def main():
global args, logger
args = get_parser().parse_args()
logger = get_logger()
# os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(x) for x in args.gpu)
logger.info(args)
assert args.classes > 1
assert args.zoom_factor in [1, 2, 4, 8]
assert (args.crop_h - 1) % 8 == 0 and (args.crop_w - 1) % 8 == 0
assert args.split in ['train', 'val', 'test']
logger.info("=> creating model ...")
logger.info("Classes: {}".format(args.classes))
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * value_scale for item in std]
train_transform = transforms.Compose([
transforms.RandScale([0.5, 2]),
transforms.RandRotate([-10, 10], padding=mean, ignore_label=args.ignore_label),
transforms.RandomGaussianBlur(),
transforms.RandomHorizontalFlip(),
transforms.Crop([args.crop_h, args.crop_w], crop_type='rand', padding=mean, ignore_label=args.ignore_label),
transforms.ToTensor()])
val_transform = transforms.Compose([transforms.Crop([args.crop_h, args.crop_w], crop_type='center', padding=mean, ignore_label=args.ignore_label),
transforms.ToTensor()])
val_data1 = datasets.SegData(split='train', data_root=args.data_root, data_list=args.val_list1, transform=val_transform)
val_loader1 = torch.utils.data.DataLoader(val_data1, batch_size=1, shuffle=False, num_workers=args.workers, pin_memory=True)
from pspnet import PSPNet
model = PSPNet(backbone = args.backbone, layers=args.layers, classes=args.classes, zoom_factor=args.zoom_factor, use_softmax=False, use_aux=False, pretrained=False, syncbn=False).cuda()
logger.info(model)
# model = torch.nn.DataParallel(model).cuda()
model = model.cuda()
cudnn.enabled = True
cudnn.benchmark = True
if os.path.isfile(args.model_path):
logger.info("=> loading checkpoint '{}'".format(args.model_path))
checkpoint = torch.load(args.model_path)
# model.load_state_dict(checkpoint['state_dict'], strict=False)
# logger.info("=> loaded checkpoint '{}'".format(args.model_path))
pretrained_dict = {k.replace('module.',''): v for k, v in checkpoint['state_dict'].items()}
dict1 = model.state_dict()
model.load_state_dict(pretrained_dict, strict=False)
else:
raise RuntimeError("=> no checkpoint found at '{}'".format(args.model_path))
cv2.setNumThreads(0)
validate(val_loader1, val_data1.data_list, model, args.classes, mean, std, args.base_size1, args.crop_h, args.crop_w, args.scales)
def build_network(snapshot):
epoch = 0
net = PSPNet()
net = nn.DataParallel(net)
if snapshot is not None:
_, epoch = os.path.basename(snapshot).split('_')
epoch = int(epoch)
net.load_state_dict(torch.load(snapshot))
logging.info("Snapshot for epoch {} loaded from {}".format(
epoch, snapshot))
net = net.cuda()
return net, epoch
def build_network(snapshot, backend):
epoch = 0
backend = backend.lower()
net = None
if backend.startswith('resnet'):
net = PSPNet(sizes=(1, 2, 3, 6),
psp_size=2048,
deep_features_size=1024,
backend=backend)
net = nn.DataParallel(net)
if snapshot is not None:
_, epoch = os.path.basename(snapshot).split('_')
epoch = int(epoch)
net.load_state_dict(torch.load(snapshot))
logging.info("Snapshot for epoch {} loaded from {}".format(
epoch, snapshot))
net = net.cuda()
return net, epoch
def main():
global args, best_record
args = parser.parse_args()
if args.augment:
transform_train = joint_transforms.Compose([
joint_transforms.FreeScale((512, 512)),
joint_transforms.RandomHorizontallyFlip(),
joint_transforms.RandomVerticallyFlip(),
joint_transforms.Rotate(90),
])
transform_val = joint_transforms.Compose(
[joint_transforms.FreeScale((512, 512))])
else:
transform_train = None
dataset_train = dataset.PRCVData('train', args.data_root,
args.label_train_list, transform_train)
dataloader_train = data.DataLoader(dataset_train,
batch_size=args.batch_size,
shuffle=True,
num_workers=8)
dataset_val = dataset.PRCVData('val', args.data_root, args.label_val_list,
transform_val)
dataloader_val = data.DataLoader(dataset_val,
batch_size=args.batch_size,
shuffle=None,
num_workers=8)
model = PSPNet(num_classes=args.num_class)
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
saved_state_dict = torch.load(args.restore_from)
new_params = model.state_dict().copy()
if args.num_class != 21:
for i in saved_state_dict:
#Scale.layer5.conv2d_list.3.weight
i_parts = i.split('.')
# print i_parts
if i_parts[0] != 'fc':
new_params[i] = saved_state_dict[i]
model.load_state_dict(new_params)
model = model.cuda()
model = torch.nn.DataParallel(model)
cudnn.benchmark = True
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.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.resume))
# define loss function (criterion) and pptimizer
criterion = torch.nn.CrossEntropyLoss(ignore_index=255).cuda()
optimizer = torch.optim.SGD([{
'params': get_1x_lr_params(model),
'lr': args.learning_rate
}, {
'params': get_10x_lr_params(model),
'lr': 10 * args.learning_rate
}],
lr=args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch)
# train for one epoch
train(dataloader_train, model, criterion, optimizer, epoch)
# evaluate on validation set
acc, mean_iou, val_loss = validate(dataloader_val, model, criterion,
args.result_pth, epoch)
is_best = mean_iou > best_record['miou']
if is_best:
best_record['epoch'] = epoch
best_record['val_loss'] = val_loss.avg
best_record['acc'] = acc
best_record['miou'] = mean_iou
save_checkpoint(
{
'epoch': epoch + 1,
'val_loss': val_loss.avg,
'accuracy': acc,
'miou': mean_iou,
'state_dict': model.state_dict(),
}, is_best)
print(
'------------------------------------------------------------------------------------------------------'
)
print('[epoch: %d], [val_loss: %5f], [acc: %.5f], [miou: %.5f]' %
(epoch, val_loss.avg, acc, mean_iou))
print(
'best record: [epoch: {epoch}], [val_loss: {val_loss:.5f}], [acc: {acc:.5f}], [miou: {miou:.5f}]'
.format(**best_record))
print(
'------------------------------------------------------------------------------------------------------'
)
def main():
global args, logger, writer
args = get_parser().parse_args()
logger = get_logger()
writer = SummaryWriter(args.save_path)
# os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(x) for x in args.gpu)
if args.dist:
dist_init(args.port, backend=args.backend)
if len(args.gpu) == 1:
args.syncbn = False
logger.info(args)
assert args.classes > 1
assert args.zoom_factor in [1, 2, 4, 8]
assert (args.crop_h - 1) % 8 == 0 and (args.crop_w - 1) % 8 == 0
world_size = 1
rank = 0
if args.dist:
rank = dist.get_rank()
world_size = dist.get_world_size()
if rank == 0:
logger.info('dist:{}'.format(args.dist))
logger.info("=> creating model ...")
logger.info("Classes: {}".format(args.classes))
# rank = dist.get_rank()
if args.bn_group > 1:
args.syncbn = True
bn_sync_stats = True
bn_group_comm = simple_group_split(world_size, rank,
world_size // args.bn_group)
else:
args.syncbn = False
bn_sync_stats = False
bn_group_comm = None
model = PSPNet(layers=args.layers,
classes=args.classes,
zoom_factor=args.zoom_factor,
syncbn=args.syncbn,
group_size=args.bn_group,
group=bn_group_comm,
sync_stats=bn_sync_stats)
if rank == 0:
logger.info(model)
# optimizer = torch.optim.SGD(model.parameters(), args.base_lr, momentum=args.momentum, weight_decay=args.weight_decay)
# newly introduced layer with lr x10
optimizer = torch.optim.SGD([{
'params': model.layer0.parameters()
}, {
'params': model.layer1.parameters()
}, {
'params': model.layer2.parameters()
}, {
'params': model.layer3.parameters()
}, {
'params': model.layer4.parameters()
}, {
'params': model.ppm.parameters(),
'lr': args.base_lr * 10
}, {
'params': model.cls.parameters(),
'lr': args.base_lr * 10
}, {
'params': model.aux.parameters(),
'lr': args.base_lr * 10
}],
lr=args.base_lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# model = torch.nn.DataParallel(model).cuda()
# if args.syncbn:
# from lib.syncbn import patch_replication_callback
# patch_replication_callback(model)
model = model.cuda()
cudnn.enabled = True
cudnn.benchmark = True
criterion = nn.NLLLoss(ignore_index=args.ignore_label)
if args.weight:
def map_func(storage, location):
return storage.cuda()
if os.path.isfile(args.weight):
logger.info("=> loading weight '{}'".format(args.weight))
checkpoint = torch.load(args.weight, map_location=map_func)
model.load_state_dict(checkpoint['state_dict'])
logger.info("=> loaded weight '{}'".format(args.weight))
else:
logger.info("=> no weight found at '{}'".format(args.weight))
if args.resume:
if os.path.isfile(args.resume):
logger.info("=> loading checkpoint '{}'".format(args.resume))
# checkpoint = torch.load(args.resume)
# args.start_epoch = checkpoint['epoch']
# model.load_state_dict(checkpoint['state_dict'])
# optimizer.load_state_dict(checkpoint['optimizer'])
model, optimizer, args.start_epoch = restore_from(
model, optimizer, args.resume)
logger.info("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, args.start_epoch))
else:
logger.info("=> no checkpoint found at '{}'".format(args.resume))
if args.dist:
broadcast_params(model)
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * value_scale for item in std]
train_transform = transforms.Compose([
transforms.RandScale([args.scale_min, args.scale_max]),
transforms.RandRotate([args.rotate_min, args.rotate_max],
padding=mean,
ignore_label=args.ignore_label),
transforms.RandomGaussianBlur(),
transforms.RandomHorizontalFlip(),
transforms.Crop([args.crop_h, args.crop_w],
crop_type='rand',
padding=mean,
ignore_label=args.ignore_label),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
train_data = datasets.SegData(split='train',
data_root=args.data_root,
data_list=args.train_list,
transform=train_transform)
train_sampler = None
if args.dist:
train_sampler = DistributedSampler(train_data)
train_loader = torch.utils.data.DataLoader(
train_data,
batch_size=args.batch_size,
shuffle=False if train_sampler else True,
num_workers=args.workers,
pin_memory=False,
sampler=train_sampler)
if args.evaluate:
val_transform = transforms.Compose([
transforms.Crop([args.crop_h, args.crop_w],
crop_type='center',
padding=mean,
ignore_label=args.ignore_label),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
val_data = datasets.SegData(split='val',
data_root=args.data_root,
data_list=args.val_list,
transform=val_transform)
val_sampler = None
if args.dist:
val_sampler = DistributedSampler(val_data)
val_loader = torch.utils.data.DataLoader(
val_data,
batch_size=args.batch_size_val,
shuffle=False,
num_workers=args.workers,
pin_memory=False,
sampler=val_sampler)
for epoch in range(args.start_epoch, args.epochs + 1):
loss_train, mIoU_train, mAcc_train, allAcc_train = train(
train_loader, model, criterion, optimizer, epoch, args.zoom_factor,
args.batch_size, args.aux_weight)
writer.add_scalar('loss_train', loss_train.cpu().numpy(), epoch)
writer.add_scalar('mIoU_train', mIoU_train, epoch)
writer.add_scalar('mAcc_train', mAcc_train, epoch)
writer.add_scalar('allAcc_train', allAcc_train, epoch)
# write parameters histogram costs lots of time
# for name, param in model.named_parameters():
# writer.add_histogram(name, param, epoch)
if args.evaluate and rank == 0:
loss_val, mIoU_val, mAcc_val, allAcc_val = validate(
val_loader, model, criterion, args.classes, args.zoom_factor)
writer.add_scalar('loss_val', loss_val.cpu().numpy(), epoch)
writer.add_scalar('mIoU_val', mIoU_val, epoch)
writer.add_scalar('mAcc_val', mAcc_val, epoch)
writer.add_scalar('allAcc_val', allAcc_val, epoch)
if epoch % args.save_step == 0 and (rank == 0):
filename = args.save_path + '/train_epoch_' + str(epoch) + '.pth'
logger.info('Saving checkpoint to: ' + filename)
torch.save(
{
'epoch': epoch,
'state_dict': model.cpu().state_dict(),
'optimizer': optimizer.state_dict()
}, filename)
def train():
print(torch.cuda.device_count())
# os.environ["CUDA_VISIBLE_DEVICES"] = '0, 1'
net = PSPNet(n_classes=1,
sizes=(1, 2, 3, 6),
psp_size=512,
deep_features_size=256,
backend='resnet34',
pretrained=False)
net = nn.DataParallel(net)
net = net.cuda()
weight = torch.ones(3)
weight[0] = 0
# print(net)
optimizer = Adam(net.parameters(), lr=1e-3)
# criterion = nn.CrossEntropyLoss()
criterion = nn.MSELoss()
loaders = prepared_train_data()
test_loaders = prepared_test_data()
# print(len(loaders))
# print(loaders)
for epoch in range(1, 100):
print('Training................')
epoch_loss = []
iteration = 0
net.train(mode=True)
for step, sample_batch in enumerate(loaders):
# print("Iter:"+str(iteration))
iteration = iteration + 1
images = sample_batch['image'].cuda()
# masks = torch.transpose(sample_batch['mask'], 1, 3)
masks = sample_batch['mask'].cuda()
# print(images.size())
# print(masks.size())
inputs = Variable(images)
targets = Variable(masks)
# print(targets.size())
outputs = net(inputs)
outputs = torch.clamp(outputs, 0., 255.)
# results = outputs.cpu().data.numpy()
#
# # print(np.shape(results))
#
# map = np.squeeze(results, axis=[0, 1])
#
# misc.imsave('./test_images/test_image_' + str(iteration) + '.png', map)
# print(outputs)
# print(outputs)
# print(outputs.size())
optimizer.zero_grad()
loss = criterion(outputs, targets)
loss.backward()
optimizer.step()
epoch_loss.append(loss.data[0])
# if iteration % 10 == 0:
# print("Epoch:{}, iteration:{}, loss:{}".format(epoch,
# step,
# loss))
# if iteration % 10 == 0:
# results = outputs.cpu().data.numpy()
# # print(np.shape(results))
# map = np.squeeze(results, axis=1)
# # print(np.shape(map))
# # map = np.transpose(map, [0, 2, 3, 1])
#
# misc.imsave('./train_image/test_image'+str(iteration)+'.png', map[0, :, :])
# if iteration % 400 == 0:
# torch.save(net, 'Models/modol-'+str(epoch)+'-'+str(iteration)+'.pth')
#
torch.save(net, 'Models/modol-' + str(epoch) + '.pth')
print('Testing........................')
net.train(mode=False)
total_m1 = 0
disc = 44
for iteration, item in enumerate(test_loaders):
images = item['image'].cuda()
# masks = torch.transpose(sample_batch['mask'], 1, 3)
masks = item['mask'].numpy()
name = item['name']
masks = np.squeeze(masks, axis=0)
test_image = Variable(images).cuda()
predict_label = net(test_image)
predict_label = torch.clamp(predict_label, 0., 255.)
results = predict_label.cpu().data.numpy()
map = np.squeeze(results, axis=[0, 1])
gt = np.zeros(shape=np.shape(masks))
gt[masks > 200] = 1
prediction = np.zeros(shape=np.shape(map))
prediction[map > disc] = 1
overlap = gt + prediction
# print(overlap.max(), overlap.min())
# print(np.shape(overlap))
image_inter = np.zeros(shape=np.shape(overlap))
image_inter[overlap > 1] = 1
num_inter = sum(sum(image_inter))
# print(np.shape(num_inter))
image_union = np.zeros(shape=np.shape(overlap))
image_union[overlap > 0] = 1
num_union = sum(sum(image_union))
# print(np.shape(num_union))
m_1 = (1 - num_inter / num_union)
print('Image name is {}, and m1 is {}'.format(name[0], m_1))
total_m1 = total_m1 + m_1
map[map > disc] = 255
#
# misc.imsave('./test_images/test_image_' + str(iteration) + '.png', map)
misc.imsave('./test_image/' + name[0], map)
print('m1 is {}'.format(total_m1 / 200))
def main():
global args, logger
args = get_parser().parse_args()
logger = get_logger()
# os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(x) for x in args.gpu)
logger.info(args)
assert args.classes > 1
assert args.zoom_factor in [1, 2, 4, 8]
assert (args.crop_h - 1) % 8 == 0 and (args.crop_w - 1) % 8 == 0
assert args.split in ['train', 'val', 'test']
logger.info("=> creating model ...")
logger.info("Classes: {}".format(args.classes))
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * value_scale for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * value_scale for item in std]
normalize = Normalize()
infer_dataset = voc12.data.VOC12ClsDataset(
args.train_list,
voc12_root=args.voc12_root,
transform=transforms.Compose([
np.asarray,
imutils.RandomCrop(441),
# normalize,
imutils.HWC_to_CHW
]))
val_loader1 = DataLoader(infer_dataset,
batch_size=1,
shuffle=False,
num_workers=args.workers,
pin_memory=True)
from pspnet import PSPNet
model = PSPNet(backbone=args.backbone,
layers=args.layers,
classes=args.classes,
zoom_factor=args.zoom_factor,
use_softmax=False,
use_aux=False,
pretrained=False,
syncbn=False).cuda()
logger.info(model)
# model = torch.nn.DataParallel(model).cuda()
model = model.cuda()
cudnn.enabled = True
cudnn.benchmark = True
if os.path.isfile(args.model_path):
logger.info("=> loading checkpoint '{}'".format(args.model_path))
checkpoint = torch.load(args.model_path)
# model.load_state_dict(checkpoint['state_dict'], strict=False)
# logger.info("=> loaded checkpoint '{}'".format(args.model_path))
pretrained_dict = {
k.replace('module.', ''): v
for k, v in checkpoint['state_dict'].items()
}
dict1 = model.state_dict()
model.load_state_dict(pretrained_dict, strict=False)
else:
raise RuntimeError("=> no checkpoint found at '{}'".format(
args.model_path))
cv2.setNumThreads(0)
validate(val_loader1, model, args.classes, mean, std, args.base_size1)
def main():
global args
# 如果有多GPU 使用多GPU训练
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
args.batch_size = args.batch_size * torch.cuda.device_count()
else:
print("Let's use", torch.cuda.current_device())
if args.resume:
assert os.path.isfile(args.resume), \
"=> no checkpoint found at '{}'".format(args.resume)
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args = checkpoint['args']
start_epoch = checkpoint['epoch'] + 1
best_result = checkpoint['best_result']
model = checkpoint['model']
optimizer = checkpoint['optimizer']
output_directory = os.path.dirname(os.path.abspath(args.resume))
print("=> loaded checkpoint (epoch {})".format(checkpoint['epoch']))
train_loader, val_loader = create_data_loaders(args)
args.resume = True
else:
train_loader, val_loader = create_data_loaders(args)
print("=> creating Model ")
model = PSPNet(n_classes=args.max_classes,
sizes=(1, 2, 3, 6),
psp_size=512,
deep_features_size=256,
backend='resnet34')
print("=> model created.")
start_epoch = 0
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.decay)
best_result = np.inf
# create results folder, if not already exists
output_directory = os.path.join(args.saved_path)
if not os.path.exists(output_directory):
os.makedirs(output_directory)
log_path = os.path.join(args.log_path, "{}".format('vocaug'))
if os.path.isdir(log_path):
shutil.rmtree(log_path)
os.makedirs(log_path)
logger = SummaryWriter(log_path)
# for multi-gpu training
if torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model).cuda()
else:
model = model.cuda()
# define loss function
weights = torch.ones(args.max_classes)
weights[0] = 0
seg_criterion = nn.NLLLoss2d(weight=weights.cuda()).cuda()
cls_criterion = nn.BCEWithLogitsLoss(weight=weights.cuda()).cuda()
# criterion = [seg_criterion, cls_criterion]
is_best = False
for epoch in range(start_epoch, args.epochs):
loss = train(train_loader, model, seg_criterion, optimizer, epoch,
logger) # train for one epoch
if loss < best_result:
best_result = loss
is_best = True
else:
is_best = False
utils.save_checkpoint(
{
'args': args,
'epoch': epoch,
'model': model,
'best_result': best_result,
'optimizer': optimizer,
}, is_best, epoch, output_directory)
if (epoch + 1) % 10 == 0:
validate(val_loader, model, epoch, logger)
from pspnet import PSPNet
model = PSPNet(backbone = 'resnet', layers=50, classes=20, zoom_factor=1, pretrained=False, syncbn=False).cuda()
checkpoint = torch.load('exp/drivable/res101_psp_coarse/model/train_epoch_14.pth')
pretrained_dict = {k.replace('module.',''): v for k, v in checkpoint['state_dict'].items()}
dict1 = model.state_dict()
print (dict1.keys(), pretrained_dict.keys())
for item in dict1:
if item not in pretrained_dict.keys():
print(item,'nbnmbkjhiuguig~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~`')
model.load_state_dict(pretrained_dict, strict=False)
model.eval()
model.cuda()
print(model)
normalize = Normalize()
infer_dataset = voc12.data.VOC12ClsDatasetMSF(args.infer_list, voc12_root=args.voc12_root,
scales=(1, 0.5, 1.5, 2.0),
inter_transform=torchvision.transforms.Compose(
[np.asarray,
normalize,
imutils.HWC_to_CHW]))
infer_data_loader = DataLoader(infer_dataset, shuffle=False, num_workers=args.num_workers, pin_memory=True)
print('data ready')
n_gpus = torch.cuda.device_count()
model_replicas = torch.nn.parallel.replicate(model, list(range(n_gpus)))
for iter, (img_name, img_list, label) in enumerate(infer_data_loader):