Upload dino_tagger.ipynb

dino_tagger.ipynb

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-{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"pb9bxY0OE81H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================\n","# Cell 1: Setup - Dependencies, Model Download & Load Tagger\n","# =============================================\n","\n","# Install required packages\n","!pip install -q torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n","!pip install -q safetensors pillow\n","\n","import torch\n","from PIL import Image\n","import os\n","import zipfile\n","from pathlib import Path\n","from google.colab import files\n","\n","print(\"Downloading model files from Hugging Face...\")\n","\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/tagger_proto.safetensors -O tagger_proto.safetensors\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/tagger_vocab_with_categories.json -O tagger_vocab_with_categories.json\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/inference_tagger_standalone.py -O inference_tagger_standalone.py\n","\n","print(\"✅ Model files downloaded.\")\n","\n","# Import and initialize the Tagger\n","from inference_tagger_standalone import Tagger\n","\n","tagger = Tagger(\n","    checkpoint_path=\"tagger_proto.safetensors\",\n","    vocab_path=\"tagger_vocab_with_categories.json\",\n","    device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n","    dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,\n","    max_size=1024\n",")\n","\n","print(f\"✅ Tagger successfully loaded on {tagger.device}\")"],"metadata":{"id":"rWxLOedwHio3","outputId":"3b4fde9b-2e32-438d-a71c-10d123580bcc","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading model files from Hugging Face...\n"]}]},{"cell_type":"code","source":["# =============================================\n","# Cell 2: Upload Images → Tag → Generate output.zip\n","# =============================================\n","\n","# Upload images (you can select multiple files at once)\n","print(\"Please upload your images (PNG, JPG, JPEG, WEBP, BMP supported)...\")\n","uploaded = files.upload()\n","\n","# Filter only image files\n","image_paths = [f for f in uploaded.keys() if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp'))]\n","\n","if not image_paths:\n","    print(\"❌ No valid image files were uploaded.\")\n","else:\n","    print(f\"✅ Uploaded {len(image_paths)} image(s). Starting processing...\")\n","\n","    # Prepare output folder\n","    output_dir = Path(\"/content/output\")\n","    output_dir.mkdir(exist_ok=True)\n","\n","    # Process each image\n","    for i, img_name in enumerate(image_paths, start=1):\n","        print(f\"[{i}/{len(image_paths)}] Processing: {img_name}\")\n","\n","        # Get tags (top 50 by confidence)\n","        tags = tagger.predict(img_name, topk=50)\n","\n","        # Save image as numbered JPG\n","        img = Image.open(img_name).convert(\"RGB\")\n","        new_img_path = output_dir / f\"{i}.jpg\"\n","        img.save(new_img_path, \"JPEG\", quality=95)\n","\n","        # Save tags as .txt (one tag per line, highest confidence first)\n","        tag_file = output_dir / f\"{i}.txt\"\n","        with open(tag_file, \"w\", encoding=\"utf-8\") as f:\n","            for tag, prob in tags:\n","                f.write(f\"{tag}\\n\")          # Change to f\"{tag} {prob:.4f}\\n\" if you want scores\n","\n","        print(f\"   → Saved {i}.jpg + {i}.txt  ({len(tags)} tags)\")\n","\n","    # Create zip file\n","    zip_path = \"/content/output.zip\"\n","    with zipfile.ZipFile(zip_path, \"w\") as zipf:\n","        for file in sorted(output_dir.iterdir()):\n","            zipf.write(file, file.name)\n","\n","    print(f\"\\n✅ Processing complete! Zip file created: {zip_path}\")\n","    files.download(zip_path)"],"metadata":{"id":"-faezdNzHoN6"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774880489123},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1774879795776},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1763646205520},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1760993725927},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1760450712160},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1756712618300},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1747490904984},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1740037333374},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1736477078136},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1725365086834}],"gpuType":"T4"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"id":"pb9bxY0OE81H"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================\n","# Cell 1: Setup - Dependencies, Model & Tagger\n","# =============================================\n","\n","!pip install -q torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118\n","!pip install -q safetensors pillow\n","\n","import torch\n","from PIL import Image\n","import os\n","import zipfile\n","from pathlib import Path\n","from google.colab import files\n","from google.colab import widgets   # for form support\n","\n","print(\"Downloading model files...\")\n","\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/tagger_proto.safetensors -O tagger_proto.safetensors\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/tagger_vocab_with_categories.json -O tagger_vocab_with_categories.json\n","!wget -q https://huggingface.co/lodestones/tagger-experiment/resolve/main/inference_tagger_standalone.py -O inference_tagger_standalone.py\n","\n","print(\"✅ Model files downloaded.\")\n","\n","# Load the Tagger\n","from inference_tagger_standalone import Tagger\n","\n","tagger = Tagger(\n","    checkpoint_path=\"tagger_proto.safetensors\",\n","    vocab_path=\"tagger_vocab_with_categories.json\",\n","    device=\"cuda\" if torch.cuda.is_available() else \"cpu\",\n","    dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,\n","    max_size=1024\n",")\n","\n","print(f\"✅ Tagger loaded successfully on {tagger.device}\")"],"metadata":{"id":"hoi7qhsSJPE0"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# =============================================\n","# Cell 2: Upload Images → Tag → Download ZIP\n","# =============================================\n","\n","# @title Tagger Settings\n","threshold_percent = 85  # @param {type:\"slider\", min:1, max:95, step:1}\n","max_tags = 75           # @param {type:\"slider\", min:5, max:150, step:5}\n","use_max_tags = False     # @param {type:\"boolean\"}\n","add_commas = True      # @param {type:\"boolean\"}\n","\n","print(f\"Settings → Threshold: {threshold_percent}% | Max tags: {max_tags} | Force max tags: {use_max_tags} | Commas: {add_commas}\\n\")\n","\n","# Upload images\n","print(\"Please upload your images (multiple files supported)...\")\n","uploaded = files.upload()\n","\n","image_paths = [f for f in uploaded.keys() if f.lower().endswith(('.png', '.jpg', '.jpeg', '.webp', '.bmp'))]\n","\n","if not image_paths:\n","    print(\"❌ No valid images uploaded.\")\n","else:\n","    print(f\"✅ {len(image_paths)} image(s) uploaded. Processing...\\n\")\n","\n","    output_dir = Path(\"/content/output\")\n","    output_dir.mkdir(exist_ok=True)\n","\n","    threshold = threshold_percent / 100.0\n","\n","    for i, img_name in enumerate(image_paths, start=1):\n","        print(f\"[{i}/{len(image_paths)}] Processing: {img_name}\")\n","\n","        # Decide prediction mode\n","        if use_max_tags:\n","            tags_list = tagger.predict(img_name, topk=max_tags, threshold=None)\n","        else:\n","            tags_list = tagger.predict(img_name, topk=None, threshold=threshold)\n","            # Limit to max_tags even when using threshold\n","            if len(tags_list) > max_tags:\n","                tags_list = tags_list[:max_tags]\n","\n","        # Save image as numbered JPG\n","        img = Image.open(img_name).convert(\"RGB\")\n","        new_img_path = output_dir / f\"{i}.jpg\"\n","        img.save(new_img_path, \"JPEG\", quality=95)\n","\n","        # Prepare tag string\n","        tags_only = [tag for tag, prob in tags_list]\n","\n","        if add_commas:\n","            tag_text = \" , \".join(tags_only)\n","        else:\n","            tag_text = \" \".join(tags_only)\n","\n","        # Save as single-line text file\n","        tag_file = output_dir / f\"{i}.txt\"\n","        with open(tag_file, \"w\", encoding=\"utf-8\") as f:\n","            f.write(tag_text)\n","\n","        print(f\"   → Saved {i}.jpg + {i}.txt  ({len(tags_list)} tags)\")\n","\n","    # Create zip\n","    zip_path = \"/content/output.zip\"\n","    with zipfile.ZipFile(zip_path, \"w\") as zipf:\n","        for file in sorted(output_dir.iterdir()):\n","            zipf.write(file, file.name)\n","\n","    print(f\"\\n✅ All done! Output saved to {zip_path}\")\n","    files.download(zip_path)"],"metadata":{"id":"-IefzYMXJRVw"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/dino_tagger.ipynb","timestamp":1774881268156},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1774879795776},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1763646205520},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1760993725927},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1760450712160},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1756712618300},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1747490904984},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1740037333374},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1736477078136},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1725365086834}],"gpuType":"T4"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0}