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95 lines
3.9 KiB
95 lines
3.9 KiB
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""VitMatte model configuration"""
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from ...backbone_utils import consolidate_backbone_kwargs_to_config
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from ...configuration_utils import PreTrainedConfig
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from ...utils import logging
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from ..auto.configuration_auto import AutoConfig
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logger = logging.get_logger(__name__)
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class VitMatteConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of [`VitMatteForImageMatting`]. It is used to
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instantiate a ViTMatte model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the ViTMatte
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[hustvl/vitmatte-small-composition-1k](https://huggingface.co/hustvl/vitmatte-small-composition-1k) architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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backbone_config (`Union[dict, "PreTrainedConfig"]`, *optional*, defaults to `VitDetConfig()`):
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The configuration of the backbone model.
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hidden_size (`int`, *optional*, defaults to 384):
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The number of input channels of the decoder.
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batch_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the batch norm layers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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convstream_hidden_sizes (`list[int]`, *optional*, defaults to `[48, 96, 192]`):
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The output channels of the ConvStream module.
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fusion_hidden_sizes (`list[int]`, *optional*, defaults to `[256, 128, 64, 32]`):
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The output channels of the Fusion blocks.
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Example:
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```python
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>>> from transformers import VitMatteConfig, VitMatteForImageMatting
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>>> # Initializing a ViTMatte hustvl/vitmatte-small-composition-1k style configuration
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>>> configuration = VitMatteConfig()
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>>> # Initializing a model (with random weights) from the hustvl/vitmatte-small-composition-1k style configuration
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>>> model = VitMatteForImageMatting(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "vitmatte"
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sub_configs = {"backbone_config": AutoConfig}
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def __init__(
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self,
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backbone_config: PreTrainedConfig | None = None,
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hidden_size: int = 384,
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batch_norm_eps: float = 1e-5,
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initializer_range: float = 0.02,
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convstream_hidden_sizes: list[int] = [48, 96, 192],
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fusion_hidden_sizes: list[int] = [256, 128, 64, 32],
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**kwargs,
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):
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backbone_config, kwargs = consolidate_backbone_kwargs_to_config(
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backbone_config=backbone_config,
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default_config_type="vitdet",
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default_config_kwargs={"out_features": ["stage4"]},
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**kwargs,
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)
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self.backbone_config = backbone_config
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self.batch_norm_eps = batch_norm_eps
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self.hidden_size = hidden_size
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self.initializer_range = initializer_range
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self.convstream_hidden_sizes = convstream_hidden_sizes
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self.fusion_hidden_sizes = fusion_hidden_sizes
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super().__init__(**kwargs)
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__all__ = ["VitMatteConfig"]
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