# 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. """VideoMAE model configuration""" from ...configuration_utils import PretrainedConfig from ...utils import logging logger = logging.get_logger(__name__) class VideoMAEConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`VideoMAEModel`]. It is used to instantiate a VideoMAE 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 VideoMAE [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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. num_frames (`int`, *optional*, defaults to 16): The number of frames in each video. tubelet_size (`int`, *optional*, defaults to 2): The number of tubelets. 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. qkv_bias (`bool`, *optional*, defaults to `True`): Whether to add a bias to the queries, keys and values. use_mean_pooling (`bool`, *optional*, defaults to `True`): Whether to mean pool the final hidden states instead of using the final hidden state of the [CLS] token. decoder_num_attention_heads (`int`, *optional*, defaults to 6): Number of attention heads for each attention layer in the decoder. decoder_hidden_size (`int`, *optional*, defaults to 384): Dimensionality of the decoder. decoder_num_hidden_layers (`int`, *optional*, defaults to 4): Number of hidden layers in the decoder. decoder_intermediate_size (`int`, *optional*, defaults to 1536): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the decoder. norm_pix_loss (`bool`, *optional*, defaults to `True`): Whether to normalize the target patch pixels. Example: ```python >>> from transformers import VideoMAEConfig, VideoMAEModel >>> # Initializing a VideoMAE videomae-base style configuration >>> configuration = VideoMAEConfig() >>> # Randomly initializing a model from the configuration >>> model = VideoMAEModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "videomae" def __init__( self, image_size=224, patch_size=16, num_channels=3, num_frames=16, tubelet_size=2, 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, qkv_bias=True, use_mean_pooling=True, decoder_num_attention_heads=6, decoder_hidden_size=384, decoder_num_hidden_layers=4, decoder_intermediate_size=1536, norm_pix_loss=True, **kwargs, ): super().__init__(**kwargs) self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.num_frames = num_frames self.tubelet_size = tubelet_size 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.qkv_bias = qkv_bias self.use_mean_pooling = use_mean_pooling self.decoder_num_attention_heads = decoder_num_attention_heads self.decoder_hidden_size = decoder_hidden_size self.decoder_num_hidden_layers = decoder_num_hidden_layers self.decoder_intermediate_size = decoder_intermediate_size self.norm_pix_loss = norm_pix_loss __all__ = ["VideoMAEConfig"]