动手学深度学习之全连接卷积神经网络

全连接卷积神经网络(FCN)

• FCN是用深度神经网络来做语义分割的奠基性工作
• 他用转置卷积层来替换CNN最后的全连接层，从而可以实现每个像素的预测
  

代码实现

%matplotlib inline
import torch
import torchvision
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l

# 使用在ImageNet数据集上预训练的ResNet-18模型来提取图像特征
pretrained_net = torchvision.models.resnet18(pretrained=True)
list(pretrained_net.children())[-3:]

[Sequential(
   (0): BasicBlock(
     (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (downsample): Sequential(
       (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
       (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     )
   )
   (1): BasicBlock(
     (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
     (relu): ReLU(inplace=True)
     (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   )
 ),
 AdaptiveAvgPool2d(output_size=(1, 1)),
 Linear(in_features=512, out_features=1000, bias=True)]

# 创建一个全卷积网络实例net
net = nn.Sequential(*list(pretrained_net.children())[:-2])  # 这一行的作用就是将之前训练好的模型拿过来，把最后两层去掉

X = torch.rand(size=(1, 3, 320, 480))
net(X).shape

/Users/tiger/opt/anaconda3/envs/d2l-zh/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  ../c10/core/TensorImpl.h:1156.)
  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)





torch.Size([1, 512, 10, 15])

# 使用1*1卷积层将输出通道数转换为Pascal VOC2012数据集的类数（21类）。 将要素地图的高度和宽度增加32倍
num_classes = 21  # 类别数
net.add_module('final_conv', nn.Conv2d(512, num_classes, kernel_size=1))  # 加上最后的1*1卷积层
net.add_module(
    'transpose_conv',
    nn.ConvTranspose2d(num_classes, num_classes, kernel_size=64, padding=16,
                       stride=32))  # 加上转置卷积，stride=32是让最后的高宽增加32倍

# 初始化转置卷积层
def bilinear_kernel(in_channels, out_channels, kernel_size):  # 实现双线性插值
    factor = (kernel_size + 1) // 2
    if kernel_size % 2 == 1:
        center = factor - 1
    else:
        center = factor - 0.5
    og = (torch.arange(kernel_size).reshape(-1, 1),
          torch.arange(kernel_size).reshape(1, -1))
    filt = (1 - torch.abs(og[0] - center) / factor) * \
           (1 - torch.abs(og[1] - center) / factor)
    weight = torch.zeros(
        (in_channels, out_channels, kernel_size, kernel_size))
    weight[range(in_channels), range(out_channels), :, :] = filt
    return weight

# 双线性插值的上采样实验
conv_trans = nn.ConvTranspose2d(3, 3, kernel_size=4, padding=1, stride=2,
                                bias=False)
conv_trans.weight.data.copy_(bilinear_kernel(3, 3, 4));

img = torchvision.transforms.ToTensor()(d2l.Image.open('/Users/tiger/Desktop/study/机器学习/李沐深度学习/d2l-zh/pytorch/img/catdog.jpg'))
X = img.unsqueeze(0)
Y = conv_trans(X)
out_img = Y[0].permute(1, 2, 0).detach()

d2l.set_figsize()
print('input image shape:', img.permute(1, 2, 0).shape)
d2l.plt.imshow(img.permute(1, 2, 0))
print('output image shape:', out_img.shape)
d2l.plt.imshow(out_img);

input image shape: torch.Size([561, 728, 3])
output image shape: torch.Size([1122, 1456, 3])

# 用双线性插值的上采样初始化转置卷积层。对于1×1卷积层，我们使用Xavier初始化参数
W = bilinear_kernel(num_classes, num_classes, 64)
net.transpose_conv.weight.data.copy_(W);

# 读取数据
batch_size, crop_size = 32, (320, 480)
train_iter, test_iter = d2l.load_data_voc(batch_size, crop_size)

read 1114 examples
read 1078 examples

# 训练
def loss(inputs, targets):
    return F.cross_entropy(inputs, targets, reduction='none').mean(1).mean(1)

num_epochs, lr, wd, devices = 5, 0.001, 1e-3, d2l.try_all_gpus()
trainer = torch.optim.SGD(net.parameters(), lr=lr, weight_decay=wd)
d2l.train_ch13(net, train_iter, test_iter, loss, trainer, num_epochs, devices)

# 预测
def predict(img):
    X = test_iter.dataset.normalize_image(img).unsqueeze(0)
    pred = net(X.to(devices[0])).argmax(dim=1)
    return pred.reshape(pred.shape[1], pred.shape[2])

# 可视化预测的类别
def label2image(pred):
    colormap = torch.tensor(d2l.VOC_COLORMAP, device=devices[0])
    X = pred.long()
    return colormap[X, :]

voc_dir = d2l.download_extract('voc2012', 'VOCdevkit/VOC2012')
test_images, test_labels = d2l.read_voc_images(voc_dir, False)
n, imgs = 4, []
for i in range(n):
    crop_rect = (0, 0, 320, 480)
    X = torchvision.transforms.functional.crop(test_images[i], *crop_rect)
    pred = label2image(predict(X))
    imgs += [
        X.permute(1, 2, 0),
        pred.cpu(),
        torchvision.transforms.functional.crop(test_labels[i],
                                               *crop_rect).permute(1, 2, 0)]
d2l.show_images(imgs[::3] + imgs[1::3] + imgs[2::3], 3, n, scale=2);