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RegNet Classification\n\nRegNet code","metadata":{"papermill":{"duration":0.038811,"end_time":"2025-01-19T23:08:02.444577","exception":false,"start_time":"2025-01-19T23:08:02.405766","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"## Import","metadata":{"papermill":{"duration":0.03602,"end_time":"2025-01-19T23:08:02.517710","exception":false,"start_time":"2025-01-19T23:08:02.481690","status":"completed"},"tags":[]}},{"cell_type":"code","source":"from __future__ import print_function\n\nimport glob\nimport os\nimport random\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom PIL import Image\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import f1_score\nfrom torch.optim.lr_scheduler import StepLR\nfrom torch.utils.data import DataLoader, Dataset\nfrom torchvision import transforms, models\nfrom tqdm.notebook import tqdm\nimport matplotlib.pyplot as plt","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","execution":{"iopub.status.busy":"2025-01-22T10:00:36.552594Z","iopub.execute_input":"2025-01-22T10:00:36.553365Z","iopub.status.idle":"2025-01-22T10:00:43.478157Z","shell.execute_reply.started":"2025-01-22T10:00:36.553291Z","shell.execute_reply":"2025-01-22T10:00:43.477245Z"},"papermill":{"duration":9.206799,"end_time":"2025-01-19T23:08:11.760636","exception":false,"start_time":"2025-01-19T23:08:02.553837","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Configuration and Reproducibility","metadata":{"papermill":{"duration":0.036677,"end_time":"2025-01-19T23:08:11.834753","exception":false,"start_time":"2025-01-19T23:08:11.798076","status":"completed"},"tags":[]}},{"cell_type":"code","source":"batch_size = 16\nepochs = 12\nlr = 5e-5\ngamma = 0.7\nseed = 42\n\n\ndef seed_everything(seed):\n    \"\"\"\n    Sets the random seed for reproducibility in python, numpy, and torch.\n    \"\"\"\n    random.seed(seed)\n    os.environ['PYTHONHASHSEED'] = str(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    torch.backends.cudnn.deterministic = True\n\nseed_everything(seed)\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nprint(\"Device used :\", device)","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:00:43.479048Z","iopub.execute_input":"2025-01-22T10:00:43.479501Z","iopub.status.idle":"2025-01-22T10:00:43.537820Z","shell.execute_reply.started":"2025-01-22T10:00:43.479479Z","shell.execute_reply":"2025-01-22T10:00:43.537042Z"},"papermill":{"duration":0.100491,"end_time":"2025-01-19T23:08:11.971213","exception":false,"start_time":"2025-01-19T23:08:11.870722","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Data Preparation and Transformation","metadata":{"papermill":{"duration":0.035804,"end_time":"2025-01-19T23:08:12.043263","exception":false,"start_time":"2025-01-19T23:08:12.007459","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Paths to the dataset\nbase_dir = '/kaggle/input/ai-vs-human-generated-dataset'\ntrain_csv_path = os.path.join(base_dir, 'train.csv')\ntest_csv_path  = os.path.join(base_dir, 'test.csv')\n\n# Reading the training CSV file\ndf_train = pd.read_csv(train_csv_path)\n# Example of a row: file_name=\"train_data/041be3153810...\", label=0 or 1\n\n# Reading the testing CSV file\ndf_test = pd.read_csv(os.path.join(base_dir, 'test.csv'))\n# Exemple: df_test['id'] = \"test_data/e25323c62af644fba97afb846261b05b.jpg\", etc.\n\n# Adding the full path to the file_name instead of just \"trainORtest_data/xxx.jpg\"\ndf_test['id'] = df_test['id'].apply(lambda x: os.path.join(base_dir, x))\ndf_train['file_name'] = df_train['file_name'].apply(lambda x: os.path.join(base_dir, x))\n\nall_image_paths = df_train['file_name'].values\nall_labels = df_train['label'].values\n\n# Splitting train/validation (95% / 5%)\ntrain_paths, val_paths, train_labels, val_labels = train_test_split(\n    all_image_paths,\n    all_labels,\n    test_size=0.05,        \n    stratify=all_labels,\n    random_state=seed\n)\n\n\n#train_paths = train_paths[:1500]\n#val_paths = val_paths[:150]\nprint(f\"Train Data: {len(train_paths)}\")\nprint(f\"Validation Data: {len(val_paths)}\")","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:00:43.538813Z","iopub.execute_input":"2025-01-22T10:00:43.539040Z","iopub.status.idle":"2025-01-22T10:00:43.872681Z","shell.execute_reply.started":"2025-01-22T10:00:43.539020Z","shell.execute_reply":"2025-01-22T10:00:43.871984Z"},"papermill":{"duration":0.067888,"end_time":"2025-01-19T23:08:12.146801","exception":false,"start_time":"2025-01-19T23:08:12.078913","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import torchvision.transforms as T\nfrom torchvision.transforms import InterpolationMode\n\ntrain_transforms = T.Compose([\n    T.Resize(224, interpolation=InterpolationMode.BICUBIC),\n    T.RandomCrop(224),\n    T.RandomHorizontalFlip(),\n    T.ToTensor(),\n    T.Normalize(mean=[0.485, 0.456, 0.406],\n                std=[0.229, 0.224, 0.225]),\n])\n\nval_transforms = T.Compose([\n    T.Resize(224, interpolation=InterpolationMode.BICUBIC),\n    T.CenterCrop(224),\n    T.ToTensor(),\n    T.Normalize(mean=[0.485, 0.456, 0.406],\n                std=[0.229, 0.224, 0.225]),\n])\n\ntest_transforms = T.Compose([\n    T.Resize(224, interpolation=InterpolationMode.BICUBIC),\n    T.CenterCrop(224),\n    T.ToTensor(),\n    T.Normalize(mean=[0.485, 0.456, 0.406],\n                std=[0.229, 0.224, 0.225]),\n])","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:00:43.874271Z","iopub.execute_input":"2025-01-22T10:00:43.874496Z","iopub.status.idle":"2025-01-22T10:00:43.880037Z","shell.execute_reply.started":"2025-01-22T10:00:43.874478Z","shell.execute_reply":"2025-01-22T10:00:43.879292Z"},"papermill":{"duration":0.04338,"end_time":"2025-01-19T23:08:12.227256","exception":false,"start_time":"2025-01-19T23:08:12.183876","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Custom Dataset Implementation","metadata":{"papermill":{"duration":0.035583,"end_time":"2025-01-19T23:08:12.299306","exception":false,"start_time":"2025-01-19T23:08:12.263723","status":"completed"},"tags":[]}},{"cell_type":"code","source":"class ImageDataset(Dataset):\n    \"\"\"\n    Dataset générique pour charger des images et leurs labels (optionnel).\n    Si labels=None, on suppose qu'on est en inference/test sans labels.\n    \"\"\"\n    def __init__(self, file_list, labels=None, transform=None):\n        self.file_list = file_list\n        self.labels = labels\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.file_list)\n\n    def __getitem__(self, idx):\n        img_path = self.file_list[idx]\n        img = Image.open(img_path).convert(\"RGB\")\n        if self.transform:\n            img = self.transform(img)\n        if self.labels is not None:\n            label = self.labels[idx]\n            return img, label\n        else:\n            # Pas de label en inference\n            return img\n\nclass TestImageDataset(Dataset):\n    def __init__(self, file_list, transform=None):\n        self.file_list = file_list\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.file_list)\n\n    def __getitem__(self, idx):\n        img_path = self.file_list[idx]\n        img = Image.open(img_path).convert(\"RGB\")\n        if self.transform:\n            img = self.transform(img)\n        return img, os.path.basename(img_path) \n","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:00:43.881207Z","iopub.execute_input":"2025-01-22T10:00:43.881421Z","iopub.status.idle":"2025-01-22T10:00:43.896174Z","shell.execute_reply.started":"2025-01-22T10:00:43.881403Z","shell.execute_reply":"2025-01-22T10:00:43.895568Z"},"papermill":{"duration":0.046066,"end_time":"2025-01-19T23:08:12.381654","exception":false,"start_time":"2025-01-19T23:08:12.335588","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## DataLoaders","metadata":{}},{"cell_type":"code","source":"train_data = ImageDataset(train_paths, train_labels, transform=train_transforms)\nval_data   = ImageDataset(val_paths,   val_labels,   transform=val_transforms)\n\ntrain_loader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True,  num_workers=4)\nval_loader   = DataLoader(dataset=val_data,   batch_size=batch_size, shuffle=False, num_workers=4)\n\nprint(f\"Train Dataset size: {len(train_data)}\")\nprint(f\"Validation Dataset size: {len(val_data)}\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-22T10:00:43.897014Z","iopub.execute_input":"2025-01-22T10:00:43.897225Z","iopub.status.idle":"2025-01-22T10:00:43.913935Z","shell.execute_reply.started":"2025-01-22T10:00:43.897206Z","shell.execute_reply":"2025-01-22T10:00:43.913001Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Model Initialization","metadata":{"papermill":{"duration":0.038764,"end_time":"2025-01-19T23:08:12.456471","exception":false,"start_time":"2025-01-19T23:08:12.417707","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Load pre-trained Regnet\nmodel = models.regnet_y_32gf(weights=models.RegNet_Y_32GF_Weights.IMAGENET1K_SWAG_E2E_V1)\n\nnum_ftrs = model.fc.in_features\n\nmodel.fc = nn.Linear(num_ftrs, 2)\n\nmodel.to(device)","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:00:43.914754Z","iopub.execute_input":"2025-01-22T10:00:43.915050Z","iopub.status.idle":"2025-01-22T10:01:20.391146Z","shell.execute_reply.started":"2025-01-22T10:00:43.915023Z","shell.execute_reply":"2025-01-22T10:01:20.390221Z"},"papermill":{"duration":6.108259,"end_time":"2025-01-19T23:08:18.600830","exception":false,"start_time":"2025-01-19T23:08:12.492571","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Training the Model","metadata":{"papermill":{"duration":0.038709,"end_time":"2025-01-19T23:08:18.678883","exception":false,"start_time":"2025-01-19T23:08:18.640174","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Define loss, optimizer, and scheduler\ncriterion = nn.CrossEntropyLoss()\noptimizer = optim.Adam(model.parameters(), lr=lr)\nscheduler = StepLR(optimizer, step_size=1, gamma=gamma)\n\ntrain_losses = []\ntrain_accuracies = []\nval_losses = []\nval_accuracies = []\nval_f1s = []","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:01:20.392857Z","iopub.execute_input":"2025-01-22T10:01:20.393178Z","iopub.status.idle":"2025-01-22T10:01:20.398746Z","shell.execute_reply.started":"2025-01-22T10:01:20.393146Z","shell.execute_reply":"2025-01-22T10:01:20.397984Z"},"papermill":{"duration":0.046927,"end_time":"2025-01-19T23:08:18.764087","exception":false,"start_time":"2025-01-19T23:08:18.717160","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"for epoch in range(epochs):\n    model.train()\n    epoch_loss = 0.0\n    epoch_accuracy = 0.0\n\n    for data, label in tqdm(train_loader, desc=f\"Training Epoch {epoch+1}\"):\n        data, label = data.to(device), label.to(device)\n\n        optimizer.zero_grad()\n        output = model(data)\n        loss = criterion(output, label)\n        loss.backward()\n        optimizer.step()\n\n        epoch_loss += loss.item()\n        preds = output.argmax(dim=1)\n        acc = (preds == label).float().mean().item()\n        epoch_accuracy += acc\n\n    epoch_loss /= len(train_loader)\n    epoch_accuracy /= len(train_loader)\n\n    train_losses.append(epoch_loss)\n    train_accuracies.append(epoch_accuracy)\n\n    # -- Validation --\n    model.eval()\n    val_loss = 0.0\n    val_acc = 0.0\n    val_pred_classes = []\n    val_labels_list = []\n\n    with torch.no_grad():\n        for data, label in tqdm(val_loader, desc=f\"Validation Epoch {epoch+1}\"):\n            data, label = data.to(device), label.to(device)\n            output = model(data)\n\n            loss = criterion(output, label)\n            val_loss += loss.item()\n\n            preds = output.argmax(dim=1)\n            acc = (preds == label).float().mean().item()\n            val_acc += acc\n\n            val_pred_classes.extend(preds.cpu().numpy())\n            val_labels_list.extend(label.cpu().numpy())\n\n    val_loss /= len(val_loader)\n    val_acc /= len(val_loader)\n    \n    val_f1 = f1_score(val_labels_list, val_pred_classes)\n\n    val_losses.append(val_loss)\n    val_accuracies.append(val_acc)\n    val_f1s.append(val_f1)\n\n    print(\n        f\"Epoch [{epoch+1}/{epochs}] \"\n        f\"Train Loss: {epoch_loss:.4f} | Train Acc: {epoch_accuracy:.4f} | \"\n        f\"Val Loss: {val_loss:.4f} | Val Acc: {val_acc:.4f} | Val F1: {val_f1:.4f}\"\n    )\n\n    scheduler.step()","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:01:20.399578Z","iopub.execute_input":"2025-01-22T10:01:20.399854Z","iopub.status.idle":"2025-01-22T10:02:31.573242Z","shell.execute_reply.started":"2025-01-22T10:01:20.399828Z","shell.execute_reply":"2025-01-22T10:02:31.572457Z"},"papermill":{"duration":257.644496,"end_time":"2025-01-19T23:12:36.447259","exception":false,"start_time":"2025-01-19T23:08:18.802763","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Save model","metadata":{}},{"cell_type":"code","source":"torch.save(model.state_dict(), \"model_weights.pth\")\n\ntorch.save(model, \"model.pth\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-22T10:02:31.574197Z","iopub.execute_input":"2025-01-22T10:02:31.574452Z","iopub.status.idle":"2025-01-22T10:02:33.099344Z","shell.execute_reply.started":"2025-01-22T10:02:31.574429Z","shell.execute_reply":"2025-01-22T10:02:33.098696Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Visualization of Loss and Accuracy","metadata":{"papermill":{"duration":0.03967,"end_time":"2025-01-19T23:12:36.528046","exception":false,"start_time":"2025-01-19T23:12:36.488376","status":"completed"},"tags":[]}},{"cell_type":"code","source":"plt.figure(figsize=(10,5))\nplt.title(\"Training and Validation Loss\")\nplt.plot(train_losses, label=\"Training\")\nplt.plot(val_losses, label=\"Validation\")\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Loss\")\nplt.legend()\nplt.show()\n\nplt.figure(figsize=(10,5))\nplt.title(\"Training and Validation Accuracy\")\nplt.plot(train_accuracies, label=\"Training\")\nplt.plot(val_accuracies, label=\"Validation\")\nplt.xlabel(\"Epoch\")\nplt.ylabel(\"Accuracy\")\nplt.legend()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:02:33.100170Z","iopub.execute_input":"2025-01-22T10:02:33.100406Z","iopub.status.idle":"2025-01-22T10:02:33.500228Z","shell.execute_reply.started":"2025-01-22T10:02:33.100385Z","shell.execute_reply":"2025-01-22T10:02:33.499356Z"},"papermill":{"duration":0.480716,"end_time":"2025-01-19T23:12:37.048400","exception":false,"start_time":"2025-01-19T23:12:36.567684","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Load model","metadata":{}},{"cell_type":"code","source":"#model.load_state_dict(torch.load(\"model_weights.pth\"))\n#model.to(device)\n\n# OR, load the full model\n#model = torch.load(\"model.pth\")\n#model.to(device)","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## Inference on Test Data and Submission Generation","metadata":{"papermill":{"duration":0.040469,"end_time":"2025-01-19T23:12:37.130462","exception":false,"start_time":"2025-01-19T23:12:37.089993","status":"completed"},"tags":[]}},{"cell_type":"code","source":"test_dataset = TestImageDataset(df_test['id'].values, transform=test_transforms)\ntest_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)\n\nmodel.eval()\npredictions = []\nimage_names = []\n\nwith torch.no_grad():\n    for data, names in tqdm(test_loader, desc=\"Predicting\"):\n        data = data.to(device)\n        outputs = model(data)\n        \n        # On récupère la classe prédite\n        preds = outputs.argmax(dim=1)  # shape [batch_size]\n        \n        predictions.extend(preds.cpu().numpy())\n        image_names.extend([f\"test_data/{name}\" for name in names])\n\n# Créer le DataFrame au format \"id,label\"\nsubmission_df = pd.DataFrame({\n    'id': image_names,\n    'label': predictions\n})","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:02:33.501302Z","iopub.execute_input":"2025-01-22T10:02:33.501641Z","iopub.status.idle":"2025-01-22T10:05:50.543313Z","shell.execute_reply.started":"2025-01-22T10:02:33.501599Z","shell.execute_reply":"2025-01-22T10:05:50.542519Z"},"papermill":{"duration":3.727844,"end_time":"2025-01-19T23:12:40.899108","exception":false,"start_time":"2025-01-19T23:12:37.171264","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"submission_df.to_csv(\"submit.csv\", index=False)\nprint(\"Submission file generated: submit.csv\")","metadata":{"execution":{"iopub.status.busy":"2025-01-22T10:05:50.545948Z","iopub.execute_input":"2025-01-22T10:05:50.546161Z","iopub.status.idle":"2025-01-22T10:05:50.583739Z","shell.execute_reply.started":"2025-01-22T10:05:50.546140Z","shell.execute_reply":"2025-01-22T10:05:50.583137Z"},"papermill":{"duration":0.054215,"end_time":"2025-01-19T23:12:40.996137","exception":false,"start_time":"2025-01-19T23:12:40.941922","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null}]}