configuration_timesformer.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.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
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""" TimeSformer model configuration"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}


class TimesformerConfig(PretrainedConfig):
    r"""
    This is the configuration class to store the configuration of a [`TimesformerModel`]. It is used to instantiate a
    TimeSformer 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 TimeSformer
    [facebook/timesformer-base-finetuned-k600](https://huggingface.co/facebook/timesformer-base-finetuned-k600)
    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 8):
            The number of frames in each video.
        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 probabilitiy 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-6):
            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.
        attention_type (`str`, *optional*, defaults to `"divided_space_time"`):
            The attention type to use. Must be one of `"divided_space_time"`, `"space_only"`, `"joint_space_time"`.
        drop_path_rate (`float`, *optional*, defaults to 0):
            The dropout ratio for stochastic depth.

    Example:

    ```python
    >>> from transformers import TimesformerConfig, TimesformerModel

    >>> # Initializing a TimeSformer timesformer-base style configuration
    >>> configuration = TimesformerConfig()

    >>> # Initializing a model from the configuration
    >>> model = TimesformerModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```"""
    model_type = "timesformer"

    def __init__(
        self,
        image_size=224,
        patch_size=16,
        num_channels=3,
        num_frames=8,
        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-6,
        qkv_bias=True,
        attention_type="divided_space_time",
        drop_path_rate=0,
        **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.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.attention_type = attention_type
        self.drop_path_rate = drop_path_rate