tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
1ba3c8e verified | # TIPSv2: image and text embeddings with Transformers | |
| This page is the Transformers contract of the TIPSv2 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/tipsv2-b14`. 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. | |
| TIPSv2 is a dual image-text encoder: Transformers loads it natively, so the checkpoint's remote code (`auto_map`) is not needed. The bfloat16 variant is not measured yet: in bfloat16, `Tipsv2Model`'s text pooling returns float32 embeddings (a float32 pooling mask promotes them), and the image-text product then fails with `expected m1 and m2 to have the same dtype`. It is re-enabled once Transformers fixes it. | |
| ## Embed an image and some text | |
| ```py runnable:tipsv2 | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from transformers import AutoProcessor, Tipsv2Model, set_seed | |
| model_id = "google/tipsv2-b14" | |
| 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 and dropout draw random numbers | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| model = Tipsv2Model.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = Tipsv2Model.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| model.eval() | |
| # Two synthetic images that differ in colour, as (H, W, 3) uint8 arrays. | |
| rng = np.random.default_rng(0) | |
| noise = rng.integers(0, 50, (2, 224, 224, 3), dtype=np.uint8) | |
| images = [Image.fromarray(noise[0] + np.array([200, 0, 0], np.uint8)), Image.fromarray(noise[1] + np.array([0, 0, 200], np.uint8))] | |
| classes = ["a photo of a red square", "a photo of a blue square"] | |
| inputs = processor(text=classes, images=images, return_tensors="pt", padding=True) | |
| inputs = inputs.to(device, dtype=model.dtype) | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| similarity = outputs.logits_per_image | |
| print(classes[similarity[0].argmax()]) | |
| ``` | |
| ## One training step | |
| ```py runnable:tipsv2:2 | |
| # return_loss=True adds the image-text contrastive loss over the batch. | |
| model.train() | |
| optimizer = torch.optim.SGD([p for p in model.parameters() if p.requires_grad], lr=0.01) | |
| loss = model(**inputs, return_loss=True).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()) # doc-builder: hide | |
| checked = [n for n in named if n.endswith("weight") and named[n].requires_grad] # doc-builder: hide | |
| checked = [next(n for n in checked if n.startswith("vision")), next(n for n in checked if n.startswith("text"))] # doc-builder: hide | |
| result = { # doc-builder: hide | |
| "runtime": { # doc-builder: hide | |
| "device": model.device.type, # doc-builder: hide | |
| "dtype": str(model.dtype).removeprefix("torch."), # doc-builder: hide | |
| "attn_implementation": model.config._attn_implementation, # doc-builder: hide | |
| }, # doc-builder: hide | |
| "logits": {"per_image": similarity.float().tolist()}, # doc-builder: hide | |
| "embeds": {"image_probe": outputs.image_embeds[:, :4].float().tolist(), "text_probe": outputs.text_embeds[:, :4].float().tolist()}, # doc-builder: hide | |
| "training": { # doc-builder: hide | |
| "loss": loss.item(), # doc-builder: hide | |
| "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}, # doc-builder: hide | |
| "updated": any(not torch.equal(p.detach().cpu(), before[n]) for n, p in model.named_parameters() if n in before), # doc-builder: hide | |
| }, # doc-builder: hide | |
| } # doc-builder: hide | |
| print(json.dumps(result)) # doc-builder: hide | |
| ``` | |