codingutf8 2021 nvidia and the huggingface inc team 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 orglicenseslicense2 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 typing import mapping from packaging import version from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging logger logging getloggername segformerpretrainedconfigarchivemap nvidiasegformerb0finetunedade512512 https huggingface convidiasegformerb0finetunedade512512resolvemainconfig json see all segformer models at https huggingface comodels filtersegformer class segformerconfigpretrainedconfig 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 nvidiasegformerb0finetunedade512512https huggingface convidiasegformerb0finetunedade512512 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args numchannels int optional defaults to 3 the number of input channels numencoderblocks int optional defaults to 4 the number of encoder blocks i e stages in the mix transformer encoder depths listint optional defaults to 2 2 2 2 the number of layers in each encoder block srratios listint optional defaults to 8 4 2 1 sequence reduction ratios in each encoder block hiddensizes listint optional defaults to 32 64 160 256 dimension of each of the encoder blocks patchsizes listint optional defaults to 7 3 3 3 patch size before each encoder block strides listint optional defaults to 4 2 2 2 stride before each encoder block numattentionheads listint optional defaults to 1 2 5 8 number of attention heads for each attention layer in each block of the transformer encoder mlpratios listint 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 hiddenact str or function optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew are supported hiddendropoutprob float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentionprobsdropoutprob float optional defaults to 0 0 the dropout ratio for the attention probabilities classifierdropoutprob float optional defaults to 0 1 the dropout probability before the classification head initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices droppathrate float optional defaults to 0 1 the dropout probability for stochastic depth used in the blocks of the transformer encoder layernormeps float optional defaults to 1e06 the epsilon used by the layer normalization layers decoderhiddensize int optional defaults to 256 the dimension of the allmlp decode head semanticlossignoreindex 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 nvidiasegformerb0finetunedade512512 style configuration configuration segformerconfig initializing a model from the nvidiasegformerb0finetunedade512512 style configuration model segformermodelconfiguration accessing the model configuration configuration model config modeltype segformer def init self numchannels3 numencoderblocks4 depths2 2 2 2 srratios8 4 2 1 hiddensizes32 64 160 256 patchsizes7 3 3 3 strides4 2 2 2 numattentionheads1 2 5 8 mlpratios4 4 4 4 hiddenactgelu hiddendropoutprob0 0 attentionprobsdropoutprob0 0 classifierdropoutprob0 1 initializerrange0 02 droppathrate0 1 layernormeps1e6 decoderhiddensize256 semanticlossignoreindex255 kwargs super initkwargs if reshapelaststage in kwargs and kwargsreshapelaststage is false warnings warn reshapelaststage is set to false in this config this argument is deprecated and will soon be removed as the behaviour will default to that of reshapelaststage true futurewarning self numchannels numchannels self numencoderblocks numencoderblocks self depths depths self srratios srratios self hiddensizes hiddensizes self patchsizes patchsizes self strides strides self mlpratios mlpratios self numattentionheads numattentionheads self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self classifierdropoutprob classifierdropoutprob self initializerrange initializerrange self droppathrate droppathrate self layernormeps layernormeps self decoderhiddensize decoderhiddensize self reshapelaststage kwargs getreshapelaststage true self semanticlossignoreindex semanticlossignoreindex class segformeronnxconfigonnxconfig torchonnxminimumversion version parse1 11 property def inputsself mappingstr mappingint str return ordereddict pixelvalues 0 batch 1 numchannels 2 height 3 width property def atolforvalidationself float return 1e4 property def defaultonnxopsetself int return 12 coding utf 8 2021 nvidia and the huggingface inc team 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 see all segformer models at https huggingface co models filter segformer 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
import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    
}


class SegformerConfig(PretrainedConfig):
    r

    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