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| # coding=utf-8 | |
| # Copyright Meta Platforms and 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. | |
| """ Data2VecVision model configuration""" | |
| from collections import OrderedDict | |
| from typing import Mapping | |
| from packaging import version | |
| from ...configuration_utils import PretrainedConfig | |
| from ...onnx import OnnxConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "facebook/data2vec-vision-base-ft": ( | |
| "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json" | |
| ), | |
| } | |
| class Data2VecVisionConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`Data2VecVisionModel`]. It is used to instantiate | |
| an Data2VecVision 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 Data2VecVision | |
| [facebook/data2vec-vision-base](https://huggingface.co/facebook/data2vec-vision-base) architecture. | |
| 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. | |
| use_mask_token (`bool`, *optional*, defaults to `False`): | |
| Whether to use a mask token for masked image modeling. | |
| use_absolute_position_embeddings (`bool`, *optional*, defaults to `False`): | |
| Whether to use BERT-style absolute position embeddings. | |
| use_relative_position_bias (`bool`, *optional*, defaults to `False`): | |
| Whether to use T5-style relative position embeddings in the self-attention layers. | |
| use_shared_relative_position_bias (`bool`, *optional*, defaults to `False`): | |
| Whether to use the same relative position embeddings across all self-attention layers of the Transformer. | |
| layer_scale_init_value (`float`, *optional*, defaults to 0.1): | |
| Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale. | |
| drop_path_rate (`float`, *optional*, defaults to 0.1): | |
| Stochastic depth rate per sample (when applied in the main path of residual layers). | |
| use_mean_pooling (`bool`, *optional*, defaults to `True`): | |
| Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the | |
| CLS token, before applying the classification head. | |
| out_indices (`List[int]`, *optional*, defaults to `[3, 5, 7, 11]`): | |
| Indices of the feature maps to use for semantic segmentation. | |
| pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`): | |
| Pooling scales used in Pooling Pyramid Module applied on the last feature map. | |
| use_auxiliary_head (`bool`, *optional*, defaults to `True`): | |
| Whether to use an auxiliary head during training. | |
| auxiliary_loss_weight (`float`, *optional*, defaults to 0.4): | |
| Weight of the cross-entropy loss of the auxiliary head. | |
| auxiliary_channels (`int`, *optional*, defaults to 256): | |
| Number of channels to use in the auxiliary head. | |
| auxiliary_num_convs (`int`, *optional*, defaults to 1): | |
| Number of convolutional layers to use in the auxiliary head. | |
| auxiliary_concat_input (`bool`, *optional*, defaults to `False`): | |
| Whether to concatenate the output of the auxiliary head with the input before the classification layer. | |
| semantic_loss_ignore_index (`int`, *optional*, defaults to 255): | |
| The index that is ignored by the loss function of the semantic segmentation model. | |
| Example: | |
| ```python | |
| >>> from transformers import Data2VecVisionConfig, Data2VecVisionModel | |
| >>> # Initializing a Data2VecVision data2vec_vision-base-patch16-224-in22k style configuration | |
| >>> configuration = Data2VecVisionConfig() | |
| >>> # Initializing a model (with random weights) from the data2vec_vision-base-patch16-224-in22k style configuration | |
| >>> model = Data2VecVisionModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "data2vec-vision" | |
| 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, | |
| use_mask_token=False, | |
| use_absolute_position_embeddings=False, | |
| use_relative_position_bias=False, | |
| use_shared_relative_position_bias=False, | |
| layer_scale_init_value=0.1, | |
| drop_path_rate=0.1, | |
| use_mean_pooling=True, | |
| out_indices=[3, 5, 7, 11], | |
| pool_scales=[1, 2, 3, 6], | |
| use_auxiliary_head=True, | |
| auxiliary_loss_weight=0.4, | |
| auxiliary_channels=256, | |
| auxiliary_num_convs=1, | |
| auxiliary_concat_input=False, | |
| semantic_loss_ignore_index=255, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| 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.use_mask_token = use_mask_token | |
| self.use_absolute_position_embeddings = use_absolute_position_embeddings | |
| self.use_relative_position_bias = use_relative_position_bias | |
| self.use_shared_relative_position_bias = use_shared_relative_position_bias | |
| self.layer_scale_init_value = layer_scale_init_value | |
| self.drop_path_rate = drop_path_rate | |
| self.use_mean_pooling = use_mean_pooling | |
| # decode head attributes (semantic segmentation) | |
| self.out_indices = out_indices | |
| self.pool_scales = pool_scales | |
| # auxiliary head attributes (semantic segmentation) | |
| self.use_auxiliary_head = use_auxiliary_head | |
| self.auxiliary_loss_weight = auxiliary_loss_weight | |
| self.auxiliary_channels = auxiliary_channels | |
| self.auxiliary_num_convs = auxiliary_num_convs | |
| self.auxiliary_concat_input = auxiliary_concat_input | |
| self.semantic_loss_ignore_index = semantic_loss_ignore_index | |
| # Copied from transformers.models.vit.configuration_vit.ViTOnnxConfig | |
| class Data2VecVisionOnnxConfig(OnnxConfig): | |
| torch_onnx_minimum_version = version.parse("1.11") | |
| def inputs(self) -> Mapping[str, Mapping[int, str]]: | |
| return OrderedDict( | |
| [ | |
| ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), | |
| ] | |
| ) | |
| def atol_for_validation(self) -> float: | |
| return 1e-4 | |