# coding=utf-8 # Copyright 2023 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 model configuration""" from collections import OrderedDict from typing import Callable, List, Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) class PvtConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`PvtModel`]. It is used to instantiate an Pvt 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 [Xrenya/pvt-tiny-224](https://huggingface.co/Xrenya/pvt-tiny-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: image_size (`int`, *optional*, defaults to 224): The input image size 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. sequence_reduction_ratios (`List[int]`, *optional*, defaults to `[8, 4, 2, 1]`): Sequence reduction ratios in each encoder block. hidden_sizes (`List[int]`, *optional*, defaults to `[64, 128, 320, 512]`): Dimension of each of the encoder blocks. patch_sizes (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`): Patch size before each encoder block. strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`): Stride 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 `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. 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. num_labels ('int', *optional*, defaults to 1000): The number of classes. Example: ```python >>> from transformers import PvtModel, PvtConfig >>> # Initializing a PVT Xrenya/pvt-tiny-224 style configuration >>> configuration = PvtConfig() >>> # Initializing a model from the Xrenya/pvt-tiny-224 style configuration >>> model = PvtModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "pvt" def __init__( self, image_size: int = 224, num_channels: int = 3, num_encoder_blocks: int = 4, depths: List[int] = [2, 2, 2, 2], sequence_reduction_ratios: List[int] = [8, 4, 2, 1], hidden_sizes: List[int] = [64, 128, 320, 512], patch_sizes: List[int] = [4, 2, 2, 2], 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: Mapping[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, num_labels: int = 1000, **kwargs, ): super().__init__(**kwargs) self.image_size = image_size self.num_channels = num_channels self.num_encoder_blocks = num_encoder_blocks self.depths = depths self.sequence_reduction_ratios = sequence_reduction_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.num_labels = num_labels self.qkv_bias = qkv_bias class PvtOnnxConfig(OnnxConfig): torch_onnx_minimum_version = version.parse("1.11") @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), ] ) @property def atol_for_validation(self) -> float: return 1e-4 @property def default_onnx_opset(self) -> int: return 12 __all__ = ["PvtConfig", "PvtOnnxConfig"]