# coding=utf-8 # Copyright 2022 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. """ViT Hybrid model configuration""" from ....configuration_utils import PretrainedConfig from ....utils import logging from ...auto.configuration_auto import CONFIG_MAPPING from ...bit import BitConfig logger = logging.get_logger(__name__) class ViTHybridConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`ViTHybridModel`]. It is used to instantiate a ViT Hybrid 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 ViT Hybrid [google/vit-hybrid-base-bit-384](https://huggingface.co/google/vit-hybrid-base-bit-384) 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 in a dictionary or the config object of the backbone. 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 to load `backbone` from the timm library. If `False`, the backbone is loaded from the transformers library. 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_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. 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. hidden_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. layer_norm_eps (`float`, *optional*, defaults to 1e-12): The epsilon used by the layer normalization layers. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. patch_size (`int`, *optional*, defaults to 1): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. backbone_featmap_shape (`List[int]`, *optional*, defaults to `[1, 1024, 24, 24]`): Used only for the `hybrid` embedding type. The shape of the feature maps of the backbone. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. Example: ```python >>> from transformers import ViTHybridConfig, ViTHybridModel >>> # Initializing a ViT Hybrid vit-hybrid-base-bit-384 style configuration >>> configuration = ViTHybridConfig() >>> # Initializing a model (with random weights) from the vit-hybrid-base-bit-384 style configuration >>> model = ViTHybridModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "vit-hybrid" def __init__( self, backbone_config=None, backbone=None, use_pretrained_backbone=False, use_timm_backbone=False, backbone_kwargs=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-12, image_size=224, patch_size=1, num_channels=3, backbone_featmap_shape=[1, 1024, 24, 24], qkv_bias=True, **kwargs, ): super().__init__(**kwargs) 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 a `BiT` backbone.") backbone_config = { "global_padding": "same", "layer_type": "bottleneck", "depths": [3, 4, 9], "out_features": ["stage3"], "embedding_dynamic_padding": True, } 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`.") if isinstance(backbone_config, dict): if "model_type" in backbone_config: backbone_config_class = CONFIG_MAPPING[backbone_config["model_type"]] else: logger.info( "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class." ) backbone_config_class = BitConfig backbone_config = backbone_config_class(**backbone_config) self.backbone_featmap_shape = backbone_featmap_shape 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.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.qkv_bias = qkv_bias