137 lines
6.2 KiB
Python
137 lines
6.2 KiB
Python
# coding=utf-8
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# Copyright 2022 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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""" BiT model configuration"""
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from ...configuration_utils import PretrainedConfig
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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 BIT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class BitConfig(BackboneConfigMixin, PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`BitModel`]. It is used to instantiate an BiT
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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 BiT
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[google/bit-50](https://huggingface.co/google/bit-50) 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 `"preactivation"`):
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The layer to use, it can be either `"preactivation"` or `"bottleneck"`.
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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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global_padding (`str`, *optional*):
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Padding strategy to use for the convolutional layers. Can be either `"valid"`, `"same"`, or `None`.
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num_groups (`int`, *optional*, defaults to 32):
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Number of groups used for the `BitGroupNormActivation` layers.
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drop_path_rate (`float`, *optional*, defaults to 0.0):
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The drop path rate for the stochastic depth.
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embedding_dynamic_padding (`bool`, *optional*, defaults to `False`):
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Whether or not to make use of dynamic padding for the embedding layer.
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output_stride (`int`, *optional*, defaults to 32):
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The output stride of the model.
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width_factor (`int`, *optional*, defaults to 1):
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The width factor for the model.
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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 BitConfig, BitModel
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>>> # Initializing a BiT bit-50 style configuration
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>>> configuration = BitConfig()
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>>> # Initializing a model (with random weights) from the bit-50 style configuration
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>>> model = BitModel(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 = "bit"
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layer_types = ["preactivation", "bottleneck"]
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supported_padding = ["SAME", "VALID"]
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def __init__(
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self,
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num_channels=3,
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embedding_size=64,
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hidden_sizes=[256, 512, 1024, 2048],
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depths=[3, 4, 6, 3],
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layer_type="preactivation",
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hidden_act="relu",
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global_padding=None,
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num_groups=32,
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drop_path_rate=0.0,
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embedding_dynamic_padding=False,
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output_stride=32,
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width_factor=1,
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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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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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if global_padding is not None:
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if global_padding.upper() in self.supported_padding:
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global_padding = global_padding.upper()
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else:
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raise ValueError(f"Padding strategy {global_padding} not supported")
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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.layer_type = layer_type
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self.hidden_act = hidden_act
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self.global_padding = global_padding
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self.num_groups = num_groups
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self.drop_path_rate = drop_path_rate
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self.embedding_dynamic_padding = embedding_dynamic_padding
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self.output_stride = output_stride
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self.width_factor = width_factor
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self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(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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