def make_val_data_loader(config):
transform_val = torchvision.transforms.Compose([
mtr.Resize(tuple(config['data_size'])),
mtr.ToTensor(),
mtr.Normalize(config['data_normalize_mean'],
config['data_normalize_std'],
elems_do=['img']),
])
val_loaders = list()
for val_dataset in config['val_datasets']:
val_set = RgbdSodDataset(val_dataset,
transform=transform_val,
max_num=0,
if_memory=False)
val_loaders.append(
DataLoader(val_set, batch_size=1, shuffle=False, pin_memory=True))
return val_loaders
def make_train_data_loader(config):
transform_train = torchvision.transforms.Compose([
mtr.RandomFlip(),
mtr.Resize(tuple(config['data_size'])),
mtr.ToTensor(),
mtr.Normalize(config['data_normalize_mean'],
config['data_normalize_std'],
elems_do=['img']),
])
# TODO maxnum ?, num_workers ?
train_set = RgbdSodDataset(datasets=config['train_datasets'],
transform=transform_train,
max_num=0,
if_memory=False)
train_loader = DataLoader(train_set,
batch_size=config['batch_size'],
shuffle=True,
num_workers=4,
pin_memory=True)
return train_loader
def __init__(self, p):
self.p = p
os.makedirs(p['snapshot_path'], exist_ok=True)
shutil.copyfile(
os.path.join('model_zoo/D3NetBenchmark/model', p['model'] + '.py'),
os.path.join(p['snapshot_path'], p['model'] + '.py'))
SetLogFile('{}/log.txt'.format(p['snapshot_path']))
if p['if_use_tensorboard']:
self.writer = SummaryWriter(p['snapshot_path'])
transform_train = torchvision.transforms.Compose([
mtr.RandomFlip(),
mtr.Resize(p['size']),
mtr.ToTensor(),
mtr.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
elems_do=['img']),
])
transform_val = torchvision.transforms.Compose([
mtr.Resize(p['size']),
mtr.ToTensor(),
mtr.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
elems_do=['img']),
])
self.train_set = RgbdSodDataset(datasets=p['train_datasets'],
transform=transform_train,
max_num=p['max_num'],
if_memory=p['if_memory'])
self.train_loader = DataLoader(self.train_set,
batch_size=p['bs'],
shuffle=True,
num_workers=p['num_workers'],
pin_memory=True)
self.val_loaders = []
for val_dataset in p['val_datasets']:
val_set = RgbdSodDataset(val_dataset,
transform=transform_val,
max_num=p['max_num'],
if_memory=p['if_memory'])
self.val_loaders.append(
DataLoader(val_set,
batch_size=1,
shuffle=False,
pin_memory=True))
self.model = MyNet()
self.model = self.model.cuda()
self.optimizer = utils.get_optimizer(p['optimizer'][0],
self.model.parameters(), p['lr'],
p['optimizer'][1])
self.scheduler = utils.get_scheduler(p['scheduler'][0], self.optimizer,
p['scheduler'][1])
self.best_metric = None
if p['resume'] != None:
print('Load checkpoint from [{}]'.format(p['resume']))
checkpoint = torch.load(p['resume'])
self.p['start_epoch'] = checkpoint['current_epoch'] + 1
self.best_metric = checkpoint['best_metric']
self.model.load_state_dict(checkpoint['model'])
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.scheduler.load_state_dict(checkpoint['scheduler'])
model_rgb = RgbNet().cuda()
model_rgbd = RgbdNet().cuda()
model_depth = DepthNet().cuda()
model_rgb.load_state_dict(torch.load(pretrained_models['RgbNet'])['model'])
model_rgbd.load_state_dict(torch.load(pretrained_models['RgbdNet'])['model'])
model_depth.load_state_dict(torch.load(pretrained_models['DepthNet'])['model'])
model_rgb.eval()
model_rgbd.eval()
model_depth.eval()
transform_test = torchvision.transforms.Compose([
mtr.Resize(size),
mtr.ToTensor(),
mtr.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225],
elems_do=['img'])
])
test_loaders = []
for test_dataset in test_datasets:
val_set = RgbdSodDataset(datasets_path + test_dataset,
transform=transform_test)
test_loaders.append(
DataLoader(val_set, batch_size=1, shuffle=False, pin_memory=True))
for index, test_loader in enumerate(test_loaders):
dataset = test_datasets[index]
print('Test [{}]'.format(dataset))
for test_dataset in test_datasets:
os.makedirs(os.path.join(result_path,tmp,test_dataset),exist_ok=True)
model_rgb=RgbNet().cuda()
model_rgbd=RgbdNet().cuda()
model_depth=DepthNet().cuda()
model_rgb.load_state_dict(torch.load(pretrained_models['RgbNet'])['model'])
model_rgbd.load_state_dict(torch.load(pretrained_models['RgbdNet'])['model'])
model_depth.load_state_dict(torch.load(pretrained_models['DepthNet'])['model'])
model_rgb.eval()
model_rgbd.eval()
model_depth.eval()
transform_test = torchvision.transforms.Compose([mtr.Resize(size),mtr.ToTensor(),mtr.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225],elems_do=['img'])])
test_loaders=[]
for test_dataset in test_datasets:
val_set=RgbdSodDataset(datasets_path+test_dataset,transform=transform_test)
test_loaders.append(DataLoader(val_set, batch_size=1, shuffle=False,pin_memory=True))
for index, test_loader in enumerate(test_loaders):
dataset=test_datasets[index]
print('Test [{}]'.format(dataset))
for i, sample_batched in enumerate(tqdm(test_loader)):
input, gt = model_rgb.get_input(sample_batched),model_rgb.get_gt(sample_batched)
with torch.no_grad():
output_rgb = model_rgb(input)