# coding=utf-8 # Copyright 2023 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. """ DINOv2 model configuration""" from collections import OrderedDict from typing import Mapping from packaging import version from transformers.configuration_utils import PretrainedConfig from transformers.onnx import OnnxConfig from transformers.utils import logging from transformers.utils.backbone_utils import ( BackboneConfigMixin, get_aligned_output_features_output_indices, ) logger = logging.get_logger(__name__) class Dinov2Config(BackboneConfigMixin, PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Dinov2Model`]. It is used to instantiate an Dinov2 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 Dinov2 [google/dinov2-base-patch16-224](https://huggingface.co/google/dinov2-base-patch16-224) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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. mlp_ratio (`int`, *optional*, defaults to 4): Ratio of the hidden size of the MLPs relative to the `hidden_size`. 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-06): 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 16): The size (resolution) of each patch. num_channels (`int`, *optional*, defaults to 3): The number of input channels. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. layerscale_value (`float`, *optional*, defaults to 1.0): Initial value to use for layer scale. drop_path_rate (`float`, *optional*, defaults to 0.0): Stochastic depth rate per sample (when applied in the main path of residual layers). use_swiglu_ffn (`bool`, *optional*, defaults to `False`): Whether to use the SwiGLU feedforward neural network. out_features (`List[str]`, *optional*): If used as backbone, list of features to output. Can be any of `"stem"`, `"stage1"`, `"stage2"`, etc. (depending on how many stages the model has). If unset and `out_indices` is set, will default to the corresponding stages. If unset and `out_indices` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. out_indices (`List[int]`, *optional*): If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how many stages the model has). If unset and `out_features` is set, will default to the corresponding stages. If unset and `out_features` is unset, will default to the last stage. Must be in the same order as defined in the `stage_names` attribute. apply_layernorm (`bool`, *optional*, defaults to `True`): Whether to apply layer normalization to the feature maps in case the model is used as backbone. reshape_hidden_states (`bool`, *optional*, defaults to `True`): Whether to reshape the feature maps to 4D tensors of shape `(batch_size, hidden_size, height, width)` in case the model is used as backbone. If `False`, the feature maps will be 3D tensors of shape `(batch_size, seq_len, hidden_size)`. Example: ```python >>> from transformers import Dinov2Config, Dinov2Model >>> # Initializing a Dinov2 dinov2-base-patch16-224 style configuration >>> configuration = Dinov2Config() >>> # Initializing a model (with random weights) from the dinov2-base-patch16-224 style configuration >>> model = Dinov2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "dinov2" def __init__( self, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, mlp_ratio=4, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-6, image_size=224, patch_size=16, num_channels=3, qkv_bias=True, layerscale_value=1.0, drop_path_rate=0.0, use_swiglu_ffn=False, out_features=None, out_indices=None, apply_layernorm=True, reshape_hidden_states=True, num_register_tokens=0, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.mlp_ratio = mlp_ratio 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 self.layerscale_value = layerscale_value self.drop_path_rate = drop_path_rate self.use_swiglu_ffn = use_swiglu_ffn self.stage_names = ["stem"] + [ f"stage{idx}" for idx in range(1, num_hidden_layers + 1) ] ( self._out_features, self._out_indices, ) = get_aligned_output_features_output_indices( out_features=out_features, out_indices=out_indices, stage_names=self.stage_names, ) self.apply_layernorm = apply_layernorm self.reshape_hidden_states = reshape_hidden_states # add register tokens self.num_register_tokens = num_register_tokens class Dinov2OnnxConfig(OnnxConfig): torch_onnx_minimum_version = version.parse("1.11") @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ( "pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}, ), ] ) @property def atol_for_validation(self) -> float: return 1e-4