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()