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* first draft * add IJepaEmbeddings class * fix copy-from for IJepa model * add weight conversion script * update attention class names in IJepa model * style changes * Add push_to_hub option to convert_ijepa_checkpoint function * add initial tests for I-JEPA * minor style changes to conversion script * make fixup related * rename conversion script * Add I-JEPA to sdpa docs * minor fixes * adjust conversion script * update conversion script * adjust sdpa docs * [run_slow] ijepa * [run-slow] ijepa * [run-slow] ijepa * [run-slow] ijepa * [run-slow] ijepa * [run-slow] ijepa * formatting issues * adjust modeling to modular code * add IJepaModel to objects to ignore in docstring checks * [run-slow] ijepa * fix formatting issues * add usage instruction snippet to docs * change pos encoding, add checkpoint for doc * add verify logits for all models * [run-slow] ijepa * update docs to include image feature extraction instructions * remove pooling layer from IJepaModel in image classification class * [run-slow] ijepa * remove pooling layer from IJepaModel constructor * update docs * [run-slow] ijepa * [run-slow] ijepa * small changes * [run-slow] ijepa * style adjustments * update copyright in init file * adjust modular ijepa * [run-slow] ijepa
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| Original file line number | Diff line number | Diff line change |
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| <!--Copyright 2024 The HuggingFace Team. All rights reserved. | ||
| Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||
| the License. You may obtain a copy of the License at | ||
| http://www.apache.org/licenses/LICENSE-2.0 | ||
| Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||
| an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||
| specific language governing permissions and limitations under the License. | ||
| ⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be | ||
| rendered properly in your Markdown viewer. | ||
| --> | ||
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| # I-JEPA | ||
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| ## Overview | ||
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| The I-JEPA model was proposed in [Image-based Joint-Embedding Predictive Architecture](https://arxiv.org/pdf/2301.08243.pdf) by Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas. | ||
| I-JEPA is a self-supervised learning method that predicts the representations of one part of an image based on other parts of the same image. This approach focuses on learning semantic features without relying on pre-defined invariances from hand-crafted data transformations, which can bias specific tasks, or on filling in pixel-level details, which often leads to less meaningful representations. | ||
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| The abstract from the paper is the following: | ||
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| This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. We introduce the Image- based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample tar- get blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transform- ers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction. | ||
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| This model was contributed by [jmtzt](https://huggingface.co/jmtzt). | ||
| The original code can be found [here](https://github.com/facebookresearch/ijepa). | ||
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| ## How to use | ||
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| Here is how to use this model for image feature extraction: | ||
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| ```python | ||
| import requests | ||
| import torch | ||
| from PIL import Image | ||
| from torch.nn.functional import cosine_similarity | ||
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| from transformers import AutoModel, AutoProcessor | ||
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| url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg" | ||
| url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg" | ||
| image_1 = Image.open(requests.get(url_1, stream=True).raw) | ||
| image_2 = Image.open(requests.get(url_2, stream=True).raw) | ||
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| model_id = "jmtzt/ijepa_vith14_1k" | ||
| processor = AutoProcessor.from_pretrained(model_id) | ||
| model = AutoModel.from_pretrained(model_id) | ||
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| @torch.no_grad() | ||
| def infer(image): | ||
| inputs = processor(image, return_tensors="pt") | ||
| outputs = model(**inputs) | ||
| return outputs.last_hidden_state.mean(dim=1) | ||
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| embed_1 = infer(image_1) | ||
| embed_2 = infer(image_2) | ||
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| similarity = cosine_similarity(embed_1, embed_2) | ||
| print(similarity) | ||
| ``` | ||
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| ## IJepaConfig | ||
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| [[autodoc]] IJepaConfig | ||
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| ## IJepaModel | ||
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| [[autodoc]] IJepaModel | ||
| - forward | ||
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| ## IJepaForImageClassification | ||
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| [[autodoc]] IJepaForImageClassification | ||
| - forward |
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@@ -117,6 +117,7 @@ | |
| idefics, | ||
| idefics2, | ||
| idefics3, | ||
| ijepa, | ||
| imagegpt, | ||
| informer, | ||
| instructblip, | ||
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| # Copyright 2023 The HuggingFace Team. All rights reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| from typing import TYPE_CHECKING | ||
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| from ...utils import ( | ||
| OptionalDependencyNotAvailable, | ||
| _LazyModule, | ||
| is_torch_available, | ||
| ) | ||
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| _import_structure = {"configuration_ijepa": ["IJepaConfig"]} | ||
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| try: | ||
| if not is_torch_available(): | ||
| raise OptionalDependencyNotAvailable() | ||
| except OptionalDependencyNotAvailable: | ||
| pass | ||
| else: | ||
| _import_structure["modeling_ijepa"] = [ | ||
| "IJepaForImageClassification", | ||
| "IJepaModel", | ||
| "IJepaPreTrainedModel", | ||
| ] | ||
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| if TYPE_CHECKING: | ||
| from .configuration_ijepa import IJepaConfig | ||
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| try: | ||
| if not is_torch_available(): | ||
| raise OptionalDependencyNotAvailable() | ||
| except OptionalDependencyNotAvailable: | ||
| pass | ||
| else: | ||
| from .modeling_ijepa import ( | ||
| IJepaForImageClassification, | ||
| IJepaModel, | ||
| IJepaPreTrainedModel, | ||
| ) | ||
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| else: | ||
| import sys | ||
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| sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) |
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| # coding=utf-8 | ||
| # Copyright 2024 The HuggingFace Inc. team. All rights reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
| """I-JEPA model configuration""" | ||
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| from ...configuration_utils import PretrainedConfig | ||
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| class IJepaConfig(PretrainedConfig): | ||
| r""" | ||
| This is the configuration class to store the configuration of a [`IJepaModel`]. It is used to instantiate an IJEPA | ||
| model according to the specified arguments, defining the model architecture. Instantiating a configuration with the | ||
| defaults will yield a similar configuration to that of the I-JEPA | ||
| [google/ijepa-base-patch16-224](https://huggingface.co/google/ijepa-base-patch16-224) architecture. | ||
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | ||
| documentation from [`PretrainedConfig`] for more information. | ||
| Args: | ||
| hidden_size (`int`, *optional*, defaults to 768): | ||
| Dimensionality of the encoder layers and the pooler layer. | ||
| num_hidden_layers (`int`, *optional*, defaults to 12): | ||
| Number of hidden layers in the Transformer encoder. | ||
| num_attention_heads (`int`, *optional*, defaults to 12): | ||
| Number of attention heads for each attention layer in the Transformer encoder. | ||
| intermediate_size (`int`, *optional*, defaults to 3072): | ||
| Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. | ||
| hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): | ||
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | ||
| `"relu"`, `"selu"` and `"gelu_new"` are supported. | ||
| hidden_dropout_prob (`float`, *optional*, defaults to 0.0): | ||
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | ||
| attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): | ||
| The dropout ratio for the attention probabilities. | ||
| initializer_range (`float`, *optional*, defaults to 0.02): | ||
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | ||
| layer_norm_eps (`float`, *optional*, defaults to 1e-12): | ||
| The epsilon used by the layer normalization layers. | ||
| image_size (`int`, *optional*, defaults to 224): | ||
| The size (resolution) of each image. | ||
| patch_size (`int`, *optional*, defaults to 16): | ||
| The size (resolution) of each patch. | ||
| num_channels (`int`, *optional*, defaults to 3): | ||
| The number of input channels. | ||
| qkv_bias (`bool`, *optional*, defaults to `True`): | ||
| Whether to add a bias to the queries, keys and values. | ||
| Example: | ||
| ```python | ||
| >>> from transformers import IJepaConfig, IJepaModel | ||
| >>> # Initializing a IJEPA ijepa-base-patch16-224 style configuration | ||
| >>> configuration = IJepaConfig() | ||
| >>> # Initializing a model (with random weights) from the ijepa-base-patch16-224 style configuration | ||
| >>> model = IJepaModel(configuration) | ||
| >>> # Accessing the model configuration | ||
| >>> configuration = model.config | ||
| ```""" | ||
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| model_type = "ijepa" | ||
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| def __init__( | ||
| self, | ||
| hidden_size=768, | ||
| num_hidden_layers=12, | ||
| num_attention_heads=12, | ||
| intermediate_size=3072, | ||
| hidden_act="gelu", | ||
| hidden_dropout_prob=0.0, | ||
| attention_probs_dropout_prob=0.0, | ||
| initializer_range=0.02, | ||
| layer_norm_eps=1e-12, | ||
| image_size=224, | ||
| patch_size=16, | ||
| num_channels=3, | ||
| qkv_bias=True, | ||
| **kwargs, | ||
| ): | ||
| super().__init__(**kwargs) | ||
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| self.hidden_size = hidden_size | ||
| self.num_hidden_layers = num_hidden_layers | ||
| self.num_attention_heads = num_attention_heads | ||
| self.intermediate_size = intermediate_size | ||
| self.hidden_act = hidden_act | ||
| self.hidden_dropout_prob = hidden_dropout_prob | ||
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | ||
| self.initializer_range = initializer_range | ||
| self.layer_norm_eps = layer_norm_eps | ||
| self.image_size = image_size | ||
| self.patch_size = patch_size | ||
| self.num_channels = num_channels | ||
| self.qkv_bias = qkv_bias |
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