modules = [] block_in_channels = in_channels block_out_channels = start_out_channels for _ in range(n_temporal_layers): if use_pyramid_pooling: use_pyramid_pooling = True pool_sizes = [(2, h, w)] else: use_pyramid_pooling = False pool_sizes = None temporal = TemporalBlock( block_in_channels, block_out_channels, use_pyramid_pooling=use_pyramid_pooling, pool_sizes=pool_sizes, ) spatial = [ Bottleneck3D(block_out_channels, block_out_channels, kernel_size=(1, 3, 3)) for _ in range(n_spatial_layers_between_temporal_layers) ] temporal_spatial_layers = nn.Sequential(temporal, *spatial) modules.extend(temporal_spatial_layers) block_in_channels = block_out_channels block_out_channels += extra_in_channels这个网络是如何传播的
时间: 2024-02-14 13:23:01 浏览: 22
这个网络是一个具有多个时间和空间层的3D卷积神经网络,每个时间层包含一个TemporalBlock,每个时间层之间有多个Bottleneck3D空间层。在每个时间层中,输入的数据通过TemporalBlock进行处理,然后通过多个Bottleneck3D空间层进行处理。每个时间层的输出数据是下一个时间层的输入数据。整个网络的输入是一个3D图像,输出是一系列的3D特征图,其中每个特征图都对应输入图像的一帧。
相关问题
class TemporalModel(nn.Module): def __init__( self, in_channels, receptive_field, input_shape, start_out_channels=64, extra_in_channels=0, n_spatial_layers_between_temporal_layers=0, use_pyramid_pooling=True): super().__init__() self.receptive_field = receptive_field n_temporal_layers = receptive_field - 1 h, w = input_shape modules = [] block_in_channels = in_channels block_out_channels = start_out_channels for _ in range(n_temporal_layers): if use_pyramid_pooling: use_pyramid_pooling = True pool_sizes = [(2, h, w)] else: use_pyramid_pooling = False pool_sizes = None temporal = TemporalBlock( block_in_channels, block_out_channels, use_pyramid_pooling=use_pyramid_pooling, pool_sizes=pool_sizes, ) spatial = [ Bottleneck3D(block_out_channels, block_out_channels, kernel_size=(1, 3, 3)) for _ in range(n_spatial_layers_between_temporal_layers) ] temporal_spatial_layers = nn.Sequential(temporal, *spatial) modules.extend(temporal_spatial_layers) block_in_channels = block_out_channels block_out_channels += extra_in_channels self.out_channels = block_in_channels self.model = nn.Sequential(*modules) def forward(self, x): # Reshape input tensor to (batch, C, time, H, W) x = x.permute(0, 2, 1, 3, 4) x = self.model(x) x = x.permute(0, 2, 1, 3, 4).contiguous() return x[:, (self.receptive_field - 1):]是如何一步步前向传播的?
首先,输入张量x的形状为(batch_size, in_channels, sequence_length, height, width)。
然后,我们将x的维度从(sequence_length, batch_size, in_channels, height, width)改变为(batch_size, sequence_length, in_channels, height, width)。
接下来,我们将模型应用于输入,并将结果从(batch_size, sequence_length, out_channels, height, width)改变为(batch_size, out_channels, sequence_length, height, width),使其与输入张量的形状匹配。
最后,我们将输出张量的维度从(batch_size, out_channels, sequence_length, height, width)改变为(batch_size, sequence_length-receptive_field+1, out_channels, height, width)。其中,sequence_length-receptive_field+1是因为我们使用的是膨胀卷积,其输出序列长度会比输入短receptive_field-1,因此我们需要将其调整为对齐原始输入序列的长度。
temporal = TemporalBlock( block_in_channels, block_out_channels, use_pyramid_pooling=use_pyramid_pooling, pool_sizes=pool_sizes, ) spatial = [ Bottleneck3D(block_out_channels, block_out_channels, kernel_size=(1, 3, 3)) for _ in range(n_spatial_layers_between_temporal_layers) ] temporal_spatial_layers = nn.Sequential(temporal, *spatial) modules.extend(temporal_spatial_layers)的网络结构
这段代码定义了一个包含两个部分的网络结构,分别是TemporalBlock和一个Bottleneck3D序列。其中,TemporalBlock是一个包含卷积、批量归一化和激活函数的3D卷积块,用于对时间维进行特征提取。Bottleneck3D是一个类似于ResNet中的瓶颈块,由1x1卷积、3x3卷积和1x1卷积组成,用于对空间维进行特征提取。具体地,每个TemporalBlock包含以下几个层:
1. 一个3D卷积层,使用receptive_field大小的膨胀卷积对时间维进行特征提取。
2. 一个批量归一化层,用于规范化卷积输出的数据分布。
3. 一个ReLU激活函数,用于增加网络的非线性性。
4. 一个可选的金字塔池化层,用于对空间维进行降采样和聚合特征。
然后,根据n_spatial_layers_between_temporal_layers的值,我们将Bottleneck3D序列添加到TemporalBlock之后,用于对空间维进行更深入的特征提取。最终,我们将TemporalBlock和Bottleneck3D序列组合成一个nn.Sequential对象,并将其添加到网络结构中去。