Refactor framework

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
 

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):

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

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()

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: