import torch from torch import nn, optim # this architecture...

Prompt

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() Send back the complete code with all the fixes. Fix each of the listed errors one by one, making sure to actually correct them so that there are 0 errors remaining. Keep the original imports, since the files exist. Write out every single character; do not abbreviate anything. Fix every error. There must be exactly one file. Do not write anything else; just output the complete code, and it must not contain any comments. Never, under any circumstances, use simplified, substitute, dummy, simulated, or fake code. Write the entire file as complete, unabridged, production-ready code in a single code block. It must be 100% error-free, a complete, error-free file, and must be submitted as a downloadable file. These requirements are mandatory and must be strictly adhered to. If no list of errors is provided, you must find all the errors and fix them. If there were comments in the original code, delete them. And most importantly: YOU MUST NEVER SIMPLIFY! 1. [L8, Unet.__init__.num_filters] — Logic: `num_filters` is never used; changing it has no effect on the hard-coded layer widths. 2. [L14–42; L150, MainModel.backward_D] — Input-shape ambiguity: The generator preserves spatial dimensions only when they are divisible by 32; an otherwise valid 65×64 input produces a 64×64 output, causing the concatenation at L150 to raise a size-mismatch `RuntimeError`. 3. [L27, Unet.model bottleneck BatchNorm2d] — Runtime: A single-image 32×32 training batch reaches this layer with shape `[1, 512, 1, 1]`, causing `ValueError` because training-mode BatchNorm requires more than one value per channel. 4. [L74–75, GANLoss label buffers] — Type: Integer arguments such as `real_label=1` and `fake_label=0` create integer target buffers, causing `BCEWithLogitsLoss` to fail with floating-point predictions. 5. [L76–79; L90, GANLoss] — Runtime: Any `gan_mode` other than `'vanilla'` or `'lsgan'` leaves `self.loss` undefined; invoking the constructed loss then raises `AttributeError`. 6. [L88, GANLoss.__call__] — Module interface: Implementing the loss by overriding `__call__` bypasses `nn.Module` dispatch and its hooks; no `forward` implementation exists, so the inherited `forward` raises `NotImplementedError`. 7. [L98–103; L112, init_weights] — Logic: An unsupported `init` value leaves convolution weights unchanged while the function still prints that the requested initialization succeeded. 8. [L107–109, init_func BatchNorm2d branch] — Runtime: For `nn.BatchNorm2d(..., affine=False)`, `weight` and `bias` are `None`; the unconditional `m.weight.data` access raises `AttributeError`. 9. [L125; L143–144, MainModel.device] — Device tracking: Standard module device moves do not update `self.device`; for example, moving a CUDA-constructed model to CPU leaves `setup_input` sending inputs to CUDA, producing an input/weight device mismatch. 10. [L170; L178, MainModel.optimize] — Training logic: The generator forward pass occurs before `self.net_G.train()`; if the generator was previously in evaluation mode, that training batch uses evaluation-mode BatchNorm behavior and does not update its running statistics. 1. [~L4–L7, header comments / `class Unet`] — Naming/ambiguity: the class is named `Unet` and the L4 comment says the architecture is taken from the U-Net colorization tutorial, but the body is a plain encoder–decoder with no skip connections (the L6 comment itself admits it is a DCGAN-style generator); name, provenance comment, and implementation contradict each other. 2. [~L8, `Unet.__init__(..., num_filters=128)`] — Dead parameter/logic: `num_filters` is never referenced; every channel width is hard-coded (64/128/256/512), so the `num_filters=64` passed at L129 is silently ignored and the default 128 has no effect. 3. [~L11, `nn.Conv2d(input_c,64,kernel_size=4,stride = 1,padding="same")`] — Ambiguity/runtime: even kernel size 4 with `padding="same"` has no symmetric padding; PyTorch pads asymmetrically (1 top/left, 2 bottom/right) and emits a UserWarning on first forward; string padding exists only in torch ≥ 1.9 and raises TypeError on older versions. 4. [~L14–L42, the five stride-2 `Conv2d` + five stride-2 `ConvTranspose2d` in `Unet`] — Runtime/logic: output H×W equals input H×W only when both are multiples of 32 (down-path applies floor(H/2) five times, up-path exactly doubles five times); for any other size `fake_color` mismatches `self.L`/`self.ab` and `torch.cat` at L150/L162 raises RuntimeError; the constraint is neither checked nor stated. 5. [~L76–L79, `GANLoss.__init__` if/elif] — Logic/runtime: no `else` branch; any `gan_mode` other than `'vanilla'`/`'lsgan'` leaves `self.loss` unassigned, so the first call at L90 fails with AttributeError. 6. [~L88, `GANLoss.__call__`] — Contract error: `nn.Module.__call__` is overridden and no `forward` is defined; forward/pre-forward hooks are bypassed and `GANcriterion.forward(...)` raises NotImplementedError. 7. [~L98–L103 with L112, `init_weights.init_func` / print] — Logic: an `init` value outside `'norm'|'xavier'|'kaiming'` falls through all branches (conv weights left at default init, only biases zeroed) while L112 still prints "model initialized with {init} initialization". 8. [~L146–L147, `MainModel.forward`] — Runtime: `self.L` (and `self.ab` used at L153/L165) are never initialized in `__init__`; calling `forward()`/`optimize()` before `setup_input()` raises AttributeError. 9. [~L157–L159 with L175, `backward_D` parity gate] — Logic: the `epoch % 2 == 0` condition is duplicated in `backward_D` and `optimize`; on odd epochs `backward_D` still runs both discriminator forward passes in train mode (BatchNorm running statistics updated, autograd graph built) and the resulting `loss_D` is discarded. 10. [~L170 vs L178, `optimize`] — Logic/order: `self.forward()` (the generator pass producing `fake_color`) executes before `self.net_G.train()`; if the generator is in eval mode when `optimize` is entered, `fake_color` and its BatchNorm behavior come from eval mode and the later `train()` call does not affect the already-computed forward used by `backward_G`. 11. [~L184–L190, trailing commented block] — Leftover dead/debug code: an inference/loading snippet containing a debugger breakpoint is left commented out in the module (stub content; inert unless re-enabled). 12. [~L186, `set_trace()`] — Broken reference: `set_trace` is never imported (no `pdb`/`IPython` import); NameError if the block is re-enabled. 13. [~L187, `map_location=device`] — Broken reference: `device` is not defined at module scope (only `self.device` exists inside `MainModel`). 14. [~L188, `torch.load("modelbatchv2.pth", ...).state_dict()`] — Runtime: assumes the checkpoint is a pickled full `MainModel` object; on torch ≥ 2.6 (`weights_only=True` default) `torch.load` raises UnpicklingError for such a file, and if the file holds a plain state_dict, `.state_dict()` raises AttributeError on a `dict`. 15. [~L189, `assert model.device.type == "cpu"`] — Logic: `MainModel.__init__` (L125) hard-codes `device` to CUDA whenever available with no override parameter, so this assertion fails on any GPU host. END OF ERROR LIST.