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| import torch | |
| from torch import nn, optim | |
| # this architecture is taken from https://github.com/moein-shariatnia/Deep-Learning/tree/main/Image%20Colorization%20Tutorial | |
| #this is actually the DCGans. in training, we had kept the class name the same as the original to avoid changing code^ | |
| class Unet(nn.Module): | |
| def __init__(self, input_c=1, output_c=2, num_filters=128): | |
| super().__init__() | |
| self.model = nn.Sequential( | |
| nn.Conv2d(input_c,64,kernel_size=4,stride = 1,padding="same"), | |
| nn.BatchNorm2d(64), | |
| nn.LeakyReLU(0.2, True), | |
| nn.Conv2d(64,128,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(128), | |
| nn.LeakyReLU(0.2, True), | |
| nn.Conv2d(128,256,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(256), | |
| nn.LeakyReLU(0.2, True), | |
| nn.Conv2d(256,256,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(256), | |
| nn.LeakyReLU(0.2, True), | |
| nn.Conv2d(256,512,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(512), | |
| nn.LeakyReLU(0.2, True), | |
| nn.Conv2d(512,512,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(512), | |
| nn.LeakyReLU(0.2, True), | |
| nn.ConvTranspose2d(512,512,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(512), | |
| nn.ReLU(True), | |
| nn.ConvTranspose2d(512,256,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(256), | |
| nn.ReLU(True), | |
| nn.ConvTranspose2d(256,256,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(256), | |
| nn.ReLU(True), | |
| nn.ConvTranspose2d(256,128,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(128), | |
| nn.ReLU(True), | |
| nn.ConvTranspose2d(128,64,kernel_size=4,stride=2,padding=1), | |
| nn.BatchNorm2d(64), | |
| nn.ReLU(True), | |
| nn.Conv2d(64,output_c, kernel_size=1,stride=1), | |
| nn.Tanh() | |
| ) | |
| def forward(self, x): | |
| return self.model(x) | |
| class PatchDiscriminator(nn.Module): | |
| def __init__(self, input_c, num_filters=64, n_down=3): # num_filters=64 | |
| super().__init__() | |
| model = [self.get_layers(input_c, num_filters, norm=False)] | |
| model += [self.get_layers(num_filters * 2 ** i, num_filters * 2 ** (i + 1), s=1 if i == (n_down-1) else 2) | |
| for i in range(n_down)] # the 'if' statement is taking care of not using | |
| # stride of 2 for the last block in this loop | |
| model += [self.get_layers(num_filters * 2 ** n_down, 1, s=1, norm=False, act=False)] # Make sure to not use normalization or | |
| # activation for the last layer of the model | |
| self.model = nn.Sequential(*model) | |
| def get_layers(self, ni, nf, k=4, s=2, p=1, norm=True, act=True): # when needing to make some repeatitive blocks of layers, | |
| layers = [nn.Conv2d(ni, nf, k, s, p, bias=not norm)] # it's always helpful to make a separate method for that purpose | |
| if norm: layers += [nn.BatchNorm2d(nf)] | |
| if act: layers += [nn.LeakyReLU(0.2, True)] #nn.LeakyReLU(0.2, True) | |
| return nn.Sequential(*layers) | |
| def forward(self, x): | |
| return self.model(x) | |
| class GANLoss(nn.Module): | |
| def __init__(self, gan_mode='vanilla', real_label=1.0, fake_label=0.0): | |
| super().__init__() | |
| self.register_buffer('real_label', torch.tensor(real_label)) | |
| self.register_buffer('fake_label', torch.tensor(fake_label)) | |
| if gan_mode == 'vanilla': | |
| self.loss = nn.BCEWithLogitsLoss() | |
| elif gan_mode == 'lsgan': | |
| self.loss = nn.MSELoss() | |
| def get_labels(self, preds, target_is_real): | |
| if target_is_real: | |
| labels = self.real_label | |
| else: | |
| labels = self.fake_label | |
| return labels.expand_as(preds) | |
| def __call__(self, preds, target_is_real): | |
| labels = self.get_labels(preds, target_is_real) | |
| loss = self.loss(preds, labels) | |
| return loss | |
| def init_weights(net, init='norm', gain=0.02): | |
| def init_func(m): | |
| classname = m.__class__.__name__ | |
| if hasattr(m, 'weight') and 'Conv' in classname: | |
| if init == 'norm': | |
| nn.init.normal_(m.weight.data, mean=0.0, std=gain) | |
| elif init == 'xavier': | |
| nn.init.xavier_normal_(m.weight.data, gain=gain) | |
| elif init == 'kaiming': | |
| nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') | |
| if hasattr(m, 'bias') and m.bias is not None: | |
| nn.init.constant_(m.bias.data, 0.0) | |
| elif 'BatchNorm2d' in classname: | |
| nn.init.normal_(m.weight.data, 1., gain) | |
| nn.init.constant_(m.bias.data, 0.) | |
| net.apply(init_func) | |
| print(f"model initialized with {init} initialization") | |
| return net | |
| def init_model(model, device): | |
| model = model.to(device) | |
| model = init_weights(model) | |
| return model | |
| class MainModel(nn.Module): | |
| def __init__(self, net_G=None, lr_G=2e-4, lr_D=2e-4, | |
| beta1=0.5, beta2=0.999, lambda_L1=100.): | |
| super().__init__() | |
| self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| self.lambda_L1 = lambda_L1 | |
| if net_G is None: | |
| self.net_G = init_model(Unet(input_c=1, output_c=2, num_filters=64), self.device) | |
| else: | |
| self.net_G = net_G.to(self.device) | |
| self.net_D = init_model(PatchDiscriminator(input_c=3, n_down=3, num_filters=64), self.device) | |
| self.GANcriterion = GANLoss(gan_mode='vanilla').to(self.device) | |
| self.L1criterion = nn.L1Loss() | |
| self.opt_G = optim.Adam(self.net_G.parameters(), lr=lr_G, betas=(beta1, beta2)) | |
| self.opt_D = optim.Adam(self.net_D.parameters(), lr=lr_D, betas=(beta1, beta2)) | |
| def set_requires_grad(self, model, requires_grad=True): | |
| for p in model.parameters(): | |
| p.requires_grad = requires_grad | |
| def setup_input(self, data): | |
| self.L = data['L'].to(self.device) | |
| self.ab = data['ab'].to(self.device) | |
| def forward(self): | |
| self.fake_color = self.net_G(self.L) | |
| def backward_D(self,epoch): | |
| fake_image = torch.cat([self.L, self.fake_color], dim=1) | |
| fake_preds = self.net_D(fake_image.detach()) | |
| self.loss_D_fake = self.GANcriterion(fake_preds, False) | |
| real_image = torch.cat([self.L, self.ab], dim=1) | |
| real_preds = self.net_D(real_image) | |
| self.loss_D_real = self.GANcriterion(real_preds, True) | |
| self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5 | |
| # offset discriminator training | |
| if epoch % 2 ==0: | |
| self.loss_D.backward() | |
| def backward_G(self): | |
| fake_image = torch.cat([self.L, self.fake_color], dim=1) | |
| fake_preds = self.net_D(fake_image) | |
| self.loss_G_GAN = self.GANcriterion(fake_preds, True) | |
| self.loss_G_L1 = self.L1criterion(self.fake_color, self.ab) * self.lambda_L1 | |
| self.loss_G = self.loss_G_GAN + self.loss_G_L1 | |
| self.loss_G.backward() | |
| def optimize(self, epoch): | |
| self.forward() | |
| self.net_D.train() | |
| self.set_requires_grad(self.net_D, True) | |
| self.opt_D.zero_grad() | |
| self.backward_D(epoch) | |
| if epoch % 2 ==0: | |
| self.opt_D.step() | |
| self.net_G.train() | |
| self.set_requires_grad(self.net_D, False) | |
| self.opt_G.zero_grad() | |
| self.backward_G() | |
| self.opt_G.step() | |
| # with torch.no_grad(): | |
| # model = MainModel() | |
| # set_trace() | |
| # # model = torch.load("modelbatchv2.pth", map_location=device) | |
| # model.load_state_dict(torch.load("modelbatchv2.pth", map_location=torch.device('cpu')).state_dict()) | |
| # assert model.device.type == "cpu" | |
| # model.eval() | |