Repository: zyxElsa/CAST_pytorch
Branch: main
Commit: 67a7d9c1e9c1
Files: 28
Total size: 169.4 KB
Directory structure:
gitextract_cjj_65gk/
├── LICENSE
├── README.md
├── data/
│ ├── __init__.py
│ ├── base_dataset.py
│ ├── image_folder.py
│ └── unaligned_dataset.py
├── experiments/
│ ├── __init__.py
│ └── __main__.py
├── models/
│ ├── MSP.py
│ ├── __init__.py
│ ├── base_model.py
│ ├── cast_model.py
│ ├── net.py
│ ├── networks.py
│ └── torch_utils.py
├── options/
│ ├── __init__.py
│ ├── base_options.py
│ ├── test_options.py
│ └── train_options.py
├── requirements.txt
├── test.py
├── train.py
└── util/
├── __init__.py
├── get_data.py
├── html.py
├── image_pool.py
├── util.py
└── visualizer.py
================================================
FILE CONTENTS
================================================
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
## Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning (CAST)
A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning (UCAST)
We provide our PyTorch implementation of the paper ''Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning''(SIGGRAPH 2022) , which is a simple yet powerful model for arbitrary image style transfer, and ''A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning''(ACM Transactions on Graphics) , which is a improved arbitrary style style transfer method.
In this work, we tackle the challenging problem of arbitrary image style transfer using a novel style feature representation learning method.
A suitable style representation, as a key component in image stylization tasks, is essential to achieve satisfactory results.
Existing deep neural network based approaches achieve reasonable results with the guidance from second-order statistics such as Gram matrix of content features.
However, they do not leverage sufficient style information, which results in artifacts such as local distortions and style inconsistency.
To address these issues, we propose to learn style representation directly from image features instead of their second-order statistics, by analyzing the similarities and differences between multiple styles and considering the style distribution.
For details see the papers [CAST](http://arxiv.org/abs/2205.09542) , [UCAST](https://arxiv.org/abs/2303.12710), and the [video](https://youtu.be/3RG2yjLKTus)
(back to top)
## Getting Started
### Prerequisites
Python 3.6 or above.
PyTorch 1.6 or above
For packages, see requirements.txt.
```sh
pip install -r requirements.txt
```
(back to top)
### Installation
Clone the repo
```sh
git clone https://github.com/zyxElsa/CAST_pytorch.git
```
(back to top)
### Datasets
Then put your content images in ./datasets/{datasets_name}/testA, and style images in ./datasets/{datasets_name}/testB.
Example directory hierarchy:
```sh
CAST_pytorch
|--- datasets
|--- {datasets_name}
|--- trainA
|--- trainB
|--- testA
|--- testB
Then, call --dataroot ./datasets/{datasets_name}
```
(back to top)
### Train
Train the CAST model:
```sh
python train.py --dataroot ./datasets/{dataset_name} --name {model_name}
```
The pretrained style classification model is saved at ./models/style_vgg.pth.
Google Drive: Check [here](https://drive.google.com/file/d/12JKlL6QsVWkz6Dag54K59PAZigFBS6PQ/view?usp=sharing)
The pretrained content encoder is saved at ./models/vgg_normalised.pth.
Google Drive: Check [here](https://drive.google.com/file/d/1DKYRWJUKbmrvEba56tuihy1N6VrNZFwl/view?usp=sharing)
(back to top)
### Test
Test the CAST or UCAST model:
```sh
python test.py --dataroot ./datasets/{dataset_name} --name {model_name}
```
The pretrained model is saved at ./checkpoints/CAST_model/*.pth.
BaiduNetdisk: Check [CAST model](https://pan.baidu.com/s/12oPk3195fntMEHdlsHNwkQ) (passwd：cast)
Google Drive: Download [CAST model](https://drive.google.com/file/d/11dZqu95QfnAgkzgR1NTJfQutz8JlwRY8/view?usp=sharing) and [UCAST model](https://drive.google.com/file/d/1rU8haiPG2BDhh5BNSwngjMKBKdutDYTJ/view?usp=sharing) (for video style transfer).
(back to top)
### Citation
```sh
@inproceedings{zhang2020cast,
author = {Zhang, Yuxin and Tang, Fan and Dong, Weiming and Huang, Haibin and Ma, Chongyang and Lee, Tong-Yee and Xu, Changsheng},
title = {Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning},
booktitle = {ACM SIGGRAPH},
year = {2022}}
```
```sh
@article{zhang2023unified,
title={A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning},
author={Zhang, Yuxin and Tang, Fan and Dong, Weiming and Huang, Haibin and Ma, Chongyang and Lee, Tong-Yee and Xu, Changsheng},
journal={ACM Transactions on Graphics},
year={2023},
publisher={ACM New York, NY}
}
```
(back to top)
## Contact
Please feel free to open an issue or contact us personally if you have questions, need help, or need explanations. Write to one of the following email addresses, and maybe put one other in the cc:
zhangyuxin2020@ia.ac.cn
(back to top)
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[product-screenshot]: images/screenshot.png
================================================
FILE: data/__init__.py
================================================
"""This package includes all the modules related to data loading and preprocessing
To add a custom dataset class called 'dummy', you need to add a file called 'dummy_dataset.py' and define a subclass 'DummyDataset' inherited from BaseDataset.
You need to implement four functions:
-- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt).
-- <__len__>: return the size of dataset.
-- <__getitem__>: get a data point from data loader.
-- : (optionally) add dataset-specific options and set default options.
Now you can use the dataset class by specifying flag '--dataset_mode dummy'.
See our template dataset class 'template_dataset.py' for more details.
"""
import importlib
import torch.utils.data
from data.base_dataset import BaseDataset
def find_dataset_using_name(dataset_name):
"""Import the module "data/[dataset_name]_dataset.py".
In the file, the class called DatasetNameDataset() will
be instantiated. It has to be a subclass of BaseDataset,
and it is case-insensitive.
"""
dataset_filename = "data." + dataset_name + "_dataset"
datasetlib = importlib.import_module(dataset_filename)
dataset = None
target_dataset_name = dataset_name.replace('_', '') + 'dataset'
for name, cls in datasetlib.__dict__.items():
if name.lower() == target_dataset_name.lower() \
and issubclass(cls, BaseDataset):
dataset = cls
if dataset is None:
raise NotImplementedError("In %s.py, there should be a subclass of BaseDataset with class name that matches %s in lowercase." % (dataset_filename, target_dataset_name))
return dataset
def get_option_setter(dataset_name):
"""Return the static method of the dataset class."""
dataset_class = find_dataset_using_name(dataset_name)
return dataset_class.modify_commandline_options
def create_dataset(opt):
"""Create a dataset given the option.
This function wraps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from data import create_dataset
>>> dataset = create_dataset(opt)
"""
data_loader = CustomDatasetDataLoader(opt)
dataset = data_loader.load_data()
return dataset
class CustomDatasetDataLoader():
"""Wrapper class of Dataset class that performs multi-threaded data loading"""
def __init__(self, opt):
"""Initialize this class
Step 1: create a dataset instance given the name [dataset_mode]
Step 2: create a multi-threaded data loader.
"""
self.opt = opt
dataset_class = find_dataset_using_name(opt.dataset_mode)
self.dataset = dataset_class(opt)
print("dataset [%s] was created" % type(self.dataset).__name__)
self.dataloader = torch.utils.data.DataLoader(
self.dataset,
batch_size=opt.batch_size,
shuffle=not opt.serial_batches,
num_workers=int(opt.num_threads),
drop_last=True if opt.isTrain else False,
)
def set_epoch(self, epoch):
self.dataset.current_epoch = epoch
def load_data(self):
return self
def __len__(self):
"""Return the number of data in the dataset"""
return min(len(self.dataset), self.opt.max_dataset_size)
def __iter__(self):
"""Return a batch of data"""
for i, data in enumerate(self.dataloader):
if i * self.opt.batch_size >= self.opt.max_dataset_size:
break
yield data
================================================
FILE: data/base_dataset.py
================================================
"""This module implements an abstract base class (ABC) 'BaseDataset' for datasets.
It also includes common transformation functions (e.g., get_transform, __scale_width), which can be later used in subclasses.
"""
import random
import numpy as np
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
from abc import ABC, abstractmethod
class BaseDataset(data.Dataset, ABC):
"""This class is an abstract base class (ABC) for datasets.
To create a subclass, you need to implement the following four functions:
-- <__init__>: initialize the class, first call BaseDataset.__init__(self, opt).
-- <__len__>: return the size of dataset.
-- <__getitem__>: get a data point.
-- : (optionally) add dataset-specific options and set default options.
"""
def __init__(self, opt):
"""Initialize the class; save the options in the class
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
self.opt = opt
self.root = opt.dataroot
self.current_epoch = 0
@staticmethod
def modify_commandline_options(parser, is_train):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
"""
return parser
@abstractmethod
def __len__(self):
"""Return the total number of images in the dataset."""
return 0
@abstractmethod
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index - - a random integer for data indexing
Returns:
a dictionary of data with their names. It ususally contains the data itself and its metadata information.
"""
pass
def get_params(opt, size):
w, h = size
new_h = h
new_w = w
if opt.preprocess == 'resize_and_crop':
new_h = new_w = opt.load_size
elif opt.preprocess == 'scale_width_and_crop':
new_w = opt.load_size
new_h = opt.load_size * h // w
x = random.randint(0, np.maximum(0, new_w - opt.crop_size))
y = random.randint(0, np.maximum(0, new_h - opt.crop_size))
flip = random.random() > 0.5
return {'crop_pos': (x, y), 'flip': flip}
def get_transform(opt, params=None, grayscale=False, method=Image.BICUBIC, convert=True):
transform_list = []
if grayscale:
transform_list.append(transforms.Grayscale(1))
if 'fixsize' in opt.preprocess:
transform_list.append(transforms.Resize(params["size"], method))
if 'resize' in opt.preprocess:
osize = [opt.load_size, opt.load_size]
if "gta2cityscapes" in opt.dataroot:
osize[0] = opt.load_size // 2
transform_list.append(transforms.Resize(osize, method))
elif 'scale_width' in opt.preprocess:
transform_list.append(transforms.Lambda(lambda img: __scale_width(img, opt.load_size, opt.crop_size, method)))
elif 'scale_shortside' in opt.preprocess:
transform_list.append(transforms.Lambda(lambda img: __scale_shortside(img, opt.load_size, opt.crop_size, method)))
if 'zoom' in opt.preprocess:
if params is None:
transform_list.append(transforms.Lambda(lambda img: __random_zoom(img, opt.load_size, opt.crop_size, method)))
else:
transform_list.append(transforms.Lambda(lambda img: __random_zoom(img, opt.load_size, opt.crop_size, method, factor=params["scale_factor"])))
if 'crop' in opt.preprocess:
if params is None or 'crop_pos' not in params:
transform_list.append(transforms.RandomCrop(opt.crop_size))
else:
transform_list.append(transforms.Lambda(lambda img: __crop(img, params['crop_pos'], opt.crop_size)))
if 'patch' in opt.preprocess:
transform_list.append(transforms.Lambda(lambda img: __patch(img, params['patch_index'], opt.crop_size)))
if 'trim' in opt.preprocess:
transform_list.append(transforms.Lambda(lambda img: __trim(img, opt.crop_size)))
# if opt.preprocess == 'none':
transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base=4, method=method)))
if not opt.no_flip:
if params is None or 'flip' not in params:
transform_list.append(transforms.RandomHorizontalFlip())
elif 'flip' in params:
transform_list.append(transforms.Lambda(lambda img: __flip(img, params['flip'])))
if convert:
transform_list += [transforms.ToTensor()]
if grayscale:
transform_list += [transforms.Normalize((0.5,), (0.5,))]
else:
transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
return transforms.Compose(transform_list)
def __make_power_2(img, base, method=Image.BICUBIC):
ow, oh = img.size
h = int(round(oh / base) * base)
w = int(round(ow / base) * base)
if h == oh and w == ow:
return img
return img.resize((w, h), method)
def __random_zoom(img, target_width, crop_width, method=Image.BICUBIC, factor=None):
if factor is None:
zoom_level = np.random.uniform(0.8, 1.0, size=[2])
else:
zoom_level = (factor[0], factor[1])
iw, ih = img.size
zoomw = max(crop_width, iw * zoom_level[0])
zoomh = max(crop_width, ih * zoom_level[1])
img = img.resize((int(round(zoomw)), int(round(zoomh))), method)
return img
def __scale_shortside(img, target_width, crop_width, method=Image.BICUBIC):
ow, oh = img.size
shortside = min(ow, oh)
if shortside >= target_width:
return img
else:
scale = target_width / shortside
return img.resize((round(ow * scale), round(oh * scale)), method)
def __trim(img, trim_width):
ow, oh = img.size
if ow > trim_width:
xstart = np.random.randint(ow - trim_width)
xend = xstart + trim_width
else:
xstart = 0
xend = ow
if oh > trim_width:
ystart = np.random.randint(oh - trim_width)
yend = ystart + trim_width
else:
ystart = 0
yend = oh
return img.crop((xstart, ystart, xend, yend))
def __scale_width(img, target_width, crop_width, method=Image.BICUBIC):
ow, oh = img.size
if ow == target_width and oh >= crop_width:
return img
w = target_width
h = int(max(target_width * oh / ow, crop_width))
return img.resize((w, h), method)
def __crop(img, pos, size):
ow, oh = img.size
x1, y1 = pos
tw = th = size
if (ow > tw or oh > th):
return img.crop((x1, y1, x1 + tw, y1 + th))
return img
def __patch(img, index, size):
ow, oh = img.size
nw, nh = ow // size, oh // size
roomx = ow - nw * size
roomy = oh - nh * size
startx = np.random.randint(int(roomx) + 1)
starty = np.random.randint(int(roomy) + 1)
index = index % (nw * nh)
ix = index // nh
iy = index % nh
gridx = startx + ix * size
gridy = starty + iy * size
return img.crop((gridx, gridy, gridx + size, gridy + size))
def __flip(img, flip):
if flip:
return img.transpose(Image.FLIP_LEFT_RIGHT)
return img
def __print_size_warning(ow, oh, w, h):
"""Print warning information about image size(only print once)"""
if not hasattr(__print_size_warning, 'has_printed'):
print("The image size needs to be a multiple of 4. "
"The loaded image size was (%d, %d), so it was adjusted to "
"(%d, %d). This adjustment will be done to all images "
"whose sizes are not multiples of 4" % (ow, oh, w, h))
__print_size_warning.has_printed = True
================================================
FILE: data/image_folder.py
================================================
"""A modified image folder class
We modify the official PyTorch image folder (https://github.com/pytorch/vision/blob/master/torchvision/datasets/folder.py)
so that this class can load images from both current directory and its subdirectories.
"""
import torch.utils.data as data
from PIL import Image
import os
import os.path
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
'.tif', '.TIF', '.tiff', '.TIFF',
]
def is_image_file(filename):
return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
def make_dataset(dir, max_dataset_size=float("inf")):
images = []
assert os.path.isdir(dir) or os.path.islink(dir), '%s is not a valid directory' % dir
for root, _, fnames in sorted(os.walk(dir, followlinks=True)):
for fname in fnames:
if is_image_file(fname):
path = os.path.join(root, fname)
images.append(path)
return images[:min(max_dataset_size, len(images))]
def default_loader(path):
return Image.open(path).convert('RGB')
class ImageFolder(data.Dataset):
def __init__(self, root, transform=None, return_paths=False,
loader=default_loader):
imgs = make_dataset(root)
if len(imgs) == 0:
raise(RuntimeError("Found 0 images in: " + root + "\n"
"Supported image extensions are: " + ",".join(IMG_EXTENSIONS)))
self.root = root
self.imgs = imgs
self.transform = transform
self.return_paths = return_paths
self.loader = loader
def __getitem__(self, index):
path = self.imgs[index]
img = self.loader(path)
if self.transform is not None:
img = self.transform(img)
if self.return_paths:
return img, path
else:
return img
def __len__(self):
return len(self.imgs)
================================================
FILE: data/unaligned_dataset.py
================================================
import os.path
from data.base_dataset import BaseDataset, get_transform
from data.image_folder import make_dataset
from PIL import Image
import random
import util.util as util
class UnalignedDataset(BaseDataset):
"""
This dataset class can load unaligned/unpaired datasets.
It requires two directories to host training images from domain A '/path/to/data/trainA'
and from domain B '/path/to/data/trainB' respectively.
You can train the model with the dataset flag '--dataroot /path/to/data'.
Similarly, you need to prepare two directories:
'/path/to/data/testA' and '/path/to/data/testB' during test time.
"""
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') # create a path '/path/to/data/trainA'
self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainB'
if opt.phase == "test" and not os.path.exists(self.dir_A) \
and os.path.exists(os.path.join(opt.dataroot, "valA")):
self.dir_A = os.path.join(opt.dataroot, "valA")
self.dir_B = os.path.join(opt.dataroot, "valB")
self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'
self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size)) # load images from '/path/to/data/trainB'
self.A_size = len(self.A_paths) # get the size of dataset A
self.B_size = len(self.B_paths) # get the size of dataset B
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index (int) -- a random integer for data indexing
Returns a dictionary that contains A, B, A_paths and B_paths
A (tensor) -- an image in the input domain
B (tensor) -- its corresponding image in the target domain
A_paths (str) -- image paths
B_paths (str) -- image paths
"""
A_path = self.A_paths[index % self.A_size] # make sure index is within then range
if self.opt.serial_batches: # make sure index is within then range
index_B = index % self.B_size
else: # randomize the index for domain B to avoid fixed pairs.
index_B = random.randint(0, self.B_size - 1)
B_path = self.B_paths[index_B]
A_img = Image.open(A_path).convert('RGB')
B_img = Image.open(B_path).convert('RGB')
# Apply image transformation
# For FastCUT mode, if in finetuning phase (learning rate is decaying),
# do not perform resize-crop data augmentation of CycleGAN.
# print('current_epoch', self.current_epoch)
is_finetuning = self.opt.isTrain and self.current_epoch > self.opt.n_epochs
modified_opt = util.copyconf(self.opt, load_size=self.opt.crop_size if is_finetuning else self.opt.load_size)
transform = get_transform(modified_opt)
A = transform(A_img)
B = transform(B_img)
return {'A': A, 'B': B, 'A_paths': A_path, 'B_paths': B_path}
def __len__(self):
"""Return the total number of images in the dataset.
As we have two datasets with potentially different number of images,
we take a maximum of
"""
return max(self.A_size, self.B_size)
================================================
FILE: experiments/__init__.py
================================================
import os
import importlib
def find_launcher_using_name(launcher_name):
# cur_dir = os.path.dirname(os.path.abspath(__file__))
# pythonfiles = glob.glob(cur_dir + '/**/*.py')
launcher_filename = "experiments.{}_launcher".format(launcher_name)
launcherlib = importlib.import_module(launcher_filename)
# In the file, the class called LauncherNameLauncher() will
# be instantiated. It has to be a subclass of BaseLauncher,
# and it is case-insensitive.
launcher = None
target_launcher_name = launcher_name.replace('_', '') + 'launcher'
for name, cls in launcherlib.__dict__.items():
if name.lower() == target_launcher_name.lower():
launcher = cls
if launcher is None:
raise ValueError("In %s.py, there should be a subclass of BaseLauncher "
"with class name that matches %s in lowercase." %
(launcher_filename, target_launcher_name))
return launcher
if __name__ == "__main__":
import sys
import pickle
assert len(sys.argv) >= 3
name = sys.argv[1]
Launcher = find_launcher_using_name(name)
cache = "/tmp/tmux_launcher/{}".format(name)
if os.path.isfile(cache):
instance = pickle.load(open(cache, 'r'))
else:
instance = Launcher()
cmd = sys.argv[2]
if cmd == "launch":
instance.launch()
elif cmd == "stop":
instance.stop()
elif cmd == "send":
expid = int(sys.argv[3])
cmd = int(sys.argv[4])
instance.send_command(expid, cmd)
os.makedirs("/tmp/tmux_launcher/", exist_ok=True)
pickle.dump(instance, open(cache, 'w'))
================================================
FILE: experiments/__main__.py
================================================
import os
import importlib
def find_launcher_using_name(launcher_name):
# cur_dir = os.path.dirname(os.path.abspath(__file__))
# pythonfiles = glob.glob(cur_dir + '/**/*.py')
launcher_filename = "experiments.{}_launcher".format(launcher_name)
launcherlib = importlib.import_module(launcher_filename)
# In the file, the class called LauncherNameLauncher() will
# be instantiated. It has to be a subclass of BaseLauncher,
# and it is case-insensitive.
launcher = None
# target_launcher_name = launcher_name.replace('_', '') + 'launcher'
for name, cls in launcherlib.__dict__.items():
if name.lower() == "launcher":
launcher = cls
if launcher is None:
raise ValueError("In %s.py, there should be a class named Launcher")
return launcher
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('name')
parser.add_argument('cmd')
parser.add_argument('id', nargs='+', type=str)
parser.add_argument('--mode', default=None)
parser.add_argument('--which_epoch', default=None)
parser.add_argument('--continue_train', action='store_true')
parser.add_argument('--subdir', default='')
parser.add_argument('--title', default='')
parser.add_argument('--gpu_id', default=None, type=int)
parser.add_argument('--phase', default='test')
opt = parser.parse_args()
name = opt.name
Launcher = find_launcher_using_name(name)
instance = Launcher()
cmd = opt.cmd
ids = 'all' if 'all' in opt.id else [int(i) for i in opt.id]
if cmd == "launch":
instance.launch(ids, continue_train=opt.continue_train)
elif cmd == "stop":
instance.stop()
elif cmd == "send":
assert False
elif cmd == "close":
instance.close()
elif cmd == "dry":
instance.dry()
elif cmd == "relaunch":
instance.close()
instance.launch(ids, continue_train=opt.continue_train)
elif cmd == "run" or cmd == "train":
assert len(ids) == 1, '%s is invalid for run command' % (' '.join(opt.id))
expid = ids[0]
instance.run_command(instance.commands(), expid,
continue_train=opt.continue_train,
gpu_id=opt.gpu_id)
elif cmd == 'launch_test':
instance.launch(ids, test=True)
elif cmd == "run_test" or cmd == "test":
test_commands = instance.test_commands()
if ids == "all":
ids = list(range(len(test_commands)))
for expid in ids:
instance.run_command(test_commands, expid, opt.which_epoch,
gpu_id=opt.gpu_id)
if expid < len(ids) - 1:
os.system("sleep 5s")
elif cmd == "print_names":
instance.print_names(ids, test=False)
elif cmd == "print_test_names":
instance.print_names(ids, test=True)
elif cmd == "create_comparison_html":
instance.create_comparison_html(name, ids, opt.subdir, opt.title, opt.phase)
else:
raise ValueError("Command not recognized")
================================================
FILE: models/MSP.py
================================================
import numpy as np
import torch.nn as nn
import torch
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from .torch_utils import concat_all_gather, get_world_size
class StyleExtractor(nn.Module):
"""Defines a PatchGAN discriminator"""
def __init__(self, encoder, gpu_ids = []):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(StyleExtractor, self).__init__()
enc_layers = list(encoder.children())
self.enc_1 = nn.Sequential(*enc_layers[:6]) # input -> relu1_1
self.enc_2 = nn.Sequential(*enc_layers[6:13]) # relu1_1 -> relu2_1
self.enc_3 = nn.Sequential(*enc_layers[13:20]) # relu2_1 -> relu3_1
self.enc_4 = nn.Sequential(*enc_layers[20:33]) # relu3_1 -> relu4_1
self.enc_5 = nn.Sequential(*enc_layers[33:46]) # relu4_1 -> relu5_1
self.enc_6 = nn.Sequential(*enc_layers[46:70]) # relu5_1 -> maxpool
# fix the encoder
for name in ['enc_1', 'enc_2','enc_3', 'enc_4', 'enc_5', 'enc_6']:
for param in getattr(self, name).parameters():
param.requires_grad = True
# Class Activation Map
# self.gap_fc0 = nn.Linear(64, 1, bias=False)
# self.gmp_fc0 = nn.Linear(64, 1, bias=False)
# self.gap_fc1 = nn.Linear(128, 1, bias=False)
# self.gmp_fc1 = nn.Linear(128, 1, bias=False)
# self.gap_fc2 = nn.Linear(256, 1, bias=False)
# self.gmp_fc2 = nn.Linear(256, 1, bias=False)
# self.gap_fc3 = nn.Linear(512, 1, bias=False)
# self.gmp_fc3 = nn.Linear(512, 1, bias=False)
# self.gap_fc4 = nn.Linear(512, 1, bias=False)
# self.gmp_fc4 = nn.Linear(512, 1, bias=False)
# self.gap_fc5 = nn.Linear(512, 1, bias=False)
# self.gmp_fc5 = nn.Linear(512, 1, bias=False)
self.conv1x1_0 = nn.Conv2d(128, 64, kernel_size=1, stride=1, bias=True)
self.conv1x1_1 = nn.Conv2d(256, 128, kernel_size=1, stride=1, bias=True)
self.conv1x1_2 = nn.Conv2d(512, 256, kernel_size=1, stride=1, bias=True)
self.conv1x1_3 = nn.Conv2d(1024, 512, kernel_size=1, stride=1, bias=True)
self.conv1x1_4 = nn.Conv2d(1024, 512, kernel_size=1, stride=1, bias=True)
self.conv1x1_5 = nn.Conv2d(1024, 512, kernel_size=1, stride=1, bias=True)
self.relu = nn.ReLU(True)
# extract relu1_1, relu2_1, relu3_1, relu4_1 from input image
def encode_with_intermediate(self, input):
results = [input]
for i in range(6):
func = getattr(self, 'enc_{:d}'.format(i + 1))
results.append(func(results[-1]))
return results[1:]
def forward(self, input, index):
"""Standard forward."""
feats = self.encode_with_intermediate(input)
codes = []
for x in index:
code = feats[x].clone()
gap = torch.nn.functional.adaptive_avg_pool2d(code, (1,1))
gmp = torch.nn.functional.adaptive_max_pool2d(code, (1,1))
conv1x1 = getattr(self, 'conv1x1_{:d}'.format(x))
code = torch.cat([gap, gmp], 1)
code = self.relu(conv1x1(code))
codes.append(code)
return codes
class Projector(nn.Module):
def __init__(self, projector, gpu_ids = []):
super(Projector, self).__init__()
self.projector0 = nn.Sequential(
nn.Linear(64, 1024),
nn.ReLU(True),
#nn.Dropout(),
nn.Linear(1024, 2048),
nn.ReLU(True),
nn.Linear(2048, 2048),
)
self.projector1 = nn.Sequential(
#nn.Dropout(),
nn.Linear(128, 1024),
nn.ReLU(True),
#nn.Dropout(),
nn.Linear(1024, 2048),
nn.ReLU(True),
nn.Linear(2048, 2048),
)
self.projector2 = nn.Sequential(
#nn.Dropout(),
nn.Linear(256,1024),
nn.ReLU(True),
#nn.Dropout(),
nn.Linear(1024, 2048),
nn.ReLU(True),
nn.Linear(2048, 2048),
)
self.projector3 = nn.Sequential(
#nn.Dropout(),
nn.Linear(512, 1024),
nn.ReLU(True),
#nn.Dropout(),
nn.Linear(1024, 2048),
nn.ReLU(True),
nn.Linear(2048, 2048),
)
self.projector4 = nn.Sequential(
#nn.Dropout(),
nn.Linear(512, 1024),
nn.ReLU(True),
#nn.Dropout(),
nn.Linear(1024, 2048),
nn.ReLU(True),
nn.Linear(2048, 2048),
)
self.projector5 = nn.Sequential(
#nn.Dropout(),
nn.Linear(512, 1024),
nn.ReLU(True),
#nn.Dropout(),
nn.Linear(1024, 2048),
nn.ReLU(True),
nn.Linear(2048, 2048),
)
def forward(self, input, index):
"""Standard forward."""
num = 0
projections = []
for x in index:
projector = getattr(self, 'projector{:d}'.format(x))
code = input[num].view(input[num].size(0), -1)
projection = projector(code).view(code.size(0), -1)
projection = nn.functional.normalize(projection)
projections.append(projection)
num += 1
return projections
def make_layers(cfg, batch_norm=True):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1)
if batch_norm:
layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = v
return nn.Sequential(*layers)
vgg = make_layers([3, 64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M',
512, 512, 512, 512, 'M', 512, 512, 'M', 512, 512, 'M'])
class InfoNCELoss(nn.Module):
def __init__(self, temperature, feature_dim, queue_size):
super().__init__()
self.tau = temperature
self.queue_size = queue_size
self.world_size = get_world_size()
data0 = torch.randn(2048, queue_size)
data0 = F.normalize(data0, dim=0)
data1 = torch.randn(2048, queue_size)
data1 = F.normalize(data1, dim=0)
data2 = torch.randn(2048, queue_size)
data2 = F.normalize(data2, dim=0)
data3 = torch.randn(2048, queue_size)
data3 = F.normalize(data3, dim=0)
data4 = torch.randn(2048, queue_size)
data4 = F.normalize(data4, dim=0)
data5 = torch.randn(2048, queue_size)
data5 = F.normalize(data5, dim=0)
self.register_buffer("queue_data_A0", data0)
self.register_buffer("queue_ptr_A0", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_B0", data0)
self.register_buffer("queue_ptr_B0", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_A2", data2)
self.register_buffer("queue_ptr_A2", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_B2", data2)
self.register_buffer("queue_ptr_B2", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_A4", data4)
self.register_buffer("queue_ptr_A4", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_B4", data4)
self.register_buffer("queue_ptr_B4", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_A1", data1)
self.register_buffer("queue_ptr_A1", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_B1", data1)
self.register_buffer("queue_ptr_B1", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_A3", data3)
self.register_buffer("queue_ptr_A3", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_B3", data3)
self.register_buffer("queue_ptr_B3", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_A5", data5)
self.register_buffer("queue_ptr_A5", torch.zeros(1, dtype=torch.long))
self.register_buffer("queue_data_B5", data5)
self.register_buffer("queue_ptr_B5", torch.zeros(1, dtype=torch.long))
def forward(self, query, key, style = 'real'):
# positive logits: Nx1
l_pos = torch.einsum("nc,nc->n", (query, key)).unsqueeze(-1)
# negative logits: NxK
if style == 'real_A0':
queue = self.queue_data_A0.clone().detach()
elif style == 'real_A1':
queue = self.queue_data_A1.clone().detach()
elif style == 'real_A2':
queue = self.queue_data_A2.clone().detach()
elif style == 'real_A3':
queue = self.queue_data_A3.clone().detach()
elif style == 'real_A4':
queue = self.queue_data_A4.clone().detach()
elif style == 'real_A5':
queue = self.queue_data_A5.clone().detach()
elif style == 'fake_A':
queue = self.queue_data_fake_A.clone().detach()
elif style == 'real_B0':
queue = self.queue_data_B0.clone().detach()
elif style == 'real_B1':
queue = self.queue_data_B1.clone().detach()
elif style == 'real_B2':
queue = self.queue_data_B2.clone().detach()
elif style == 'real_B3':
queue = self.queue_data_B3.clone().detach()
elif style == 'real_B4':
queue = self.queue_data_B4.clone().detach()
elif style == 'real_B5':
queue = self.queue_data_B5.clone().detach()
elif style == 'fake_B':
queue = self.queue_data_fake_B.clone().detach()
else:
raise NotImplementedError('QUEUE: style is not recognized')
l_neg = torch.einsum("nc,ck->nk", (query, queue))
# logits: Nx(1+K)
logits = torch.cat((l_pos, l_neg), dim=1)
# labels: positive key indicators
labels = torch.zeros(logits.size(0), dtype=torch.long, device=query.device)
return F.cross_entropy(logits / self.tau, labels)
@torch.no_grad()
def dequeue_and_enqueue(self, keys, style = 'real'):
# gather from all gpus
if self.world_size > 1:
keys = concat_all_gather(keys, self.world_size)
batch_size = keys.size(0)
# replace the keys at ptr (dequeue and enqueue)
if style == 'real_A0':
ptr = int(self.queue_ptr_A0)
assert self.queue_size % batch_size == 0
self.queue_data_A0[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_A0[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_A1':
ptr = int(self.queue_ptr_A1)
assert self.queue_size % batch_size == 0
self.queue_data_A1[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_A1[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_A2':
ptr = int(self.queue_ptr_A2)
assert self.queue_size % batch_size == 0
self.queue_data_A2[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_A2[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_A3':
ptr = int(self.queue_ptr_A3)
assert self.queue_size % batch_size == 0
self.queue_data_A3[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_A3[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_A4':
ptr = int(self.queue_ptr_A4)
assert self.queue_size % batch_size == 0
self.queue_data_A4[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_A4[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_A5':
ptr = int(self.queue_ptr_A5)
assert self.queue_size % batch_size == 0
self.queue_data_A5[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_A5[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_B0':
ptr = int(self.queue_ptr_B0)
assert self.queue_size % batch_size == 0
self.queue_data_B0[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_B0[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_B1':
ptr = int(self.queue_ptr_B1)
assert self.queue_size % batch_size == 0
self.queue_data_B1[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_B1[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_B2':
ptr = int(self.queue_ptr_B2)
assert self.queue_size % batch_size == 0
self.queue_data_B2[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_B2[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_B3':
ptr = int(self.queue_ptr_B3)
assert self.queue_size % batch_size == 0
self.queue_data_B3[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_B3[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_B4':
ptr = int(self.queue_ptr_B4)
assert self.queue_size % batch_size == 0
self.queue_data_B4[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_B4[0] = (ptr + batch_size) % self.queue_size
elif style == 'real_B5':
ptr = int(self.queue_ptr_B5)
assert self.queue_size % batch_size == 0
self.queue_data_B5[:, ptr:ptr + batch_size] = keys.T
self.queue_ptr_B5[0] = (ptr + batch_size) % self.queue_size
else:
raise NotImplementedError('QUEUE: style is not recognized')
================================================
FILE: models/__init__.py
================================================
"""This package contains modules related to objective functions, optimizations, and network architectures.
To add a custom model class called 'dummy', you need to add a file called 'dummy_model.py' and define a subclass DummyModel inherited from BaseModel.
You need to implement the following five functions:
-- <__init__>: initialize the class; first call BaseModel.__init__(self, opt).
-- : unpack data from dataset and apply preprocessing.
-- : produce intermediate results.
-- : calculate loss, gradients, and update network weights.
-- : (optionally) add model-specific options and set default options.
In the function <__init__>, you need to define four lists:
-- self.loss_names (str list): specify the training losses that you want to plot and save.
-- self.model_names (str list): define networks used in our training.
-- self.visual_names (str list): specify the images that you want to display and save.
-- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an usage.
Now you can use the model class by specifying flag '--model dummy'.
See our template model class 'template_model.py' for more details.
"""
import importlib
from models.base_model import BaseModel
def find_model_using_name(model_name):
"""Import the module "models/[model_name]_model.py".
In the file, the class called DatasetNameModel() will
be instantiated. It has to be a subclass of BaseModel,
and it is case-insensitive.
"""
model_filename = "models." + model_name + "_model"
modellib = importlib.import_module(model_filename)
model = None
target_model_name = model_name.replace('_', '') + 'model'
for name, cls in modellib.__dict__.items():
if name.lower() == target_model_name.lower() \
and issubclass(cls, BaseModel):
model = cls
if model is None:
print("In %s.py, there should be a subclass of BaseModel with class name that matches %s in lowercase." % (model_filename, target_model_name))
exit(0)
return model
def get_option_setter(model_name):
"""Return the static method of the model class."""
model_class = find_model_using_name(model_name)
return model_class.modify_commandline_options
def create_model(opt):
"""Create a model given the option.
This function warps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from models import create_model
>>> model = create_model(opt)
"""
model = find_model_using_name(opt.model)
instance = model(opt)
print("model [%s] was created" % type(instance).__name__)
return instance
================================================
FILE: models/base_model.py
================================================
import os
import torch
from collections import OrderedDict
from abc import ABC, abstractmethod
from . import networks
class BaseModel(ABC):
"""This class is an abstract base class (ABC) for models.
To create a subclass, you need to implement the following five functions:
-- <__init__>: initialize the class; first call BaseModel.__init__(self, opt).
-- : unpack data from dataset and apply preprocessing.
-- : produce intermediate results.
-- : calculate losses, gradients, and update network weights.
-- : (optionally) add model-specific options and set default options.
"""
def __init__(self, opt):
"""Initialize the BaseModel class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
When creating your custom class, you need to implement your own initialization.
In this fucntion, you should first call
Then, you need to define four lists:
-- self.loss_names (str list): specify the training losses that you want to plot and save.
-- self.model_names (str list): specify the images that you want to display and save.
-- self.visual_names (str list): define networks used in our training.
-- self.optimizers (optimizer list): define and initialize optimizers. You can define one optimizer for each network. If two networks are updated at the same time, you can use itertools.chain to group them. See cycle_gan_model.py for an example.
"""
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.device = torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu') # get device name: CPU or GPU
self.save_dir = os.path.join(opt.checkpoints_dir, opt.name) # save all the checkpoints to save_dir
if opt.preprocess != 'scale_width': # with [scale_width], input images might have different sizes, which hurts the performance of cudnn.benchmark.
torch.backends.cudnn.benchmark = True
self.loss_names = []
self.model_names = []
self.visual_names = []
self.optimizers = []
self.image_paths = []
self.metric = 0 # used for learning rate policy 'plateau'
@staticmethod
def dict_grad_hook_factory(add_func=lambda x: x):
saved_dict = dict()
def hook_gen(name):
def grad_hook(grad):
saved_vals = add_func(grad)
saved_dict[name] = saved_vals
return grad_hook
return hook_gen, saved_dict
@staticmethod
def modify_commandline_options(parser, is_train):
"""Add new model-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
the modified parser.
"""
return parser
@abstractmethod
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input (dict): includes the data itself and its metadata information.
"""
pass
@abstractmethod
def forward(self):
"""Run forward pass; called by both functions and ."""
pass
@abstractmethod
def optimize_parameters(self):
"""Calculate losses, gradients, and update network weights; called in every training iteration"""
pass
def setup(self, opt):
"""Load and print networks; create schedulers
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
if self.isTrain:
self.schedulers = [networks.get_scheduler(optimizer, opt) for optimizer in self.optimizers]
if not self.isTrain or opt.continue_train:
load_suffix = opt.epoch
self.load_networks(load_suffix)
self.print_networks(opt.verbose)
def parallelize(self):
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, 'net' + name)
setattr(self, 'net' + name, torch.nn.DataParallel(net, self.opt.gpu_ids))
def data_dependent_initialize(self, data):
pass
def eval(self):
"""Make models eval mode during test time"""
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, 'net' + name)
net.eval()
def test(self):
"""Forward function used in test time.
This function wraps function in no_grad() so we don't save intermediate steps for backprop
It also calls to produce additional visualization results
"""
with torch.no_grad():
self.forward()
self.compute_visuals()
def compute_visuals(self):
"""Calculate additional output images for visdom and HTML visualization"""
pass
def get_image_paths(self):
""" Return image paths that are used to load current data"""
return self.image_paths
def update_learning_rate(self):
"""Update learning rates for all the networks; called at the end of every epoch"""
for scheduler in self.schedulers:
if self.opt.lr_policy == 'plateau':
scheduler.step(self.metric)
else:
scheduler.step()
lr = self.optimizers[0].param_groups[0]['lr']
print('learning rate = %.7f' % lr)
def get_current_visuals(self):
"""Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
visual_ret = OrderedDict()
for name in self.visual_names:
if isinstance(name, str):
visual_ret[name] = getattr(self, name)
return visual_ret
def get_current_losses(self):
"""Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
errors_ret = OrderedDict()
for name in self.loss_names:
if isinstance(name, str):
errors_ret[name] = float(getattr(self, 'loss_' + name)) # float(...) works for both scalar tensor and float number
return errors_ret
def save_networks(self, epoch):
"""Save all the networks to the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
for name in self.model_names:
if isinstance(name, str):
save_filename = '%s_net_%s.pth' % (epoch, name)
save_path = os.path.join(self.save_dir, save_filename)
net = getattr(self, 'net' + name)
if len(self.gpu_ids) > 0 and torch.cuda.is_available():
torch.save(net.module.cpu().state_dict(), save_path)
net.cuda(self.gpu_ids[0])
else:
torch.save(net.cpu().state_dict(), save_path)
def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
"""Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
key = keys[i]
if i + 1 == len(keys): # at the end, pointing to a parameter/buffer
if module.__class__.__name__.startswith('InstanceNorm') and \
(key == 'running_mean' or key == 'running_var'):
if getattr(module, key) is None:
state_dict.pop('.'.join(keys))
if module.__class__.__name__.startswith('InstanceNorm') and \
(key == 'num_batches_tracked'):
state_dict.pop('.'.join(keys))
else:
self.__patch_instance_norm_state_dict(state_dict, getattr(module, key), keys, i + 1)
def load_networks(self, epoch):
"""Load all the networks from the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
for name in self.model_names:
if isinstance(name, str):
load_filename = '%s_net_%s.pth' % (epoch, name)
if self.opt.isTrain and self.opt.pretrained_name is not None:
load_dir = os.path.join(self.opt.checkpoints_dir, self.opt.pretrained_name)
else:
load_dir = self.save_dir
load_path = os.path.join(load_dir, load_filename)
net = getattr(self, 'net' + name)
if isinstance(net, torch.nn.DataParallel):
net = net.module
print('loading the model from %s' % load_path)
# if you are using PyTorch newer than 0.4 (e.g., built from
# GitHub source), you can remove str() on self.device
state_dict = torch.load(load_path, map_location=str(self.device))
if hasattr(state_dict, '_metadata'):
del state_dict._metadata
# patch InstanceNorm checkpoints prior to 0.4
# for key in list(state_dict.keys()): # need to copy keys here because we mutate in loop
# self.__patch_instance_norm_state_dict(state_dict, net, key.split('.'))
net.load_state_dict(state_dict)
def print_networks(self, verbose):
"""Print the total number of parameters in the network and (if verbose) network architecture
Parameters:
verbose (bool) -- if verbose: print the network architecture
"""
print('---------- Networks initialized -------------')
for name in self.model_names:
if isinstance(name, str):
net = getattr(self, 'net' + name)
num_params = 0
for param in net.parameters():
num_params += param.numel()
if verbose:
print(net)
print('[Network %s] Total number of parameters : %.3f M' % (name, num_params / 1e6))
print('-----------------------------------------------')
def set_requires_grad(self, nets, requires_grad=False):
"""Set requies_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
"""
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad
def generate_visuals_for_evaluation(self, data, mode):
return {}
================================================
FILE: models/cast_model.py
================================================
import itertools
import torch
from .base_model import BaseModel
from . import networks
from . import net
from . import MSP
import util.util as util
from util.image_pool import ImagePool
import torch.nn as nn
from torch.nn import init
import kornia.augmentation as K
class CASTModel(BaseModel):
""" This class implements CAST model.
This code is inspired by DCLGAN
"""
@staticmethod
def modify_commandline_options(parser, is_train=True):
""" Configures options specific for CAST """
parser.add_argument('--CAST_mode', type=str, default="CAST", choices='CAST')
parser.add_argument('--lambda_GAN_G_A', type=float, default=0.1, help='weight for GAN loss：GAN(G(Ic, Is))')
parser.add_argument('--lambda_GAN_G_B', type=float, default=0.1, help='weight for GAN loss：GAN(G(Is, Ic))')
parser.add_argument('--lambda_GAN_D_A', type=float, default=1.0, help='weight for GAN loss：GAN(G(Is, Ic))')
parser.add_argument('--lambda_GAN_D_B', type=float, default=1.0, help='weight for GAN loss：GAN(G(Ic, Is))')
parser.add_argument('--lambda_NCE_G', type=float, default=0.05, help='weight for NCE loss: NCE(G(Ic, Is), Is)')
parser.add_argument('--lambda_NCE_D', type=float, default=1.0, help='weight for NCE loss: NCE(I, I+, I-)')
parser.add_argument('--lambda_CYC', type=float, default=4.0, help='weight for l1 reconstructe loss:||Ic - G(G(Ic, Is),Ic)||')
parser.add_argument('--nce_layers', type=str, default='0,1,2,3', help='compute NCE loss on which layers')
parser.set_defaults(pool_size=0) # no image pooling
opt, _ = parser.parse_known_args()
# Set default parameters for CAST.
if opt.CAST_mode.lower() == "cast":
pass
else:
raise ValueError(opt.CAST_mode)
return parser
def __init__(self, opt):
BaseModel.__init__(self, opt)
# specify the training losses you want to print out.
# The training/test scripts will call
self.loss_names = ['G']
self.visual_names = ['real_A', 'fake_B', 'real_B']
if self.opt.lambda_GAN_G_A > 0.0 and self.isTrain:
self.loss_names += [ 'G_A']
if self.opt.lambda_GAN_G_B > 0.0 and self.isTrain:
self.loss_names += [ 'G_B']
if self.opt.lambda_GAN_D_A > 0.0 and self.isTrain:
self.loss_names += ['D_A']
if self.opt.lambda_GAN_D_B > 0.0 and self.isTrain:
self.loss_names += ['D_B']
if self.opt.lambda_NCE_G > 0.0 and self.isTrain:
self.loss_names += [ 'G_NCE_style']
if self.opt.lambda_NCE_D > 0.0 and self.isTrain:
self.loss_names += [ 'NCE_D']
if self.opt.lambda_CYC > 0.0 and self.isTrain:
self.visual_names += ['rec_A', 'rec_B']
self.loss_names += ['cyc']
if self.isTrain:
self.model_names = ['AE','Dec_A', 'Dec_B', 'D', 'P_style', 'D_A', 'D_B']
else: # during test time, only load G
self.model_names = ['AE','Dec_A', 'Dec_B']
# define networks
vgg = net.vgg
vgg.load_state_dict(torch.load('models/vgg_normalised.pth'))
vgg = nn.Sequential(*list(vgg.children())[:31])
self.netAE = net.ADAIN_Encoder(vgg, self.gpu_ids)
self.netDec_A = net.Decoder(self.gpu_ids)
self.netDec_B = net.Decoder(self.gpu_ids)
init_net(self.netAE, 'normal', 0.02, self.gpu_ids)
init_net(self.netDec_A, 'normal', 0.02, self.gpu_ids)
init_net(self.netDec_B, 'normal', 0.02, self.gpu_ids)
if self.isTrain:
style_vgg = MSP.vgg
style_vgg.load_state_dict(torch.load('models/style_vgg.pth'))
style_vgg = nn.Sequential(*list(style_vgg.children()))
self.netD = MSP.StyleExtractor(style_vgg, self.gpu_ids)
self.netP_style = MSP.Projector(self.gpu_ids)
init_net(self.netD, 'normal', 0.02, self.gpu_ids)
init_net(self.netP_style, 'normal', 0.02, self.gpu_ids)
self.netD_A = networks.define_D(opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D,
opt.crop_size, opt.feature_dim, opt.max_conv_dim,
opt.normD, opt.init_type, opt.init_gain, opt.no_antialias,
self.gpu_ids, opt)
self.netD_B = networks.define_D(opt.output_nc, opt.ndf, opt.netD, opt.n_layers_D,
opt.crop_size, opt.feature_dim, opt.max_conv_dim,
opt.normD, opt.init_type, opt.init_gain, opt.no_antialias,
self.gpu_ids, opt)
self.fake_pool = ImagePool(opt.pool_size) # create image buffer to store previously generated images
self.criterionGAN = networks.GANLoss(opt.gan_mode).to(self.device)
self.nce_layers = [int(i) for i in self.opt.nce_layers.split(',')]
self.nce_loss = MSP.InfoNCELoss(opt.temperature, opt.hypersphere_dim,
opt.queue_size).to(self.device)
self.mse_loss = nn.MSELoss()
self.patch_sampler = K.RandomResizedCrop((256,256),scale=(0.8,1.0),ratio=(0.75,1.33)).to(self.device)
self.criterionCyc = torch.nn.L1Loss().to(self.device)
self.optimizer_G = torch.optim.Adam(itertools.chain(self.netAE.parameters(), self.netDec_A.parameters(), self.netDec_B.parameters()),
lr=opt.lr_G, betas=(opt.beta1, opt.beta2))
self.optimizer_D = torch.optim.Adam(itertools.chain(self.netD_A.parameters(), self.netD_B.parameters()),
lr=opt.lr_D, betas=(opt.beta1, opt.beta2))
self.optimizer_D_NCE = torch.optim.Adam(itertools.chain(self.netD.parameters(), self.netP_style.parameters()),
lr=opt.lr_D_NCE, betas=(opt.beta1, opt.beta2))
self.optimizers.append(self.optimizer_G)
self.optimizers.append(self.optimizer_D)
self.optimizers.append(self.optimizer_D_NCE)
def optimize_parameters(self):
# forward
self.forward()
# update D
if self.opt.lambda_GAN_D_A > 0.0 or self.opt.lambda_GAN_D_B > 0.0:
self.set_requires_grad([self.netD_A, self.netD_B], True)
self.set_requires_grad([self.netD, self.netP_style, self.netAE, self.netDec_A,self.netDec_B ], False)
self.optimizer_D.zero_grad()
self.loss_D = self.backward_D()
self.loss_D.backward(retain_graph=True)
self.optimizer_D.step()
# update MSP
if self.opt.lambda_NCE_D > 0.0:
self.set_requires_grad([self.netD, self.netP_style], True)
self.set_requires_grad([self.netAE, self.netDec_A,self.netDec_B, self.netD_A, self.netD_B ], False)
self.optimizer_D_NCE.zero_grad()
self.loss_NCE_D = self.backward_D_NCEloss()
self.loss_NCE_D.backward(retain_graph=True)
self.optimizer_D_NCE.step()
# update G
self.set_requires_grad([self.netD, self.netP_style, self.netD_A, self.netD_B], False)
self.set_requires_grad([self.netAE, self.netDec_A,self.netDec_B], True)
self.optimizer_G.zero_grad()
self.loss_G = self.compute_G_loss()
self.loss_G.backward()
self.optimizer_G.step()
def set_input(self, input):
"""Unpack input data from the dataloader and perform necessary pre-processing steps.
Parameters:
input (dict): include the data itself and its metadata information.
The option 'direction' can be used to swap domain A and domain B.
"""
AtoB = self.opt.direction == 'AtoB'
self.real_A = input['A' if AtoB else 'B'].to(self.device)
self.real_B = input['B' if AtoB else 'A'].to(self.device)
self.image_paths = input['A_paths' if AtoB else 'B_paths']
def forward(self):
"""Run forward pass; called by both functions and ."""
self.real_A_feat = self.netAE(self.real_A, self.real_B) # G_A(A)
self.fake_B = self.netDec_B(self.real_A_feat)
if self.isTrain:
self.real_B_feat = self.netAE(self.real_B, self.real_A) # G_A(A)
self.fake_A = self.netDec_A(self.real_B_feat)
if self.opt.lambda_CYC > 0.0:
self.rec_A_feat = self.netAE(self.fake_B, self.real_A)
self.rec_B_feat = self.netAE(self.fake_A, self.real_B)
self.rec_A = self.netDec_A(self.rec_A_feat)
self.rec_B = self.netDec_B(self.rec_B_feat)
def backward_D_basic(self, netD, content,style, fake):
"""Calculate GAN loss for the discriminator
Parameters:
netD (network) -- the discriminator D
real (tensor array) -- real images
fake (tensor array) -- images generated by a generator
Return the discriminator loss.
We also call loss_D.backward() to calculate the gradients.
"""
loss_D_real = loss_D_fake = 0
# Real
pred_real = netD(style)
loss_D_real = self.criterionGAN(pred_real, True)
# Fake
pred_fake = netD(fake.detach())
loss_D_fake = self.criterionGAN(pred_fake, False)
# Combined loss and calculate gradients
loss_D = (loss_D_real + loss_D_fake)*0.5
return loss_D
def backward_D_NCEloss(self):
"""
Calculate NCE loss for the discriminator
"""
#query_A = query_B =0.0
real_A = self.netD(self.patch_sampler(self.real_A), self.nce_layers)
real_B = self.netD(self.patch_sampler(self.real_B), self.nce_layers)
real_Ax = self.netD(self.patch_sampler(self.real_A), self.nce_layers)
real_Bx = self.netD(self.patch_sampler(self.real_B), self.nce_layers)
query_A = self.netP_style(real_A, self.nce_layers)
query_B = self.netP_style(real_B, self.nce_layers)
query_Ax = self.netP_style(real_Ax, self.nce_layers)
query_Bx = self.netP_style(real_Bx, self.nce_layers)
num = 0
loss_D_cont_A = 0
loss_D_cont_B = 0
for x in self.nce_layers:
#self.nce_loss.dequeue_and_enqueue(query_A[num], 'real_A{:d}'.format(x))
self.nce_loss.dequeue_and_enqueue(query_B[num], 'real_B{:d}'.format(x))
#loss_D_cont_A += self.nce_loss(query_A[num], query_Ax[num], 'real_B{:d}'.format(x))
loss_D_cont_B += self.nce_loss(query_B[num], query_Bx[num], 'real_B{:d}'.format(x))
num += 1
loss_NCE_D = (loss_D_cont_A + loss_D_cont_B) * 0.5 * self.opt.lambda_NCE_D
return loss_NCE_D
def backward_D(self):
"""Calculate GAN loss for discriminator D"""
if self.opt.lambda_GAN_D_B > 0.0:
fake_B = self.fake_pool.query(self.fake_B)
self.loss_D_B = self.backward_D_basic(self.netD_B, self.real_A, self.real_B, fake_B) * self.opt.lambda_GAN_D_B
else:
self.loss_D_B = 0
if self.opt.lambda_GAN_D_A > 0.0:
fake_A = self.fake_pool.query(self.fake_A)
self.loss_D_A = self.backward_D_basic(self.netD_A, self.real_B, self.real_A, fake_A) * self.opt.lambda_GAN_D_A
else:
self.loss_D_A = 0
self.loss_D = (self.loss_D_B + self.loss_D_A) * 0.5
return self.loss_D
def compute_G_loss(self):
"""Calculate GAN and NCE loss for the generator"""
# First, G(A) should fake the discriminator
if self.opt.lambda_GAN_G_A > 0.0:
pred_fakeB = self.netD_B(self.fake_B)
self.loss_G_A = self.criterionGAN(pred_fakeB, True).mean() * self.opt.lambda_GAN_G_A
else:
self.loss_G_A = 0.0
if self.opt.lambda_GAN_G_B > 0.0:
pred_fakeA = self.netD_A(self.fake_A)
self.loss_G_B = self.criterionGAN(pred_fakeA, True).mean() * self.opt.lambda_GAN_G_B
else:
self.loss_G_B = 0.0
# Calculate the style contrastive loss.
if self.opt.lambda_NCE_G > 0.0:
real_A = self.patch_sampler(self.real_A)
real_B = self.patch_sampler(self.real_B)
fake_A = self.patch_sampler(self.fake_A)
fake_B = self.patch_sampler(self.fake_B)
key_A = self.netP_style(self.netD(real_A, self.nce_layers),self.nce_layers)
key_B = self.netP_style(self.netD(real_B, self.nce_layers),self.nce_layers)
query_A = self.netP_style(self.netD(fake_A, self.nce_layers),self.nce_layers)
query_B = self.netP_style(self.netD(fake_B, self.nce_layers),self.nce_layers)
num = 0
self.loss_G_NCE_style_A = 0
self.loss_G_NCE_style_B = 0
for x in self.nce_layers:
#self.loss_G_NCE_style_A += self.nce_loss(query_A[num], key_A[num], 'real_B{:d}'.format(x))
self.loss_G_NCE_style_B += self.nce_loss(query_B[num], key_B[num], 'real_B{:d}'.format(x))
num += 1
else:
self.loss_G_NCE_style_A = 0
self.loss_G_NCE_style_B = 0
self.loss_G_NCE_style = (self.loss_G_NCE_style_A + self.loss_G_NCE_style_B) * 0.5 * self.opt.lambda_NCE_G
#L1 Cycle Loss
if self.opt.lambda_CYC > 0.0:
self.loss_cyc_A = self.criterionCyc(self.rec_A, self.real_A) * self.opt.lambda_CYC
self.loss_cyc_B = self.criterionCyc(self.rec_B, self.real_B) * self.opt.lambda_CYC
else:
self.loss_cyc_A = 0
self.loss_cyc_B = 0
self.loss_cyc = (self.loss_cyc_A + self.loss_cyc_B) * 0.5
self.loss_G = self.loss_cyc + self.loss_G_NCE_style + (self.loss_G_A + self.loss_G_B) * 0.5
return self.loss_G
def init_weights(net, init_type='normal', init_gain=0.02):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
init.normal_(m.weight.data, 1.0, init_gain)
init.constant_(m.bias.data, 0.0)
print('initialize network with %s' % init_type)
net.apply(init_func) # apply the initialization function
def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[]):
"""Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
Parameters:
net (network) -- the network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Return an initialized network.
"""
if len(gpu_ids) > 0:
assert(torch.cuda.is_available())
net.to(gpu_ids[0])
net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs
init_weights(net, init_type, init_gain=init_gain)
return net
================================================
FILE: models/net.py
================================================
import torch.nn as nn
import torch
vgg = nn.Sequential(
nn.Conv2d(3, 3, (1, 1)),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(3, 64, (3, 3)),
nn.ReLU(), # relu1-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 64, (3, 3)),
nn.ReLU(), # relu1-2
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 128, (3, 3)),
nn.ReLU(), # relu2-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 128, (3, 3)),
nn.ReLU(), # relu2-2
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 256, (3, 3)),
nn.ReLU(), # relu3-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(), # relu3-4
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 512, (3, 3)),
nn.ReLU(), # relu4-1, this is the last layer used
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu4-4
nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-1
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-2
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU(), # relu5-3
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 512, (3, 3)),
nn.ReLU() # relu5-4
)
class ADAIN_Encoder(nn.Module):
def __init__(self, encoder, gpu_ids=[]):
super(ADAIN_Encoder, self).__init__()
enc_layers = list(encoder.children())
self.enc_1 = nn.Sequential(*enc_layers[:4]) # input -> relu1_1 64
self.enc_2 = nn.Sequential(*enc_layers[4:11]) # relu1_1 -> relu2_1 128
self.enc_3 = nn.Sequential(*enc_layers[11:18]) # relu2_1 -> relu3_1 256
self.enc_4 = nn.Sequential(*enc_layers[18:31]) # relu3_1 -> relu4_1 512
self.mse_loss = nn.MSELoss()
# fix the encoder
for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4']:
for param in getattr(self, name).parameters():
param.requires_grad = False
# extract relu1_1, relu2_1, relu3_1, relu4_1 from input image
def encode_with_intermediate(self, input):
results = [input]
for i in range(4):
func = getattr(self, 'enc_{:d}'.format(i + 1))
results.append(func(results[-1]))
return results[1:]
def calc_mean_std(self, feat, eps=1e-5):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.size()
assert (len(size) == 4)
N, C = size[:2]
feat_var = feat.view(N, C, -1).var(dim=2) + eps
feat_std = feat_var.sqrt().view(N, C, 1, 1)
feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return feat_mean, feat_std
def adain(self, content_feat, style_feat):
assert (content_feat.size()[:2] == style_feat.size()[:2])
size = content_feat.size()
style_mean, style_std = self.calc_mean_std(style_feat)
content_mean, content_std = self.calc_mean_std(content_feat)
normalized_feat = (content_feat - content_mean.expand(
size)) / content_std.expand(size)
return normalized_feat * style_std.expand(size) + style_mean.expand(size)
def forward(self, content, style, encoded_only = False):
style_feats = self.encode_with_intermediate(style)
content_feats = self.encode_with_intermediate(content)
if encoded_only:
return content_feats[-1], style_feats[-1]
else:
adain_feat = self.adain(content_feats[-1], style_feats[-1])
return adain_feat
class Decoder(nn.Module):
def __init__(self, gpu_ids=[]):
super(Decoder, self).__init__()
decoder = [
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(512, 256, (3, 3)),
nn.ReLU(), # 256
nn.Upsample(scale_factor=2, mode='nearest'),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 256, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(256, 128, (3, 3)),
nn.ReLU(),# 128
nn.Upsample(scale_factor=2, mode='nearest'),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 128, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(128, 64, (3, 3)),
nn.ReLU(),# 64
nn.Upsample(scale_factor=2, mode='nearest'),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 64, (3, 3)),
nn.ReLU(),
nn.ReflectionPad2d((1, 1, 1, 1)),
nn.Conv2d(64, 3, (3, 3))
]
self.decoder = nn.Sequential(*decoder)
def forward(self, adain_feat):
fake_image = self.decoder(adain_feat)
return fake_image
================================================
FILE: models/networks.py
================================================
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
import functools
from torch.optim import lr_scheduler
import numpy as np
from torch.nn.parameter import Parameter
###############################################################################
# Helper Functions
###############################################################################
def get_filter(filt_size=3):
if(filt_size == 1):
a = np.array([1., ])
elif(filt_size == 2):
a = np.array([1., 1.])
elif(filt_size == 3):
a = np.array([1., 2., 1.])
elif(filt_size == 4):
a = np.array([1., 3., 3., 1.])
elif(filt_size == 5):
a = np.array([1., 4., 6., 4., 1.])
elif(filt_size == 6):
a = np.array([1., 5., 10., 10., 5., 1.])
elif(filt_size == 7):
a = np.array([1., 6., 15., 20., 15., 6., 1.])
filt = torch.Tensor(a[:, None] * a[None, :])
filt = filt / torch.sum(filt)
return filt
class Downsample(nn.Module):
def __init__(self, channels, pad_type='reflect', filt_size=3, stride=2, pad_off=0):
super(Downsample, self).__init__()
self.filt_size = filt_size
self.pad_off = pad_off
self.pad_sizes = [int(1. * (filt_size - 1) / 2), int(np.ceil(1. * (filt_size - 1) / 2)), int(1. * (filt_size - 1) / 2), int(np.ceil(1. * (filt_size - 1) / 2))]
self.pad_sizes = [pad_size + pad_off for pad_size in self.pad_sizes]
self.stride = stride
self.off = int((self.stride - 1) / 2.)
self.channels = channels
filt = get_filter(filt_size=self.filt_size)
self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
self.pad = get_pad_layer(pad_type)(self.pad_sizes)
def forward(self, inp):
if(self.filt_size == 1):
if(self.pad_off == 0):
return inp[:, :, ::self.stride, ::self.stride]
else:
return self.pad(inp)[:, :, ::self.stride, ::self.stride]
else:
return F.conv2d(self.pad(inp), self.filt, stride=self.stride, groups=inp.shape[1])
class Upsample2(nn.Module):
def __init__(self, scale_factor, mode='nearest'):
super().__init__()
self.factor = scale_factor
self.mode = mode
def forward(self, x):
return torch.nn.functional.interpolate(x, scale_factor=self.factor, mode=self.mode)
class Upsample(nn.Module):
def __init__(self, channels, pad_type='repl', filt_size=4, stride=2):
super(Upsample, self).__init__()
self.filt_size = filt_size
self.filt_odd = np.mod(filt_size, 2) == 1
self.pad_size = int((filt_size - 1) / 2)
self.stride = stride
self.off = int((self.stride - 1) / 2.)
self.channels = channels
filt = get_filter(filt_size=self.filt_size) * (stride**2)
self.register_buffer('filt', filt[None, None, :, :].repeat((self.channels, 1, 1, 1)))
self.pad = get_pad_layer(pad_type)([1, 1, 1, 1])
def forward(self, inp):
ret_val = F.conv_transpose2d(self.pad(inp), self.filt, stride=self.stride, padding=1 + self.pad_size, groups=inp.shape[1])[:, :, 1:, 1:]
if(self.filt_odd):
return ret_val
else:
return ret_val[:, :, :-1, :-1]
def get_pad_layer(pad_type):
if(pad_type in ['refl', 'reflect']):
PadLayer = nn.ReflectionPad2d
elif(pad_type in ['repl', 'replicate']):
PadLayer = nn.ReplicationPad2d
elif(pad_type == 'zero'):
PadLayer = nn.ZeroPad2d
else:
print('Pad type [%s] not recognized' % pad_type)
return PadLayer
class Identity(nn.Module):
def forward(self, x):
return x
def get_norm_layer(norm_type='instance'):
"""Return a normalization layer
Parameters:
norm_type (str) -- the name of the normalization layer: batch | instance | none
For BatchNorm, we use learnable affine parameters and track running statistics (mean/stddev).
For InstanceNorm, we do not use learnable affine parameters. We do not track running statistics.
"""
if norm_type == 'batch':
norm_layer = functools.partial(nn.BatchNorm2d, affine=True, track_running_stats=True)
elif norm_type == 'instance':
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
elif norm_type == 'none':
def norm_layer(x):
return Identity()
else:
raise NotImplementedError('normalization layer [%s] is not found' % norm_type)
return norm_layer
def get_scheduler(optimizer, opt):
"""Return a learning rate scheduler
Parameters:
optimizer -- the optimizer of the network
opt (option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions．
opt.lr_policy is the name of learning rate policy: linear | step | plateau | cosine
For 'linear', we keep the same learning rate for the first epochs
and linearly decay the rate to zero over the next epochs.
For other schedulers (step, plateau, and cosine), we use the default PyTorch schedulers.
See https://pytorch.org/docs/stable/optim.html for more details.
"""
if opt.lr_policy == 'linear':
def lambda_rule(epoch):
lr_l = 1.0 - max(0, epoch + opt.epoch_count - opt.n_epochs) / float(opt.n_epochs_decay + 1)
return lr_l
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda_rule)
elif opt.lr_policy == 'step':
scheduler = lr_scheduler.StepLR(optimizer, step_size=opt.lr_decay_iters, gamma=0.1)
elif opt.lr_policy == 'plateau':
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.2, threshold=0.01, patience=5)
elif opt.lr_policy == 'cosine':
scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.n_epochs, eta_min=0)
else:
return NotImplementedError('learning rate policy [%s] is not implemented', opt.lr_policy)
return scheduler
def init_weights(net, init_type='kaiming', init_gain=0.02, debug=False):
"""Initialize network weights.
Parameters:
net (network) -- network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
We use 'normal' in the original pix2pix and CycleGAN paper. But xavier and kaiming might
work better for some applications. Feel free to try yourself.
"""
def init_func(m): # define the initialization function
classname = m.__class__.__name__
if hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1):
if debug:
print(classname)
if init_type == 'normal':
init.normal_(m.weight.data, 0.0, init_gain)
elif init_type == 'xavier':
init.xavier_normal_(m.weight.data, gain=init_gain)
elif init_type == 'kaiming':
init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
elif init_type == 'orthogonal':
init.orthogonal_(m.weight.data, gain=init_gain)
else:
raise NotImplementedError('initialization method [%s] is not implemented' % init_type)
if hasattr(m, 'bias') and m.bias is not None:
init.constant_(m.bias.data, 0.0)
elif classname.find('BatchNorm2d') != -1: # BatchNorm Layer's weight is not a matrix; only normal distribution applies.
init.normal_(m.weight.data, 1.0, init_gain)
init.constant_(m.bias.data, 0.0)
net.apply(init_func) # apply the initialization function
def init_net(net, init_type='normal', init_gain=0.02, gpu_ids=[], debug=False, initialize_weights=True):
"""Initialize a network: 1. register CPU/GPU device (with multi-GPU support); 2. initialize the network weights
Parameters:
net (network) -- the network to be initialized
init_type (str) -- the name of an initialization method: normal | xavier | kaiming | orthogonal
gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Return an initialized network.
"""
if len(gpu_ids) > 0:
assert(torch.cuda.is_available())
net.to(gpu_ids[0])
# if not amp:
# net = torch.nn.DataParallel(net, gpu_ids) # multi-GPUs for non-AMP training
if initialize_weights:
init_weights(net, init_type, init_gain=init_gain, debug=debug)
return net
def define_D(input_nc, ndf, netD, n_layers_D=3, image_size = 256, feature_dim = 256, max_conv_dim = 512,norm='batch', init_type='normal', init_gain=0.02, no_antialias=False, gpu_ids=[], opt=None):
"""Create a discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the first conv layer
netD (str) -- the architecture's name: basic | n_layers | pixel
n_layers_D (int) -- the number of conv layers in the discriminator; effective when netD=='n_layers'
norm (str) -- the type of normalization layers used in the network.
init_type (str) -- the name of the initialization method.
init_gain (float) -- scaling factor for normal, xavier and orthogonal.
gpu_ids (int list) -- which GPUs the network runs on: e.g., 0,1,2
Returns a discriminator
Our current implementation provides three types of discriminators:
[basic]: 'PatchGAN' classifier described in the original pix2pix paper.
It can classify whether 70×70 overlapping patches are real or fake.
Such a patch-level discriminator architecture has fewer parameters
than a full-image discriminator and can work on arbitrarily-sized images
in a fully convolutional fashion.
[n_layers]: With this mode, you cna specify the number of conv layers in the discriminator
with the parameter (default=3 as used in [basic] (PatchGAN).)
[pixel]: 1x1 PixelGAN discriminator can classify whether a pixel is real or not.
It encourages greater color diversity but has no effect on spatial statistics.
The discriminator has been initialized by . It uses Leaky RELU for non-linearity.
"""
net = None
norm_layer = get_norm_layer(norm_type=norm)
if netD == 'basic': # default PatchGAN classifier
net = NLayerDiscriminator(input_nc, ndf, n_layers=3, image_size = image_size, norm_layer=norm_layer, no_antialias=no_antialias,)
elif netD == 'n_layers': # more options
net = NLayerDiscriminator(input_nc, ndf, n_layers_D, norm_layer=norm_layer, no_antialias=no_antialias,)
elif netD == 'pixel': # classify if each pixel is real or fake
net = PixelDiscriminator(input_nc, ndf, norm_layer=norm_layer)
elif 'stylegan2' in netD:
net = StyleGAN2Discriminator(input_nc, ndf, n_layers_D, no_antialias=no_antialias, opt=opt)
elif netD == 'NCE': # default PatchGAN classifier
net = NCEDiscriminator(input_nc, ndf, n_layers=3, image_size = image_size, feature_dim = feature_dim, max_conv_dim = max_conv_dim, norm_layer=norm_layer, no_antialias=no_antialias,)
else:
raise NotImplementedError('Discriminator model name [%s] is not recognized' % netD)
return init_net(net, init_type, init_gain, gpu_ids,
initialize_weights=('stylegan2' not in netD))
##############################################################################
# Classes
##############################################################################
class GANLoss(nn.Module):
"""Define different GAN objectives.
The GANLoss class abstracts away the need to create the target label tensor
that has the same size as the input.
"""
def __init__(self, gan_mode, target_real_label=1.0, target_fake_label=0.0):
""" Initialize the GANLoss class.
Parameters:
gan_mode (str) - - the type of GAN objective. It currently supports vanilla, lsgan, and wgangp.
target_real_label (bool) - - label for a real image
target_fake_label (bool) - - label of a fake image
Note: Do not use sigmoid as the last layer of Discriminator.
LSGAN needs no sigmoid. vanilla GANs will handle it with BCEWithLogitsLoss.
"""
super(GANLoss, self).__init__()
self.register_buffer('real_label', torch.tensor(target_real_label))
self.register_buffer('fake_label', torch.tensor(target_fake_label))
self.gan_mode = gan_mode
if gan_mode == 'lsgan':
self.loss = nn.MSELoss()
elif gan_mode == 'vanilla':
self.loss = nn.BCEWithLogitsLoss()
elif gan_mode in ['wgangp', 'nonsaturating']:
self.loss = None
elif gan_mode == "hinge":
self.loss = None
else:
raise NotImplementedError('gan mode %s not implemented' % gan_mode)
def get_target_tensor(self, prediction, target_is_real):
"""Create label tensors with the same size as the input.
Parameters:
prediction (tensor) - - tpyically the prediction from a discriminator
target_is_real (bool) - - if the ground truth label is for real images or fake images
Returns:
A label tensor filled with ground truth label, and with the size of the input
"""
if target_is_real:
target_tensor = self.real_label
else:
target_tensor = self.fake_label
return target_tensor.expand_as(prediction)
def __call__(self, prediction, target_is_real):
"""Calculate loss given Discriminator's output and grount truth labels.
Parameters:
prediction (tensor) - - tpyically the prediction output from a discriminator
target_is_real (bool) - - if the ground truth label is for real images or fake images
Returns:
the calculated loss.
"""
bs = prediction.size(0)
if self.gan_mode in ['lsgan', 'vanilla']:
target_tensor = self.get_target_tensor(prediction, target_is_real)
loss = self.loss(prediction, target_tensor)
elif self.gan_mode == 'wgangp':
if target_is_real:
loss = -prediction.mean()
else:
loss = prediction.mean()
elif self.gan_mode == 'nonsaturating':
if target_is_real:
loss = F.softplus(-prediction).view(bs, -1).mean(dim=1)
else:
loss = F.softplus(prediction).view(bs, -1).mean(dim=1)
elif self.gan_mode == 'hinge':
if target_is_real:
minvalue = torch.min(prediction - 1, torch.zeros(prediction.shape).to(prediction.device))
loss = -torch.mean(minvalue)
else:
minvalue = torch.min(-prediction - 1,torch.zeros(prediction.shape).to(prediction.device))
loss = -torch.mean(minvalue)
return loss
def cal_gradient_penalty(netD, real_data, fake_data, device, type='mixed', constant=1.0, lambda_gp=10.0):
"""Calculate the gradient penalty loss, used in WGAN-GP paper https://arxiv.org/abs/1704.00028
Arguments:
netD (network) -- discriminator network
real_data (tensor array) -- real images
fake_data (tensor array) -- generated images from the generator
device (str) -- GPU / CPU: from torch.device('cuda:{}'.format(self.gpu_ids[0])) if self.gpu_ids else torch.device('cpu')
type (str) -- if we mix real and fake data or not [real | fake | mixed].
constant (float) -- the constant used in formula ( | |gradient||_2 - constant)^2
lambda_gp (float) -- weight for this loss
Returns the gradient penalty loss
"""
if lambda_gp > 0.0:
if type == 'real': # either use real images, fake images, or a linear interpolation of two.
interpolatesv = real_data
elif type == 'fake':
interpolatesv = fake_data
elif type == 'mixed':
alpha = torch.rand(real_data.shape[0], 1, device=device)
alpha = alpha.expand(real_data.shape[0], real_data.nelement() // real_data.shape[0]).contiguous().view(*real_data.shape)
interpolatesv = alpha * real_data + ((1 - alpha) * fake_data)
else:
raise NotImplementedError('{} not implemented'.format(type))
interpolatesv.requires_grad_(True)
disc_interpolates = netD(interpolatesv)
gradients = torch.autograd.grad(outputs=disc_interpolates, inputs=interpolatesv,
grad_outputs=torch.ones(disc_interpolates.size()).to(device),
create_graph=True, retain_graph=True, only_inputs=True)
gradients = gradients[0].view(real_data.size(0), -1) # flat the data
gradient_penalty = (((gradients + 1e-16).norm(2, dim=1) - constant) ** 2).mean() * lambda_gp # added eps
return gradient_penalty, gradients
else:
return 0.0, None
class Normalize(nn.Module):
def __init__(self, power=2):
super(Normalize, self).__init__()
self.power = power
def forward(self, x):
norm = x.pow(self.power).sum(1, keepdim=True).pow(1. / self.power)
out = x.div(norm + 1e-7)
return out
##################################################################################
# Sequential Models
##################################################################################
class ResBlocks(nn.Module):
def __init__(self, num_blocks, dim, norm='inst', activation='relu', pad_type='zero', nz=0):
super(ResBlocks, self).__init__()
self.model = []
for i in range(num_blocks):
self.model += [ResBlock(dim, norm=norm, activation=activation, pad_type=pad_type, nz=nz)]
self.model = nn.Sequential(*self.model)
def forward(self, x):
return self.model(x)
##################################################################################
# Basic Blocks
##################################################################################
def cat_feature(x, y):
y_expand = y.view(y.size(0), y.size(1), 1, 1).expand(
y.size(0), y.size(1), x.size(2), x.size(3))
x_cat = torch.cat([x, y_expand], 1)
return x_cat
class Conv2dBlock(nn.Module):
def __init__(self, input_dim, output_dim, kernel_size, stride,
padding=0, norm='none', activation='relu', pad_type='zero'):
super(Conv2dBlock, self).__init__()
self.use_bias = True
# initialize padding
if pad_type == 'reflect':
self.pad = nn.ReflectionPad2d(padding)
elif pad_type == 'zero':
self.pad = nn.ZeroPad2d(padding)
else:
assert 0, "Unsupported padding type: {}".format(pad_type)
# initialize normalization
norm_dim = output_dim
if norm == 'batch':
self.norm = nn.BatchNorm2d(norm_dim)
elif norm == 'inst':
self.norm = nn.InstanceNorm2d(norm_dim, track_running_stats=False)
elif norm == 'ln':
self.norm = LayerNorm(norm_dim)
elif norm == 'none':
self.norm = None
else:
assert 0, "Unsupported normalization: {}".format(norm)
# initialize activation
if activation == 'relu':
self.activation = nn.ReLU(inplace=True)
elif activation == 'lrelu':
self.activation = nn.LeakyReLU(0.2, inplace=True)
elif activation == 'prelu':
self.activation = nn.PReLU()
elif activation == 'selu':
self.activation = nn.SELU(inplace=True)
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'none':
self.activation = None
else:
assert 0, "Unsupported activation: {}".format(activation)
# initialize convolution
self.conv = nn.Conv2d(input_dim, output_dim, kernel_size, stride, bias=self.use_bias)
def forward(self, x):
x = self.conv(self.pad(x))
if self.norm:
x = self.norm(x)
if self.activation:
x = self.activation(x)
return x
class LinearBlock(nn.Module):
def __init__(self, input_dim, output_dim, norm='none', activation='relu'):
super(LinearBlock, self).__init__()
use_bias = True
# initialize fully connected layer
self.fc = nn.Linear(input_dim, output_dim, bias=use_bias)
# initialize normalization
norm_dim = output_dim
if norm == 'batch':
self.norm = nn.BatchNorm1d(norm_dim)
elif norm == 'inst':
self.norm = nn.InstanceNorm1d(norm_dim)
elif norm == 'ln':
self.norm = LayerNorm(norm_dim)
elif norm == 'none':
self.norm = None
else:
assert 0, "Unsupported normalization: {}".format(norm)
# initialize activation
if activation == 'relu':
self.activation = nn.ReLU(inplace=True)
elif activation == 'lrelu':
self.activation = nn.LeakyReLU(0.2, inplace=True)
elif activation == 'prelu':
self.activation = nn.PReLU()
elif activation == 'selu':
self.activation = nn.SELU(inplace=True)
elif activation == 'tanh':
self.activation = nn.Tanh()
elif activation == 'none':
self.activation = None
else:
assert 0, "Unsupported activation: {}".format(activation)
def forward(self, x):
out = self.fc(x)
if self.norm:
out = self.norm(out)
if self.activation:
out = self.activation(out)
return out
##################################################################################
# Normalization layers
##################################################################################
class LayerNorm(nn.Module):
def __init__(self, num_features, eps=1e-5, affine=True):
super(LayerNorm, self).__init__()
self.num_features = num_features
self.affine = affine
self.eps = eps
if self.affine:
self.gamma = nn.Parameter(torch.Tensor(num_features).uniform_())
self.beta = nn.Parameter(torch.zeros(num_features))
def forward(self, x):
shape = [-1] + [1] * (x.dim() - 1)
mean = x.view(x.size(0), -1).mean(1).view(*shape)
std = x.view(x.size(0), -1).std(1).view(*shape)
x = (x - mean) / (std + self.eps)
if self.affine:
shape = [1, -1] + [1] * (x.dim() - 2)
x = x * self.gamma.view(*shape) + self.beta.view(*shape)
return x
class NLayerDiscriminator(nn.Module):
"""Defines a PatchGAN discriminator"""
def __init__(self, input_nc, ndf=64, n_layers=3, image_size = 256,norm_layer=nn.BatchNorm2d, no_antialias=False):
"""Construct a PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
n_layers (int) -- the number of conv layers in the discriminator
norm_layer -- normalization layer
"""
super(NLayerDiscriminator, self).__init__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
kw = 4
padw = 1
if(no_antialias):
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw), nn.LeakyReLU(0.2, True)]
else:
sequence = [nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=1, padding=padw), nn.LeakyReLU(0.2, True), Downsample(ndf)]
nf_mult = 1
nf_mult_prev = 1
for n in range(1, n_layers): # gradually increase the number of filters
nf_mult_prev = nf_mult
nf_mult = min(2 ** n, 8)
if(no_antialias):
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=2, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
else:
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True),
Downsample(ndf * nf_mult)]
nf_mult_prev = nf_mult
nf_mult = min(2 ** n_layers, 8)
sequence += [
nn.Conv2d(ndf * nf_mult_prev, ndf * nf_mult, kernel_size=kw, stride=1, padding=padw, bias=use_bias),
norm_layer(ndf * nf_mult),
nn.LeakyReLU(0.2, True)
]
sequence += [nn.Conv2d(ndf * nf_mult, 1, kernel_size=kw, stride=1, padding=padw)] # output 1 channel prediction map
self.model = nn.Sequential(*sequence)
def forward(self, input):
"""Standard forward."""
logit = self.model(input)
return logit
class PixelDiscriminator(nn.Module):
"""Defines a 1x1 PatchGAN discriminator (pixelGAN)"""
def __init__(self, input_nc, ndf=64, norm_layer=nn.BatchNorm2d):
"""Construct a 1x1 PatchGAN discriminator
Parameters:
input_nc (int) -- the number of channels in input images
ndf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
"""
super(PixelDiscriminator, self).__init__()
if type(norm_layer) == functools.partial: # no need to use bias as BatchNorm2d has affine parameters
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
self.net = [
nn.Conv2d(input_nc, ndf, kernel_size=1, stride=1, padding=0),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf, ndf * 2, kernel_size=1, stride=1, padding=0, bias=use_bias),
norm_layer(ndf * 2),
nn.LeakyReLU(0.2, True),
nn.Conv2d(ndf * 2, 1, kernel_size=1, stride=1, padding=0, bias=use_bias)]
self.net = nn.Sequential(*self.net)
def forward(self, input):
"""Standard forward."""
return self.net(input)
class PatchDiscriminator(NLayerDiscriminator):
"""Defines a PatchGAN discriminator"""
def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, no_antialias=False):
super().__init__(input_nc, ndf, 2, norm_layer, no_antialias)
def forward(self, input):
B, C, H, W = input.size(0), input.size(1), input.size(2), input.size(3)
size = 16
Y = H // size
X = W // size
input = input.view(B, C, Y, size, X, size)
input = input.permute(0, 2, 4, 1, 3, 5).contiguous().view(B * Y * X, C, size, size)
return super().forward(input)
class GroupedChannelNorm(nn.Module):
def __init__(self, num_groups):
super().__init__()
self.num_groups = num_groups
def forward(self, x):
shape = list(x.shape)
new_shape = [shape[0], self.num_groups, shape[1] // self.num_groups] + shape[2:]
x = x.view(*new_shape)
mean = x.mean(dim=2, keepdim=True)
std = x.std(dim=2, keepdim=True)
x_norm = (x - mean) / (std + 1e-7)
return x_norm.view(*shape)
================================================
FILE: models/torch_utils.py
================================================
import os
import random
import numpy as np
import torch
import torch.nn as nn
@torch.no_grad()
def concat_all_gather(tensor, world_size):
tensors_gather = [
torch.ones_like(tensor) for _ in range(world_size)]
torch.distributed.all_gather(tensors_gather, tensor, async_op=False)
output = torch.cat(tensors_gather, dim=0)
return output
def get_rank(group=None):
try:
return torch.distributed.get_rank(group)
except:
return 0
def get_world_size(group=None):
try:
return torch.distributed.get_world_size(group)
except:
return 1
def kaiming_init(mod):
if isinstance(mod, (nn.Conv2d, nn.Linear)):
if mod.weight.requires_grad:
nn.init.kaiming_normal_(mod.weight, a=0.2, mode="fan_in")
if mod.bias is not None:
nn.init.zeros_(mod.bias)
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
os.environ["PYTHONHASHSEED"] = str(seed)
@torch.no_grad()
def update_average(net, net_ema, m=0.999):
net = net.module if hasattr(net, "module") else net
for p, p_ema in zip(net.parameters(), net_ema.parameters()):
p_ema.data.mul_(m).add_((1.0 - m) * p.detach().data)
def warmup_learning_rate(optimizer, lr, train_step, warmup_step):
if train_step > warmup_step or warmup_step == 0:
return lr
ratio = min(1.0, train_step/warmup_step)
lr_w = ratio * lr
for param_group in optimizer.param_groups:
param_group["lr"] = lr_w
return lr_w
================================================
FILE: options/__init__.py
================================================
"""This package options includes option modules: training options, test options, and basic options (used in both training and test)."""
================================================
FILE: options/base_options.py
================================================
import argparse
import os
from util import util
import torch
import models
import data
class BaseOptions():
"""This class defines options used during both training and test time.
It also implements several helper functions such as parsing, printing, and saving the options.
It also gathers additional options defined in functions in both dataset class and model class.
"""
def __init__(self, cmd_line=None):
"""Reset the class; indicates the class hasn't been initailized"""
self.initialized = False
self.cmd_line = None
if cmd_line is not None:
self.cmd_line = cmd_line.split()
def initialize(self, parser):
"""Define the common options that are used in both training and test."""
# basic parameters
parser.add_argument('--dataroot', default='placeholder', help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
parser.add_argument('--name', type=str, default='experiment_name', help='name of the experiment. It decides where to store samples and models')
parser.add_argument('--easy_label', type=str, default='experiment_name', help='Interpretable name')
parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU')
parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here')
# model parameters
parser.add_argument('--model', type=str, default='cast', help='chooses which model to use.')
parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB and 1 for grayscale')
parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB and 1 for grayscale')
parser.add_argument('--ngf', type=int, default=64, help='# of gen filters in the last conv layer')
parser.add_argument('--ndf', type=int, default=64, help='# of discrim filters in the first conv layer')
parser.add_argument('--netD', type=str, default='basic', choices=['basic', 'n_layers', 'pixel', 'patch', 'tilestylegan2', 'stylegan2'], help='specify discriminator architecture. The basic model is a 70x70 PatchGAN. n_layers allows you to specify the layers in the discriminator')
parser.add_argument('--netG', type=str, default='resnet_9blocks', choices=['resnet_9blocks', 'resnet_6blocks', 'unet_256', 'unet_128', 'stylegan2', 'smallstylegan2', 'resnet_cat', 'cluit', 'SA2_2'], help='specify generator architecture')
parser.add_argument('--n_layers_D', type=int, default=3, help='only used if netD==n_layers')
parser.add_argument('--normG', type=str, default='instance', choices=['instance', 'batch', 'none'], help='instance normalization or batch normalization for G')
parser.add_argument('--normD', type=str, default='instance', choices=['instance', 'batch', 'none'], help='instance normalization or batch normalization for D')
parser.add_argument('--init_type', type=str, default='xavier', choices=['normal', 'xavier', 'kaiming', 'orthogonal'], help='network initialization')
parser.add_argument('--init_gain', type=float, default=0.02, help='scaling factor for normal, xavier and orthogonal.')
parser.add_argument('--no_dropout', type=util.str2bool, nargs='?', const=True, default=True,
help='no dropout for the generator')
parser.add_argument('--no_antialias', action='store_true', help='if specified, use stride=2 convs instead of antialiased-downsampling (sad)')
parser.add_argument('--no_antialias_up', action='store_true', help='if specified, use [upconv(learned filter)] instead of [upconv(hard-coded [1,3,3,1] filter), conv]')
# Model parameters.
parser.add_argument("--style-dim", default=256, type=int)
parser.add_argument("--feature_dim", default=256, type=int)
parser.add_argument("--hypersphere-dim", default=256, type=int)
parser.add_argument("--queue-size", default=4096, type=int)
parser.add_argument("--temperature", default=0.07, type=float)
parser.add_argument("--max_conv_dim", default=512, type=int)
# dataset parameters
parser.add_argument('--dataset_mode', type=str, default='unaligned', help='chooses how datasets are loaded. [unaligned | aligned | single | colorization]')
parser.add_argument('--direction', type=str, default='AtoB', help='AtoB or BtoA')
parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly')
parser.add_argument('--num_threads', default=4, type=int, help='# threads for loading data')
parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
parser.add_argument('--load_size', type=int, default=286, help='scale images to this size')
parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size')
parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.')
parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | crop | scale_width | scale_width_and_crop | none]')
parser.add_argument('--no_flip', action='store_true', help='if specified, do not flip the images for data augmentation')
parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML')
parser.add_argument('--random_scale_max', type=float, default=3.0,
help='(used for single image translation) Randomly scale the image by the specified factor as data augmentation.')
# additional parameters
parser.add_argument('--epoch', type=str, default='latest', help='which epoch to load? set to latest to use latest cached model')
parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information')
parser.add_argument('--suffix', default='', type=str, help='customized suffix: opt.name = opt.name + suffix: e.g., {model}_{netG}_size{load_size}')
self.initialized = True
return parser
def gather_options(self):
"""Initialize our parser with basic options(only once).
Add additional model-specific and dataset-specific options.
These options are defined in the function
in model and dataset classes.
"""
if not self.initialized: # check if it has been initialized
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser = self.initialize(parser)
# get the basic options
if self.cmd_line is None:
opt, _ = parser.parse_known_args()
else:
opt, _ = parser.parse_known_args(self.cmd_line)
# modify model-related parser options
model_name = opt.model
model_option_setter = models.get_option_setter(model_name)
parser = model_option_setter(parser, self.isTrain)
if self.cmd_line is None:
opt, _ = parser.parse_known_args() # parse again with new defaults
else:
opt, _ = parser.parse_known_args(self.cmd_line) # parse again with new defaults
# modify dataset-related parser options
dataset_name = opt.dataset_mode
dataset_option_setter = data.get_option_setter(dataset_name)
parser = dataset_option_setter(parser, self.isTrain)
# save and return the parser
self.parser = parser
if self.cmd_line is None:
return parser.parse_args()
else:
return parser.parse_args(self.cmd_line)
def print_options(self, opt):
"""Print and save options
It will print both current options and default values(if different).
It will save options into a text file / [checkpoints_dir] / opt.txt
"""
message = ''
message += '----------------- Options ---------------\n'
for k, v in sorted(vars(opt).items()):
comment = ''
default = self.parser.get_default(k)
if v != default:
comment = '\t[default: %s]' % str(default)
message += '{:>25}: {:<30}{}\n'.format(str(k), str(v), comment)
message += '----------------- End -------------------'
print(message)
# save to the disk
expr_dir = os.path.join(opt.checkpoints_dir, opt.name)
util.mkdirs(expr_dir)
file_name = os.path.join(expr_dir, '{}_opt.txt'.format(opt.phase))
try:
with open(file_name, 'wt') as opt_file:
opt_file.write(message)
opt_file.write('\n')
except PermissionError as error:
print("permission error {}".format(error))
pass
def parse(self):
"""Parse our options, create checkpoints directory suffix, and set up gpu device."""
opt = self.gather_options()
opt.isTrain = self.isTrain # train or test
# process opt.suffix
if opt.suffix:
suffix = ('_' + opt.suffix.format(**vars(opt))) if opt.suffix != '' else ''
opt.name = opt.name + suffix
self.print_options(opt)
# set gpu ids
str_ids = opt.gpu_ids.split(',')
opt.gpu_ids = []
for str_id in str_ids:
id = int(str_id)
if id >= 0:
opt.gpu_ids.append(id)
if len(opt.gpu_ids) > 0:
torch.cuda.set_device(opt.gpu_ids[0])
self.opt = opt
return self.opt
================================================
FILE: options/test_options.py
================================================
from .base_options import BaseOptions
class TestOptions(BaseOptions):
"""This class includes test options.
It also includes shared options defined in BaseOptions.
"""
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser) # define shared options
parser.add_argument('--results_dir', type=str, default='./results/', help='saves results here.')
parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc')
# Dropout and Batchnorm has different behavioir during training and test.
parser.add_argument('--eval', action='store_true', help='use eval mode during test time.')
# Set the default = 5000 to test the whole test set.
parser.add_argument('--num_test', type=int, default=5000, help='how many test images to run')
# To avoid cropping, the load_size should be the same as crop_size
parser.set_defaults(load_size=parser.get_default('crop_size'))
self.isTrain = False
return parser
================================================
FILE: options/train_options.py
================================================
from .base_options import BaseOptions
class TrainOptions(BaseOptions):
"""This class includes training options.
It also includes shared options defined in BaseOptions.
"""
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser)
# visdom and HTML visualization parameters
parser.add_argument('--display_freq', type=int, default=400, help='frequency of showing training results on screen')
parser.add_argument('--display_ncols', type=int, default=4, help='if positive, display all images in a single visdom web panel with certain number of images per row.')
parser.add_argument('--display_id', type=int, default=None, help='window id of the web display. Default is random window id')
parser.add_argument('--display_server', type=str, default="http://localhost", help='visdom server of the web display')
parser.add_argument('--display_env', type=str, default='main', help='visdom display environment name (default is "main")')
parser.add_argument('--display_port', type=int, default=8097, help='visdom port of the web display')
parser.add_argument('--update_html_freq', type=int, default=100, help='frequency of saving training results to html')
parser.add_argument('--print_freq', type=int, default=100, help='frequency of showing training results on console')
parser.add_argument('--no_html', action='store_true', help='do not save intermediate training results to [opt.checkpoints_dir]/[opt.name]/web/')
# network saving and loading parameters
parser.add_argument('--save_latest_freq', type=int, default=5000, help='frequency of saving the latest results')
parser.add_argument('--save_epoch_freq', type=int, default=50, help='frequency of saving checkpoints at the end of epochs')
parser.add_argument('--evaluation_freq', type=int, default=5000, help='evaluation freq')
parser.add_argument('--save_by_iter', action='store_true', help='whether saves model by iteration')
parser.add_argument('--continue_train', action='store_true', help='continue training: load the latest model')
parser.add_argument('--epoch_count', type=int, default=1, help='the starting epoch count, we save the model by , +, ...')
parser.add_argument('--phase', type=str, default='train', help='train, val, test, etc')
parser.add_argument('--pretrained_name', type=str, default=None, help='resume training from another checkpoint')
# training parameters
parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs with the initial learning rate')
parser.add_argument('--n_epochs_decay', type=int, default=200, help='number of epochs to linearly decay learning rate to zero')
parser.add_argument('--beta1', type=float, default=0.5, help='momentum term of adam')
parser.add_argument('--beta2', type=float, default=0.999, help='momentum term of adam')
parser.add_argument('--lr_G', type=float, default=0.0001, help='initial learning rate for adam')
parser.add_argument('--lr_D', type=float, default=0.0001, help='initial learning rate for adam')
parser.add_argument('--lr_D_NCE', type=float, default=0.0001, help='initial learning rate for adam')
parser.add_argument('--gan_mode', type=str, default='hinge', help='the type of GAN objective. [vanilla| lsgan | wgangp| hinge]. vanilla GAN loss is the cross-entropy objective used in the original GAN paper.')
parser.add_argument('--pool_size', type=int, default=50, help='the size of image buffer that stores previously generated images')
parser.add_argument('--lr_policy', type=str, default='linear', help='learning rate policy. [linear | step | plateau | cosine]')
parser.add_argument('--lr_decay_iters', type=int, default=50, help='multiply by a gamma every lr_decay_iters iterations')
self.isTrain = True
return parser
================================================
FILE: requirements.txt
================================================
torch>=1.6.0
torchvision>=0.7.0
dominate>=2.4.0
visdom>=0.1.8.8
packaging
GPUtil>=1.4.0
scipy
Pillow>=6.1.0
numpy>=1.16.4
kornia
================================================
FILE: test.py
================================================
import os
from options.test_options import TestOptions
from data import create_dataset
from models import create_model
from util.visualizer import save_images
from util import html
import util.util as util
if __name__ == '__main__':
opt = TestOptions().parse() # get test options
# hard-code some parameters for test
opt.num_threads = 0 # test code only supports num_threads = 1
opt.batch_size = 1 # test code only supports batch_size = 1
opt.serial_batches = True # disable data shuffling; comment this line if results on randomly chosen images are needed.
opt.no_flip = True # no flip; comment this line if results on flipped images are needed.
opt.display_id = -1 # no visdom display; the test code saves the results to a HTML file.
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
# train_dataset = create_dataset(util.copyconf(opt, phase="train"))
model = create_model(opt) # create a model given opt.model and other options
# create a webpage for viewing the results
web_dir = os.path.join(opt.results_dir, opt.name, '{}_{}'.format(opt.phase, opt.epoch)) # define the website directory
print('creating web directory', web_dir)
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.epoch))
for i, data in enumerate(dataset):
if i == 0:
model.setup(opt) # regular setup: load and print networks; create schedulers
model.parallelize()
if opt.eval:
model.eval()
if i >= opt.num_test: # only apply our model to opt.num_test images.
break
model.set_input(data) # unpack data from data loader
model.test() # run inference
visuals = model.get_current_visuals() # get image results
img_path = model.get_image_paths() # get image paths
if i % 5 == 0: # save images to an HTML file
print('processing (%04d)-th image... %s' % (i, img_path))
save_images(webpage, visuals, img_path, width=opt.display_winsize)
webpage.save() # save the HTML
================================================
FILE: train.py
================================================
import time
import torch
from options.train_options import TrainOptions
from data import create_dataset
from models import create_model
from util.visualizer import Visualizer
if __name__ == '__main__':
opt = TrainOptions().parse() # get training options
dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options
dataset_size = len(dataset) # get the number of images in the dataset.
model = create_model(opt) # create a model given opt.model and other options
print('The number of training images = %d' % dataset_size)
visualizer = Visualizer(opt) # create a visualizer that display/save images and plots
opt.visualizer = visualizer
total_iters = 0 # the total number of training iterations
optimize_time = 0.1
times = []
for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1): # outer loop for different epochs; we save the model by , +
epoch_start_time = time.time() # timer for entire epoch
iter_data_time = time.time() # timer for data loading per iteration
epoch_iter = 0 # the number of training iterations in current epoch, reset to 0 every epoch
visualizer.reset() # reset the visualizer: make sure it saves the results to HTML at least once every epoch
dataset.set_epoch(epoch)
for i, data in enumerate(dataset): # inner loop within one epoch
iter_start_time = time.time() # timer for computation per iteration
if total_iters % opt.print_freq == 0:
t_data = iter_start_time - iter_data_time
batch_size = data["A"].size(0)
total_iters += batch_size
epoch_iter += batch_size
if len(opt.gpu_ids) > 0:
torch.cuda.synchronize()
optimize_start_time = time.time()
if epoch == opt.epoch_count and i == 0:
model.setup(opt) # regular setup: load and print networks; create schedulers
model.parallelize()
model.set_input(data) # unpack data from dataset and apply preprocessing
model.optimize_parameters() # calculate loss functions, get gradients, update network weights
if len(opt.gpu_ids) > 0:
torch.cuda.synchronize()
optimize_time = (time.time() - optimize_start_time) / batch_size * 0.005 + 0.995 * optimize_time
if total_iters % opt.display_freq == 0: # display images on visdom and save images to a HTML file
save_result = total_iters % opt.update_html_freq == 0
model.compute_visuals()
visualizer.display_current_results(model.get_current_visuals(), epoch, save_result)
if total_iters % opt.print_freq == 0: # print training losses and save logging information to the disk
losses = model.get_current_losses()
visualizer.print_current_losses(epoch, epoch_iter, losses, optimize_time, t_data)
if opt.display_id is None or opt.display_id > 0:
visualizer.plot_current_losses(epoch, float(epoch_iter) / dataset_size, losses)
if total_iters % opt.save_latest_freq == 0: # cache our latest model every iterations
print('saving the latest model (epoch %d, total_iters %d)' % (epoch, total_iters))
print(opt.name) # it's useful to occasionally show the experiment name on console
save_suffix = 'iter_%d' % total_iters if opt.save_by_iter else 'latest'
model.save_networks(save_suffix)
iter_data_time = time.time()
if epoch % opt.save_epoch_freq == 0: # cache our model every epochs
print('saving the model at the end of epoch %d, iters %d' % (epoch, total_iters))
model.save_networks(str(epoch)+'_'+str(total_iters))
model.save_networks(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' % (epoch, opt.n_epochs + opt.n_epochs_decay, time.time() - epoch_start_time))
model.update_learning_rate() # update learning rates at the end of every epoch.
================================================
FILE: util/__init__.py
================================================
"""This package includes a miscellaneous collection of useful helper functions."""
from util import *
================================================
FILE: util/get_data.py
================================================
from __future__ import print_function
import os
import tarfile
import requests
from warnings import warn
from zipfile import ZipFile
from bs4 import BeautifulSoup
from os.path import abspath, isdir, join, basename
class GetData(object):
"""A Python script for downloading CycleGAN or pix2pix datasets.
Parameters:
technique (str) -- One of: 'cyclegan' or 'pix2pix'.
verbose (bool) -- If True, print additional information.
Examples:
>>> from util.get_data import GetData
>>> gd = GetData(technique='cyclegan')
>>> new_data_path = gd.get(save_path='./datasets') # options will be displayed.
Alternatively, You can use bash scripts: 'scripts/download_pix2pix_model.sh'
and 'scripts/download_cyclegan_model.sh'.
"""
def __init__(self, technique='cyclegan', verbose=True):
url_dict = {
'pix2pix': 'http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/',
'cyclegan': 'https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets'
}
self.url = url_dict.get(technique.lower())
self._verbose = verbose
def _print(self, text):
if self._verbose:
print(text)
@staticmethod
def _get_options(r):
soup = BeautifulSoup(r.text, 'lxml')
options = [h.text for h in soup.find_all('a', href=True)
if h.text.endswith(('.zip', 'tar.gz'))]
return options
def _present_options(self):
r = requests.get(self.url)
options = self._get_options(r)
print('Options:\n')
for i, o in enumerate(options):
print("{0}: {1}".format(i, o))
choice = input("\nPlease enter the number of the "
"dataset above you wish to download:")
return options[int(choice)]
def _download_data(self, dataset_url, save_path):
if not isdir(save_path):
os.makedirs(save_path)
base = basename(dataset_url)
temp_save_path = join(save_path, base)
with open(temp_save_path, "wb") as f:
r = requests.get(dataset_url)
f.write(r.content)
if base.endswith('.tar.gz'):
obj = tarfile.open(temp_save_path)
elif base.endswith('.zip'):
obj = ZipFile(temp_save_path, 'r')
else:
raise ValueError("Unknown File Type: {0}.".format(base))
self._print("Unpacking Data...")
obj.extractall(save_path)
obj.close()
os.remove(temp_save_path)
def get(self, save_path, dataset=None):
"""
Download a dataset.
Parameters:
save_path (str) -- A directory to save the data to.
dataset (str) -- (optional). A specific dataset to download.
Note: this must include the file extension.
If None, options will be presented for you
to choose from.
Returns:
save_path_full (str) -- the absolute path to the downloaded data.
"""
if dataset is None:
selected_dataset = self._present_options()
else:
selected_dataset = dataset
save_path_full = join(save_path, selected_dataset.split('.')[0])
if isdir(save_path_full):
warn("\n'{0}' already exists. Voiding Download.".format(
save_path_full))
else:
self._print('Downloading Data...')
url = "{0}/{1}".format(self.url, selected_dataset)
self._download_data(url, save_path=save_path)
return abspath(save_path_full)
================================================
FILE: util/html.py
================================================
import dominate
from dominate.tags import meta, h3, table, tr, td, p, a, img, br
import os
class HTML:
"""This HTML class allows us to save images and write texts into a single HTML file.
It consists of functions such as (add a text header to the HTML file),
(add a row of images to the HTML file), and (save the HTML to the disk).
It is based on Python library 'dominate', a Python library for creating and manipulating HTML documents using a DOM API.
"""
def __init__(self, web_dir, title, refresh=0):
"""Initialize the HTML classes
Parameters:
web_dir (str) -- a directory that stores the webpage. HTML file will be created at /index.html; images will be saved at 0:
with self.doc.head:
meta(http_equiv="refresh", content=str(refresh))
def get_image_dir(self):
"""Return the directory that stores images"""
return self.img_dir
def add_header(self, text):
"""Insert a header to the HTML file
Parameters:
text (str) -- the header text
"""
with self.doc:
h3(text)
def add_images(self, ims, txts, links, width=400):
"""add images to the HTML file
Parameters:
ims (str list) -- a list of image paths
txts (str list) -- a list of image names shown on the website
links (str list) -- a list of hyperref links; when you click an image, it will redirect you to a new page
"""
self.t = table(border=1, style="table-layout: fixed;") # Insert a table
self.doc.add(self.t)
with self.t:
with tr():
for im, txt, link in zip(ims, txts, links):
with td(style="word-wrap: break-word;", halign="center", valign="top"):
with p():
with a(href=os.path.join('images', link)):
img(style="width:%dpx" % width, src=os.path.join('images', im))
br()
p(txt)
def save(self):
"""save the current content to the HMTL file"""
html_file = '%s/index.html' % self.web_dir
f = open(html_file, 'wt')
f.write(self.doc.render())
f.close()
if __name__ == '__main__': # we show an example usage here.
html = HTML('web/', 'test_html')
html.add_header('hello world')
ims, txts, links = [], [], []
for n in range(4):
ims.append('image_%d.png' % n)
txts.append('text_%d' % n)
links.append('image_%d.png' % n)
html.add_images(ims, txts, links)
html.save()
================================================
FILE: util/image_pool.py
================================================
import random
import torch
class ImagePool():
"""This class implements an image buffer that stores previously generated images.
This buffer enables us to update discriminators using a history of generated images
rather than the ones produced by the latest generators.
"""
def __init__(self, pool_size):
"""Initialize the ImagePool class
Parameters:
pool_size (int) -- the size of image buffer, if pool_size=0, no buffer will be created
"""
self.pool_size = pool_size
if self.pool_size > 0: # create an empty pool
self.num_imgs = 0
self.images = []
def query(self, images):
"""Return an image from the pool.
Parameters:
images: the latest generated images from the generator
Returns images from the buffer.
By 50/100, the buffer will return input images.
By 50/100, the buffer will return images previously stored in the buffer,
and insert the current images to the buffer.
"""
if self.pool_size == 0: # if the buffer size is 0, do nothing
return images
return_images = []
for image in images:
image = torch.unsqueeze(image.data, 0)
if self.num_imgs < self.pool_size: # if the buffer is not full; keep inserting current images to the buffer
self.num_imgs = self.num_imgs + 1
self.images.append(image)
return_images.append(image)
else:
p = random.uniform(0, 1)
if p > 0.5: # by 50% chance, the buffer will return a previously stored image, and insert the current image into the buffer
random_id = random.randint(0, self.pool_size - 1) # randint is inclusive
tmp = self.images[random_id].clone()
self.images[random_id] = image
return_images.append(tmp)
else: # by another 50% chance, the buffer will return the current image
return_images.append(image)
return_images = torch.cat(return_images, 0) # collect all the images and return
return return_images
================================================
FILE: util/util.py
================================================
"""This module contains simple helper functions """
from __future__ import print_function
import torch
import numpy as np
from PIL import Image
import os
import importlib
import argparse
from argparse import Namespace
import torchvision
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def copyconf(default_opt, **kwargs):
conf = Namespace(**vars(default_opt))
for key in kwargs:
setattr(conf, key, kwargs[key])
return conf
def find_class_in_module(target_cls_name, module):
target_cls_name = target_cls_name.replace('_', '').lower()
clslib = importlib.import_module(module)
cls = None
for name, clsobj in clslib.__dict__.items():
if name.lower() == target_cls_name:
cls = clsobj
assert cls is not None, "In %s, there should be a class whose name matches %s in lowercase without underscore(_)" % (module, target_cls_name)
return cls
def tensor2im(input_image, imtype=np.uint8):
""""Converts a Tensor array into a numpy image array.
Parameters:
input_image (tensor) -- the input image tensor array
imtype (type) -- the desired type of the converted numpy array
"""
if not isinstance(input_image, np.ndarray):
if isinstance(input_image, torch.Tensor): # get the data from a variable
image_tensor = input_image.data
else:
return input_image
image_numpy = image_tensor[0].clamp(-1.0, 1.0).cpu().float().numpy() # convert it into a numpy array
if image_numpy.shape[0] == 1: # grayscale to RGB
image_numpy = np.tile(image_numpy, (3, 1, 1))
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0 # post-processing: tranpose and scaling
else: # if it is a numpy array, do nothing
image_numpy = input_image
return image_numpy.astype(imtype)
def diagnose_network(net, name='network'):
"""Calculate and print the mean of average absolute(gradients)
Parameters:
net (torch network) -- Torch network
name (str) -- the name of the network
"""
mean = 0.0
count = 0
for param in net.parameters():
if param.grad is not None:
mean += torch.mean(torch.abs(param.grad.data))
count += 1
if count > 0:
mean = mean / count
print(name)
print(mean)
def save_image(image_numpy, image_path, aspect_ratio=1.0):
"""Save a numpy image to the disk
Parameters:
image_numpy (numpy array) -- input numpy array
image_path (str) -- the path of the image
"""
image_pil = Image.fromarray(image_numpy)
h, w, _ = image_numpy.shape
if aspect_ratio is None:
pass
elif aspect_ratio > 1.0:
image_pil = image_pil.resize((h, int(w * aspect_ratio)), Image.BICUBIC)
elif aspect_ratio < 1.0:
image_pil = image_pil.resize((int(h / aspect_ratio), w), Image.BICUBIC)
image_pil.save(image_path)
def print_numpy(x, val=True, shp=False):
"""Print the mean, min, max, median, std, and size of a numpy array
Parameters:
val (bool) -- if print the values of the numpy array
shp (bool) -- if print the shape of the numpy array
"""
x = x.astype(np.float64)
if shp:
print('shape,', x.shape)
if val:
x = x.flatten()
print('mean = %3.3f, min = %3.3f, max = %3.3f, median = %3.3f, std=%3.3f' % (
np.mean(x), np.min(x), np.max(x), np.median(x), np.std(x)))
def mkdirs(paths):
"""create empty directories if they don't exist
Parameters:
paths (str list) -- a list of directory paths
"""
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
"""create a single empty directory if it didn't exist
Parameters:
path (str) -- a single directory path
"""
if not os.path.exists(path):
os.makedirs(path)
def correct_resize_label(t, size):
device = t.device
t = t.detach().cpu()
resized = []
for i in range(t.size(0)):
one_t = t[i, :1]
one_np = np.transpose(one_t.numpy().astype(np.uint8), (1, 2, 0))
one_np = one_np[:, :, 0]
one_image = Image.fromarray(one_np).resize(size, Image.NEAREST)
resized_t = torch.from_numpy(np.array(one_image)).long()
resized.append(resized_t)
return torch.stack(resized, dim=0).to(device)
def correct_resize(t, size, mode=Image.BICUBIC):
device = t.device
t = t.detach().cpu()
resized = []
for i in range(t.size(0)):
one_t = t[i:i + 1]
one_image = Image.fromarray(tensor2im(one_t)).resize(size, Image.BICUBIC)
resized_t = torchvision.transforms.functional.to_tensor(one_image) * 2 - 1.0
resized.append(resized_t)
return torch.stack(resized, dim=0).to(device)
================================================
FILE: util/visualizer.py
================================================
import numpy as np
import os
import sys
import ntpath
import time
from . import util, html
from subprocess import Popen, PIPE
if sys.version_info[0] == 2:
VisdomExceptionBase = Exception
else:
VisdomExceptionBase = ConnectionError
def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
"""Save images to the disk.
Parameters:
webpage (the HTML class) -- the HTML webpage class that stores these imaegs (see html.py for more details)
visuals (OrderedDict) -- an ordered dictionary that stores (name, images (either tensor or numpy) ) pairs
image_path (str) -- the string is used to create image paths
aspect_ratio (float) -- the aspect ratio of saved images
width (int) -- the images will be resized to width x width
This function will save images stored in 'visuals' to the HTML file specified by 'webpage'.
"""
image_dir = webpage.get_image_dir()
short_path = ntpath.basename(image_path[0])
name = os.path.splitext(short_path)[0]
webpage.add_header(name)
ims, txts, links = [], [], []
for label, im_data in visuals.items():
im = util.tensor2im(im_data)
image_name = '%s_%s.png' % (name, label)
os.makedirs(os.path.join(image_dir, label), exist_ok=True)
save_path = os.path.join(image_dir, image_name)
util.save_image(im, save_path, aspect_ratio=aspect_ratio)
ims.append(image_name)
txts.append(label)
links.append(image_name)
webpage.add_images(ims, txts, links, width=width)
class Visualizer():
"""This class includes several functions that can display/save images and print/save logging information.
It uses a Python library 'visdom' for display, and a Python library 'dominate' (wrapped in 'HTML') for creating HTML files with images.
"""
def __init__(self, opt):
"""Initialize the Visualizer class
Parameters:
opt -- stores all the experiment flags; needs to be a subclass of BaseOptions
Step 1: Cache the training/test options
Step 2: connect to a visdom server
Step 3: create an HTML object for saveing HTML filters
Step 4: create a logging file to store training losses
"""
self.opt = opt # cache the option
if opt.display_id is None:
self.display_id = np.random.randint(100000) * 10 # just a random display id
else:
self.display_id = opt.display_id
self.use_html = opt.isTrain and not opt.no_html
self.win_size = opt.display_winsize
self.name = opt.name
self.port = opt.display_port
self.saved = False
if self.display_id > 0: # connect to a visdom server given and
import visdom
self.plot_data = {}
self.ncols = opt.display_ncols
if "tensorboard_base_url" not in os.environ:
self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env)
else:
self.vis = visdom.Visdom(port=2004,
base_url=os.environ['tensorboard_base_url'] + '/visdom')
if not self.vis.check_connection():
self.create_visdom_connections()
if self.use_html: # create an HTML object at /web/; images will be saved under /web/images/
self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
self.img_dir = os.path.join(self.web_dir, 'images')
print('create web directory %s...' % self.web_dir)
util.mkdirs([self.web_dir, self.img_dir])
# create a logging file to store training losses
self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
with open(self.log_name, "a") as log_file:
now = time.strftime("%c")
log_file.write('================ Training Loss (%s) ================\n' % now)
def reset(self):
"""Reset the self.saved status"""
self.saved = False
def create_visdom_connections(self):
"""If the program could not connect to Visdom server, this function will start a new server at port < self.port > """
cmd = sys.executable + ' -m visdom.server -p %d &>/dev/null &' % self.port
print('\n\nCould not connect to Visdom server. \n Trying to start a server....')
print('Command: %s' % cmd)
Popen(cmd, shell=True, stdout=PIPE, stderr=PIPE)
def display_current_results(self, visuals, epoch, save_result):
"""Display current results on visdom; save current results to an HTML file.
Parameters:
visuals (OrderedDict) - - dictionary of images to display or save
epoch (int) - - the current epoch
save_result (bool) - - if save the current results to an HTML file
"""
if self.display_id > 0: # show images in the browser using visdom
ncols = self.ncols
if ncols > 0: # show all the images in one visdom panel
ncols = min(ncols, len(visuals))
h, w = next(iter(visuals.values())).shape[:2]
table_css = """""" % (w, h) # create a table css
# create a table of images.
title = self.name
label_html = ''
label_html_row = ''
images = []
idx = 0
for label, image in visuals.items():
image_numpy = util.tensor2im(image)
label_html_row += '%s | ' % label
images.append(image_numpy.transpose([2, 0, 1]))
idx += 1
if idx % ncols == 0:
label_html += '%s
' % label_html_row
label_html_row = ''
white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255
while idx % ncols != 0:
images.append(white_image)
label_html_row += ' | '
idx += 1
if label_html_row != '':
label_html += '%s
' % label_html_row
try:
self.vis.images(images, ncols, 2, self.display_id + 1,
None, dict(title=title + ' images'))
label_html = '' % label_html
self.vis.text(table_css + label_html, win=self.display_id + 2,
opts=dict(title=title + ' labels'))
except VisdomExceptionBase:
self.create_visdom_connections()
else: # show each image in a separate visdom panel;
idx = 1
try:
for label, image in visuals.items():
image_numpy = util.tensor2im(image)
self.vis.image(
image_numpy.transpose([2, 0, 1]),
self.display_id + idx,
None,
dict(title=label)
)
idx += 1
except VisdomExceptionBase:
self.create_visdom_connections()
if self.use_html and (save_result or not self.saved): # save images to an HTML file if they haven't been saved.
self.saved = True
# save images to the disk
for label, image in visuals.items():
image_numpy = util.tensor2im(image)
img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
util.save_image(image_numpy, img_path)
# update website
webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, refresh=0)
for n in range(epoch, 0, -1):
webpage.add_header('epoch [%d]' % n)
ims, txts, links = [], [], []
for label, image_numpy in visuals.items():
image_numpy = util.tensor2im(image)
img_path = 'epoch%.3d_%s.png' % (n, label)
ims.append(img_path)
txts.append(label)
links.append(img_path)
webpage.add_images(ims, txts, links, width=self.win_size)
webpage.save()
def plot_current_losses(self, epoch, counter_ratio, losses):
"""display the current losses on visdom display: dictionary of error labels and values
Parameters:
epoch (int) -- current epoch
counter_ratio (float) -- progress (percentage) in the current epoch, between 0 to 1
losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
"""
if len(losses) == 0:
return
plot_name = '_'.join(list(losses.keys()))
if plot_name not in self.plot_data:
self.plot_data[plot_name] = {'X': [], 'Y': [], 'legend': list(losses.keys())}
plot_data = self.plot_data[plot_name]
plot_id = list(self.plot_data.keys()).index(plot_name)
plot_data['X'].append(epoch + counter_ratio)
plot_data['Y'].append([losses[k] for k in plot_data['legend']])
try:
self.vis.line(
X=np.stack([np.array(plot_data['X'])] * len(plot_data['legend']), 1),
Y=np.array(plot_data['Y']),
opts={
'title': self.name,
'legend': plot_data['legend'],
'xlabel': 'epoch',
'ylabel': 'loss'},
win=self.display_id - plot_id)
except VisdomExceptionBase:
self.create_visdom_connections()
# losses: same format as |losses| of plot_current_losses
def print_current_losses(self, epoch, iters, losses, t_comp, t_data):
"""print current losses on console; also save the losses to the disk
Parameters:
epoch (int) -- current epoch
iters (int) -- current training iteration during this epoch (reset to 0 at the end of every epoch)
losses (OrderedDict) -- training losses stored in the format of (name, float) pairs
t_comp (float) -- computational time per data point (normalized by batch_size)
t_data (float) -- data loading time per data point (normalized by batch_size)
"""
message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, iters, t_comp, t_data)
for k, v in losses.items():
message += '%s: %.3f ' % (k, v)
print(message) # print the message
with open(self.log_name, "a") as log_file:
log_file.write('%s\n' % message) # save the message