本次参考《动手学深度学习》（此书用的是MXNet，本次实践使用的是pytorch框架）进行FCN在VOC2012数据集上的实践。
首先需要下载VOC数据集，书中已经讲下载已经封装好了，直接调用一下函数即可。当然不想装MXNet也可以很容易通过百度下载。
from mxnet.gluon import data as gdata, utils as gutils
import os
import sys
import tarfile
def download_voc_pascal(data_dir='../data'):
voc_dir = os.path.join(data_dir, 'VOCdevkit/VOC2012')
url = ('http://host.robots.ox.ac.uk/pascal/VOC/voc2012'
'/VOCtrainval_11-May-2012.tar')
sha1 = '4e443f8a2eca6b1dac8a6c57641b67dd40621a49'
fname = gutils.download(url, data_dir, sha1_hash=sha1)
with tarfile.open(fname, 'r') as f:
f.extractall(data_dir)
return voc_dir
voc_dir = download_voc_pascal()
数据集将会放置在../data/VOCdevkit/VOC2012路径下。进入../data/VOCdevkit/VOC2012路径后，我们可以获取数据集的不同组成部分。其中ImageSets/Segmentation路径包含了指定训练和测试样本的文本文件，而JPEGImages和SegmentationClass路径下分别包含了样本的输入图像和标签。这里的标签也是图像格式，其尺寸和它所标注的输入图像的尺寸相同。标签中颜色相同的像素属于同一个语义类别。下面定义read_images函数将输入图像和标签全部读进内存。
voc_root = '../data/VOCdevkit/VOC2012'
# 读取图片和标签路径成为列表
def read_images(root=voc_root, train=True):
txt_fname = root + '/ImageSets/Segmentation/' + ('train.txt' if train else 'val.txt')
with open(txt_fname, 'r') as f:
images = f.read().split()
data = [os.path.join(root, 'JPEGImages', i+'.jpg') for i in images]
label = [os.path.join(root, 'SegmentationClass', i+'.png') for i in images]
return data, label
在标签图像中，白色和黑色分别代表边框和背景，而其他不同的颜色则对应不同的类别。
接下来，我们列出标签中每个RGB颜色的值及其标注的类别。
classes = ['background','aeroplane','bicycle','bird','boat',
'bottle','bus','car','cat','chair','cow','diningtable',
'dog','horse','motorbike','person','potted plant',
'sheep','sofa','train','tv/monitor']
colormap = [[0,0,0],[128,0,0],[0,128,0], [128,128,0], [0,0,128],
[128,0,128],[0,128,128],[128,128,128],[64,0,0],[192,0,0],
[64,128,0],[192,128,0],[64,0,128],[192,0,128],
[64,128,128],[192,128,128],[0,64,0],[128,64,0],
[0,192,0],[128,192,0],[0,64,128]]
有了上面定义的两个常量以后，我们可以很容易地查找标签中每个像素的类别索引。
cm2lbl = np.zeros(256**3) # 256**3个色彩
for i,cm in enumerate(colormap): # 将其中21个色彩对应索引
cm2lbl[(cm[0]*256+cm[1])*256+cm[2]] = i
# 图像转索引矩阵
def image2label(im):
data = np.array(im, dtype='int32')
idx = (data[:, :, 0] * 256 + data[:, :, 1]) * 256 + data[:, :, 2]
return np.array(cm2lbl[idx], dtype='int64')
我们可以测试一下：
可以看到像素[128,0,0]对应的索引为(128256+0)256+0，索引出的值为1，然后再通过1对classes进行索引，得到‘aeroplane’。
函数
image2label()将一个图像标签转换成一个二维矩阵，其中每个值的取值范围为0-20，代表着它是属于21个类别中的哪一类像素。
在之前图像分类中，我们通过缩放图像使其符合模型的输入形状。然而在语义分割里，这样做需要将预测的像素类别重新映射回原始尺寸的输入图像。这样的映射难以做到精确，尤其在不同语义的分割区域。为了避免这个问题，我们将图像裁剪成固定尺寸而不是缩放。具体来说，我们使用随机裁剪，并对输入图像和标签裁剪相同区域。
貌似pytorch里没有这个实现方法，这里用下面的函数进行图像和标签的随机裁剪。
import random
# 随机裁剪图像和标签
def rand_crop(data,label, height, width):
x1 = random.randint(0, data.size[0] - width)
y1 = random.randint(0, data.size[1] - height)
x2 = x1 + width
y2 = y1 + height
data=data.crop((x1, y1, x2, y2))
label=label.crop((x1, y1, x2, y2))
return data,label
运行此代码两次查看效果：
data = Image.open('../data/VOCdevkit/VOC2012/JPEGImages/2007_000032.jpg')
label = Image.open('../data/VOCdevkit/VOC2012/SegmentationClass/2007_000032.png').convert('RGB')
data, label = rand_crop(data, label, 200, 300)
plt.subplot(1,2,1)
plt.imshow(data)
plt.subplot(1,2,2)
plt.imshow(label)
自定义数据集
def img_transforms(im, label, crop_size):
im, label = rand_crop(im, label, *crop_size)
im_tfs = tfs.Compose([
tfs.ToTensor(),
tfs.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
im = im_tfs(im)
label = image2label(label)
label = torch.from_numpy(label)
return im, label
class VOCSegDataset(Dataset):
'''
voc dataset
'''
def __init__(self, train, crop_size, transforms):
self.crop_size = crop_size
self.transforms = transforms
data_list, label_list = read_images(train=train)
self.data_list = self._filter(data_list)
self.label_list = self._filter(label_list)
print('Read ' + str(len(self.data_list)) + ' images')
def _filter(self, images): # 过滤掉图片大小小于 crop 大小的图片
return [im for im in images if (Image.open(im).size[1] >= self.crop_size[0] and
Image.open(im).size[0] >= self.crop_size[1])]
def __getitem__(self, idx):
img = self.data_list[idx]
label = self.label_list[idx]
img = Image.open(img)
label = Image.open(label).convert('RGB')
img, label = self.transforms(img, label, self.crop_size)
return img, label
def __len__(self):
return len(self.data_list)
读取数据集：
input_shape = (320, 480)
voc_train = VOCSegDataset(True, input_shape, img_transforms)
voc_test = VOCSegDataset(False, input_shape, img_transforms)
train_data = DataLoader(voc_train, 16, shuffle=True, num_workers=1)
valid_data = DataLoader(voc_test, 16, num_workers=1)
FCN模型在此笔记已经实现了。
我们只需要将网络实例化，在定义谢谢参数即可进行训练了。
device = torch.device('cuda')
net = fcn(num_classes)
criterion = nn.NLLLoss()
optimizer = torch.optim.SGD(net.parameters(), lr=1e-3, weight_decay=1e-4)
好了，开始训练！
net = net.to(device)
for e in range(80):
print('epoch:{}/80'.format(e))
print('-' * 20)
train_loss = 0
train_acc = 0
train_acc_cls = 0
train_mean_iu = 0
train_fwavacc = 0
prev_time = datetime.now()
net = net.train()
for data, label in train_data:
im = data.to(device)
label = label.to(device)
# print(im.shape, label.shape)
# forward
out = net(im)
# print(out.shape, type(out))
loss = criterion(nn.LogSoftmax(dim=1)(out), label)
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
label_pred = out.max(dim=1)[1].data.cpu().numpy()
label_true = label.data.cpu().numpy()
for lbt, lbp in zip(label_true, label_pred):
acc, acc_cls, mean_iu, fwavacc = label_accuracy_score(lbt, lbp, num_classes)
train_acc += acc
train_acc_cls += acc_cls
train_mean_iu += mean_iu
train_fwavacc += fwavacc
print('Train Loss: {:.5f}, Train Acc: {:.5f}, Train Mean IU: {:.5f}'.format(train_loss / len(train_data), train_acc / len(voc_train), train_mean_iu / len(voc_train)))
net = net.eval()
eval_loss = 0
eval_acc = 0
eval_acc_cls = 0
eval_mean_iu = 0
eval_fwavacc = 0
for data, label in valid_data:
im = data.to(device)
label = label.to(device)
# forward
out = net(im)
loss = criterion(nn.LogSoftmax(dim=1)(out), label)
eval_loss += loss.item()
label_pred = out.max(dim=1)[1].data.cpu().numpy()
label_true = label.data.cpu().numpy()
for lbt, lbp in zip(label_true, label_pred):
acc, acc_cls, mean_iu, fwavacc = label_accuracy_score(lbt, lbp, num_classes)
eval_acc += acc
eval_acc_cls += acc_cls
eval_mean_iu += mean_iu
eval_fwavacc += fwavacc
print('Valid Loss: {:.5f}, Valid Acc: {:.5f}, Valid Mean IU: {:.5f} '.format(eval_loss / len(valid_data), eval_acc / len(voc_test), eval_mean_iu / len(voc_test)))
cur_time = datetime.now()
h, remainder = divmod((cur_time - prev_time).seconds, 3600)
m, s = divmod(remainder, 60)
time_str = 'Time: {:.0f}:{:.0f}:{:.0f}'.format(h, m, s)
print(time_str)
print()
部分训练日志如下：
epoch:76/80
--------------------
Train Loss: 0.31527, Train Acc: 0.89842, Train Mean IU: 0.54611
Valid Loss: 0.42469, Valid Acc: 0.86795, Valid Mean IU: 0.50423
Time: 0:0:45
epoch:77/80
--------------------
Train Loss: 0.31699, Train Acc: 0.89788, Train Mean IU: 0.55095
Valid Loss: 0.42091, Valid Acc: 0.87019, Valid Mean IU: 0.51235
Time: 0:0:45
epoch:78/80
--------------------
Train Loss: 0.32034, Train Acc: 0.89675, Train Mean IU: 0.54414
Valid Loss: 0.42173, Valid Acc: 0.86962, Valid Mean IU: 0.51024
Time: 0:0:45
可以看到经过80次迭代，验证集的meanIOU为51%左右。
最后可视化一下结果。第一列为原始图像，第二列为图像标签，第三列为预测的结果。
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