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# coding=utf-8#
# 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.
"""ResNet model configuration"""
from transformers import PretrainedConfig
class ResNet10Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`ResNetModel`]. It is used to instantiate an
ResNet 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 ResNet
[microsoft/resnet-50](https://huggingface.co./microsoft/resnet-50) 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.
embedding_size (`int`, *optional*, defaults to 64):
Dimensionality (hidden size) for the embedding layer.
hidden_sizes (`List[int]`, *optional*, defaults to `[256, 512, 1024, 2048]`):
Dimensionality (hidden size) at each stage.
depths (`List[int]`, *optional*, defaults to `[3, 4, 6, 3]`):
Depth (number of layers) for each stage.
layer_type (`str`, *optional*, defaults to `"bottleneck"`):
The layer to use, it can be either `"basic"` (used for smaller models, like resnet-18 or resnet-34) or
`"bottleneck"` (used for larger models like resnet-50 and above).
hidden_act (`str`, *optional*, defaults to `"relu"`):
The non-linear activation function in each block. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"`
are supported.
downsample_in_first_stage (`bool`, *optional*, defaults to `False`):
If `True`, the first stage will downsample the inputs using a `stride` of 2.
Example:
```python
>>> from transformers import AutoConfig, AutoModel
>>> # Initializing a ResNet resnet-50 style configuration
>>> configuration = AutoConfig.from_pretrained("helper2424/resnet10")
>>> # Initializing a model (with random weights) from the resnet-50 style configuration
>>> model = AutoModel.from_pretrained("helper2424/resnet10")
>>> # Accessing the model configuration
>>> model.config = configuration
```
"""
model_type = "resnet10"
def __init__(
self,
num_channels=3,
embedding_size=64,
hidden_sizes=[64, 128, 256, 512],
depths=[1, 1, 1, 1],
hidden_act="relu",
pooler="avg",
**kwargs,
):
super().__init__(**kwargs)
self.num_channels = num_channels
self.embedding_size = embedding_size
self.hidden_sizes = hidden_sizes
self.depths = depths
self.hidden_act = hidden_act
self.pooler = pooler
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