# coding=utf-8 # Copyright 2023 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. """MobileViTV2 model configuration""" 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__) class MobileViTV2Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`MobileViTV2Model`]. It is used to instantiate a MobileViTV2 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 MobileViTV2 [apple/mobilevitv2-1.0](https://huggingface.co/apple/mobilevitv2-1.0) 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. image_size (`int`, *optional*, defaults to 256): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 2): The size (resolution) of each patch. expand_ratio (`float`, *optional*, defaults to 2.0): Expansion factor for the MobileNetv2 layers. hidden_act (`str` or `function`, *optional*, defaults to `"swish"`): The non-linear activation function (function or string) in the Transformer encoder and convolution layers. conv_kernel_size (`int`, *optional*, defaults to 3): The size of the convolutional kernel in the MobileViTV2 layer. output_stride (`int`, *optional*, defaults to 32): The ratio of the spatial resolution of the output to the resolution of the input image. classifier_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for attached classifiers. 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. aspp_out_channels (`int`, *optional*, defaults to 512): Number of output channels used in the ASPP layer for semantic segmentation. atrous_rates (`List[int]`, *optional*, defaults to `[6, 12, 18]`): Dilation (atrous) factors used in the ASPP layer for semantic segmentation. aspp_dropout_prob (`float`, *optional*, defaults to 0.1): The dropout ratio for the ASPP layer for semantic segmentation. semantic_loss_ignore_index (`int`, *optional*, defaults to 255): The index that is ignored by the loss function of the semantic segmentation model. n_attn_blocks (`List[int]`, *optional*, defaults to `[2, 4, 3]`): The number of attention blocks in each MobileViTV2Layer base_attn_unit_dims (`List[int]`, *optional*, defaults to `[128, 192, 256]`): The base multiplier for dimensions of attention blocks in each MobileViTV2Layer width_multiplier (`float`, *optional*, defaults to 1.0): The width multiplier for MobileViTV2. ffn_multiplier (`int`, *optional*, defaults to 2): The FFN multiplier for MobileViTV2. attn_dropout (`float`, *optional*, defaults to 0.0): The dropout in the attention layer. ffn_dropout (`float`, *optional*, defaults to 0.0): The dropout between FFN layers. Example: ```python >>> from transformers import MobileViTV2Config, MobileViTV2Model >>> # Initializing a mobilevitv2-small style configuration >>> configuration = MobileViTV2Config() >>> # Initializing a model from the mobilevitv2-small style configuration >>> model = MobileViTV2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "mobilevitv2" def __init__( self, num_channels=3, image_size=256, patch_size=2, expand_ratio=2.0, hidden_act="swish", conv_kernel_size=3, output_stride=32, classifier_dropout_prob=0.1, initializer_range=0.02, layer_norm_eps=1e-5, aspp_out_channels=512, atrous_rates=[6, 12, 18], aspp_dropout_prob=0.1, semantic_loss_ignore_index=255, n_attn_blocks=[2, 4, 3], base_attn_unit_dims=[128, 192, 256], width_multiplier=1.0, ffn_multiplier=2, attn_dropout=0.0, ffn_dropout=0.0, **kwargs, ): super().__init__(**kwargs) self.num_channels = num_channels self.image_size = image_size self.patch_size = patch_size self.expand_ratio = expand_ratio self.hidden_act = hidden_act self.conv_kernel_size = conv_kernel_size self.output_stride = output_stride self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.n_attn_blocks = n_attn_blocks self.base_attn_unit_dims = base_attn_unit_dims self.width_multiplier = width_multiplier self.ffn_multiplier = ffn_multiplier self.ffn_dropout = ffn_dropout self.attn_dropout = attn_dropout self.classifier_dropout_prob = classifier_dropout_prob # decode head attributes for semantic segmentation self.aspp_out_channels = aspp_out_channels self.atrous_rates = atrous_rates self.aspp_dropout_prob = aspp_dropout_prob self.semantic_loss_ignore_index = semantic_loss_ignore_index class MobileViTV2OnnxConfig(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 outputs(self) -> Mapping[str, Mapping[int, str]]: if self.task == "image-classification": return OrderedDict([("logits", {0: "batch"})]) else: return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]) @property def atol_for_validation(self) -> float: return 1e-4 __all__ = ["MobileViTV2Config", "MobileViTV2OnnxConfig"]