Spaces:
Sleeping
Sleeping
# 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 | |
# limitations under the License. | |
""" ConvNeXT 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__) | |
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(BackboneConfigMixin, 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. | |
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. | |
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. | |
Example: | |
```python | |
>>> from transformers import ConvNextConfig, ConvNextModel | |
>>> # Initializing a ConvNext convnext-tiny-224 style configuration | |
>>> configuration = ConvNextConfig() | |
>>> # Initializing a model (with random weights) 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, | |
layer_scale_init_value=1e-6, | |
drop_path_rate=0.0, | |
image_size=224, | |
out_features=None, | |
out_indices=None, | |
**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 | |
self.stage_names = ["stem"] + [f"stage{idx}" for idx in range(1, len(self.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 ConvNextOnnxConfig(OnnxConfig): | |
torch_onnx_minimum_version = version.parse("1.11") | |
def inputs(self) -> Mapping[str, Mapping[int, str]]: | |
return OrderedDict( | |
[ | |
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), | |
] | |
) | |
def atol_for_validation(self) -> float: | |
return 1e-5 | |