/usr/local/lib/python3.6/site-packages/transformers/models/convnext
Edit: /usr/local/lib/python3.6/site-packages/transformers/models/convnext/configuration_convnext.py (4472B)
# coding=utf-8
# Copyright 2022 Meta Platforms, Inc. 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
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""" ConvNeXT model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"facebook/convnext-tiny-224": "https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json",
# See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}
class ConvNextConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ConvNextModel`]. It is used to instantiate an
ConvNeXT 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 ConvNeXT
[facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-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:
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
patch_size (`int`, optional, defaults to 4):
Patch size to use in the patch embedding layer.
num_stages (`int`, optional, defaults to 4):
The number of stages in the model.
hidden_sizes (`List[int]`, *optional*, defaults to [96, 192, 384, 768]):
Dimensionality (hidden size) at each stage.
depths (`List[int]`, *optional*, defaults to [3, 3, 9, 3]):
Depth (number of blocks) for each stage.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
`"selu"` and `"gelu_new"` are supported.
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-12):
The epsilon used by the layer normalization layers.
layer_scale_init_value (`float`, *optional*, defaults to 1e-6):
The initial value for the layer scale.
drop_path_rate (`float`, *optional*, defaults to 0.0):
The drop rate for stochastic depth.
Example:
```python
>>> from transformers import ConvNextModel, ConvNextConfig
>>> # Initializing a ConvNext convnext-tiny-224 style configuration
>>> configuration = ConvNextConfig()
>>> # Initializing a model from the convnext-tiny-224 style configuration
>>> model = ConvNextModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "convnext"
def __init__(
self,
num_channels=3,
patch_size=4,
num_stages=4,
hidden_sizes=None,
depths=None,
hidden_act="gelu",
initializer_range=0.02,
layer_norm_eps=1e-12,
is_encoder_decoder=False,
layer_scale_init_value=1e-6,
drop_path_rate=0.0,
image_size=224,
**kwargs
):
super().__init__(**kwargs)
self.num_channels = num_channels
self.patch_size = patch_size
self.num_stages = num_stages
self.hidden_sizes = [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
self.depths = [3, 3, 9, 3] if depths is None else depths
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.layer_norm_eps = layer_norm_eps
self.layer_scale_init_value = layer_scale_init_value
self.drop_path_rate = drop_path_rate
self.image_size = image_size