126 lines
5.7 KiB
Python
126 lines
5.7 KiB
Python
# coding=utf-8
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# Copyright 2024 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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"""VitPose model configuration"""
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from typing import Optional
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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from ...utils.backbone_utils import verify_backbone_config_arguments
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from ..auto.configuration_auto import CONFIG_MAPPING
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logger = logging.get_logger(__name__)
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class VitPoseConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`VitPoseForPoseEstimation`]. It is used to instantiate a
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VitPose model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the VitPose
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[usyd-community/vitpose-base-simple](https://huggingface.co/usyd-community/vitpose-base-simple) 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 `VitPoseBackboneConfig()`):
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The configuration of the backbone model. Currently, only `backbone_config` with `vitpose_backbone` as `model_type` is supported.
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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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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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scale_factor (`int`, *optional*, defaults to 4):
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Factor to upscale the feature maps coming from the ViT backbone.
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use_simple_decoder (`bool`, *optional*, defaults to `True`):
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Whether to use a `VitPoseSimpleDecoder` to decode the feature maps from the backbone into heatmaps. Otherwise it uses `VitPoseClassicDecoder`.
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Example:
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```python
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>>> from transformers import VitPoseConfig, VitPoseForPoseEstimation
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>>> # Initializing a VitPose configuration
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>>> configuration = VitPoseConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = VitPoseForPoseEstimation(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 = "vitpose"
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def __init__(
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self,
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backbone_config: Optional[PretrainedConfig] = None,
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backbone: Optional[str] = None,
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use_pretrained_backbone: bool = False,
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use_timm_backbone: bool = False,
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backbone_kwargs: Optional[dict] = None,
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initializer_range: float = 0.02,
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scale_factor: int = 4,
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use_simple_decoder: bool = True,
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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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logger.info(
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"`use_pretrained_backbone` is `True`. For the pure inference purpose of VitPose weight do not set this value."
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)
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if use_timm_backbone:
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raise ValueError("use_timm_backbone set `True` is not supported at the moment.")
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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 `VitPose` backbone.")
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backbone_config = CONFIG_MAPPING["vitpose_backbone"](out_indices=[4])
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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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verify_backbone_config_arguments(
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use_timm_backbone=use_timm_backbone,
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use_pretrained_backbone=use_pretrained_backbone,
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backbone=backbone,
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backbone_config=backbone_config,
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backbone_kwargs=backbone_kwargs,
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)
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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.initializer_range = initializer_range
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self.scale_factor = scale_factor
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self.use_simple_decoder = use_simple_decoder
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__all__ = ["VitPoseConfig"]
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