classSuperResolutionModel(nn.Module):def__init__(self, upscale_factor):super(SuperResolutionModel, self).__init__() self.conv1 = ConvBlock(3,64, kernel_size=9, stride=1, padding=4) self.conv2 = ConvBlock(64,32, kernel_size=1, stride=1, padding=0) self.upscale = UpscaleBlock(32,...
self.conv_bn1 = ConvBatchNorm(in_channels+out_channels, out_channels) self.conv_bn2 = ConvBatchNorm(out_channels, out_channels) defforward(self, x, x_skip): # note : x_skip is the skip connection and x is the input from the pre...
DenseNet的主要构建模块是稠密块(dense block)和过渡层(transition layer)。前者定义了输入和输出是如何连结的,后者则用来控制通道数,使之不过大。 1. 稠密块介绍及其实现 DenseNet使用了ResNet改良版的“批量归一化、激活和卷积”结构,我们首先在conv_block函数里实现这个结构。 import time import torch from torch ...
这里实现连续的两个conv3×3+ReLu class conv_block(nn.Module): def __init__(self, in_channels, out_channels, padding=0): super().__init__() self.conv = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=3,stride=1,padding=padding), nn.BatchNorm2d(out_channels), nn...
class ConvNextBlock(nn.Module):def __init__(self,in_channels,out_channels,mult=2,time_embedding_dim=None,norm=True,group=8,):super().__init__()self.mlp = (nn.Sequential(nn.GELU(), nn.Linear(time_embedding_dim, in_channels))if time_embedding...
self.down_path=nn.ModuleList()#根据深度决定实现几个卷积块以及上采样foriinrange(depth):self.down_path.append(UNetConvBlock(prev_channels,2**(wf+i),padding,batch_norm))prev_channels=2**(wf+i)self.up_path=nn.ModuleList()foriinreversed(range(depth-1)):self.up_path.append(UNetUpBlock(prev...
在上述代码中,我们定义了一个SEBlock类,它是SENet的基本构建块。SEBlock通过一个全局平均池化层和两个全连接层来实现通道注意力。在使用SEBlock时,将其插入到模型中需要增强通道注意力的位置。 然后之前的博客使用迁移学习Resnet50模块进行猫狗二分类,然后我们可以添加SE注意力机制。
import torch # vgg块 def vgg_block(nums_conv, in_channels, out_channels): blk = [] for i in range(nums_conv): if i == 0: blk.append( torch.nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=(3, 3), padding=1)) blk.append(torch.nn.ReLU()) else: bl...
conv1(input_batch) block_out = self.relu1(block_out) block_out = self.conv2(block_out) block_out = self.relu2(block_out) return self.maxpool(block_out) 然后是对整个模型的构建。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 class LunaModel(nn.Module): def __init__(self, in...
nn.Conv2d(in_channels,out_channels,kernel_size, stride=stride,padding=padding,bias=not(use_batchnorm)), nn.ReLU(inplace=True), ] ifuse_batchnorm: layers.insert(1,nn.BatchNorm2d(out_channels,**batchnorm_params)) self.block=nn.Sequential(*layers) ...