def forward(self, *inputs): (x,) = inputs x_paths = [] for conv in self.convolution_paths: x_paths.append(conv(x)) x_residual = torch.cat(x_paths, dim=1) if self.use_pyramid_pooling: x_pool = self.pyramid_pooling(x) x_residual = torch.cat([x_residual, x_pool], dim=1) x_residual = self.aggregation(x_residual) if self.out_channels != self.in_channels: x = self.projection(x) x = x + x_residual return x网络计算过程
时间: 2023-12-21 13:04:37 浏览: 90
该模块的 forward 方法接收一个输入 x,并通过 3 个卷积路径分别对输入进行卷积,将卷积的结果拼接一个张量 x_residual。如果 use_pyramid_pool 为 True,则对输入进行 spatio-temporal pyramid pooling 操作,并将池化的结果与 x_residual 拼接起来。最后,将拼接后的张量 x_residual 通过一个 1x1x1 卷积层进行特征聚合,并将聚合的结果与输入张量 x 相加,得到最终输出。
如果输入和输出通道数不同,则通过投影层将输入 x 的通道数调整为 out_channels。
相关问题
解释一下这段代码:class ResnetBlock(Model): def __init__(self, filters, strides=1,residual_path=False): super(ResnetBlock, self).__init__() self.filters = filters self.strides = strides self.residual_path = residual_path self.c1 = Conv2D(filters, (3, 3), strides=strides, padding='same', use_bias=False) self.b1 = BatchNormalization() self.a1 = Activation('relu') self.c2 = Conv2D(filters, (3, 3), strides=1, padding='same', use_bias=False) self.b2 = BatchNormalization() if residual_path: self.down_c1 = Conv2D(filters, (1, 1),strides=strides, padding='same', use_bias=False) self.down_b1 = BatchNormalization() self.a2 = Activation('relu') def call(self, inputs): residual = inputs x = self.c1(inputs) x = self.b1(x) x = self.a1(x) x = self.c2(x) y = self.b2(x) if self.residual_path: residual = self.down_c1(inputs) residual = self.down_b1(residual) out = self.a2(y + residual) return out class ResNet18(Model): def __init__(self, block_list, initial_filters=64): super(ResNet18, self).__init__() self.num_blocks = len(block_list) self.block_list = block_list self.out_filters = initial_filters self.c1 = Conv2D(self.out_filters, (3, 3), strides=1, padding='same', use_bias=False, kernel_initializer='he_normal') self.b1 = BatchNormalization() self.a1 = Activation('relu') self.blocks = tf.keras.models.Sequential() for block_id in range(len(block_list)): for layer_id in range(block_list[block_id]): if block_id != 0 and layer_id == 0: block = ResnetBlock(self.out_filters, strides=2, residual_path=True) else: block = ResnetBlock(self.out_filters, residual_path=False) self.blocks.add(block) self.out_filters *= 2 self.p1 = tf.keras.layers.GlobalAveragePooling2D() self.f1 = tf.keras.layers.Dense(41, activation='tanh') def call(self, inputs): x = self.c1(inputs) x = self.b1(x) x = self.a1(x) x = self.blocks(x) x = self.p1(x) y = self.f1(x) return y
这段代码定义了两个类:ResnetBlock和ResNet18。ResnetBlock是ResNet18的基本模块,用于构建ResNet18的主体结构。ResnetBlock包含两个卷积层和批归一化层,以及一个残差路径。ResNet18是一个由ResnetBlock组成的深度卷积神经网络,包含多个ResnetBlock,每个ResnetBlock中包含多个卷积层和批归一化层。在ResNet18的定义中,先定义了第一个卷积层、批归一化层和激活层,然后通过循环创建多个ResnetBlock,并在每个ResnetBlock中根据需要定义残差路径,最后通过全局平均池化层和全连接层输出预测结果。
class Partial_conv3(nn.Module): def init(self, dim, n_div, forward): super().init() self.dim_conv3 = dim // n_div self.dim_untouched = dim - self.dim_conv3 self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False) self.global_pool = GlobalAvgPool2d() if forward == 'slicing': self.forward = self.forward_slicing elif forward == 'split_cat': self.forward = self.forward_split_cat else: raise NotImplementedError def forward_slicing(self, x: Tensor) -> Tensor: # only for inference x = x.clone() # !!! Keep the original input intact for the residual connection later x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :]) return x def forward_split_cat(self, x: Tensor) -> Tensor: x1, x2 = torch.split(x, [self.dim_conv3, self.dim_untouched], dim=1) x1 = self.global_pool(x1) x1 = self.partial_conv3(x1) x = torch.cat((x1, x2), 1) return x这段代码有什么错误
这段代码并没有明显的语法错误,但是存在一些潜在的问题和需要改进的地方:
1. 类名应该以大写字母开头,应该将 `Partial_conv3` 改为 `PartialConv3`。
2. `super().init()` 应该改为 `super().__init__()`。
3. `self.forward` 方法应该在 `init` 方法中定义,在 `init` 中加入 `self.forward = None`。
4. `forward_split_cat` 方法中,应该先使用 `self.partial_conv3` 对 `x1` 进行卷积操作,再将 `x1` 和 `x2` 进行拼接,即应该将 `x1 = self.partial_conv3(x1)` 放在 `torch.cat((x1, x2), 1)` 之前。
5. `dim` 应该改为 `in_channels`,以更加清晰地表示输入张量的通道数。
6. `forward` 方法的输入参数应该改为 `inputs`,以更加符合命名规范。
以下是修改后的代码:
```
class PartialConv3(nn.Module):
def __init__(self, in_channels, n_div, forward):
super().__init__()
self.dim_conv3 = in_channels // n_div
self.dim_untouched = in_channels - self.dim_conv3
self.partial_conv3 = nn.Conv2d(self.dim_conv3, self.dim_conv3, 3, 1, 1, bias=False)
self.global_pool = GlobalAvgPool2d()
self.forward = None
if forward == 'slicing':
self.forward = self.forward_slicing
elif forward == 'split_cat':
self.forward = self.forward_split_cat
else:
raise NotImplementedError
def forward_slicing(self, inputs: Tensor) -> Tensor:
# only for inference
x = inputs.clone() # !!! Keep the original input intact for the residual connection later
x[:, :self.dim_conv3, :, :] = self.partial_conv3(x[:, :self.dim_conv3, :, :])
return x
def forward_split_cat(self, inputs: Tensor) -> Tensor:
x1, x2 = torch.split(inputs, [self.dim_conv3, self.dim_untouched], dim=1)
x1 = self.partial_conv3(x1)
x1 = self.global_pool(x1)
x = torch.cat((x1, x2), 1)
return x
```
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