全卷积网络¶

In [1]:

%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

In [2]:

pretrained_net = torchvision.models.resnet18(pretrained=True)
list(pretrained_net.children())[-3:]

Out[2]:

[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

In [4]:

net = nn.Sequential(*list(pretrained_net.children())[:-2])

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

Out[4]:

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

使用$1\times1$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

In [5]:

num_classes = 21
net.add_module('final_conv', nn.Conv2d(512, num_classes, kernel_size=1))
net.add_module(
    'transpose_conv',
    nn.ConvTranspose2d(num_classes, num_classes, kernel_size=64, padding=16,
                       stride=32))

初始化转置卷积层

In [6]:

def bilinear_kernel(in_channels, out_channels, kernel_size):
    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

In [9]:

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('../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$1\times 1$卷积层，我们使用Xavier初始化参数

In [10]:

W = bilinear_kernel(num_classes, num_classes, 64)
net.transpose_conv.weight.data.copy_(W);

读取数据集

In [11]:

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

In [12]:

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)

loss 0.450, train acc 0.861, test acc 0.851
221.5 examples/sec on [device(type='cuda', index=0), device(type='cuda', index=1)]

预测

In [13]:

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

In [15]:

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);