# coding=utf-8 # Copyright 2022 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. """Dilated Neighborhood Attention Transformer model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices logger = logging.get_logger(__name__) class DinatConfig(BackboneConfigMixin, PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DinatModel`]. It is used to instantiate a Dinat 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 Dinat [shi-labs/dinat-mini-in1k-224](https://huggingface.co/shi-labs/dinat-mini-in1k-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: patch_size (`int`, *optional*, defaults to 4): The size (resolution) of each patch. NOTE: Only patch size of 4 is supported at the moment. num_channels (`int`, *optional*, defaults to 3): The number of input channels. embed_dim (`int`, *optional*, defaults to 64): Dimensionality of patch embedding. depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 5]`): Number of layers in each level of the encoder. num_heads (`List[int]`, *optional*, defaults to `[2, 4, 8, 16]`): Number of attention heads in each layer of the Transformer encoder. kernel_size (`int`, *optional*, defaults to 7): Neighborhood Attention kernel size. 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]]`): Dilation value of each NA layer in the Transformer encoder. mlp_ratio (`float`, *optional*, defaults to 3.0): Ratio of MLP hidden dimensionality to embedding dimensionality. qkv_bias (`bool`, *optional*, defaults to `True`): Whether or not a learnable bias should be added to the queries, keys and values. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings and encoder. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. drop_path_rate (`float`, *optional*, defaults to 0.1): Stochastic depth rate. hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported. 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-05): The epsilon used by the layer normalization layers. layer_scale_init_value (`float`, *optional*, defaults to 0.0): The initial value for the layer scale. Disabled if <=0. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. out_indices (`List[int]`, *optional*): If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. Example: ```python >>> from transformers import DinatConfig, DinatModel >>> # Initializing a Dinat shi-labs/dinat-mini-in1k-224 style configuration >>> configuration = DinatConfig() >>> # Initializing a model (with random weights) from the shi-labs/dinat-mini-in1k-224 style configuration >>> model = DinatModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "dinat" attribute_map = { "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers", } def __init__( self, patch_size=4, num_channels=3, embed_dim=64, depths=[3, 4, 6, 5], num_heads=[2, 4, 8, 16], kernel_size=7, dilations=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]], mlp_ratio=3.0, qkv_bias=True, hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, drop_path_rate=0.1, hidden_act="gelu", initializer_range=0.02, layer_norm_eps=1e-5, layer_scale_init_value=0.0, out_features=None, out_indices=None, **kwargs, ): super().__init__(**kwargs) self.patch_size = patch_size self.num_channels = num_channels self.embed_dim = embed_dim self.depths = depths self.num_layers = len(depths) self.num_heads = num_heads self.kernel_size = kernel_size self.dilations = dilations self.mlp_ratio = mlp_ratio self.qkv_bias = qkv_bias self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.drop_path_rate = drop_path_rate self.hidden_act = hidden_act self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel # this indicates the channel dimension after the last stage of the model self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1)) self.layer_scale_init_value = layer_scale_init_value self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)] self._out_features, self._out_indices = get_aligned_output_features_output_indices( out_features=out_features, out_indices=out_indices, stage_names=self.stage_names ) __all__ = ["DinatConfig"]