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Source code for mmaction.models.common.conv_audio

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple, Union

import torch
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmengine.model.weight_init import constant_init, kaiming_init
from torch.nn.modules.utils import _pair

from mmaction.registry import MODELS


[docs]@MODELS.register_module() class ConvAudio(nn.Module): """Conv2d module for AudioResNet backbone. <https://arxiv.org/abs/2001.08740>`_. Args: in_channels (int): Same as ``nn.Conv2d``. out_channels (int): Same as ``nn.Conv2d``. kernel_size (Union[int, Tuple[int]]): Same as ``nn.Conv2d``. op (str): Operation to merge the output of freq and time feature map. Choices are ``sum`` and ``concat``. Defaults to ``concat``. stride (Union[int, Tuple[int]]): Same as ``nn.Conv2d``. Defaults to 1. padding (Union[int, Tuple[int]]): Same as ``nn.Conv2d``. Defaults to 0. dilation (Union[int, Tuple[int]]): Same as ``nn.Conv2d``. Defaults to 1. groups (int): Same as ``nn.Conv2d``. Defaults to 1. bias (Union[bool, str]): If specified as ``auto``, it will be decided by the ``norm_cfg``. Bias will be set as True if ``norm_cfg`` is None, otherwise False. Defaults to False. """ def __init__(self, in_channels: int, out_channels: int, kernel_size: Union[int, Tuple[int]], op: str = 'concat', stride: Union[int, Tuple[int]] = 1, padding: Union[int, Tuple[int]] = 0, dilation: Union[int, Tuple[int]] = 1, groups: int = 1, bias: Union[bool, str] = False) -> None: super().__init__() kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = kernel_size assert op in ['concat', 'sum'] self.op = op self.stride = stride self.padding = padding self.dilation = dilation self.groups = groups self.bias = bias self.output_padding = (0, 0) self.transposed = False self.conv_1 = ConvModule( in_channels, out_channels, kernel_size=(kernel_size[0], 1), stride=stride, padding=(kernel_size[0] // 2, 0), bias=bias, conv_cfg=dict(type='Conv'), norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU')) self.conv_2 = ConvModule( in_channels, out_channels, kernel_size=(1, kernel_size[1]), stride=stride, padding=(0, kernel_size[1] // 2), bias=bias, conv_cfg=dict(type='Conv'), norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU')) self.init_weights()
[docs] def forward(self, x: torch.Tensor) -> torch.Tensor: """Defines the computation performed at every call. Args: x (torch.Tensor): The input data. Returns: torch.Tensor: The output of the module. """ x_1 = self.conv_1(x) x_2 = self.conv_2(x) if self.op == 'concat': out = torch.cat([x_1, x_2], 1) else: out = x_1 + x_2 return out
[docs] def init_weights(self) -> None: """Initiate the parameters from scratch.""" kaiming_init(self.conv_1.conv) kaiming_init(self.conv_2.conv) constant_init(self.conv_1.bn, 1, bias=0) constant_init(self.conv_2.bn, 1, bias=0)
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