147 lines
6.6 KiB
Python
147 lines
6.6 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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""" CvT 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 CVT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class CvtConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`CvtModel`]. It is used to instantiate a CvT model
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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 CvT
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[microsoft/cvt-13](https://huggingface.co/microsoft/cvt-13) 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_sizes (`List[int]`, *optional*, defaults to `[7, 3, 3]`):
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The kernel size of each encoder's patch embedding.
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patch_stride (`List[int]`, *optional*, defaults to `[4, 2, 2]`):
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The stride size of each encoder's patch embedding.
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patch_padding (`List[int]`, *optional*, defaults to `[2, 1, 1]`):
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The padding size of each encoder's patch embedding.
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embed_dim (`List[int]`, *optional*, defaults to `[64, 192, 384]`):
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Dimension of each of the encoder blocks.
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num_heads (`List[int]`, *optional*, defaults to `[1, 3, 6]`):
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Number of attention heads for each attention layer in each block of the Transformer encoder.
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depth (`List[int]`, *optional*, defaults to `[1, 2, 10]`):
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The number of layers in each encoder block.
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mlp_ratios (`List[float]`, *optional*, defaults to `[4.0, 4.0, 4.0, 4.0]`):
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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_drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`):
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The dropout ratio for the attention probabilities.
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drop_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.0]`):
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The dropout ratio for the patch embeddings probabilities.
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drop_path_rate (`List[float]`, *optional*, defaults to `[0.0, 0.0, 0.1]`):
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The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
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qkv_bias (`List[bool]`, *optional*, defaults to `[True, True, True]`):
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The bias bool for query, key and value in attentions
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cls_token (`List[bool]`, *optional*, defaults to `[False, False, True]`):
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Whether or not to add a classification token to the output of each of the last 3 stages.
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qkv_projection_method (`List[string]`, *optional*, defaults to ["dw_bn", "dw_bn", "dw_bn"]`):
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The projection method for query, key and value Default is depth-wise convolutions with batch norm. For
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Linear projection use "avg".
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kernel_qkv (`List[int]`, *optional*, defaults to `[3, 3, 3]`):
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The kernel size for query, key and value in attention layer
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padding_kv (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
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The padding size for key and value in attention layer
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stride_kv (`List[int]`, *optional*, defaults to `[2, 2, 2]`):
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The stride size for key and value in attention layer
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padding_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
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The padding size for query in attention layer
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stride_q (`List[int]`, *optional*, defaults to `[1, 1, 1]`):
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The stride size for query in attention layer
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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-6):
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The epsilon used by the layer normalization layers.
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Example:
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```python
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>>> from transformers import CvtConfig, CvtModel
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>>> # Initializing a Cvt msft/cvt style configuration
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>>> configuration = CvtConfig()
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>>> # Initializing a model (with random weights) from the msft/cvt style configuration
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>>> model = CvtModel(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 = "cvt"
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def __init__(
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self,
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num_channels=3,
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patch_sizes=[7, 3, 3],
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patch_stride=[4, 2, 2],
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patch_padding=[2, 1, 1],
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embed_dim=[64, 192, 384],
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num_heads=[1, 3, 6],
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depth=[1, 2, 10],
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mlp_ratio=[4.0, 4.0, 4.0],
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attention_drop_rate=[0.0, 0.0, 0.0],
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drop_rate=[0.0, 0.0, 0.0],
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drop_path_rate=[0.0, 0.0, 0.1],
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qkv_bias=[True, True, True],
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cls_token=[False, False, True],
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qkv_projection_method=["dw_bn", "dw_bn", "dw_bn"],
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kernel_qkv=[3, 3, 3],
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padding_kv=[1, 1, 1],
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stride_kv=[2, 2, 2],
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padding_q=[1, 1, 1],
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stride_q=[1, 1, 1],
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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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_sizes = patch_sizes
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self.patch_stride = patch_stride
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self.patch_padding = patch_padding
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.depth = depth
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self.mlp_ratio = mlp_ratio
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self.attention_drop_rate = attention_drop_rate
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self.drop_rate = drop_rate
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self.drop_path_rate = drop_path_rate
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self.qkv_bias = qkv_bias
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self.cls_token = cls_token
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self.qkv_projection_method = qkv_projection_method
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self.kernel_qkv = kernel_qkv
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self.padding_kv = padding_kv
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self.stride_kv = stride_kv
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self.padding_q = padding_q
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self.stride_q = stride_q
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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