158 lines
7.4 KiB
Python
158 lines
7.4 KiB
Python
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# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" VitDet model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import VITDET_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class VitDetConfig(BackboneConfigMixin, PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`VitDetModel`]. It is used to instantiate an
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VitDet model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the VitDet
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[google/vitdet-base-patch16-224](https://huggingface.co/google/vitdet-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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mlp_ratio (`int`, *optional*, defaults to 4):
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Ratio of mlp hidden dim to embedding dim.
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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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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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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-06):
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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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pretrain_image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image during pretraining.
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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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drop_path_rate (`float`, *optional*, defaults to 0.0):
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Stochastic depth rate.
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window_block_indices (`List[int]`, *optional*, defaults to `[]`):
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List of indices of blocks that should have window attention instead of regular global self-attention.
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residual_block_indices (`List[int]`, *optional*, defaults to `[]`):
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List of indices of blocks that should have an extra residual block after the MLP.
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use_absolute_position_embeddings (`bool`, *optional*, defaults to `True`):
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Whether to add absolute position embeddings to the patch embeddings.
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use_relative_position_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to add relative position embeddings to the attention maps.
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window_size (`int`, *optional*, defaults to 0):
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The size of the attention window.
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out_features (`List[str]`, *optional*):
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If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
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(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
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corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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out_indices (`List[int]`, *optional*):
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If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
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many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
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If unset and `out_features` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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Example:
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```python
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>>> from transformers import VitDetConfig, VitDetModel
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>>> # Initializing a VitDet configuration
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>>> configuration = VitDetConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = VitDetModel(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 = "vitdet"
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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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mlp_ratio=4,
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hidden_act="gelu",
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dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-6,
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image_size=224,
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pretrain_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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drop_path_rate=0.0,
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window_block_indices=[],
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residual_block_indices=[],
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use_absolute_position_embeddings=True,
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use_relative_position_embeddings=False,
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window_size=0,
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out_features=None,
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out_indices=None,
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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.mlp_ratio = mlp_ratio
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self.hidden_act = hidden_act
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self.dropout_prob = 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.pretrain_image_size = pretrain_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.drop_path_rate = drop_path_rate
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self.window_block_indices = window_block_indices
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self.residual_block_indices = residual_block_indices
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self.use_absolute_position_embeddings = use_absolute_position_embeddings
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self.use_relative_position_embeddings = use_relative_position_embeddings
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self.window_size = window_size
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self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, self.num_hidden_layers + 1)]
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self._out_features, self._out_indices = get_aligned_output_features_output_indices(
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out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
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)
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