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"""ViT model configuration""" |
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from collections import OrderedDict |
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from typing import Mapping |
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from packaging import version |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.onnx import OnnxConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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class ViTConfig(PretrainedConfig): |
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r""" |
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This is the configuration class to store the configuration of a [`ViTModel`]. It is used to instantiate an ViT |
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the |
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defaults will yield a similar configuration to that of the ViT |
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[google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) architecture. |
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the |
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documentation from [`PretrainedConfig`] for more information. |
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Args: |
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hidden_size (`int`, *optional*, defaults to 768): |
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Dimensionality of the encoder layers and the pooler layer. |
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num_hidden_layers (`int`, *optional*, defaults to 12): |
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Number of hidden layers in the Transformer encoder. |
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num_attention_heads (`int`, *optional*, defaults to 12): |
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Number of attention heads for each attention layer in the Transformer encoder. |
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intermediate_size (`int`, *optional*, defaults to 3072): |
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. |
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): |
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, |
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`"relu"`, `"selu"` and `"gelu_new"` are supported. |
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0): |
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. |
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): |
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The dropout ratio for the attention probabilities. |
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initializer_range (`float`, *optional*, defaults to 0.02): |
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices. |
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layer_norm_eps (`float`, *optional*, defaults to 1e-12): |
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The epsilon used by the layer normalization layers. |
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image_size (`int`, *optional*, defaults to 224): |
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The size (resolution) of each image. |
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patch_size (`int`, *optional*, defaults to 16): |
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The size (resolution) of each patch. |
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num_channels (`int`, *optional*, defaults to 3): |
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The number of input channels. |
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qkv_bias (`bool`, *optional*, defaults to `True`): |
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Whether to add a bias to the queries, keys and values. |
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encoder_stride (`int`, *optional*, defaults to 16): |
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Factor to increase the spatial resolution by in the decoder head for masked image modeling. |
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Example: |
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```python |
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>>> from transformers import ViTConfig, ViTModel |
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>>> # Initializing a ViT vit-base-patch16-224 style configuration |
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>>> configuration = ViTConfig() |
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>>> # Initializing a model (with random weights) from the vit-base-patch16-224 style configuration |
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>>> model = ViTModel(configuration) |
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>>> # Accessing the model configuration |
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>>> configuration = model.config |
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```""" |
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model_type = "vit" |
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def __init__( |
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self, |
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hidden_size=768, |
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num_hidden_layers=12, |
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num_attention_heads=12, |
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intermediate_size=3072, |
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hidden_act="gelu", |
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hidden_dropout_prob=0.0, |
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attention_probs_dropout_prob=0.0, |
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initializer_range=0.02, |
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layer_norm_eps=1e-12, |
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image_size=224, |
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patch_size=16, |
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num_channels=3, |
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qkv_bias=True, |
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encoder_stride=16, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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self.hidden_size = hidden_size |
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self.num_hidden_layers = num_hidden_layers |
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self.num_attention_heads = num_attention_heads |
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self.intermediate_size = intermediate_size |
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self.hidden_act = hidden_act |
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self.hidden_dropout_prob = hidden_dropout_prob |
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self.attention_probs_dropout_prob = attention_probs_dropout_prob |
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self.initializer_range = initializer_range |
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self.layer_norm_eps = layer_norm_eps |
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self.image_size = image_size |
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self.patch_size = patch_size |
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self.num_channels = num_channels |
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self.qkv_bias = qkv_bias |
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self.encoder_stride = encoder_stride |
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class ViTOnnxConfig(OnnxConfig): |
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torch_onnx_minimum_version = version.parse("1.11") |
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@property |
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def inputs(self) -> Mapping[str, Mapping[int, str]]: |
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return OrderedDict( |
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[ |
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("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), |
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] |
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) |
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@property |
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def atol_for_validation(self) -> float: |
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return 1e-4 |
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