153 lines
7.3 KiB
Python
153 lines
7.3 KiB
Python
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# coding=utf-8
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# Copyright 2022 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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""" Dilated Neighborhood Attention Transformer 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 DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class DinatConfig(BackboneConfigMixin, PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat
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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 Dinat
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[shi-labs/dinat-mini-in1k-224](https://huggingface.co/shi-labs/dinat-mini-in1k-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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patch_size (`int`, *optional*, defaults to 4):
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The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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embed_dim (`int`, *optional*, defaults to 64):
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Dimensionality of patch embedding.
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depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 5]`):
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Number of layers in each level of the encoder.
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num_heads (`List[int]`, *optional*, defaults to `[2, 4, 8, 16]`):
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Number of attention heads in each layer of the Transformer encoder.
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kernel_size (`int`, *optional*, defaults to 7):
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Neighborhood Attention kernel size.
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dilations (`List[List[int]]`, *optional*, defaults to `[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]]`):
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Dilation value of each NA layer in the Transformer encoder.
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mlp_ratio (`float`, *optional*, defaults to 3.0):
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Ratio of MLP hidden dimensionality to embedding dimensionality.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether or not a learnable bias should be added to the queries, keys and values.
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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 and encoder.
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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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drop_path_rate (`float`, *optional*, defaults to 0.1):
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Stochastic depth rate.
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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. If string, `"gelu"`, `"relu"`,
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`"selu"` and `"gelu_new"` are supported.
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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-05):
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The epsilon used by the layer normalization layers.
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layer_scale_init_value (`float`, *optional*, defaults to 0.0):
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The initial value for the layer scale. Disabled if <=0.
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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 DinatConfig, DinatModel
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>>> # Initializing a Dinat shi-labs/dinat-mini-in1k-224 style configuration
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>>> configuration = DinatConfig()
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>>> # Initializing a model (with random weights) from the shi-labs/dinat-mini-in1k-224 style configuration
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>>> model = DinatModel(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 = "dinat"
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attribute_map = {
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"num_attention_heads": "num_heads",
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"num_hidden_layers": "num_layers",
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}
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def __init__(
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self,
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patch_size=4,
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num_channels=3,
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embed_dim=64,
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depths=[3, 4, 6, 5],
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num_heads=[2, 4, 8, 16],
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kernel_size=7,
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dilations=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]],
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mlp_ratio=3.0,
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qkv_bias=True,
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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drop_path_rate=0.1,
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hidden_act="gelu",
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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layer_scale_init_value=0.0,
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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.patch_size = patch_size
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self.num_channels = num_channels
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self.embed_dim = embed_dim
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self.depths = depths
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self.num_layers = len(depths)
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self.num_heads = num_heads
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self.kernel_size = kernel_size
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self.dilations = dilations
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self.mlp_ratio = mlp_ratio
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self.qkv_bias = qkv_bias
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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.drop_path_rate = drop_path_rate
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self.hidden_act = hidden_act
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self.layer_norm_eps = layer_norm_eps
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self.initializer_range = initializer_range
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# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
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# this indicates the channel dimension after the last stage of the model
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self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
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self.layer_scale_init_value = layer_scale_init_value
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self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 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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