170 lines
7.5 KiB
Python
170 lines
7.5 KiB
Python
# coding=utf-8
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# Copyright 2023 Google Research, 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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""" EfficientNet model configuration"""
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from collections import OrderedDict
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from typing import List, 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 EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class EfficientNetConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`EfficientNetModel`]. It is used to instantiate an
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EfficientNet 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 EfficientNet
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[google/efficientnet-b7](https://huggingface.co/google/efficientnet-b7) 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 600):
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The input image size.
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width_coefficient (`float`, *optional*, defaults to 2.0):
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Scaling coefficient for network width at each stage.
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depth_coefficient (`float`, *optional*, defaults to 3.1):
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Scaling coefficient for network depth at each stage.
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depth_divisor `int`, *optional*, defaults to 8):
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A unit of network width.
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kernel_sizes (`List[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`):
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List of kernel sizes to be used in each block.
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in_channels (`List[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`):
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List of input channel sizes to be used in each block for convolutional layers.
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out_channels (`List[int]`, *optional*, defaults to `[16, 24, 40, 80, 112, 192, 320]`):
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List of output channel sizes to be used in each block for convolutional layers.
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depthwise_padding (`List[int]`, *optional*, defaults to `[]`):
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List of block indices with square padding.
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strides (`List[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`):
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List of stride sizes to be used in each block for convolutional layers.
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num_block_repeats (`List[int]`, *optional*, defaults to `[1, 2, 2, 3, 3, 4, 1]`):
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List of the number of times each block is to repeated.
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expand_ratios (`List[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`):
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List of scaling coefficient of each block.
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squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25):
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Squeeze expansion ratio.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
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`"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported.
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hiddem_dim (`int`, *optional*, defaults to 1280):
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The hidden dimension of the layer before the classification head.
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pooling_type (`str` or `function`, *optional*, defaults to `"mean"`):
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Type of final pooling to be applied before the dense classification head. Available options are [`"mean"`,
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`"max"`]
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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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batch_norm_eps (`float`, *optional*, defaults to 1e-3):
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The epsilon used by the batch normalization layers.
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batch_norm_momentum (`float`, *optional*, defaults to 0.99):
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The momentum used by the batch normalization layers.
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dropout_rate (`float`, *optional*, defaults to 0.5):
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The dropout rate to be applied before final classifier layer.
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drop_connect_rate (`float`, *optional*, defaults to 0.2):
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The drop rate for skip connections.
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Example:
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```python
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>>> from transformers import EfficientNetConfig, EfficientNetModel
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>>> # Initializing a EfficientNet efficientnet-b7 style configuration
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>>> configuration = EfficientNetConfig()
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>>> # Initializing a model (with random weights) from the efficientnet-b7 style configuration
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>>> model = EfficientNetModel(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 = "efficientnet"
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def __init__(
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self,
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num_channels: int = 3,
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image_size: int = 600,
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width_coefficient: float = 2.0,
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depth_coefficient: float = 3.1,
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depth_divisor: int = 8,
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kernel_sizes: List[int] = [3, 3, 5, 3, 5, 5, 3],
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in_channels: List[int] = [32, 16, 24, 40, 80, 112, 192],
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out_channels: List[int] = [16, 24, 40, 80, 112, 192, 320],
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depthwise_padding: List[int] = [],
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strides: List[int] = [1, 2, 2, 2, 1, 2, 1],
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num_block_repeats: List[int] = [1, 2, 2, 3, 3, 4, 1],
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expand_ratios: List[int] = [1, 6, 6, 6, 6, 6, 6],
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squeeze_expansion_ratio: float = 0.25,
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hidden_act: str = "swish",
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hidden_dim: int = 2560,
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pooling_type: str = "mean",
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initializer_range: float = 0.02,
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batch_norm_eps: float = 0.001,
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batch_norm_momentum: float = 0.99,
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dropout_rate: float = 0.5,
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drop_connect_rate: float = 0.2,
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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.width_coefficient = width_coefficient
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self.depth_coefficient = depth_coefficient
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self.depth_divisor = depth_divisor
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self.kernel_sizes = kernel_sizes
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.depthwise_padding = depthwise_padding
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self.strides = strides
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self.num_block_repeats = num_block_repeats
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self.expand_ratios = expand_ratios
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self.squeeze_expansion_ratio = squeeze_expansion_ratio
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self.hidden_act = hidden_act
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self.hidden_dim = hidden_dim
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self.pooling_type = pooling_type
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self.initializer_range = initializer_range
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self.batch_norm_eps = batch_norm_eps
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self.batch_norm_momentum = batch_norm_momentum
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self.dropout_rate = dropout_rate
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self.drop_connect_rate = drop_connect_rate
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self.num_hidden_layers = sum(num_block_repeats) * 4
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class EfficientNetOnnxConfig(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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