diff --git a/src/core/data.py b/src/core/data.py
index b1fcbcde851b31e441a21404260db3e208c647db..13a2ef4b30f0aa9590c0b7701ad56772eb467849 100644
--- a/src/core/data.py
+++ b/src/core/data.py
@@ -53,11 +53,13 @@ class DatasetBase(data.Dataset, metaclass=ABCMeta):
raise FileNotFoundError
# phase stands for the working mode,
# 'train' for training and 'eval' for validating or testing.
- assert phase in ('train', 'eval')
+ if phase not in ('train', 'eval'):
+ raise ValueError("Invalid phase")
# subset is the sub-dataset to use.
# For some datasets there are three subsets,
# while for others there are only train and test(val).
- assert subset in ('train', 'val', 'test')
+ if subset not in ('train', 'val', 'test'):
+ raise ValueError("Invalid subset")
self.phase = phase
self.transforms = transforms
self.repeats = int(repeats)
diff --git a/src/core/factories.py b/src/core/factories.py
index c39e4268a448f5a590cf49d6cc4604df28452856..a0ff531d013c06e98c27f2d3be86b8646aae0492 100644
--- a/src/core/factories.py
+++ b/src/core/factories.py
@@ -57,7 +57,8 @@ class Duck(Sequence, ABC):
class DuckMeta(ABCMeta):
def __new__(cls, name, bases, attrs):
- assert len(bases) == 1 # Multiple inheritance is not yet supported.
+ if len(bases) > 1:
+ raise NotImplementedError("Multiple inheritance is not yet supported.")
members = dict(getmembers(bases[0])) # Trade space for time
for k in attrs['__ava__']:
diff --git a/src/core/misc.py b/src/core/misc.py
index 20122ae9fa123695878477e01b261088c317d312..ae4b4287cbd241749e8c3fc3f1f2981ebff1f74d 100644
--- a/src/core/misc.py
+++ b/src/core/misc.py
@@ -225,7 +225,8 @@ class _Tree:
r"""
This is different from a travasal in that this search allows early stop.
"""
- assert mode in ('name', 'path', 'val')
+ if mode not in ('name', 'path', 'val'):
+ raise NotImplementedError("Invalid mode")
if mode == 'path':
nodes = self.parse_path(tar)
root = self.root
diff --git a/src/data/augmentations.py b/src/data/augmentations.py
index 473372326f94ef3ef1a01ebbf6d9aea32cd9e92f..826182944dea5bc6dc6d4a7cc48d151269177c46 100644
--- a/src/data/augmentations.py
+++ b/src/data/augmentations.py
@@ -61,7 +61,8 @@ class Transform:
class Compose:
def __init__(self, *tfs):
- assert len(tfs) > 0
+ if len(tfs) == 0:
+ raise ValueError("The transformation sequence should contain at least one element.")
self.tfs = tfs
def __call__(self, *x):
@@ -80,7 +81,8 @@ class Compose:
class Choose:
def __init__(self, *tfs):
- assert len(tfs) > 1
+ if len(tfs) < 2:
+ raise ValueError("The transformation sequence should contain at least two elements.")
self.tfs = tfs
def __call__(self, *x):
@@ -94,7 +96,8 @@ class Scale(Transform):
def __init__(self, scale=(0.5, 1.0), prob_apply=1.0):
super(Scale, self).__init__(rand_state=_isseq(scale), prob_apply=prob_apply)
if _isseq(scale):
- assert len(scale) == 2
+ if len(scale) != 2:
+ raise ValueError
self.scale = tuple(scale)
else:
self.scale = float(scale)
@@ -136,7 +139,8 @@ class FlipRotate(Transform):
def __init__(self, direction=None, prob_apply=1.0):
super(FlipRotate, self).__init__(rand_state=(direction is None), prob_apply=prob_apply)
if direction is not None:
- assert direction in self._DIRECTIONS
+ if direction not in self._DIRECTIONS:
+ raise ValueError("Invalid direction")
self.direction = direction
def _transform(self, x, params):
@@ -206,7 +210,8 @@ class Crop(Transform):
_no_bounds = (bounds is None)
super(Crop, self).__init__(rand_state=_no_bounds, prob_apply=prob_apply)
if _no_bounds:
- assert crop_size is not None
+ if crop_size is None:
+ raise TypeError("crop_size should be specified if bounds is set to None.")
else:
if not((_isseq(bounds) and len(bounds)==4) or (isinstance(bounds, str) and bounds in self._INNER_BOUNDS)):
raise ValueError("Invalid bounds")
@@ -237,7 +242,8 @@ class Crop(Transform):
left, top, right, lower = bounds
return x[top:lower, left:right]
else:
- assert self.crop_size <= (h, w)
+ if self.crop_size > (h, w):
+ raise ValueError("Image size is smaller than cropping size.")
ch, cw = self.crop_size
if (ch,cw) == (h,w):
return x
@@ -262,7 +268,8 @@ class CenterCrop(Transform):
ch, cw = self.crop_size
- assert ch<=h and cw<=w
+ if ch>h or cw>w:
+ raise ValueError("Image size is smaller than cropping size.")
offset_up = (h-ch)//2
offset_left = (w-cw)//2
diff --git a/src/utils/metrics.py b/src/utils/metrics.py
index f4668b254334a30d53ff92f7a2a8e280cbb709fe..291151e62ff723f8f7524bbef94a0288e45fb7b9 100644
--- a/src/utils/metrics.py
+++ b/src/utils/metrics.py
@@ -8,11 +8,11 @@ class AverageMeter:
def __init__(self, callback=None, calc_avg=True):
super().__init__()
if callback is not None:
- self.compute = callback
- self.calc_avg = calc_avg
+ self.calculate = callback
+ self.calc_avg = bool(calc_avg)
self.reset()
- def compute(self, *args):
+ def calculate(self, *args):
if len(args) == 1:
return args[0]
else:
@@ -25,58 +25,59 @@ class AverageMeter:
if self.calc_avg:
self.avg = 0
- for attr in filter(lambda a: not a.startswith('__'), dir(self)):
- obj = getattr(self, attr)
- if isinstance(obj, AverageMeter):
- AverageMeter.reset(obj)
-
def update(self, *args, n=1):
- self.val = self.compute(*args)
+ self.val = self.calculate(*args)
self.sum += self.val * n
self.count += n
if self.calc_avg:
self.avg = self.sum / self.count
def __repr__(self):
- return "val: {} avg: {} cnt: {}".format(self.val, self.avg, self.count)
+ if self.calc_avg:
+ return "val: {} avg: {} cnt: {}".format(self.val, self.avg, self.count)
+ else:
+ return "val: {} cnt: {}".format(self.val, self.count)
# These metrics only for numpy arrays
class Metric(AverageMeter):
__name__ = 'Metric'
def __init__(self, n_classes=2, mode='separ', reduction='binary'):
- assert mode in ('accum', 'separ')
- assert reduction in ('mean', 'none', 'binary')
- super().__init__(None, mode!='accum')
+ if mode not in ('accum', 'separ'):
+ raise ValueError("Invalid working mode")
+ if reduction not in ('mean', 'none', 'binary'):
+ raise ValueError("Invalid reduction type")
self._cm = AverageMeter(partial(metrics.confusion_matrix, labels=np.arange(n_classes)), False)
self.mode = mode
if reduction == 'binary' and n_classes != 2:
raise ValueError("Binary reduction only works in 2-class cases.")
self.reduction = reduction
+ super().__init__(None, mode!='accum')
- def _compute(self, cm):
+ def _calculate_metric(self, cm):
raise NotImplementedError
- def compute(self, cm):
+ def calculate(self, pred, true, n=1):
+ self._cm.update(true.ravel(), pred.ravel())
+ if self.mode == 'accum':
+ cm = self._cm.sum
+ elif self.mode == 'separ':
+ cm = self._cm.val
+
if self.reduction == 'none':
# Do not reduce size
- return self._compute(cm)
+ return self._calculate_metric(cm)
elif self.reduction == 'mean':
# Micro averaging
- return self._compute(cm).mean()
- else:
+ return self._calculate_metric(cm).mean()
+ elif self.reduction == 'binary':
# The pos_class be 1
- return self._compute(cm)[1]
+ return self._calculate_metric(cm)[1]
- def update(self, pred, true, n=1):
- self._cm.update(true.ravel(), pred.ravel())
- if self.mode == 'accum':
- cm = self._cm.sum
- elif self.mode == 'separ':
- cm = self._cm.val
- else:
- raise NotImplementedError
- super().update(cm, n=n)
+ def reset(self):
+ super().reset()
+ # Reset the confusion matrix
+ self._cm.reset()
def __repr__(self):
return self.__name__+" "+super().__repr__()
@@ -84,13 +85,13 @@ class Metric(AverageMeter):
class Precision(Metric):
__name__ = 'Prec.'
- def _compute(self, cm):
+ def _calculate_metric(self, cm):
return np.nan_to_num(np.diag(cm)/cm.sum(axis=0))
class Recall(Metric):
__name__ = 'Recall'
- def _compute(self, cm):
+ def _calculate_metric(self, cm):
return np.nan_to_num(np.diag(cm)/cm.sum(axis=1))
@@ -98,13 +99,14 @@ class Accuracy(Metric):
__name__ = 'OA'
def __init__(self, n_classes=2, mode='separ'):
super().__init__(n_classes=n_classes, mode=mode, reduction='none')
- def _compute(self, cm):
+
+ def _calculate_metric(self, cm):
return np.nan_to_num(np.diag(cm).sum()/cm.sum())
class F1Score(Metric):
__name__ = 'F1'
- def _compute(self, cm):
+ def _calculate_metric(self, cm):
prec = np.nan_to_num(np.diag(cm)/cm.sum(axis=0))
recall = np.nan_to_num(np.diag(cm)/cm.sum(axis=1))
return np.nan_to_num(2*(prec*recall) / (prec+recall))
\ No newline at end of file
diff --git a/src/utils/utils.py b/src/utils/utils.py
index eb08225cd052702a012c2c663488af88b52691ec..8e0a7c77880c4f6993f44861ee7b1bdf5249ecc7 100644
--- a/src/utils/utils.py
+++ b/src/utils/utils.py
@@ -19,7 +19,7 @@ def mod_crop(blob, N):
nh = h - h % N
nw = w - w % N
return blob[..., :nh, :nw]
-
+
class HookHelper:
def __init__(self, model, fetch_dict, out_dict, hook_type='forward_out'):
@@ -38,7 +38,8 @@ class HookHelper:
self.out_dict = weakref.WeakValueDictionary(out_dict)
self._handles = []
- assert hook_type in ('forward_in', 'forward_out', 'backward_out')
+ if hook_type not in ('forward_in', 'forward_out', 'backward_out'):
+ raise NotImplementedError("Hook type is not implemented.")
def _proto_hook(x, entry):
# x should be a tensor or a tuple