# coding=utf-8 # Copyright 2024 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. """DepthAnything model configuration""" import copy from ...configuration_utils import PretrainedConfig from ...utils import logging from ...utils.backbone_utils import verify_backbone_config_arguments from ..auto.configuration_auto import CONFIG_MAPPING logger = logging.get_logger(__name__) class DepthAnythingConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`DepthAnythingModel`]. It is used to instantiate a DepthAnything 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 DepthAnything [LiheYoung/depth-anything-small-hf](https://huggingface.co/LiheYoung/depth-anything-small-hf) 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[str, Any], PretrainedConfig]`, *optional*): The configuration of the backbone model. Only used in case `is_hybrid` is `True` or in case you want to leverage the [`AutoBackbone`] API. backbone (`str`, *optional*): Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. use_pretrained_backbone (`bool`, *optional*, defaults to `False`): Whether to use pretrained weights for the backbone. use_timm_backbone (`bool`, *optional*, defaults to `False`): Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`] API. backbone_kwargs (`dict`, *optional*): Keyword arguments to be passed to AutoBackbone when loading from a checkpoint e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. patch_size (`int`, *optional*, defaults to 14): The size of the patches to extract from the backbone features. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. reassemble_hidden_size (`int`, *optional*, defaults to 384): The number of input channels of the reassemble layers. reassemble_factors (`List[int]`, *optional*, defaults to `[4, 2, 1, 0.5]`): The up/downsampling factors of the reassemble layers. neck_hidden_sizes (`List[str]`, *optional*, defaults to `[48, 96, 192, 384]`): The hidden sizes to project to for the feature maps of the backbone. fusion_hidden_size (`int`, *optional*, defaults to 64): The number of channels before fusion. head_in_index (`int`, *optional*, defaults to -1): The index of the features to use in the depth estimation head. head_hidden_size (`int`, *optional*, defaults to 32): The number of output channels in the second convolution of the depth estimation head. depth_estimation_type (`str`, *optional*, defaults to `"relative"`): The type of depth estimation to use. Can be one of `["relative", "metric"]`. max_depth (`float`, *optional*): The maximum depth to use for the "metric" depth estimation head. 20 should be used for indoor models and 80 for outdoor models. For "relative" depth estimation, this value is ignored. Example: ```python >>> from transformers import DepthAnythingConfig, DepthAnythingForDepthEstimation >>> # Initializing a DepthAnything small style configuration >>> configuration = DepthAnythingConfig() >>> # Initializing a model from the DepthAnything small style configuration >>> model = DepthAnythingForDepthEstimation(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "depth_anything" def __init__( self, backbone_config=None, backbone=None, use_pretrained_backbone=False, use_timm_backbone=False, backbone_kwargs=None, patch_size=14, initializer_range=0.02, reassemble_hidden_size=384, reassemble_factors=[4, 2, 1, 0.5], neck_hidden_sizes=[48, 96, 192, 384], fusion_hidden_size=64, head_in_index=-1, head_hidden_size=32, depth_estimation_type="relative", max_depth=None, **kwargs, ): super().__init__(**kwargs) if backbone_config is None and backbone is None: logger.info("`backbone_config` is `None`. Initializing the config with the default `Dinov2` backbone.") backbone_config = CONFIG_MAPPING["dinov2"]( image_size=518, hidden_size=384, num_attention_heads=6, out_indices=[9, 10, 11, 12], apply_layernorm=True, reshape_hidden_states=False, ) elif isinstance(backbone_config, dict): backbone_model_type = backbone_config.get("model_type") config_class = CONFIG_MAPPING[backbone_model_type] backbone_config = config_class.from_dict(backbone_config) verify_backbone_config_arguments( use_timm_backbone=use_timm_backbone, use_pretrained_backbone=use_pretrained_backbone, backbone=backbone, backbone_config=backbone_config, backbone_kwargs=backbone_kwargs, ) self.backbone_config = backbone_config self.backbone = backbone self.use_pretrained_backbone = use_pretrained_backbone self.use_timm_backbone = use_timm_backbone self.backbone_kwargs = backbone_kwargs self.reassemble_hidden_size = reassemble_hidden_size self.patch_size = patch_size self.initializer_range = initializer_range self.reassemble_factors = reassemble_factors self.neck_hidden_sizes = neck_hidden_sizes self.fusion_hidden_size = fusion_hidden_size self.head_in_index = head_in_index self.head_hidden_size = head_hidden_size if depth_estimation_type not in ["relative", "metric"]: raise ValueError("depth_estimation_type must be one of ['relative', 'metric']") self.depth_estimation_type = depth_estimation_type self.max_depth = max_depth if max_depth else 1 def to_dict(self): """ Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`]. Returns: `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance, """ output = copy.deepcopy(self.__dict__) if output["backbone_config"] is not None: output["backbone_config"] = self.backbone_config.to_dict() output["model_type"] = self.__class__.model_type return output __all__ = ["DepthAnythingConfig"]