Upload qwen3vl-detect.py with huggingface_hub

qwen3vl-detect.py

@@ -0,0 +1,591 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "datasets>=4.0.0",
+#     "huggingface-hub",
+#     "pillow",
+#     "toolz",
+#     "torch",
+#     "tqdm",
+#     "transformers",
+#     "vllm>=0.15.1",
+# ]
+# ///
+
+"""
+Instruction-oriented object detection with Qwen3-VL via vLLM.
+
+Takes an HF dataset with an image column, runs each image through Qwen3-VL with
+a free-form detection prompt, parses bounding-box JSON from the response, and
+pushes a labelled dataset back to the Hub. Designed as a VLM-as-labeller
+primitive for bootstrapping object-detection datasets.
+
+The prompt is free-form: "detect all components and identify their reference
+designators with bbox_2d, label, sub_label" or "detect every car and its
+colour". Qwen3-VL emits bbox JSON on a 0-1000 normalised scale; we extract,
+denormalise to original-image pixel coords, and store as ints ready for
+downstream labelling tools (Label Studio, FiftyOne, COCO conversion, etc.).
+
+Sibling: `uv-scripts/sam3/detect-objects.py` does class-prompted detection.
+This script is the instruction-prompted counterpart.
+
+Output columns added:
+- detections: list[{bbox: [x1,y1,x2,y2] in ORIGINAL-IMAGE PIXELS, label, sub_label}]
+- raw_response: full model text (for QA, re-parsing, audit)
+- inference_info: JSON with model, prompt, image_size, min/max pixels, timestamp
+"""
+
+import argparse
+import io
+import json
+import logging
+import os
+import re
+import sys
+from datetime import datetime
+from typing import Any, Optional, Union
+
+import torch
+from datasets import load_dataset
+from huggingface_hub import login
+from PIL import Image
+from toolz import partition_all
+from tqdm.auto import tqdm
+from transformers import AutoProcessor
+from vllm import LLM, SamplingParams
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+DEFAULT_MODEL = "Qwen/Qwen3.6-35B-A3B"
+
+# Qwen-VL bbox prompt template — used when no --prompt is supplied. The
+# "bbox_2d / label / sub_label" schema matches the original demo (app.py) so
+# the same prompts work here.
+DEFAULT_PROMPT = (
+    "Detect every distinct object in the image. For each object, output a JSON "
+    "object with keys: bbox_2d (an array of four numbers [x1, y1, x2, y2]), "
+    "label (the object category), and sub_label (a short descriptive attribute "
+    'or "" if none applies). Return a JSON array of these objects. Example: '
+    '[{"bbox_2d": [x1, y1, x2, y2], "label": "car", "sub_label": "red"}].'
+)
+
+
+def eval_pixel_string(s: str) -> int:
+    """Evaluate a pixel expression like '1024*32*32' to an integer.
+
+    Lifted from app.py:284-289. Lets users write `--min-image-tokens 1024`
+    and have it become 1024*32*32 pixels (Qwen-VL uses 32x32 patches).
+    """
+    try:
+        return int(eval(s.strip().replace(" ", ""), {"__builtins__": {}}))
+    except Exception as e:
+        raise ValueError(f"Invalid pixel expression: {s}") from e
+
+
+def to_pil(image: Union[Image.Image, dict, str]) -> Image.Image:
+    """Convert HF dataset image entries (PIL / dict-with-bytes / path) to PIL."""
+    if isinstance(image, Image.Image):
+        return image
+    if isinstance(image, dict) and "bytes" in image:
+        return Image.open(io.BytesIO(image["bytes"]))
+    if isinstance(image, str):
+        return Image.open(image)
+    raise ValueError(f"Unsupported image type: {type(image)}")
+
+
+def parse_bboxes_from_response(text: str) -> list[dict[str, Any]]:
+    """Extract bbox objects from a model response.
+
+    Lifted from app.py:147-194. Tolerates fenced code blocks, comments,
+    trailing commas, and malformed JSON. Falls back to regex bbox extraction
+    if no parseable objects are found.
+    """
+    results: list[dict[str, Any]] = []
+
+    pattern = r'\{[^{}]*"bbox_2d"\s*:\s*\[[\d\s.,\-]+\][^{}]*\}'
+    for match in re.findall(pattern, text, re.DOTALL):
+        try:
+            obj = json.loads(match)
+            if "bbox_2d" in obj:
+                results.append(obj)
+                continue
+        except json.JSONDecodeError:
+            pass
+        try:
+            cleaned = re.sub(r"#.*$", "", match, flags=re.MULTILINE).strip()
+            cleaned = re.sub(r",\s*}", "}", cleaned)
+            cleaned = re.sub(r",\s*\]", "]", cleaned)
+            obj = json.loads(cleaned)
+            if "bbox_2d" in obj:
+                results.append(obj)
+        except json.JSONDecodeError:
+            continue
+
+    if not results:
+        bbox_pattern = (
+            r'"bbox_2d"\s*:\s*\[\s*([\d.\-]+)\s*,\s*([\d.\-]+)\s*,\s*'
+            r"([\d.\-]+)\s*,\s*([\d.\-]+)\s*\]"
+        )
+        for i, (x1, y1, x2, y2) in enumerate(re.findall(bbox_pattern, text)):
+            results.append(
+                {
+                    "bbox_2d": [float(x1), float(y1), float(x2), float(y2)],
+                    "label": f"object_{i + 1}",
+                    "sub_label": "",
+                }
+            )
+
+    return results
+
+
+def denormalize_bbox(bbox: list[float], width: int, height: int) -> list[int]:
+    """Convert a Qwen-VL 0-1000 normalised bbox to original-image pixel coords.
+
+    Empirically confirmed (smoke runs on cppe-5 and Beyond Words, May 2026): the
+    instruction-tuned Qwen3-VL family emits `bbox_2d` values in the 0-1000
+    normalised space regardless of input image size or smart-resize behaviour.
+    Multiply by W/1000 and H/1000 to recover pixel coords on the original image,
+    then round to ints and clip to image bounds for safety.
+    """
+    if len(bbox) != 4:
+        return []
+    sx, sy = width / 1000.0, height / 1000.0
+    x1, y1, x2, y2 = bbox
+    return [
+        max(0, min(width, round(x1 * sx))),
+        max(0, min(height, round(y1 * sy))),
+        max(0, min(width, round(x2 * sx))),
+        max(0, min(height, round(y2 * sy))),
+    ]
+
+
+def normalise_detection(obj: dict[str, Any], width: int, height: int) -> dict[str, Any]:
+    """Coerce a parsed bbox dict to the canonical detection schema.
+
+    Output: {bbox: list[int] length 4 in ORIGINAL-IMAGE PIXELS, label, sub_label}
+    """
+    raw = obj.get("bbox_2d", [])
+    raw = [float(x) for x in raw] if isinstance(raw, list) and len(raw) == 4 else []
+    return {
+        "bbox": denormalize_bbox(raw, width, height),
+        "label": str(obj.get("label", "")),
+        "sub_label": str(obj.get("sub_label") or ""),
+    }
+
+
+def main(
+    input_dataset: str,
+    output_dataset: str,
+    prompt: str,
+    image_column: str = "image",
+    model: str = DEFAULT_MODEL,
+    batch_size: int = 8,
+    max_samples: Optional[int] = None,
+    split: str = "train",
+    # Defaults below assume Qwen3.6-35B-A3B on a100-large (80 GB) via the
+    # vllm/vllm-openai image. For smaller GPUs use Qwen/Qwen3.5-9B and lower
+    # budgets, e.g.:
+    #   --max-model-len 12288 --max-image-tokens 4096 --gpu-memory-utilization 0.92
+    max_model_len: int = 32768,
+    max_tokens: int = 8192,
+    min_image_tokens: int = 1024,
+    max_image_tokens: int = 9800,
+    gpu_memory_utilization: float = 0.90,
+    tensor_parallel_size: Optional[int] = None,
+    temperature: float = 0.0,
+    repetition_penalty: float = 1.05,
+    grayscale: bool = False,
+    hf_token: Optional[str] = None,
+    private: bool = False,
+    shuffle: bool = False,
+    seed: int = 42,
+    create_pr: bool = False,
+) -> None:
+    if not torch.cuda.is_available():
+        logger.error("CUDA is not available. This script requires a GPU.")
+        logger.error("For cloud execution: hf jobs uv run --flavor l4x1 ...")
+        sys.exit(1)
+    logger.info("CUDA OK — GPU: %s", torch.cuda.get_device_name(0))
+
+    HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
+    if HF_TOKEN:
+        login(token=HF_TOKEN)
+
+    start_time = datetime.now()
+
+    min_pixels = eval_pixel_string(str(min_image_tokens)) * 32 * 32
+    max_pixels = eval_pixel_string(str(max_image_tokens)) * 32 * 32
+    logger.info(
+        "Pixel budget: min=%d (%d tokens), max=%d (%d tokens)",
+        min_pixels,
+        min_image_tokens,
+        max_pixels,
+        max_image_tokens,
+    )
+
+    logger.info("Loading dataset: %s (split=%s)", input_dataset, split)
+    dataset = load_dataset(input_dataset, split=split)
+
+    if image_column not in dataset.column_names:
+        raise ValueError(
+            f"Column '{image_column}' not found. Available: {dataset.column_names}"
+        )
+
+    if shuffle:
+        logger.info("Shuffling dataset (seed=%d)", seed)
+        dataset = dataset.shuffle(seed=seed)
+
+    if max_samples:
+        dataset = dataset.select(range(min(max_samples, len(dataset))))
+        logger.info("Limited to %d samples", len(dataset))
+
+    if tensor_parallel_size is None:
+        tensor_parallel_size = torch.cuda.device_count()
+        logger.info(
+            "Auto-detected %d GPU(s) for tensor parallelism", tensor_parallel_size
+        )
+
+    logger.info("Loading vLLM model: %s", model)
+    # Cap max_num_seqs to our batch_size: hybrid-Mamba architectures (Qwen3.6's
+    # qwen3_5_moe uses Gated DeltaNet) allocate per-sequence SSM cache blocks
+    # separately from KV cache; vLLM's default 256 can exceed available blocks
+    # and crash CUDA graph capture. We never decode > batch_size concurrently.
+    llm_kwargs: dict[str, Any] = {
+        "model": model,
+        "trust_remote_code": True,
+        "gpu_memory_utilization": gpu_memory_utilization,
+        "tensor_parallel_size": tensor_parallel_size,
+        "limit_mm_per_prompt": {"image": 1},
+        "mm_processor_kwargs": {
+            "min_pixels": min_pixels,
+            "max_pixels": max_pixels,
+        },
+        "max_num_seqs": max(batch_size, 8),
+    }
+    if max_model_len:
+        llm_kwargs["max_model_len"] = max_model_len
+
+    llm = LLM(**llm_kwargs)
+    processor = AutoProcessor.from_pretrained(model, trust_remote_code=True)
+
+    # repetition_penalty > 1.0 prevents the catastrophic loop where the model
+    # emits the same detection JSON object hundreds of times until it hits the
+    # token cap (observed on Qwen3-VL-30B-A3B-Instruct on cppe-5 row 3/5).
+    sampling_params = SamplingParams(
+        temperature=temperature,
+        max_tokens=max_tokens,
+        repetition_penalty=repetition_penalty,
+    )
+    logger.info(
+        "Sampling: temperature=%.2f  repetition_penalty=%.2f  max_tokens=%d",
+        temperature,
+        repetition_penalty,
+        max_tokens,
+    )
+
+    logger.info("Processing %d images in batches of %d", len(dataset), batch_size)
+    logger.info("Prompt: %s", prompt[:200] + ("..." if len(prompt) > 200 else ""))
+
+    all_detections: list[list[dict[str, Any]]] = []
+    all_raw: list[str] = []
+    all_sizes: list[list[int]] = []
+
+    for batch_indices in tqdm(
+        partition_all(batch_size, range(len(dataset))),
+        total=(len(dataset) + batch_size - 1) // batch_size,
+        desc="Qwen3-VL detection",
+    ):
+        batch_indices = list(batch_indices)
+        batch_inputs: list[dict[str, Any]] = []
+        batch_sizes: list[list[int]] = []
+
+        for idx in batch_indices:
+            pil_img = to_pil(dataset[idx][image_column]).convert("RGB")
+            if grayscale:
+                # Convert to luminance and replicate to 3 channels; useful for
+                # discoloured/sepia historical scans where the colour channel
+                # is misleading noise rather than signal.
+                pil_img = pil_img.convert("L").convert("RGB")
+            batch_sizes.append([pil_img.width, pil_img.height])
+            messages = [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image"},
+                        {"type": "text", "text": prompt},
+                    ],
+                }
+            ]
+            templated_prompt = processor.apply_chat_template(
+                messages, add_generation_prompt=True, tokenize=False
+            )
+            batch_inputs.append(
+                {
+                    "prompt": templated_prompt,
+                    "multi_modal_data": {"image": pil_img},
+                }
+            )
+
+        try:
+            outputs = llm.generate(batch_inputs, sampling_params, use_tqdm=False)
+        except Exception as e:
+            logger.error("Batch failed: %s", e)
+            for size in batch_sizes:
+                all_detections.append([])
+                all_raw.append(f"[ERROR] {e}")
+                all_sizes.append(size)
+            continue
+
+        for output, size in zip(outputs, batch_sizes):
+            text = output.outputs[0].text if output.outputs else ""
+            parsed = parse_bboxes_from_response(text)
+            W, H = size
+            all_detections.append([normalise_detection(o, W, H) for o in parsed])
+            all_raw.append(text)
+            all_sizes.append(size)
+
+    processing_duration = datetime.now() - start_time
+    logger.info(
+        "Processing complete in %.1f min", processing_duration.total_seconds() / 60
+    )
+
+    # Per-row inference_info so the image_size travels with the row (bbox
+    # coordinate-frame work in v2 needs this).
+    inference_info_rows: list[str] = []
+    for size in all_sizes:
+        inference_info_rows.append(
+            json.dumps(
+                {
+                    "model_id": model,
+                    "prompt": prompt,
+                    "image_size": size,
+                    "min_pixels": min_pixels,
+                    "max_pixels": max_pixels,
+                    "script": "qwen3vl-detect.py",
+                    "timestamp": datetime.now().isoformat(timespec="seconds"),
+                }
+            )
+        )
+
+    logger.info("Adding columns to dataset")
+    dataset = dataset.add_column("detections", all_detections)
+    dataset = dataset.add_column("raw_response", all_raw)
+    dataset = dataset.add_column("inference_info", inference_info_rows)
+
+    logger.info("Pushing to %s", output_dataset)
+    dataset.push_to_hub(
+        output_dataset,
+        private=private,
+        token=HF_TOKEN,
+        create_pr=create_pr,
+        commit_message=(f"Qwen3-VL detection: {model} on {len(dataset)} samples"),
+    )
+
+    n_with_dets = sum(1 for d in all_detections if d)
+    total_dets = sum(len(d) for d in all_detections)
+    logger.info("Done.")
+    logger.info("  Rows with >=1 detection: %d / %d", n_with_dets, len(all_detections))
+    logger.info("  Total detections: %d", total_dets)
+    logger.info("  Output: https://huggingface.co/datasets/%s", output_dataset)
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(
+        description="Instruction-oriented object detection with Qwen3-VL (vLLM batch).",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Generic detection
+  uv run qwen3vl-detect.py \\
+      input-dataset username/output-dataset \\
+      --prompt "Detect all visible objects, return JSON with bbox_2d, label, sub_label"
+
+  # Component detection (matches the demo's example 1)
+  uv run qwen3vl-detect.py \\
+      pcb-images username/labelled-pcbs \\
+      --prompt-file prompts/components.txt \\
+      --max-samples 20
+
+  # HF Jobs (local script upload)
+  hf jobs uv run --flavor l4x1 \\
+      -e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \\
+      ./qwen3vl-detect.py \\
+      input-dataset username/output-dataset \\
+      --prompt "detect all photographs" --max-samples 5
+
+Notes:
+  Default --model is Qwen/Qwen3.6-35B-A3B (needs A100/80GB via
+  vllm/vllm-openai:latest image). For smaller GPUs use Qwen/Qwen3.5-9B
+  and lower --max-model-len / --max-image-tokens.
+        """,
+    )
+    parser.add_argument("input_dataset", help="Input dataset ID on the Hub")
+    parser.add_argument("output_dataset", help="Output dataset ID on the Hub")
+
+    prompt_group = parser.add_mutually_exclusive_group()
+    prompt_group.add_argument(
+        "--prompt",
+        default=None,
+        help="Detection instruction (free-form). Defaults to a generic JSON-bbox prompt.",
+    )
+    prompt_group.add_argument(
+        "--prompt-file",
+        default=None,
+        help="Path to a text file containing the detection prompt.",
+    )
+
+    parser.add_argument(
+        "--image-column", default="image", help="Image column name (default: image)"
+    )
+    parser.add_argument(
+        "--model", default=DEFAULT_MODEL, help=f"VLM (default: {DEFAULT_MODEL})"
+    )
+    parser.add_argument(
+        "--batch-size", type=int, default=8, help="Batch size (default: 8)"
+    )
+    parser.add_argument(
+        "--max-samples", type=int, default=None, help="Cap rows processed (for testing)"
+    )
+    parser.add_argument(
+        "--split", default="train", help="Dataset split (default: train)"
+    )
+    parser.add_argument(
+        "--max-model-len",
+        type=int,
+        default=32768,
+        help="vLLM max_model_len (default: 32768, tuned for A100/80GB). "
+        "Lower to ~12288 on L4-class GPUs.",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=8192,
+        help="Max generation tokens (default: 8192)",
+    )
+    parser.add_argument(
+        "--min-image-tokens",
+        type=int,
+        default=1024,
+        help="Min image tokens (32x32 patches); default 1024",
+    )
+    parser.add_argument(
+        "--max-image-tokens",
+        type=int,
+        default=9800,
+        help="Max image tokens (32x32 patches); default 9800 (A100/80GB). "
+        "Lower to ~4096 on L4-class GPUs.",
+    )
+    parser.add_argument(
+        "--gpu-memory-utilization",
+        type=float,
+        default=0.90,
+        help="GPU memory utilisation (default: 0.90)",
+    )
+    parser.add_argument(
+        "--tensor-parallel-size",
+        type=int,
+        default=None,
+        help="Tensor-parallel GPUs (default: auto)",
+    )
+    parser.add_argument(
+        "--temperature",
+        type=float,
+        default=0.0,
+        help="Sampling temperature (default: 0.0)",
+    )
+    parser.add_argument(
+        "--repetition-penalty",
+        type=float,
+        default=1.05,
+        help="vLLM repetition_penalty (default: 1.05). Prevents the duplicate-detection loop "
+        "observed on Qwen3-VL-30B-A3B. Set to 1.0 to disable.",
+    )
+    parser.add_argument(
+        "--grayscale",
+        action="store_true",
+        help="Convert each image to greyscale (L channel replicated to RGB) before "
+        "inference. Useful for sepia/discoloured historical scans where the colour "
+        "channel is noise rather than signal.",
+    )
+    parser.add_argument(
+        "--hf-token", default=None, help="HF token (or set HF_TOKEN env)"
+    )
+    parser.add_argument(
+        "--private", action="store_true", help="Push output dataset as private"
+    )
+    parser.add_argument(
+        "--shuffle", action="store_true", help="Shuffle before processing"
+    )
+    parser.add_argument(
+        "--seed", type=int, default=42, help="Shuffle seed (default: 42)"
+    )
+    parser.add_argument(
+        "--create-pr",
+        action="store_true",
+        help="Push as PR instead of direct commit (useful for parallel runs)",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    parser = build_parser()
+
+    if len(sys.argv) == 1:
+        parser.print_help()
+        print("\n" + "=" * 60)
+        print("Example HF Jobs command:")
+        print("=" * 60)
+        print(
+            """
+hf jobs uv run \\
+    --image vllm/vllm-openai:latest \\
+    --flavor a100-large \\
+    --python /usr/bin/python3 \\
+    -e PYTHONPATH=/usr/local/lib/python3.12/dist-packages \\
+    -s HF_TOKEN \\
+    ./qwen3vl-detect.py \\
+    INPUT_DATASET OUTPUT_DATASET \\
+    --prompt "detect all visible objects" \\
+    --max-samples 5
+            """
+        )
+        sys.exit(0)
+
+    args = parser.parse_args()
+
+    if args.prompt_file:
+        with open(args.prompt_file, "r", encoding="utf-8") as f:
+            prompt = f.read().strip()
+    elif args.prompt:
+        prompt = args.prompt
+    else:
+        prompt = DEFAULT_PROMPT
+
+    main(
+        input_dataset=args.input_dataset,
+        output_dataset=args.output_dataset,
+        prompt=prompt,
+        image_column=args.image_column,
+        model=args.model,
+        batch_size=args.batch_size,
+        max_samples=args.max_samples,
+        split=args.split,
+        max_model_len=args.max_model_len,
+        max_tokens=args.max_tokens,
+        min_image_tokens=args.min_image_tokens,
+        max_image_tokens=args.max_image_tokens,
+        gpu_memory_utilization=args.gpu_memory_utilization,
+        tensor_parallel_size=args.tensor_parallel_size,
+        temperature=args.temperature,
+        repetition_penalty=args.repetition_penalty,
+        grayscale=args.grayscale,
+        hf_token=args.hf_token,
+        private=args.private,
+        shuffle=args.shuffle,
+        seed=args.seed,
+        create_pr=args.create_pr,
+    )