143 lines
6.1 KiB
Python
143 lines
6.1 KiB
Python
# coding=utf-8
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# Copyright 2022 Meta Platforms, Inc. and 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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""" ConvNeXT 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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from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class ConvNextConfig(BackboneConfigMixin, PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`ConvNextModel`]. It is used to instantiate an
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ConvNeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the ConvNeXT
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[facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) 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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patch_size (`int`, optional, defaults to 4):
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Patch size to use in the patch embedding layer.
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num_stages (`int`, optional, defaults to 4):
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The number of stages in the model.
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hidden_sizes (`List[int]`, *optional*, defaults to [96, 192, 384, 768]):
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Dimensionality (hidden size) at each stage.
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depths (`List[int]`, *optional*, defaults to [3, 3, 9, 3]):
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Depth (number of blocks) for each stage.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
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`"selu"` and `"gelu_new"` are supported.
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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-12):
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The epsilon used by the layer normalization layers.
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layer_scale_init_value (`float`, *optional*, defaults to 1e-6):
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The initial value for the layer scale.
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drop_path_rate (`float`, *optional*, defaults to 0.0):
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The drop rate for stochastic depth.
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out_features (`List[str]`, *optional*):
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If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
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(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
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corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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out_indices (`List[int]`, *optional*):
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If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
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many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
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If unset and `out_features` is unset, will default to the last stage. Must be in the
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same order as defined in the `stage_names` attribute.
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Example:
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```python
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>>> from transformers import ConvNextConfig, ConvNextModel
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>>> # Initializing a ConvNext convnext-tiny-224 style configuration
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>>> configuration = ConvNextConfig()
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>>> # Initializing a model (with random weights) from the convnext-tiny-224 style configuration
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>>> model = ConvNextModel(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 = "convnext"
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def __init__(
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self,
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num_channels=3,
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patch_size=4,
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num_stages=4,
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hidden_sizes=None,
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depths=None,
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hidden_act="gelu",
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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layer_scale_init_value=1e-6,
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drop_path_rate=0.0,
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image_size=224,
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out_features=None,
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out_indices=None,
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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.patch_size = patch_size
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self.num_stages = num_stages
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self.hidden_sizes = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
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self.depths = [3, 3, 9, 3] if depths is None else depths
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self.hidden_act = hidden_act
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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.layer_scale_init_value = layer_scale_init_value
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self.drop_path_rate = drop_path_rate
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self.image_size = image_size
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self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.depths) + 1)]
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self._out_features, self._out_indices = get_aligned_output_features_output_indices(
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out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
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)
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class ConvNextOnnxConfig(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(
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[
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("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
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]
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)
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@property
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def atol_for_validation(self) -> float:
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return 1e-5
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