def load_nyu(ds='train', n_sp=300, sp='rgb'):
# trigger cache.....
dataset = NYUSegmentation()
file_names = dataset.get_split(ds)
if ds == "test":
reorder = np.array([2, 0, 3, 1])
else:
reorder = None
# load image to generate superpixels
result = Parallel(n_jobs=-1)(delayed(load_single_file)(dataset, f, n_sp, sp, reorder=reorder)
for f in file_names)
X, Y, superpixels = zip(*result)
return DataBunch(X, Y, file_names, superpixels)
def load_nyu(ds='train', n_sp=300, sp='rgb'):
# trigger cache.....
dataset = NYUSegmentation()
file_names = dataset.get_split(ds)
if ds == "test":
reorder = np.array([2, 0, 3, 1])
else:
reorder = None
# load image to generate superpixels
result = Parallel(n_jobs=-1)(
delayed(load_single_file)(dataset, f, n_sp, sp, reorder=reorder)
for f in file_names)
X, Y, superpixels = zip(*result)
return DataBunch(X, Y, file_names, superpixels)
def load_nyu_pixelwise(ds='train'):
if ds == "test":
reorder = np.array([2, 0, 3, 1])
else:
reorder = np.arange(4)
# trigger cache.
dataset = NYUSegmentation()
file_names, X, Y = [], [], []
for file_name in dataset.get_split(ds):
print(file_name)
file_names.append(file_name)
gt = dataset.get_ground_truth(file_name)
prediction = get_probabilities(file_name, dataset.directory)
Y.append(gt)
X.append(prediction[:, :, reorder])
return DataBunchNoSP(X, Y, file_names)
def load_nyu_pixelwise(ds='train'):
if ds == "test":
reorder = np.array([2, 0, 3, 1])
else:
reorder = np.arange(4)
# trigger cache.
dataset = NYUSegmentation()
file_names, X, Y = [], [], []
for file_name in dataset.get_split(ds):
print(file_name)
file_names.append(file_name)
gt = dataset.get_ground_truth(file_name)
prediction = get_probabilities(file_name, dataset.directory)
Y.append(gt)
X.append(prediction[:, :, reorder])
return DataBunchNoSP(X, Y, file_names)
