{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.chdir('../')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'c:\\\\mlops project\\\\image-colorization-mlops'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%pwd" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from dataclasses import dataclass\n", "from pathlib import Path\n", "\n", "@dataclass(frozen=True)\n", "class DataTransformationConfig:\n", " root_dir : Path\n", " data_path_black : Path\n", " data_path_grey : Path\n", " BATCH_SIZE : int\n", " IMAGE_SIZE : list\n", " DATA_RANGE: int\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from src.imagecolorization.constants import *\n", "from src.imagecolorization.utils.common import read_yaml, create_directories\n", "\n", "class ConfigurationManager:\n", " def __init__(\n", " self,\n", " config_filepath = CONFIG_FILE_PATH,\n", " params_filepath = PARAMS_FILE_PATH):\n", "\n", " self.config = read_yaml(config_filepath)\n", " self.params = read_yaml(params_filepath)\n", "\n", " create_directories([self.config.artifacts_root])\n", " \n", " \n", " def get_data_transformation_config(self) -> DataTransformationConfig:\n", " config = self.config.data_transformation\n", " params = self.params\n", " \n", " data_transformation_cofig = DataTransformationConfig(\n", " root_dir= config.root_dir,\n", " data_path_black=config.data_path_black,\n", " data_path_grey=config.data_path_grey,\n", " BATCH_SIZE=params.BATCH_SIZE,\n", " IMAGE_SIZE=params.IMAGE_SIZE,\n", " DATA_RANGE=params.DATA_RANGE\n", " )\n", " \n", " return data_transformation_cofig" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import torch\n", "from torch.utils.data import Dataset\n", "from torchvision import transforms\n", "\n", " \n", " \n", " \n", "class ImageColorizationDataset:\n", " def __init__(self, dataset, image_size, transform = None):\n", " self.dataset = dataset\n", " self.transform = transform\n", " self.image_size = tuple(image_size)\n", " \n", " def __len__(self):\n", " return len(self.dataset[0])\n", " \n", " def __getitem__(self, idx):\n", " L = np.array(self.dataset[0][idx]).reshape(self.image_size)\n", " L = transforms.ToTensor()(L)\n", " \n", " ab = np.array(self.dataset[1][idx])\n", " ab = transforms.ToTensor()(ab)\n", " \n", " return ab , L" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from torch.utils.data import DataLoader\n", "import gc\n", "import os\n", "import numpy as np\n", "import torch\n", "from src.imagecolorization.logging import logger\n", "\n", "class DataTransformation:\n", " def __init__(self, config: DataTransformationConfig):\n", " self.config = config\n", " \n", " def load_data(self):\n", " ab_df = np.load(self.config.data_path_black)[:self.config.DATA_RANGE]\n", " l_df = np.load(self.config.data_path_grey)[:self.config.DATA_RANGE]\n", " dataset = (l_df, ab_df)\n", " gc.collect()\n", " return dataset\n", " \n", " def get_datasets(self, dataset):\n", " train_dataset = ImageColorizationDataset(\n", " dataset=dataset,\n", " image_size=self.config.IMAGE_SIZE\n", " )\n", " test_dataset = ImageColorizationDataset(\n", " dataset=dataset,\n", " image_size=self.config.IMAGE_SIZE\n", " )\n", " \n", " return train_dataset, test_dataset\n", " \n", " \n", " \n", " def save_datasets(self, train_dataset, test_dataset):\n", " # Ensure the directory exists\n", " os.makedirs(self.config.root_dir, exist_ok=True)\n", "\n", " train_dataset_path = os.path.join(self.config.root_dir, 'train_dataset.pt')\n", " test_dataset_path = os.path.join(self.config.root_dir, 'test_dataset.pt')\n", "\n", " try:\n", " # Save the datasets\n", " torch.save(train_dataset, train_dataset_path)\n", " torch.save(test_dataset, test_dataset_path)\n", "\n", " logger.info(f\"Train dataset saved at: {train_dataset_path}\")\n", " logger.info(f\"Test dataset saved at: {test_dataset_path}\")\n", " except Exception as e:\n", " logger.error(f\"Error saving datasets: {str(e)}\")\n", " raise e" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2024-08-24 23:35:18,235: INFO: common: yaml file: config\\config.yaml loaded successfully]\n", "[2024-08-24 23:35:18,243: INFO: common: yaml file: params.yaml loaded successfully]\n", "[2024-08-24 23:35:18,245: INFO: common: created directory at: artifacts]\n", "[2024-08-24 23:35:34,541: INFO: 2411080742: Train dataset saved at: artifacts/data_transformation\\train_dataset.pt]\n", "[2024-08-24 23:35:34,552: INFO: 2411080742: Test dataset saved at: artifacts/data_transformation\\test_dataset.pt]\n", "Train dataset type: \n", "Train dataset length: 5000\n", "First item in train dataset: (tensor([[[0.5059, 0.4941, 0.4941, ..., 0.4902, 0.4863, 0.4863],\n", " [0.4980, 0.4941, 0.4941, ..., 0.4980, 0.4902, 0.4824],\n", " [0.4980, 0.4980, 0.4980, ..., 0.4902, 0.4941, 0.5020],\n", " ...,\n", " [0.4941, 0.4941, 0.4980, ..., 0.4941, 0.4941, 0.4941],\n", " [0.4941, 0.4980, 0.4941, ..., 0.4941, 0.4941, 0.4941],\n", " [0.4980, 0.4980, 0.5020, ..., 0.4941, 0.4863, 0.4941]],\n", "\n", " [[0.5333, 0.5255, 0.5294, ..., 0.5176, 0.5255, 0.5294],\n", " [0.5373, 0.5333, 0.5216, ..., 0.5137, 0.5216, 0.5373],\n", " [0.5412, 0.5255, 0.5255, ..., 0.5137, 0.5137, 0.5216],\n", " ...,\n", " [0.5137, 0.5137, 0.5137, ..., 0.5098, 0.5098, 0.5098],\n", " [0.5137, 0.5137, 0.5176, ..., 0.5098, 0.5098, 0.5098],\n", " [0.5137, 0.5216, 0.5294, ..., 0.5098, 0.5098, 0.5098]]]), tensor([[[0.9294, 0.5294, 0.2941, ..., 0.1373, 0.1451, 0.2471],\n", " [0.9176, 0.5961, 0.2824, ..., 0.1608, 0.1922, 0.1843],\n", " [0.8431, 0.8471, 0.4078, ..., 0.2863, 0.1882, 0.3216],\n", " ...,\n", " [0.1569, 0.1765, 0.1490, ..., 0.0431, 0.0314, 0.0314],\n", " [0.1569, 0.2196, 0.1843, ..., 0.0314, 0.0275, 0.0392],\n", " [0.1647, 0.2353, 0.3098, ..., 0.0471, 0.0510, 0.0588]]]))\n" ] } ], "source": [ "try:\n", " config = ConfigurationManager()\n", " data_transformation_config = config.get_data_transformation_config()\n", " data_transformation = DataTransformation(config=data_transformation_config)\n", " \n", " # Load the dataset\n", " dataset = data_transformation.load_data()\n", " \n", " # Get the datasets using the loaded dataset\n", " train_dataset, test_dataset = data_transformation.get_datasets(dataset)\n", " \n", " # Perform any further operations (e.g., saving the dataset)\n", " data_transformation.save_datasets(train_dataset, test_dataset)\n", " \n", " # Print information about the train dataset\n", " print(f\"Train dataset type: {type(train_dataset)}\")\n", " print(f\"Train dataset length: {len(train_dataset)}\")\n", " print(f\"First item in train dataset: {train_dataset[0]}\")\n", " \n", "except Exception as e:\n", " raise e" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\azizu\\AppData\\Local\\Temp\\ipykernel_28412\\4121028732.py:4: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " test_dataset = torch.load('artifacts/data_transformation/test_dataset.pt')\n", "C:\\Users\\azizu\\AppData\\Local\\Temp\\ipykernel_28412\\4121028732.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", " train_dataset = torch.load('artifacts/data_transformation/train_dataset.pt')\n" ] } ], "source": [ "from torch.utils.data import Dataset, DataLoader\n", "\n", "\n", "test_dataset = torch.load('artifacts/data_transformation/test_dataset.pt')\n", "train_dataset = torch.load('artifacts/data_transformation/train_dataset.pt')\n", "\n", "\n", "train_loader1 = DataLoader(\n", " train_dataset,\n", " shuffle=True,\n", " batch_size=1\n", ")\n", "test_loader1 = DataLoader(\n", " test_dataset,\n", " shuffle=True,\n", " batch_size=1\n", ")\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train loader batch - ab shape: torch.Size([1, 2, 224, 224]), L shape: torch.Size([1, 1, 224, 224])\n", "Test loader batch - ab shape: torch.Size([1, 2, 224, 224]), L shape: torch.Size([1, 1, 224, 224])\n" ] } ], "source": [ "# Print shapes for train dataloader\n", "for batch in train_loader1:\n", " ab, L = batch\n", " print(f\"Train loader batch - ab shape: {ab.shape}, L shape: {L.shape}\")\n", " break # We only need to check one batch\n", "\n", "# Print shapes for test dataloader\n", "for batch in test_loader1:\n", " ab, L = batch\n", " print(f\"Test loader batch - ab shape: {ab.shape}, L shape: {L.shape}\")\n", " break # We only need to check one batch" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from skimage.color import rgb2lab, lab2rgb\n", "import gc\n", "import matplotlib.pylab as plt\n", "\n", "def lab_to_rgb(L, ab):\n", " L = L * 100\n", " ab = (ab - 0.5) * 128 * 2\n", " Lab = torch.cat([L, ab], dim = 2).numpy()\n", " rgb_img = []\n", " for img in Lab:\n", " img_rgb = lab2rgb(img)\n", " rgb_img.append(img_rgb)\n", " \n", " return np.stack(rgb_img, axis = 0)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def display_progress(cond, real, fake, current_epoch = 0, figsize=(20,15)):\n", " \"\"\"\n", " Save cond, real (original) and generated (fake)\n", " images in one panel \n", " \"\"\"\n", " cond = cond.detach().cpu().permute(1, 2, 0) \n", " real = real.detach().cpu().permute(1, 2, 0)\n", " fake = fake.detach().cpu().permute(1, 2, 0)\n", " \n", " images = [cond, real, fake]\n", " titles = ['input','real','generated']\n", " print(f'Epoch: {current_epoch}')\n", " fig, ax = plt.subplots(1, 3, figsize=figsize)\n", " for idx,img in enumerate(images):\n", " if idx == 0:\n", " ab = torch.zeros((224,224,2))\n", " img = torch.cat([images[0]* 100, ab], dim=2).numpy()\n", " imgan = lab2rgb(img)\n", " else:\n", " imgan = lab_to_rgb(images[0],img)\n", " ax[idx].imshow(imgan)\n", " ax[idx].axis(\"off\")\n", " for idx, title in enumerate(titles): \n", " ax[idx].set_title('{}'.format(title))\n", " plt.show()\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import pytorch_lightning as pl\n", "from src.imagecolorization.conponents.model_trainer import Generator,Critic\n", "from torch import nn, optim\n", "\n", "class CWGAN(pl.LightningModule):\n", " def __init__(self, in_channels, out_channels, learning_rate=0.0002, lambda_recon=100, display_step=10, lambda_gp=10, lambda_r1=10):\n", " super().__init__()\n", " self.save_hyperparameters()\n", " self.display_step = display_step\n", " self.generator = Generator(in_channels, out_channels)\n", " self.critic = Critic(in_channels + out_channels) # Ensure Critic is initialized with the correct input channels\n", " self.lambda_recon = lambda_recon\n", " self.lambda_gp = lambda_gp\n", " self.lambda_r1 = lambda_r1\n", " self.recon_criterion = nn.L1Loss()\n", " self.generator_losses, self.critic_losses = [], []\n", " self.automatic_optimization = False\n", " \n", " def configure_optimizers(self):\n", " optimizer_G = optim.Adam(self.generator.parameters(), lr=self.hparams.learning_rate, betas=(0.5, 0.9))\n", " optimizer_C = optim.Adam(self.critic.parameters(), lr=self.hparams.learning_rate, betas=(0.5, 0.9))\n", " return [optimizer_C, optimizer_G]\n", " \n", " def generator_step(self, real_images, conditioned_images, optimizer_G):\n", " # WGAN has only a reconstruction loss\n", " optimizer_G.zero_grad()\n", " fake_images = self.generator(conditioned_images)\n", " recon_loss = self.recon_criterion(fake_images, real_images)\n", " recon_loss.backward()\n", " optimizer_G.step()\n", " self.generator_losses.append(recon_loss.item())\n", " \n", " def critic_step(self, real_images, conditioned_images, optimizer_C):\n", " optimizer_C.zero_grad()\n", " fake_images = self.generator(conditioned_images)\n", " fake_input = torch.cat((fake_images, conditioned_images), 1)\n", " real_input = torch.cat((real_images, conditioned_images), 1)\n", " fake_logits = self.critic(fake_input)\n", " real_logits = self.critic(real_input)\n", "\n", " # Compute the loss for the critic\n", " loss_C = real_logits.mean() - fake_logits.mean()\n", "\n", " # Compute the gradient penalty\n", " alpha = torch.rand(real_images.size(0), 1, 1, 1, requires_grad=True).to(real_images.device)\n", " interpolated = (alpha * real_images + (1 - alpha) * fake_images.detach()).requires_grad_(True)\n", " interpolated_logits = self.critic(interpolated, conditioned_images)\n", " \n", " gradients = torch.autograd.grad(outputs=interpolated_logits, inputs=interpolated,\n", " grad_outputs=torch.ones_like(interpolated_logits), create_graph=True, retain_graph=True)[0]\n", " gradients = gradients.view(len(gradients), -1)\n", " gradients_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n", " loss_C += self.lambda_gp * gradients_penalty\n", " \n", " # Compute the R1 regularization loss\n", " r1_reg = gradients.pow(2).sum(1).mean()\n", " loss_C += self.lambda_r1 * r1_reg\n", "\n", " # Backpropagation\n", " loss_C.backward()\n", " optimizer_C.step()\n", " self.critic_losses.append(loss_C.item())\n", "\n", " \n", " def training_step(self, batch, batch_idx):\n", " real, condition = batch\n", " optimizer_C, optimizer_G = self.optimizers() # Access optimizers\n", " \n", " # Debugging shapes\n", " print(f\"Real images shape: {real.shape}\")\n", " print(f\"Conditioned images shape: {condition.shape}\")\n", " \n", " # Update the critic\n", " self.critic_step(real, condition, optimizer_C)\n", " \n", " # Update the generator\n", " self.generator_step(real, condition, optimizer_G)\n", " \n", " \n", " # Logging and saving models\n", " gen_mean = sum(self.generator_losses[-self.display_step:]) / self.display_step\n", " crit_mean = sum(self.critic_losses[-self.display_step:]) / self.display_step\n", " if self.current_epoch % self.display_step == 0 and batch_idx == 0:\n", " fake = self.generator(condition).detach()\n", " print(f\"Epoch {self.current_epoch}: Generator loss: {gen_mean}, Critic loss: {crit_mean}\")\n", " display_progress(condition[0], real[0], fake[0], self.current_epoch)\n", " \n", " # Save models every 10 epochs\n", " if (self.current_epoch + 1) % 10 == 0 and batch_idx == self.trainer.num_training_batches - 1:\n", " torch.save(self.generator.state_dict(), f\"/kaggle/working/cwgan_generator_epoch_{self.current_epoch+1}.pt\")\n", " torch.save(self.critic.state_dict(), f\"/kaggle/working/cwgan_critic_epoch_{self.current_epoch+1}.pt\")\n", " print(f\"Saved models at epoch {self.current_epoch+1}\")\n", "\n", " # Final save at epoch 150\n", " if self.current_epoch == 149 and batch_idx == self.trainer.num_training_batches - 1:\n", " torch.save(self.generator.state_dict(), \"cwgan_generator_final.pt\")\n", " torch.save(self.critic.state_dict(), \"cwgan_critic_final.pt\")\n", " print(\"Saved final models at epoch 150\")\n", " " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from torch import nn, optim\n", "\n", "gc.collect()\n", "cwgan = CWGAN(in_channels = 1, out_channels = 2 ,learning_rate=2e-4, lambda_recon=100, display_step=10)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2024-08-25 00:04:53,828: INFO: rank_zero: GPU available: True (cuda), used: True]\n", "[2024-08-25 00:04:53,829: INFO: rank_zero: TPU available: False, using: 0 TPU cores]\n", "[2024-08-25 00:04:53,830: INFO: rank_zero: IPU available: False, using: 0 IPUs]\n", "[2024-08-25 00:04:53,831: INFO: rank_zero: HPU available: False, using: 0 HPUs]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "[2024-08-25 00:04:54,549: INFO: cuda: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]]\n", "[2024-08-25 00:04:54,552: INFO: model_summary: \n", " | Name | Type | Params\n", "----------------------------------------------\n", "0 | generator | Generator | 8.2 M \n", "1 | critic | Critic | 2.8 M \n", "2 | recon_criterion | L1Loss | 0 \n", "----------------------------------------------\n", "11.0 M Trainable params\n", "0 Non-trainable params\n", "11.0 M Total params\n", "43.893 Total estimated model params size (MB)]\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a1c97e80cdd746b2afa3ae30771ee058", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Training: | | 0/? [00:00 2\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcwgan\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader1\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:545\u001b[0m, in \u001b[0;36mTrainer.fit\u001b[1;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[0;32m 543\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstatus \u001b[38;5;241m=\u001b[39m TrainerStatus\u001b[38;5;241m.\u001b[39mRUNNING\n\u001b[0;32m 544\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 545\u001b[0m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_and_handle_interrupt\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 546\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit_impl\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_dataloaders\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatamodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\n\u001b[0;32m 547\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\trainer\\call.py:44\u001b[0m, in \u001b[0;36m_call_and_handle_interrupt\u001b[1;34m(trainer, trainer_fn, *args, **kwargs)\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 43\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mlauncher\u001b[38;5;241m.\u001b[39mlaunch(trainer_fn, \u001b[38;5;241m*\u001b[39margs, trainer\u001b[38;5;241m=\u001b[39mtrainer, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m---> 44\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtrainer_fn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 46\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m _TunerExitException:\n\u001b[0;32m 47\u001b[0m _call_teardown_hook(trainer)\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:581\u001b[0m, in \u001b[0;36mTrainer._fit_impl\u001b[1;34m(self, model, train_dataloaders, val_dataloaders, datamodule, ckpt_path)\u001b[0m\n\u001b[0;32m 574\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 575\u001b[0m ckpt_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_checkpoint_connector\u001b[38;5;241m.\u001b[39m_select_ckpt_path(\n\u001b[0;32m 576\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mfn,\n\u001b[0;32m 577\u001b[0m ckpt_path,\n\u001b[0;32m 578\u001b[0m model_provided\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m 579\u001b[0m model_connected\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 580\u001b[0m )\n\u001b[1;32m--> 581\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mckpt_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mckpt_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 583\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mstopped\n\u001b[0;32m 584\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:990\u001b[0m, in \u001b[0;36mTrainer._run\u001b[1;34m(self, model, ckpt_path)\u001b[0m\n\u001b[0;32m 985\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_signal_connector\u001b[38;5;241m.\u001b[39mregister_signal_handlers()\n\u001b[0;32m 987\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m 988\u001b[0m \u001b[38;5;66;03m# RUN THE TRAINER\u001b[39;00m\n\u001b[0;32m 989\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[1;32m--> 990\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_run_stage\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 992\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m 993\u001b[0m \u001b[38;5;66;03m# POST-Training CLEAN UP\u001b[39;00m\n\u001b[0;32m 994\u001b[0m \u001b[38;5;66;03m# ----------------------------\u001b[39;00m\n\u001b[0;32m 995\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: trainer tearing down\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\trainer\\trainer.py:1036\u001b[0m, in \u001b[0;36mTrainer._run_stage\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1034\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_run_sanity_check()\n\u001b[0;32m 1035\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mset_detect_anomaly(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_detect_anomaly):\n\u001b[1;32m-> 1036\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1037\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 1038\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnexpected state \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\loops\\fit_loop.py:202\u001b[0m, in \u001b[0;36m_FitLoop.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 201\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_start()\n\u001b[1;32m--> 202\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 203\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end()\n\u001b[0;32m 204\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\loops\\fit_loop.py:359\u001b[0m, in \u001b[0;36m_FitLoop.advance\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 357\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_training_epoch\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m 358\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_fetcher \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m--> 359\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mepoch_loop\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_fetcher\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\loops\\training_epoch_loop.py:136\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.run\u001b[1;34m(self, data_fetcher)\u001b[0m\n\u001b[0;32m 134\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdone:\n\u001b[0;32m 135\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 136\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata_fetcher\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 137\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_advance_end(data_fetcher)\n\u001b[0;32m 138\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\loops\\training_epoch_loop.py:242\u001b[0m, in \u001b[0;36m_TrainingEpochLoop.advance\u001b[1;34m(self, data_fetcher)\u001b[0m\n\u001b[0;32m 240\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mautomatic_optimization\u001b[38;5;241m.\u001b[39mrun(trainer\u001b[38;5;241m.\u001b[39moptimizers[\u001b[38;5;241m0\u001b[39m], batch_idx, kwargs)\n\u001b[0;32m 241\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 242\u001b[0m batch_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmanual_optimization\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 244\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbatch_progress\u001b[38;5;241m.\u001b[39mincrement_processed()\n\u001b[0;32m 246\u001b[0m \u001b[38;5;66;03m# update non-plateau LR schedulers\u001b[39;00m\n\u001b[0;32m 247\u001b[0m \u001b[38;5;66;03m# update epoch-interval ones only when we are at the end of training epoch\u001b[39;00m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\loops\\optimization\\manual.py:92\u001b[0m, in \u001b[0;36m_ManualOptimization.run\u001b[1;34m(self, kwargs)\u001b[0m\n\u001b[0;32m 90\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_run_start()\n\u001b[0;32m 91\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m suppress(\u001b[38;5;167;01mStopIteration\u001b[39;00m): \u001b[38;5;66;03m# no loop to break at this level\u001b[39;00m\n\u001b[1;32m---> 92\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madvance\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 93\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_restarting \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 94\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mon_run_end()\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\loops\\optimization\\manual.py:112\u001b[0m, in \u001b[0;36m_ManualOptimization.advance\u001b[1;34m(self, kwargs)\u001b[0m\n\u001b[0;32m 109\u001b[0m trainer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\n\u001b[0;32m 111\u001b[0m \u001b[38;5;66;03m# manually capture logged metrics\u001b[39;00m\n\u001b[1;32m--> 112\u001b[0m training_step_output \u001b[38;5;241m=\u001b[39m \u001b[43mcall\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_strategy_hook\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrainer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtraining_step\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 113\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m kwargs \u001b[38;5;66;03m# release the batch from memory\u001b[39;00m\n\u001b[0;32m 114\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39mpost_training_step() \u001b[38;5;66;03m# unused hook - call anyway for backward compatibility\u001b[39;00m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\trainer\\call.py:309\u001b[0m, in \u001b[0;36m_call_strategy_hook\u001b[1;34m(trainer, hook_name, *args, **kwargs)\u001b[0m\n\u001b[0;32m 306\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m 308\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m trainer\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mprofile(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[Strategy]\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrainer\u001b[38;5;241m.\u001b[39mstrategy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mhook_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m--> 309\u001b[0m output \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 311\u001b[0m \u001b[38;5;66;03m# restore current_fx when nested context\u001b[39;00m\n\u001b[0;32m 312\u001b[0m pl_module\u001b[38;5;241m.\u001b[39m_current_fx_name \u001b[38;5;241m=\u001b[39m prev_fx_name\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pytorch_lightning\\strategies\\strategy.py:382\u001b[0m, in \u001b[0;36mStrategy.training_step\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 380\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module:\n\u001b[0;32m 381\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_redirection(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlightning_module, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtraining_step\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m--> 382\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlightning_module\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "Cell \u001b[1;32mIn[13], line 74\u001b[0m, in \u001b[0;36mCWGAN.training_step\u001b[1;34m(self, batch, batch_idx)\u001b[0m\n\u001b[0;32m 71\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConditioned images shape: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcondition\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 73\u001b[0m \u001b[38;5;66;03m# Update the critic\u001b[39;00m\n\u001b[1;32m---> 74\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcritic_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreal\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcondition\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer_C\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 76\u001b[0m \u001b[38;5;66;03m# Update the generator\u001b[39;00m\n\u001b[0;32m 77\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgenerator_step(real, condition, optimizer_G)\n", "Cell \u001b[1;32mIn[13], line 38\u001b[0m, in \u001b[0;36mCWGAN.critic_step\u001b[1;34m(self, real_images, conditioned_images, optimizer_C)\u001b[0m\n\u001b[0;32m 36\u001b[0m fake_input \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat((fake_images, conditioned_images), \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m 37\u001b[0m real_input \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mcat((real_images, conditioned_images), \u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m---> 38\u001b[0m fake_logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcritic\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfake_input\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 39\u001b[0m real_logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcritic(real_input)\n\u001b[0;32m 41\u001b[0m \u001b[38;5;66;03m# Compute the loss for the critic\u001b[39;00m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1551\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1553\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", "File \u001b[1;32mc:\\Users\\azizu\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1560\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1561\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1562\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1565\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n", "\u001b[1;31mTypeError\u001b[0m: Critic.forward() missing 1 required positional argument: 'l'" ] } ], "source": [ "trainer = pl.Trainer(max_epochs=150)\n", "trainer.fit(cwgan, train_loader1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 2 }