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Publish MIC usage, slim fixture and reviewed Transformers expectations
fb35353 verified | # Grounding DINO: zero-shot object detection with Transformers | |
| This page is the Transformers contract of the Grounding DINO MIC and also a usage example. [doc-builder](https://github.com/huggingface/doc-builder/blob/main/docs/runnable-code-blocks.md) renders the `runnable` blocks below as documentation, and its pytest plugin runs them as written against `IDEA-Research/grounding-dino-tiny`. The MIC runner runs the same blocks against each slim fixture and each checkpoint in [`integration.json`](../../integration.json). Lines marked `# doc-builder: hide` only matter there: they swap in the pinned checkpoint or variant and print the result record. | |
| ## Detect objects from text prompts | |
| ```py runnable:grounding_dino | |
| import torch | |
| from PIL import Image | |
| import numpy as np | |
| from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "IDEA-Research/grounding-dino-tiny" | |
| model_id = os.environ.get("MIC_MODEL", model_id) # doc-builder: hide | |
| variant = json.loads(os.environ.get("MIC_MODEL_KWARGS", "{}")) # doc-builder: hide | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| set_seed(0) | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| # Two synthetic images with different text prompts (lowercase phrases, each ending with a dot) | |
| rng = np.random.default_rng(0) | |
| image1 = Image.fromarray(rng.integers(0, 255, (320, 480, 3), dtype=np.uint8)) | |
| image2 = Image.fromarray(rng.integers(0, 255, (240, 320, 3), dtype=np.uint8)) | |
| text1 = "a cat. a remote control." | |
| text2 = "a person. a dog." | |
| inputs = processor(images=[image1, image2], text=[text1, text2], return_tensors="pt", padding=True) | |
| inputs = inputs.to(device, dtype=model.dtype) # casts only floating tensors (pixel_values) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| # Compact probes for both images: first 3 queries, their first 4 token scores and boxes | |
| class_scores = outputs.logits[:, :3, :4].float().tolist() | |
| box_preds = outputs.pred_boxes[:, :3].float().tolist() | |
| results = processor.post_process_grounded_object_detection( | |
| outputs, inputs.input_ids, threshold=0.3, text_threshold=0.25, target_sizes=[image.size[::-1] for image in (image1, image2)] | |
| ) | |
| print(f"detections per image: {[len(result['boxes']) for result in results]}") | |
| ``` | |
| ## One training step | |
| ```py runnable:grounding_dino:2 | |
| # Labels: class_labels index the phrases of each prompt; boxes are normalized (cx, cy, w, h) | |
| labels = [ | |
| { | |
| "class_labels": torch.tensor([0], dtype=torch.long, device=device), # "a cat" | |
| "boxes": torch.tensor([[0.5, 0.5, 0.2, 0.3]], dtype=torch.float32, device=device), | |
| }, | |
| { | |
| "class_labels": torch.tensor([1], dtype=torch.long, device=device), # "a dog" | |
| "boxes": torch.tensor([[0.6, 0.6, 0.3, 0.3]], dtype=torch.float32, device=device), | |
| }, | |
| ] | |
| model.train() | |
| optimizer = torch.optim.SGD(model.parameters(), lr=0.01) | |
| # The Hungarian matcher uses torch.cdist, which has no bfloat16 kernel: autocast runs it in float32 | |
| with torch.autocast(device, dtype=model.dtype, enabled=model.dtype != torch.float32): | |
| loss = model(**inputs, labels=labels).loss | |
| loss.backward() | |
| before = {n: p.detach().cpu().clone() for n, p in model.named_parameters() if p.requires_grad} # doc-builder: hide | |
| optimizer.step() | |
| print(f"loss: {loss.item():.4f}") | |
| if os.environ.get("MIC_RUNNER"): # doc-builder: hide | |
| named = dict(model.named_parameters()) | |
| checked = ("model.text_backbone.embeddings.word_embeddings.weight", "model.decoder.layers.0.self_attn.query.weight") | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "class_scores": class_scores, | |
| "box_preds": box_preds, | |
| "training": { | |
| "loss": loss.item(), | |
| "gradients": {n: bool(named[n].grad is not None and torch.count_nonzero(named[n].grad) > 0 and torch.isfinite(named[n].grad).all()) for n in checked}, | |
| "updated": any(not torch.equal(p.detach().cpu(), before[n]) for n, p in model.named_parameters() if n in before), | |
| }, | |
| } | |
| print(json.dumps(result)) | |
| ``` | |