169 lines
7.0 KiB
Python
169 lines
7.0 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" MobileViTV2 model configuration"""
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from collections import OrderedDict
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from typing import Mapping
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from packaging import version
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from ...configuration_utils import PretrainedConfig
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from ...onnx import OnnxConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class MobileViTV2Config(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MobileViTV2Model`]. It is used to instantiate a
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MobileViTV2 model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the MobileViTV2
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[apple/mobilevitv2-1.0](https://huggingface.co/apple/mobilevitv2-1.0) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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image_size (`int`, *optional*, defaults to 256):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 2):
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The size (resolution) of each patch.
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expand_ratio (`float`, *optional*, defaults to 2.0):
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Expansion factor for the MobileNetv2 layers.
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hidden_act (`str` or `function`, *optional*, defaults to `"swish"`):
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The non-linear activation function (function or string) in the Transformer encoder and convolution layers.
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conv_kernel_size (`int`, *optional*, defaults to 3):
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The size of the convolutional kernel in the MobileViTV2 layer.
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output_stride (`int`, *optional*, defaults to 32):
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The ratio of the spatial resolution of the output to the resolution of the input image.
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classifier_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for attached classifiers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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aspp_out_channels (`int`, *optional*, defaults to 512):
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Number of output channels used in the ASPP layer for semantic segmentation.
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atrous_rates (`List[int]`, *optional*, defaults to `[6, 12, 18]`):
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Dilation (atrous) factors used in the ASPP layer for semantic segmentation.
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aspp_dropout_prob (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the ASPP layer for semantic segmentation.
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semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
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The index that is ignored by the loss function of the semantic segmentation model.
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n_attn_blocks (`List[int]`, *optional*, defaults to `[2, 4, 3]`):
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The number of attention blocks in each MobileViTV2Layer
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base_attn_unit_dims (`List[int]`, *optional*, defaults to `[128, 192, 256]`):
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The base multiplier for dimensions of attention blocks in each MobileViTV2Layer
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width_multiplier (`float`, *optional*, defaults to 1.0):
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The width multiplier for MobileViTV2.
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ffn_multiplier (`int`, *optional*, defaults to 2):
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The FFN multiplier for MobileViTV2.
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attn_dropout (`float`, *optional*, defaults to 0.0):
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The dropout in the attention layer.
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ffn_dropout (`float`, *optional*, defaults to 0.0):
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The dropout between FFN layers.
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Example:
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```python
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>>> from transformers import MobileViTV2Config, MobileViTV2Model
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>>> # Initializing a mobilevitv2-small style configuration
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>>> configuration = MobileViTV2Config()
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>>> # Initializing a model from the mobilevitv2-small style configuration
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>>> model = MobileViTV2Model(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "mobilevitv2"
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def __init__(
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self,
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num_channels=3,
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image_size=256,
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patch_size=2,
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expand_ratio=2.0,
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hidden_act="swish",
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conv_kernel_size=3,
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output_stride=32,
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classifier_dropout_prob=0.1,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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aspp_out_channels=512,
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atrous_rates=[6, 12, 18],
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aspp_dropout_prob=0.1,
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semantic_loss_ignore_index=255,
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n_attn_blocks=[2, 4, 3],
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base_attn_unit_dims=[128, 192, 256],
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width_multiplier=1.0,
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ffn_multiplier=2,
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attn_dropout=0.0,
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ffn_dropout=0.0,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.num_channels = num_channels
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self.image_size = image_size
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self.patch_size = patch_size
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self.expand_ratio = expand_ratio
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self.hidden_act = hidden_act
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self.conv_kernel_size = conv_kernel_size
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self.output_stride = output_stride
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.n_attn_blocks = n_attn_blocks
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self.base_attn_unit_dims = base_attn_unit_dims
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self.width_multiplier = width_multiplier
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self.ffn_multiplier = ffn_multiplier
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self.ffn_dropout = ffn_dropout
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self.attn_dropout = attn_dropout
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self.classifier_dropout_prob = classifier_dropout_prob
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# decode head attributes for semantic segmentation
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self.aspp_out_channels = aspp_out_channels
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self.atrous_rates = atrous_rates
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self.aspp_dropout_prob = aspp_dropout_prob
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self.semantic_loss_ignore_index = semantic_loss_ignore_index
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class MobileViTV2OnnxConfig(OnnxConfig):
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torch_onnx_minimum_version = version.parse("1.11")
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@property
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def inputs(self) -> Mapping[str, Mapping[int, str]]:
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return OrderedDict([("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"})])
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@property
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def outputs(self) -> Mapping[str, Mapping[int, str]]:
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if self.task == "image-classification":
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return OrderedDict([("logits", {0: "batch"})])
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else:
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return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})])
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@property
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def atol_for_validation(self) -> float:
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return 1e-4
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