149 lines
6.5 KiB
Python
149 lines
6.5 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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""" VideoMAE model configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class VideoMAEConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`VideoMAEModel`]. It is used to instantiate a
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VideoMAE model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the VideoMAE
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[MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) 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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image_size (`int`, *optional*, defaults to 224):
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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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num_frames (`int`, *optional*, defaults to 16):
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The number of frames in each video.
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tubelet_size (`int`, *optional*, defaults to 2):
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The number of tubelets.
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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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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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use_mean_pooling (`bool`, *optional*, defaults to `True`):
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Whether to mean pool the final hidden states instead of using the final hidden state of the [CLS] token.
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decoder_num_attention_heads (`int`, *optional*, defaults to 6):
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Number of attention heads for each attention layer in the decoder.
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decoder_hidden_size (`int`, *optional*, defaults to 384):
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Dimensionality of the decoder.
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decoder_num_hidden_layers (`int`, *optional*, defaults to 4):
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Number of hidden layers in the decoder.
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decoder_intermediate_size (`int`, *optional*, defaults to 1536):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the decoder.
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norm_pix_loss (`bool`, *optional*, defaults to `True`):
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Whether to normalize the target patch pixels.
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Example:
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```python
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>>> from transformers import VideoMAEConfig, VideoMAEModel
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>>> # Initializing a VideoMAE videomae-base style configuration
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>>> configuration = VideoMAEConfig()
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>>> # Randomly initializing a model from the configuration
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>>> model = VideoMAEModel(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 = "videomae"
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def __init__(
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self,
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image_size=224,
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patch_size=16,
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num_channels=3,
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num_frames=16,
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tubelet_size=2,
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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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qkv_bias=True,
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use_mean_pooling=True,
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decoder_num_attention_heads=6,
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decoder_hidden_size=384,
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decoder_num_hidden_layers=4,
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decoder_intermediate_size=1536,
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norm_pix_loss=True,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_frames = num_frames
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self.tubelet_size = tubelet_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.qkv_bias = qkv_bias
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self.use_mean_pooling = use_mean_pooling
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self.decoder_num_attention_heads = decoder_num_attention_heads
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self.decoder_hidden_size = decoder_hidden_size
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self.decoder_num_hidden_layers = decoder_num_hidden_layers
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self.decoder_intermediate_size = decoder_intermediate_size
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self.norm_pix_loss = norm_pix_loss
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