def main():
from common.vis_utils import show_batch
from torchvision.utils import make_grid
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from mapnet.config import MapNetConfigurator
from common.utils import get_configuration
scene = 'full'
num_workers = 4
transform = transforms.Compose([
transforms.Scale(256),
# transforms.CenterCrop(224),
transforms.ToTensor()])
config = get_configuration(MapNetConfigurator())
config.uniform_sampling = False
config.mask_sampling = False
config.mask_image = True
data_path = "/home/drinkingcoder/Dataset/robotcar/"
dset = RobotCar(scene, data_path, train=True, real=False,
transform=transform, data_dir=osp.join("..", "data", "RobotCar"),
config=config)
print 'Loaded RobotCar scene {:s}, length = {:d}'.format(scene, len(dset))
# plot the poses
plt.figure()
plt.plot(dset.poses[:, 0], dset.poses[:, 1])
plt.show()
print(len(dset))
data_loader = data.DataLoader(dset, batch_size=10, shuffle=True,
num_workers=num_workers)
#plt.imshow(dset.muimg.data.numpy()[0, :, :])
#plt.imshow(dset[0].data.numpy())
batch_count = 0
N = 2
for batch in data_loader:
print 'Minibatch {:d}'.format(batch_count)
show_batch(make_grid(batch[0], nrow=5, padding=25, normalize=True))
batch_count += 1
if batch_count >= N:
break
def main():
"""
visualizes the dataset
"""
from common.vis_utils import show_batch, show_stereo_batch
from torchvision.utils import make_grid
import torchvision.transforms as transforms
seq = 'chess'
mode = 2
num_workers = 6
transform = transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
dset = SevenScenes(seq,
'../data/deepslam_data/7Scenes',
True,
transform,
mode=mode)
print(
('Loaded 7Scenes sequence {:s}, length = {:d}'.format(seq, len(dset))))
data_loader = data.DataLoader(dset,
batch_size=10,
shuffle=True,
num_workers=num_workers)
batch_count = 0
N = 2
for batch in data_loader:
print(('Minibatch {:d}'.format(batch_count)))
if mode < 2:
show_batch(make_grid(batch[0], nrow=1, padding=25, normalize=True))
elif mode == 2:
lb = make_grid(batch[0][0], nrow=1, padding=25, normalize=True)
rb = make_grid(batch[0][1], nrow=1, padding=25, normalize=True)
show_stereo_batch(lb, rb)
batch_count += 1
if batch_count >= N:
break
def main():
from common.vis_utils import show_batch
from torchvision.utils import make_grid
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
from mapnet.config import MapNetConfigurator
from common.utils import get_configuration
scene = 'KingsCollege'
num_workers = 4
transform = transforms.Compose([
transforms.Scale(256),
# transforms.CenterCrop(224),
transforms.ToTensor()])
config = get_configuration(MapNetConfigurator())
data_path = "/home/drinkingcoder/Dataset/Cambridge/"
dset = Cambridge(scene, data_path, train=True, real=False,
transform=transform, data_dir=osp.join("..", "data", "Cambridge", "KingsCollege"),
config=config)
print 'Loaded RobotCar scene {:s}, length = {:d}'.format(scene, len(dset))
# plot the poses
plt.figure()
plt.scatter(dset.poses[:, 0], dset.poses[:, 1])
plt.show()
plt.figure()
plt.scatter(dset.poses[:, 3], dset.poses[:, 5])
plt.show()
print(len(dset))
data_loader = data.DataLoader(dset, batch_size=10, shuffle=True,
num_workers=num_workers)
batch_count = 0
N = 2
for batch in data_loader:
print 'Minibatch {:d}'.format(batch_count)
show_batch(make_grid(batch[0], nrow=5, padding=25, normalize=True))
batch_count += 1
if batch_count >= N:
break
def main():
from common.vis_utils import show_batch
from torchvision.utils import make_grid
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
scene = 'loop'
num_workers = 4
transform = transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor()
])
data_path = osp.join('..', 'data', 'deepslam_data', 'RobotCar')
dset = RobotCar(scene,
data_path,
train=True,
real=True,
transform=transform)
print('Loaded RobotCar scene {:s}, length = {:d}'.format(scene, len(dset)))
# plot the poses
plt.figure()
plt.plot(dset.poses[:, 0], dset.poses[:, 1])
plt.show()
data_loader = data.DataLoader(dset,
batch_size=10,
shuffle=True,
num_workers=num_workers)
batch_count = 0
N = 2
for batch in data_loader:
print('Minibatch {:d}'.format(batch_count))
show_batch(make_grid(batch[0], nrow=5, padding=25, normalize=True))
batch_count += 1
if batch_count >= N:
break
def main():
"""
visualizes the dataset
"""
from common.vis_utils import show_batch, show_stereo_batch
from torchvision.utils import make_grid
import torchvision.transforms as transforms
mode = int(sys.argv[1])
num_workers = 6
transform = transforms.Compose([
transforms.Scale(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
concatenate_inputs = (mode == 3)
if mode == 0:
input_types = 'left'
elif mode == 1 and not dual_output:
input_types = 'depth'
elif mode == 2 and not dual_output:
input_types = ['left', 'depth']
elif mode == 3:
input_types = ['left', 'label_colorized']
dset = DeepLoc('../data/deepslam_data/DeepLoc',
True,
transform,
input_types=input_types,
output_types=[],
concatenate_inputs=concatenate_inputs)
print('Loaded DeepLoc, length = {:d}'.format(len(dset)))
data_loader = data.DataLoader(dset,
batch_size=10,
shuffle=True,
num_workers=num_workers)
batch_count = 0
N = 2
for batch in data_loader:
print('Minibatch {:d}'.format(batch_count))
if mode < 2:
show_batch(make_grid(batch[0], nrow=1, padding=25, normalize=True))
elif mode == 2:
lb = make_grid(batch[0][0], nrow=1, padding=25, normalize=True)
rb = make_grid(batch[0][1], nrow=1, padding=25, normalize=True)
show_stereo_batch(lb, rb)
elif mode == 3:
print(len(batch))
for elm in batch:
print(elm.shape)
lb = make_grid(batch[0][0][:3], nrow=1, padding=25, normalize=True)
rb = make_grid(batch[0][0][3:], nrow=1, padding=25, normalize=True)
show_stereo_batch(lb, rb)
batch_count += 1
if batch_count >= N:
break
