163 lines
6.9 KiB
Python
163 lines
6.9 KiB
Python
# coding=utf-8
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# Copyright 2023 Authors: Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan,
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# Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao and The HuggingFace Inc. team.
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# 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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""" Pvt model configuration"""
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from collections import OrderedDict
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from typing import Callable, 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 PVT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class PvtConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PvtModel`]. It is used to instantiate an Pvt
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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 Pvt
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[Xrenya/pvt-tiny-224](https://huggingface.co/Xrenya/pvt-tiny-224) 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 input image size
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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num_encoder_blocks (`int`, *optional*, defaults to 4):
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The number of encoder blocks (i.e. stages in the Mix Transformer encoder).
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depths (`List[int]`, *optional*, defaults to `[2, 2, 2, 2]`):
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The number of layers in each encoder block.
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sequence_reduction_ratios (`List[int]`, *optional*, defaults to `[8, 4, 2, 1]`):
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Sequence reduction ratios in each encoder block.
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hidden_sizes (`List[int]`, *optional*, defaults to `[64, 128, 320, 512]`):
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Dimension of each of the encoder blocks.
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patch_sizes (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
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Patch size before each encoder block.
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strides (`List[int]`, *optional*, defaults to `[4, 2, 2, 2]`):
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Stride before each encoder block.
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num_attention_heads (`List[int]`, *optional*, defaults to `[1, 2, 5, 8]`):
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Number of attention heads for each attention layer in each block of the Transformer encoder.
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mlp_ratios (`List[int]`, *optional*, defaults to `[8, 8, 4, 4]`):
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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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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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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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drop_path_rate (`float`, *optional*, defaults to 0.0):
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The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether or not a learnable bias should be added to the queries, keys and values.
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num_labels ('int', *optional*, defaults to 1000):
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The number of classes.
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Example:
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```python
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>>> from transformers import PvtModel, PvtConfig
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>>> # Initializing a PVT Xrenya/pvt-tiny-224 style configuration
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>>> configuration = PvtConfig()
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>>> # Initializing a model from the Xrenya/pvt-tiny-224 style configuration
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>>> model = PvtModel(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 = "pvt"
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def __init__(
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self,
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image_size: int = 224,
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num_channels: int = 3,
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num_encoder_blocks: int = 4,
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depths: List[int] = [2, 2, 2, 2],
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sequence_reduction_ratios: List[int] = [8, 4, 2, 1],
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hidden_sizes: List[int] = [64, 128, 320, 512],
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patch_sizes: List[int] = [4, 2, 2, 2],
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strides: List[int] = [4, 2, 2, 2],
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num_attention_heads: List[int] = [1, 2, 5, 8],
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mlp_ratios: List[int] = [8, 8, 4, 4],
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hidden_act: Mapping[str, Callable] = "gelu",
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hidden_dropout_prob: float = 0.0,
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attention_probs_dropout_prob: float = 0.0,
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initializer_range: float = 0.02,
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drop_path_rate: float = 0.0,
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layer_norm_eps: float = 1e-6,
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qkv_bias: bool = True,
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num_labels: int = 1000,
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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.num_encoder_blocks = num_encoder_blocks
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self.depths = depths
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self.sequence_reduction_ratios = sequence_reduction_ratios
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self.hidden_sizes = hidden_sizes
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self.patch_sizes = patch_sizes
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self.strides = strides
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self.mlp_ratios = mlp_ratios
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.drop_path_rate = drop_path_rate
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self.layer_norm_eps = layer_norm_eps
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self.num_labels = num_labels
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self.qkv_bias = qkv_bias
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class PvtOnnxConfig(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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@property
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def default_onnx_opset(self) -> int:
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return 12
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