95 lines
3.9 KiB
Python
95 lines
3.9 KiB
Python
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# 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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""" RegNet model configuration"""
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from ...configuration_utils import PretrainedConfig
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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 REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class RegNetConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`RegNetModel`]. It is used to instantiate a RegNet
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the RegNet
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[facebook/regnet-y-040](https://huggingface.co/facebook/regnet-y-040) 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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embedding_size (`int`, *optional*, defaults to 64):
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Dimensionality (hidden size) for the embedding layer.
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hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
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Dimensionality (hidden size) at each stage.
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depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
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Depth (number of layers) for each stage.
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layer_type (`str`, *optional*, defaults to `"y"`):
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The layer to use, it can be either `"x" or `"y"`. An `x` layer is a ResNet's BottleNeck layer with
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`reduction` fixed to `1`. While a `y` layer is a `x` but with squeeze and excitation. Please refer to the
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paper for a detailed explanation of how these layers were constructed.
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hidden_act (`str`, *optional*, defaults to `"relu"`):
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The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
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are supported.
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downsample_in_first_stage (`bool`, *optional*, defaults to `False`):
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If `True`, the first stage will downsample the inputs using a `stride` of 2.
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Example:
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```python
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>>> from transformers import RegNetConfig, RegNetModel
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>>> # Initializing a RegNet regnet-y-40 style configuration
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>>> configuration = RegNetConfig()
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>>> # Initializing a model from the regnet-y-40 style configuration
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>>> model = RegNetModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "regnet"
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layer_types = ["x", "y"]
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def __init__(
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self,
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num_channels=3,
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embedding_size=32,
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hidden_sizes=[128, 192, 512, 1088],
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depths=[2, 6, 12, 2],
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groups_width=64,
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layer_type="y",
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hidden_act="relu",
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**kwargs,
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):
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super().__init__(**kwargs)
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if layer_type not in self.layer_types:
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raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types)}")
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self.num_channels = num_channels
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self.embedding_size = embedding_size
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self.hidden_sizes = hidden_sizes
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self.depths = depths
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self.groups_width = groups_width
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self.layer_type = layer_type
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self.hidden_act = hidden_act
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# always downsample in the first stage
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self.downsample_in_first_stage = True
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