利用前向勾子获取神经网络中间层的输出并将其进行保存（示例详解）
代码示例：
# 激活字典，用于保存每次的中间特征
activation = {}# 将 forward_hook 函数定义在 upsample_v2 外部
def forward_hook(name):def hook(module, input, output):activation[name] = output.detach()return hookdef upsample_v2(in_channels, out_channels, upscale, kernel_size=3):layers = []# Define mid channel stages (three times reduction)mid_channels = [256, 128, 64] # 512 32 32 -> 256 64 64 -> 128 128 128 -> 64 256 256 -> 2 256 256scale_factor_per_step = upscale ** (1/3) # Calculate the scaling for each stepcurrent_in_channels = in_channels# Upsample and reduce channels in 3 stepsfor step, mid_channel in enumerate(mid_channels):# Conv layer to reduce number of channelsconv = nn.Conv2d(current_in_channels, mid_channel, kernel_size=kernel_size, padding=1, bias=False)nn.init.kaiming_normal_(conv.weight.data, nonlinearity='relu')layers.append(conv)# ReLU activationrelu = nn.ReLU()layers.append(relu)# Upsampling layerup = nn.Upsample(scale_factor=scale_factor_per_step, mode='bilinear', align_corners=True)layers.append(up)layers[-1].register_forward_hook(forward_hook(f'step_{step}'))# Update current in_channels for the next layercurrent_in_channels = mid_channelconv = nn.Conv2d(current_in_channels, out_channels, kernel_size=kernel_size, padding=1, bias=False)nn.init.kaiming_normal_(conv.weight.data, nonlinearity='relu')layers.append(conv)return nn.Sequential(*layers)def forward_hook(name):def hook(module, input, output):activation[name] = output.detach()return hookforward_hook布置了抓取函数。其中，module代表你下面勾的那一层，input代表那一层的输入，output定义那一层的输出，我们常常只使用output。
layers[-1].register_forward_hook(forward_hook(f'step_{step}'))这里定义了我需要捕获的那一层，layers[-1]代表我要捕获当前layers的最后一层，即上采用层，由于循环了三次，所以最后勾取的应当是三份中间层输出。