139 lines
6.6 KiB
Python
139 lines
6.6 KiB
Python
# coding=utf-8
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# Copyright 2022 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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""" UperNet model configuration"""
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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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class UperNetConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of an [`UperNetForSemanticSegmentation`]. It is used to
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instantiate an UperNet 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 UperNet
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[openmmlab/upernet-convnext-tiny](https://huggingface.co/openmmlab/upernet-convnext-tiny) 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 `ResNetConfig()`):
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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*, `False`):
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Whether to use pretrained weights for the backbone.
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use_timm_backbone (`bool`, *optional*, `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 512):
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The number of hidden units in the convolutional 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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pool_scales (`Tuple[int]`, *optional*, defaults to `[1, 2, 3, 6]`):
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Pooling scales used in Pooling Pyramid Module applied on the last feature map.
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use_auxiliary_head (`bool`, *optional*, defaults to `True`):
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Whether to use an auxiliary head during training.
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auxiliary_loss_weight (`float`, *optional*, defaults to 0.4):
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Weight of the cross-entropy loss of the auxiliary head.
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auxiliary_channels (`int`, *optional*, defaults to 256):
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Number of channels to use in the auxiliary head.
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auxiliary_num_convs (`int`, *optional*, defaults to 1):
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Number of convolutional layers to use in the auxiliary head.
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auxiliary_concat_input (`bool`, *optional*, defaults to `False`):
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Whether to concatenate the output of the auxiliary head with the input before the classification layer.
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loss_ignore_index (`int`, *optional*, defaults to 255):
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The index that is ignored by the loss function.
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Examples:
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```python
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>>> from transformers import UperNetConfig, UperNetForSemanticSegmentation
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>>> # Initializing a configuration
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>>> configuration = UperNetConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = UperNetForSemanticSegmentation(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 = "upernet"
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def __init__(
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self,
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backbone_config=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=512,
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initializer_range=0.02,
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pool_scales=[1, 2, 3, 6],
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use_auxiliary_head=True,
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auxiliary_loss_weight=0.4,
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auxiliary_in_channels=384,
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auxiliary_channels=256,
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auxiliary_num_convs=1,
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auxiliary_concat_input=False,
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loss_ignore_index=255,
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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 `ResNet` backbone.")
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backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "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.hidden_size = hidden_size
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self.initializer_range = initializer_range
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self.pool_scales = pool_scales
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self.use_auxiliary_head = use_auxiliary_head
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self.auxiliary_loss_weight = auxiliary_loss_weight
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self.auxiliary_in_channels = auxiliary_in_channels
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self.auxiliary_channels = auxiliary_channels
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self.auxiliary_num_convs = auxiliary_num_convs
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self.auxiliary_concat_input = auxiliary_concat_input
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self.loss_ignore_index = loss_ignore_index
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