tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
09d714a verified | # OWL-ViT: open-vocabulary object detection with Transformers | |
| This page is the Transformers contract of the OWL-ViT 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 `google/owlvit-base-patch32`. 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 described by text | |
| ```py runnable:detect | |
| import torch | |
| from transformers import AutoProcessor, OwlViTForObjectDetection, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "google/owlvit-base-patch32" | |
| 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) # seeds torch, NumPy, and Python: loading, dropout, and data augmentation draw random numbers | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| model = OwlViTForObjectDetection.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = OwlViTForObjectDetection.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| model.eval() | |
| # Two synthetic images and two text queries per image: a real batch makes padding and batching meaningful. | |
| texts = [["a photo of a cat", "a photo of a dog"], ["a red car", "a blue car"]] | |
| images = [torch.rand(3, 768, 768), torch.rand(3, 768, 768)] | |
| # OWL-ViT's processor concatenates the per-image query encodings, so every image must carry the | |
| # same number of queries; padding=True then pads the queries to a common token length. | |
| # OWL-ViT pads each image's queries on its own: pad to max_length so they concatenate. | |
| inputs = processor(text=texts, images=images, return_tensors="pt", padding="max_length") | |
| inputs = inputs.to(device, dtype=model.dtype) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| # Keep only the top few detections per image, never every post-processed box. | |
| target_sizes = torch.tensor([(768, 768), (768, 768)], device=device) | |
| results = processor.post_process_grounded_object_detection( | |
| outputs=outputs, target_sizes=target_sizes, threshold=0.0, text_labels=texts | |
| ) | |
| detections = { | |
| "boxes": [r["boxes"][:3].float().cpu().tolist() for r in results], | |
| "scores": [r["scores"][:3].float().cpu().tolist() for r in results], | |
| "labels": [r["text_labels"][:3] for r in results], | |
| } | |
| print(results[0]["boxes"][:3]) | |
| ``` | |
| ## One training step | |
| ```py runnable:detect:2 | |
| # OWL-ViT's public forward returns no loss, so build a small detection loss from its outputs: | |
| # match each target box to the nearest predicted box and use class BCE plus box L1. | |
| labels = [ | |
| { | |
| "class_labels": torch.tensor([0, 1], device=device, dtype=torch.int64), | |
| "boxes": torch.tensor([[0.5, 0.5, 0.2, 0.2], [0.3, 0.3, 0.1, 0.1]], device=device, dtype=torch.float32), | |
| }, | |
| { | |
| "class_labels": torch.tensor([0], device=device, dtype=torch.int64), | |
| "boxes": torch.tensor([[0.6, 0.6, 0.3, 0.3]], device=device, dtype=torch.float32), | |
| }, | |
| ] | |
| model.train() | |
| optimizer = torch.optim.SGD([p for p in model.parameters() if p.requires_grad], lr=0.01) | |
| with torch.autocast(device_type=model.device.type, dtype=model.dtype, enabled=model.dtype != torch.float32): | |
| outputs = model(**inputs) | |
| pred_boxes = outputs.pred_boxes # (batch, num_queries, 4), normalized cx, cy, w, h | |
| pred_logits = outputs.logits # (batch, num_queries, num_queries_text) | |
| loss = 0.0 | |
| for i, target in enumerate(labels): | |
| boxes = target["boxes"] | |
| classes = target["class_labels"] | |
| # Nearest predicted box for each target box. | |
| dist = torch.cdist(boxes, pred_boxes[i]) | |
| idx = dist.argmin(dim=1) | |
| loss = loss + torch.nn.functional.l1_loss(pred_boxes[i][idx], boxes) | |
| # Score every text query of the matched boxes: the zero targets keep a gradient | |
| # even where the fixture's logits for the wanted class are saturated. | |
| wanted = torch.nn.functional.one_hot(classes, pred_logits.shape[-1]).float() | |
| loss = loss + torch.nn.functional.binary_cross_entropy_with_logits(pred_logits[i][idx].float(), wanted) | |
| loss = loss / len(labels) | |
| 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 = ("class_head.dense0.weight", "owlvit.vision_model.encoder.layers.0.self_attn.q_proj.weight") | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "detections": detections, | |
| "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)) | |
| ``` | |