towhee.models.utils.create_resnet_basic_3d_module.create_resnet_basic_3d_module

towhee.models.utils.create_resnet_basic_3d_module.create_resnet_basic_3d_module(*, in_channels, out_channels, conv_kernel_size=(3, 7, 7), conv_stride=(1, 2, 2), conv_padding=(1, 3, 3), conv_bias=False, conv=<class 'torch.nn.modules.conv.Conv3d'>, pool=<class 'torch.nn.modules.pooling.MaxPool3d'>, pool_kernel_size=(1, 3, 3), pool_stride=(1, 2, 2), pool_padding=(0, 1, 1), norm=<class 'torch.nn.modules.batchnorm.BatchNorm3d'>, norm_eps=1e-05, norm_momentum=0.1, activation=<class 'torch.nn.modules.activation.ReLU'>) → Module[source]

ResNet basic 3D stem module. It performs spatiotemporal Convolution, BN, and activation following by a spatiotemporal pooling.

Conv3d

↓

Normalization

↓

Activation

↓

Pool3d

Parameters:

  • in_channels (int) – Input channel size of the convolution.

  • out_channels (int) – Output channel size of the convolution.

  • conv_kernel_size (Tuple) – Convolutional kernel size(s).

  • conv_stride (Tuple) – Convolutional stride size(s).

  • conv_padding (Tuple) – Convolutional padding size(s).

  • conv_bias (bool) – Convolutional bias. If true, adds a learnable bias to the output.

  • conv (callable) – Callable used to build the convolution layer.

  • pool (Callable) – A callable that constructs pooling layer, options include: nn.AvgPool3d, nn.MaxPool3d, and None (not performing pooling).

  • pool_kernel_size (Tuple) – Pooling kernel size(s).

  • pool_stride (Tuple) – Pooling stride size(s).

  • pool_padding (Tuple) – Pooling padding size(s).

  • norm (Callable) – A callable that constructs normalization layer, options include nn.BatchNorm3d, None (not performing normalization).

  • norm_eps (float) – Normalization epsilon.

  • norm_momentum (float) – Normalization momentum.

  • activation (Callable) – A callable that constructs activation layer, options include: nn.ReLU, nn.Softmax, nn.Sigmoid, and None (not performing activation).