class TestDataset(Dataset):
def __init__(self, config, split='test'):
self.config = config
if config.test_data_dir:
self.db = WaterDataset(config.test_data_dir, split=split)
else:
self.db = WaterDataset(config.data_dir, split=split)
#self.db = aaaDataset(config.data_dir, config.num_of_samples, split=split)
def __getitem__(self, idx):
label, datas = self.db.get_example(idx)
#label, datas = self.db.get_example(idx, 'test')
#label = t.from_numpy(np.array(label).astype(np.float32))
label = t.from_numpy(np.array(label))
datas = np.array(datas)
datas = t.from_numpy(datas)
datas = datas.contiguous().view(1, -1)
# TODO: check whose stride is negative to fix this instead copy all
return label, datas
def __len__(self):
return len(self.db)
class TrainDataset(Dataset):
def __init__(self, config, split='train'):
self.config = config
self.db = WaterDataset(config.data_dir, split=split)
def __getitem__(self, idx):
label, datas = self.db.get_example(idx)
label = t.from_numpy(np.array(label))
datas = np.array(datas)
datas = t.from_numpy(datas)
datas = datas.contiguous().view(1, -1)
# TODO: check whose stride is negative to fix this instead copy all
return label, datas
def __len__(self):
return len(self.db)
def __init__(self, config, split='test'):
self.config = config
self.db = WaterDataset(config.data_dir, split=split)
def __init__(self, config, split='test'):
self.config = config
if config.test_data_dir:
self.db = WaterDataset(config.test_data_dir, split=split)
else:
self.db = WaterDataset(config.data_dir, split=split)
def __init__(self, config, split='train'):
self.config = config
print(config.data_dir)
self.db = WaterDataset(config.data_dir,
config.num_of_samples,
split=split)
# Normalize by softmax
all_E[:, :, f + 1] = torch.clamp(all_E[:, :, f + 1], 1e-7, 1 - 1e-7)
all_E[:, :, f + 1] = torch.log(
(all_E[:, :, f + 1] / (1 - all_E[:, :, f + 1])))
all_E[:, :, f + 1] = F.softmax(Variable(all_E[:, :, f + 1]),
dim=1).data
return all_E
if MO:
Testset = DAVIS(DAVIS_ROOT, imset='2017/val.txt', multi_object=True)
if SO:
Testset = DAVIS(DAVIS_ROOT, imset='2016/val.txt')
if test_case is not None:
Testset = WaterDataset(water_root, test_case)
Testloader = data.DataLoader(Testset,
batch_size=1,
shuffle=False,
num_workers=2,
pin_memory=True)
model = nn.DataParallel(RGMP())
if torch.cuda.is_available():
model.cuda()
model.load_state_dict(torch.load('weights.pth'))
model.eval() # turn-off BN
for seq, (all_F, all_M, info) in enumerate(Testloader):
all_F, all_M = all_F[0], all_M[0]