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configuration_swin.py
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configuration_swin.py
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# coding=utf-8
# Copyright 2022 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.
""" Swin Transformer model configuration"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
logger = logging.get_logger(__name__)
class SwinConfig(BackboneConfigMixin, PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`SwinModel`]. It is used to instantiate a Swin
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the Swin
[microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224)
architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 4):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
embed_dim (`int`, *optional*, defaults to 96):
Dimensionality of patch embedding.
depths (`list(int)`, *optional*, defaults to `[2, 2, 6, 2]`):
Depth of each layer in the Transformer encoder.
num_heads (`list(int)`, *optional*, defaults to `[3, 6, 12, 24]`):
Number of attention heads in each layer of the Transformer encoder.
window_size (`int`, *optional*, defaults to 7):
Size of windows.
mlp_ratio (`float`, *optional*, defaults to 4.0):
Ratio of MLP hidden dimensionality to embedding dimensionality.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings and encoder.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
use_absolute_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to add absolute position embeddings to the patch embeddings.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the layer normalization layers.
encoder_stride (`int`, *optional*, defaults to 32):
Factor to increase the spatial resolution by in the decoder head for masked image modeling.
out_features (`List[str]`, *optional*):
If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc.
(depending on how many stages the model has). If unset and `out_indices` is set, will default to the
corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
out_indices (`List[int]`, *optional*):
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and `out_features` is set, will default to the corresponding stages.
If unset and `out_features` is unset, will default to the last stage. Must be in the
same order as defined in the `stage_names` attribute.
Example:
```python
>>> from transformers import SwinConfig, SwinModel
>>> # Initializing a Swin microsoft/swin-tiny-patch4-window7-224 style configuration
>>> configuration = SwinConfig()
>>> # Initializing a model (with random weights) from the microsoft/swin-tiny-patch4-window7-224 style configuration
>>> model = SwinModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "swin"
attribute_map = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__(
self,
image_size=224,
patch_size=4,
num_channels=3,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.0,
qkv_bias=True,
hidden_dropout_prob=0.0,
attention_probs_dropout_prob=0.0,
drop_path_rate=0.1,
hidden_act="gelu",
use_absolute_embeddings=False,
initializer_range=0.02,
layer_norm_eps=1e-5,
encoder_stride=32,
out_features=None,
out_indices=None,
**kwargs,
):
super().__init__(**kwargs)
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.embed_dim = embed_dim
self.depths = depths
self.num_layers = len(depths)
self.num_heads = num_heads
self.window_size = window_size
self.mlp_ratio = mlp_ratio
self.qkv_bias = qkv_bias
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.drop_path_rate = drop_path_rate
self.hidden_act = hidden_act
self.use_absolute_embeddings = use_absolute_embeddings
self.layer_norm_eps = layer_norm_eps
self.initializer_range = initializer_range
self.encoder_stride = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
self.hidden_size = int(embed_dim * 2 ** (len(depths) - 1))
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(depths) + 1)]
self._out_features, self._out_indices = get_aligned_output_features_output_indices(
out_features=out_features, out_indices=out_indices, stage_names=self.stage_names
)
class SwinOnnxConfig(OnnxConfig):
torch_onnx_minimum_version = version.parse("1.11")
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
]
)
@property
def atol_for_validation(self) -> float:
return 1e-4