diff --git a/src/core/data.py b/src/core/data.py
index 13a2ef4b30f0aa9590c0b7701ad56772eb467849..e5e7032e81f76313c3bf972d6de28f72923dbcce 100644
--- a/src/core/data.py
+++ b/src/core/data.py
@@ -53,16 +53,16 @@ class DatasetBase(data.Dataset, metaclass=ABCMeta):
raise FileNotFoundError
# phase stands for the working mode,
# 'train' for training and 'eval' for validating or testing.
- if phase not in ('train', 'eval'):
- raise ValueError("Invalid phase")
+ # 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).
- if subset not in ('train', 'val', 'test'):
- raise ValueError("Invalid subset")
+ # if subset not in ('train', 'val', 'test'):
+ # raise ValueError("Invalid subset")
self.phase = phase
self.transforms = transforms
- self.repeats = int(repeats)
+ self.repeats = repeats
# Use 'train' subset during training.
self.subset = 'train' if self.phase == 'train' else subset
diff --git a/src/data/augmentations.py b/src/data/augmentations.py
index 826182944dea5bc6dc6d4a7cc48d151269177c46..45b697a451c5519bb030bbf9a06829ad5f6c3617 100644
--- a/src/data/augmentations.py
+++ b/src/data/augmentations.py
@@ -31,8 +31,8 @@ def _isseq(x): return isinstance(x, (tuple, list))
class Transform:
def __init__(self, rand_state=False, prob_apply=1.0):
- self._rand_state = bool(rand_state)
- self.prob_apply = float(prob_apply)
+ self._rand_state = rand_state
+ self.prob_apply = prob_apply
def _transform(self, x, params):
raise NotImplementedError
@@ -100,7 +100,7 @@ class Scale(Transform):
raise ValueError
self.scale = tuple(scale)
else:
- self.scale = float(scale)
+ self.scale = scale
def _transform(self, x, params):
if self._rand_state:
@@ -138,10 +138,7 @@ class FlipRotate(Transform):
_DIRECTIONS = ('ud', 'lr', '90', '180', '270')
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:
- if direction not in self._DIRECTIONS:
- raise ValueError("Invalid direction")
- self.direction = direction
+ self.direction = direction
def _transform(self, x, params):
if self._rand_state:
@@ -212,9 +209,6 @@ class Crop(Transform):
if _no_bounds:
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")
self.bounds = bounds
self.crop_size = crop_size if _isseq(crop_size) else (crop_size, crop_size)
@@ -287,12 +281,12 @@ class Shift(Transform):
if _isseq(xshift):
self.xshift = tuple(xshift)
else:
- self.xshift = float(xshift)
+ self.xshift = xshift
if _isseq(yshift):
self.yshift = tuple(yshift)
else:
- self.yshift = float(yshift)
+ self.yshift = yshift
self.circular = circular
@@ -368,12 +362,12 @@ class ContrastBrightScale(_ValueTransform):
if _isseq(alpha):
self.alpha = tuple(alpha)
else:
- self.alpha = float(alpha)
+ self.alpha = alpha
if _isseq(beta):
self.beta = tuple(beta)
else:
- self.beta = float(beta)
+ self.beta = beta
@_ValueTransform.keep_range
def _transform(self, x, params):
@@ -406,8 +400,8 @@ class BrightnessScale(ContrastBrightScale):
class AddGaussNoise(_ValueTransform):
def __init__(self, mu=0.0, sigma=0.1, prob_apply=1.0, limit=(0, 255)):
super().__init__(True, prob_apply, limit)
- self.mu = float(mu)
- self.sigma = float(sigma)
+ self.mu = mu
+ self.sigma = sigma
@_ValueTransform.keep_range
def _transform(self, x, params):
diff --git a/src/impl/trainers/cd_trainer.py b/src/impl/trainers/cd_trainer.py
index e0710868f9b7529f3c8b56aa67e6aa609fa5273f..85a885432ae21e957c6203dbae5bbad5c785f63e 100644
--- a/src/impl/trainers/cd_trainer.py
+++ b/src/impl/trainers/cd_trainer.py
@@ -58,7 +58,7 @@ class CDTrainer(Trainer):
self.out_dir = self.ctx['out_dir']
self.save = (self.ctx['save_on'] or self.out_dir) and not self.debug
- self.val_iters = float(self.ctx['val_iters'])
+ self.val_iters = self.ctx['val_iters']
def init_learning_rate(self):
# Set learning rate adjustment strategy
diff --git a/src/utils/metrics.py b/src/utils/metrics.py
index 291151e62ff723f8f7524bbef94a0288e45fb7b9..13a9baae4602caf5830838250935d54a100c1b30 100644
--- a/src/utils/metrics.py
+++ b/src/utils/metrics.py
@@ -9,7 +9,7 @@ class AverageMeter:
super().__init__()
if callback is not None:
self.calculate = callback
- self.calc_avg = bool(calc_avg)
+ self.calc_avg = calc_avg
self.reset()
def calculate(self, *args):
@@ -43,10 +43,6 @@ class AverageMeter:
class Metric(AverageMeter):
__name__ = 'Metric'
def __init__(self, n_classes=2, mode='separ', reduction='binary'):
- 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:
@@ -63,6 +59,8 @@ class Metric(AverageMeter):
cm = self._cm.sum
elif self.mode == 'separ':
cm = self._cm.val
+ else:
+ raise ValueError("Invalid working mode")
if self.reduction == 'none':
# Do not reduce size
@@ -73,6 +71,8 @@ class Metric(AverageMeter):
elif self.reduction == 'binary':
# The pos_class be 1
return self._calculate_metric(cm)[1]
+ else:
+ raise ValueError("Invalid reduction type")
def reset(self):
super().reset()
diff --git a/src/utils/utils.py b/src/utils/utils.py
index 6bdadb11b67d6030c3da6338ed023c1f6ec89f3b..f6afd01a1a90562d27f4abe39be155fb4e468f7f 100644
--- a/src/utils/utils.py
+++ b/src/utils/utils.py
@@ -55,9 +55,6 @@ class HookHelper:
self.fetch_dict = fetch_dict
self.out_dict = out_dict
self._handles = []
-
- if hook_type not in ('forward_in', 'forward_out', 'backward'):
- raise NotImplementedError("Hook type is not implemented.")
self.hook_type = hook_type
def __enter__(self):
@@ -106,7 +103,7 @@ class HookHelper:
)
)
else:
- raise NotImplementedError
+ raise NotImplementedError("Hook type is not implemented.")
def __exit__(self, exc_type, exc_val, ext_tb):
for handle in self._handles: