# coding=utf-8 # Copyright 2024 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, # Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao 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. """Pvt V2 model configuration""" from typing import Callable, List, Tuple, Union 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 PvtV2Config(BackboneConfigMixin, PretrainedConfig): r""" This is the configuration class to store the configuration of a [`PvtV2Model`]. It is used to instantiate a Pvt V2 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 Pvt V2 B0 [OpenGVLab/pvt_v2_b0](https://huggingface.co/OpenGVLab/pvt_v2_b0) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: image_size (`Union[int, Tuple[int, int]]`, *optional*, defaults to 224): The input image size. Pass int value for square image, or tuple of (height, width). num_channels (`int`, *optional*, defaults to 3): The number of input channels. num_encoder_blocks (`[int]`, *optional*, defaults to 4): The number of encoder blocks (i.e. stages in the Mix Transformer encoder). depths (`List[int]`, *optional*, defaults to `[2, 2, 2, 2]`): The number of layers in each encoder block. sr_ratios (`List[int]`, *optional*, defaults to `[8, 4, 2, 1]`): Spatial reduction ratios in each encoder block. hidden_sizes (`List[int]`, *optional*, defaults to `[32, 64, 160, 256]`): Dimension of each of the encoder blocks. patch_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3, 3]`): Patch size for overlapping patch embedding before each encoder block. strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`): Stride for overlapping patch embedding before each encoder block. num_attention_heads (`List[int]`, *optional*, defaults to `[1, 2, 5, 8]`): Number of attention heads for each attention layer in each block of the Transformer encoder. mlp_ratios (`List[int]`, *optional*, defaults to `[8, 8, 4, 4]`): Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the encoder blocks. hidden_act (`str` or `Callable`, *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. drop_path_rate (`float`, *optional*, defaults to 0.0): The dropout probability for stochastic depth, used in the blocks of the Transformer encoder. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. qkv_bias (`bool`, *optional*, defaults to `True`): Whether or not a learnable bias should be added to the queries, keys and values. linear_attention (`bool`, *optional*, defaults to `False`): Use linear attention complexity. If set to True, `sr_ratio` is ignored and average pooling is used for dimensionality reduction in the attention layers rather than strided convolution. 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. 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. Example: ```python >>> from transformers import PvtV2Model, PvtV2Config >>> # Initializing a pvt_v2_b0 style configuration >>> configuration = PvtV2Config() >>> # Initializing a model from the OpenGVLab/pvt_v2_b0 style configuration >>> model = PvtV2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "pvt_v2" def __init__( self, image_size: Union[int, Tuple[int, int]] = 224, num_channels: int = 3, num_encoder_blocks: int = 4, depths: List[int] = [2, 2, 2, 2], sr_ratios: List[int] = [8, 4, 2, 1], hidden_sizes: List[int] = [32, 64, 160, 256], patch_sizes: List[int] = [7, 3, 3, 3], strides: List[int] = [4, 2, 2, 2], num_attention_heads: List[int] = [1, 2, 5, 8], mlp_ratios: List[int] = [8, 8, 4, 4], hidden_act: Union[str, Callable] = "gelu", hidden_dropout_prob: float = 0.0, attention_probs_dropout_prob: float = 0.0, initializer_range: float = 0.02, drop_path_rate: float = 0.0, layer_norm_eps: float = 1e-6, qkv_bias: bool = True, linear_attention: bool = False, out_features=None, out_indices=None, **kwargs, ): super().__init__(**kwargs) image_size = (image_size, image_size) if isinstance(image_size, int) else image_size self.image_size = image_size self.num_channels = num_channels self.num_encoder_blocks = num_encoder_blocks self.depths = depths self.sr_ratios = sr_ratios self.hidden_sizes = hidden_sizes self.patch_sizes = patch_sizes self.strides = strides self.mlp_ratios = mlp_ratios self.num_attention_heads = num_attention_heads 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.drop_path_rate = drop_path_rate self.layer_norm_eps = layer_norm_eps self.qkv_bias = qkv_bias self.linear_attention = linear_attention self.stage_names = [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__ = ["PvtV2Config"]