├── .gitignore
├── LICENSE
├── README.md
├── checkpoints
├── discriminator_final.pth
├── generator_final.pth
└── generator_pretrain.pth
├── models.py
├── output
├── high_res_fake
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├── high_res_real
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├── low_res
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│ └── 99.png
└── training_results.png
├── test
├── train
└── utils.py
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--------------------------------------------------------------------------------
/README.md:
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1 | # PyTorch-SRGAN
2 | A modern PyTorch implementation of SRGAN
3 |
4 | It is deeply based on __Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network__ paper published by the Twitter team (https://arxiv.org/abs/1609.04802) but I replaced activations by Swish (https://arxiv.org/abs/1710.05941)
5 |
6 | You can start training out-of-the-box with the CIFAR-10 or CIFAR-100 datasets, to emulate the paper results however, you will need to download and clean the ImageNet dataset yourself. Results and weights are provided for the ImageNet dataset.
7 |
8 | Contributions are welcome!
9 |
10 | ## Requirements
11 |
12 | * PyTorch
13 | * torchvision
14 | * tensorboard_logger (https://github.com/TeamHG-Memex/tensorboard_logger)
15 |
16 | ## Training
17 |
18 | ```
19 | usage: train [-h] [--dataset DATASET] [--dataroot DATAROOT]
20 | [--workers WORKERS] [--batchSize BATCHSIZE]
21 | [--imageSize IMAGESIZE] [--upSampling UPSAMPLING]
22 | [--nEpochs NEPOCHS] [--generatorLR GENERATORLR]
23 | [--discriminatorLR DISCRIMINATORLR] [--cuda] [--nGPU NGPU]
24 | [--generatorWeights GENERATORWEIGHTS]
25 | [--discriminatorWeights DISCRIMINATORWEIGHTS] [--out OUT]
26 | ```
27 |
28 | Example: ```./train --cuda```
29 |
30 | This will start a training session in the GPU. First it will pre-train the generator using MSE error for 2 epochs, then it will train the full GAN (generator + discriminator) for 100 epochs, using content (mse + vgg) and adversarial loss. Although weights are already provided in the repository, this script will also generate them in the checkpoints file.
31 |
32 | ## Testing
33 |
34 | ```
35 | usage: test [-h] [--dataset DATASET] [--dataroot DATAROOT] [--workers WORKERS]
36 | [--batchSize BATCHSIZE] [--imageSize IMAGESIZE]
37 | [--upSampling UPSAMPLING] [--cuda] [--nGPU NGPU]
38 | [--generatorWeights GENERATORWEIGHTS]
39 | [--discriminatorWeights DISCRIMINATORWEIGHTS]
40 |
41 | ```
42 |
43 | Example: ```./test --cuda```
44 |
45 | This will start a testing session in the GPU. It will display mean error values and save the generated images in the output directory, all three versions: low resolution, high resolution (original) and high resolution (generated).
46 |
47 | ## Results
48 |
49 | ### Training
50 | The following results have been obtained with the current training setup:
51 |
52 | * Dataset: 350K randomly selected ImageNet samples
53 | * Input image size: 24x24
54 | * Output image size: 96x96 (16x)
55 |
56 | Other training parameters are the default of _train_ script
57 |
58 | 
59 |
60 | ### Testing
61 | Testing has been executed on 128 randomly selected ImageNet samples (disjoint from training set)
62 |
63 | ```[7/8] Discriminator_Loss: 1.4123 Generator_Loss (Content/Advers/Total): 0.0901/0.6152/0.0908```
64 |
65 | ### Examples
66 | See more under the _output_ directory
67 |
68 | __High resolution / Low resolution / Recovered High Resolution__
69 |
70 | 
71 |
72 | 
73 |
74 | 
75 |
76 | 
77 |
78 | 
79 |
80 | 
81 |
82 | 
83 |
84 | 
85 |
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/checkpoints/discriminator_final.pth:
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/checkpoints/generator_final.pth:
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https://raw.githubusercontent.com/aitorzip/PyTorch-SRGAN/5377e3cb63b72db299f610ef21cfb9f1ed2bff5b/checkpoints/generator_final.pth
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/checkpoints/generator_pretrain.pth:
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https://raw.githubusercontent.com/aitorzip/PyTorch-SRGAN/5377e3cb63b72db299f610ef21cfb9f1ed2bff5b/checkpoints/generator_pretrain.pth
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/models.py:
--------------------------------------------------------------------------------
1 | # -*- coding: utf-8 -*-
2 | """Implements SRGAN models: https://arxiv.org/abs/1609.04802
3 |
4 | TODO:
5 |
6 | """
7 |
8 | import torch
9 | import torch.nn as nn
10 | import torch.nn.functional as F
11 | from torch.autograd import Variable
12 |
13 | def swish(x):
14 | return x * F.sigmoid(x)
15 |
16 | class FeatureExtractor(nn.Module):
17 | def __init__(self, cnn, feature_layer=11):
18 | super(FeatureExtractor, self).__init__()
19 | self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)])
20 |
21 | def forward(self, x):
22 | return self.features(x)
23 |
24 |
25 | class residualBlock(nn.Module):
26 | def __init__(self, in_channels=64, k=3, n=64, s=1):
27 | super(residualBlock, self).__init__()
28 |
29 | self.conv1 = nn.Conv2d(in_channels, n, k, stride=s, padding=1)
30 | self.bn1 = nn.BatchNorm2d(n)
31 | self.conv2 = nn.Conv2d(n, n, k, stride=s, padding=1)
32 | self.bn2 = nn.BatchNorm2d(n)
33 |
34 | def forward(self, x):
35 | y = swish(self.bn1(self.conv1(x)))
36 | return self.bn2(self.conv2(y)) + x
37 |
38 | class upsampleBlock(nn.Module):
39 | # Implements resize-convolution
40 | def __init__(self, in_channels, out_channels):
41 | super(upsampleBlock, self).__init__()
42 | self.conv = nn.Conv2d(in_channels, out_channels, 3, stride=1, padding=1)
43 | self.shuffler = nn.PixelShuffle(2)
44 |
45 | def forward(self, x):
46 | return swish(self.shuffler(self.conv(x)))
47 |
48 | class Generator(nn.Module):
49 | def __init__(self, n_residual_blocks, upsample_factor):
50 | super(Generator, self).__init__()
51 | self.n_residual_blocks = n_residual_blocks
52 | self.upsample_factor = upsample_factor
53 |
54 | self.conv1 = nn.Conv2d(3, 64, 9, stride=1, padding=4)
55 |
56 | for i in range(self.n_residual_blocks):
57 | self.add_module('residual_block' + str(i+1), residualBlock())
58 |
59 | self.conv2 = nn.Conv2d(64, 64, 3, stride=1, padding=1)
60 | self.bn2 = nn.BatchNorm2d(64)
61 |
62 | for i in range(self.upsample_factor/2):
63 | self.add_module('upsample' + str(i+1), upsampleBlock(64, 256))
64 |
65 | self.conv3 = nn.Conv2d(64, 3, 9, stride=1, padding=4)
66 |
67 | def forward(self, x):
68 | x = swish(self.conv1(x))
69 |
70 | y = x.clone()
71 | for i in range(self.n_residual_blocks):
72 | y = self.__getattr__('residual_block' + str(i+1))(y)
73 |
74 | x = self.bn2(self.conv2(y)) + x
75 |
76 | for i in range(self.upsample_factor/2):
77 | x = self.__getattr__('upsample' + str(i+1))(x)
78 |
79 | return self.conv3(x)
80 |
81 | class Discriminator(nn.Module):
82 | def __init__(self):
83 | super(Discriminator, self).__init__()
84 | self.conv1 = nn.Conv2d(3, 64, 3, stride=1, padding=1)
85 |
86 | self.conv2 = nn.Conv2d(64, 64, 3, stride=2, padding=1)
87 | self.bn2 = nn.BatchNorm2d(64)
88 | self.conv3 = nn.Conv2d(64, 128, 3, stride=1, padding=1)
89 | self.bn3 = nn.BatchNorm2d(128)
90 | self.conv4 = nn.Conv2d(128, 128, 3, stride=2, padding=1)
91 | self.bn4 = nn.BatchNorm2d(128)
92 | self.conv5 = nn.Conv2d(128, 256, 3, stride=1, padding=1)
93 | self.bn5 = nn.BatchNorm2d(256)
94 | self.conv6 = nn.Conv2d(256, 256, 3, stride=2, padding=1)
95 | self.bn6 = nn.BatchNorm2d(256)
96 | self.conv7 = nn.Conv2d(256, 512, 3, stride=1, padding=1)
97 | self.bn7 = nn.BatchNorm2d(512)
98 | self.conv8 = nn.Conv2d(512, 512, 3, stride=2, padding=1)
99 | self.bn8 = nn.BatchNorm2d(512)
100 |
101 | # Replaced original paper FC layers with FCN
102 | self.conv9 = nn.Conv2d(512, 1, 1, stride=1, padding=1)
103 |
104 | def forward(self, x):
105 | x = swish(self.conv1(x))
106 |
107 | x = swish(self.bn2(self.conv2(x)))
108 | x = swish(self.bn3(self.conv3(x)))
109 | x = swish(self.bn4(self.conv4(x)))
110 | x = swish(self.bn5(self.conv5(x)))
111 | x = swish(self.bn6(self.conv6(x)))
112 | x = swish(self.bn7(self.conv7(x)))
113 | x = swish(self.bn8(self.conv8(x)))
114 |
115 | x = self.conv9(x)
116 | return F.sigmoid(F.avg_pool2d(x, x.size()[2:])).view(x.size()[0], -1)
117 |
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1 | #!/usr/bin/env python
2 |
3 | import argparse
4 | import sys
5 | import os
6 |
7 | import torch
8 | import torch.nn as nn
9 | from torch.autograd import Variable
10 |
11 | import torchvision
12 | import torchvision.datasets as datasets
13 | import torchvision.transforms as transforms
14 | from torchvision.utils import save_image
15 |
16 | from models import Generator, Discriminator, FeatureExtractor
17 |
18 | parser = argparse.ArgumentParser()
19 | parser.add_argument('--dataset', type=str, default='cifar100', help='cifar10 | cifar100 | folder')
20 | parser.add_argument('--dataroot', type=str, default='./data', help='path to dataset')
21 | parser.add_argument('--workers', type=int, default=2, help='number of data loading workers')
22 | parser.add_argument('--batchSize', type=int, default=16, help='input batch size')
23 | parser.add_argument('--imageSize', type=int, default=15, help='the low resolution image size')
24 | parser.add_argument('--upSampling', type=int, default=2, help='low to high resolution scaling factor')
25 | parser.add_argument('--cuda', action='store_true', help='enables cuda')
26 | parser.add_argument('--nGPU', type=int, default=1, help='number of GPUs to use')
27 | parser.add_argument('--generatorWeights', type=str, default='checkpoints/generator_final.pth', help="path to generator weights (to continue training)")
28 | parser.add_argument('--discriminatorWeights', type=str, default='checkpoints/discriminator_final.pth', help="path to discriminator weights (to continue training)")
29 |
30 | opt = parser.parse_args()
31 | print(opt)
32 |
33 | try:
34 | os.makedirs('output/high_res_fake')
35 | os.makedirs('output/high_res_real')
36 | os.makedirs('output/low_res')
37 | except OSError:
38 | pass
39 |
40 |
41 | if torch.cuda.is_available() and not opt.cuda:
42 | print("WARNING: You have a CUDA device, so you should probably run with --cuda")
43 |
44 | transform = transforms.Compose([transforms.RandomCrop(opt.imageSize*opt.upSampling),
45 | transforms.ToTensor()])
46 |
47 | normalize = transforms.Normalize(mean = [0.485, 0.456, 0.406],
48 | std = [0.229, 0.224, 0.225])
49 |
50 | scale = transforms.Compose([transforms.ToPILImage(),
51 | transforms.Scale(opt.imageSize),
52 | transforms.ToTensor(),
53 | transforms.Normalize(mean = [0.485, 0.456, 0.406],
54 | std = [0.229, 0.224, 0.225])
55 | ])
56 |
57 | # Equivalent to un-normalizing ImageNet (for correct visualization)
58 | unnormalize = transforms.Normalize(mean = [-2.118, -2.036, -1.804], std = [4.367, 4.464, 4.444])
59 |
60 | if opt.dataset == 'folder':
61 | # folder dataset
62 | dataset = datasets.ImageFolder(root=opt.dataroot, transform=transform)
63 | elif opt.dataset == 'cifar10':
64 | dataset = datasets.CIFAR10(root=opt.dataroot, download=True, train=False, transform=transform)
65 | elif opt.dataset == 'cifar100':
66 | dataset = datasets.CIFAR100(root=opt.dataroot, download=True, train=False, transform=transform)
67 | assert dataset
68 |
69 | dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,
70 | shuffle=False, num_workers=int(opt.workers))
71 |
72 | generator = Generator(16, opt.upSampling)
73 | if opt.generatorWeights != '':
74 | generator.load_state_dict(torch.load(opt.generatorWeights))
75 | print generator
76 |
77 | discriminator = Discriminator()
78 | if opt.discriminatorWeights != '':
79 | discriminator.load_state_dict(torch.load(opt.discriminatorWeights))
80 | print discriminator
81 |
82 | # For the content loss
83 | feature_extractor = FeatureExtractor(torchvision.models.vgg19(pretrained=True))
84 | print feature_extractor
85 | content_criterion = nn.MSELoss()
86 | adversarial_criterion = nn.BCELoss()
87 |
88 | target_real = Variable(torch.ones(opt.batchSize,1))
89 | target_fake = Variable(torch.zeros(opt.batchSize,1))
90 |
91 | # if gpu is to be used
92 | if opt.cuda:
93 | generator.cuda()
94 | discriminator.cuda()
95 | feature_extractor.cuda()
96 | content_criterion.cuda()
97 | adversarial_criterion.cuda()
98 | target_real = target_real.cuda()
99 | target_fake = target_fake.cuda()
100 |
101 | low_res = torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize)
102 |
103 | print 'Test started...'
104 | mean_generator_content_loss = 0.0
105 | mean_generator_adversarial_loss = 0.0
106 | mean_generator_total_loss = 0.0
107 | mean_discriminator_loss = 0.0
108 |
109 | # Set evaluation mode (not training)
110 | generator.eval()
111 | discriminator.eval()
112 |
113 | for i, data in enumerate(dataloader):
114 | # Generate data
115 | high_res_real, _ = data
116 |
117 | # Downsample images to low resolution
118 | for j in range(opt.batchSize):
119 | low_res[j] = scale(high_res_real[j])
120 | high_res_real[j] = normalize(high_res_real[j])
121 |
122 | # Generate real and fake inputs
123 | if opt.cuda:
124 | high_res_real = Variable(high_res_real.cuda())
125 | high_res_fake = generator(Variable(low_res).cuda())
126 | else:
127 | high_res_real = Variable(high_res_real)
128 | high_res_fake = generator(Variable(low_res))
129 |
130 | ######### Test discriminator #########
131 |
132 | discriminator_loss = adversarial_criterion(discriminator(high_res_real), target_real) + \
133 | adversarial_criterion(discriminator(Variable(high_res_fake.data)), target_fake)
134 | mean_discriminator_loss += discriminator_loss.data[0]
135 |
136 | ######### Test generator #########
137 |
138 | real_features = Variable(feature_extractor(high_res_real).data)
139 | fake_features = feature_extractor(high_res_fake)
140 |
141 | generator_content_loss = content_criterion(high_res_fake, high_res_real) + 0.006*content_criterion(fake_features, real_features)
142 | mean_generator_content_loss += generator_content_loss.data[0]
143 | generator_adversarial_loss = adversarial_criterion(discriminator(high_res_fake), target_real)
144 | mean_generator_adversarial_loss += generator_adversarial_loss.data[0]
145 |
146 | generator_total_loss = generator_content_loss + 1e-3*generator_adversarial_loss
147 | mean_generator_total_loss += generator_total_loss.data[0]
148 |
149 | ######### Status and display #########
150 | sys.stdout.write('\r[%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f' % (i, len(dataloader),
151 | discriminator_loss.data[0], generator_content_loss.data[0], generator_adversarial_loss.data[0], generator_total_loss.data[0]))
152 |
153 | for j in range(opt.batchSize):
154 | save_image(unnormalize(high_res_real.data[j]), 'output/high_res_real/' + str(i*opt.batchSize + j) + '.png')
155 | save_image(unnormalize(high_res_fake.data[j]), 'output/high_res_fake/' + str(i*opt.batchSize + j) + '.png')
156 | save_image(unnormalize(low_res[j]), 'output/low_res/' + str(i*opt.batchSize + j) + '.png')
157 |
158 | sys.stdout.write('\r[%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f\n' % (i, len(dataloader),
159 | mean_discriminator_loss/len(dataloader), mean_generator_content_loss/len(dataloader),
160 | mean_generator_adversarial_loss/len(dataloader), mean_generator_total_loss/len(dataloader)))
--------------------------------------------------------------------------------
/train:
--------------------------------------------------------------------------------
1 | #!/usr/bin/env python
2 |
3 | import argparse
4 | import os
5 | import sys
6 |
7 | import torch
8 | import torch.optim as optim
9 | import torch.optim.lr_scheduler as lr_scheduler
10 | import torch.nn as nn
11 | from torch.autograd import Variable
12 |
13 | import torchvision
14 | import torchvision.datasets as datasets
15 | import torchvision.transforms as transforms
16 |
17 | from tensorboard_logger import configure, log_value
18 |
19 | from models import Generator, Discriminator, FeatureExtractor
20 | from utils import Visualizer
21 |
22 | parser = argparse.ArgumentParser()
23 | parser.add_argument('--dataset', type=str, default='cifar100', help='cifar10 | cifar100 | folder')
24 | parser.add_argument('--dataroot', type=str, default='./data', help='path to dataset')
25 | parser.add_argument('--workers', type=int, default=2, help='number of data loading workers')
26 | parser.add_argument('--batchSize', type=int, default=16, help='input batch size')
27 | parser.add_argument('--imageSize', type=int, default=15, help='the low resolution image size')
28 | parser.add_argument('--upSampling', type=int, default=2, help='low to high resolution scaling factor')
29 | parser.add_argument('--nEpochs', type=int, default=100, help='number of epochs to train for')
30 | parser.add_argument('--generatorLR', type=float, default=0.0001, help='learning rate for generator')
31 | parser.add_argument('--discriminatorLR', type=float, default=0.0001, help='learning rate for discriminator')
32 | parser.add_argument('--cuda', action='store_true', help='enables cuda')
33 | parser.add_argument('--nGPU', type=int, default=1, help='number of GPUs to use')
34 | parser.add_argument('--generatorWeights', type=str, default='', help="path to generator weights (to continue training)")
35 | parser.add_argument('--discriminatorWeights', type=str, default='', help="path to discriminator weights (to continue training)")
36 | parser.add_argument('--out', type=str, default='checkpoints', help='folder to output model checkpoints')
37 |
38 | opt = parser.parse_args()
39 | print(opt)
40 |
41 | try:
42 | os.makedirs(opt.out)
43 | except OSError:
44 | pass
45 |
46 | if torch.cuda.is_available() and not opt.cuda:
47 | print("WARNING: You have a CUDA device, so you should probably run with --cuda")
48 |
49 | transform = transforms.Compose([transforms.RandomCrop(opt.imageSize*opt.upSampling),
50 | transforms.ToTensor()])
51 |
52 | normalize = transforms.Normalize(mean = [0.485, 0.456, 0.406],
53 | std = [0.229, 0.224, 0.225])
54 |
55 | scale = transforms.Compose([transforms.ToPILImage(),
56 | transforms.Scale(opt.imageSize),
57 | transforms.ToTensor(),
58 | transforms.Normalize(mean = [0.485, 0.456, 0.406],
59 | std = [0.229, 0.224, 0.225])
60 | ])
61 |
62 | if opt.dataset == 'folder':
63 | # folder dataset
64 | dataset = datasets.ImageFolder(root=opt.dataroot, transform=transform)
65 | elif opt.dataset == 'cifar10':
66 | dataset = datasets.CIFAR10(root=opt.dataroot, train=True, download=True, transform=transform)
67 | elif opt.dataset == 'cifar100':
68 | dataset = datasets.CIFAR100(root=opt.dataroot, train=True, download=True, transform=transform)
69 | assert dataset
70 |
71 | dataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batchSize,
72 | shuffle=True, num_workers=int(opt.workers))
73 |
74 | generator = Generator(16, opt.upSampling)
75 | if opt.generatorWeights != '':
76 | generator.load_state_dict(torch.load(opt.generatorWeights))
77 | print generator
78 |
79 | discriminator = Discriminator()
80 | if opt.discriminatorWeights != '':
81 | discriminator.load_state_dict(torch.load(opt.discriminatorWeights))
82 | print discriminator
83 |
84 | # For the content loss
85 | feature_extractor = FeatureExtractor(torchvision.models.vgg19(pretrained=True))
86 | print feature_extractor
87 | content_criterion = nn.MSELoss()
88 | adversarial_criterion = nn.BCELoss()
89 |
90 | ones_const = Variable(torch.ones(opt.batchSize, 1))
91 |
92 | # if gpu is to be used
93 | if opt.cuda:
94 | generator.cuda()
95 | discriminator.cuda()
96 | feature_extractor.cuda()
97 | content_criterion.cuda()
98 | adversarial_criterion.cuda()
99 | ones_const = ones_const.cuda()
100 |
101 | optim_generator = optim.Adam(generator.parameters(), lr=opt.generatorLR)
102 | optim_discriminator = optim.Adam(discriminator.parameters(), lr=opt.discriminatorLR)
103 |
104 | configure('logs/' + opt.dataset + '-' + str(opt.batchSize) + '-' + str(opt.generatorLR) + '-' + str(opt.discriminatorLR), flush_secs=5)
105 | visualizer = Visualizer(image_size=opt.imageSize*opt.upSampling)
106 |
107 | low_res = torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize)
108 |
109 | # Pre-train generator using raw MSE loss
110 | print 'Generator pre-training'
111 | for epoch in range(2):
112 | mean_generator_content_loss = 0.0
113 |
114 | for i, data in enumerate(dataloader):
115 | # Generate data
116 | high_res_real, _ = data
117 |
118 | # Downsample images to low resolution
119 | for j in range(opt.batchSize):
120 | low_res[j] = scale(high_res_real[j])
121 | high_res_real[j] = normalize(high_res_real[j])
122 |
123 | # Generate real and fake inputs
124 | if opt.cuda:
125 | high_res_real = Variable(high_res_real.cuda())
126 | high_res_fake = generator(Variable(low_res).cuda())
127 | else:
128 | high_res_real = Variable(high_res_real)
129 | high_res_fake = generator(Variable(low_res))
130 |
131 | ######### Train generator #########
132 | generator.zero_grad()
133 |
134 | generator_content_loss = content_criterion(high_res_fake, high_res_real)
135 | mean_generator_content_loss += generator_content_loss.data[0]
136 |
137 | generator_content_loss.backward()
138 | optim_generator.step()
139 |
140 | ######### Status and display #########
141 | sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f' % (epoch, 2, i, len(dataloader), generator_content_loss.data[0]))
142 | visualizer.show(low_res, high_res_real.cpu().data, high_res_fake.cpu().data)
143 |
144 | sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f\n' % (epoch, 2, i, len(dataloader), mean_generator_content_loss/len(dataloader)))
145 | log_value('generator_mse_loss', mean_generator_content_loss/len(dataloader), epoch)
146 |
147 | # Do checkpointing
148 | torch.save(generator.state_dict(), '%s/generator_pretrain.pth' % opt.out)
149 |
150 | # SRGAN training
151 | optim_generator = optim.Adam(generator.parameters(), lr=opt.generatorLR*0.1)
152 | optim_discriminator = optim.Adam(discriminator.parameters(), lr=opt.discriminatorLR*0.1)
153 |
154 | print 'SRGAN training'
155 | for epoch in range(opt.nEpochs):
156 | mean_generator_content_loss = 0.0
157 | mean_generator_adversarial_loss = 0.0
158 | mean_generator_total_loss = 0.0
159 | mean_discriminator_loss = 0.0
160 |
161 | for i, data in enumerate(dataloader):
162 | # Generate data
163 | high_res_real, _ = data
164 |
165 | # Downsample images to low resolution
166 | for j in range(opt.batchSize):
167 | low_res[j] = scale(high_res_real[j])
168 | high_res_real[j] = normalize(high_res_real[j])
169 |
170 | # Generate real and fake inputs
171 | if opt.cuda:
172 | high_res_real = Variable(high_res_real.cuda())
173 | high_res_fake = generator(Variable(low_res).cuda())
174 | target_real = Variable(torch.rand(opt.batchSize,1)*0.5 + 0.7).cuda()
175 | target_fake = Variable(torch.rand(opt.batchSize,1)*0.3).cuda()
176 | else:
177 | high_res_real = Variable(high_res_real)
178 | high_res_fake = generator(Variable(low_res))
179 | target_real = Variable(torch.rand(opt.batchSize,1)*0.5 + 0.7)
180 | target_fake = Variable(torch.rand(opt.batchSize,1)*0.3)
181 |
182 | ######### Train discriminator #########
183 | discriminator.zero_grad()
184 |
185 | discriminator_loss = adversarial_criterion(discriminator(high_res_real), target_real) + \
186 | adversarial_criterion(discriminator(Variable(high_res_fake.data)), target_fake)
187 | mean_discriminator_loss += discriminator_loss.data[0]
188 |
189 | discriminator_loss.backward()
190 | optim_discriminator.step()
191 |
192 | ######### Train generator #########
193 | generator.zero_grad()
194 |
195 | real_features = Variable(feature_extractor(high_res_real).data)
196 | fake_features = feature_extractor(high_res_fake)
197 |
198 | generator_content_loss = content_criterion(high_res_fake, high_res_real) + 0.006*content_criterion(fake_features, real_features)
199 | mean_generator_content_loss += generator_content_loss.data[0]
200 | generator_adversarial_loss = adversarial_criterion(discriminator(high_res_fake), ones_const)
201 | mean_generator_adversarial_loss += generator_adversarial_loss.data[0]
202 |
203 | generator_total_loss = generator_content_loss + 1e-3*generator_adversarial_loss
204 | mean_generator_total_loss += generator_total_loss.data[0]
205 |
206 | generator_total_loss.backward()
207 | optim_generator.step()
208 |
209 | ######### Status and display #########
210 | sys.stdout.write('\r[%d/%d][%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f' % (epoch, opt.nEpochs, i, len(dataloader),
211 | discriminator_loss.data[0], generator_content_loss.data[0], generator_adversarial_loss.data[0], generator_total_loss.data[0]))
212 | visualizer.show(low_res, high_res_real.cpu().data, high_res_fake.cpu().data)
213 |
214 | sys.stdout.write('\r[%d/%d][%d/%d] Discriminator_Loss: %.4f Generator_Loss (Content/Advers/Total): %.4f/%.4f/%.4f\n' % (epoch, opt.nEpochs, i, len(dataloader),
215 | mean_discriminator_loss/len(dataloader), mean_generator_content_loss/len(dataloader),
216 | mean_generator_adversarial_loss/len(dataloader), mean_generator_total_loss/len(dataloader)))
217 |
218 | log_value('generator_content_loss', mean_generator_content_loss/len(dataloader), epoch)
219 | log_value('generator_adversarial_loss', mean_generator_adversarial_loss/len(dataloader), epoch)
220 | log_value('generator_total_loss', mean_generator_total_loss/len(dataloader), epoch)
221 | log_value('discriminator_loss', mean_discriminator_loss/len(dataloader), epoch)
222 |
223 | # Do checkpointing
224 | torch.save(generator.state_dict(), '%s/generator_final.pth' % opt.out)
225 | torch.save(discriminator.state_dict(), '%s/discriminator_final.pth' % opt.out)
226 |
227 | # Avoid closing
228 | while True:
229 | pass
230 |
--------------------------------------------------------------------------------
/utils.py:
--------------------------------------------------------------------------------
1 | # -*- coding: utf-8 -*-
2 | """Implements some utils
3 |
4 | TODO:
5 | """
6 |
7 | import random
8 |
9 | from torchvision import transforms
10 | import matplotlib.pyplot as plt
11 |
12 | class Visualizer:
13 | def __init__(self, show_step=10, image_size=30):
14 | self.transform = transforms.Compose([transforms.Normalize(mean = [-2.118, -2.036, -1.804], # Equivalent to un-normalizing ImageNet (for correct visualization)
15 | std = [4.367, 4.464, 4.444]),
16 | transforms.ToPILImage(),
17 | transforms.Scale(image_size)])
18 |
19 | self.show_step = show_step
20 | self.step = 0
21 |
22 | self.figure, (self.lr_plot, self.hr_plot, self.fake_plot) = plt.subplots(1,3)
23 | self.figure.show()
24 |
25 | self.lr_image_ph = None
26 | self.hr_image_ph = None
27 | self.fake_hr_image_ph = None
28 |
29 | def show(self, inputsG, inputsD_real, inputsD_fake):
30 |
31 | self.step += 1
32 | if self.step == self.show_step:
33 | self.step = 0
34 |
35 | i = random.randint(0, inputsG.size(0) -1)
36 |
37 | lr_image = self.transform(inputsG[i])
38 | hr_image = self.transform(inputsD_real[i])
39 | fake_hr_image = self.transform(inputsD_fake[i])
40 |
41 | if self.lr_image_ph is None:
42 | self.lr_image_ph = self.lr_plot.imshow(lr_image)
43 | self.hr_image_ph = self.hr_plot.imshow(hr_image)
44 | self.fake_hr_image_ph = self.fake_plot.imshow(fake_hr_image)
45 | else:
46 | self.lr_image_ph.set_data(lr_image)
47 | self.hr_image_ph.set_data(hr_image)
48 | self.fake_hr_image_ph.set_data(fake_hr_image)
49 |
50 | self.figure.canvas.draw()
51 |
--------------------------------------------------------------------------------