| # ViT: image classification with Transformers |
|
|
| This page is the Transformers contract of the 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/vit-base-patch16-224`. 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. |
|
|
| ## Classify two synthetic images |
|
|
| ```py runnable:vit_classify |
| import torch |
| from transformers import AutoConfig, AutoModelForImageClassification, AutoImageProcessor, set_seed |
| import json # doc-builder: hide |
| import os # doc-builder: hide |
| import numpy as np |
| |
| model_id = "google/vit-base-patch16-224" |
| 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 |
| |
| config = AutoConfig.from_pretrained(model_id) |
| processor = AutoImageProcessor.from_pretrained(model_id) |
| model = AutoModelForImageClassification.from_pretrained(model_id).to(device) |
| if variant: # doc-builder: hide |
| model = AutoModelForImageClassification.from_pretrained(model_id, **variant).to(device) |
| |
| # Two distinct synthetic images (224x224x3, uint8) |
| img1 = np.zeros((224, 224, 3), dtype=np.uint8) |
| img1[:112, :112] = 255 # top-left white quadrant |
| img2 = np.zeros((224, 224, 3), dtype=np.uint8) |
| img2[112:, 112:] = 255 # bottom-right white quadrant |
| |
| inputs = processor(images=[img1, img2], return_tensors="pt").to(device) |
| |
| model.eval() |
| with torch.no_grad(): |
| logits = model(**inputs).logits |
| |
| top5 = logits.topk(5, dim=-1) |
| top5_indices = top5.indices.tolist() |
| top5_probs = torch.softmax(logits, dim=-1).topk(5, dim=-1).values.float().tolist() |
| |
| print(f"Top-5 labels: {top5_indices}") |
| print(f"Top-5 probs: {[[f'{p:.4f}' for p in row] for row in top5_probs]}") |
| ``` |
|
|
| ## One training step |
|
|
| ```py runnable:vit_classify:2 |
| labels = torch.tensor([0, 1], device=device) |
| |
| model.train() |
| optimizer = torch.optim.SGD(model.parameters(), lr=0.01) |
| loss = model(**inputs, labels=labels).loss |
| loss.backward() |
| before = {name: p.detach().clone() for name, p in model.named_parameters()} # 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 = tuple(n for n, p in named.items() if p.grad is not None and p.ndim >= 2 and torch.count_nonzero(p.grad) > 0)[:2] |
| result = { |
| "runtime": { |
| "device": model.device.type, |
| "dtype": str(next(model.parameters()).dtype).removeprefix("torch."), |
| "attn_implementation": model.config._attn_implementation, |
| }, |
| "top5_labels": top5_indices, |
| "top5_probs": top5_probs, |
| "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, before[n]) for n, p in named.items()), |
| }, |
| } |
| print(json.dumps(result)) |
| ``` |
|
|
