AutoAndroidController/py/venv/Lib/site-packages/transformers/models/videomae/configuration_videomae.py

# coding=utf-8
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""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"]