class SizeBlock(nn.Module): def __init__(self, conv): super(SizeBlock, self).__init__() self.conv, inc = nc2dc(conv) self.glob = nn.Sequential( nn.Linear(2, 64), nn.ReLU(inplace=True), nn.Linear(64, 32) ) self.local = nn.Sequential( nn.Conv2d(inc, 32, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 32, 3, padding=1) ) self.fuse = nn.Sequential( nn.Conv2d(64, 32, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(32, 3 * 3 * 2, 3, padding=1) ) self.relu = nn.ReLU() def forward(self, x, bsize): b, c, h, w = x.shape g_offset = self.glob(bsize) g_offset = g_offset.view(b, -1, 1, 1).repeat(1, 1, h, w).contiguous() l_offset = self.local(x) offset = self.fuse(torch.cat((g_offset, l_offset), dim=1)) fea = self.conv(x, offset) return self.relu(fea)和class ResBase(nn.Module): def __init__(self, res_name): super(ResBase, self).__init__() # model_resnet = res_dict[res_name](pretrained=False, norm_layer=BN_2D) model_resnet = res_dict[res_name](pretrained=True) self.sizeblock = SizeBlock self.conv1 = model_resnet.conv1 self.bn1 = model_resnet.bn1 self.relu = model_resnet.relu self.maxpool = model_resnet.maxpool self.layer1 = model_resnet.layer1 self.layer2 = model_resnet.layer2 self.layer3 = model_resnet.layer3 self.layer4 = model_resnet.layer4 self.avgpool = model_resnet.avgpool self.in_features = model_resnet.fc.in_features def forward(self, x, msize): print(x.shape) # torch.Size([8, 3, 384, 384]) x = self.sizeblock(x, msize) x = self.conv1(x) print(x.shape) # torch.Size([8, 64, 192, 192]) x = self.bn1(x) x = self.relu(x) # x = self.self.selist[1](x, msize) x = self.maxpool(x) print(x.shape) # torch.Size([8, 64, 96, 96]) x = self.layer1(x) print(x.shape) # torch.Size([8, 256, 96, 96]) # x = self.self.selist[2](x, msize) x = self.layer2(x) print(x.shape) # torch.Size([8, 512, 48, 48]) # x = self.self.selist[3](x, msize) x = self.layer3(x) # print(x.shape) # torch.Size([8, 1024, 24, 24]) x = self.layer4(x) # print(x.shape) # torch.Size([8, 2048, 12, 12]) x = self.avgpool(x) print(x.shape) # torch.Size([8, 2048, 1, 1]) x = x.view(x.size(0), -1) print(x.shape) # torch.Size([8, 2048]) a = input() return x,如何使用SizeBlock的forward函数
时间: 2023-05-31 12:07:23 浏览: 62
这是两个PyTorch的神经网络模块的定义代码。第一个模块是一个尺寸块,包含一个全局特征模块和一个本地特征模块,同时还有一个融合模块。该模块的输入是一组图像和一个块大小,并通过卷积层返回处理后的特征图像。第二个模块是一个残差基础模块,继承了PyTorch的nn.Module类。
相关问题
class BasicBlock(nn.Module): def __init__(self, net_depth, dim, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=nn.Sigmoid): super().__init__() self.norm = norm_layer(dim) self.conv = conv_layer(net_depth, dim, kernel_size, gate_act) def forward(self, x): identity = x x = self.norm(x) x = self.conv(x) x = identity + x return x转化为Paddle框架写法
class BasicBlock(fluid.dygraph.Layer):
def __init__(self, net_depth, dim, kernel_size=3, conv_layer=ConvLayer, norm_layer=nn.BatchNorm2d, gate_act=fluid.dygraph.nn.functional.sigmoid):
super(BasicBlock, self).__init__()
self.norm = norm_layer(dim)
self.conv = conv_layer(net_depth, dim, kernel_size, gate_act)
def forward(self, x):
identity = x
x = self.norm(x)
x = self.conv(x)
x = identity + x
return x
class ConvNet(nn.Module): def __init__(self): super(ConvNet, self).__init__() self.conv1 = nn.Conv2d(3, 32, 3) self.max_pool1 = nn.MaxPool2d(2) self.conv2 = nn.Conv2d(32, 64, 3) self.max_pool2 = nn.MaxPool2d(2)
这是一个简单的卷积神经网络模型,命名为ConvNet。下面是对该模型代码的解释:
- `class ConvNet(nn.Module):`:定义一个名为ConvNet的类,继承自nn.Module类,表示这是一个神经网络模型。
- `def __init__(self):`:定义构造函数,初始化ConvNet类的对象。
- `super(ConvNet, self).__init__()`:调用nn.Module父类的构造函数,初始化继承自nn.Module类的对象。
- `self.conv1 = nn.Conv2d(3, 32, 3)`:定义一个卷积层,输入通道数为3,输出通道数为32,卷积核大小为3x3。
- `self.max_pool1 = nn.MaxPool2d(2)`:定义一个最大池化层,池化核大小为2x2。
- `self.conv2 = nn.Conv2d(32, 64, 3)`:定义第二个卷积层,输入通道数为32,输出通道数为64,卷积核大小为3x3。
- `self.max_pool2 = nn.MaxPool2d(2)`:定义第二个最大池化层,池化核大小为2x2。
该模型总共有两个卷积层和两个最大池化层,卷积层使用ReLU激活函数。其中,第一个卷积层的输入通道数为3,即RGB三通道,输出通道数为32,第二个卷积层的输入通道数为32,输出通道数为64。两个最大池化层的池化核大小都为2x2,即将每个2x2的区域中的最大值作为输出。