clean stuff + remove redundant arg batch_size (issue #9)

data.py

@@ -136,9 +136,10 @@ def __len__(self):
 
 class ActiveLearningSampler(IterableDataset):
 
-    def __init__(self, dataset, split='training'):
+    def __init__(self, dataset, batch_size=6, split='training'):
         self.dataset = dataset
         self.split = split
+        self.batch_size = batch_size
         self.possibility = {}
         self.min_possibility = {}
 
@@ -163,7 +164,7 @@ def __len__(self):
     def spatially_regular_gen(self):
         # Choosing the least known point as center of a new cloud each time.
 
-        for i in range(self.n_samples * cfg.batch_size):  # num_per_epoch
+        for i in range(self.n_samples * self.batch_size):  # num_per_epoch
             # t0 = time.time()
             if cfg.sampling_type=='active_learning':
                 # Generator loop
@@ -229,8 +230,17 @@ def spatially_regular_gen(self):
 def data_loaders(dir, sampling_method='active_learning', **kwargs):
     if sampling_method == 'active_learning':
         dataset = CloudsDataset(dir / 'train')
-        val_sampler = ActiveLearningSampler(dataset, split='validation')
-        train_sampler = ActiveLearningSampler(dataset, split='training')
+        batch_size = kwargs.get('batch_size', 6)
+        val_sampler = ActiveLearningSampler(
+            dataset,
+            batch_size=batch_size,
+            split='validation'
+        )
+        train_sampler = ActiveLearningSampler(
+            dataset,
+            batch_size=batch_size,
+            split='training'
+        )
         return DataLoader(train_sampler, **kwargs), DataLoader(val_sampler, **kwargs)
 
     if sampling_method == 'naive':

train.py

@@ -68,18 +68,10 @@ def train(args):
 
     print('Computing weights...', end='\t')
     samples_per_class = np.array(cfg.class_weights)
-    # weight = samples_per_class / float(sum(samples_per_class))
-    # class_weights = 1 / (weight + 0.02)
-    # effective = 1.0 - np.power(0.99, samples_per_class)
-    # class_weights = (1 - 0.99) / effective
-    # class_weights = class_weights / (np.sum(class_weights) * num_classes)
-    # class_weights = class_weights / float(sum(class_weights))
-    # weights = torch.tensor(class_weights).float().to(args.gpu)
+
     n_samples = torch.tensor(cfg.class_weights, dtype=torch.float, device=args.gpu)
     ratio_samples = n_samples / n_samples.sum()
     weights = 1 / (ratio_samples + 0.02)
-    #weights = F.softmin(n_samples)
-    # weights = (1/ratio_samples) / (1/ratio_samples).sum()
 
     print('Done.')
     print('Weights:', weights)
@@ -160,14 +152,6 @@ def train(args):
                 } for iou, val_iou in zip(ious, val_ious)
             ]
 
-            # acc_dicts = [
-            #     {
-            #         f'{i:02d}_train_acc':    acc,
-            #         f'{}':  val_acc
-            #     }
-            #     for i, (acc, val_accs) in enumerate(zip(accs, val_accs))
-            # ]
-
             t1 = time.time()
             d = t1 - t0
             # Display results

utils/tools.py

@@ -12,25 +12,11 @@
 
 class Config:
     num_points = 40960  # Number of input points
-    # num_classes = 14  # Number of valid classes
     sub_grid_size = 0.04  # preprocess_parameter
 
-    batch_size = 6
     train_steps = 200  # Number of steps per epochs
     val_steps = 100    # Number of validation steps per epoch
-    #
-    # sub_sampling_ratio = [4, 4, 4, 4, 2]  # sampling ratio of random sampling at each layer
-    # d_out = [16, 64, 128, 256, 512]  # feature dimension
-    #
-    # noise_init = 3.5  # noise initial parameter
-    # max_epoch = 100  # maximum epoch during training
-    # learning_rate = 1e-2  # initial learning rate
-    # lr_decays = {i: 0.95 for i in range(0, 500)}  # decay rate of learning rate
-    #
-    # train_sum_dir = 'train_log'
-    # saving = True
-    # saving_path = None
-    #
+
     sampling_type = 'active_learning'
     class_weights = [1938651, 1242339, 608870, 1699694, 2794560, 195000, 115990, 549838, 531470, 292971, 196633, 59032, 209046, 39321]