145 lines
5.7 KiB
Python
145 lines
5.7 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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""" LeViT 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 LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class LevitConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`LevitModel`]. It is used to instantiate a LeViT
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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 LeViT
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[facebook/levit-128S](https://huggingface.co/facebook/levit-128S) 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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image_size (`int`, *optional*, defaults to 224):
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The size of the input image.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input image.
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kernel_size (`int`, *optional*, defaults to 3):
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The kernel size for the initial convolution layers of patch embedding.
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stride (`int`, *optional*, defaults to 2):
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The stride size for the initial convolution layers of patch embedding.
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padding (`int`, *optional*, defaults to 1):
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The padding size for the initial convolution layers of patch embedding.
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patch_size (`int`, *optional*, defaults to 16):
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The patch size for embeddings.
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hidden_sizes (`List[int]`, *optional*, defaults to `[128, 256, 384]`):
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Dimension of each of the encoder blocks.
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num_attention_heads (`List[int]`, *optional*, defaults to `[4, 8, 12]`):
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Number of attention heads for each attention layer in each block of the Transformer encoder.
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depths (`List[int]`, *optional*, defaults to `[4, 4, 4]`):
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The number of layers in each encoder block.
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key_dim (`List[int]`, *optional*, defaults to `[16, 16, 16]`):
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The size of key in each of the encoder blocks.
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drop_path_rate (`int`, *optional*, defaults to 0):
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The dropout probability for stochastic depths, used in the blocks of the Transformer encoder.
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mlp_ratios (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
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Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
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encoder blocks.
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attention_ratios (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
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Ratio of the size of the output dimension compared to input dimension of attention layers.
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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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Example:
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```python
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>>> from transformers import LevitConfig, LevitModel
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>>> # Initializing a LeViT levit-128S style configuration
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>>> configuration = LevitConfig()
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>>> # Initializing a model (with random weights) from the levit-128S style configuration
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>>> model = LevitModel(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 = "levit"
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def __init__(
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self,
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image_size=224,
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num_channels=3,
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kernel_size=3,
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stride=2,
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padding=1,
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patch_size=16,
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hidden_sizes=[128, 256, 384],
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num_attention_heads=[4, 8, 12],
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depths=[4, 4, 4],
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key_dim=[16, 16, 16],
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drop_path_rate=0,
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mlp_ratio=[2, 2, 2],
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attention_ratio=[2, 2, 2],
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initializer_range=0.02,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.image_size = image_size
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self.num_channels = num_channels
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self.kernel_size = kernel_size
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self.stride = stride
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self.padding = padding
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self.hidden_sizes = hidden_sizes
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self.num_attention_heads = num_attention_heads
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self.depths = depths
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self.key_dim = key_dim
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self.drop_path_rate = drop_path_rate
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self.patch_size = patch_size
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self.attention_ratio = attention_ratio
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self.mlp_ratio = mlp_ratio
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self.initializer_range = initializer_range
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self.down_ops = [
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["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
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["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
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]
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# Copied from transformers.models.vit.configuration_vit.ViTOnnxConfig
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class LevitOnnxConfig(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-4
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