{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "code", "colab": { "base_uri": "https://localhost:8080/" }, "id": "6-DZJrfyqkkK", "outputId": "2dd4df56-c9be-4475-a053-74fccafaabce" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/tinkerspace/.local/lib/python3.12/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", " warnings.warn(\n", "/home/tinkerspace/.local/lib/python3.12/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n", " warnings.warn(msg)\n" ] } ], "source": [ "import torch\n", "import torch.nn as nn\n", "from torchvision import models, transforms\n", "from PIL import Image\n", "import os\n", "\n", "# Load Pretrained ResNet Model\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "model = models.resnet50(pretrained=True).to(device)\n", "model.eval()\n", "\n", "# Remove the last fully connected layer to get feature vectors\n", "model = nn.Sequential(*list(model.children())[:-1])\n", "\n", "# Transform for input images\n", "transform = transforms.Compose([\n", " transforms.Resize((224, 224)), # ResNet expects 224x224 input\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", "])\n", "\n", "# Feature extraction function\n", "def extract_features(image_path, model):\n", " image = Image.open(image_path).convert('RGB')\n", " image = transform(image).unsqueeze(0).to(device)\n", " with torch.no_grad():\n", " features = model(image).squeeze().cpu().numpy()\n", " return features\n", "\n", "# Directory paths for game frames and real-world images\n", "real_dir = '/home/tinkerspace/extracted_images'\n", "frame_dir = '/home/tinkerspace/extracted_frames'\n", "frame_features_dir = '/home/tinkerspace/frames_features'\n", "real_features_dir = '/home/tinkerspace/real_features'\n", "\n", "# Ensure directories exist\n", "os.makedirs(frame_features_dir, exist_ok=True)\n", "os.makedirs(real_features_dir, exist_ok=True)\n", "\n", "# Extract and save features\n", "def save_features(real_dir, save_dir, model):\n", " for img_file in os.listdir(real_dir):\n", " img_path = os.path.join(real_dir, img_file)\n", " features = extract_features(img_path, model)\n", " save_path = os.path.join(save_dir, img_file.split('.')[0] + '.pt')\n", " torch.save(features, save_path)\n", "\n", "# save_features(frame_dir, frame_features_dir, model)\n", "# save_features(real_dir, real_features_dir, model)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_QoG3KMcqk7l", "outputId": "a68ffecd-bce4-4bbc-d4f3-6da006cb2958" }, "outputs": [], "source": [ "for i in range(1, 11):\n", " url = f\"https://huggingface.co/datasets/aoxo/photorealism-style-adapter-gta-v/resolve/main/images_{i}.zip\"\n", " !wget {url}\n", " !unzip -o \"/home/tinkerspace/images_{i}.zip\" # Use -o to overwrite if needed\n", " !rm \"/home/tinkerspace/images_{i}.zip\"\n", " save_features(frame_dir, frame_features_dir, model)\n", " save_features(real_dir, real_features_dir, model)\n", " !rm -rf \"/home/tinkerspace/kaggle\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_92001/1589225413.py:11: 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", " loaded_feat = torch.load(feature_path, map_location='cpu') # Load directly to CPU\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Frame batch 1/6916 processed and saved to matches_frame_batch_1.json\n", "Frame batch 2/6916 processed and saved to matches_frame_batch_2.json\n", "Frame batch 3/6916 processed and saved to matches_frame_batch_3.json\n", "Frame batch 4/6916 processed and saved to matches_frame_batch_4.json\n", "Frame batch 5/6916 processed and saved to matches_frame_batch_5.json\n", "Frame batch 6/6916 processed and saved to matches_frame_batch_6.json\n", "Frame batch 7/6916 processed and saved to matches_frame_batch_7.json\n", "Frame batch 8/6916 processed and saved to matches_frame_batch_8.json\n", "Frame batch 9/6916 processed and saved to matches_frame_batch_9.json\n", "Frame batch 10/6916 processed and saved to matches_frame_batch_10.json\n", "Frame batch 11/6916 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return features\n", "\n", "# Function to compute cosine similarities for a batch\n", "def compute_similarities(args):\n", " batch_tensors, real_matrix_norm, real_names, top_n = args\n", " batch_tensor_norm = batch_tensors / batch_tensors.norm(dim=1, keepdim=True) # In-place normalization\n", " similarities = batch_tensor_norm @ real_matrix_norm.T # Matrix multiplication\n", "\n", " results = []\n", " for j in range(batch_tensor_norm.size(0)):\n", " top_indices = torch.topk(similarities[j], top_n).indices.cpu().numpy() # Get top indices and move to CPU\n", " top_matches = [(real_names[idx], similarities[j][idx].item()) for idx in top_indices] # Get the scores\n", " results.append(top_matches)\n", " \n", " return results\n", "\n", "# Compute cosine similarities and save incrementally\n", "def find_top_matches_and_save(frame_features, real_features, top_n=10, frame_batch_size=5, real_batch_size=200):\n", " real_names = list(real_features.keys())\n", " real_matrix = torch.stack(list(real_features.values())).cuda() # Move to GPU\n", " real_matrix_norm = real_matrix / real_matrix.norm(dim=1, keepdim=True)\n", "\n", " frame_names = list(frame_features.keys())\n", " total_frame_batches = len(frame_names) // frame_batch_size + (1 if len(frame_names) % frame_batch_size > 0 else 0)\n", "\n", " for frame_batch_index in range(total_frame_batches):\n", " batch_names = frame_names[frame_batch_index * frame_batch_size:(frame_batch_index + 1) * frame_batch_size]\n", " batch_tensors = torch.stack([frame_features[name] for name in batch_names]).cuda() # Move to GPU\n", "\n", " # Compute similarities\n", " similarities = compute_similarities((batch_tensors, real_matrix_norm, real_names, top_n))\n", " \n", " # Write to JSON file\n", " json_file = f'matches_frame_batch_{frame_batch_index + 1}.json'\n", " with open(json_file, 'w') as f:\n", " json.dump({batch_names[j]: similarities[j] for j in range(len(batch_names))}, f)\n", "\n", " # Clear cache\n", " torch.cuda.empty_cache()\n", "\n", " # Update progress\n", " print(f'Frame batch {frame_batch_index + 1}/{total_frame_batches} processed and saved to {json_file}')\n", "\n", " # Optionally, process real features in batches\n", " total_real_batches = len(real_names) // real_batch_size + (1 if len(real_names) % real_batch_size > 0 else 0)\n", "\n", " for real_batch_index in range(total_real_batches):\n", " batch_indices = slice(real_batch_index * real_batch_size, (real_batch_index + 1) * real_batch_size)\n", " batch_real_names = real_names[batch_indices]\n", " batch_real_tensors = real_matrix[batch_indices] # Already on GPU\n", "\n", " # Optionally do something with the real features batch here if needed\n", " # For instance, save to a separate file or process further\n", "\n", " # Clear cache\n", " torch.cuda.empty_cache()\n", " print(f'Real features batch {real_batch_index + 1}/{total_real_batches} processed.')\n", "\n", "# Load features\n", "frame_features = load_features(frame_features_dir)\n", "real_features = load_features(real_features_dir)\n", "\n", "# Find top matches and save incrementally\n", "find_top_matches_and_save(frame_features, real_features, top_n=10, frame_batch_size=100, real_batch_size=1000)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Combined JSON file saved as: /home/tinkerspace/jsons/cyberpunk-china-losangeles.json\n" ] } ], "source": [ "import json\n", "import os\n", "\n", "def combine_json_files(directory, output_file):\n", " combined_data = {}\n", "\n", " # List all JSON files in the specified directory\n", " for filename in os.listdir(directory):\n", " if filename.endswith('.json'):\n", " file_path = os.path.join(directory, filename)\n", " with open(file_path, 'r') as f:\n", " data = json.load(f)\n", " combined_data.update(data) # Merge data into combined_data\n", "\n", " # Write the combined data to the output JSON file\n", " with open(output_file, 'w') as f:\n", " json.dump(combined_data, f, indent=4)\n", "\n", " print(f'Combined JSON file saved as: {output_file}')\n", "\n", "# Specify the directory containing the JSON files and the output file name\n", "json_directory = '/home/tinkerspace/jsons'\n", "output_json_file = '/home/tinkerspace/jsons/cyberpunk-china-losangeles.json'\n", "\n", "# Combine the JSON files\n", "combine_json_files(json_directory, output_json_file)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 0 }