def __getitem__(self, index):
im, label = self._inputs[index, ...].copy(), self._labels[index]
im = transforms.CHW2HWC(im) # CHW, RGB -> HWC, RGB
if cfg.TASK == 'rot':
im, label = prepare_rot(im,
dataset="cifar10",
split=self._split,
mean=_MEAN,
sd=_SD)
elif cfg.TASK == 'col':
im, label = prepare_col(im,
dataset="cifar10",
split=self._split,
nbrs=self._nbrs,
mean=_MEAN,
sd=_SD)
elif cfg.TASK == 'jig':
im, label = prepare_jig(im,
dataset="cifar10",
split=self._split,
perms=self._perms,
mean=_MEAN,
sd=_SD)
else:
im = prepare_im(im,
dataset="cifar10",
split=self._split,
mean=_MEAN,
sd=_SD)
return im, label
def __getitem__(self, index):
# Load the image
im = cv2.imread(os.path.join(self._data_path,
self._imdb[index]["im_path"].split('_')[0],
self._imdb[index]["im_path"]))
while im is None:
logger.info("cv2.imread failed for {}; replacing".format(
self._imdb[index]["im_path"]))
index = np.random.randint(len(self._imdb))
im = cv2.imread(os.path.join(self._data_path,
self._imdb[index]["im_path"].split('_')[0],
self._imdb[index]["im_path"]))
im = im.astype(np.float32, copy=False)
im = im[:, :, ::-1] # HWC, BGR -> HWC, RGB
if cfg.TASK == 'rot':
im, label = prepare_rot(im,
dataset="imagenet22k",
split=self._split,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
elif cfg.TASK == 'col':
im, label = prepare_col(im,
dataset="imagenet22k",
split=self._split,
nbrs=self._nbrs,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
elif cfg.TASK == 'jig':
im, label = prepare_jig(im,
dataset="imagenet22k",
split=self._split,
perms=self._perms,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
else:
# Prepare the image for training / testing
im = prepare_im(im,
dataset="imagenet22k",
split=self._split,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
# Retrieve the label
label = self._imdb[index]["class"]
return im.copy(), label
def __getitem__(self, index):
# Load the image
im = cv2.imread(self._imdb[index]["im_path"])
im = im.astype(np.float32, copy=False)
im = im[:, :, ::-1] # HWC, BGR -> HWC, RGB
if cfg.TASK == 'rot':
im, label = prepare_rot(im,
dataset="cityscapes",
split=self._split,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
elif cfg.TASK == 'col':
im, label = prepare_col(im,
dataset="cityscapes",
split=self._split,
nbrs=self._nbrs,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
elif cfg.TASK == 'jig':
im, label = prepare_jig(im,
dataset="cityscapes",
split=self._split,
perms=self._perms,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
else:
# Prepare the image for training / testing
label = cv2.imread(self._imdb[index]["class"],
cv2.IMREAD_UNCHANGED)
label = label.astype(np.int64, copy=False)
im, label = prepare_seg(im,
label,
split=self._split,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
return im.copy(), label
def __getitem__(self, index):
# Load the image
im = cv2.imread(self._imdb[index]["im_path"])
im = im.astype(np.float32, copy=False)
im = im[:, :, ::-1] # HWC, BGR -> HWC, RGB
if cfg.TASK == 'rot':
im, label = prepare_rot(im,
dataset="imagenet",
split=self._split,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
elif cfg.TASK == 'col':
im, label = prepare_col(im,
dataset="imagenet",
split=self._split,
nbrs=self._nbrs,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
elif cfg.TASK == 'jig':
im, label = prepare_jig(im,
dataset="imagenet",
split=self._split,
perms=self._perms,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
else:
# Prepare the image for training / testing
im = prepare_im(im,
dataset="imagenet",
split=self._split,
mean=_MEAN,
sd=_SD,
eig_vals=_EIG_VALS,
eig_vecs=_EIG_VECS)
# Retrieve the label
label = self._imdb[index]["class"]
return im.copy(), label