update

dygraph/datasets/optic_disc_seg.py

@@ -12,13 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import print_function
-
 import os
-import random
 
 from paddle.fluid.io import Dataset
-import cv2
 
 from utils.download import download_file_and_uncompress
 

dygraph/models/unet.py

@@ -12,10 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import paddle.fluid as fluid
 from paddle.fluid.dygraph import Conv2D, BatchNorm, Pool2D
 
@@ -23,8 +19,8 @@ from paddle.fluid.dygraph import Conv2D, BatchNorm, Pool2D
 class UNet(fluid.dygraph.Layer):
     def __init__(self, num_classes, ignore_index=255):
         super().__init__()
-        self.encode = Encoder()
-        self.decode = Decode()
+        self.encode = UnetEncoder()
+        self.decode = UnetDecode()
         self.get_logit = GetLogit(64, num_classes)
         self.ignore_index = ignore_index
         self.EPS = 1e-5
@@ -61,7 +57,7 @@ class UNet(fluid.dygraph.Layer):
         return avg_loss
 
 
-class Encoder(fluid.dygraph.Layer):
+class UnetEncoder(fluid.dygraph.Layer):
     def __init__(self):
         super().__init__()
         self.double_conv = DoubleConv(3, 64)
@@ -84,7 +80,7 @@ class Encoder(fluid.dygraph.Layer):
         return x, short_cuts
 
 
-class Decode(fluid.dygraph.Layer):
+class UnetDecode(fluid.dygraph.Layer):
     def __init__(self):
         super().__init__()
         self.up1 = Up(512, 256)

dygraph/utils/distributed.py

@@ -41,36 +41,8 @@ class DistributedBatchSampler(BatchSampler):
             batch indices. Default False.
         drop_last(bool): whether drop the last incomplete batch dataset size
             is not divisible by the batch size. Default False
-
-    Examples:
-        .. code-block:: python
-
-            import numpy as np
-
-            from hapi.datasets import MNIST
-            from hapi.distributed import DistributedBatchSampler
-
-            class MnistDataset(MNIST):
-                def __init__(self, mode, return_label=True):
-                    super(MnistDataset, self).__init__(mode=mode)
-                    self.return_label = return_label
-
-                def __getitem__(self, idx):
-                    img = np.reshape(self.images[idx], [1, 28, 28])
-                    if self.return_label:
-                        return img, np.array(self.labels[idx]).astype('int64')
-                    return img,
-
-                def __len__(self):
-                    return len(self.images)
-
-            train_dataset = MnistDataset(mode='train')
-            dist_train_dataloader = DistributedBatchSampler(train_dataset, batch_size=64)
-
-            for data in dist_train_dataloader:
-                # do something
-                break
     """
+
     def __init__(self, dataset, batch_size, shuffle=False, drop_last=False):
         self.dataset = dataset
 
@@ -111,9 +83,8 @@ class DistributedBatchSampler(BatchSampler):
 
             indices = indices[len(indices) - last_batch_size:]
             subsampled_indices.extend(
-                indices[self.local_rank *
-                        last_local_batch_size:(self.local_rank + 1) *
-                        last_local_batch_size])
+                indices[self.local_rank * last_local_batch_size:
+                        (self.local_rank + 1) * last_local_batch_size])
             return subsampled_indices
 
         if self.nranks > 1: