def AlbumentationTrainTransform(self):
tf = tc.Compose([
ta.HorizontalFlip(),
ta.Cutout(num_holes=1, max_h_size=16, max_w_size=16),
tp.ToTensor(dict(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
])
return lambda img: tf(image=np.array(img))["image"]
def AlbumentationTestTransform(self):
tf = tc.Compose([
tp.ToTensor(
dict(mean=(0.4802, 0.4481, 0.3975),
std=(0.2302, 0.2265, 0.2262)))
])
return lambda img: tf(image=np.array(img))["image"]
def AlbumentationTrainTransform(self):
tf = tc.Compose([ta.PadIfNeeded(4, 4, always_apply=True),
ta.RandomCrop(height=32, width=32, always_apply=True),
ta.Cutout(num_holes = 1, max_h_size=8, max_w_size=8, always_apply=True),
ta.HorizontalFlip(),
tp.ToTensor(dict (mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)))
])
return lambda img: tf(image = np.array(img))["image"]
def AlbumentationTrainTransform(self):
tf = tc.Compose([ta.HorizontalFlip(p=0.5),
ta.Rotate(limit=(-20, 20)),
# ta.VerticalFlip(p=0.5),
# ta.Cutout(num_holes=3, max_h_size=8, max_w_size=8, p=0.5),
# ta.Blur(),
# ta.ChannelShuffle(),
# ta.InvertImg(),
ta.RandomCrop(height=30, width=30, p=5.0),
ta.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
tp.ToTensor()
])
return lambda img: tf(image = np.array(img))["image"]
night_augmentation=None,
output_types=['pose', 'label'])
tf = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor()
])
L = len(test)
print('Dataset has {:d} entries'.format(L))
for i in tqdm.tqdm(range(len(test)),
total=L,
desc='All there?',
leave=False):
test[i]
imgs = torch.stack(
[tf(test[i][0]) for i in range(L // 16 - 7, L // 16 + 7)]
#+ [tf(test[i][0]) for i in range(2*(L//3)-7, 2*(L//3)+7)]
)
grid = utils.make_grid(imgs, 7)
plt.imshow(grid.permute(1, 2, 0))
plt.show()
elif test_name == 'synthetic':
test = AachenDayNight('../data/deepslam_data/AachenDayNight/',
True,
224,
use_synthetic=True,
use_stylization=16,
output_types=['pose'])
print('Length synthetic dataset: {:d}'.format(len(test)))
"""
loader = AachenDayNight('../data/deepslam_data/AachenDayNight/', True, verbose=True)
def AlbumentationTestTransform(self):
tf = tc.Compose([ta.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
tp.ToTensor()
# tp.ToTensor(dict(mean=(0.4914, 0.4822, 0.4465), std=(0.247, 0.2435, 0.2616)))
])
return lambda img: tf(image = np.array(img))["image"]