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celinelee
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python_fn_calls

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format(v)
range(1, 9)
format(v)
format(group, num_views)
os.path.abspath(os.path.expanduser(root)
np.random.RandomState(seed)
format(image_size)
self.get_paths(images_dir, group)
self.get_paths(self.root + '/train_masks' + suff, group)
len(self.data_path)
len(self.data_path)
len(self.data_path)
KFold(n_splits=n_folds, shuffle=True, random_state=self.rs)
list(kf.split(np.arange(num_cars)
np.unique(map(lambda x: os.path.basename(x[:-len('_01.jpg')
os.path.basename(self.data_path[128 + 1])
format(2)
int(TRAIN_FRAC * (len(self.data_path)
self.rs.permutation(len(self.data_path)
dict()
img_indices.extend(range(car_id * num_views, (car_id + 1)
format(self.subset, fold_id, n_folds)
self.get_test_paths(is_hq, group)
reload_all_test_paths(self)
ValueError('Only possible for test!')
self.get_test_paths(self.is_hq, self.group)
get_paths(dir_path, group)
range(1, 9)
join(dir_path, f)
os.listdir(dir_path)
isfile(join(dir_path, f)
range(1, 9)
format(group)
format(group)
format(group + 8)
format(group + 8)
path.endswith(ending)
final_paths.append(path)
final_paths.sort()
np.asarray(final_paths)
get_test_paths(is_hq, group='all')
CARVANA.get_paths(join(config.input_data_dir, 'test')
__getitem__(self, index)
tuple (img, target)
Image.open(self.data_path[index])
Image.open(self.labels_path[index])
np.asarray(target)
target.convert('L')
self.transform(img, target)
self.transform(img)
target.max()
mask (max val = {})
format(target.max()
assert ((target > 0)
sum()
Path(self.data_path[index])
Path(self.data_path[index])
__len__(self)
len(self.data_path)
CarvanaPlus(Dataset)
ValueError('No test split available')
__getitem__(self, index)
__len__(self)
len(self.carvana)
im_show(img_list)
transforms.ToPILImage()
len(img_list)
Exception("len(img_list)
enumerate(img_list)
np.array(to_PIL(img)
plt.subplot(100 + 10 * len(img_list)
plt.imshow(img)
fig.axes.get_xaxis()
set_visible(False)
fig.axes.get_yaxis()
set_visible(False)
plt.show()
save_checkpoint(state, is_best, filepath='checkpoint.pth.tar')
torch.save(state, filepath)
os.path.join(os.path.dirname(filepath)
six.add_metaclass(abc.ABCMeta)
_BaseDeterministic(distribution.Distribution)
pmf(x; loc)
Abs(x - loc)
Abs(loc)
point (or batch of points)
statistics (e.g., mean, mode, variance)
tf.name_scope(name)
dtype_util.common_dtype([loc, atol, rtol], dtype_hint=tf.float32)
super(_BaseDeterministic, self)
dtype_util.is_floating(self._loc.dtype)
_slack(self, loc)
tf.abs(loc)
loc(self)
Point (or batch of points)
atol(self)
rtol(self)
_entropy(self)
tf.zeros(self.batch_shape_tensor()
_mean(self)