File size: 3,834 Bytes
89ae45d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | # Swin: image classification with Transformers
This page is the Transformers contract of the Swin 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 `microsoft/swin-tiny-patch4-window7-224`. The MIC runner runs the same blocks against the slim fixture and the 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 images
```py runnable:classify
import torch
from transformers import AutoImageProcessor, AutoModelForImageClassification, set_seed
import json # doc-builder: hide
import os # doc-builder: hide
model_id = "microsoft/swin-tiny-patch4-window7-224"
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
processor = AutoImageProcessor.from_pretrained(model_id)
model = AutoModelForImageClassification.from_pretrained(model_id).to(device)
if variant: # doc-builder: hide
del model # doc-builder: hide
model = AutoModelForImageClassification.from_pretrained(model_id, **variant).to(device) # doc-builder: hide
# Two small synthetic images: a red-to-blue gradient and a green-to-white gradient.
# Build each as (3, H, W) explicitly so the channel dimension is unambiguous.
height = width = 224
red_blue = torch.cat([
torch.linspace(1, 0, width).view(1, 1, width).expand(1, height, width),
torch.zeros(1, height, width),
torch.linspace(0, 1, width).view(1, 1, width).expand(1, height, width),
], dim=0).mul(255).byte()
green_white = torch.cat([
torch.linspace(0, 1, width).view(1, 1, width).expand(1, height, width),
torch.linspace(1, 0, width).view(1, 1, width).expand(1, height, width),
torch.ones(1, height, width),
], dim=0).mul(255).byte()
images = [red_blue, green_white]
inputs = processor(images=images, return_tensors="pt")
inputs = inputs.to(device, dtype=model.dtype) # cast floating pixel_values, keep integer ids
with torch.no_grad():
logits = model(**inputs).logits
predicted = logits.argmax(dim=-1)
print([model.config.id2label[i] for i in predicted.tolist()])
```
## One training step
```py runnable: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 = {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 = ("classifier.weight", "swin.encoder.layers.0.blocks.0.attention.q_proj.weight")
result = {
"runtime": {
"device": model.device.type,
"dtype": str(model.dtype).removeprefix("torch."),
"attn_implementation": model.config._attn_implementation,
},
"predicted_classes": predicted.tolist(),
"logits": {"probe": logits[:, :5].float().tolist()},
"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))
```
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