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- # Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan,
- # Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team.
- # All rights reserved.
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- """Pvt model configuration"""
- from huggingface_hub.dataclasses import strict
- from ...configuration_utils import PreTrainedConfig
- from ...utils import auto_docstring
- @auto_docstring(checkpoint="Xrenya/pvt-tiny-224")
- @strict
- class PvtConfig(PreTrainedConfig):
- r"""
- num_encoder_blocks (`int`, *optional*, defaults to 4):
- The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
- depths (`list[int]`, *optional*, defaults to `[2, 2, 2, 2]`):
- The number of layers in each encoder block.
- sequence_reduction_ratios (`list[int]`, *optional*, defaults to `[8, 4, 2, 1]`):
- Sequence reduction ratios in each encoder block.
- patch_sizes (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
- Patch size before each encoder block.
- strides (`list[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
- Stride before each encoder block.
- num_attention_heads (`list[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
- Number of attention heads for each attention layer in each block of the Transformer encoder.
- mlp_ratios (`list[int]`, *optional*, defaults to `[8, 8, 4, 4]`):
- Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
- encoder blocks.
- num_labels ('int', *optional*, defaults to 1000):
- The number of classes.
- Example:
- ```python
- >>> from transformers import PvtModel, PvtConfig
- >>> # Initializing a PVT Xrenya/pvt-tiny-224 style configuration
- >>> configuration = PvtConfig()
- >>> # Initializing a model from the Xrenya/pvt-tiny-224 style configuration
- >>> model = PvtModel(configuration)
- >>> # Accessing the model configuration
- >>> configuration = model.config
- ```"""
- model_type = "pvt"
- image_size: int | list[int] | tuple[int, int] = 224
- num_channels: int = 3
- num_encoder_blocks: int = 4
- depths: list[int] | tuple[int, ...] = (2, 2, 2, 2)
- sequence_reduction_ratios: list[int] | tuple[int, ...] = (8, 4, 2, 1)
- hidden_sizes: list[int] | tuple[int, ...] = (64, 128, 320, 512)
- patch_sizes: list[int] | tuple[int, ...] = (4, 2, 2, 2)
- strides: list[int] | tuple[int, ...] = (4, 2, 2, 2)
- num_attention_heads: list[int] | tuple[int, ...] = (1, 2, 5, 8)
- mlp_ratios: list[int] | tuple[int, ...] = (8, 8, 4, 4)
- hidden_act: str = "gelu"
- hidden_dropout_prob: float | int = 0.0
- attention_probs_dropout_prob: float | int = 0.0
- initializer_range: float = 0.02
- drop_path_rate: float | int = 0.0
- layer_norm_eps: float = 1e-6
- qkv_bias: bool = True
- num_labels: int = 1000
- __all__ = ["PvtConfig"]
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