137 lines
6.3 KiB
Python
137 lines
6.3 KiB
Python
# coding=utf-8
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# 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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import copy
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from typing import List
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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 CONFIG_MAPPING
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import VITMATTE_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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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 (`PretrainedConfig` or `dict`, *optional*, defaults to `VitDetConfig()`):
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The configuration of the backbone model.
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backbone (`str`, *optional*):
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Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this
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will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone`
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is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights.
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use_pretrained_backbone (`bool`, *optional*, defaults to `False`):
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Whether to use pretrained weights for the backbone.
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use_timm_backbone (`bool`, *optional*, defaults to `False`):
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Whether to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers
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library.
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backbone_kwargs (`dict`, *optional*):
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Keyword arguments to be passed to AutoBackbone when loading from a checkpoint
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e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set.
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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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def __init__(
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self,
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backbone_config: PretrainedConfig = None,
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backbone=None,
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use_pretrained_backbone=False,
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use_timm_backbone=False,
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backbone_kwargs=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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super().__init__(**kwargs)
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if use_pretrained_backbone:
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raise ValueError("Pretrained backbones are not supported yet.")
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if backbone_config is not None and backbone is not None:
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raise ValueError("You can't specify both `backbone` and `backbone_config`.")
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if backbone_config is None and backbone is None:
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logger.info("`backbone_config` is `None`. Initializing the config with the default `VitDet` backbone.")
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backbone_config = CONFIG_MAPPING["vitdet"](out_features=["stage4"])
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elif isinstance(backbone_config, dict):
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backbone_model_type = backbone_config.get("model_type")
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config_class = CONFIG_MAPPING[backbone_model_type]
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backbone_config = config_class.from_dict(backbone_config)
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if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None:
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raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.")
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self.backbone_config = backbone_config
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self.backbone = backbone
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self.use_pretrained_backbone = use_pretrained_backbone
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self.use_timm_backbone = use_timm_backbone
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self.backbone_kwargs = backbone_kwargs
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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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def to_dict(self):
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"""
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Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns:
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`Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
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"""
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output = copy.deepcopy(self.__dict__)
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output["backbone_config"] = self.backbone_config.to_dict()
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output["model_type"] = self.__class__.model_type
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return output
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