285 lines
14 KiB
Python
285 lines
14 KiB
Python
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# 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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""" DPT model configuration"""
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import copy
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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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from ..bit import BitConfig
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class DPTConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`DPTModel`]. It is used to instantiate an DPT
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the DPT
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[Intel/dpt-large](https://huggingface.co/Intel/dpt-large) 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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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` are supported.
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hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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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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layer_norm_eps (`float`, *optional*, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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image_size (`int`, *optional*, defaults to 384):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 16):
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The size (resolution) of each patch.
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num_channels (`int`, *optional*, defaults to 3):
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The number of input channels.
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is_hybrid (`bool`, *optional*, defaults to `False`):
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Whether to use a hybrid backbone. Useful in the context of loading DPT-Hybrid models.
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qkv_bias (`bool`, *optional*, defaults to `True`):
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Whether to add a bias to the queries, keys and values.
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backbone_out_indices (`List[int]`, *optional*, defaults to `[2, 5, 8, 11]`):
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Indices of the intermediate hidden states to use from backbone.
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readout_type (`str`, *optional*, defaults to `"project"`):
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The readout type to use when processing the readout token (CLS token) of the intermediate hidden states of
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the ViT backbone. Can be one of [`"ignore"`, `"add"`, `"project"`].
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- "ignore" simply ignores the CLS token.
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- "add" passes the information from the CLS token to all other tokens by adding the representations.
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- "project" passes information to the other tokens by concatenating the readout to all other tokens before
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projecting the
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representation to the original feature dimension D using a linear layer followed by a GELU non-linearity.
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reassemble_factors (`List[int]`, *optional*, defaults to `[4, 2, 1, 0.5]`):
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The up/downsampling factors of the reassemble layers.
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neck_hidden_sizes (`List[str]`, *optional*, defaults to `[96, 192, 384, 768]`):
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The hidden sizes to project to for the feature maps of the backbone.
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fusion_hidden_size (`int`, *optional*, defaults to 256):
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The number of channels before fusion.
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head_in_index (`int`, *optional*, defaults to -1):
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The index of the features to use in the heads.
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use_batch_norm_in_fusion_residual (`bool`, *optional*, defaults to `False`):
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Whether to use batch normalization in the pre-activate residual units of the fusion blocks.
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use_bias_in_fusion_residual (`bool`, *optional*, defaults to `True`):
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Whether to use bias in the pre-activate residual units of the fusion blocks.
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add_projection (`bool`, *optional*, defaults to `False`):
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Whether to add a projection layer before the depth estimation head.
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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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semantic_loss_ignore_index (`int`, *optional*, defaults to 255):
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The index that is ignored by the loss function of the semantic segmentation model.
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semantic_classifier_dropout (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the semantic classification head.
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backbone_featmap_shape (`List[int]`, *optional*, defaults to `[1, 1024, 24, 24]`):
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Used only for the `hybrid` embedding type. The shape of the feature maps of the backbone.
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neck_ignore_stages (`List[int]`, *optional*, defaults to `[0, 1]`):
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Used only for the `hybrid` embedding type. The stages of the readout layers to ignore.
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backbone_config (`Union[Dict[str, Any], PretrainedConfig]`, *optional*):
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The configuration of the backbone model. Only used in case `is_hybrid` is `True` or in case you want to
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leverage the [`AutoBackbone`] API.
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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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Example:
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```python
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>>> from transformers import DPTModel, DPTConfig
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>>> # Initializing a DPT dpt-large style configuration
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>>> configuration = DPTConfig()
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>>> # Initializing a model from the dpt-large style configuration
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>>> model = DPTModel(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 = "dpt"
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def __init__(
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self,
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hidden_size=768,
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num_hidden_layers=12,
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num_attention_heads=12,
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intermediate_size=3072,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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image_size=384,
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patch_size=16,
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num_channels=3,
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is_hybrid=False,
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qkv_bias=True,
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backbone_out_indices=[2, 5, 8, 11],
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readout_type="project",
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reassemble_factors=[4, 2, 1, 0.5],
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neck_hidden_sizes=[96, 192, 384, 768],
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fusion_hidden_size=256,
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head_in_index=-1,
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use_batch_norm_in_fusion_residual=False,
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use_bias_in_fusion_residual=None,
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add_projection=False,
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use_auxiliary_head=True,
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auxiliary_loss_weight=0.4,
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semantic_loss_ignore_index=255,
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semantic_classifier_dropout=0.1,
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backbone_featmap_shape=[1, 1024, 24, 24],
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neck_ignore_stages=[0, 1],
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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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**kwargs,
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):
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super().__init__(**kwargs)
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self.hidden_size = hidden_size
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self.is_hybrid = is_hybrid
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if use_pretrained_backbone:
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raise ValueError("Pretrained backbones are not supported yet.")
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use_autobackbone = False
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if self.is_hybrid:
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if backbone_config is None and backbone is None:
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logger.info("Initializing the config with a `BiT` backbone.")
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backbone_config = {
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"global_padding": "same",
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"layer_type": "bottleneck",
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"depths": [3, 4, 9],
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"out_features": ["stage1", "stage2", "stage3"],
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"embedding_dynamic_padding": True,
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}
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backbone_config = BitConfig(**backbone_config)
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elif isinstance(backbone_config, dict):
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logger.info("Initializing the config with a `BiT` backbone.")
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backbone_config = BitConfig(**backbone_config)
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elif isinstance(backbone_config, PretrainedConfig):
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backbone_config = backbone_config
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else:
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raise ValueError(
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f"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."
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)
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self.backbone_config = backbone_config
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self.backbone_featmap_shape = backbone_featmap_shape
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self.neck_ignore_stages = neck_ignore_stages
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if readout_type != "project":
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raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode.")
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elif backbone_config is not None:
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use_autobackbone = True
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if 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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self.backbone_config = backbone_config
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self.backbone_featmap_shape = None
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self.neck_ignore_stages = []
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else:
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self.backbone_config = backbone_config
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self.backbone_featmap_shape = None
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self.neck_ignore_stages = []
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if use_autobackbone and 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_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 = 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.num_hidden_layers = None if use_autobackbone else num_hidden_layers
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self.num_attention_heads = None if use_autobackbone else num_attention_heads
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self.intermediate_size = None if use_autobackbone else intermediate_size
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self.hidden_dropout_prob = None if use_autobackbone else hidden_dropout_prob
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self.attention_probs_dropout_prob = None if use_autobackbone else attention_probs_dropout_prob
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self.layer_norm_eps = None if use_autobackbone else layer_norm_eps
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self.image_size = None if use_autobackbone else image_size
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self.patch_size = None if use_autobackbone else patch_size
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self.num_channels = None if use_autobackbone else num_channels
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self.qkv_bias = None if use_autobackbone else qkv_bias
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self.backbone_out_indices = None if use_autobackbone else backbone_out_indices
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if readout_type not in ["ignore", "add", "project"]:
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raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']")
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.readout_type = readout_type
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self.reassemble_factors = reassemble_factors
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self.neck_hidden_sizes = neck_hidden_sizes
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self.fusion_hidden_size = fusion_hidden_size
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self.head_in_index = head_in_index
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self.use_batch_norm_in_fusion_residual = use_batch_norm_in_fusion_residual
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self.use_bias_in_fusion_residual = use_bias_in_fusion_residual
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self.add_projection = add_projection
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# auxiliary head attributes (semantic segmentation)
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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.semantic_loss_ignore_index = semantic_loss_ignore_index
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self.semantic_classifier_dropout = semantic_classifier_dropout
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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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if output["backbone_config"] is not None:
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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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