# coding=utf-8 # Copyright 2021 NVIDIA 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. """SegFormer model configuration""" import warnings from collections import OrderedDict from collections.abc import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) class SegformerConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`SegformerModel`]. It is used to instantiate an SegFormer 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 SegFormer [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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]`): Sequence 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 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 `[4, 4, 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. classifier_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout probability before the classification head. 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 0.1): 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. decoder_hidden_size (`int`, *optional*, defaults to 256): The dimension of the all-MLP decode head. semantic_loss_ignore_index (`int`, *optional*, defaults to 255): The index that is ignored by the loss function of the semantic segmentation model. Example: ```python >>> from transformers import SegformerModel, SegformerConfig >>> # Initializing a SegFormer nvidia/segformer-b0-finetuned-ade-512-512 style configuration >>> configuration = SegformerConfig() >>> # Initializing a model from the nvidia/segformer-b0-finetuned-ade-512-512 style configuration >>> model = SegformerModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "segformer" def __init__( self, num_channels=3, num_encoder_blocks=4, depths=[2, 2, 2, 2], sr_ratios=[8, 4, 2, 1], hidden_sizes=[32, 64, 160, 256], patch_sizes=[7, 3, 3, 3], strides=[4, 2, 2, 2], num_attention_heads=[1, 2, 5, 8], mlp_ratios=[4, 4, 4, 4], hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, classifier_dropout_prob=0.1, initializer_range=0.02, drop_path_rate=0.1, layer_norm_eps=1e-6, decoder_hidden_size=256, semantic_loss_ignore_index=255, **kwargs, ): super().__init__(**kwargs) if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False: warnings.warn( "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be" " removed, as the behaviour will default to that of reshape_last_stage = True.", FutureWarning, ) 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.classifier_dropout_prob = classifier_dropout_prob self.initializer_range = initializer_range self.drop_path_rate = drop_path_rate self.layer_norm_eps = layer_norm_eps self.decoder_hidden_size = decoder_hidden_size self.reshape_last_stage = kwargs.get("reshape_last_stage", True) self.semantic_loss_ignore_index = semantic_loss_ignore_index class SegformerOnnxConfig(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__ = ["SegformerConfig", "SegformerOnnxConfig"]