# import numpy as np
import torch.optim as optim
# import os
import time
import loadData
startTime = time.time()
#%%
# 读取数据
numOfBatch = 9
pixel = 64
flag = 0
root = f'./{numOfBatch}/data'
batchSize = 2
train_data = loadData.MyDataset(txt=f'files_train.txt')
test_data = loadData.MyDataset(txt=f'files_test.txt')
train_loader = DataLoader(dataset=train_data,
batch_size=batchSize,
shuffle=True,
num_workers=2)
test_loader = DataLoader(dataset=test_data,
batch_size=batchSize,
shuffle=False,
num_workers=2)
print('num_of_trainData:', len(train_data))
print('num_of_testData:', len(test_data))
#%%
kernelSize = 3
def trainValSegmentation(args):
if not os.path.isfile(args.cached_data_file):
dataLoader = ld.LoadData(args.data_dir, args.classes, args.attrClasses,
args.cached_data_file)
if dataLoader is None:
print("Error while cacheing the data.")
exit(-1)
data = dataLoader.processData()
else:
print("load cacheing data.")
data = pickle.load(open(args.cached_data_file, 'rb'))
# only unet for segmentation now.
# model= unet.UNet(args.classes)
# model = r18unet.ResNetUNet(args.classes)
model = mobileunet.MobileUNet(args.classes)
print("UNet done...")
# if args.onGPU == True:
model = model.cuda()
# devices_ids=[2,3], device_ids=range(2)
# device = torch.device('cuda:' + str(devices_ids[0]))
# model = model.to(device)
if args.visNet == True:
x = Variable(torch.randn(1, 3, args.inwidth, args.inheight))
if args.onGPU == True:
x = x.cuda()
print("before forward...")
y = model.forward(x)
print("after forward...")
g = viz.make_dot(y)
# g1 = viz.make_dot(y1)
g.render(args.save_dir + '/model', view=False)
model = torch.nn.DataParallel(model)
n_param = sum([np.prod(param.size()) for param in model.parameters()])
print('network parameters: ' + str(n_param))
#define optimization criteria
weight = torch.from_numpy(data['classWeights'])
print(weight)
if args.onGPU == True:
weight = weight.cuda()
criteria = CrossEntropyLoss2d(weight)
# if args.onGPU == True:
# criteria = criteria.cuda()
trainDatasetNoZoom = myTransforms.Compose([
myTransforms.RandomCropResize(args.inwidth, args.inheight),
# myTransforms.RandomHorizontalFlip(),
myTransforms.ToTensor(args.scaleIn)
])
trainDatasetWithZoom = myTransforms.Compose([
# myTransforms.Zoom(512,512),
myTransforms.RandomCropResize(args.inwidth, args.inheight),
myTransforms.RandomHorizontalFlip(),
myTransforms.ToTensor(args.scaleIn)
])
valDataset = myTransforms.Compose([
myTransforms.RandomCropResize(args.inwidth, args.inheight),
myTransforms.ToTensor(args.scaleIn)
])
trainLoaderNoZoom = torch.utils.data.DataLoader(
ld.MyDataset(data['trainIm'],
data['trainAnnot'],
transform=trainDatasetNoZoom),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
trainLoaderWithZoom = torch.utils.data.DataLoader(
ld.MyDataset(data['trainIm'],
data['trainAnnot'],
transform=trainDatasetWithZoom),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
valLoader = torch.utils.data.DataLoader(ld.MyDataset(data['valIm'],
data['valAnnot'],
transform=valDataset),
batch_size=args.batch_size_val,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True)
#define the optimizer
optimizer = torch.optim.Adam(model.parameters(),
args.lr, (0.9, 0.999),
eps=1e-08,
weight_decay=2e-4)
# optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.99, weight_decay=5e-4)
# optimizer = torch.optim.SGD([
# {'params': [param for name, param in model.named_parameters() if name[-4:] == 'bias'],
# 'lr': 2 * args.lr},
# {'params': [param for name, param in model.named_parameters() if name[-4:] != 'bias'],
# 'lr': args.lr, 'weight_decay': 5e-4}
# ], momentum=0.99)
if args.onGPU == True:
cudnn.benchmark = True
start_epoch = 0
if args.resume:
if os.path.isfile(args.resumeLoc):
print("=> loading checkpoint '{}'".format(args.resumeLoc))
checkpoint = torch.load(args.resumeLoc)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch{})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resumeLoc))
logfileLoc = args.save_dir + os.sep + args.logFile
print(logfileLoc)
if os.path.isfile(logfileLoc):
logger = open(logfileLoc, 'a')
logger.write("parameters: %s" % (str(n_param)))
logger.write("\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t" %
('Epoch', 'Loss(Tr)', 'Loss(val)', 'Overall acc(Tr)',
'Overall acc(val)', 'mIOU (tr)', 'mIOU (val'))
logger.flush()
else:
logger = open(logfileLoc, 'w')
logger.write("Parameters: %s" % (str(n_param)))
logger.write("\n%s\t%s\t%s\t%s\t%s\t%s\t%s\t" %
('Epoch', 'Loss(Tr)', 'Loss(val)', 'Overall acc(Tr)',
'Overall acc(val)', 'mIOU (tr)', 'mIOU (val'))
logger.flush()
#lr scheduler
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer,
milestones=[30, 60, 90],
gamma=0.1)
best_model_acc = 0
for epoch in range(start_epoch, args.max_epochs):
scheduler.step(epoch)
lr = 0
for param_group in optimizer.param_groups:
lr = param_group['lr']
# train(args,trainLoaderWithZoom,model,criteria,optimizer,epoch)
lossTr, overall_acc_tr, per_class_acc_tr, per_class_iu_tr, mIOU_tr = train(
args, trainLoaderNoZoom, model, criteria, optimizer, epoch)
# print(per_class_acc_tr,per_class_iu_tr)
lossVal, overall_acc_val, per_class_acc_val, per_class_iu_val, mIOU_val = val(
args, valLoader, model, criteria)
#save_checkpoint
torch.save(
{
'epoch': epoch + 1,
'arch': str(model),
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lossTr': lossTr,
'lossVal': lossVal,
'iouTr': mIOU_tr,
'iouVal': mIOU_val,
}, args.save_dir + '/checkpoint.pth.tar')
#save model also
# if overall_acc_val > best_model_acc:
# best_model_acc = overall_acc_val
model_file_name = args.save_dir + '/model_' + str(epoch + 1) + '.pth'
torch.save(model.state_dict(), model_file_name)
with open('../acc/acc_' + str(epoch) + '.txt', 'w') as log:
log.write(
"\nEpoch: %d\t Overall Acc (Tr): %.4f\t Overall Acc (Val): %.4f\t mIOU (Tr): %.4f\t mIOU (Val): %.4f"
% (epoch, overall_acc_tr, overall_acc_val, mIOU_tr, mIOU_val))
log.write('\n')
log.write('Per Class Training Acc: ' + str(per_class_acc_tr))
log.write('\n')
log.write('Per Class Validation Acc: ' + str(per_class_acc_val))
log.write('\n')
log.write('Per Class Training mIOU: ' + str(per_class_iu_tr))
log.write('\n')
log.write('Per Class Validation mIOU: ' + str(per_class_iu_val))
logger.write(
"\n%d\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.6f" %
(epoch, lossTr, lossVal, overall_acc_tr, overall_acc_val, mIOU_tr,
mIOU_val, lr))
logger.flush()
print("Epoch : " + str(epoch) + ' Details')
print(
"\nEpoch No.: %d\tTrain Loss = %.4f\tVal Loss = %.4f\t Train acc = %.4f\t Val acc = %.4f\t mIOU(tr) = %.4f\t mIOU(val) = %.4f"
% (epoch, lossTr, lossVal, overall_acc_tr, overall_acc_val,
mIOU_tr, mIOU_val))
logger.close()
# 读取数据
transforms = tv.transforms.Compose([
# transforms.CenterCrop(64),
# transforms.CenterCrop(128),
transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
numOfBatch = 9
pixel = 64
flag = 0
root = f'./{numOfBatch}/data'
batchSize = 2
# train_data = loadData.MyDataset(txt=f'{root}_{pixel}_{flag}_train.txt', transform=transforms)
# test_data = loadData.MyDataset(txt=f'{root}_{pixel}_{flag}_test.txt', transform=transforms)
train_data = loadData.MyDataset(txt=f'files_train.txt', transform=transforms)
test_data = loadData.MyDataset(txt=f'files_test.txt', transform=transforms)
train_loader = DataLoader(dataset=train_data,
batch_size=batchSize,
shuffle=True,
num_workers=2)
test_loader = DataLoader(dataset=test_data,
batch_size=batchSize,
shuffle=False,
num_workers=2)
print('num_of_trainData:', len(train_data))
print('num_of_testData:', len(test_data))
#%%
net = LeNetModel.LeNet()
# transforms.CenterCrop(128),
transforms.ToTensor(),
# transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
numOfBatch = 5
dirPath = f'./{numOfBatch}/test_result'
targetFiles = sorted(os.listdir(dirPath))
erroCounter = 0
realLabels = []
predictedLabels = []
for targetFile in targetFiles:
batchSize = 2
test_data = loadData.MyDataset(txt=f'{dirPath}/{targetFile}',
transform=transforms)
test_loader = DataLoader(dataset=test_data,
batch_size=batchSize,
shuffle=False,
num_workers=2)
correct = 0
total = 0
realLabel = []
predictedLabel = []
for data in test_loader:
inputs, labels = data
inputs, labels = inputs.cuda(), labels.cuda()
realLabel.append([int(label) for label in labels])
outputs = net(inputs)