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# Copyright 2023 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.
"""VitMatte model configuration"""
from ...backbone_utils import consolidate_backbone_kwargs_to_config
from ...configuration_utils import PreTrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
logger = logging.get_logger(__name__)
class VitMatteConfig(PreTrainedConfig):
r"""
This is the configuration class to store the configuration of [`VitMatteForImageMatting`]. It is used to
instantiate a ViTMatte 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 ViTMatte
[hustvl/vitmatte-small-composition-1k](https://huggingface.co/hustvl/vitmatte-small-composition-1k) architecture.
Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PreTrainedConfig`] for more information.
Args:
backbone_config (`Union[dict, "PreTrainedConfig"]`, *optional*, defaults to `VitDetConfig()`):
The configuration of the backbone model.
hidden_size (`int`, *optional*, defaults to 384):
The number of input channels of the decoder.
batch_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the batch norm layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
convstream_hidden_sizes (`list[int]`, *optional*, defaults to `[48, 96, 192]`):
The output channels of the ConvStream module.
fusion_hidden_sizes (`list[int]`, *optional*, defaults to `[256, 128, 64, 32]`):
The output channels of the Fusion blocks.
Example:
```python
>>> from transformers import VitMatteConfig, VitMatteForImageMatting
>>> # Initializing a ViTMatte hustvl/vitmatte-small-composition-1k style configuration
>>> configuration = VitMatteConfig()
>>> # Initializing a model (with random weights) from the hustvl/vitmatte-small-composition-1k style configuration
>>> model = VitMatteForImageMatting(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "vitmatte"
sub_configs = {"backbone_config": AutoConfig}
def __init__(
self,
backbone_config: PreTrainedConfig | None = None,
hidden_size: int = 384,
batch_norm_eps: float = 1e-5,
initializer_range: float = 0.02,
convstream_hidden_sizes: list[int] = [48, 96, 192],
fusion_hidden_sizes: list[int] = [256, 128, 64, 32],
**kwargs,
):
backbone_config, kwargs = consolidate_backbone_kwargs_to_config(
backbone_config=backbone_config,
default_config_type="vitdet",
default_config_kwargs={"out_features": ["stage4"]},
**kwargs,
)
self.backbone_config = backbone_config
self.batch_norm_eps = batch_norm_eps
self.hidden_size = hidden_size
self.initializer_range = initializer_range
self.convstream_hidden_sizes = convstream_hidden_sizes
self.fusion_hidden_sizes = fusion_hidden_sizes
super().__init__(**kwargs)
__all__ = ["VitMatteConfig"]