Llama-3.1-8B-DALv0.1
/
venv
/lib
/python3.12
/site-packages
/transformers
/models
/zoedepth
/configuration_zoedepth.py
| # 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. | |
| """ZoeDepth model configuration""" | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| from ..auto.configuration_auto import CONFIG_MAPPING | |
| logger = logging.get_logger(__name__) | |
| ZOEDEPTH_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "Intel/zoedepth-nyu": "https://huggingface.co/Intel/zoedepth-nyu/resolve/main/config.json", | |
| } | |
| class ZoeDepthConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`ZoeDepthForDepthEstimation`]. It is used to instantiate an ZoeDepth | |
| 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 ZoeDepth | |
| [Intel/zoedepth-nyu](https://huggingface.co/Intel/zoedepth-nyu) 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*, defaults to `BeitConfig()`): | |
| The configuration of the backbone model. | |
| 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. | |
| 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. | |
| hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
| `"relu"`, `"selu"` and `"gelu_new"` are supported. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| batch_norm_eps (`float`, *optional*, defaults to 1e-05): | |
| The epsilon used by the batch normalization layers. | |
| readout_type (`str`, *optional*, defaults to `"project"`): | |
| The readout type to use when processing the readout token (CLS token) of the intermediate hidden states of | |
| the ViT backbone. Can be one of [`"ignore"`, `"add"`, `"project"`]. | |
| - "ignore" simply ignores the CLS token. | |
| - "add" passes the information from the CLS token to all other tokens by adding the representations. | |
| - "project" passes information to the other tokens by concatenating the readout to all other tokens before | |
| projecting the | |
| representation to the original feature dimension D using a linear layer followed by a GELU non-linearity. | |
| 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 `[96, 192, 384, 768]`): | |
| The hidden sizes to project to for the feature maps of the backbone. | |
| fusion_hidden_size (`int`, *optional*, defaults to 256): | |
| The number of channels before fusion. | |
| head_in_index (`int`, *optional*, defaults to -1): | |
| The index of the features to use in the heads. | |
| use_batch_norm_in_fusion_residual (`bool`, *optional*, defaults to `False`): | |
| Whether to use batch normalization in the pre-activate residual units of the fusion blocks. | |
| use_bias_in_fusion_residual (`bool`, *optional*, defaults to `True`): | |
| Whether to use bias in the pre-activate residual units of the fusion blocks. | |
| num_relative_features (`int`, *optional*, defaults to 32): | |
| The number of features to use in the relative depth estimation head. | |
| add_projection (`bool`, *optional*, defaults to `False`): | |
| Whether to add a projection layer before the depth estimation head. | |
| bottleneck_features (`int`, *optional*, defaults to 256): | |
| The number of features in the bottleneck layer. | |
| num_attractors (`List[int], *optional*, defaults to `[16, 8, 4, 1]`): | |
| The number of attractors to use in each stage. | |
| bin_embedding_dim (`int`, *optional*, defaults to 128): | |
| The dimension of the bin embeddings. | |
| attractor_alpha (`int`, *optional*, defaults to 1000): | |
| The alpha value to use in the attractor. | |
| attractor_gamma (`int`, *optional*, defaults to 2): | |
| The gamma value to use in the attractor. | |
| attractor_kind (`str`, *optional*, defaults to `"mean"`): | |
| The kind of attractor to use. Can be one of [`"mean"`, `"sum"`]. | |
| min_temp (`float`, *optional*, defaults to 0.0212): | |
| The minimum temperature value to consider. | |
| max_temp (`float`, *optional*, defaults to 50.0): | |
| The maximum temperature value to consider. | |
| bin_centers_type (`str`, *optional*, defaults to `"softplus"`): | |
| Activation type used for bin centers. Can be "normed" or "softplus". For "normed" bin centers, linear normalization trick | |
| is applied. This results in bounded bin centers. For "softplus", softplus activation is used and thus are unbounded. | |
| bin_configurations (`List[dict]`, *optional*, defaults to `[{'n_bins': 64, 'min_depth': 0.001, 'max_depth': 10.0}]`): | |
| Configuration for each of the bin heads. | |
| Each configuration should consist of the following keys: | |
| - name (`str`): The name of the bin head - only required in case of multiple bin configurations. | |
| - `n_bins` (`int`): The number of bins to use. | |
| - `min_depth` (`float`): The minimum depth value to consider. | |
| - `max_depth` (`float`): The maximum depth value to consider. | |
| In case only a single configuration is passed, the model will use a single head with the specified configuration. | |
| In case multiple configurations are passed, the model will use multiple heads with the specified configurations. | |
| num_patch_transformer_layers (`int`, *optional*): | |
| The number of transformer layers to use in the patch transformer. Only used in case of multiple bin configurations. | |
| patch_transformer_hidden_size (`int`, *optional*): | |
| The hidden size to use in the patch transformer. Only used in case of multiple bin configurations. | |
| patch_transformer_intermediate_size (`int`, *optional*): | |
| The intermediate size to use in the patch transformer. Only used in case of multiple bin configurations. | |
| patch_transformer_num_attention_heads (`int`, *optional*): | |
| The number of attention heads to use in the patch transformer. Only used in case of multiple bin configurations. | |
| Example: | |
| ```python | |
| >>> from transformers import ZoeDepthConfig, ZoeDepthForDepthEstimation | |
| >>> # Initializing a ZoeDepth zoedepth-large style configuration | |
| >>> configuration = ZoeDepthConfig() | |
| >>> # Initializing a model from the zoedepth-large style configuration | |
| >>> model = ZoeDepthForDepthEstimation(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "zoedepth" | |
| def __init__( | |
| self, | |
| backbone_config=None, | |
| backbone=None, | |
| use_pretrained_backbone=False, | |
| backbone_kwargs=None, | |
| hidden_act="gelu", | |
| initializer_range=0.02, | |
| batch_norm_eps=1e-05, | |
| readout_type="project", | |
| reassemble_factors=[4, 2, 1, 0.5], | |
| neck_hidden_sizes=[96, 192, 384, 768], | |
| fusion_hidden_size=256, | |
| head_in_index=-1, | |
| use_batch_norm_in_fusion_residual=False, | |
| use_bias_in_fusion_residual=None, | |
| num_relative_features=32, | |
| add_projection=False, | |
| bottleneck_features=256, | |
| num_attractors=[16, 8, 4, 1], | |
| bin_embedding_dim=128, | |
| attractor_alpha=1000, | |
| attractor_gamma=2, | |
| attractor_kind="mean", | |
| min_temp=0.0212, | |
| max_temp=50.0, | |
| bin_centers_type="softplus", | |
| bin_configurations=[{"n_bins": 64, "min_depth": 0.001, "max_depth": 10.0}], | |
| num_patch_transformer_layers=None, | |
| patch_transformer_hidden_size=None, | |
| patch_transformer_intermediate_size=None, | |
| patch_transformer_num_attention_heads=None, | |
| **kwargs, | |
| ): | |
| super().__init__(**kwargs) | |
| if readout_type not in ["ignore", "add", "project"]: | |
| raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']") | |
| if attractor_kind not in ["mean", "sum"]: | |
| raise ValueError("Attractor_kind must be one of ['mean', 'sum']") | |
| if use_pretrained_backbone: | |
| raise ValueError("Pretrained backbones are not supported yet.") | |
| if backbone_config is not None and backbone is not None: | |
| raise ValueError("You can't specify both `backbone` and `backbone_config`.") | |
| if backbone_config is None and backbone is None: | |
| logger.info("`backbone_config` is `None`. Initializing the config with the default `BEiT` backbone.") | |
| backbone_config = CONFIG_MAPPING["beit"]( | |
| image_size=384, | |
| num_hidden_layers=24, | |
| hidden_size=1024, | |
| intermediate_size=4096, | |
| num_attention_heads=16, | |
| use_relative_position_bias=True, | |
| reshape_hidden_states=False, | |
| out_features=["stage6", "stage12", "stage18", "stage24"], | |
| ) | |
| 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) | |
| if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None: | |
| raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.") | |
| self.backbone_config = backbone_config | |
| self.backbone = backbone | |
| self.hidden_act = hidden_act | |
| self.use_pretrained_backbone = use_pretrained_backbone | |
| self.initializer_range = initializer_range | |
| self.batch_norm_eps = batch_norm_eps | |
| self.readout_type = readout_type | |
| 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.use_batch_norm_in_fusion_residual = use_batch_norm_in_fusion_residual | |
| self.use_bias_in_fusion_residual = use_bias_in_fusion_residual | |
| self.num_relative_features = num_relative_features | |
| self.add_projection = add_projection | |
| self.bottleneck_features = bottleneck_features | |
| self.num_attractors = num_attractors | |
| self.bin_embedding_dim = bin_embedding_dim | |
| self.attractor_alpha = attractor_alpha | |
| self.attractor_gamma = attractor_gamma | |
| self.attractor_kind = attractor_kind | |
| self.min_temp = min_temp | |
| self.max_temp = max_temp | |
| self.bin_centers_type = bin_centers_type | |
| self.bin_configurations = bin_configurations | |
| self.num_patch_transformer_layers = num_patch_transformer_layers | |
| self.patch_transformer_hidden_size = patch_transformer_hidden_size | |
| self.patch_transformer_intermediate_size = patch_transformer_intermediate_size | |
| self.patch_transformer_num_attention_heads = patch_transformer_num_attention_heads | |