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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T12:54:49.334286Z",
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},
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Path to dataset files: /kaggle/input/cifake-real-and-ai-generated-synthetic-images\n"
]
}
],
"source": [
"import kagglehub\n",
"path = kagglehub.dataset_download(\"birdy654/cifake-real-and-ai-generated-synthetic-images\")\n",
"print(\"Path to dataset files:\", path)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T12:54:51.497912Z",
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},
"trusted": true
},
"outputs": [
{
"data": {
"text/plain": [
"'/kaggle/input/cifake-real-and-ai-generated-synthetic-images'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"path"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T12:54:52.950084Z",
"iopub.status.busy": "2025-04-04T12:54:52.949766Z",
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},
"trusted": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0m\u001b[01;34mtest\u001b[0m/ \u001b[01;34mtrain\u001b[0m/\n"
]
}
],
"source": [
"ls '/kaggle/input/cifake-real-and-ai-generated-synthetic-images'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2025-04-04T12:54:54.425136Z",
"iopub.status.busy": "2025-04-04T12:54:54.424819Z",
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"shell.execute_reply": "2025-04-04T12:54:54.435720Z",
"shell.execute_reply.started": "2025-04-04T12:54:54.425108Z"
},
"trusted": true
},
"outputs": [],
"source": [
"import os\n",
"import glob\n",
"\n",
"data_dir = str(path)\n",
"\n",
"training_dir = os.path.join(data_dir,\"train\")\n",
"if not os.path.isdir(training_dir):\n",
" os.mkdir(training_dir)\n",
"\n",
"dog_training_dir = os.path.join(training_dir,\"REAL\")\n",
"if not os.path.isdir(dog_training_dir):\n",
" os.mkdir(dog_training_dir)\n",
"\n",
"\n",
"cat_training_dir = os.path.join(training_dir,\"FAKE\")\n",
"if not os.path.isdir(cat_training_dir):\n",
" os.mkdir(cat_training_dir)\n",
"\n",
"\n",
"validation_dir = os.path.join(data_dir,\"test\")\n",
"if not os.path.isdir(validation_dir):\n",
" os.mkdir(validation_dir)\n",
"\n",
"dog_validation_dir = os.path.join(validation_dir,\"REAL\")\n",
"if not os.path.isdir(dog_validation_dir):\n",
" os.mkdir(dog_validation_dir)\n",
"\n",
"\n",
"cat_validation_dir = os.path.join(validation_dir,\"FAKE\")\n",
"if not os.path.isdir(cat_validation_dir):\n",
" os.mkdir(cat_validation_dir)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T12:54:57.162476Z",
"iopub.status.busy": "2025-04-04T12:54:57.162155Z",
"iopub.status.idle": "2025-04-04T12:54:57.169644Z",
"shell.execute_reply": "2025-04-04T12:54:57.168755Z",
"shell.execute_reply.started": "2025-04-04T12:54:57.162453Z"
},
"trusted": true
},
"outputs": [],
"source": [
"import shutil\n",
"\n",
"split_size = 0.80\n",
"cat_imgs_size = len(glob.glob(\"/content/data/train/FAKE*\"))\n",
"dog_imgs_size = len(glob.glob(\"/content/data/train/REAL*\"))\n",
"\n",
"for i,img in enumerate(glob.glob(\"/content/data/train/FAKE*\")):\n",
" if i < (cat_imgs_size * split_size):\n",
" shutil.move(img,cat_training_dir)\n",
" else:\n",
" shutil.move(img,cat_validation_dir)\n",
"\n",
"for i,img in enumerate(glob.glob(\"/content/data/train/REAL*\")):\n",
" if i < (dog_imgs_size * split_size):\n",
" shutil.move(img,dog_training_dir)\n",
" else:\n",
" shutil.move(img,dog_validation_dir)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T12:55:00.489855Z",
"iopub.status.busy": "2025-04-04T12:55:00.489560Z",
"iopub.status.idle": "2025-04-04T12:56:01.137810Z",
"shell.execute_reply": "2025-04-04T12:56:01.137109Z",
"shell.execute_reply.started": "2025-04-04T12:55:00.489835Z"
},
"trusted": true
},
"outputs": [],
"source": [
"import torch\n",
"import torchvision\n",
"from torchvision import datasets, transforms\n",
"\n",
"traindir = path+\"/train\"\n",
"testdir = path+\"/test\"\n",
"\n",
"train_transforms = transforms.Compose([transforms.Resize((224,224)),\n",
" transforms.ToTensor(), \n",
" torchvision.transforms.Normalize(\n",
" mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225],\n",
" ),\n",
" ])\n",
"test_transforms = transforms.Compose([transforms.Resize((224,224)),\n",
" transforms.ToTensor(),\n",
" torchvision.transforms.Normalize(\n",
" mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225],\n",
" ),\n",
" ])\n",
"\n",
"train_data = datasets.ImageFolder(traindir,transform=train_transforms)\n",
"test_data = datasets.ImageFolder(testdir,transform=test_transforms)\n",
"\n",
"trainloader = torch.utils.data.DataLoader(train_data, shuffle = True, batch_size=16)\n",
"testloader = torch.utils.data.DataLoader(test_data, shuffle = True, batch_size=16)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T12:56:11.815228Z",
"iopub.status.busy": "2025-04-04T12:56:11.814667Z",
"iopub.status.idle": "2025-04-04T12:56:11.820038Z",
"shell.execute_reply": "2025-04-04T12:56:11.818970Z",
"shell.execute_reply.started": "2025-04-04T12:56:11.815175Z"
},
"trusted": true
},
"outputs": [],
"source": [
"def make_train_step(model, optimizer, loss_fn):\n",
" def train_step(x,y):\n",
" yhat = model(x)\n",
" model.train()\n",
" loss = loss_fn(yhat,y)\n",
"\n",
" loss.backward()\n",
" optimizer.step()\n",
" optimizer.zero_grad()\n",
" #optimizer.cleargrads()\n",
"\n",
" return loss\n",
" return train_step"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T12:56:24.422670Z",
"iopub.status.busy": "2025-04-04T12:56:24.422329Z",
"iopub.status.idle": "2025-04-04T12:56:24.664399Z",
"shell.execute_reply": "2025-04-04T12:56:24.663683Z",
"shell.execute_reply.started": "2025-04-04T12:56:24.422644Z"
},
"trusted": true
},
"outputs": [],
"source": [
"from torchvision import datasets, models, transforms\n",
"import torch.nn as nn\n",
"\n",
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"model = models.resnet18(pretrained=True)\n",
"\n",
"for params in model.parameters():\n",
" params.requires_grad_ = False\n",
"\n",
"nr_filters = model.fc.in_features \n",
"model.fc = nn.Linear(nr_filters, 1)\n",
"\n",
"model = model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T12:56:26.628077Z",
"iopub.status.busy": "2025-04-04T12:56:26.627763Z",
"iopub.status.idle": "2025-04-04T12:56:26.632616Z",
"shell.execute_reply": "2025-04-04T12:56:26.631736Z",
"shell.execute_reply.started": "2025-04-04T12:56:26.628054Z"
},
"trusted": true
},
"outputs": [],
"source": [
"from torch.nn.modules.loss import BCEWithLogitsLoss\n",
"from torch.optim import lr_scheduler\n",
"\n",
"loss_fn = BCEWithLogitsLoss()\n",
"optimizer = torch.optim.Adam(model.fc.parameters()) \n",
"\n",
"train_step = make_train_step(model, optimizer, loss_fn)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T12:56:29.126146Z",
"iopub.status.busy": "2025-04-04T12:56:29.125852Z",
"iopub.status.idle": "2025-04-04T12:57:41.905138Z",
"shell.execute_reply": "2025-04-04T12:57:41.904311Z",
"shell.execute_reply.started": "2025-04-04T12:56:29.126124Z"
},
"trusted": true
},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-fecf91831327>\u001b[0m in \u001b[0;36m<cell line: 19>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Set model to train mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainloader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 27\u001b[0m \u001b[0mx_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[0mx_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tqdm/std.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1180\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1181\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1182\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1183\u001b[0m \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 699\u001b[0m \u001b[0;31m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_reset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# type: ignore[call-arg]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 701\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 702\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_yielded\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 703\u001b[0m if (\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m_next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_next_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 756\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_next_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 757\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dataset_fetcher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfetch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# may raise StopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 758\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 759\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpin_memory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_pin_memory_device\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36mfetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 50\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitems__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 51\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0midx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpossibly_batched_index\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m 243\u001b[0m \"\"\"\n\u001b[1;32m 244\u001b[0m \u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0msample\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36mdefault_loader\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0maccimage_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpil_loader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 285\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torchvision/datasets/folder.py\u001b[0m in \u001b[0;36mpil_loader\u001b[0;34m(path)\u001b[0m\n\u001b[1;32m 261\u001b[0m \u001b[0;31m# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 262\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 264\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"RGB\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/PIL/Image.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m 3478\u001b[0m \u001b[0mexclusive_fp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3480\u001b[0;31m \u001b[0mprefix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m16\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3482\u001b[0m \u001b[0mpreinit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"%%capture\n",
"!pip install tqdm\n",
"\n",
"from tqdm import tqdm\n",
"import torch\n",
"\n",
"losses = []\n",
"val_losses = []\n",
"\n",
"epoch_train_losses = []\n",
"epoch_test_losses = []\n",
"\n",
"n_epochs = 10\n",
"early_stopping_tolerance = 3\n",
"early_stopping_threshold = 0.03\n",
"early_stopping_counter = 0 \n",
"\n",
"best_loss = float(\"inf\") \n",
"\n",
"for epoch in range(n_epochs):\n",
" optimizer.zero_grad()\n",
"\n",
" epoch_loss = 0\n",
" model.train() \n",
"\n",
" for i, data in tqdm(enumerate(trainloader), total=len(trainloader)):\n",
" x_batch, y_batch = data\n",
" x_batch = x_batch.to(device)\n",
" y_batch = y_batch.unsqueeze(1).float().to(device)\n",
"\n",
" loss = train_step(x_batch, y_batch)\n",
" epoch_loss += loss / len(trainloader)\n",
" losses.append(loss)\n",
"\n",
" epoch_train_losses.append(epoch_loss)\n",
" print(f\"\\nEpoch: {epoch+1}, train loss: {epoch_loss:.4f}\")\n",
"\n",
" model.eval()\n",
" with torch.no_grad():\n",
" cum_loss = 0\n",
" for x_batch, y_batch in testloader:\n",
" x_batch = x_batch.to(device)\n",
" y_batch = y_batch.unsqueeze(1).float().to(device)\n",
"\n",
" yhat = model(x_batch)\n",
" val_loss = loss_fn(yhat, y_batch)\n",
" cum_loss += val_loss.item() / len(testloader)\n",
" val_losses.append(val_loss.item())\n",
"\n",
" epoch_test_losses.append(cum_loss)\n",
" print(f\"Epoch: {epoch+1}, val loss: {cum_loss:.4f}\")\n",
"\n",
" if cum_loss < best_loss:\n",
" best_loss = cum_loss\n",
" best_model_wts = model.state_dict()\n",
" early_stopping_counter = 0\n",
" else:\n",
" early_stopping_counter += 1\n",
"\n",
" if early_stopping_counter == early_stopping_tolerance or best_loss <= early_stopping_threshold:\n",
" print(\"\\nTerminating: early stopping\")\n",
" break\n",
"\n",
"model.load_state_dict(best_model_wts)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"trusted": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T13:00:51.514501Z",
"iopub.status.busy": "2025-04-04T13:00:51.514093Z",
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"trusted": true
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fri Apr 4 13:00:51 2025 \n",
"+-----------------------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 560.35.03 Driver Version: 560.35.03 CUDA Version: 12.6 |\n",
"|-----------------------------------------+------------------------+----------------------+\n",
"| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n",
"| | | MIG M. |\n",
"|=========================================+========================+======================|\n",
"| 0 Tesla P100-PCIE-16GB Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 36C P0 32W / 250W | 929MiB / 16384MiB | 0% Default |\n",
"| | | N/A |\n",
"+-----------------------------------------+------------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------------------+\n",
"| Processes: |\n",
"| GPU GI CI PID Type Process name GPU Memory |\n",
"| ID ID Usage |\n",
"|=========================================================================================|\n",
"+-----------------------------------------------------------------------------------------+\n"
]
}
],
"source": [
"!nvidia-smi"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (4.67.1)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6250/6250 [07:21<00:00, 14.15it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Epoch: 1, train loss: 0.3295\n",
"Epoch: 1, val loss: 0.2714\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6250/6250 [07:49<00:00, 13.32it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Epoch: 2, train loss: 0.3302\n",
"Epoch: 2, val loss: 0.2683\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6250/6250 [06:44<00:00, 15.47it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Epoch: 3, train loss: 0.3320\n",
"Epoch: 3, val loss: 0.2689\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6250/6250 [06:30<00:00, 15.99it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Epoch: 4, train loss: 0.3316\n",
"Epoch: 4, val loss: 0.2745\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 6250/6250 [06:30<00:00, 16.01it/s]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Epoch: 5, train loss: 0.3331\n",
"Epoch: 5, val loss: 0.2716\n",
"\n",
"Terminating: early stopping\n"
]
},
{
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"!pip install tqdm\n",
"\n",
"from tqdm import tqdm\n",
"import torch\n",
"\n",
"losses = []\n",
"val_losses = []\n",
"\n",
"epoch_train_losses = []\n",
"epoch_test_losses = []\n",
"\n",
"n_epochs = 10\n",
"early_stopping_tolerance = 3\n",
"early_stopping_threshold = 0.03\n",
"early_stopping_counter = 0\n",
"\n",
"best_loss = float(\"inf\")\n",
"\n",
"for epoch in range(n_epochs):\n",
" optimizer.zero_grad() \n",
"\n",
" epoch_loss = 0\n",
" model.train()\n",
"\n",
" for i, data in tqdm(enumerate(trainloader), total=len(trainloader)):\n",
" x_batch, y_batch = data\n",
" x_batch = x_batch.to(device)\n",
" y_batch = y_batch.unsqueeze(1).float().to(device)\n",
"\n",
" loss = train_step(x_batch, y_batch) \n",
" loss_value = loss.item() \n",
"\n",
" epoch_loss += loss_value / len(trainloader)\n",
" losses.append(loss_value) \n",
" epoch_train_losses.append(epoch_loss)\n",
" print(f\"\\nEpoch: {epoch+1}, train loss: {epoch_loss:.4f}\")\n",
"\n",
" model.eval()\n",
" with torch.no_grad():\n",
" cum_loss = 0\n",
" for x_batch, y_batch in testloader:\n",
" x_batch = x_batch.to(device)\n",
" y_batch = y_batch.unsqueeze(1).float().to(device)\n",
"\n",
" yhat = model(x_batch)\n",
" val_loss = loss_fn(yhat, y_batch)\n",
" val_loss_value = val_loss.item() \n",
" cum_loss += val_loss_value / len(testloader)\n",
" val_losses.append(val_loss_value) \n",
"\n",
" epoch_test_losses.append(cum_loss)\n",
" print(f\"Epoch: {epoch+1}, val loss: {cum_loss:.4f}\")\n",
"\n",
" if cum_loss < best_loss:\n",
" best_loss = cum_loss\n",
" best_model_wts = model.state_dict()\n",
" early_stopping_counter = 0\n",
" else:\n",
" early_stopping_counter += 1\n",
"\n",
" if early_stopping_counter == early_stopping_tolerance or best_loss <= early_stopping_threshold:\n",
" print(\"\\nTerminating: early stopping\")\n",
" break\n",
"\n",
"model.load_state_dict(best_model_wts)\n"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T14:34:04.284009Z",
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"outputs": [],
"source": [
"\n",
"torch.save(model.state_dict(), \"my_model.pth\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T14:34:16.117938Z",
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},
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"outputs": [],
"source": [
"from safetensors.torch import save_file\n",
"\n",
"save_file(model.state_dict(), \"my_model.safetensors\")\n",
"\n",
"import h5py\n",
"\n",
"state_dict = model.state_dict()\n",
"\n",
"with h5py.File(\"my_model.h5\", \"w\") as f:\n",
" for key, tensor in state_dict.items():\n",
" f.create_dataset(key, data=tensor.cpu().numpy())\n"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T14:40:54.022447Z",
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},
"trusted": true
},
"outputs": [],
"source": [
"#inference\n",
"import os\n",
"import torch\n",
"from torchvision import models, transforms\n",
"from torch.utils.data import Dataset, DataLoader\n",
"from PIL import Image\n",
"import pandas as pd\n",
"\n",
"class InferenceDataset(Dataset):\n",
" def __init__(self, folder, transform):\n",
" self.paths = [os.path.join(folder, f) for f in os.listdir(folder)\n",
" if f.lower().endswith((\"png\", \"jpg\", \"jpeg\"))]\n",
" self.transform = transform\n",
"\n",
" def __len__(self):\n",
" return len(self.paths)\n",
"\n",
" def __getitem__(self, idx):\n",
" img = Image.open(self.paths[idx]).convert(\"RGB\")\n",
" return self.transform(img), self.paths[idx]\n",
"\n",
"def run_inference(image_folder, output_csv=\"predictions.csv\"):\n",
" device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
" model = models.resnet18(pretrained=True)\n",
" for p in model.parameters():\n",
" p.requires_grad = False\n",
" model.fc = torch.nn.Linear(model.fc.in_features, 1)\n",
" model = model.to(device)\n",
" model.eval()\n",
"\n",
" transform = transforms.Compose([\n",
" transforms.Resize((224, 224)),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize([0.485, 0.456, 0.406],\n",
" [0.229, 0.224, 0.225])\n",
" ])\n",
"\n",
" dataset = InferenceDataset(image_folder, transform)\n",
" loader = DataLoader(dataset, batch_size=1, shuffle=False)\n",
"\n",
" results = []\n",
" with torch.no_grad():\n",
" for img, path in loader:\n",
" img = img.to(device)\n",
" pred = torch.sigmoid(model(img)).item()\n",
" label = \"REAL\" if pred >= 0.5 else \"FAKE\"\n",
" results.append({\"image_path\": path[0], \"prediction\": label, \"score\": pred})\n",
"\n",
" pd.DataFrame(results).to_csv(output_csv, index=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"execution": {
"iopub.execute_input": "2025-04-04T14:41:16.510441Z",
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},
"trusted": true
},
"outputs": [],
"source": [
"final_path = \"/kaggle/input/finald/Test datasets/Test_dataset_2\"\n",
"run_inference(final_path, \"outputdata1.csv\")\n",
"run_inference(final_path, \"outputdata2.csv\")"
]
}
],
"metadata": {
"kaggle": {
"accelerator": "gpu",
"dataSources": [
{
"datasetId": 3041726,
"sourceId": 5256696,
"sourceType": "datasetVersion"
},
{
"datasetId": 7049439,
"sourceId": 11276085,
"sourceType": "datasetVersion"
}
],
"dockerImageVersionId": 30919,
"isGpuEnabled": true,
"isInternetEnabled": true,
"language": "python",
"sourceType": "notebook"
},
"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": "3.10.12"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
|