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e94a4807df
* Add usage example for DINOv2 * More explicit shape names * More verbose text * Moved example to Notes section * Indentation
210 lines
10 KiB
Markdown
210 lines
10 KiB
Markdown
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# DINOv2
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[DINOv2](https://huggingface.co/papers/2304.07193) is a vision foundation model that uses [ViT](./vit) as a feature extractor for multiple downstream tasks like image classification and depth estimation. It focuses on stabilizing and accelerating training through techniques like a faster memory-efficient attention, sequence packing, improved stochastic depth, Fully Sharded Data Parallel (FSDP), and model distillation.
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You can find all the original DINOv2 checkpoints under the [Dinov2](https://huggingface.co/collections/facebook/dinov2-6526c98554b3d2576e071ce3) collection.
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> [!TIP]
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> Click on the DINOv2 models in the right sidebar for more examples of how to apply DINOv2 to different vision tasks.
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The example below demonstrates how to obtain an image embedding with [`Pipeline`] or the [`AutoModel`] class.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipe = pipeline(
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task="image-classification",
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model="facebook/dinov2-small-imagenet1k-1-layer",
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torch_dtype=torch.float16,
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device=0
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)
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pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import requests
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from transformers import AutoImageProcessor, AutoModelForImageClassification
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from PIL import Image
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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processor = AutoImageProcessor.from_pretrained("facebook/dinov2-small-imagenet1k-1-layer")
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model = AutoModelForImageClassification.from_pretrained(
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"facebook/dinov2-small-imagenet1k-1-layer",
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torch_dtype=torch.float16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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inputs = processor(images=image, return_tensors="pt")
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logits = model(**inputs).logits
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predicted_class_idx = logits.argmax(-1).item()
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print("Predicted class:", model.config.id2label[predicted_class_idx])
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```
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</hfoption>
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</hfoptions>
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Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
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The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
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```py
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# pip install torchao
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import requests
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from transformers import TorchAoConfig, AutoImageProcessor, AutoModelForImageClassification
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from torchao.quantization import Int4WeightOnlyConfig
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from PIL import Image
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
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image = Image.open(requests.get(url, stream=True).raw)
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processor = AutoImageProcessor.from_pretrained('facebook/dinov2-giant-imagenet1k-1-layer')
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quant_config = Int4WeightOnlyConfig(group_size=128)
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quantization_config = TorchAoConfig(quant_type=quant_config)
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model = AutoModelForImageClassification.from_pretrained(
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'facebook/dinov2-giant-imagenet1k-1-layer',
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torch_dtype=torch.bfloat16,
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device_map="auto",
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quantization_config=quantization_config
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)
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inputs = processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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logits = outputs.logits
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predicted_class_idx = logits.argmax(-1).item()
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print("Predicted class:", model.config.id2label[predicted_class_idx])
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```
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## Notes
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- The example below shows how to split the output tensor into:
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- one embedding for the whole image, commonly referred to as a `CLS` token,
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useful for classification and retrieval
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- a set of local embeddings, one for each `14x14` patch of the input image,
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useful for dense tasks, such as semantic segmentation
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```py
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from transformers import AutoImageProcessor, AutoModel
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from PIL import Image
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import requests
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
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image = Image.open(requests.get(url, stream=True).raw)
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print(image.height, image.width) # [480, 640]
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processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
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model = AutoModel.from_pretrained('facebook/dinov2-base')
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patch_size = model.config.patch_size
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inputs = processor(images=image, return_tensors="pt")
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print(inputs.pixel_values.shape) # [1, 3, 224, 224]
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batch_size, rgb, img_height, img_width = inputs.pixel_values.shape
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num_patches_height, num_patches_width = img_height // patch_size, img_width // patch_size
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num_patches_flat = num_patches_height * num_patches_width
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outputs = model(**inputs)
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last_hidden_states = outputs[0]
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print(last_hidden_states.shape) # [1, 1 + 256, 768]
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assert last_hidden_states.shape == (batch_size, 1 + num_patches_flat, model.config.hidden_size)
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cls_token = last_hidden_states[:, 0, :]
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patch_features = last_hidden_states[:, 1:, :].unflatten(1, (num_patches_height, num_patches_width))
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```
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- Use [torch.jit.trace](https://pytorch.org/docs/stable/generated/torch.jit.trace.html) to speedup inference.
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However, it will produce some mismatched elements. The difference between the original and traced model is 1e-4.
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```py
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import torch
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from transformers import AutoImageProcessor, AutoModel
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from PIL import Image
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import requests
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
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image = Image.open(requests.get(url, stream=True).raw)
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processor = AutoImageProcessor.from_pretrained('facebook/dinov2-base')
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model = AutoModel.from_pretrained('facebook/dinov2-base')
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inputs = processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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last_hidden_states = outputs[0]
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# We have to force return_dict=False for tracing
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model.config.return_dict = False
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with torch.no_grad():
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traced_model = torch.jit.trace(model, [inputs.pixel_values])
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traced_outputs = traced_model(inputs.pixel_values)
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print((last_hidden_states - traced_outputs[0]).abs().max())
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```
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## Dinov2Config
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[[autodoc]] Dinov2Config
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<frameworkcontent>
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<pt>
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## Dinov2Model
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[[autodoc]] Dinov2Model
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- forward
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## Dinov2ForImageClassification
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[[autodoc]] Dinov2ForImageClassification
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- forward
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</pt>
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<jax>
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## FlaxDinov2Model
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[[autodoc]] FlaxDinov2Model
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- __call__
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## FlaxDinov2ForImageClassification
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[[autodoc]] FlaxDinov2ForImageClassification
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- __call__
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</jax>
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</frameworkcontent>
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