = samples[:, 3] < (max_dist - 1e-3)
    samples[mask, 4:] = sample_grads(udf_func, samples[mask, :3], max_batch // 4)

    # separate values in udf and gradients
    udf = samples[:, 3]
    udf = udf.reshape(N, N, N)
    grads = samples[:, 4:]
    grads = grads.reshape(N, N, N, 3)

    return udf, grads


def get_mesh_from_udf(
    udf_func: Callable[[Tensor], Tensor],
    coords_range: Tuple[float, float],
    max_dist: float,
    N: int = 128,
    smooth_borders: bool = True,
    differentiable: bool = True,
    max_batch: int = 2**12,
    use_fast_grid_filler: bool = True,
) -> Tuple[Tensor, Tensor]:
    """
    Computes a triangulated mesh from udf.

    Args:
        udf_func: Function to call to get the udf values.
        coords_range: The udf coordinates range.
        max_dist: The udf clipping distance.
        N: Grid resolution.
        smooth_borders: Do we smooth borders with a Laplacian?
        differentiable: Do we need the mesh to be differentiable wrt the UDF?
        max_batch: The maximum number of points that we can evaluate simultaneously.
        use_fast_grid_filler: Use coarse to fine UDF grid evaluator, with cached coordinates.

    Returns:
        - Vertices of the mesh.
        - Faces of the mesh.
    """
    # sample udf grid
    if not use_fast_grid_filler:
        udf, gradients = get_udf_and_grads(
            udf_func, coords_range, max_dist, N, max_batch
        )
    else:
        fast_grid_filler = GridFiller(N)
        udf, gradients = fast_grid_filler.fill_grid(udf_func, max_batch)
    udf[udf < 0] = 0

    # run custom marching cubes on it
    N = udf.shape[0]
    spacing = (coords_range[1] - coords_range[0]) / (N - 1)
    udf = udf.cpu().detach().numpy()
    gradients = gradients.cpu().detach().numpy()
    vertices, faces, _, _ = udf_mc_lewiner(udf, gradients, spacing=[spacing] * 3)

    # shift vertices according to the given range
    vertices += coords_range[0]

    mesh = trimesh.Trimesh(vertices, faces, process=False)

    # remove faces whose vertices feature big udf values
    # check not only vertices but also points in the middle of edges
    points = np.vstack(
        (
            mesh.vertices[mesh.edges[:, 0]],
            mesh.vertices[mesh.edges[:, 1]],
            (mesh.vertices[mesh.edges[:, 0]] + mesh.vertices[mesh.edges[:, 1]]) / 2,
        )
    )
    face_idxs = np.hstack([mesh.edges_face] * 3)

    points = torch.from_numpy(points).float().cuda()
    udf = sample_udf(udf_func, points, max_batch)
    udf = udf.cpu().numpy()

    # th_dist is the threshold udf to consider a point on the surface.
    th_dist = 1 / N

    mask = udf > th_dist
    face_idxs_to_remove = np.unique(face_idxs[mask])
    face_mask = np.full(mesh.faces.shape[0], True)
    face_mask[face_idxs_to_remove] = False
    filtered_faces = mesh.faces[face_mask]
    mesh = trimesh.Trimesh(mesh.vertices, filtered_faces)

    # remove NaNs, flat triangles, duplicate faces
    mesh = mesh.process(validate=False)
    mesh.remove_duplicate_faces()
    mesh.remove_degenerate_faces()
    # fill single triangle holes
    mesh.fill_holes()

    # re-process the mesh until it is stable:
    mesh_2 = trimesh.Trimesh(mesh.vertices, mesh.faces)
    n_verts, n_faces, n_iter = 0, 0, 0
    while (n_verts, n_faces) != (
        len(mesh_2.vertices),
        len(mesh_2.faces),
    ) and n_iter < 10:
        mesh_2 = mesh_2.process(validate=False)
        mesh_2.remove_duplicate_faces()
        mesh_2.remove_degenerate_faces()
        (n_verts, n_faces) = (len(mesh_2.vertices), len(mesh_2.faces))
        n_iter += 1
        mesh_2 = trimesh.Trimesh(mesh_2.vertices, mesh_2.faces)

    mesh = trimesh.Trimesh(mesh_2.vertices, mesh_2.faces)

    if smooth_borders:
        # identify borders: those appearing only once
        border_edges = trimesh.grouping.group_rows(mesh.edges_sorted, require_count=1)

        # build a dictionnary of (u,l): l is the list of vertices that are adjacent to u
        neighbours = defaultdict(lambda: [])
        for u, v in mesh.edges_sorted[border_edges]:
            neighbours[u].append(v)
            neighbours[v].append(u)
        border_vertices = np.array(list(neighbours.keys()))

        # build a sparse matrix for computing laplacian
        pos_i, pos_j = [], []
        for k, ns in enumerate(neighbours.values()):
            for j in ns:
                pos_i.append(k)
                pos_j.append(j)

        sparse = coo_matrix(
            (np.ones(len(pos_i)), (pos_i, pos_j)),  # put ones at these locations
            shape=(len(border_vertices), len(mesh.vertices)),
        )

        # smoothing operation:
        lambda_ = 0.3
        for _ in range(20):
            border_neighbouring_averages = sparse @ mesh.vertices / sparse.sum(axis=1)
            laplacian = border_neighbouring_averages - mesh.vertices[border_vertices]
            mesh.vertices[border_vertices] = (
                mesh.vertices[border_vertices] + lambda_ * laplacian
            )

    final_verts = torch.tensor(mesh.vertices).float().cuda()
    final_faces = torch.tensor(mesh.faces).long().cuda()

    if differentiable:
        # use the mesh to compute normals
        normals = trimesh.geometry.weighted_vertex_normals(
            vertex_count=len(mesh.vertices),
            faces=mesh.faces,
            face_normals=mesh.face_normals,
            face_angles=mesh.face_angles,
        )

        # evaluate the udf around each vertex, based on normals
        normals = torch.tensor(normals).float().cuda()
        verts = torch.tensor(mesh.vertices).float().cuda()
        xyz_s1 = verts + th_dist * normals
        xyz_s2 = verts - th_dist * normals
        s1 = sample_udf(udf_func, xyz_s1, max_batch, True).unsqueeze(-1)
        s2 = sample_udf(udf_func, xyz_s2, max_batch, True).unsqueeze(-1)
        # re-plug differentiability here, by this rewriting trick
        z1 = th_dist * s1 * normals - th_dist * s2 * normals
        z2 = (th_dist * s1 * normals - th_dist * s2 * normals).detach()
        new_verts = verts - z1 + z2

        # identify borders
        border_edges = trimesh.grouping.group_rows(mesh.edges_sorted, require_count=1)

        # build a dictionnary of (u,v) edges, such that each vertex on the border
        # gets associated to exactly one border edge
        border_edges_dict = {}
        for u, v in mesh.edges_sorted[border_edges]:
            border_edges_dict[u] = v
            border_edges_dict[v] = u
        u_v_border = np.array(list(border_edges_dict.items()))
        u_border = u_v_border[:, 0]  # split border edges (u,v) into u and v arrays
        v_border = u_v_border[:, 1]

        # for each vertex on the border, take the cross product between
        # its normal and the border's edge
        normals_border = normals[u_border]
        edge_border = mesh.vertices[v_border] - mesh.vertices[u_border]
        edge_border = torch.tensor(edge_border).float().cuda()
        out_vec = torch.cross(edge_border, normals_border, dim=1)
        out_vec = out_vec / (
            torch.norm(out_vec, dim=1, keepdim=True) + 1e-6
        )  # make it unit length

        # then we need to orient the out_vec such that they point outwards
        # to do so, we evaluate at +- their offset, and take the corresponding max udf value
        border_verts = torch.tensor(mesh.vertices[u_border]).float().cuda()
        xyz_s1_border = border_verts + 3 * th_dist * out_vec
        xyz_s2_border = border_verts - 3 * th_dist * out_vec
        s1_border = sample_udf(udf_func, xyz_s1_border, max_batch, True).unsqueeze(-1)
        s2_border = sample_udf(udf_func, xyz_s2_border, max_batch, True).unsqueeze(-1)
        s1s2 = torch.stack((s1_border, s2_border))
        sign_out_vec = -torch.argmax(s1s2, dim=0) * 2 + 1
        out_vec = sign_out_vec * out_vec

        # filter out the verts borders for which a displacement of out_vec still present
        # a udf < th_dist, i.e. verts classified as borders which are not really so
        mask = ((s1_border + s2_border)[:, 0] > th_dist).detach().cpu().numpy()
        u_border_filtered = u_border[mask]
        out_vec_filtered = out_vec[(s1_border + s2_border)[:, 0] > th_dist]
        out_df_filtered = torch.max(s1_border, s2_border)[
            (s1_border + s2_border) > th_dist
        ]

        # plug gradients to verts positions (fake zero, just to pass grads)
        s_border = (th_dist * (out_df_filtered - out_df_filtered.detach())).unsqueeze(
            -1
        )
        new_verts[u_border_filtered] = (
            new_verts[u_border_filtered] - s_border * out_vec_filtered
        )

        final_verts = new_verts
        final_faces = torch.tensor(mesh.faces).long().cuda()

    return final_verts, final_faces


================================================
FILE: meshudf/setup.py
================================================
# python setup.py build_ext --inplace
from setuptools import setup
from Cython.Build import cythonize
import numpy as np
import os

includes_numpy = '-I ' + np.get_include() + ' '
os.environ['CFLAGS'] = includes_numpy + (os.environ['CFLAGS'] if 'CFLAGS' in os.environ else '')

setup(
    name="My MC",
    ext_modules=cythonize("_marching_cubes_lewiner_cy.pyx", include_path=[np.get_include()], language="c++"),
)


================================================
FILE: meshudf/setup.sh
================================================
python3 setup.py build_ext --inplace

================================================
FILE: models/cfg_sampler.py
================================================
import numpy as np
import torch
import torch.nn as nn
from copy import deepcopy

# A wrapper model for Classifier-free guidance **SAMPLING** only
# https://arxiv.org/abs/2207.12598
class ClassifierFreeSampleModel(nn.Module):

    def __init__(self, model):
        super().__init__()
        self.model = model  # model is the actual model to run

        #assert self.model.cond_mask_prob > 0, 'Cannot run a guided diffusion on a model that has not been trained with no conditions'

        self.cond_mode = self.model.cond_mode
        self.clip_version = self.model.clip_version

    def forward(self, x, timesteps, y=None):
        cond_mode = self.model.cond_mode
        assert cond_mode in ['text', 'action']
        y_uncond = deepcopy(y)
        y_uncond['uncond'] = True
        out = self.model(x, timesteps, y)
        out_uncond = self.model(x, timesteps, y_uncond)
        return out_uncond + (y['scale'].view(-1, 1, 1) * (out - out_uncond))



================================================
FILE: models/mdm.py
================================================
import torch
import torch.nn as nn
import clip

import torch
import torch.nn as nn
from models.openaimodel import UNetModel

class MDM(nn.Module):

    def __init__(self, modeltype, num_actions, dropout=0.1, activation="gelu", legacy=False, dataset='deepfasion3d', clip_dim=512,
                 arch='OpenUNet', clip_version=None, **kargs):
        super().__init__()

        self.legacy = legacy
        self.modeltype = modeltype
        self.num_actions = num_actions

        self.dataset = dataset

        self.dropout = dropout

        self.activation = activation
        self.clip_dim = clip_dim

        self.cond_mode = kargs.get('cond_mode', 'no_cond')
        self.cond_mask_prob = kargs.get('cond_mask_prob', 0.)
        self.arch = arch

        if self.arch == 'OpenUNet':
            num_classes=None
            if 'category' in self.cond_mode:
                num_classes = self.num_actions
            self.Unet = UNetModel(
                in_channels=1,
                model_channels=224,
                out_channels=1,
                num_res_blocks=2,
                attention_resolutions=[ 4, 2, 1 ],
                dropout=0,
                channel_mult=(1, 2, 4, 4),
                conv_resample=True,
                dims=1,
                num_classes=num_classes,
                use_checkpoint=True,
                use_fp16=False,
                num_heads=8,
                num_head_channels=-1,
                num_heads_upsample=-1,
                use_scale_shift_norm=False,
                resblock_updown=False,
                use_new_attention_order=False,
                use_spatial_transformer=False,    # custom transformer support
                transformer_depth=1,              # custom transformer support
                context_dim=clip_dim,                 # custom transformer support
                n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
                legacy=False,)

        self.clip_version = clip_version
        if self.cond_mode != 'no_cond':
            if 'text' in self.cond_mode:
                #self.embed_text = nn.Linear(self.clip_dim, self.latent_dim)
                print('EMBED TEXT')
                print('Loading CLIP...')

                self.clip_version = clip_version
                self.clip_model = self.load_and_freeze_clip(clip_version)

            if 'sketch' in self.cond_mode:
                self.clip_version = clip_version

    def parameters_wo_clip(self):
        return [p for name, p in self.named_parameters() if not name.startswith('clip_model.')]

    def load_and_freeze_clip(self, clip_version):
        clip_model, _ = clip.load(clip_version, device='cpu',
                                                jit=False)  # Must set jit=False for training
        # Freeze CLIP weights
        clip_model.eval()
        for p in clip_model.parameters():
            p.requires_grad = False

        return clip_model


    def encode_text(self, raw_text):
        device = next(self.parameters()).device
        texts = clip.tokenize(raw_text, truncate=True).to(device) 
        return self.clip_model.encode_text(texts).float()

    def forward(self, x, timesteps, y=None):
        """
        x: [batch_size, njoints, nfeats, max_frames], denoted x_t in the paper
        timesteps: [batch_size] (int)
        """
        if 'text' in self.cond_mode:
            enc_text = self.encode_text(y['text'])


        if 'sketch' in self.cond_mode or 'img' in self.cond_mode:
            context = y['context']
            output = self.Unet(x, timesteps=timesteps, context=context)
        elif self.cond_mode == 'no_cond':
            output = self.Unet(x, timesteps=timesteps, y=None)
        elif 'text' in self.cond_mode:
            output = self.Unet(x, timesteps=timesteps, context=enc_text)
        else:
            output = self.Unet(x, timesteps=timesteps, y=y['action_text'])

        return output

    def train(self, *args, **kwargs):
        super().train(*args, **kwargs)




================================================
FILE: models/models.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F

from einops import rearrange

class LinearAttention(nn.Module):
    def __init__(self, dim, heads=4, dim_head=32):
        super().__init__()
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv3d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Conv3d(hidden_dim, dim, 1)

    def forward(self, x):
        b, c, d, h, w = x.shape
        qkv = self.to_qkv(x)
        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
        k = k.softmax(dim=-1)  
        context = torch.einsum('bhdn,bhen->bhde', k, v)
        out = torch.einsum('bhde,bhdn->bhen', context, q)
        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
        return self.to_out(out)

class LinAttnBlock(LinearAttention):
    """to match AttnBlock usage"""
    def __init__(self, in_channels):
        super().__init__(dim=in_channels, heads=1, dim_head=in_channels)

class Downsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            # no asymmetric padding in torch conv, must do it ourselves
            self.conv = torch.nn.Conv3d(in_channels,
                                        in_channels,
                                        kernel_size=3,
                                        stride=2,
                                        padding=0)

    def forward(self, x):
        if self.with_conv:
            pad = (0,1,0,1,0,1)
            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
            x = self.conv(x)
        else:
            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
        return x


class Upsample(nn.Module):
    def __init__(self, in_channels, with_conv):
        super().__init__()
        self.with_conv = with_conv
        if self.with_conv:
            self.conv = torch.nn.Conv3d(in_channels,
                                        in_channels,
                                        kernel_size=3,
                                        stride=1,
                                        padding=1)

    def forward(self, x):
        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="trilinear")
        if self.with_conv:
            x = self.conv(x)
        return x


class AttnBlock(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv3d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.k = torch.nn.Conv3d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.v = torch.nn.Conv3d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.proj_out = torch.nn.Conv3d(in_channels,
                                        in_channels,
                                        kernel_size=1,
                                        stride=1,
                                        padding=0)


    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b,c,d,h,w = q.shape
        q = q.reshape(b,c,h*w*d)
        q = q.permute(0,2,1)   # b,hw,c
        k = k.reshape(b,c,h*w*d) # b,c,hw
        w_ = torch.bmm(q,k)     # b,hw,hw    w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
        w_ = w_ * (int(c)**(-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = v.reshape(b,c,h*w*d)
        w_ = w_.permute(0,2,1)   # b,hw,hw (first hw of k, second of q)
        h_ = torch.bmm(v,w_)     # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
        h_ = h_.reshape(b,c,d,h,w)

        h_ = self.proj_out(h_)

        return x+h_



def make_attn(in_channels, attn_type="vanilla"):
    assert attn_type in ["vanilla", "linear", "none"], f'attn_type {attn_type} unknown'
    print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
    if attn_type == "vanilla":
        return AttnBlock(in_channels)
    elif attn_type == "none":
        return nn.Identity(in_channels)
    else:
        return LinAttnBlock(in_channels)

def Normalize(in_channels, num_groups=4):
    return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)

def nonlinearity(x):
    # swish
    #return x*torch.sigmoid(x)
    return F.relu(x) ##test abs z


class ResnetBlock(nn.Module):
    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
                 dropout, temb_channels=512):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
        self.out_channels = out_channels
        self.use_conv_shortcut = conv_shortcut

        self.norm1 = Normalize(in_channels)
        self.conv1 = torch.nn.Conv3d(in_channels,
                                     out_channels,
                                     kernel_size=3,
                                     stride=1,
                                     padding=1)
        if temb_channels > 0:
            self.temb_proj = torch.nn.Linear(temb_channels,
                                             out_channels)
        self.norm2 = Normalize(out_channels)
        self.dropout = torch.nn.Dropout(dropout)
        self.conv2 = torch.nn.Conv3d(out_channels,
                                     out_channels,
                                     kernel_size=3,
                                     stride=1,
                                     padding=1)
        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                self.conv_shortcut = torch.nn.Conv3d(in_channels,
                                                     out_channels,
                                                     kernel_size=3,
                                                     stride=1,
                                                     padding=1)
            else:
                self.nin_shortcut = torch.nn.Conv3d(in_channels,
                                                    out_channels,
                                                    kernel_size=1,
                                                    stride=1,
                                                    padding=0)

    def forward(self, x, temb):
        h = x
        h = self.norm1(h)
        h = nonlinearity(h)
        h = self.conv1(h)

        if temb is not None:
            h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]

        h = self.norm2(h)
        h = nonlinearity(h)
        h = self.dropout(h)
        h = self.conv2(h)

        if self.in_channels != self.out_channels:
            if self.use_conv_shortcut:
                x = self.conv_shortcut(x)
            else:
                x = self.nin_shortcut(x)

        return x+h





###
#Old version of auto encoder
###
class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels, conv_type=nn.Conv3d, mid_channels=None):
        super(DoubleConv, self).__init__()
        if not mid_channels:
            mid_channels = out_channels
        #print('debug')
        self.double_conv = nn.Sequential(
            conv_type(in_channels, mid_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(mid_channels),
            nn.ReLU(inplace=True),
            conv_type(mid_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm3d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)


class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels, conv_type=nn.Conv3d):
        super(Down, self).__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool3d(2),
            DoubleConv(in_channels, out_channels, conv_type=conv_type)
        )

    def forward(self, x):
        return self.maxpool_conv(x)


class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, trilinear=True):
        super(Up, self).__init__()

        # if trilinear, use the normal convolutions to reduce the number of channels
        if trilinear:
            self.up = nn.Upsample(scale_factor=2, mode='trilinear', align_corners=True)
            self.conv = DoubleConv(in_channels, out_channels, mid_channels=in_channels // 2)
        else:
            self.up = nn.ConvTranspose3d(in_channels, in_channels // 2, kernel_size=2, stride=2)
            self.conv = DoubleConv(in_channels, out_channels)


    def forward(self, x1):
        x = self.up(x1)
        # input is CHW
        # diffY = x2.size()[2] - x1.size()[2]
        # diffX = x2.size()[3] - x1.size()[3]

        # x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
        #                 diffY // 2, diffY - diffY // 2])
        # if you have padding issues, see
        # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
        # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
        #x = torch.cat([x2, x1], dim=1)
        return self.conv(x)




class DepthwiseSeparableConv3d(nn.Module):
    def __init__(self, nin, nout, kernel_size, padding, kernels_per_layer=1):
        super(DepthwiseSeparableConv3d, self).__init__()
        self.depthwise = nn.Conv3d(nin, nin * kernels_per_layer, kernel_size=kernel_size, padding=padding, groups=nin)
        self.pointwise = nn.Conv3d(nin * kernels_per_layer, nout, kernel_size=1)

    def forward(self, x):
        out = self.depthwise(x)
        out = self.pointwise(out)
        return out


class Autoencoder_Old(nn.Module):
    def __init__(self, n_channels=1, width_multiplier=1, trilinear=True, use_ds_conv=False) -> None:
        super(Autoencoder_Old, self).__init__()
        # _channels = (32, 64, 128, 256, 512)
        _channels = (32, 64, 128, 256)
        #_channels = (64, 128, 256, 512)
        
        self.n_channels = n_channels
        self.channels = [int(c*width_multiplier) for c in _channels]
        self.trilinear = trilinear
        self.convtype = DepthwiseSeparableConv3d if use_ds_conv else nn.Conv3d

        self.inc = DoubleConv(n_channels, self.channels[0], conv_type=self.convtype)
        self.down1 = Down(self.channels[0], self.channels[1], conv_type=self.convtype)
        self.down2 = Down(self.channels[1], self.channels[2], conv_type=self.convtype)
        factor = 2 if trilinear else 1
        self.down3 = Down(self.channels[2], self.channels[3], conv_type=self.convtype)

        self.up1 = Up(self.channels[3], self.channels[2], trilinear)
        self.up2 = Up(self.channels[2], self.channels[1], trilinear)
        self.up3 = Up(self.channels[1], n_channels, trilinear)


    def encode(self, x):
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        
        return x4

    def decode(self, z):
        x = self.up1(z)
        x = self.up2(x)
        x = self.up3(x)

        return x

    def forward(self, input):
        z = self.encode(input)
        dec = self.decode(z)

        return dec

================================================
FILE: models/openaimodel.py
================================================
from abc import abstractmethod
from functools import partial
import math
from typing import Iterable

import numpy as np
import torch as th
import torch.nn as nn
import torch.nn.functional as F

from utils.ldm_utils import (
    checkpoint,
    conv_nd,
    linear,
    avg_pool_nd,
    zero_module,
    normalization,
    timestep_embedding,
)
from modules.attention import SpatialTransformer


# dummy replace
def convert_module_to_f16(x):
    pass

def convert_module_to_f32(x):
    pass


## go
class AttentionPool2d(nn.Module):
    """
    Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
    """

    def __init__(
        self,
        spacial_dim: int,
        embed_dim: int,
        num_heads_channels: int,
        output_dim: int = None,
    ):
        super().__init__()
        self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
        self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
        self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
        self.num_heads = embed_dim // num_heads_channels
        self.attention = QKVAttention(self.num_heads)

    def forward(self, x):
        b, c, *_spatial = x.shape
        x = x.reshape(b, c, -1)  # NC(HW)
        x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1)  # NC(HW+1)
        x = x + self.positional_embedding[None, :, :].to(x.dtype)  # NC(HW+1)
        x = self.qkv_proj(x)
        x = self.attention(x)
        x = self.c_proj(x)
        return x[:, :, 0]


class TimestepBlock(nn.Module):
    """
    Any module where forward() takes timestep embeddings as a second argument.
    """

    @abstractmethod
    def forward(self, x, emb):
        """
        Apply the module to `x` given `emb` timestep embeddings.
        """


class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
    """
    A sequential module that passes timestep embeddings to the children that
    support it as an extra input.
    """

    def forward(self, x, emb, context=None):
        for layer in self:
            if isinstance(layer, TimestepBlock):
                x = layer(x, emb)
            elif isinstance(layer, SpatialTransformer):
                x = layer(x, context)
            else:
                x = layer(x)
        return x


class Upsample(nn.Module):
    """
    An upsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 upsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=3, out_channels=None, padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        if use_conv:
            self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)

    def forward(self, x):
        assert x.shape[1] == self.channels
        if self.dims == 3:
            x = F.interpolate(
                x, (x.shape[2] * 2, x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
            )
        else:
            x = F.interpolate(x, scale_factor=2, mode="nearest")
        if self.use_conv:
            x = self.conv(x)
        return x

class TransposedUpsample(nn.Module):
    'Learned 2x upsampling without padding'
    def __init__(self, channels, out_channels=None, ks=5):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels

        self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)

    def forward(self,x):
        return self.up(x)


class Downsample(nn.Module):
    """
    A downsampling layer with an optional convolution.
    :param channels: channels in the inputs and outputs.
    :param use_conv: a bool determining if a convolution is applied.
    :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
                 downsampling occurs in the inner-two dimensions.
    """

    def __init__(self, channels, use_conv, dims=3, out_channels=None,padding=1):
        super().__init__()
        self.channels = channels
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.dims = dims
        stride = 2 if dims != 3 else (2, 2, 2)
        if use_conv:
            self.op = conv_nd(
                dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
            )
        else:
            assert self.channels == self.out_channels
            self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)

    def forward(self, x):
        assert x.shape[1] == self.channels
        return self.op(x)


class ResBlock(TimestepBlock):
    """
    A residual block that can optionally change the number of channels.
    :param channels: the number of input channels.
    :param emb_channels: the number of timestep embedding channels.
    :param dropout: the rate of dropout.
    :param out_channels: if specified, the number of out channels.
    :param use_conv: if True and out_channels is specified, use a spatial
        convolution instead of a smaller 1x1 convolution to change the
        channels in the skip connection.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param use_checkpoint: if True, use gradient checkpointing on this module.
    :param up: if True, use this block for upsampling.
    :param down: if True, use this block for downsampling.
    """

    def __init__(
        self,
        channels,
        emb_channels,
        dropout,
        out_channels=None,
        use_conv=False,
        use_scale_shift_norm=False,
        dims=3,
        use_checkpoint=False,
        up=False,
        down=False,
    ):
        super().__init__()
        self.channels = channels
        self.emb_channels = emb_channels
        self.dropout = dropout
        self.out_channels = out_channels or channels
        self.use_conv = use_conv
        self.use_checkpoint = use_checkpoint
        self.use_scale_shift_norm = use_scale_shift_norm

        self.in_layers = nn.Sequential(
            normalization(channels),
            nn.SiLU(),
            conv_nd(dims, channels, self.out_channels, 3, padding=1),
        )

        self.updown = up or down

        if up:
            self.h_upd = Upsample(channels, False, dims)
            self.x_upd = Upsample(channels, False, dims)
        elif down:
            self.h_upd = Downsample(channels, False, dims)
            self.x_upd = Downsample(channels, False, dims)
        else:
            self.h_upd = self.x_upd = nn.Identity()

        self.emb_layers = nn.Sequential(
            nn.SiLU(),
            linear(
                emb_channels,
                2 * self.out_channels if use_scale_shift_norm else self.out_channels,
            ),
        )
        self.out_layers = nn.Sequential(
            normalization(self.out_channels),
            nn.SiLU(),
            nn.Dropout(p=dropout),
            zero_module(
                conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
            ),
        )

        if self.out_channels == channels:
            self.skip_connection = nn.Identity()
        elif use_conv:
            self.skip_connection = conv_nd(
                dims, channels, self.out_channels, 3, padding=1
            )
        else:
            self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)

    def forward(self, x, emb):
        """
        Apply the block to a Tensor, conditioned on a timestep embedding.
        :param x: an [N x C x ...] Tensor of features.
        :param emb: an [N x emb_channels] Tensor of timestep embeddings.
        :return: an [N x C x ...] Tensor of outputs.
        """
        return checkpoint(
            self._forward, (x, emb), self.parameters(), self.use_checkpoint
        )


    def _forward(self, x, emb):
        if self.updown:
            in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
            h = in_rest(x)
            h = self.h_upd(h)
            x = self.x_upd(x)
            h = in_conv(h)
        else:
            h = self.in_layers(x)
        emb_out = self.emb_layers(emb).type(h.dtype)
        while len(emb_out.shape) < len(h.shape):
            emb_out = emb_out[..., None]
        if self.use_scale_shift_norm:
            out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
            scale, shift = th.chunk(emb_out, 2, dim=1)
            h = out_norm(h) * (1 + scale) + shift
            h = out_rest(h)
        else:
            h = h + emb_out
            h = self.out_layers(h)
        return self.skip_connection(x) + h


class AttentionBlock(nn.Module):
    """
    An attention block that allows spatial positions to attend to each other.
    Originally ported from here, but adapted to the N-d case.
    https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
    """

    def __init__(
        self,
        channels,
        num_heads=1,
        num_head_channels=-1,
        use_checkpoint=False,
        use_new_attention_order=False,
    ):
        super().__init__()
        self.channels = channels
        if num_head_channels == -1:
            self.num_heads = num_heads
        else:
            assert (
                channels % num_head_channels == 0
            ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
            self.num_heads = channels // num_head_channels
        self.use_checkpoint = use_checkpoint
        self.norm = normalization(channels)
        self.qkv = conv_nd(1, channels, channels * 3, 1)
        if use_new_attention_order:
            # split qkv before split heads
            self.attention = QKVAttention(self.num_heads)
        else:
            # split heads before split qkv
            self.attention = QKVAttentionLegacy(self.num_heads)

        self.proj_out = zero_module(conv_nd(1, channels, channels, 1))

    def forward(self, x):
        return checkpoint(self._forward, (x,), self.parameters(), True)   # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
        #return pt_checkpoint(self._forward, x)  # pytorch

    def _forward(self, x):
        b, c, *spatial = x.shape
        x = x.reshape(b, c, -1)
        qkv = self.qkv(self.norm(x))
        h = self.attention(qkv)
        h = self.proj_out(h)
        return (x + h).reshape(b, c, *spatial)


def count_flops_attn(model, _x, y):
    """
    A counter for the `thop` package to count the operations in an
    attention operation.
    Meant to be used like:
        macs, params = thop.profile(
            model,
            inputs=(inputs, timestamps),
            custom_ops={QKVAttention: QKVAttention.count_flops},
        )
    """
    b, c, *spatial = y[0].shape
    num_spatial = int(np.prod(spatial))
    # We perform two matmuls with the same number of ops.
    # The first computes the weight matrix, the second computes
    # the combination of the value vectors.
    matmul_ops = 2 * b * (num_spatial ** 2) * c
    model.total_ops += th.DoubleTensor([matmul_ops])


class QKVAttentionLegacy(nn.Module):
    """
    A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts", q * scale, k * scale
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v)
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)


class QKVAttention(nn.Module):
    """
    A module which performs QKV attention and splits in a different order.
    """

    def __init__(self, n_heads):
        super().__init__()
        self.n_heads = n_heads

    def forward(self, qkv):
        """
        Apply QKV attention.
        :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
        :return: an [N x (H * C) x T] tensor after attention.
        """
        bs, width, length = qkv.shape
        assert width % (3 * self.n_heads) == 0
        ch = width // (3 * self.n_heads)
        q, k, v = qkv.chunk(3, dim=1)
        scale = 1 / math.sqrt(math.sqrt(ch))
        weight = th.einsum(
            "bct,bcs->bts",
            (q * scale).view(bs * self.n_heads, ch, length),
            (k * scale).view(bs * self.n_heads, ch, length),
        )  # More stable with f16 than dividing afterwards
        weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
        a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
        return a.reshape(bs, -1, length)

    @staticmethod
    def count_flops(model, _x, y):
        return count_flops_attn(model, _x, y)


class UNetModel(nn.Module):
    """
    The full UNet model with attention and timestep embedding.
    :param in_channels: channels in the input Tensor.
    :param model_channels: base channel count for the model.
    :param out_channels: channels in the output Tensor.
    :param num_res_blocks: number of residual blocks per downsample.
    :param attention_resolutions: a collection of downsample rates at which
        attention will take place. May be a set, list, or tuple.
        For example, if this contains 4, then at 4x downsampling, attention
        will be used.
    :param dropout: the dropout probability.
    :param channel_mult: channel multiplier for each level of the UNet.
    :param conv_resample: if True, use learned convolutions for upsampling and
        downsampling.
    :param dims: determines if the signal is 1D, 2D, or 3D.
    :param num_classes: if specified (as an int), then this model will be
        class-conditional with `num_classes` classes.
    :param use_checkpoint: use gradient checkpointing to reduce memory usage.
    :param num_heads: the number of attention heads in each attention layer.
    :param num_heads_channels: if specified, ignore num_heads and instead use
                               a fixed channel width per attention head.
    :param num_heads_upsample: works with num_heads to set a different number
                               of heads for upsampling. Deprecated.
    :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
    :param resblock_updown: use residual blocks for up/downsampling.
    :param use_new_attention_order: use a different attention pattern for potentially
                                    increased efficiency.
    """

    def __init__(
        self,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=3,
        num_classes=None,
        use_checkpoint=False,
        use_fp16=False,
        num_heads=-1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
        use_spatial_transformer=False,    # custom transformer support
        transformer_depth=1,              # custom transformer support
        context_dim=None,                 # custom transformer support
        n_embed=None,                     # custom support for prediction of discrete ids into codebook of first stage vq model
        legacy=True,
    ):
        super().__init__()
        # if use_spatial_transformer:
        #     assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'

        # if context_dim is not None:
        #     assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
        #     from omegaconf.listconfig import ListConfig
        #     if type(context_dim) == ListConfig:
        #         context_dim = list(context_dim)

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        if num_heads == -1:
            assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'

        if num_head_channels == -1:
            assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'

        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.num_classes = num_classes
        self.use_checkpoint = use_checkpoint
        self.dtype = th.float16 if use_fp16 else th.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample
        self.predict_codebook_ids = n_embed is not None

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )
        if self.num_classes is not None:
            self.label_emb = nn.Embedding(num_classes, time_embed_dim)
        if context_dim is not None:
            #print(context_dim)
            self.sketch_emb = nn.Linear(context_dim, time_embed_dim)

        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )
        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                layers = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=mult * model_channels,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = mult * model_channels
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        #num_heads = 1
                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads,
                            num_head_channels=dim_head,
                            use_new_attention_order=use_new_attention_order,
                        ) if not use_spatial_transformer else SpatialTransformer(
                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
                        )
                    )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        if num_head_channels == -1:
            dim_head = ch // num_heads
        else:
            num_heads = ch // num_head_channels
            dim_head = num_head_channels
        if legacy:
            #num_heads = 1
            dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
        self.middle_block = TimestepEmbedSequential(
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            AttentionBlock(
                ch,
                use_checkpoint=use_checkpoint,
                num_heads=num_heads,
                num_head_channels=dim_head,
                use_new_attention_order=use_new_attention_order,
            ) if not use_spatial_transformer else SpatialTransformer(
                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
                        ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self._feature_size += ch

        self.output_blocks = nn.ModuleList([])
        for level, mult in list(enumerate(channel_mult))[::-1]:
            for i in range(num_res_blocks + 1):
                ich = input_block_chans.pop()
                layers = [
                    ResBlock(
                        ch + ich,
                        time_embed_dim,
                        dropout,
                        out_channels=model_channels * mult,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = model_channels * mult
                if ds in attention_resolutions:
                    if num_head_channels == -1:
                        dim_head = ch // num_heads
                    else:
                        num_heads = ch // num_head_channels
                        dim_head = num_head_channels
                    if legacy:
                        #num_heads = 1
                        dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads_upsample,
                            num_head_channels=dim_head,
                            use_new_attention_order=use_new_attention_order,
                        ) if not use_spatial_transformer else SpatialTransformer(
                            ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim
                        )
                    )
                if level and i == num_res_blocks:
                    out_ch = ch
                    layers.append(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            up=True,
                        )
                        if resblock_updown
                        else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
                    )
                    ds //= 2
                self.output_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch

        self.out = nn.Sequential(
            normalization(ch),
            nn.SiLU(),
            zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
        )
        if self.predict_codebook_ids:
            self.id_predictor = nn.Sequential(
            normalization(ch),
            conv_nd(dims, model_channels, n_embed, 1),
            #nn.LogSoftmax(dim=1)  # change to cross_entropy and produce non-normalized logits
        )

    def convert_to_fp16(self):
        """
        Convert the torso of the model to float16.
        """
        self.input_blocks.apply(convert_module_to_f16)
        self.middle_block.apply(convert_module_to_f16)
        self.output_blocks.apply(convert_module_to_f16)

    def convert_to_fp32(self):
        """
        Convert the torso of the model to float32.
        """
        self.input_blocks.apply(convert_module_to_f32)
        self.middle_block.apply(convert_module_to_f32)
        self.output_blocks.apply(convert_module_to_f32)

    def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
        """
        Apply the model to an input batch.
        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :param context: conditioning plugged in via crossattn
        :param y: an [N] Tensor of labels, if class-conditional.
        :return: an [N x C x ...] Tensor of outputs.
        """
        # print(x.shape)
        assert (y is not None) == (
            self.num_classes is not None
        ), "must specify y if and only if the model is class-conditional"
        hs = []
        t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
        emb = self.time_embed(t_emb)

        if self.num_classes is not None:
            #print('test_shape', y.shape, (x.shape[0],))
            assert y.shape == (x.shape[0],)
            emb = emb + self.label_emb(y)
        if context is not None:
            #print(context.shape, x.shape)
            assert context.shape[0] == x.shape[0]
            #print(context.dtype, self.sketch_emb.weight.dtype)
            emb = emb + self.sketch_emb(context)

        h = x.type(self.dtype)
        for module in self.input_blocks:
            h = module(h, emb, context)
            hs.append(h)
        h = self.middle_block(h, emb, context)
        for module in self.output_blocks:
            h = th.cat([h, hs.pop()], dim=1)
            h = module(h, emb, context)
        h = h.type(x.dtype)
        if self.predict_codebook_ids:
            return self.id_predictor(h)
        else:
            return self.out(h)


class EncoderUNetModel(nn.Module):
    """
    The half UNet model with attention and timestep embedding.
    For usage, see UNet.
    """

    def __init__(
        self,
        image_size,
        in_channels,
        model_channels,
        out_channels,
        num_res_blocks,
        attention_resolutions,
        dropout=0,
        channel_mult=(1, 2, 4, 8),
        conv_resample=True,
        dims=3,
        use_checkpoint=False,
        use_fp16=False,
        num_heads=1,
        num_head_channels=-1,
        num_heads_upsample=-1,
        use_scale_shift_norm=False,
        resblock_updown=False,
        use_new_attention_order=False,
        pool="adaptive",
        *args,
        **kwargs
    ):
        super().__init__()

        if num_heads_upsample == -1:
            num_heads_upsample = num_heads

        self.in_channels = in_channels
        self.model_channels = model_channels
        self.out_channels = out_channels
        self.num_res_blocks = num_res_blocks
        self.attention_resolutions = attention_resolutions
        self.dropout = dropout
        self.channel_mult = channel_mult
        self.conv_resample = conv_resample
        self.use_checkpoint = use_checkpoint
        self.dtype = th.float16 if use_fp16 else th.float32
        self.num_heads = num_heads
        self.num_head_channels = num_head_channels
        self.num_heads_upsample = num_heads_upsample

        time_embed_dim = model_channels * 4
        self.time_embed = nn.Sequential(
            linear(model_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, time_embed_dim),
        )

        self.input_blocks = nn.ModuleList(
            [
                TimestepEmbedSequential(
                    conv_nd(dims, in_channels, model_channels, 3, padding=1)
                )
            ]
        )
        self._feature_size = model_channels
        input_block_chans = [model_channels]
        ch = model_channels
        ds = 1
        for level, mult in enumerate(channel_mult):
            for _ in range(num_res_blocks):
                layers = [
                    ResBlock(
                        ch,
                        time_embed_dim,
                        dropout,
                        out_channels=mult * model_channels,
                        dims=dims,
                        use_checkpoint=use_checkpoint,
                        use_scale_shift_norm=use_scale_shift_norm,
                    )
                ]
                ch = mult * model_channels
                if ds in attention_resolutions:
                    layers.append(
                        AttentionBlock(
                            ch,
                            use_checkpoint=use_checkpoint,
                            num_heads=num_heads,
                            num_head_channels=num_head_channels,
                            use_new_attention_order=use_new_attention_order,
                        )
                    )
                self.input_blocks.append(TimestepEmbedSequential(*layers))
                self._feature_size += ch
                input_block_chans.append(ch)
            if level != len(channel_mult) - 1:
                out_ch = ch
                self.input_blocks.append(
                    TimestepEmbedSequential(
                        ResBlock(
                            ch,
                            time_embed_dim,
                            dropout,
                            out_channels=out_ch,
                            dims=dims,
                            use_checkpoint=use_checkpoint,
                            use_scale_shift_norm=use_scale_shift_norm,
                            down=True,
                        )
                        if resblock_updown
                        else Downsample(
                            ch, conv_resample, dims=dims, out_channels=out_ch
                        )
                    )
                )
                ch = out_ch
                input_block_chans.append(ch)
                ds *= 2
                self._feature_size += ch

        self.middle_block = TimestepEmbedSequential(
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
            AttentionBlock(
                ch,
                use_checkpoint=use_checkpoint,
                num_heads=num_heads,
                num_head_channels=num_head_channels,
                use_new_attention_order=use_new_attention_order,
            ),
            ResBlock(
                ch,
                time_embed_dim,
                dropout,
                dims=dims,
                use_checkpoint=use_checkpoint,
                use_scale_shift_norm=use_scale_shift_norm,
            ),
        )
        self._feature_size += ch
        self.pool = pool
        if pool == "adaptive":
            self.out = nn.Sequential(
                normalization(ch),
                nn.SiLU(),
                nn.AdaptiveAvgPool2d((1, 1)),
                zero_module(conv_nd(dims, ch, out_channels, 1)),
                nn.Flatten(),
            )
        elif pool == "attention":
            assert num_head_channels != -1
            self.out = nn.Sequential(
                normalization(ch),
                nn.SiLU(),
                AttentionPool2d(
                    (image_size // ds), ch, num_head_channels, out_channels
                ),
            )
        elif pool == "spatial":
            self.out = nn.Sequential(
                nn.Linear(self._feature_size, 2048),
                nn.ReLU(),
                nn.Linear(2048, self.out_channels),
            )
        elif pool == "spatial_v2":
            self.out = nn.Sequential(
                nn.Linear(self._feature_size, 2048),
                normalization(2048),
                nn.SiLU(),
                nn.Linear(2048, self.out_channels),
            )
        else:
            raise NotImplementedError(f"Unexpected {pool} pooling")

    def convert_to_fp16(self):
        """
        Convert the torso of the model to float16.
        """
        self.input_blocks.apply(convert_module_to_f16)
        self.middle_block.apply(convert_module_to_f16)

    def convert_to_fp32(self):
        """
        Convert the torso of the model to float32.
        """
        self.input_blocks.apply(convert_module_to_f32)
        self.middle_block.apply(convert_module_to_f32)

    def forward(self, x, timesteps):
        """
        Apply the model to an input batch.
        :param x: an [N x C x ...] Tensor of inputs.
        :param timesteps: a 1-D batch of timesteps.
        :return: an [N x K] Tensor of outputs.
        """
        emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))

        results = []
        h = x.type(self.dtype)
        for module in self.input_blocks:
            h = module(h, emb)
            if self.pool.startswith("spatial"):
                results.append(h.type(x.dtype).mean(dim=(2, 3)))
        h = self.middle_block(h, emb)
        if self.pool.startswith("spatial"):
            results.append(h.type(x.dtype).mean(dim=(2, 3)))
            h = th.cat(results, axis=-1)
            return self.out(h)
        else:
            h = h.type(x.dtype)
            return self.out(h)



================================================
FILE: modules/attention.py
================================================
from inspect import isfunction
import math
import torch
import torch.nn.functional as F
from torch import nn, einsum
from einops import rearrange, repeat

from utils.ldm_utils import checkpoint


def exists(val):
    return val is not None


def uniq(arr):
    return{el: True for el in arr}.keys()


def default(val, d):
    if exists(val):
        return val
    return d() if isfunction(d) else d


def max_neg_value(t):
    return -torch.finfo(t.dtype).max


def init_(tensor):
    dim = tensor.shape[-1]
    std = 1 / math.sqrt(dim)
    tensor.uniform_(-std, std)
    return tensor


# feedforward
class GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)


class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        project_in = nn.Sequential(
            nn.Linear(dim, inner_dim),
            nn.GELU()
        ) if not glu else GEGLU(dim, inner_dim)

        self.net = nn.Sequential(
            project_in,
            nn.Dropout(dropout),
            nn.Linear(inner_dim, dim_out)
        )

    def forward(self, x):
        return self.net(x)


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def Normalize(in_channels):
    return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)


class LinearAttention(nn.Module):
    def __init__(self, dim, heads=4, dim_head=32):
        super().__init__()
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias = False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)

    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x)
        q, k, v = rearrange(qkv, 'b (qkv heads c) h w -> qkv b heads c (h w)', heads = self.heads, qkv=3)
        k = k.softmax(dim=-1)  
        context = torch.einsum('bhdn,bhen->bhde', k, v)
        out = torch.einsum('bhde,bhdn->bhen', context, q)
        out = rearrange(out, 'b heads c (h w) -> b (heads c) h w', heads=self.heads, h=h, w=w)
        return self.to_out(out)


class SpatialSelfAttention(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = Normalize(in_channels)
        self.q = torch.nn.Conv2d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.k = torch.nn.Conv2d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.v = torch.nn.Conv2d(in_channels,
                                 in_channels,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)
        self.proj_out = torch.nn.Conv2d(in_channels,
                                        in_channels,
                                        kernel_size=1,
                                        stride=1,
                                        padding=0)

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b,c,h,w = q.shape
        q = rearrange(q, 'b c h w -> b (h w) c')
        k = rearrange(k, 'b c h w -> b c (h w)')
        w_ = torch.einsum('bij,bjk->bik', q, k)

        w_ = w_ * (int(c)**(-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = rearrange(v, 'b c h w -> b c (h w)')
        w_ = rearrange(w_, 'b i j -> b j i')
        h_ = torch.einsum('bij,bjk->bik', v, w_)
        h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
        h_ = self.proj_out(h_)

        return x+h_


class CrossAttention(nn.Module):
    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.scale = dim_head ** -0.5
        self.heads = heads

        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(
            nn.Linear(inner_dim, query_dim),
            nn.Dropout(dropout)
        )

    def forward(self, x, context=None, mask=None):
        h = self.heads

        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
        v = self.to_v(context)

        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))

        sim = einsum('b i d, b j d -> b i j', q, k) * self.scale

        if exists(mask):
            mask = rearrange(mask, 'b ... -> b (...)')
            max_neg_value = -torch.finfo(sim.dtype).max
            mask = repeat(mask, 'b j -> (b h) () j', h=h)
            sim.masked_fill_(~mask, max_neg_value)

        # attention, what we cannot get enough of
        attn = sim.softmax(dim=-1)

        out = einsum('b i j, b j d -> b i d', attn, v)
        out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
        return self.to_out(out)


class BasicTransformerBlock(nn.Module):
    def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True):
        super().__init__()
        self.attn1 = CrossAttention(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout)  # is a self-attention
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = CrossAttention(query_dim=dim, context_dim=context_dim,
                                    heads=n_heads, dim_head=d_head, dropout=dropout)  # is self-attn if context is none
        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward(self, x, context=None):
        return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)

    def _forward(self, x, context=None):
        x = self.attn1(self.norm1(x)) + x
        x = self.attn2(self.norm2(x), context=context) + x
        x = self.ff(self.norm3(x)) + x
        return x


class SpatialTransformer(nn.Module):
    """
    Transformer block for image-like data.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """
    def __init__(self, in_channels, n_heads, d_head,
                 depth=1, dropout=0., context_dim=None):
        super().__init__()
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = Normalize(in_channels)

        self.proj_in = nn.Conv2d(in_channels,
                                 inner_dim,
                                 kernel_size=1,
                                 stride=1,
                                 padding=0)

        self.transformer_blocks = nn.ModuleList(
            [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim)
                for d in range(depth)]
        )

        self.proj_out = zero_module(nn.Conv2d(inner_dim,
                                              in_channels,
                                              kernel_size=1,
                                              stride=1,
                                              padding=0))

    def forward(self, x, context=None):
        # note: if no context is given, cross-attention defaults to self-attention
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        x = self.proj_in(x)
        x = rearrange(x, 'b c h w -> b (h w) c')
        for block in self.transformer_blocks:
            x = block(x, context=context)
        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
        x = self.proj_out(x)
        return x + x_in

================================================
FILE: sample/generate_cat.py
================================================
from utils.fixseed import fixseed
import os

import torch
from utils.parser_util import generate_args
from utils.model_util import create_model_and_diffusion, load_model_wo_clip
from utils import dist_util
from models.cfg_sampler import ClassifierFreeSampleModel


from AutoEncoder.models.coordsenc import CoordsEncoder
from AutoEncoder.models.cbndec import CbnDecoder
from meshudf.meshudf import get_mesh_from_udf
from utils.utils import get_o3d_mesh_from_tensors

import open3d as o3d
from torch import Tensor
import pymeshlab as ml


cat2name = {0: 'long_sleeve_upper', 
            1: 'short_sleeve_upper', 
            2: 'no_sleeve_upper', 
            3: 'long_sleeve_dress', 
            4: 'short_sleeve_dress', 
            5: 'no_sleeve_dress', 
            8: 'dress', 
            6: 'long_pants', 
            7: 'short_pants'}

def main():
    args = generate_args()
    out_path = args.output_dir
    os.makedirs(out_path, exist_ok=True)

    dist_util.setup_dist(args.device)


    print("Creating model and diffusion...")
    model, diffusion = create_model_and_diffusion(args)

    print(f"Loading checkpoints from [{args.model_path}]...")
    state_dict = torch.load(args.model_path, map_location='cpu')
    load_model_wo_clip(model, state_dict)

    if args.guidance_param != 1:
        model = ClassifierFreeSampleModel(model)   # wrapping model with the classifier-free sampler
    model.to(dist_util.dev())
    model.eval()  # disable random masking

    cond = {}
    cond["y"] = {}
    category = [args.category]*args.num_samples
    
    cond['y']['action'] = torch.tensor(category, dtype=torch.float).cuda()
    cond['y']['action_text'] = torch.tensor(category, dtype=torch.int64).cuda()
    print("You are running coditional generaion")
    print("Conidtion: ", cond['y']['action'] )
    print(cond['y']['action'].shape)


    ckpt = torch.load(args.ae_dir)
    print(f'Load Drape From: {args.ae_dir}')

    latent_size = 32
    coords_encoder = CoordsEncoder()

    hidden_dim = 512
    num_hidden_layers = 5
    decoder = CbnDecoder(
        coords_encoder.out_dim,
        latent_size,
        hidden_dim,
        num_hidden_layers,
    )
    decoder.load_state_dict(ckpt["decoder"], strict=True)
    decoder = decoder.cuda()
    decoder.eval()
    for param in decoder.parameters():
        param.requires_grad = False

    args.batch_size = args.num_samples
    sample_fn = diffusion.p_sample_loop
    sample = sample_fn(
        model,
        (
        args.batch_size, 1, latent_size), # torch.Size([1, 1, 128, 128, 128])
        clip_denoised=False,
        model_kwargs=cond,
        skip_timesteps=0,  # 0 is the default value - i.e. don't skip any step
        init_image=None,
        progress=True,
        dump_steps=None,
        noise=None,
        const_noise=False,
    )

    udf_max_dist = 0.1
    for k in range(args.batch_size):

        lat = sample[k]

        def udf_func(c: Tensor) -> Tensor:

            c = coords_encoder.encode(c.unsqueeze(0))
            p = decoder(c, lat).squeeze(0)
            p = torch.sigmoid(p)
            p = (1 - p) * udf_max_dist
            return p

        v, t = get_mesh_from_udf(
            udf_func,
            coords_range=(-1, 1),
            max_dist=udf_max_dist,
            N=args.resolution,
            max_batch=2**16,
            differentiable=False,
        )

        pred_mesh_o3d = get_o3d_mesh_from_tensors(v, t)
        mesh_path = os.path.join(out_path, cat2name[args.category], f'{k}.obj')
        os.makedirs(os.path.dirname(mesh_path), exist_ok=True)
        o3d.io.write_triangle_mesh(mesh_path, pred_mesh_o3d)

        print(f'saved results to {mesh_path}')
        ms = ml.MeshSet()
        ms.set_verbosity(False)
        ms.load_new_mesh(mesh_path)
        ms.apply_coord_laplacian_smoothing()
        ms.meshing_remove_connected_component_by_face_number(mincomponentsize=2500)
        ms.save_current_mesh(mesh_path)


if __name__ == "__main__":
    main()


================================================
FILE: sample/generate_image.py
================================================

import sys

from utils.fixseed import fixseed
import os
import time
import numpy as np
import torch
from utils.parser_util import generate_args
from utils.model_util import create_model_and_diffusion, load_model_wo_clip
from utils import dist_util
from models.cfg_sampler import ClassifierFreeSampleModel

from AutoEncoder.models.coordsenc import CoordsEncoder
from AutoEncoder.models.cbndec import CbnDecoder
from meshudf.meshudf import get_mesh_from_udf
from utils.utils import get_o3d_mesh_from_tensors
from data_loaders.dataset import mask2bbox, crop_square, _convert_image_to_rgb, _transform_rgb

import trimesh
import open3d as o3d
from PIL import Image
from torch import Tensor
from utils.utils import GridFiller
import pymeshlab as ml


from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
try:
    from torchvision.transforms import InterpolationMode
    BICUBIC = InterpolationMode.BICUBIC
except ImportError:
    BICUBIC = Image.BICUBIC
import clip


def load_and_freeze_clip(clip_version):
    clip_model, _ = clip.load(clip_version, device='cpu',
                                            jit=False)  # Must set jit=False for training
    # clip.model.convert_weights(
    #     clip_model)  # Actually this line is unnecessary since clip by default already on float16

    # Freeze CLIP weights
    clip_model.eval()
    for p in clip_model.parameters():
        p.requires_grad = False

    return clip_model

def main():
    args = generate_args()

    os.makedirs(args.output_dir, exist_ok=True)

    dist_util.setup_dist(args.device)

    print("Creating model and diffusion...")
    model, diffusion = create_model_and_diffusion(args)

    print(f"Loading checkpoints from [{args.model_path}]...")
    state_dict = torch.load(args.model_path, map_location='cpu')
    load_model_wo_clip(model, state_dict)

    if args.guidance_param != 1:
        model = ClassifierFreeSampleModel(model)   # wrapping model with the classifier-free sampler
    model.to(dist_util.dev())
    model.eval()  # disable random masking

    clip_version = 'ViT-B/32'
    clip_model = load_and_freeze_clip(clip_version)
    clip_preprocess = _transform_rgb(224)

    ckpt = torch.load(args.ae_dir)
    print(f'Load AutoEncoder From: {args.ae_dir}')

    latent_size = 64
    coords_encoder = CoordsEncoder()
    hidden_dim = 512
    num_hidden_layers = 5
    decoder = CbnDecoder(
        coords_encoder.out_dim,
        latent_size,
        hidden_dim,
        num_hidden_layers,
    )
    decoder.load_state_dict(ckpt["decoder"], strict=True)
    decoder = decoder.cuda()
    decoder.eval()
    for param in decoder.parameters():
        param.requires_grad = False

    img_path = args.image_path
    img_name = img_path.split('/')[-1]
    img_id = img_name.split('.')[0]
    mask_path = args.mask_path

    img_np = np.array(Image.open(img_path).convert('RGB'))
    mask_np = np.array(Image.open(mask_path).convert('1'))

    # get bbox from mask
    x0, y0, x1, y1 = mask2bbox(mask_np)
    bbox = [x0, y0, x1, y1]
    
    r = 0.7
    img_comp = img_np * mask_np[:, :, None] + (1 - mask_np[:, :, None]) * (r*255 + (1 - r) * img_np)
    img_comp = crop_square(img_comp.astype(np.uint8), bbox)

    img_clean = img_np * mask_np[:, :, None]
    img_clean = crop_square(img_clean.astype(np.uint8), bbox)

    img = clip_preprocess(img_clean).unsqueeze(0)

    cond = {}
    cond["y"] = {}
    cond['y']['context'] = clip_model.encode_image(img).cuda()

    print("You are running conditional generaion")
    print("Conidtion: image, Image path: ", img_path, mask_path)

    args.batch_size = 1
    sample_fn = diffusion.p_sample_loop
    sample = sample_fn(
        model,
        (
        args.batch_size, 1, latent_size),
        clip_denoised=False,
        model_kwargs=cond,
        skip_timesteps=0,  # 0 is the default value - i.e. don't skip any step
        init_image=None,
        progress=True,
        dump_steps=None,
        noise=None,
        const_noise=False,
    )


    udf_max_dist = 0.1

    lat = sample[0]

    def udf_func(c: Tensor) -> Tensor:
        c = coords_encoder.encode(c.unsqueeze(0))
        p = decoder(c, lat).squeeze(0)
        p = torch.sigmoid(p)
        p = (1 - p) * udf_max_dist
        return p
    mesh_path = os.path.join(args.output_dir, f'{img_id}.obj')
    os.makedirs(os.path.dirname(mesh_path), exist_ok=True)

    if args.watertight:
        size = args.resolution
        fast_grid_filler = GridFiller(size)
        udf, _ = fast_grid_filler.fill_grid(udf_func, max_batch=2**16)
        udf[udf < 0] = 0

        # keep the max component of the extracted mesh
        import mcubes
        vertices, faces = mcubes.marching_cubes(udf.detach().cpu().numpy(), 0.01)
        mesh = trimesh.Trimesh(vertices, faces)
        components = mesh.split(only_watertight=False)
        bbox = []
        for k, c in enumerate(components):
            bbmin = c.vertices.min(0)
            bbmax = c.vertices.max(0)
            bbox.append((bbmax - bbmin).max())
            
        max_component = np.argmax(bbox)
        mesh = components[max_component]
        mesh.vertices = mesh.vertices * (2.0 / size) - 1.0  # normalize it to [-1, 1]
        os.makedirs(os.path.dirname(mesh_path), exist_ok=True)
        trimesh.Trimesh(vertices=vertices, faces=faces).export(mesh_path)
    else:
        v, t = get_mesh_from_udf(
            udf_func,
            coords_range=(-1, 1),
            max_dist=udf_max_dist,
            N=args.resolution,
            max_batch=2**16,
            differentiable=False,
        )

        pred_mesh_o3d = get_o3d_mesh_from_tensors(v, t)
        o3d.io.write_triangle_mesh(mesh_path, pred_mesh_o3d)
        ms = ml.MeshSet()
        ms.set_verbosity(False)
        ms.load_new_mesh(mesh_path)
        ms.apply_coord_laplacian_smoothing()
        ms.meshing_remove_connected_component_by_face_number(mincomponentsize=2500)
        ms.save_current_mesh(mesh_path)
        print(f'saved results to {mesh_path}')



if __name__ == "__main__":
    main()


================================================
FILE: sample/generate_sketch.py
================================================

from utils.fixseed import fixseed
import os
import torch
from utils.parser_util import generate_args
from utils.model_util import create_model_and_diffusion, load_model_wo_clip
from utils import dist_util
from models.cfg_sampler import ClassifierFreeSampleModel

from AutoEncoder.models.coordsenc import CoordsEncoder
from AutoEncoder.models.cbndec import CbnDecoder
from meshudf.meshudf import get_mesh_from_udf
from utils.utils import get_o3d_mesh_from_tensors
from data_loaders.dataset import _convert_image_to_rgb

import open3d as o3d
from PIL import Image
from torch import Tensor
import pymeshlab as ml


from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
try:
    from torchvision.transforms import InterpolationMode
    BICUBIC = InterpolationMode.BICUBIC
except ImportError:
    BICUBIC = Image.BICUBIC
import clip


def _transform(n_px):
    return Compose([
        Resize(n_px, interpolation=BICUBIC),
        CenterCrop(n_px),
        _convert_image_to_rgb,
        ToTensor(),
        Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
    ])

def load_and_freeze_clip(clip_version):
    clip_model, _ = clip.load(clip_version, device='cpu',
                                            jit=False)  # Must set jit=False for training
    # Freeze CLIP weights
    clip_model.eval()
    for p in clip_model.parameters():
        p.requires_grad = False

    return clip_model

def main():
    args = generate_args()

    out_path = args.output_dir
    os.makedirs(out_path, exist_ok=True)

    dist_util.setup_dist(args.device)

    args.batch_size = 1 
    print("Creating model and diffusion...")
    model, diffusion = create_model_and_diffusion(args)

    print(f"Loading checkpoints from [{args.model_path}]...")
    state_dict = torch.load(args.model_path, map_location='cpu')
    load_model_wo_clip(model, state_dict)

    if args.guidance_param != 1:
        model = ClassifierFreeSampleModel(model)   # wrapping model with the classifier-free sampler
    model.to(dist_util.dev())
    model.eval()  # disable random masking

    print(f'using sketch: {args.sketch_path}')


    clip_version = 'ViT-B/32'
    img = Image.open(args.sketch_path)
    clip_preprocess = _transform(224)
    img = clip_preprocess(img).unsqueeze(0)
    clip_model = load_and_freeze_clip(clip_version)

    cond = {}
    cond["y"] = {}
    cond['y']['context'] = clip_model.encode_image(img).cuda()

    print("You are running conditional generaion")
    print("Conidtion: sketch image, Image path: ", args.sketch_path)

    ckpt = torch.load(args.ae_dir)
    print(f'Load AutoEncoder From: {args.ae_dir}')

    coords_encoder = CoordsEncoder()

    hidden_dim = 512
    num_hidden_layers = 5
    decoder = CbnDecoder(
        coords_encoder.out_dim,
        32,
        hidden_dim,
        num_hidden_layers,
    )
    decoder.load_state_dict(ckpt["decoder"], strict=True)
    decoder = decoder.cuda()
    decoder.eval()
    for param in decoder.parameters():
        param.requires_grad = False

    sample_fn = diffusion.p_sample_loop
    sample = sample_fn(
        model,
        (
        args.batch_size, 1, 32),
        clip_denoised=False,
        model_kwargs=cond,
        skip_timesteps=0,  # 0 is the default value - i.e. don't skip any step
        init_image=None,
        progress=True,
        dump_steps=None,
        noise=None,
        const_noise=False,
    )

    udf_max_dist = 0.1

    if args.sketch_path is not None:
        id = args.sketch_path.split('/')[-1][:-4]
        
    lat = sample[0]

    def udf_func(c: Tensor) -> Tensor:
        c = coords_encoder.encode(c.unsqueeze(0))
        p = decoder(c, lat).squeeze(0)
        p = torch.sigmoid(p)
        p = (1 - p) * udf_max_dist
        return p

    v, t = get_mesh_from_udf(
        udf_func,
        coords_range=(-1, 1),
        max_dist=udf_max_dist,
        N=args.resolution,
        max_batch=2**16,
        differentiable=False,
    )

    pred_mesh_o3d = get_o3d_mesh_from_tensors(v, t)
    mesh_path = os.path.join(args.output_dir, f'sketch_{id}.obj')
    os.makedirs(os.path.dirname(mesh_path), exist_ok=True)
    o3d.io.write_triangle_mesh(mesh_path, pred_mesh_o3d)
    ms = ml.MeshSet()
    ms.set_verbosity(False)
    ms.load_new_mesh(mesh_path)
    ms.apply_coord_laplacian_smoothing()
    ms.meshing_remove_connected_component_by_face_number(mincomponentsize=2500)
    ms.save_current_mesh(mesh_path)


if __name__ == "__main__":
    main()


================================================
FILE: sample/generate_text.py
================================================

import os

import numpy as np
import torch
from utils.parser_util import generate_args
from utils.model_util import create_model_and_diffusion, load_model_wo_clip
from utils import dist_util
from models.cfg_sampler import ClassifierFreeSampleModel

from AutoEncoder.models.coordsenc import CoordsEncoder
from AutoEncoder.models.cbndec import CbnDecoder
from meshudf.meshudf import get_mesh_from_udf
from utils.utils import get_o3d_mesh_from_tensors

import trimesh
import open3d as o3d
from PIL import Image
from torch import Tensor
from utils.utils import GridFiller
import pymeshlab as ml

from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
try:
    from torchvision.transforms import InterpolationMode
    BICUBIC = InterpolationMode.BICUBIC
except ImportError:
    BICUBIC = Image.BICUBIC
import clip


def load_and_freeze_clip(clip_version):
    clip_model, _ = clip.load(clip_version, device='cpu',
                                            jit=False)  # Must set jit=False for training
    # clip.model.convert_weights(
    #     clip_model)  # Actually this line is unnecessary since clip by default already on float16

    # Freeze CLIP weights
    clip_model.eval()
    for p in clip_model.parameters():
        p.requires_grad = False

    return clip_model

def main():
    args = generate_args()

    out_path = args.output_dir
    os.makedirs(out_path, exist_ok=True)

    args.batch_size = args.num_samples  # Sampling a single batch from the testset, with exactly args.num_samples

    print("Creating model and diffusion...")
    model, diffusion = create_model_and_diffusion(args)

    print(args.guidance_param)
    if args.guidance_param != 1: ## 3 for text
        model = ClassifierFreeSampleModel(model)   # wrapping model with the classifier-free sampler

    print(f"Loading checkpoints from [{args.model_path}]...")
    state_dict = torch.load(args.model_path, map_location='cpu')
    load_model_wo_clip(model, state_dict)


    model.to(dist_util.dev())
    model.eval()  # disable random masking

    print("You are running conditional generaion")

    import csv
    text2name = {}

    cond = {}
    cond['y'] = {}
    

    ckpt = torch.load(args.ae_dir)
    print(f'Load AutoEncoder From: {args.ae_dir}')

    latent_size = 64
    coords_encoder = CoordsEncoder()

    hidden_dim = 512
    num_hidden_layers = 5
    decoder = CbnDecoder(
        coords_encoder.out_dim,
        latent_size,
        hidden_dim,
        num_hidden_layers,
    )
    decoder.load_state_dict(ckpt["decoder"], strict=True)
    decoder = decoder.cuda()
    decoder.eval()
    for param in decoder.parameters():
        param.requires_grad = False

    cond['y']['text'] = [args.prompt]*args.batch_size
    # for name in names:
    print("You are running conditional generaion")
    print("Conidtion sample: ", cond['y']['text'][0])

    args.batch_size = len(cond['y']['text'])
    sample_fn = diffusion.p_sample_loop
    sample = sample_fn(
        model,
        (
        args.batch_size, 1, latent_size),
        clip_denoised=False,
        model_kwargs=cond,
        skip_timesteps=0,  # 0 is the default value - i.e. don't skip any step
        init_image=None,
        progress=True,
        dump_steps=None,
        noise=None,
        const_noise=False,
    )

    bs = len(cond['y']['text'])
    udf_max_dist = 0.1
    for k in range(bs):
        id = k
        lat = sample[k]
        def udf_func(c: Tensor) -> Tensor:
            c = coords_encoder.encode(c.unsqueeze(0))
            p = decoder(c, lat).squeeze(0)
            p = torch.sigmoid(p)
            p = (1 - p) * udf_max_dist
            return p

        mesh_path = os.path.join(args.output_dir, cond['y']['text'][k].replace(" ", "-").replace(".", "")[:100]+f'_{id}.obj')

        if args.watertight:
            size = args.resolution
            fast_grid_filler = GridFiller(size)
            udf, _ = fast_grid_filler.fill_grid(udf_func, max_batch=2**16)
            udf[udf < 0] = 0

            # keep the max component of the extracted mesh
            import mcubes
            vertices, faces = mcubes.marching_cubes(udf.detach().cpu().numpy(), 0.01)
            mesh = trimesh.Trimesh(vertices, faces)
            components = mesh.split(only_watertight=False)
            bbox = []
            for k, c in enumerate(components):
                bbmin = c.vertices.min(0)
                bbmax = c.vertices.max(0)
                bbox.append((bbmax - bbmin).max())
                
            max_component = np.argmax(bbox)
            mesh = components[max_component]
            mesh.vertices = mesh.vertices * (2.0 / size) - 1.0  # normalize it to [-1, 1]
            os.makedirs(os.path.dirname(mesh_path), exist_ok=True)
            trimesh.Trimesh(vertices=vertices, faces=faces).export(mesh_path)
            ms = ml.MeshSet()
            ms.set_verbosity(False)
            ms.load_new_mesh(mesh_path)
            ms.meshing_remove_connected_component_by_face_number(mincomponentsize=5000)
            ms.save_current_mesh(mesh_path)
        else:
            v, t = get_mesh_from_udf(
                udf_func,
                coords_range=(-1, 1),
                max_dist=udf_max_dist,
                N=args.resolution,
                max_batch=2**16,
                differentiable=False,
            )
            pred_mesh_o3d = get_o3d_mesh_from_tensors(v, t)
            os.makedirs(os.path.dirname(mesh_path), exist_ok=True)
            o3d.io.write_triangle_mesh(mesh_path, pred_mesh_o3d)

    print(f"saved to: {args.output_dir}")



if __name__ == "__main__":
    main()


================================================
FILE: sample/generate_uncond.py
================================================

from utils.fixseed import fixseed
import os
import torch
from utils.parser_util import generate_args
from utils.model_util import create_model_and_diffusion, load_model_wo_clip
from utils import dist_util
from models.cfg_sampler import ClassifierFreeSampleModel

from AutoEncoder.models.coordsenc import CoordsEncoder
from AutoEncoder.models.cbndec import CbnDecoder
from meshudf.meshudf import get_mesh_from_udf
from utils.utils import get_o3d_mesh_from_tensors

import open3d as o3d
from torch import Tensor
import pymeshlab as ml
import torch.nn.functional as F


def main():
    args = generate_args()

    out_path = args.output_dir
    os.makedirs(out_path, exist_ok=True)

    dist_util.setup_dist(args.device)

    assert args.num_samples <= args.batch_size, \
        f'Please either increase batch_size({args.batch_size}) or reduce num_samples({args.num_samples})'

    args.batch_size = args.num_samples  # Sampling a single batch from the testset, with exactly args.num_samples


    print("Creating model and diffusion...")
    model, diffusion = create_model_and_diffusion(args)

    print(f"Loading checkpoints from [{args.model_path}]...")
    state_dict = torch.load(args.model_path, map_location='cpu')
    load_model_wo_clip(model, state_dict)

    if args.guidance_param != 1:
        model = ClassifierFreeSampleModel(model)   # wrapping model with the classifier-free sampler
    model.to(dist_util.dev())
    model.eval()  # disable random masking

    cond = {}
    cond["y"] = {}
    print("You are running unconditional generaion")


    ckpt = torch.load(args.ae_dir)
    print(f'Load AutoEncoder From: {args.ae_dir}')

    latent_size = 32
    coords_encoder = CoordsEncoder()

    hidden_dim = 512
    num_hidden_layers = 5
    decoder = CbnDecoder(
        coords_encoder.out_dim,
        latent_size,
        hidden_dim,
        num_hidden_layers,
    )
    decoder.load_state_dict(ckpt["decoder"], strict=True)
    decoder = decoder.cuda()
    decoder.eval()
    for param in decoder.parameters():
        param.requires_grad = False

    sample_fn = diffusion.p_sample_loop
    sample = sample_fn(
        model,
        (
        args.batch_size, 1, latent_size), # torch.Size([1, 1, 128, 128, 128])
        clip_denoised=False,
        model_kwargs=cond,
        skip_timesteps=0,  # 0 is the default value - i.e. don't skip any step
        init_image=None,
        progress=True,
        dump_steps=None,
        noise=None,
        const_noise=False,
    )


    bs = args.batch_size
    udf_max_dist = 0.1
    for k in range(bs):

        id = k

        lat = sample[k]

        def udf_func(c: Tensor) -> Tensor:
            c = coords_encoder.encode(c.unsqueeze(0))
            p = decoder(c, lat).squeeze(0)
            p = torch.sigmoid(p)
            p = (1 - p) * udf_max_dist
            return p


        v, t = get_mesh_from_udf(
            udf_func,
            coords_range=(-1, 1),
            max_dist=udf_max_dist,
            N=args.resolution,
            max_batch=2**16,
            differentiable=False,
        )

        pred_mesh_o3d = get_o3d_mesh_from_tensors(v, t)
        mesh_path = os.path.join(args.output_dir, f'{id}.obj')
        os.makedirs(os.path.dirname(mesh_path), exist_ok=True)
        o3d.io.write_triangle_mesh(mesh_path, pred_mesh_o3d)
        ms = ml.MeshSet()
        ms.set_verbosity(False)
        ms.load_new_mesh(mesh_path)
        ms.apply_coord_laplacian_smoothing()
        ms.meshing_remove_connected_component_by_face_number(mincomponentsize=2500)
        ms.save_current_mesh(mesh_path)
    print(f'saved results to {mesh_path}')





if __name__ == "__main__":
    main()


================================================
FILE: train_diffcloth.py
================================================
# This code is based on https://github.com/openai/guided-diffusion
"""
Train a diffusion model on images.
"""

import os
import json
from utils.fixseed import fixseed
from utils.parser_util import train_args
from utils import dist_util
from utils.logger import setup_logger
from utils.comm import synchronize, is_main_process, get_rank, get_world_size, all_gather
from utils.miscellaneous import mkdir, set_seed
from training_loop_single import TrainLoop
from models.cfg_sampler import ClassifierFreeSampleModel

from utils.model_util import create_model_and_diffusion

from data_loaders.dataset import UDFs3d
import os
import torch
import numpy as np

class TrainPlatform:
    def __init__(self, save_dir):
        pass

    def report_scalar(self, name, value, iteration, group_name=None):
        pass

    def report_args(self, args, name):
        pass

    def close(self):
        pass


class TensorboardPlatform(TrainPlatform):
    def __init__(self, save_dir):
        from torch.utils.tensorboard import SummaryWriter
        self.writer = SummaryWriter(log_dir=save_dir)

    def report_scalar(self, name, value, iteration, group_name=None):
        self.writer.add_scalar(f'{group_name}/{name}', value, iteration)

    def close(self):
        self.writer.close()


class NoPlatform(TrainPlatform):
    def __init__(self, save_dir):
        pass


def make_data_sampler(dataset, shuffle, distributed):
    if distributed:
        return torch.utils.data.distributed.DistributedSampler(dataset, shuffle=shuffle)
    if shuffle:
        sampler = torch.utils.data.sampler.RandomSampler(dataset)
    else:
        sampler = torch.utils.data.sampler.SequentialSampler(dataset)
    return sampler


class IterationBasedBatchSampler(torch.utils.data.sampler.BatchSampler):
    """
    Wraps a BatchSampler, resampling from it until
    a specified number of iterations have been sampled
    """

    def __init__(self, batch_sampler, num_iterations, start_iter=0):
        self.batch_sampler = batch_sampler
        self.num_iterations = num_iterations
        self.start_iter = start_iter

    def __iter__(self):
        iteration = self.start_iter
        while iteration <= self.num_iterations:
            # if the underlying sampler has a set_epoch method, like
            # DistributedSampler, used for making each process see
            # a different split of the dataset, then set it
            if hasattr(self.batch_sampler.sampler, "set_epoch"):
                self.batch_sampler.sampler.set_epoch(iteration)
            for batch in self.batch_sampler:
                iteration += 1
                if iteration > self.num_iterations:
                    break
                yield batch

    def __len__(self):
        return self.num_iterations

def make_batch_data_sampler(sampler, images_per_gpu, num_iters=None, start_iter=0):
    batch_sampler = torch.utils.data.sampler.BatchSampler(
        sampler, images_per_gpu, drop_last=False
    )
    return batch_sampler

def main():
    global logger
    args = train_args()
    fixseed(args.seed)

    os.makedirs(args.save_dir, exist_ok=True)

    args.num_gpus = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
    args.distributed = args.num_gpus > 1
    args.device = torch.device(args.device)

    if args.distributed:
        print("Init distributed training on local rank {} ({}), world size {}".format(args.local_rank, int(os.environ["LOCAL_RANK"]), args.num_gpus))
        torch.cuda.set_device(args.local_rank)
        torch.distributed.init_process_group(
            backend='nccl', init_method='env://'
        )
        args.local_rank = int(os.environ["LOCAL_RANK"])
        synchronize()
    logger = setup_logger("Diffcloth", args.save_dir, get_rank())

    if args.save_dir is None:
        raise FileNotFoundError('save_dir was not specified.')
    elif os.path.exists(args.save_dir) and not args.overwrite:
        raise FileExistsError('save_dir [{}] already exists.'.format(args.save_dir))
    elif not os.path.exists(args.save_dir):
        os.makedirs(args.save_dir)
    args_path = os.path.join(args.save_dir, 'args.json')
    with open(args_path, 'w') as fw:
        args4print = args
        args4print.device = str(args4print.device)
        json.dump(vars(args4print), fw, indent=4, sort_keys=True)
    logger.info("Using {} GPUs".format(args.num_gpus))

    logger.info("creating data loader...")


    name = args.dataset
    dset_root = args.data_dir
    dset_category = 'train'
    dataset_train = UDFs3d(name, dset_root, dset_category, args.cond_mode)

    shuffle = True
    
    args.batch_size = 2 #4
    images_per_gpu = args.batch_size
    images_per_batch = images_per_gpu * get_world_size()
    iters_per_batch = len(dataset_train) // images_per_batch
    num_iters = args.num_steps
    start_iter = 0
    logger.info("Train with {} images per GPU.".format(images_per_gpu))
    logger.info("Total batch size {}".format(images_per_batch))
    logger.info("Total training steps {}".format(num_iters))
 
    sampler = make_data_sampler(dataset_train, shuffle, args.distributed)
    
    batch_sampler = make_batch_data_sampler(
        sampler, images_per_gpu, num_iters, start_iter
    )

    dataloader_train = torch.utils.data.DataLoader(
        dataset_train, args.batch_size, shuffle=True, num_workers=6,
        pin_memory=True,
    )
    if args.unconstrained:
        args.num_actions = None
    

    logger.info("creating model and diffusion...")
    diff_model, diffusion = create_model_and_diffusion(args)
   
    if args.guidance_param != 1:
        print('classifier free')
        diff_model = ClassifierFreeSampleModel(diff_model)   # wrapping model with the classifier-free sampler

    print(args.guidance_param)
    if args.distributed:
        args.gpu = args.local_rank
        args.world_size = torch.distributed.get_world_size()
        diff_model = diff_model.to(args.local_rank)
     
        diff_model = torch.nn.parallel.DistributedDataParallel(
            diff_model, device_ids=[args.local_rank], 
            output_device=args.local_rank,
            find_unused_parameters=True,
        )

    else:
        diff_model = diff_model.cuda()

    inds = np.random.choice(100000, 10000, replace=False)
    TrainLoop(args, diff_model, diffusion, dataloader_train, logger).run_loop(inds=inds)




if __name__ == "__main__":
    main()


================================================
FILE: training_loop_single.py
================================================
import functools
import os
import time
import numpy as np

import blobfile as bf
import torch
from torch.optim import AdamW

from diffusion import logger
from utils import dist_util
from diffusion.fp16_util import MixedPrecisionTrainer
from diffusion.resample import LossAwareSampler
from utils.comm import is_main_process

from diffusion.resample import create_named_schedule_sampler


from torch.utils.tensorboard import SummaryWriter

from utils.utils import random_point_sampling

from AutoEncoder.models.dgcnn import Dgcnn
import clip

# For ImageNet experiments, this was a good default value.
# We found that the lg_loss_scale quickly climbed to
# 20-21 within the first ~1K steps of training.
INITIAL_LOG_LOSS_SCALE = 20.0


class TrainLoop:
    def __init__(self, args, model, diffusion, data, logger=None):
        self.args = args
        self.dataset = args.dataset
        self.data_dir = args.data_dir
        self.grid_size = args.grid_size
        self.logger = logger


        print(args.clip_value)
        print("Apply clip: ", args.clip_value)

        self.model = model
        self.diffusion = diffusion
        if args.distributed:
            self.cond_mode = model.module.cond_mode
            self.clip_version = model.module.clip_version
        else:
            self.clip_version = model.clip_version
            self.cond_mode = model.cond_mode

        self.data = data
        self.batch_size = args.batch_size
        self.microbatch = args.batch_size  # deprecating this option
        self.lr = args.lr
        self.log_interval = args.log_interval
        self.save_interval = args.save_interval
        self.resume_checkpoint = args.resume_checkpoint
        self.use_fp16 = False  # deprecating this option
        self.fp16_scale_growth = 1e-3  # deprecating this option
        self.weight_decay = args.weight_decay
        self.lr_anneal_steps = args.lr_anneal_steps
        
        self.step = 0
        self.resume_step = 0
        self.global_batch = self.batch_size # * dist.get_world_size()
        self.num_steps = args.num_steps
        print('num_actions: ', args.num_actions)
        print('dataloader length: ', len(self.data))
        self.num_epochs = self.num_steps // len(self.data) + 1

        self.sync_cuda = torch.cuda.is_available()

        print("batch_size:", self.batch_size)

        self._load_and_sync_parameters()
        self.mp_trainer = MixedPrecisionTrainer(
            model=self.model,
            use_fp16=self.use_fp16,
            fp16_scale_growth=self.fp16_scale_growth,
        )

        self.save_dir = args.save_dir
        self.overwrite = args.overwrite

        self.opt = AdamW(
            self.mp_trainer.master_params, lr=self.lr, weight_decay=self.weight_decay
        )

        self.device = torch.device("cpu")
        if torch.cuda.is_available() and dist_util.dev() != 'cpu':
            self.device = torch.device(dist_util.dev())

        self.schedule_sampler_type = 'uniform'
        self.schedule_sampler = create_named_schedule_sampler(self.schedule_sampler_type, diffusion)
        self.eval_wrapper, self.eval_data, self.eval_gt_data = None, None, None

        self.use_ddp = False
        self.ddp_model = self.model
        self.log_writer = SummaryWriter(self.save_dir+"/logs") # added.

        if 'text' or 'img' in args.cond_mode:
            latent_size = 64
        else:
            latent_size = 32
        encoder = Dgcnn(latent_size)
        self.encoder = encoder.eval()
        ckpt = torch.load(args.ae_dir)
        self.encoder.load_state_dict(ckpt["encoder"], strict=True)

        for param in self.encoder.parameters():
            param.requires_grad = False

        if args.distributed:
            self.local_rank = int(os.environ["LOCAL_RANK"])
            self.vqvae = self.vqvae.to(device=self.local_rank)
            self.device = self.local_rank
        else:
            self.encoder = self.encoder.to(device=self.args.device)

        if 'sketch' in self.cond_mode or 'img' in self.cond_mode:
            print('EMBED SKETCH IMAGE')
            print('Loading CLIP...')
            self.clip_model = self.load_and_freeze_clip(self.clip_version)

    def _load_and_sync_parameters(self):
        resume_checkpoint = find_resume_checkpoint() or self.resume_checkpoint

        if resume_checkpoint:
            self.resume_step = parse_resume_step_from_filename(resume_checkpoint)

            self.logger.info(f"loading model from checkpoint: {resume_checkpoint}...")
            
            sd = dist_util.load_state_dict(
                    resume_checkpoint, map_location="cpu"
                )

            self.model.load_state_dict({k:v for k, v in sd.items()}, 
                                       strict=False
            )

    def load_and_freeze_clip(self, clip_version):
        clip_model, _ = clip.load(clip_version, device='cpu',
                                                jit=False)  # Must set jit=False for training

        # Freeze CLIP weights
        clip_model.eval()
        for p in clip_model.parameters():
            p.requires_grad = False

        return clip_model

    def worker_init_fn(self, worker_id):
        np.random.seed(int(time.time() * 10000000) % 10000000 + worker_id)


    def _load_optimizer_state(self):
        main_checkpoint = find_resume_checkpoint() or self.resume_checkpoint
        opt_checkpoint = bf.join(
            bf.dirname(main_checkpoint), f"opt{self.resume_step:09}.pt"
        )
        if bf.exists(opt_checkpoint):
            self.logger.log(f"loading optimizer state from checkpoint: {opt_checkpoint}")
            
            state_dict = dist_util.load_state_dict(
                opt_checkpoint, map_location=dist_util.dev()
            )
            self.opt.load_state_dict(state_dict)

    def randbool(self, *size):
        return torch.randint(2, size) == torch.randint(2, size)
    def run_loop(self, inds=None):
        loss_L1 = torch.nn.L1Loss().cuda(self.device)
        for epoch in range(self.num_epochs):
            print(f'Starting epoch {epoch}')

            for i, batch in enumerate(self.data):

                if 'sketch' in self.cond_mode or 'img' in self.cond_mode:
                    _, _, pcds, coords, gt_udf, gt_grad, img = batch
                elif 'text' in self.cond_mode:
                    _, _, pcds, coords, gt_udf, gt_grad, text = batch
                elif 'category' in self.cond_mode:
                    _, _, pcds, coords, gt_udf, gt_grad, label = batch
                else:
                    _, _, pcds, coords, gt_udf, gt_grad = batch
                
                loss_args = {}
                pcds = pcds.cuda()

                num_points_pcd = 10000
                pcds = random_point_sampling(pcds, num_points_pcd, inds=inds)
                latent_codes = self.encoder(pcds).unsqueeze(1)


                if not (not self.lr_anneal_steps or self.step + self.resume_step < self.lr_anneal_steps):
                    break

                cond = {}
                cond["y"] = {}
                if self.cond_mode == "no_cond":
                    cond['y']['mask'] = torch.ones(latent_codes.shape, dtype=torch.bool).cuda()
                elif self.cond_mode == "category":
                    cond['y']['action'] = torch.tensor(label, dtype=torch.float).cuda()
                    cond['y']['action_text'] = torch.tensor(label, dtype=torch.int64).cuda()
                elif self.cond_mode == 'sketch' or self.cond_mode == 'img':
                    cond['y']['context'] = self.clip_model.encode_image(img).cuda()
                elif self.cond_mode == 'text':
                    cond['y']['text'] = text
                    cond['y']['scale'] = self.args.guidance_param * torch.ones((len(text),1)).cuda()

                self.run_step(latent_codes, cond, loss_L1)

                if (self.step % self.log_interval == 0) and ((self.args.distributed and is_main_process()==True) or not self.args.distributed):
                    info_dict = logger.get_current().name2val
                    for k,v in info_dict.items():
                        if k == 'loss':

                            log_str = 'step[{}]: loss[{:0.5f}]'.format(self.step+self.resume_step, v)
                            self.logger.info(log_str)
                            self.log_writer.add_scalar('Loss/loss', float(info_dict['loss']), self.step+self.resume_step)

                        if k in ['step', 'samples'] or '_q' in k:
                            continue

                if self.step % self.save_interval == 0:
                    if self.args.distributed:
                        if is_main_process()==True:
                            self.save_distributed()
                    else:
                        self.save()
                    self.model.eval()
                    self.evaluate()
                    self.model.train()

                    # Run for a finite amount of time in integration tests.
                    if os.environ.get("DIFFUSION_TRAINING_TEST", "") and self.step > 0:
                        return
                self.step += 1
            if not (not self.lr_anneal_steps or self.step + self.resume_step < self.lr_anneal_steps):
                break
        # Save the last checkpoint if it wasn't already saved.
        if (self.step - 1) % self.save_interval != 0:
            if is_main_process()==True:
                self.save()
            self.evaluate()

    def evaluate(self):

        return


    def run_step(self, batch, cond, loss_L1, loss_args=None):
        self.forward_backward(batch, cond, loss_L1, loss_args=loss_args)
        self.mp_trainer.optimize(self.opt)
        self._anneal_lr()
        self.log_step()

    def forward_backward(self, batch, cond, loss_L1, loss_args=None):
        self.mp_trainer.zero_grad()
        for i in range(0, batch.shape[0], self.microbatch):
            # Eliminates the microbatch feature
            assert i == 0
            assert self.microbatch == self.batch_size
            micro = batch 
            micro_cond = cond
            t, weights = self.schedule_sampler.sample(micro.shape[0], self.device)

            compute_losses = functools.partial(
                self.diffusion.training_losses,
                self.ddp_model,
                micro,  # [bs, ch, image_size, image_size]
                t,  # [bs](int) sampled timesteps
                loss_L1,
                model_kwargs=micro_cond,
                dataset=self.data.dataset,
                loss_args=loss_args
            )
            
            losses = compute_losses()
            
            if isinstance(self.schedule_sampler, LossAwareSampler):
                self.schedule_sampler.update_with_local_losses(
                    t, losses["loss"].detach()
                )

            weights = weights.to(losses["loss"].device)

            loss = losses["loss"]
            

            log_loss_dict(
                self.diffusion, t, {k: v for k, v in losses.items()}
            )
            
            self.mp_trainer.backward(loss)

    def _anneal_lr(self, gama=0.9):
        if self.step == 0:
            return
        if (self.step) % 1000 == 0:
            if self.lr <= 1e-7:
                return
            lr = self.lr * gama
            self.lr = lr
            for param_group in self.opt.param_groups:
                param_group["lr"] = lr
            print(f'adjust_lr:{lr}')

    def log_step(self):
        logger.logkv("step", self.step + self.resume_step)
        logger.logkv("samples", (self.step + self.resume_step + 1) * self.global_batch)


    def ckpt_file_name(self):
        return f"model{(self.step+self.resume_step):09d}.pt"


    def save_distributed(self):
        print("main thread saving state dict.")
        model_to_save = self.model.module if hasattr(self.model, 'module') else self.model
        
        state_dict = model_to_save.state_dict()

        def save_checkpoint_dis(state_dict):

            # Do not save CLIP weights
            clip_weights = [e for e in state_dict.keys() if e.startswith('clip_model.')]
            for e in clip_weights:
                del state_dict[e]

            logger.log(f"saving model...")
            filename = self.ckpt_file_name()
            with bf.BlobFile(bf.join(self.save_dir, filename), "wb") as f:
                torch.save(state_dict, f)

        save_checkpoint_dis(state_dict)


    def save(self):
        def save_checkpoint(params):
            state_dict = self.mp_trainer.master_params_to_state_dict(params)

            # Do not save CLIP weights
            clip_weights = [e for e in state_dict.keys() if e.startswith('clip_model.')]
            for e in clip_weights:
                del state_dict[e]

            logger.log(f"saving model...")
            filename = self.ckpt_file_name()
            with bf.BlobFile(bf.join(self.save_dir, filename), "wb") as f:
                torch.save(state_dict, f)

        save_checkpoint(self.mp_trainer.master_params)



def parse_resume_step_from_filename(filename):
    """
    Parse filenames of the form path/to/modelNNNNNN.pt, where NNNNNN is the
    checkpoint's number of steps.
    """
    split = filename.split("model")
    if len(split) < 2:
        return 0
    split1 = split[-1].split(".")[0]
    try:
        return int(split1)
    except ValueError:
        return 0


def get_blob_logdir():
    # You can change this to be a separate path to save checkpoints to
    # a blobstore or some external drive.
    return logger.get_dir()


def find_resume_checkpoint():
    # On your infrastructure, you may want to override this to automatically
    # discover the latest checkpoint on your blob storage, etc.
    return None


def log_loss_dict(diffusion, ts, losses):
    for key, values in losses.items():
        logger.logkv_mean(key, values.item())
        

================================================
FILE: utils/PYTORCH3D_LICENSE
================================================
BSD License

For PyTorch3D software

Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.

Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:

 * Redistributions of source code must retain the above copyright notice, this
    list of conditions and the following disclaimer.

 * Redistributions in binary form must reproduce the above copyright notice,
    this list of conditions and the following disclaimer in the documentation
       and/or other materials provided with the distribution.

 * Neither the name Facebook nor the names of its contributors may be used to
    endorse or promote products derived from this software without specific
       prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

================================================
FILE: utils/__init__.py
================================================
from .logger import *



================================================
FILE: utils/comm.py
================================================
"""
Copyright (c) Microsoft Corporation.
Licensed under the MIT license.

This file contains primitives for multi-gpu communication.
This is useful when doing distributed training.
"""

import pickle
import time

import torch
import torch.distributed as dist


def get_world_size():
    if not dist.is_available():
        return 1
    if not dist.is_initialized():
        return 1
    return dist.get_world_size()


def get_rank():
    if not dist.is_available():
        return 0
    if not dist.is_initialized():
        return 0
    return dist.get_rank()


def is_main_process():
    return get_rank() == 0


def synchronize():
    """
    Helper function to synchronize (barrier) among all processes when
    using distributed training
    """
    if not dist.is_available():
        return
    if not dist.is_initialized():
        return
    world_size = dist.get_world_size()
    if world_size == 1:
        return
    dist.barrier()


def gather_on_master(data):
    """Same as all_gather, but gathers data on master process only, using CPU.
    Thus, this does not work with NCCL backend unless they add CPU support.

    The memory consumption of this function is ~ 3x of data size. While in
    principal, it should be ~2x, it's not easy to force Python to release
    memory immediately and thus, peak memory usage could be up to 3x.
    """
    world_size = get_world_size()
    if world_size == 1:
        return [data]

    # serialized to a Tensor
    buffer = pickle.dumps(data)
    # trying to optimize memory, but in fact, it's not guaranteed to be released
    del data
    storage = torch.ByteStorage.from_buffer(buffer)
    del buffer
    tensor = torch.ByteTensor(storage)

    # obtain Tensor size of each rank
    local_size = torch.LongTensor([tensor.numel()])
    size_list = [torch.LongTensor([0]) for _ in range(world_size)]
    dist.all_gather(size_list, local_size)
    size_list = [int(size.item()) for size in size_list]
    max_size = max(size_list)

    if local_size != max_size:
        padding = torch.ByteTensor(size=(max_size - local_size,))
        tensor = torch.cat((tensor, padding), dim=0)
        del padding

    if is_main_process():
        tensor_list = []
        for _ in size_list:
            tensor_list.append(torch.ByteTensor(size=(max_size,)))
        dist.gather(tensor, gather_list=tensor_list, dst=0)
        del tensor
    else:
        dist.gather(tensor, gather_list=[], dst=0)
        del tensor
        return

    data_list = []
    for tensor in tensor_list:
        buffer = tensor.cpu().numpy().tobytes()
        del tensor
        data_list.append(pickle.loads(buffer))
        del buffer

    return data_list


def all_gather(data):
    """
    Run all_gather on arbitrary picklable data (not necessarily tensors)
    Args:
        data: any picklable object
    Returns:
        list[data]: list of data gathered from each rank
    """
    world_size = get_world_size()
    if world_size == 1:
        return [data]

    # serialized to a Tensor
    buffer = pickle.dumps(data)
    storage = torch.ByteStorage.from_buffer(buffer)
    tensor = torch.ByteTensor(storage).to("cuda")

    # obtain Tensor size of each rank
    local_size = torch.LongTensor([tensor.numel()]).to("cuda")
    size_list = [torch.LongTensor([0]).to("cuda") for _ in range(world_size)]
    dist.all_gather(size_list, local_size)
    size_list = [int(size.item()) for size in size_list]
    max_size = max(size_list)

    # receiving Tensor from all ranks
    # we pad the tensor because torch all_gather does not support
    # gathering tensors of different shapes
    tensor_list = []
    for _ in size_list:
        tensor_list.append(torch.ByteTensor(size=(max_size,)).to("cuda"))
    if local_size != max_size:
        padding = torch.ByteTensor(size=(max_size - local_size,)).to("cuda")
        tensor = torch.cat((tensor, padding), dim=0)
    dist.all_gather(tensor_list, tensor)

    data_list = []
    for size, tensor in zip(size_list, tensor_list):
        buffer = tensor.cpu().numpy().tobytes()[:size]
        data_list.append(pickle.loads(buffer))

    return data_list


def reduce_dict(input_dict, average=True):
    """
    Args:
        input_dict (dict): all the values will be reduced
        average (bool): whether to do average or sum
    Reduce the values in the dictionary from all processes so that process with rank
    0 has the averaged results. Returns a dict with the same fields as
    input_dict, after reduction.
    """
    world_size = get_world_size()
    if world_size < 2:
        return input_dict
    with torch.no_grad():
        names = []
        values = []
        # sort the keys so that they are consistent across processes
        for k in sorted(input_dict.keys()):
            names.append(k)
            values.append(input_dict[k])
        values = torch.stack(values, dim=0)
        dist.reduce(values, dst=0)
        if dist.get_rank() == 0 and average:
            # only main process gets accumulated, so only divide by
            # world_size in this case
            values /= world_size
        reduced_dict = {k: v for k, v in zip(names, values)}
    return reduced_dict


================================================
FILE: utils/dist_util.py
================================================
"""
Helpers for distributed training.
"""

import socket

import torch as th
import torch.distributed as dist

# Change this to reflect your cluster layout.
# The GPU for a given rank is (rank % GPUS_PER_NODE).
GPUS_PER_NODE = 8

SETUP_RETRY_COUNT = 3

used_device = 0

def setup_dist(device=0):
    """
    Setup a distributed process group.
    """
    global used_device
    used_device = device
    if dist.is_initialized():
        return
    # os.environ["CUDA_VISIBLE_DEVICES"] = str(device) # f"{MPI.COMM_WORLD.Get_rank() % GPUS_PER_NODE}"

    # comm = MPI.COMM_WORLD
    # backend = "gloo" if not th.cuda.is_available() else "nccl"

    # if backend == "gloo":
    #     hostname = "localhost"
    # else:
    #     hostname = socket.gethostbyname(socket.getfqdn())
    # os.environ["MASTER_ADDR"] = comm.bcast(hostname, root=0)
    # os.environ["RANK"] = str(comm.rank)
    # os.environ["WORLD_SIZE"] = str(comm.size)

    # port = comm.bcast(_find_free_port(), root=used_device)
    # os.environ["MASTER_PORT"] = str(port)
    # dist.init_process_group(backend=backend, init_method="env://")


def dev():
    """
    Get the device to use for torch.distributed.
    """
    global used_device
    if th.cuda.is_available() and used_device>=0:
        return th.device(f"cuda:{used_device}")
    return th.device("cpu")


def load_state_dict(path, **kwargs):
    """
    Load a PyTorch file without redundant fetches across MPI ranks.
    """
    return th.load(path, **kwargs)


def sync_params(params):
    """
    Synchronize a sequence of Tensors across ranks from rank 0.
    """
    for p in params:
        with th.no_grad():
            dist.broadcast(p, 0)


def _find_free_port():
    try:
        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        s.bind(("", 0))
        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
        return s.getsockname()[1]
    finally:
        s.close()


================================================
FILE: utils/fixseed.py
================================================
import numpy as np
import torch
import random


def fixseed(seed):
    print(f'setting seed: {seed}')
    torch.backends.cudnn.benchmark = False
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.random.manual_seed(seed)





================================================
FILE: utils/ldm_utils.py
================================================
# adopted from
# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
# and
# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
# and
# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
#
# thanks!


import os
import math
import torch
import torch.nn as nn
import numpy as np
from einops import repeat
import importlib

def instantiate_from_config(config):
    if not "target" in config:
        if config == '__is_first_stage__':
            return None
        elif config == "__is_unconditional__":
            return None
        raise KeyError("Expected key `target` to instantiate.")
    return get_obj_from_str(config["target"])(**config.get("params", dict()))

def get_obj_from_str(string, reload=False):
    module, cls = string.rsplit(".", 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)

def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
    if schedule == "linear":
        betas = (
                torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
        )

    elif schedule == "cosine":
        timesteps = (
                torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
        )
        alphas = timesteps / (1 + cosine_s) * np.pi / 2
        alphas = torch.cos(alphas).pow(2)
        alphas = alphas / alphas[0]
        betas = 1 - alphas[1:] / alphas[:-1]
        betas = np.clip(betas, a_min=0, a_max=0.999)

    elif schedule == "sqrt_linear":
        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
    elif schedule == "sqrt":
        betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
    else:
        raise ValueError(f"schedule '{schedule}' unknown.")
    return betas.numpy()


def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
    if ddim_discr_method == 'uniform':
        c = num_ddpm_timesteps // num_ddim_timesteps
        ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
    elif ddim_discr_method == 'quad':
        ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
    else:
        raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')

    # assert ddim_timesteps.shape[0] == num_ddim_timesteps
    # add one to get the final alpha values right (the ones from first scale to data during sampling)
    steps_out = ddim_timesteps + 1
    if verbose:
        print(f'Selected timesteps for ddim sampler: {steps_out}')
    return steps_out


def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
    # select alphas for computing the variance schedule
    alphas = alphacums[ddim_timesteps]
    alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())

    # according the the formula provided in https://arxiv.org/abs/2010.02502
    sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
    if verbose:
        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
        print(f'For the chosen value of eta, which is {eta}, '
              f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
    return sigmas, alphas, alphas_prev


def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
    """
    Create a beta schedule that discretizes the given alpha_t_bar function,
    which defines the cumulative product of (1-beta) over time from t = [0,1].
    :param num_diffusion_timesteps: the number of betas to produce.
    :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
                      produces the cumulative product of (1-beta) up to that
                      part of the diffusion process.
    :param max_beta: the maximum beta to use; use values lower than 1 to
                     prevent singularities.
    """
    betas = []
    for i in range(num_diffusion_timesteps):
        t1 = i / num_diffusion_timesteps
        t2 = (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
    return np.array(betas)


def extract_into_tensor(a, t, x_shape):
    b, *_ = t.shape
    out = a.gather(-1, t)
    return out.reshape(b, *((1,) * (len(x_shape) - 1)))


def checkpoint(func, inputs, params, flag):
    """
    Evaluate a function without caching intermediate activations, allowing for
    reduced memory at the expense of extra compute in the backward pass.
    :param func: the function to evaluate.
    :param inputs: the argument sequence to pass to `func`.
    :param params: a sequence of parameters `func` depends on but does not
                   explicitly take as arguments.
    :param flag: if False, disable gradient checkpointing.
    """
    if flag:
        args = tuple(inputs) + tuple(params)
        return CheckpointFunction.apply(func, len(inputs), *args)
    else:
        return func(*inputs)


class CheckpointFunction(torch.autograd.Function):
    @staticmethod
    def forward(ctx, run_function, length, *args):
        ctx.run_function = run_function
        ctx.input_tensors = list(args[:length])
        ctx.input_params = list(args[length:])

        with torch.no_grad():
            output_tensors = ctx.run_function(*ctx.input_tensors)
        return output_tensors

    @staticmethod
    def backward(ctx, *output_grads):
        ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
        with torch.enable_grad():
            # Fixes a bug where the first op in run_function modifies the
            # Tensor storage in place, which is not allowed for detach()'d
            # Tensors.
            shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
            output_tensors = ctx.run_function(*shallow_copies)
        input_grads = torch.autograd.grad(
            output_tensors,
            ctx.input_tensors + ctx.input_params,
            output_grads,
            allow_unused=True,
        )
        del ctx.input_tensors
        del ctx.input_params
        del output_tensors
        return (None, None) + input_grads


def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
    """
    Create sinusoidal timestep embeddings.
    :param timesteps: a 1-D Tensor of N indices, one per batch element.
                      These may be fractional.
    :param dim: the dimension of the output.
    :param max_period: controls the minimum frequency of the embeddings.
    :return: an [N x dim] Tensor of positional embeddings.
    """
    if not repeat_only:
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
        ).to(device=timesteps.device)
        args = timesteps[:, None].float() * freqs[None]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
    else:
        embedding = repeat(timesteps, 'b -> b d', d=dim)
    return embedding


def zero_module(module):
    """
    Zero out the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().zero_()
    return module


def scale_module(module, scale):
    """
    Scale the parameters of a module and return it.
    """
    for p in module.parameters():
        p.detach().mul_(scale)
    return module


def mean_flat(tensor):
    """
    Take the mean over all non-batch dimensions.
    """
    return tensor.mean(dim=list(range(1, len(tensor.shape))))


def normalization(channels):
    """
    Make a standard normalization layer.
    :param channels: number of input channels.
    :return: an nn.Module for normalization.
    """
    return GroupNorm32(32, channels)


# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
class SiLU(nn.Module):
    def forward(self, x):
        return x * torch.sigmoid(x)


class GroupNorm32(nn.GroupNorm):
    def forward(self, x):
        return super().forward(x.float()).type(x.dtype)

def conv_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D convolution module.
    """
    if dims == 1:
        return nn.Conv1d(*args, **kwargs)
    elif dims == 2:
        return nn.Conv2d(*args, **kwargs)
    elif dims == 3:
        return nn.Conv3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


def linear(*args, **kwargs):
    """
    Create a linear module.
    """
    return nn.Linear(*args, **kwargs)


def avg_pool_nd(dims, *args, **kwargs):
    """
    Create a 1D, 2D, or 3D average pooling module.
    """
    if dims == 1:
        return nn.AvgPool1d(*args, **kwargs)
    elif dims == 2:
        return nn.AvgPool2d(*args, **kwargs)
    elif dims == 3:
        return nn.AvgPool3d(*args, **kwargs)
    raise ValueError(f"unsupported dimensions: {dims}")


class HybridConditioner(nn.Module):

    def __init__(self, c_concat_config, c_crossattn_config):
        super().__init__()
        self.concat_conditioner = instantiate_from_config(c_concat_config)
        self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)

    def forward(self, c_concat, c_crossattn):
        c_concat = self.concat_conditioner(c_concat)
        c_crossattn = self.crossattn_conditioner(c_crossattn)
        return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}


def noise_like(shape, device, repeat=False):
    repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
    noise = lambda: torch.randn(shape, device=device)
    return repeat_noise() if repeat else noise()

================================================
FILE: utils/logger.py
================================================
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import sys
from logging import StreamHandler, Handler, getLevelName


# this class is a copy of logging.FileHandler except we end self.close()
# at the end of each emit. While closing file and reopening file after each
# write is not efficient, it allows us to see partial logs when writing to
# fused Azure blobs, which is very convenient
class FileHandler(StreamHandler):
    """
    A handler class which writes formatted logging records to disk files.
    """
    def __init__(self, filename, mode='a', encoding=None, delay=False):
        """
        Open the specified file and use it as the stream for logging.
        """
        # Issue #27493: add support for Path objects to be passed in
        filename = os.fspath(filename)
        #keep the absolute path, otherwise derived classes which use this
        #may come a cropper when the current directory changes
        self.baseFilename = os.path.abspath(filename)
        self.mode = mode
        self.encoding = encoding
        self.delay = delay
        if delay:
            #We don't open the stream, but we still need to call the
            #Handler constructor to set level, formatter, lock etc.
            Handler.__init__(self)
            self.stream = None
        else:
            StreamHandler.__init__(self, self._open())

    def close(self):
        """
        Closes the stream.
        """
        self.acquire()
        try:
            try:
                if self.stream:
                    try:
                        self.flush()
                    finally:
                        stream = self.stream
                        self.stream = None
                        if hasattr(stream, "close"):
                            stream.close()
            finally:
                # Issue #19523: call unconditionally to
                # prevent a handler leak when delay is set
                StreamHandler.close(self)
        finally:
            self.release()

    def _open(self):
        """
        Open the current base file with the (original) mode and encoding.
        Return the resulting stream.
        """
        return open(self.baseFilename, self.mode, encoding=self.encoding)

    def emit(self, record):
        """
        Emit a record.

        If the stream was not opened because 'delay' was specified in the
        constructor, open it before calling the superclass's emit.
        """
        if self.stream is None:
            self.stream = self._open()
        StreamHandler.emit(self, record)
        self.close()

    def __repr__(self):
        level = getLevelName(self.level)
        return '<%s %s (%s)>' % (self.__class__.__name__, self.baseFilename, level)


def setup_logger(name, save_dir, distributed_rank, filename="log.txt"):
    logger = logging.getLogger(name)
    logger.setLevel(logging.DEBUG)
    # don't log results for the non-master process
    if distributed_rank > 0:
        return logger
    ch = logging.StreamHandler(stream=sys.stdout)
    ch.setLevel(logging.DEBUG)
    formatter = logging.Formatter("%(asctime)s %(name)s %(levelname)s: %(message)s")
    ch.setFormatter(formatter)
    logger.addHandler(ch)

    if save_dir:
        fh = FileHandler(os.path.join(save_dir, filename))
        fh.setLevel(logging.DEBUG)
        fh.setFormatter(formatter)
        logger.addHandler(fh)

    return logger


================================================
FILE: utils/misc.py
================================================
import torch


def to_numpy(tensor):
    if torch.is_tensor(tensor):
        return tensor.cpu().numpy()
    elif type(tensor).__module__ != 'numpy':
        raise ValueError("Cannot convert {} to numpy array".format(
            type(tensor)))
    return tensor


def to_torch(ndarray):
    if type(ndarray).__module__ == 'numpy':
        return torch.from_numpy(ndarray)
    elif not torch.is_tensor(ndarray):
        raise ValueError("Cannot convert {} to torch tensor".format(
            type(ndarray)))
    return ndarray


def cleanexit():
    import sys
    import os
    try:
        sys.exit(0)
    except SystemExit:
        os._exit(0)

def load_model_wo_clip(model, state_dict):
    missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
    assert len(unexpected_keys) == 0
    assert all([k.startswith('clip_model.') for k in missing_keys])

def freeze_joints(x, joints_to_freeze):
    # Freezes selected joint *rotations* as they appear in the first frame
    # x [bs, [root+n_joints], joint_dim(6), seqlen]
    frozen = x.detach().clone()
    frozen[:, joints_to_freeze, :, :] = frozen[:, joints_to_freeze, :, :1]
    return frozen


================================================
FILE: utils/miscellaneous.py
================================================
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import errno
import os
import os.path as op
import re
import logging
import numpy as np
import torch
import random
import shutil
from .comm import is_main_process
import yaml


def mkdir(path):
    # if it is the current folder, skip.
    # otherwise the original code will raise FileNotFoundError
    if path == '':
        return
    try:
        os.makedirs(path)
    except OSError as e:
        if e.errno != errno.EEXIST:
            raise


def save_config(cfg, path):
    if is_main_process():
        with open(path, 'w') as f:
            f.write(cfg.dump())


def config_iteration(output_dir, max_iter):
    save_file = os.path.join(output_dir, 'last_checkpoint')
    iteration = -1
    if os.path.exists(save_file):
        with open(save_file, 'r') as f:
            fname = f.read().strip()
        model_name = os.path.basename(fname)
        model_path = os.path.dirname(fname)
        if model_name.startswith('model_') and len(model_name) == 17:
            iteration = int(model_name[-11:-4])
        elif model_name == "model_final":
            iteration = max_iter
        elif model_path.startswith('checkpoint-') and len(model_path) == 18:
            iteration = int(model_path.split('-')[-1])
    return iteration


def get_matching_parameters(model, regexp, none_on_empty=True):
    """Returns parameters matching regular expression"""
    if not regexp:
        if none_on_empty:
            return {}
        else:
            return dict(model.named_parameters())
    compiled_pattern = re.compile(regexp)
    params = {}
    for weight_name, weight in model.named_parameters():
        if compiled_pattern.match(weight_name):
            params[weight_name] = weight
    return params


def freeze_weights(model, regexp):
    """Freeze weights based on regular expression."""
    logger = logging.getLogger("maskrcnn_benchmark.trainer")
    for weight_name, weight in get_matching_parameters(model, regexp).items():
        weight.requires_grad = False
        logger.info("Disabled training of {}".format(weight_name))


def unfreeze_weights(model, regexp, backbone_freeze_at=-1,
        is_distributed=False):
    """Unfreeze weights based on regular expression.
    This is helpful during training to unfreeze freezed weights after
    other unfreezed weights have been trained for some iterations.
    """
    logger = logging.getLogger("maskrcnn_benchmark.trainer")
    for weight_name, weight in get_matching_parameters(model, regexp).items():
        weight.requires_grad = True
        logger.info("Enabled training of {}".format(weight_name))
    if backbone_freeze_at >= 0:
        logger.info("Freeze backbone at stage: {}".format(backbone_freeze_at))
        if is_distributed:
            model.module.backbone.body._freeze_backbone(backbone_freeze_at)
        else:
            model.backbone.body._freeze_backbone(backbone_freeze_at)


def delete_tsv_files(tsvs):
    for t in tsvs:
        if op.isfile(t):
            try_delete(t)
        line = op.splitext(t)[0] + '.lineidx'
        if op.isfile(line):
            try_delete(line)


def concat_files(ins, out):
    mkdir(op.dirname(out))
    out_tmp = out + '.tmp'
    with open(out_tmp, 'wb') as fp_out:
        for i, f in enumerate(ins):
            logging.info('concating {}/{} - {}'.format(i, len(ins), f))
            with open(f, 'rb') as fp_in:
                shutil.copyfileobj(fp_in, fp_out, 1024*1024*10)
    os.rename(out_tmp, out)


def concat_tsv_files(tsvs, out_tsv):
    concat_files(tsvs, out_tsv)
    sizes = [os.stat(t).st_size for t in tsvs]
    sizes = np.cumsum(sizes)
    all_idx = []
    for i, t in enumerate(tsvs):
        for idx in load_list_file(op.splitext(t)[0] + '.lineidx'):
            if i == 0:
                all_idx.append(idx)
            else:
                all_idx.append(str(int(idx) + sizes[i - 1]))
    with open(op.splitext(out_tsv)[0] + '.lineidx', 'w') as f:
        f.write('\n'.join(all_idx))


def load_list_file(fname):
    with open(fname, 'r') as fp:
        lines = fp.readlines()
    result = [line.strip() for line in lines]
    if len(result) > 0 and result[-1] == '':
        result = result[:-1]
    return result


def try_once(func):
    def func_wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logging.info('ignore error \n{}'.format(str(e)))
    return func_wrapper


@try_once
def try_delete(f):
    os.remove(f)


def set_seed(seed, n_gpu):
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if n_gpu > 0:
        torch.cuda.manual_seed_all(seed)


def print_and_run_cmd(cmd):
    print(cmd)
    os.system(cmd)


def write_to_yaml_file(context, file_name):
    with open(file_name, 'w') as fp:
        yaml.dump(context, fp, encoding='utf-8')


def load_from_yaml_file(yaml_file):
    with open(yaml_file, 'r') as fp:
        return yaml.load(fp, Loader=yaml.CLoader)




================================================
FILE: utils/model_util.py
================================================
from models.mdm import MDM
from diffusion import gaussian_diffusion as gd
from diffusion.respace import SpacedDiffusion, space_timesteps


def load_model_wo_clip(model, state_dict):
    missing_keys, _ = model.load_state_dict(state_dict, strict=False)

    assert all([k.startswith('clip_model.') for k in missing_keys])


def create_model_and_diffusion(args):
    model = MDM(**get_model_args(args))

    diffusion = create_gaussian_diffusion(args)
    return model, diffusion


def get_model_args(args):
    '''
    we do not set action number here.
    '''
    # default args
    clip_version = 'ViT-B/32'
    cond_mode = args.cond_mode

    return {'modeltype': '', 'num_actions': args.num_actions,
            'dropout': 0.1, 'activation': "gelu", 'cond_mode': cond_mode,
            'arch': args.arch, 'clip_version': clip_version, 'dataset': args.dataset}


def create_gaussian_diffusion(args):
    #
    # default params
    predict_xstart = True  # we always predict x_start (a.k.a. x0), that's our deal!

    steps = 1000
    scale_beta = 1.  # no scaling
    timestep_respacing = ''  # can be used for ddim sampling, we don't use it.
    learn_sigma = False
    rescale_timesteps = False

    betas = gd.get_named_beta_schedule(args.noise_schedule, steps, scale_beta)
    loss_type = gd.LossType.MSE

    if not timestep_respacing:
        timestep_respacing = [steps]

    return SpacedDiffusion(
        use_timesteps=space_timesteps(steps, timestep_respacing),
        betas=betas,
        model_mean_type=(
            gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
        ),
        model_var_type=(
            (
                gd.ModelVarType.FIXED_LARGE
                if not args.sigma_small
                else gd.ModelVarType.FIXED_SMALL
            )
            if not learn_sigma
            else gd.ModelVarType.LEARNED_RANGE
        ),
        loss_type=loss_type,
        rescale_timesteps=rescale_timesteps,
        args = args,
    )

================================================
FILE: utils/parser_util.py
================================================
from argparse import ArgumentParser
import argparse
import os
import json


def parse_and_load_from_model(parser):
    # args according to the loaded model
    # do not try to specify them from cmd line since they will be overwritten
    add_data_options(parser)
    add_model_options(parser)
    add_diffusion_options(parser)
    args = parser.parse_args()
    args_to_overwrite = []
    for group_name in ['dataset', 'model', 'diffusion']:
        args_to_overwrite += get_args_per_group_name(parser, args, group_name)

    if args.cond_mask_prob == 0:
        args.guidance_param = 1
    return args


def get_args_per_group_name(parser, args, group_name):
    for group in parser._action_groups:
        if group.title == group_name:
            group_dict = {a.dest: getattr(args, a.dest, None) for a in group._group_actions}
            return list(argparse.Namespace(**group_dict).__dict__.keys())
    return ValueError('group_name was not found.')

def get_model_path_from_args():
    try:
        dummy_parser = ArgumentParser()
        dummy_parser.add_argument('model_path')
        dummy_args, _ = dummy_parser.parse_known_args()
        return dummy_args.model_path
    except:
        raise ValueError('model_path argument must be specified.')


def add_base_options(parser):
    group = parser.add_argument_group('base')
    group.add_argument("--num_actions", default=9, type=int, help="num_classes.")
    group.add_argument("--cuda", default=True, type=bool, help="Use cuda device, otherwise use CPU.")
    group.add_argument("--device", default=0, type=int, help="Device id to use.")
    group.add_argument("--seed", default=10, type=int, help="For fixing random seed.")
    group.add_argument("--batch_size", default=64, type=int, help="Batch size during training.")
    group.add_argument("--distributed", default=False, type=bool, help="Use ddp to train model")


def add_diffusion_options(parser):
    group = parser.add_argument_group('diffusion')
    group.add_argument("--noise_schedule", default='cosine', choices=['linear', 'cosine'], type=str,
                       help="Noise schedule type")
    group.add_argument("--diffusion_steps", default=1000, type=int,
                       help="Number of diffusion steps (denoted T in the paper)")
    group.add_argument("--sigma_small", default=True, type=bool, help="Use smaller sigma values.")


def add_model_options(parser):
    group = parser.add_argument_group('model')
    group.add_argument("--arch", default='OpenUNet',
                       choices=['OpenUNet'], type=str,
                       help="Architecture types as reported in the paper.")
    group.add_argument("--cond_mask_prob", default=0, type=float,
                       help="The probability of masking the condition during training."
                            " For classifier-free guidance learning.")
    group.add_argument("--unconstrained", action='store_true',
                       help="Model is trained unconditionally. That is, it is constrained by neither text nor action.")
    group.add_argument("--cond_mode",
                       choices=['no_cond', 'text', 'sketch', 'category', 'img'], type=str,required=True,
                       help="condition type")


def add_data_options(parser):
    group = parser.add_argument_group('dataset')
    group.add_argument("--dataset", default='deepfashion3d', choices=['deepfashion3d', 'text2shape', 'pix3d', 'kcars'
                                                                ], type=str,
                       help="Dataset name (choose from list).")
    group.add_argument("--data_dir", default="", type=str,
                       help="If empty, will use defaults according to the specified dataset.")


def add_training_options(parser):
    group = parser.add_argument_group('training')
    group.add_argument("--save_dir", required=True, type=str,
                       help="Path to save checkpoints and results.")

    group.add_argument("--ae_dir", required=False, type=str,
                       help="Path to save checkpoints and results.")

    group.add_argument("--num_workers", default=4, type=int, help="num_workers.")

    group.add_argument("--grid_size", default=128, type=int, help="grid size.")
    group.add_argument("--overwrite", action='store_true',
                       help="If True, will enable to use an already existing save_dir.")

    group.add_argument("--lr", default=1e-4, type=float, help="Learning rate.")
    group.add_argument("--weight_decay", default=0.0, type=float, help="Optimizer weight decay.")
    group.add_argument("--lr_anneal_steps", default=0, type=int, help="Number of learning rate anneal steps.")

    group.add_argument("--log_interval", default=10, type=int,
                       help="Log losses each N steps")
    group.add_argument("--save_interval", default=50_000, type=int,
                       help="Save checkpoints and run evaluation each N steps")
    group.add_argument("--num_steps", default=600000, type=int,
                       help="Training will stop after the specified number of steps.")
    group.add_argument("--resume_checkpoint", default="", type=str,
                       help="If not empty, will start from the specified checkpoint (path to model###.pt file).")
    group.add_argument("--clip_value", default=0.1, type=float, help="max_clipping value (0-max).")
    group.add_argument("--guidance_param", default=1.0, type=float, help="Classifier Free Guidance")#3.0


def add_sampling_options(parser):
    group = parser.add_argument_group('sampling')
    group.add_argument("--model_path", required=True, type=str,
                       help="Path to model####.pt file to be sampled.")
    group.add_argument("--output_dir", default='', type=str,
                       help="Path to results dir (auto created by the script). "
                            "If empty, will create dir in parallel to checkpoint.")
    group.add_argument("--num_samples", default=1, type=int,
                       help="Maximal number of prompts to sample, "
                            "if loading dataset from file, this field will be ignored.")
    group.add_argument("--guidance_param", default=1.0, type=float,
                       help="For classifier-free sampling - specifies the s parameter, as defined in the paper.")
    group.add_argument("--if_clip", action='store_true',
                       help="If True, will run evaluation during training.")
    group.add_argument("--clip_value", default=0.1, type=float, help="max_clipping value (0-max).")



def add_generate_options(parser):
    group = parser.add_argument_group('generate')

    group.add_argument("--grid_size", default=128, type=int, help="grid size.")
    group.add_argument("--category", default=0, type=int, required=False,
                        help="Condition category.")
    group.add_argument("--sketch_path", default=None, type=str, required=False,
                       help="Path to the condition sketch image.")
    group.add_argument("--image_path", default=None, type=str, required=False,
                       help="Path to the condition image.")
    group.add_argument("--mask_path", default=None, type=str, required=False,
                       help="Path to the condition mask.")
    group.add_argument("--prompt", default=None, type=str, required=False,
                       help="text prompt for generation.")
    group.add_argument("--watertight",  action='store_true',
                       help="mesh attributes.")
    group.add_argument("--resolution",  default=512, type=int, required=False,
                       help="mesh resolution.")
    group.add_argument("--ae_dir", default=None, type=str, help="Path to ae")



def train_args():
    parser = ArgumentParser()
    add_base_options(parser)
    add_data_options(parser)
    add_model_options(parser)
    add_diffusion_options(parser)
    add_training_options(parser)
    parser.add_argument("--local_rank", type=int)
    return parser.parse_args()


def generate_args():
    parser = ArgumentParser()
    # args specified by the user: (all other will be loaded from the model)
    add_base_options(parser)
    add_sampling_options(parser)
    add_generate_options(parser)
    return parse_and_load_from_model(parser)


def evaluation_parser():
    parser = ArgumentParser()
    # args specified by the user: (all other will be loaded from the model)
    add_base_options(parser)
    return parse_and_load_from_model(parser)

================================================
FILE: utils/utils.py
================================================
import torch
from typing import Iterable, List, Tuple, Union, Callable
from torch import Tensor
import open3d as o3d
import numpy as np
import math

def batchify(inputs: List[Tensor], required_dim: int) -> Tuple[bool, List[Tensor]]:
    """Batchify input tensors if needed.
    All the input tensors with a number of dimensions smaller than
    required_dim will be expanded with a leading batch dimension.
    Args:
        inputs: The tensors to batchify.
        required_dim: The required number of dimensions.
    Returns:
        - A flag that indicates wether one of the inputs has been batchified.
        - The batchified tensors.
    """
    results: List[Tensor] = []
    has_changed = False

    for t in inputs:
        has_changed = len(t.shape) < required_dim or has_changed
        batched_t = torch.unsqueeze(t, dim=0) if has_changed else t
        results.append(batched_t)

    return has_changed, results


def unbatchify(inputs: List[Tensor]) -> List[Tensor]:
    """Remove batch dimension from input tensors.
    Args:
        inputs: The tensors to unbatchify.
    Returns:
        The unbatchified tensors.
    """
    results: List[Tensor] = []
    for t in inputs:
        unbatched_t = torch.squeeze(t, dim=0)
        results.append(unbatched_t)

    return results

def random_point_sampling(pcd: Tensor, num_points: int, inds=None) -> Tensor:
    """Sample the requested number of points from the given point cloud(s).
    Points are sampled randomly. If num_points is greater than NUM_POINTS,
    then points are sampled with replacement.
    Args:
        pcd: The input point cloud(s) with shape ([B,] NUM_POINTS, D).
        num_points: The number of points to sample.
    Returns:
        The sampled points with shape ([B,] NUM_SAMPLED_POINTS, D).
    """
    #print(pcd.shape)
    batched, [pcd] = batchify([pcd], 3)
    
    batch_size, original_num_points, _ = pcd.shape
    #print(batch_size, original_num_points)
    #torch.random.manual_seed(10)
    if inds is None:
        #print(original_num_points, num_points)
        weights = torch.ones((batch_size, original_num_points), dtype=torch.float)
        weights = weights.to(pcd.device)
        replacement = original_num_points < num_points
        indices_to_sample = torch.multinomial(weights, num_points, replacement=replacement)
    else:
        #print(original_num_points, num_points)
        indices_to_sample = inds
    #print(indices_to_sample)

    batch_indices = torch.arange(batch_size).reshape(batch_size, 1)
    sampled_points = pcd[batch_indices, indices_to_sample]

    if batched:
        [sampled_points] = unbatchify([sampled_points])

    return sampled_points

def get_o3d_mesh_from_tensors(
    vertices: Union[Tensor, np.ndarray],
    triangles: Union[Tensor, np.ndarray],
) -> o3d.geometry.TriangleMesh:
    """Get open3d mesh from either numpy arrays or torch tensors.
    The input vertices must have shape (NUM_VERTICES, D), where D
    can be 3 (only X,Y,Z), 6 (X,Y,Z and normals) or 9 (X,Y,Z, normals and colors).
    The input triangles must have shape (NUM_TRIANGLES, D), where D can be 3
    (only vertex indices) or 6 (vertex indices and normals).
    Args:
        vertices: The numpy array or torch tensor with vertices
            with shape (NUM_VERTICES, D).
        triangles: The numpy array or torch tensor with triangles
            with shape (NUM_TRIANGLES, D).
    Returns:
        The open3d mesh.
    """
    mesh_o3d = o3d.geometry.TriangleMesh()

    if isinstance(vertices, Tensor):
        v = vertices.clone().detach().cpu().numpy()
    else:
        v = np.copy(vertices)

    if isinstance(triangles, Tensor):
        t = triangles.clone().detach().cpu().numpy()
    else:
        t = np.copy(triangles)

    mesh_o3d.vertices = o3d.utility.Vector3dVector(v[:, :3])

    if v.shape[1] == 6:
        mesh_o3d.vertex_normals = o3d.utility.Vector3dVector(v[:, 3:6])

    if v.shape[1] == 9:
        mesh_o3d.vertex_colors = o3d.utility.Vector3dVector(v[:, 6:9])

    mesh_o3d.triangles = o3d.utility.Vector3iVector(t[:, :3])

    if t.shape[1] == 6:
        mesh_o3d.triangle_normals = o3d.utility.Vector3dVector(t[:, 3:6])

    return mesh_o3d

def compute_gradients(x: Tensor, y: Tensor) -> Tensor:
    grad_outputs = torch.ones_like(y)
    grads = torch.autograd.grad(y, x, grad_outputs=grad_outputs, create_graph=True)[0]
    return grads

def sample_udf(
    udf_func: Callable[[Tensor], Tensor],
    coords: Tensor,
    max_batch: int,
    grad: bool = False,
) -> Tensor:
    udf = torch.zeros(coords.shape[0]).cuda()
    start = 0

    while start < coords.shape[0]:
        end = min(start + max_batch, coords.shape[0])
        p = coords[start:end]
        if grad:
            udf[start:end] = udf_func(p)
        else:
            with torch.no_grad():
                udf[start:end] = udf_func(p)
        start = end

    return udf



class GridFiller:
    """
    Coarse to fine method for querying an SDF network, using cached grids.
    #
    We start by evaluating the field on a low resolution grid, and then
    iteratively subdivide each voxel and re-evaluate the field only
    where needed until we reach a desired grid resolution.
    We subdivide voxels if the field absolute value on any of the voxel corners
    is smaller than the voxel diagonal √2∆x, where ∆x denotes voxel size.
    #
    In practice the coarsest level is here hardcoded to be 32**3.
    """

    def __init__(
        self,
        final_resolution: int,
        voxel_origin: Tuple[int, int, int] = (-1, -1, -1),
        cube_side_length: float = 2.0,
    ):
        # Save attributes
        self.N_max = final_resolution
        self.num_samples = final_resolution**3
        self.N_levels = [32 * (2**i) for i in range(int(math.log2(self.N_max) - 4))]
        self.voxel_origin = voxel_origin
        self.cube_side_length = cube_side_length

        # Construct grid, and precompute sparse masks, from 32 (coarsest grid) to final_resolution
        """
        Create one empty grid (N,N,N,7) where the 7 channels are (x,y,z, UDF, +3 for gradients).
        """
        voxel_size = self.cube_side_length / (self.N_max - 1)
        self.voxel_size = voxel_size
        overall_index = torch.arange(0, self.N_max**3, 1, out=torch.LongTensor())
        samples = torch.zeros(self.N_max**3, 7)

        # Transform the first 3 columns to be the x, y, z indices.
        samples[:, 2] = overall_index % self.N_max
        samples[:, 1] = (
            torch.div(overall_index, self.N_max, rounding_mode="floor") % self.N_max
        )
        samples[:, 0] = (
            torch.div(
                torch.div(overall_index, self.N_max, rounding_mode="floor"),
                self.N_max,
                rounding_mode="floor",
            )
            % self.N_max
        )

        # Then transform the first 3 columns to be the x, y, z coordinates.
        samples[:, 0] = (samples[:, 0] * voxel_size) + voxel_origin[2]
        samples[:, 1] = (samples[:, 1] * voxel_size) + voxel_origin[1]
        samples[:, 2] = (samples[:, 2] * voxel_size) + voxel_origin[0]
        samples.requires_grad = False
        #samples.pin_memory()
        #self.samples = samples.cuda()
        self.samples = samples

        """
        Precompute binary masks for adressing the above grid at different resolutions.
        """
        mask = torch.zeros(self.N_max**3).bool()
        mask = mask.reshape(self.N_max, self.N_max, self.N_max)

        # Fill dictionaries with precomputed masks.
        self.masks_coarse = {}
        self.masks_coarse_no_recompute = {}
        self.idxs_coarse_neighbors_blocks = {}
        for i, N in enumerate(self.N_levels):
            #### 1: Subsample coarsely.
            mask_coarse = mask.clone()
            mask_coarse[
                :: self.N_max // N, :: self.N_max // N, :: self.N_max // N
            ] = True

            # (N_max**3) array, with True only for indices of the coarse sampling (N**3 locations):
            mask_coarse = mask_coarse.reshape(-1)
            self.masks_coarse[i] = mask_coarse.clone().cuda()

            #### 2: Compute the indices of neighboring blocks.
            neighbors_block_coarse = mask.clone()
            neighbors_block_coarse[
                : self.N_max // N, : self.N_max // N, : self.N_max // N
            ] = True
            neighbors_block_coarse = neighbors_block_coarse.reshape(-1)
            # Shape (N**3 / 64, 64): idxs_coarse_neighbors_blocks[i] represents the (N_max // N)**3 indices covered by coarse point i.
            idxs_coarse_neighbors_blocks = torch.where(mask_coarse)[0].reshape(
                -1, 1
            ) + torch.where(neighbors_block_coarse)[0].reshape(1, -1)
            self.idxs_coarse_neighbors_blocks[
                i
            ] = idxs_coarse_neighbors_blocks.clone().cuda()

            #### 3: For levels finer than the coarsest one, do not recompute already queried SDFs.
            if i > 0:
                mask_coarse_no_recompute = mask_coarse.clone()
                mask_coarse_no_recompute[self.masks_coarse[i - 1]] = False
                self.masks_coarse_no_recompute[
                    i
                ] = mask_coarse_no_recompute.clone().cuda()

    def fill_grid(
        self, udf_func: Callable[[Tensor], Tensor], max_batch: int
    ) -> Tuple[Tensor, Tensor]:
        with torch.no_grad():
            samples = self.samples.clone()
            close_surface_masks = {}
            idxs_coarse_neighbors_blocks_LOCAL = {}

            for level, N in enumerate(self.N_levels):
                """Prepare masks based on previous levels"""
                if level == 0:
                    mask_coarse = self.masks_coarse[level]
                    idxs_coarse_neighbors_blocks = self.idxs_coarse_neighbors_blocks[
                        level
                    ].clone()
                    mask_coarse_no_recompute = self.masks_coarse[level]
                else:
                    # Mask using previous queries: binary mask.
                    mask_coarse = self.masks_coarse[level].clone()
                    for l in range(level):
                        mask_coarse[
                            idxs_coarse_neighbors_blocks_LOCAL[l][
                                ~close_surface_masks[l]
                            ]
                        ] = False

                    # Compute the corresponding indices tensor.
                    if N < self.N_max:
                        idxs_coarse_neighbors_blocks = (
                            self.idxs_coarse_neighbors_blocks[level].clone()
                        )
                        idxs_coarse_neighbors_blocks = idxs_coarse_neighbors_blocks[
                            mask_coarse[self.masks_coarse[level]]
                        ]
                    else:
                        idxs_coarse_neighbors_blocks = (
                            self.idxs_coarse_neighbors_blocks[level]
                        )

                    # The no_recompute version does not query the decoder for nodes that have
                    # already been computed at coarser levels.
                    mask_coarse_no_recompute = self.masks_coarse_no_recompute[
                        level
                    ].clone()
                    for l in range(level):
                        mask_coarse_no_recompute[
                            idxs_coarse_neighbors_blocks_LOCAL[l][
                                ~close_surface_masks[l]
                            ]
                        ] = False
                idxs_coarse_neighbors_blocks_LOCAL[level] = idxs_coarse_neighbors_blocks

                """ Query the network """
                xyz = samples[mask_coarse_no_recompute, 0:3].cuda()
                # Query and fill grid.
                samples[mask_coarse_no_recompute, 3] = sample_udf(
                    udf_func, xyz, max_batch=max_batch
                ).cpu()

                """ Prepare next levels queries """
                if N < self.N_max:
                    ## Which samples are close to the surface?
                    step_size = 2.0 / N
                    close_surface_mask = (
                        torch.abs(samples[mask_coarse, 3]) < 1.5 * 1.7 * step_size
                    )
                    close_surface_masks[level] = close_surface_mask

                    # For those far of the surface, we can ignore them for the future and copy the high value to their neighbors
                    samples[
                        idxs_coarse_neighbors_blocks[~close_surface_mask], 3
                    ] = samples[mask_coarse, 3][~close_surface_mask].unsqueeze(-1)

            udf_values = samples[:, 3]
            udf_values = udf_values.reshape(self.N_max, self.N_max, self.N_max)

        #torch.cuda.empty_cache()
        # Compute gradients only where the predicted udf value is small.
        # mask_gradients = samples[:, 3] < (2.5 * self.cube_side_length / self.N_max)
        # samples[mask_gradients, 4:] = sample_grads(
        #     udf_func, samples[mask_gradients, :3].cuda(), max_batch=max_batch
        # ).cpu()
        # gradients = samples[:, 4:]
        # gradients = gradients.reshape(self.N_max, self.N_max, self.N_max, 3)
        gradients = None
        del samples
        torch.cuda.empty_cache()
        return udf_values, gradients