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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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================================================
FILE: AutoEncoder/dataset_info_files/ShapeNet_filelists/02691156_test.lst
================================================
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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
SYMBOL INDEX (635 symbols across 52 files)
FILE: AutoEncoder/data/dataset.py
class UdfsDataset (line 14) | class UdfsDataset(Dataset):
method __init__ (line 15) | def __init__(self, name: str, root: Path, split: str) -> None:
method __len__ (line 63) | def __len__(self) -> int:
method get_training_idxes (line 66) | def get_training_idxes(self):
method update_training_idxes (line 69) | def update_training_idxes(self, new_idxes):
method val_del_idxes (line 75) | def val_del_idxes(self):
method __getitem__ (line 79) | def __getitem__(self, index: int) -> T_ITEM:
method get_mesh (line 90) | def get_mesh(self, index: int) -> Tuple[Tensor, Tensor]:
FILE: AutoEncoder/encdec/DynamicSampler.py
class DummyDataset (line 13) | class DummyDataset(Dataset):
method __init__ (line 14) | def __init__(self):
method __getitem__ (line 17) | def __getitem__(self, index):
method __len__ (line 20) | def __len__(self):
method get_training_idxes (line 23) | def get_training_idxes(self):
method update_training_idxes (line 26) | def update_training_idxes(self, new_idxes):
class SequenceSampler (line 29) | class SequenceSampler(Sampler):
method __init__ (line 30) | def __init__(self, training_idxes, N):
method __iter__ (line 34) | def __iter__(self) -> Iterator[int]:
method __len__ (line 38) | def __len__(self) -> int:
method update_training_idxes (line 41) | def update_training_idxes(self, new_add_idxes):
class SequenceSampler_Train (line 44) | class SequenceSampler_Train(Sampler):
method __init__ (line 45) | def __init__(self, training_idxes):
method __iter__ (line 49) | def __iter__(self) -> Iterator[int]:
method __len__ (line 53) | def __len__(self) -> int:
method update_training_idxes (line 56) | def update_training_idxes(self, new_add_idxes):
class WeightedDynamicSampler (line 59) | class WeightedDynamicSampler(WeightedRandomSampler):
method __init__ (line 61) | def __init__(self, weights: Sequence[float], num_samples: int,
method __iter__ (line 79) | def __iter__(self) -> Iterator[int]:
method __len__ (line 83) | def __len__(self) -> int:
method update_weights (line 86) | def update_weights(self, weights):
class DynamicBatchSampler (line 90) | class DynamicBatchSampler(BatchSampler):
method update_weights (line 92) | def update_weights(self, weights):
method update_training_idxes (line 95) | def update_training_idxes(self, training_idxes):
FILE: AutoEncoder/encdec/export_meshes.py
function main (line 39) | def main() -> None:
FILE: AutoEncoder/encdec/normalized_obj.py
function obj_normalization (line 4) | def obj_normalization(input_mesh_file, output_file_path):
function box_center_normalization (line 21) | def box_center_normalization(input_mesh_file, output_file_path):
FILE: AutoEncoder/encdec/preprocess_udfs.py
function PrepareOneUDF (line 110) | def PrepareOneUDF(sub_id, split):
FILE: AutoEncoder/encdec/train_encdec.py
function main (line 24) | def main() -> None:
FILE: AutoEncoder/models/cbndec.py
class DecoderConditionalBatchNorm (line 4) | class DecoderConditionalBatchNorm(nn.Module):
method __init__ (line 5) | def __init__(
method forward (line 35) | def forward(self, points: Tensor, conditions: Tensor) -> Tensor:
class ConditionalBatchNorm1d (line 50) | class ConditionalBatchNorm1d(nn.Module):
method __init__ (line 51) | def __init__(self, c_dim: int, f_dim: int) -> None:
method reset_parameters (line 62) | def reset_parameters(self) -> None:
method forward (line 68) | def forward(self, x: Tensor, c: Tensor) -> Tensor:
class ConditionalResnetBlock1d (line 85) | class ConditionalResnetBlock1d(nn.Module):
method __init__ (line 86) | def __init__(self, c_dim: int, size_in: int) -> None:
method forward (line 99) | def forward(self, x: Tensor, c: Tensor) -> Tensor:
class CbnDecoder (line 106) | class CbnDecoder(nn.Module):
method __init__ (line 107) | def __init__(
method forward (line 127) | def forward(self, coords_emb, latent_codes) -> Tensor:
FILE: AutoEncoder/models/coordsenc.py
class CoordsEncoder (line 7) | class CoordsEncoder:
method __init__ (line 8) | def __init__(
method create_encoding_fn (line 25) | def create_encoding_fn(self) -> None:
method encode (line 50) | def encode(self, inputs: Tensor) -> Tensor:
FILE: AutoEncoder/models/dgcnn.py
function get_graph_feature (line 9) | def get_graph_feature(x: Tensor, indices: Tensor) -> Tensor:
class Dgcnn (line 27) | class Dgcnn(nn.Module):
method __init__ (line 28) | def __init__(
method block_forward (line 55) | def block_forward(
method forward (line 77) | def forward(self, x: Tensor, latent_index=None) -> Tensor:
FILE: AutoEncoder/trainers/encdec.py
class EncoderDecoderTrainer (line 35) | class EncoderDecoderTrainer:
method __init__ (line 36) | def __init__(self) -> None:
method train (line 115) | def train(self) -> None:
method val (line 228) | def val(self) -> float:
method save_ckpt (line 299) | def save_ckpt(self, all: bool = False, best=False) -> None:
method restore_from_last_ckpt (line 325) | def restore_from_last_ckpt(self) -> None:
FILE: AutoEncoder/trainers/test.py
class DynamicWeightedRandomSampler (line 10) | class DynamicWeightedRandomSampler(WeightedRandomSampler):
method update_distribution (line 16) | def update_distribution(self, weights):
method __len__ (line 19) | def __len__(self):
class BatchSampler (line 22) | class BatchSampler(Sampler):
method __init__ (line 23) | def __init__(self, sampler, batch_size, drop_last):
method __iter__ (line 28) | def __iter__(self):
method __len__ (line 37) | def __len__(self):
FILE: AutoEncoder/utils.py
function read_mesh (line 13) | def read_mesh(
function get_tensor_mesh_from_o3d (line 41) | def get_tensor_mesh_from_o3d(
function get_o3d_mesh_from_tensors (line 81) | def get_o3d_mesh_from_tensors(
function progress_bar (line 126) | def progress_bar(iterable: Iterable, desc: str = "", num_cols: int = 60)...
function get_tensor_pcd_from_o3d (line 141) | def get_tensor_pcd_from_o3d(
function sample_points_around_pcd (line 167) | def sample_points_around_pcd(
function compute_udf_and_gradients (line 223) | def compute_udf_and_gradients(
function compute_sdf_and_gradients (line 242) | def compute_sdf_and_gradients(
function compute_udf_from_mesh (line 268) | def compute_udf_from_mesh(
function compute_sdf_from_mesh (line 317) | def compute_sdf_from_mesh(
function compute_gradients (line 365) | def compute_gradients(x: Tensor, y: Tensor) -> Tensor:
function batchify (line 371) | def batchify(inputs: List[Tensor], required_dim: int) -> Tuple[bool, Lis...
function unbatchify (line 393) | def unbatchify(inputs: List[Tensor]) -> List[Tensor]:
function random_point_sampling (line 408) | def random_point_sampling(pcd: Tensor, num_points: int) -> Tensor:
FILE: CLIP/clip.py
function _download (line 43) | def _download(url: str, root: str):
function _convert_image_to_rgb (line 75) | def _convert_image_to_rgb(image):
function _transform (line 79) | def _transform(n_px):
function available_models (line 89) | def available_models() -> List[str]:
function load (line 94) | def load(name: str, device: Union[str, torch.device] = "cuda" if torch.c...
function tokenize (line 197) | def tokenize(texts: Union[str, List[str]], context_length: int = 77, tru...
FILE: CLIP/clip/clip.py
function _download (line 43) | def _download(url: str, root: str):
function _convert_image_to_rgb (line 75) | def _convert_image_to_rgb(image):
function _transform (line 79) | def _transform(n_px):
function available_models (line 89) | def available_models() -> List[str]:
function load (line 94) | def load(name: str, device: Union[str, torch.device] = "cuda" if torch.c...
function tokenize (line 197) | def tokenize(texts: Union[str, List[str]], context_length: int = 77, tru...
FILE: CLIP/clip/model.py
class Bottleneck (line 10) | class Bottleneck(nn.Module):
method __init__ (line 13) | def __init__(self, inplanes, planes, stride=1):
method forward (line 42) | def forward(self, x: torch.Tensor):
class AttentionPool2d (line 58) | class AttentionPool2d(nn.Module):
method __init__ (line 59) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
method forward (line 68) | def forward(self, x):
class ModifiedResNet (line 94) | class ModifiedResNet(nn.Module):
method __init__ (line 102) | def __init__(self, layers, output_dim, heads, input_resolution=224, wi...
method _make_layer (line 129) | def _make_layer(self, planes, blocks, stride=1):
method forward (line 138) | def forward(self, x):
class LayerNorm (line 157) | class LayerNorm(nn.LayerNorm):
method forward (line 160) | def forward(self, x: torch.Tensor):
class QuickGELU (line 166) | class QuickGELU(nn.Module):
method forward (line 167) | def forward(self, x: torch.Tensor):
class ResidualAttentionBlock (line 171) | class ResidualAttentionBlock(nn.Module):
method __init__ (line 172) | def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor ...
method attention (line 185) | def attention(self, x: torch.Tensor):
method forward (line 189) | def forward(self, x: torch.Tensor):
class Transformer (line 195) | class Transformer(nn.Module):
method __init__ (line 196) | def __init__(self, width: int, layers: int, heads: int, attn_mask: tor...
method forward (line 202) | def forward(self, x: torch.Tensor):
class VisionTransformer (line 206) | class VisionTransformer(nn.Module):
method __init__ (line 207) | def __init__(self, input_resolution: int, patch_size: int, width: int,...
method forward (line 223) | def forward(self, x: torch.Tensor):
class CLIP (line 243) | class CLIP(nn.Module):
method __init__ (line 244) | def __init__(self,
method initialize_parameters (line 299) | def initialize_parameters(self):
method build_attention_mask (line 328) | def build_attention_mask(self):
method dtype (line 337) | def dtype(self):
method encode_image (line 340) | def encode_image(self, image):
method encode_text (line 343) | def encode_text(self, text):
method forward (line 358) | def forward(self, image, text):
function convert_weights (line 375) | def convert_weights(model: nn.Module):
function build_model (line 399) | def build_model(state_dict: dict):
FILE: CLIP/clip/simple_tokenizer.py
function default_bpe (line 11) | def default_bpe():
function bytes_to_unicode (line 16) | def bytes_to_unicode():
function get_pairs (line 38) | def get_pairs(word):
function basic_clean (line 50) | def basic_clean(text):
function whitespace_clean (line 56) | def whitespace_clean(text):
class SimpleTokenizer (line 62) | class SimpleTokenizer(object):
method __init__ (line 63) | def __init__(self, bpe_path: str = default_bpe()):
method bpe (line 80) | def bpe(self, token):
method encode (line 121) | def encode(self, text):
method decode (line 129) | def decode(self, tokens):
FILE: CLIP/hubconf.py
function _create_hub_entrypoint (line 10) | def _create_hub_entrypoint(model):
function tokenize (line 37) | def tokenize():
FILE: CLIP/model.py
class Bottleneck (line 10) | class Bottleneck(nn.Module):
method __init__ (line 13) | def __init__(self, inplanes, planes, stride=1):
method forward (line 42) | def forward(self, x: torch.Tensor):
class AttentionPool2d (line 58) | class AttentionPool2d(nn.Module):
method __init__ (line 59) | def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, o...
method forward (line 68) | def forward(self, x):
class ModifiedResNet (line 94) | class ModifiedResNet(nn.Module):
method __init__ (line 102) | def __init__(self, layers, output_dim, heads, input_resolution=224, wi...
method _make_layer (line 129) | def _make_layer(self, planes, blocks, stride=1):
method forward (line 138) | def forward(self, x):
class LayerNorm (line 157) | class LayerNorm(nn.LayerNorm):
method forward (line 160) | def forward(self, x: torch.Tensor):
class QuickGELU (line 166) | class QuickGELU(nn.Module):
method forward (line 167) | def forward(self, x: torch.Tensor):
class ResidualAttentionBlock (line 171) | class ResidualAttentionBlock(nn.Module):
method __init__ (line 172) | def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor ...
method attention (line 185) | def attention(self, x: torch.Tensor):
method forward (line 189) | def forward(self, x: torch.Tensor):
class Transformer (line 195) | class Transformer(nn.Module):
method __init__ (line 196) | def __init__(self, width: int, layers: int, heads: int, attn_mask: tor...
method forward (line 202) | def forward(self, x: torch.Tensor):
class VisionTransformer (line 206) | class VisionTransformer(nn.Module):
method __init__ (line 207) | def __init__(self, input_resolution: int, patch_size: int, width: int,...
method forward (line 223) | def forward(self, x: torch.Tensor):
class CLIP (line 243) | class CLIP(nn.Module):
method __init__ (line 244) | def __init__(self,
method initialize_parameters (line 299) | def initialize_parameters(self):
method build_attention_mask (line 328) | def build_attention_mask(self):
method dtype (line 337) | def dtype(self):
method encode_image (line 340) | def encode_image(self, image):
method encode_text (line 343) | def encode_text(self, text):
method forward (line 358) | def forward(self, image, text):
function convert_weights (line 375) | def convert_weights(model: nn.Module):
function build_model (line 399) | def build_model(state_dict: dict):
FILE: CLIP/simple_tokenizer.py
function default_bpe (line 11) | def default_bpe():
function bytes_to_unicode (line 16) | def bytes_to_unicode():
function get_pairs (line 38) | def get_pairs(word):
function basic_clean (line 50) | def basic_clean(text):
function whitespace_clean (line 56) | def whitespace_clean(text):
class SimpleTokenizer (line 62) | class SimpleTokenizer(object):
method __init__ (line 63) | def __init__(self, bpe_path: str = default_bpe()):
method bpe (line 80) | def bpe(self, token):
method encode (line 121) | def encode(self, text):
method decode (line 129) | def decode(self, tokens):
FILE: CLIP/tests/test_consistency.py
function test_consistency (line 10) | def test_consistency(model_name):
FILE: data_loaders/dataset.py
function mask2bbox (line 19) | def mask2bbox(mask):
function crop_square (line 29) | def crop_square(img, bbox, img_size_h=256, img_size_w=256):
function _convert_image_to_rgb (line 77) | def _convert_image_to_rgb(image):
function _transform (line 80) | def _transform(n_px):
function _transform_rgb (line 88) | def _transform_rgb(n_px):
class UDFs3d (line 96) | class UDFs3d(Dataset):
method __init__ (line 97) | def __init__(self, name: str, root: Path, split: str, cond: str) -> None:
method __len__ (line 193) | def __len__(self) -> int:
method __getitem__ (line 199) | def __getitem__(self, index: int) -> T_ITEM:
method get_mesh (line 253) | def get_mesh(self, index: int) -> Tuple[Tensor, Tensor]:
FILE: diffusion/fp16_util.py
function convert_module_to_f16 (line 15) | def convert_module_to_f16(l):
function convert_module_to_f32 (line 25) | def convert_module_to_f32(l):
function make_master_params (line 35) | def make_master_params(param_groups_and_shapes):
function model_grads_to_master_grads (line 52) | def model_grads_to_master_grads(param_groups_and_shapes, master_params):
function master_params_to_model_params (line 65) | def master_params_to_model_params(param_groups_and_shapes, master_params):
function unflatten_master_params (line 78) | def unflatten_master_params(param_group, master_param):
function get_param_groups_and_shapes (line 82) | def get_param_groups_and_shapes(named_model_params):
function master_params_to_state_dict (line 95) | def master_params_to_state_dict(
function state_dict_to_master_params (line 116) | def state_dict_to_master_params(model, state_dict, use_fp16):
function zero_master_grads (line 128) | def zero_master_grads(master_params):
function zero_grad (line 133) | def zero_grad(model_params):
function param_grad_or_zeros (line 141) | def param_grad_or_zeros(param):
class MixedPrecisionTrainer (line 148) | class MixedPrecisionTrainer:
method __init__ (line 149) | def __init__(
method zero_grad (line 173) | def zero_grad(self):
method backward (line 176) | def backward(self, loss: th.Tensor):
method optimize (line 183) | def optimize(self, opt: th.optim.Optimizer):
method _optimize_fp16 (line 189) | def _optimize_fp16(self, opt: th.optim.Optimizer):
method _optimize_normal (line 209) | def _optimize_normal(self, opt: th.optim.Optimizer):
method _compute_norms (line 216) | def _compute_norms(self, grad_scale=1.0):
method master_params_to_state_dict (line 226) | def master_params_to_state_dict(self, master_params):
method state_dict_to_master_params (line 231) | def state_dict_to_master_params(self, state_dict):
function check_overflow (line 235) | def check_overflow(value):
FILE: diffusion/gaussian_diffusion.py
function get_named_beta_schedule (line 23) | def get_named_beta_schedule(schedule_name, num_diffusion_timesteps, scal...
function betas_for_alpha_bar (line 50) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
class ModelMeanType (line 70) | class ModelMeanType(enum.Enum):
class ModelVarType (line 80) | class ModelVarType(enum.Enum):
class LossType (line 94) | class LossType(enum.Enum):
method is_vb (line 102) | def is_vb(self):
class GaussianDiffusion (line 106) | class GaussianDiffusion:
method __init__ (line 123) | def __init__(
method masked_l2 (line 184) | def masked_l2(self, a, b, mask):
method q_mean_variance (line 195) | def q_mean_variance(self, x_start, t):
method q_sample (line 212) | def q_sample(self, x_start, t, noise=None):
method q_posterior_mean_variance (line 234) | def q_posterior_mean_variance(self, x_start, x_t, t):
method p_mean_variance (line 258) | def p_mean_variance(
method _predict_xstart_from_eps (line 365) | def _predict_xstart_from_eps(self, x_t, t, eps):
method _predict_xstart_from_xprev (line 372) | def _predict_xstart_from_xprev(self, x_t, t, xprev):
method _predict_eps_from_xstart (line 382) | def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
method _scale_timesteps (line 388) | def _scale_timesteps(self, t):
method condition_mean (line 393) | def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
method condition_mean_with_grad (line 408) | def condition_mean_with_grad(self, cond_fn, p_mean_var, x, t, model_kw...
method condition_score (line 423) | def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
method condition_score_with_grad (line 447) | def condition_score_with_grad(self, cond_fn, p_mean_var, x, t, model_k...
method p_sample (line 471) | def p_sample(
method p_sample_with_grad (line 522) | def p_sample_with_grad(
method p_sample_loop (line 570) | def p_sample_loop(
method p_sample_loop_progressive (line 635) | def p_sample_loop_progressive(
method ddim_sample (line 711) | def ddim_sample(
method ddim_sample_with_grad (line 763) | def ddim_sample_with_grad(
method ddim_reverse_sample (line 820) | def ddim_reverse_sample(
method ddim_sample_loop (line 858) | def ddim_sample_loop(
method ddim_sample_loop_progressive (line 907) | def ddim_sample_loop_progressive(
method plms_sample (line 974) | def plms_sample(
method plms_sample_loop (line 1058) | def plms_sample_loop(
method plms_sample_loop_progressive (line 1100) | def plms_sample_loop_progressive(
method _vb_terms_bpd (line 1171) | def _vb_terms_bpd(
method training_losses (line 1206) | def training_losses(self, model, x_start, t, loss_L1, loss_args=None, ...
function _extract_into_tensor (line 1329) | def _extract_into_tensor(arr, timesteps, broadcast_shape):
FILE: diffusion/logger.py
class KVWriter (line 26) | class KVWriter(object):
method writekvs (line 27) | def writekvs(self, kvs):
class SeqWriter (line 31) | class SeqWriter(object):
method writeseq (line 32) | def writeseq(self, seq):
class HumanOutputFormat (line 36) | class HumanOutputFormat(KVWriter, SeqWriter):
method __init__ (line 37) | def __init__(self, filename_or_file):
method writekvs (line 48) | def writekvs(self, kvs):
method _truncate (line 80) | def _truncate(self, s):
method writeseq (line 84) | def writeseq(self, seq):
method close (line 93) | def close(self):
class JSONOutputFormat (line 98) | class JSONOutputFormat(KVWriter):
method __init__ (line 99) | def __init__(self, filename):
method writekvs (line 102) | def writekvs(self, kvs):
method close (line 109) | def close(self):
class CSVOutputFormat (line 113) | class CSVOutputFormat(KVWriter):
method __init__ (line 114) | def __init__(self, filename):
method writekvs (line 119) | def writekvs(self, kvs):
method close (line 146) | def close(self):
class TensorBoardOutputFormat (line 150) | class TensorBoardOutputFormat(KVWriter):
method __init__ (line 155) | def __init__(self, dir):
method writekvs (line 171) | def writekvs(self, kvs):
method close (line 185) | def close(self):
function make_output_format (line 191) | def make_output_format(format, ev_dir, log_suffix=""):
function logkv (line 212) | def logkv(key, val):
function logkv_mean (line 221) | def logkv_mean(key, val):
function logkvs (line 228) | def logkvs(d):
function dumpkvs (line 236) | def dumpkvs():
function getkvs (line 243) | def getkvs():
function log (line 247) | def log(*args, level=INFO):
function debug (line 254) | def debug(*args):
function info (line 258) | def info(*args):
function warn (line 262) | def warn(*args):
function error (line 266) | def error(*args):
function set_level (line 270) | def set_level(level):
function set_comm (line 277) | def set_comm(comm):
function get_dir (line 281) | def get_dir():
function profile_kv (line 294) | def profile_kv(scopename):
function profile (line 303) | def profile(n):
function get_current (line 325) | def get_current():
class Logger (line 332) | class Logger(object):
method __init__ (line 337) | def __init__(self, dir, output_formats, comm=None):
method logkv (line 347) | def logkv(self, key, val):
method logkv_mean (line 350) | def logkv_mean(self, key, val):
method dumpkvs (line 355) | def dumpkvs(self):
method log (line 376) | def log(self, *args, level=INFO):
method set_level (line 382) | def set_level(self, level):
method set_comm (line 385) | def set_comm(self, comm):
method get_dir (line 388) | def get_dir(self):
method close (line 391) | def close(self):
method _do_log (line 397) | def _do_log(self, args):
function get_rank_without_mpi_import (line 403) | def get_rank_without_mpi_import():
function mpi_weighted_mean (line 412) | def mpi_weighted_mean(comm, local_name2valcount):
function configure (line 442) | def configure(dir=None, format_strs=None, comm=None, log_suffix=""):
function _configure_default_logger (line 474) | def _configure_default_logger():
function reset (line 479) | def reset():
function scoped_configure (line 487) | def scoped_configure(dir=None, format_strs=None, comm=None):
FILE: diffusion/losses.py
function normal_kl (line 12) | def normal_kl(mean1, logvar1, mean2, logvar2):
function approx_standard_normal_cdf (line 42) | def approx_standard_normal_cdf(x):
function discretized_gaussian_log_likelihood (line 50) | def discretized_gaussian_log_likelihood(x, *, means, log_scales):
FILE: diffusion/nn.py
class SiLU (line 13) | class SiLU(nn.Module):
method forward (line 14) | def forward(self, x):
class GroupNorm32 (line 18) | class GroupNorm32(nn.GroupNorm):
method forward (line 19) | def forward(self, x):
function conv_nd (line 23) | def conv_nd(dims, *args, **kwargs):
function linear (line 36) | def linear(*args, **kwargs):
function avg_pool_nd (line 43) | def avg_pool_nd(dims, *args, **kwargs):
function update_ema (line 56) | def update_ema(target_params, source_params, rate=0.99):
function zero_module (line 69) | def zero_module(module):
function scale_module (line 78) | def scale_module(module, scale):
function mean_flat (line 87) | def mean_flat(tensor):
function sum_flat (line 93) | def sum_flat(tensor):
function normalization (line 100) | def normalization(channels):
function timestep_embedding (line 110) | def timestep_embedding(timesteps, dim, max_period=10000):
function checkpoint (line 131) | def checkpoint(func, inputs, params, flag):
class CheckpointFunction (line 148) | class CheckpointFunction(th.autograd.Function):
method forward (line 151) | def forward(ctx, run_function, length, *args):
method backward (line 161) | def backward(ctx, *output_grads):
FILE: diffusion/resample.py
function create_named_schedule_sampler (line 8) | def create_named_schedule_sampler(name, diffusion):
class ScheduleSampler (line 23) | class ScheduleSampler(ABC):
method weights (line 35) | def weights(self):
method sample (line 42) | def sample(self, batch_size, device):
class UniformSampler (line 61) | class UniformSampler(ScheduleSampler):
method __init__ (line 62) | def __init__(self, diffusion):
method weights (line 66) | def weights(self):
class LossAwareSampler (line 70) | class LossAwareSampler(ScheduleSampler):
method update_with_local_losses (line 71) | def update_with_local_losses(self, local_ts, local_losses):
method update_with_all_losses (line 107) | def update_with_all_losses(self, ts, losses):
class LossSecondMomentResampler (line 124) | class LossSecondMomentResampler(LossAwareSampler):
method __init__ (line 125) | def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
method weights (line 134) | def weights(self):
method update_with_all_losses (line 143) | def update_with_all_losses(self, ts, losses):
method _warmed_up (line 153) | def _warmed_up(self):
FILE: diffusion/respace.py
function space_timesteps (line 7) | def space_timesteps(num_timesteps, section_counts):
class SpacedDiffusion (line 63) | class SpacedDiffusion(GaussianDiffusion):
method __init__ (line 72) | def __init__(self, use_timesteps, **kwargs):
method p_mean_variance (line 88) | def p_mean_variance(
method training_losses (line 93) | def training_losses(
method condition_mean (line 98) | def condition_mean(self, cond_fn, *args, **kwargs):
method condition_score (line 101) | def condition_score(self, cond_fn, *args, **kwargs):
method _wrap_model (line 104) | def _wrap_model(self, model):
method _scale_timesteps (line 111) | def _scale_timesteps(self, t):
class _WrappedModel (line 116) | class _WrappedModel:
method __init__ (line 117) | def __init__(self, model, timestep_map, rescale_timesteps, original_nu...
method __call__ (line 123) | def __call__(self, x, ts, **kwargs):
FILE: meshudf/_marching_cubes_lewiner.py
function marching_cubes_lewiner (line 9) | def marching_cubes_lewiner(
function udf_mc_lewiner (line 87) | def udf_mc_lewiner(
function _to_array (line 157) | def _to_array(args):
function _get_mc_luts (line 224) | def _get_mc_luts():
FILE: meshudf/meshudf.py
class GridFiller (line 23) | class GridFiller:
method __init__ (line 36) | def __init__(
method fill_grid (line 123) | def fill_grid(
function sample_udf (line 209) | def sample_udf(
function sample_grads (line 231) | def sample_grads(
function get_udf_and_grads (line 254) | def get_udf_and_grads(
function get_mesh_from_udf (line 307) | def get_mesh_from_udf(
FILE: models/cfg_sampler.py
class ClassifierFreeSampleModel (line 8) | class ClassifierFreeSampleModel(nn.Module):
method __init__ (line 10) | def __init__(self, model):
method forward (line 19) | def forward(self, x, timesteps, y=None):
FILE: models/mdm.py
class MDM (line 9) | class MDM(nn.Module):
method __init__ (line 11) | def __init__(self, modeltype, num_actions, dropout=0.1, activation="ge...
method parameters_wo_clip (line 72) | def parameters_wo_clip(self):
method load_and_freeze_clip (line 75) | def load_and_freeze_clip(self, clip_version):
method encode_text (line 86) | def encode_text(self, raw_text):
method forward (line 91) | def forward(self, x, timesteps, y=None):
method train (line 112) | def train(self, *args, **kwargs):
FILE: models/models.py
class LinearAttention (line 7) | class LinearAttention(nn.Module):
method __init__ (line 8) | def __init__(self, dim, heads=4, dim_head=32):
method forward (line 15) | def forward(self, x):
class LinAttnBlock (line 25) | class LinAttnBlock(LinearAttention):
method __init__ (line 27) | def __init__(self, in_channels):
class Downsample (line 30) | class Downsample(nn.Module):
method __init__ (line 31) | def __init__(self, in_channels, with_conv):
method forward (line 42) | def forward(self, x):
class Upsample (line 52) | class Upsample(nn.Module):
method __init__ (line 53) | def __init__(self, in_channels, with_conv):
method forward (line 63) | def forward(self, x):
class AttnBlock (line 70) | class AttnBlock(nn.Module):
method __init__ (line 71) | def __init__(self, in_channels):
method forward (line 98) | def forward(self, x):
function make_attn (line 126) | def make_attn(in_channels, attn_type="vanilla"):
function Normalize (line 136) | def Normalize(in_channels, num_groups=4):
function nonlinearity (line 139) | def nonlinearity(x):
class ResnetBlock (line 145) | class ResnetBlock(nn.Module):
method __init__ (line 146) | def __init__(self, *, in_channels, out_channels=None, conv_shortcut=Fa...
method forward (line 184) | def forward(self, x, temb):
class DoubleConv (line 213) | class DoubleConv(nn.Module):
method __init__ (line 216) | def __init__(self, in_channels, out_channels, conv_type=nn.Conv3d, mid...
method forward (line 230) | def forward(self, x):
class Down (line 234) | class Down(nn.Module):
method __init__ (line 237) | def __init__(self, in_channels, out_channels, conv_type=nn.Conv3d):
method forward (line 244) | def forward(self, x):
class Up (line 248) | class Up(nn.Module):
method __init__ (line 251) | def __init__(self, in_channels, out_channels, trilinear=True):
method forward (line 263) | def forward(self, x1):
class DepthwiseSeparableConv3d (line 280) | class DepthwiseSeparableConv3d(nn.Module):
method __init__ (line 281) | def __init__(self, nin, nout, kernel_size, padding, kernels_per_layer=1):
method forward (line 286) | def forward(self, x):
class Autoencoder_Old (line 292) | class Autoencoder_Old(nn.Module):
method __init__ (line 293) | def __init__(self, n_channels=1, width_multiplier=1, trilinear=True, u...
method encode (line 315) | def encode(self, x):
method decode (line 323) | def decode(self, z):
method forward (line 330) | def forward(self, input):
FILE: models/openaimodel.py
function convert_module_to_f16 (line 24) | def convert_module_to_f16(x):
function convert_module_to_f32 (line 27) | def convert_module_to_f32(x):
class AttentionPool2d (line 32) | class AttentionPool2d(nn.Module):
method __init__ (line 37) | def __init__(
method forward (line 51) | def forward(self, x):
class TimestepBlock (line 62) | class TimestepBlock(nn.Module):
method forward (line 68) | def forward(self, x, emb):
class TimestepEmbedSequential (line 74) | class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
method forward (line 80) | def forward(self, x, emb, context=None):
class Upsample (line 91) | class Upsample(nn.Module):
method __init__ (line 100) | def __init__(self, channels, use_conv, dims=3, out_channels=None, padd...
method forward (line 109) | def forward(self, x):
class TransposedUpsample (line 121) | class TransposedUpsample(nn.Module):
method __init__ (line 123) | def __init__(self, channels, out_channels=None, ks=5):
method forward (line 130) | def forward(self,x):
class Downsample (line 134) | class Downsample(nn.Module):
method __init__ (line 143) | def __init__(self, channels, use_conv, dims=3, out_channels=None,paddi...
method forward (line 158) | def forward(self, x):
class ResBlock (line 163) | class ResBlock(TimestepBlock):
method __init__ (line 179) | def __init__(
method forward (line 243) | def forward(self, x, emb):
method _forward (line 255) | def _forward(self, x, emb):
class AttentionBlock (line 278) | class AttentionBlock(nn.Module):
method __init__ (line 285) | def __init__(
method forward (line 314) | def forward(self, x):
method _forward (line 318) | def _forward(self, x):
function count_flops_attn (line 327) | def count_flops_attn(model, _x, y):
class QKVAttentionLegacy (line 347) | class QKVAttentionLegacy(nn.Module):
method __init__ (line 352) | def __init__(self, n_heads):
method forward (line 356) | def forward(self, qkv):
method count_flops (line 375) | def count_flops(model, _x, y):
class QKVAttention (line 379) | class QKVAttention(nn.Module):
method __init__ (line 384) | def __init__(self, n_heads):
method forward (line 388) | def forward(self, qkv):
method count_flops (line 409) | def count_flops(model, _x, y):
class UNetModel (line 413) | class UNetModel(nn.Module):
method __init__ (line 443) | def __init__(
method convert_to_fp16 (line 694) | def convert_to_fp16(self):
method convert_to_fp32 (line 702) | def convert_to_fp32(self):
method forward (line 710) | def forward(self, x, timesteps=None, context=None, y=None, **kwargs):
class EncoderUNetModel (line 752) | class EncoderUNetModel(nn.Module):
method __init__ (line 758) | def __init__(
method convert_to_fp16 (line 931) | def convert_to_fp16(self):
method convert_to_fp32 (line 938) | def convert_to_fp32(self):
method forward (line 945) | def forward(self, x, timesteps):
FILE: modules/attention.py
function exists (line 11) | def exists(val):
function uniq (line 15) | def uniq(arr):
function default (line 19) | def default(val, d):
function max_neg_value (line 25) | def max_neg_value(t):
function init_ (line 29) | def init_(tensor):
class GEGLU (line 37) | class GEGLU(nn.Module):
method __init__ (line 38) | def __init__(self, dim_in, dim_out):
method forward (line 42) | def forward(self, x):
class FeedForward (line 47) | class FeedForward(nn.Module):
method __init__ (line 48) | def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
method forward (line 63) | def forward(self, x):
function zero_module (line 67) | def zero_module(module):
function Normalize (line 76) | def Normalize(in_channels):
class LinearAttention (line 80) | class LinearAttention(nn.Module):
method __init__ (line 81) | def __init__(self, dim, heads=4, dim_head=32):
method forward (line 88) | def forward(self, x):
class SpatialSelfAttention (line 99) | class SpatialSelfAttention(nn.Module):
method __init__ (line 100) | def __init__(self, in_channels):
method forward (line 126) | def forward(self, x):
class CrossAttention (line 152) | class CrossAttention(nn.Module):
method __init__ (line 153) | def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, ...
method forward (line 170) | def forward(self, x, context=None, mask=None):
class BasicTransformerBlock (line 196) | class BasicTransformerBlock(nn.Module):
method __init__ (line 197) | def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None,...
method forward (line 208) | def forward(self, x, context=None):
method _forward (line 211) | def _forward(self, x, context=None):
class SpatialTransformer (line 218) | class SpatialTransformer(nn.Module):
method __init__ (line 226) | def __init__(self, in_channels, n_heads, d_head,
method forward (line 250) | def forward(self, x, context=None):
FILE: sample/generate_cat.py
function main (line 31) | def main():
FILE: sample/generate_image.py
function load_and_freeze_clip (line 37) | def load_and_freeze_clip(clip_version):
function main (line 50) | def main():
FILE: sample/generate_sketch.py
function _transform (line 31) | def _transform(n_px):
function load_and_freeze_clip (line 40) | def load_and_freeze_clip(clip_version):
function main (line 50) | def main():
FILE: sample/generate_text.py
function load_and_freeze_clip (line 32) | def load_and_freeze_clip(clip_version):
function main (line 45) | def main():
FILE: sample/generate_uncond.py
function main (line 21) | def main():
FILE: train_diffcloth.py
class TrainPlatform (line 24) | class TrainPlatform:
method __init__ (line 25) | def __init__(self, save_dir):
method report_scalar (line 28) | def report_scalar(self, name, value, iteration, group_name=None):
method report_args (line 31) | def report_args(self, args, name):
method close (line 34) | def close(self):
class TensorboardPlatform (line 38) | class TensorboardPlatform(TrainPlatform):
method __init__ (line 39) | def __init__(self, save_dir):
method report_scalar (line 43) | def report_scalar(self, name, value, iteration, group_name=None):
method close (line 46) | def close(self):
class NoPlatform (line 50) | class NoPlatform(TrainPlatform):
method __init__ (line 51) | def __init__(self, save_dir):
function make_data_sampler (line 55) | def make_data_sampler(dataset, shuffle, distributed):
class IterationBasedBatchSampler (line 65) | class IterationBasedBatchSampler(torch.utils.data.sampler.BatchSampler):
method __init__ (line 71) | def __init__(self, batch_sampler, num_iterations, start_iter=0):
method __iter__ (line 76) | def __iter__(self):
method __len__ (line 90) | def __len__(self):
function make_batch_data_sampler (line 93) | def make_batch_data_sampler(sampler, images_per_gpu, num_iters=None, sta...
function main (line 99) | def main():
FILE: training_loop_single.py
class TrainLoop (line 32) | class TrainLoop:
method __init__ (line 33) | def __init__(self, args, model, diffusion, data, logger=None):
method _load_and_sync_parameters (line 127) | def _load_and_sync_parameters(self):
method load_and_freeze_clip (line 143) | def load_and_freeze_clip(self, clip_version):
method worker_init_fn (line 154) | def worker_init_fn(self, worker_id):
method _load_optimizer_state (line 158) | def _load_optimizer_state(self):
method randbool (line 171) | def randbool(self, *size):
method run_loop (line 173) | def run_loop(self, inds=None):
method evaluate (line 249) | def evaluate(self):
method run_step (line 254) | def run_step(self, batch, cond, loss_L1, loss_args=None):
method forward_backward (line 260) | def forward_backward(self, batch, cond, loss_L1, loss_args=None):
method _anneal_lr (line 299) | def _anneal_lr(self, gama=0.9):
method log_step (line 311) | def log_step(self):
method ckpt_file_name (line 316) | def ckpt_file_name(self):
method save_distributed (line 320) | def save_distributed(self):
method save (line 341) | def save(self):
function parse_resume_step_from_filename (line 359) | def parse_resume_step_from_filename(filename):
function get_blob_logdir (line 374) | def get_blob_logdir():
function find_resume_checkpoint (line 380) | def find_resume_checkpoint():
function log_loss_dict (line 386) | def log_loss_dict(diffusion, ts, losses):
FILE: utils/comm.py
function get_world_size (line 16) | def get_world_size():
function get_rank (line 24) | def get_rank():
function is_main_process (line 32) | def is_main_process():
function synchronize (line 36) | def synchronize():
function gather_on_master (line 51) | def gather_on_master(data):
function all_gather (line 104) | def all_gather(data):
function reduce_dict (line 147) | def reduce_dict(input_dict, average=True):
FILE: utils/dist_util.py
function setup_dist (line 18) | def setup_dist(device=0):
function dev (line 44) | def dev():
function load_state_dict (line 54) | def load_state_dict(path, **kwargs):
function sync_params (line 61) | def sync_params(params):
function _find_free_port (line 70) | def _find_free_port():
FILE: utils/fixseed.py
function fixseed (line 6) | def fixseed(seed):
FILE: utils/ldm_utils.py
function instantiate_from_config (line 19) | def instantiate_from_config(config):
function get_obj_from_str (line 28) | def get_obj_from_str(string, reload=False):
function make_beta_schedule (line 35) | def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_e...
function make_ddim_timesteps (line 60) | def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_...
function make_ddim_sampling_parameters (line 77) | def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbos...
function betas_for_alpha_bar (line 91) | def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.9...
function extract_into_tensor (line 110) | def extract_into_tensor(a, t, x_shape):
function checkpoint (line 116) | def checkpoint(func, inputs, params, flag):
class CheckpointFunction (line 133) | class CheckpointFunction(torch.autograd.Function):
method forward (line 135) | def forward(ctx, run_function, length, *args):
method backward (line 145) | def backward(ctx, *output_grads):
function timestep_embedding (line 165) | def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=Fal...
function zero_module (line 188) | def zero_module(module):
function scale_module (line 197) | def scale_module(module, scale):
function mean_flat (line 206) | def mean_flat(tensor):
function normalization (line 213) | def normalization(channels):
class SiLU (line 223) | class SiLU(nn.Module):
method forward (line 224) | def forward(self, x):
class GroupNorm32 (line 228) | class GroupNorm32(nn.GroupNorm):
method forward (line 229) | def forward(self, x):
function conv_nd (line 232) | def conv_nd(dims, *args, **kwargs):
function linear (line 245) | def linear(*args, **kwargs):
function avg_pool_nd (line 252) | def avg_pool_nd(dims, *args, **kwargs):
class HybridConditioner (line 265) | class HybridConditioner(nn.Module):
method __init__ (line 267) | def __init__(self, c_concat_config, c_crossattn_config):
method forward (line 272) | def forward(self, c_concat, c_crossattn):
function noise_like (line 278) | def noise_like(shape, device, repeat=False):
FILE: utils/logger.py
class FileHandler (line 12) | class FileHandler(StreamHandler):
method __init__ (line 16) | def __init__(self, filename, mode='a', encoding=None, delay=False):
method close (line 36) | def close(self):
method _open (line 58) | def _open(self):
method emit (line 65) | def emit(self, record):
method __repr__ (line 77) | def __repr__(self):
function setup_logger (line 82) | def setup_logger(name, save_dir, distributed_rank, filename="log.txt"):
FILE: utils/misc.py
function to_numpy (line 4) | def to_numpy(tensor):
function to_torch (line 13) | def to_torch(ndarray):
function cleanexit (line 22) | def cleanexit():
function load_model_wo_clip (line 30) | def load_model_wo_clip(model, state_dict):
function freeze_joints (line 35) | def freeze_joints(x, joints_to_freeze):
FILE: utils/miscellaneous.py
function mkdir (line 15) | def mkdir(path):
function save_config (line 27) | def save_config(cfg, path):
function config_iteration (line 33) | def config_iteration(output_dir, max_iter):
function get_matching_parameters (line 50) | def get_matching_parameters(model, regexp, none_on_empty=True):
function freeze_weights (line 65) | def freeze_weights(model, regexp):
function unfreeze_weights (line 73) | def unfreeze_weights(model, regexp, backbone_freeze_at=-1,
function delete_tsv_files (line 91) | def delete_tsv_files(tsvs):
function concat_files (line 100) | def concat_files(ins, out):
function concat_tsv_files (line 111) | def concat_tsv_files(tsvs, out_tsv):
function load_list_file (line 126) | def load_list_file(fname):
function try_once (line 135) | def try_once(func):
function try_delete (line 145) | def try_delete(f):
function set_seed (line 149) | def set_seed(seed, n_gpu):
function print_and_run_cmd (line 157) | def print_and_run_cmd(cmd):
function write_to_yaml_file (line 162) | def write_to_yaml_file(context, file_name):
function load_from_yaml_file (line 167) | def load_from_yaml_file(yaml_file):
FILE: utils/model_util.py
function load_model_wo_clip (line 6) | def load_model_wo_clip(model, state_dict):
function create_model_and_diffusion (line 12) | def create_model_and_diffusion(args):
function get_model_args (line 19) | def get_model_args(args):
function create_gaussian_diffusion (line 32) | def create_gaussian_diffusion(args):
FILE: utils/parser_util.py
function parse_and_load_from_model (line 7) | def parse_and_load_from_model(parser):
function get_args_per_group_name (line 23) | def get_args_per_group_name(parser, args, group_name):
function get_model_path_from_args (line 30) | def get_model_path_from_args():
function add_base_options (line 40) | def add_base_options(parser):
function add_diffusion_options (line 50) | def add_diffusion_options(parser):
function add_model_options (line 59) | def add_model_options(parser):
function add_data_options (line 74) | def add_data_options(parser):
function add_training_options (line 83) | def add_training_options(parser):
function add_sampling_options (line 113) | def add_sampling_options(parser):
function add_generate_options (line 131) | def add_generate_options(parser):
function train_args (line 153) | def train_args():
function generate_args (line 164) | def generate_args():
function evaluation_parser (line 173) | def evaluation_parser():
FILE: utils/utils.py
function batchify (line 8) | def batchify(inputs: List[Tensor], required_dim: int) -> Tuple[bool, Lis...
function unbatchify (line 30) | def unbatchify(inputs: List[Tensor]) -> List[Tensor]:
function random_point_sampling (line 44) | def random_point_sampling(pcd: Tensor, num_points: int, inds=None) -> Te...
function get_o3d_mesh_from_tensors (line 79) | def get_o3d_mesh_from_tensors(
function compute_gradients (line 123) | def compute_gradients(x: Tensor, y: Tensor) -> Tensor:
function sample_udf (line 128) | def sample_udf(
class GridFiller (line 151) | class GridFiller:
method __init__ (line 164) | def __init__(
method fill_grid (line 252) | def fill_grid(
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{
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"chars": 15250,
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{
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"chars": 60892,
"preview": "d526a340408764d8ec0f2eef0b7e551e\ne7b9477eaf6e2b64fe755e3418b63110\n1e9b2d5d65a8b7c29fe8663d47f73813\nda64c56e36024add5b903"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/03691459_test.lst",
"chars": 10520,
"preview": "8c18ef2a9170a8144884161c20b458b0\n7ba65164444ed5be3df08ed7d70af1ee\n710014b815369e1c2bcea2cd4cc7b042\n3e21834bd08c078963de4"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/03691459_train.lst",
"chars": 41866,
"preview": "b10f7a407b4a3722296ecd3bbf65f1a3\n48a2091454502770408d050f12518231\na88fabb4286706afbb9b205a4c97698a\nc3fbe6332f413bd2bddb0"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04090263_test.lst",
"chars": 15627,
"preview": "c518e4b872a0841e0652a919e7ad7e6\n9af23e8759e070ef35d753857de68a9a\nd2b09323f863db7a71ed4e55cca1417d\n74a38b74bb57a2af6a405a"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04090263_train.lst",
"chars": 62329,
"preview": "5909ebf625e0d04dfd6d10d22bbd142\n4c41c172046a4a5e9a6e43b878d5b335\ndecdf532717c45767a9bdfb16682d944\n528bc52f211ff34ed2a72b"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04256520_test.lst",
"chars": 20734,
"preview": "3b8f2b955ee9a904b3c42e318f3affc\n3a98a93f381ff5fb4b3c42e318f3affc\n9d5461070e008c453bdc60e3df3aace8\na7f45bd27efe50af4dbf8d"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04256520_train.lst",
"chars": 83077,
"preview": "fbe08af112773184e6fe3612af521500\n8180afee86f2075519fb4103277a6b93\nad023095f1868d3019fb4103277a6b93\ndfda3c467eb6066fe6fe3"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04379243_test.lst",
"chars": 55182,
"preview": "8343d98e3710f5bee1b32bbe69d5bc15\nb7cead95e18b570d2c97486f63c12d76\n86b48365b2bd587e61830bc1b4d6c5ea\n237e15796ba16d86e1b32"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04379243_train.lst",
"chars": 220388,
"preview": "7ec59be3e55081f788292be2b58381eb\n69d4ba3043aee963dc2bc8a782be94b8\n3398658268644c2539eff6a5994f7472\nf18c9ab4ebdcdb8ac8595"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04401088_test.lst",
"chars": 7102,
"preview": "45301b15dce2724e483a0f6665d9ffe0\nc4b66fac952d4ef7d9039fb3a1715e96\neaf85cce060622c88de42b14089e0e7c\nacda976ba347a3e58ba35"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04401088_train.lst",
"chars": 11381,
"preview": "611afaaa1671ac8cc56f78d9daf213b\n9f3ad4e5f16f40b3d0f9f958bc79097c\n21a7e90867b64433316979ee2b237f2b\n74e5759913a2ac208b0d3d"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04530566_test.lst",
"chars": 12393,
"preview": "b497c390de93b4dcba21fe79619ae253\nc991fc4636615e64ed01ae0c90548352\n5a0ca7a6df97542e69c3c818538357ad\n2fb7d918e4a738fd97556"
},
{
"path": "AutoEncoder/dataset_info_files/ShapeNet_filelists/04530566_train.lst",
"chars": 48443,
"preview": "98a61aa6d78f6f49de91bdb310db8e7e\n82be57a43d5d1e14fa286e2f06a75d07\n54daf1472c51bb47a97a590141e0046\nf4923b4ce88bdda3a9142e"
},
{
"path": "AutoEncoder/dataset_info_files/info-pix3d.json",
"chars": 591,
"preview": "{\n \"lst_dir\": \"./dataset_info_files/pix3d_filelists\",\n \"cats\": {\n \"bed\": 0,\n \"bookcase\": 1,\n "
},
{
"path": "AutoEncoder/dataset_info_files/info-shapenet.json",
"chars": 952,
"preview": "{\n \"lst_dir\": \"../dataset_info_files/ShapeNet_filelists\",\n \"cats\": {\n \"watercraft\": \"04530566\",\n \"ri"
},
{
"path": "AutoEncoder/encdec/DynamicSampler.py",
"chars": 4728,
"preview": "\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nfrom torchvision im"
},
{
"path": "AutoEncoder/encdec/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "AutoEncoder/encdec/export_meshes.py",
"chars": 4628,
"preview": "import sys\n\nsys.path.append(\"..\")\n\nfrom pathlib import Path\nfrom typing import Any, Dict\n\n\nimport torch\nfrom hesiod impo"
},
{
"path": "AutoEncoder/encdec/normalized_obj.py",
"chars": 1548,
"preview": "import trimesh\nimport os\n\ndef obj_normalization(input_mesh_file, output_file_path):\n '''\n Normalize the obj file b"
},
{
"path": "AutoEncoder/encdec/preprocess_udfs.py",
"chars": 4546,
"preview": "import sys\n\nsys.path.append(\"..\")\n\nfrom pathlib import Path\nimport os\n\nimport numpy as np\nfrom utils import (\n comput"
},
{
"path": "AutoEncoder/encdec/train_encdec.py",
"chars": 483,
"preview": "import sys\n\nsys.path.append(\"..\")\n\nfrom hesiod import hmain\n\nfrom trainers.encdec import EncoderDecoderTrainer\n\n\n\nif len"
},
{
"path": "AutoEncoder/models/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "AutoEncoder/models/cbndec.py",
"chars": 3814,
"preview": "from einops import repeat\nfrom torch import Tensor, nn\n\nclass DecoderConditionalBatchNorm(nn.Module):\n def __init__(\n"
},
{
"path": "AutoEncoder/models/coordsenc.py",
"chars": 1549,
"preview": "from typing import Callable, Tuple\n\nimport torch\nfrom torch import Tensor\n\n\nclass CoordsEncoder:\n def __init__(\n "
},
{
"path": "AutoEncoder/models/dgcnn.py",
"chars": 3607,
"preview": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\nfrom pytorch3d.o"
},
{
"path": "AutoEncoder/trainers/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "AutoEncoder/trainers/encdec.py",
"chars": 12216,
"preview": "from pathlib import Path\nfrom typing import Any, Dict\n\nimport torch\nimport torch.nn.functional as F\nfrom hesiod import g"
},
{
"path": "AutoEncoder/trainers/test.py",
"chars": 1068,
"preview": "import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nfrom torchvision impo"
},
{
"path": "AutoEncoder/utils.py",
"chars": 15652,
"preview": "from pathlib import Path\nfrom typing import Iterable, List, Tuple, Union\n\nimport numpy as np\nimport open3d as o3d\nimport"
},
{
"path": "CLIP/.gitignore",
"chars": 105,
"preview": "__pycache__/\n*.py[cod]\n*$py.class\n*.egg-info\n.pytest_cache\n.ipynb_checkpoints\n\nthumbs.db\n.DS_Store\n.idea\n"
},
{
"path": "CLIP/LICENSE",
"chars": 1064,
"preview": "MIT License\n\nCopyright (c) 2021 OpenAI\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof "
},
{
"path": "CLIP/MANIFEST.in",
"chars": 42,
"preview": "include clip/bpe_simple_vocab_16e6.txt.gz\n"
},
{
"path": "CLIP/README.md",
"chars": 7302,
"preview": "# CLIP\n\n[[Blog]](https://openai.com/blog/clip/) [[Paper]](https://arxiv.org/abs/2103.00020) [[Model Card]](model-card.md"
},
{
"path": "CLIP/__init__.py",
"chars": 20,
"preview": "from .clip import *\n"
},
{
"path": "CLIP/clip/__init__.py",
"chars": 20,
"preview": "from .clip import *\n"
},
{
"path": "CLIP/clip/clip.py",
"chars": 9396,
"preview": "import hashlib\nimport os\nimport urllib\nimport warnings\nfrom typing import Any, Union, List\nfrom pkg_resources import pac"
},
{
"path": "CLIP/clip/model.py",
"chars": 17374,
"preview": "from collections import OrderedDict\nfrom typing import Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.fun"
},
{
"path": "CLIP/clip/simple_tokenizer.py",
"chars": 4628,
"preview": "import gzip\nimport html\nimport os\nfrom functools import lru_cache\n\nimport ftfy\nimport regex as re\n\n\n@lru_cache()\ndef def"
},
{
"path": "CLIP/clip.py",
"chars": 9396,
"preview": "import hashlib\nimport os\nimport urllib\nimport warnings\nfrom typing import Any, Union, List\nfrom pkg_resources import pac"
},
{
"path": "CLIP/data/country211.md",
"chars": 983,
"preview": "# The Country211 Dataset\n\nIn the paper, we used an image classification dataset called Country211, to evaluate the model"
},
{
"path": "CLIP/data/prompts.md",
"chars": 72430,
"preview": "# Prompts for Image Classification\n\nBelow are the class names and templates that are used for collecting the zero-shot c"
},
{
"path": "CLIP/data/rendered-sst2.md",
"chars": 598,
"preview": "# The Rendered SST2 Dataset\n\nIn the paper, we used an image classification dataset called Rendered SST2, to evaluate the"
},
{
"path": "CLIP/data/yfcc100m.md",
"chars": 916,
"preview": "# The YFCC100M Subset\n\nIn the paper, we performed a dataset ablation using a subset of the YFCC100M dataset and showed t"
},
{
"path": "CLIP/hubconf.py",
"chars": 1382,
"preview": "from clip.clip import tokenize as _tokenize, load as _load, available_models as _available_models\nimport re\nimport strin"
},
{
"path": "CLIP/model-card.md",
"chars": 7717,
"preview": "# Model Card: CLIP\n\nInspired by [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993) an"
},
{
"path": "CLIP/model.py",
"chars": 17374,
"preview": "from collections import OrderedDict\nfrom typing import Tuple, Union\n\nimport numpy as np\nimport torch\nimport torch.nn.fun"
},
{
"path": "CLIP/requirements.txt",
"chars": 34,
"preview": "ftfy\nregex\ntqdm\ntorch\ntorchvision\n"
},
{
"path": "CLIP/setup.py",
"chars": 491,
"preview": "import os\n\nimport pkg_resources\nfrom setuptools import setup, find_packages\n\nsetup(\n name=\"clip\",\n py_modules=[\"cl"
},
{
"path": "CLIP/simple_tokenizer.py",
"chars": 4628,
"preview": "import gzip\nimport html\nimport os\nfrom functools import lru_cache\n\nimport ftfy\nimport regex as re\n\n\n@lru_cache()\ndef def"
},
{
"path": "CLIP/tests/test_consistency.py",
"chars": 812,
"preview": "import numpy as np\nimport pytest\nimport torch\nfrom PIL import Image\n\nimport clip\n\n\n@pytest.mark.parametrize('model_name'"
},
{
"path": "README.md",
"chars": 5822,
"preview": "# Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models (ECCV 2024)\n\n<img src=\"./asset"
},
{
"path": "data_loaders/dataset.py",
"chars": 9127,
"preview": "import pickle\nfrom pathlib import Path\nfrom typing import Tuple\n\nimport numpy as np\nimport torch\nfrom torch import Tenso"
},
{
"path": "diffusion/fp16_util.py",
"chars": 7949,
"preview": "\"\"\"\nHelpers to train with 16-bit precision.\n\"\"\"\n\nimport numpy as np\nimport torch as th\nimport torch.nn as nn\nfrom torch."
},
{
"path": "diffusion/gaussian_diffusion.py",
"chars": 50069,
"preview": "# This code is based on https://github.com/openai/guided-diffusion\n\"\"\"\nThis code started out as a PyTorch port of Ho et "
},
{
"path": "diffusion/logger.py",
"chars": 13979,
"preview": "\"\"\"\nLogger copied from OpenAI baselines to avoid extra RL-based dependencies:\nhttps://github.com/openai/baselines/blob/e"
},
{
"path": "diffusion/losses.py",
"chars": 2600,
"preview": "# This code is based on https://github.com/openai/guided-diffusion\n\"\"\"\nHelpers for various likelihood-based losses. Thes"
},
{
"path": "diffusion/nn.py",
"chars": 6050,
"preview": "# This code is based on https://github.com/openai/guided-diffusion\n\"\"\"\nVarious utilities for neural networks.\n\"\"\"\n\nimpor"
},
{
"path": "diffusion/resample.py",
"chars": 5689,
"preview": "from abc import ABC, abstractmethod\n\nimport numpy as np\nimport torch as th\nimport torch.distributed as dist\n\n\ndef create"
},
{
"path": "diffusion/respace.py",
"chars": 5334,
"preview": "# This code is based on https://github.com/openai/guided-diffusion\nimport numpy as np\nimport torch as th\n\nfrom .gaussian"
},
{
"path": "environment.yaml",
"chars": 6791,
"preview": "name: SurfD\nchannels:\n - iopath\n - fvcore\n - pytorch\n - conda-forge\n - defaults\ndependencies:\n - _libgcc_mutex=0.1"
},
{
"path": "meshudf/_marching_cubes_lewiner.py",
"chars": 8665,
"preview": "import base64\n\nimport numpy as np\n\nimport meshudf._marching_cubes_lewiner_luts as mcluts\nfrom meshudf import _marching_c"
},
{
"path": "meshudf/_marching_cubes_lewiner_cy.pyx",
"chars": 116620,
"preview": "#cython: cdivision=True\n#cython: boundscheck=False\n#cython: nonecheck=False\n#cython: wraparound=False\n\n\"\"\"\nThis is an im"
},
{
"path": "meshudf/_marching_cubes_lewiner_luts.py",
"chars": 27021,
"preview": "# -*- coding: utf-8 -*-\n# This file was auto-generated from `mc_meta/LookUpTable.h` by\n# `mc_meta/createluts.py`.\n\n#stat"
},
{
"path": "meshudf/meshudf.py",
"chars": 20270,
"preview": "\"\"\" \nMeshing algorithm for UDFs from\n\"Meshudf: Fast and differentiable meshing of unsigned distance field networks.\" \nGu"
},
{
"path": "meshudf/setup.py",
"chars": 415,
"preview": "# python setup.py build_ext --inplace\nfrom setuptools import setup\nfrom Cython.Build import cythonize\nimport numpy as np"
},
{
"path": "meshudf/setup.sh",
"chars": 36,
"preview": "python3 setup.py build_ext --inplace"
},
{
"path": "models/cfg_sampler.py",
"chars": 957,
"preview": "import numpy as np\nimport torch\nimport torch.nn as nn\nfrom copy import deepcopy\n\n# A wrapper model for Classifier-free g"
},
{
"path": "models/mdm.py",
"chars": 4080,
"preview": "import torch\nimport torch.nn as nn\nimport clip\n\nimport torch\nimport torch.nn as nn\nfrom models.openaimodel import UNetMo"
},
{
"path": "models/models.py",
"chars": 12254,
"preview": "import torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\nfrom einops import rearrange\r\n\r\nclass LinearAtte"
},
{
"path": "models/openaimodel.py",
"chars": 36317,
"preview": "from abc import abstractmethod\r\nfrom functools import partial\r\nimport math\r\nfrom typing import Iterable\r\n\r\nimport numpy "
},
{
"path": "modules/attention.py",
"chars": 8773,
"preview": "from inspect import isfunction\r\nimport math\r\nimport torch\r\nimport torch.nn.functional as F\r\nfrom torch import nn, einsum"
},
{
"path": "sample/generate_cat.py",
"chars": 3983,
"preview": "from utils.fixseed import fixseed\nimport os\n\nimport torch\nfrom utils.parser_util import generate_args\nfrom utils.model_u"
},
{
"path": "sample/generate_image.py",
"chars": 6062,
"preview": "\nimport sys\n\nfrom utils.fixseed import fixseed\nimport os\nimport time\nimport numpy as np\nimport torch\nfrom utils.parser_u"
},
{
"path": "sample/generate_sketch.py",
"chars": 4509,
"preview": "\nfrom utils.fixseed import fixseed\nimport os\nimport torch\nfrom utils.parser_util import generate_args\nfrom utils.model_u"
},
{
"path": "sample/generate_text.py",
"chars": 5628,
"preview": "\nimport os\n\nimport numpy as np\nimport torch\nfrom utils.parser_util import generate_args\nfrom utils.model_util import cre"
},
{
"path": "sample/generate_uncond.py",
"chars": 3669,
"preview": "\nfrom utils.fixseed import fixseed\nimport os\nimport torch\nfrom utils.parser_util import generate_args\nfrom utils.model_u"
},
{
"path": "train_diffcloth.py",
"chars": 6398,
"preview": "# This code is based on https://github.com/openai/guided-diffusion\n\"\"\"\nTrain a diffusion model on images.\n\"\"\"\n\nimport os"
},
{
"path": "training_loop_single.py",
"chars": 13914,
"preview": "import functools\nimport os\nimport time\nimport numpy as np\n\nimport blobfile as bf\nimport torch\nfrom torch.optim import Ad"
},
{
"path": "utils/PYTORCH3D_LICENSE",
"chars": 1546,
"preview": "BSD License\n\nFor PyTorch3D software\n\nCopyright (c) Facebook, Inc. and its affiliates. All rights reserved.\n\nRedistributi"
},
{
"path": "utils/__init__.py",
"chars": 23,
"preview": "from .logger import *\n\n"
},
{
"path": "utils/comm.py",
"chars": 5183,
"preview": "\"\"\"\nCopyright (c) Microsoft Corporation.\nLicensed under the MIT license.\n\nThis file contains primitives for multi-gpu co"
},
{
"path": "utils/dist_util.py",
"chars": 1926,
"preview": "\"\"\"\nHelpers for distributed training.\n\"\"\"\n\nimport socket\n\nimport torch as th\nimport torch.distributed as dist\n\n# Change "
},
{
"path": "utils/fixseed.py",
"chars": 291,
"preview": "import numpy as np\nimport torch\nimport random\n\n\ndef fixseed(seed):\n print(f'setting seed: {seed}')\n torch.backends"
},
{
"path": "utils/ldm_utils.py",
"chars": 10421,
"preview": "# adopted from\r\n# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py\r\n# and"
},
{
"path": "utils/logger.py",
"chars": 3455,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nimport logging\nimport os\nimport sys\nfrom logging"
},
{
"path": "utils/misc.py",
"chars": 1174,
"preview": "import torch\n\n\ndef to_numpy(tensor):\n if torch.is_tensor(tensor):\n return tensor.cpu().numpy()\n elif type(t"
},
{
"path": "utils/miscellaneous.py",
"chars": 5017,
"preview": "# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.\nimport errno\nimport os\nimport os.path as op\nimpo"
},
{
"path": "utils/model_util.py",
"chars": 1997,
"preview": "from models.mdm import MDM\nfrom diffusion import gaussian_diffusion as gd\nfrom diffusion.respace import SpacedDiffusion,"
},
{
"path": "utils/parser_util.py",
"chars": 8491,
"preview": "from argparse import ArgumentParser\nimport argparse\nimport os\nimport json\n\n\ndef parse_and_load_from_model(parser):\n #"
},
{
"path": "utils/utils.py",
"chars": 13712,
"preview": "import torch\r\nfrom typing import Iterable, List, Tuple, Union, Callable\r\nfrom torch import Tensor\r\nimport open3d as o3d\r"
}
]
About this extraction
This page contains the full source code of the Yzmblog/SurfD GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 112 files (2.0 MB), approximately 531.9k tokens, and a symbol index with 635 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.