update datasets

dygraph/datasets/dataset.py

@@ -25,8 +25,7 @@ class Dataset(fluid.io.Dataset):
     Args:
         data_dir: The dataset directory.
         num_classes: Number of classes.
-        image_set: Which part of dataset to use. Generally, image_set is of ('train', 'val', 'test'). Default: 'train'.
-        mode: Dataset usage. it is one of ('train', 'eva', 'test'). Default: 'train'.
+        mode: which part of dataset to use. it is one of ('train', 'val', 'test'). Default: 'train'.
         train_list: The train dataset file. When image_set is 'train', train_list is necessary.
             The contents of train_list file are as follow:
             image1.jpg ground_truth1.png
@@ -46,7 +45,6 @@ class Dataset(fluid.io.Dataset):
     def __init__(self,
                  data_dir,
                  num_classes,
-                 image_set='train',
                  mode='train',
                  train_list=None,
                  val_list=None,
@@ -59,21 +57,16 @@ class Dataset(fluid.io.Dataset):
         self.mode = mode
         self.num_classes = num_classes
 
-        if image_set.lower() not in ['train', 'val', 'test']:
+        if mode.lower() not in ['train', 'val', 'test']:
             raise Exception(
-                "image_set should be one of ('train', 'val', 'test'), but got {}."
-                .format(image_set))
-
-        if mode.lower() not in ['train', 'eval', 'test']:
-            raise Exception(
-                "mode should be 'train', 'eval' or 'test', but got {}.".format(
+                "mode should be 'train', 'val' or 'test', but got {}.".format(
                     mode))
 
         if self.transforms is None:
             raise Exception("transforms is necessary, but it is None.")
 
         self.data_dir = data_dir
-        if image_set == 'train':
+        if mode == 'train':
             if train_list is None:
                 raise Exception(
                     'When mode is "train", train_list is necessary, but it is None.'
@@ -83,10 +76,10 @@ class Dataset(fluid.io.Dataset):
                     'train_list is not found: {}'.format(train_list))
             else:
                 file_list = train_list
-        elif image_set == 'eval':
+        elif mode == 'val':
             if val_list is None:
                 raise Exception(
-                    'When mode is "eval", val_list is necessary, but it is None.'
+                    'When mode is "val", val_list is necessary, but it is None.'
                 )
             elif not os.path.exists(val_list):
                 raise Exception('val_list is not found: {}'.format(val_list))
@@ -106,9 +99,9 @@ class Dataset(fluid.io.Dataset):
             for line in f:
                 items = line.strip().split(separator)
                 if len(items) != 2:
-                    if mode == 'train' or mode == 'eval':
+                    if mode == 'train' or mode == 'val':
                         raise Exception(
-                            "File list format incorrect! It should be"
+                            "File list format incorrect! In training or evaluation task it should be"
                             " image_name{}label_name\\n".format(separator))
                     image_path = os.path.join(self.data_dir, items[0])
                     grt_path = None
@@ -119,19 +112,19 @@ class Dataset(fluid.io.Dataset):
 
     def __getitem__(self, idx):
         image_path, grt_path = self.file_list[idx]
-        if self.mode == 'train':
-            im, im_info, label = self.transforms(im=image_path, label=grt_path)
-            return im, label
-        elif self.mode == 'eval':
+        if self.mode == 'test':
+            im, im_info, _ = self.transforms(im=image_path)
+            im = im[np.newaxis, ...]
+            return im, im_info, image_path
+        elif self.mode == 'val':
             im, im_info, _ = self.transforms(im=image_path)
             im = im[np.newaxis, ...]
             label = np.asarray(Image.open(grt_path))
             label = label[np.newaxis, np.newaxis, :, :]
             return im, im_info, label
-        if self.mode == 'test':
-            im, im_info, _ = self.transforms(im=image_path)
-            im = im[np.newaxis, ...]
-            return im, im_info, image_path
+        else:
+            im, im_info, label = self.transforms(im=image_path, label=grt_path)
+            return im, label
 
     def __len__(self):
         return len(self.file_list)

dygraph/datasets/optic_disc_seg.py

@@ -25,7 +25,6 @@ class OpticDiscSeg(Dataset):
     def __init__(self,
                  data_dir=None,
                  transforms=None,
-                 image_set='train',
                  mode='train',
                  download=True):
         self.data_dir = data_dir
@@ -34,14 +33,9 @@ class OpticDiscSeg(Dataset):
         self.mode = mode
         self.num_classes = 2
 
-        if image_set.lower() not in ['train', 'val', 'test']:
+        if mode.lower() not in ['train', 'val', 'test']:
             raise Exception(
-                "image_set should be one of ('train', 'val', 'test'), but got {}."
-                .format(image_set))
-
-        if mode.lower() not in ['train', 'eval', 'test']:
-            raise Exception(
-                "mode should be 'train', 'eval' or 'test', but got {}.".format(
+                "mode should be 'train', 'val' or 'test', but got {}.".format(
                     mode))
 
         if self.transforms is None:
@@ -53,9 +47,9 @@ class OpticDiscSeg(Dataset):
             self.data_dir = download_file_and_uncompress(
                 url=URL, savepath=DATA_HOME, extrapath=DATA_HOME)
 
-        if image_set == 'train':
+        if mode == 'train':
             file_list = os.path.join(self.data_dir, 'train_list.txt')
-        elif image_set == 'val':
+        elif mode == 'val':
             file_list = os.path.join(self.data_dir, 'val_list.txt')
         else:
             file_list = os.path.join(self.data_dir, 'test_list.txt')
@@ -64,7 +58,7 @@ class OpticDiscSeg(Dataset):
             for line in f:
                 items = line.strip().split()
                 if len(items) != 2:
-                    if mode == 'train' or mode == 'eval':
+                    if mode == 'train' or mode == 'val':
                         raise Exception(
                             "File list format incorrect! It should be"
                             " image_name label_name\\n")

dygraph/datasets/voc.py

@@ -25,15 +25,13 @@ class PascalVOC(Dataset):
     please run the voc_augment.py in tools.
     Args:
         data_dir: The dataset directory.
-        image_set: Which part of dataset to use. Generally, image_set is of ('train', 'val', 'trainval', 'trainaug). Default: 'train'.
-        mode: Dataset usage. it is one of ('train', 'eva', 'test'). Default: 'train'.
+        mode: Which part of dataset to use.. it is one of ('train', 'val', 'test'). Default: 'train'.
         transforms: Transforms for image.
         download: Whether to download dataset if data_dir is None.
     """
 
     def __init__(self,
                  data_dir=None,
-                 image_set='train',
                  mode='train',
                  transforms=None,
                  download=True):
@@ -43,22 +41,17 @@ class PascalVOC(Dataset):
         self.file_list = list()
         self.num_classes = 21
 
-        if image_set.lower() not in ['train', 'val', 'trainval', 'trainaug']:
+        if mode.lower() not in ['train', 'trainval', 'trainaug', 'val']:
             raise Exception(
-                "image_set should be one of ('train', 'val', 'trainval', 'trainaug'), but got {}."
-                .format(image_set))
-
-        if mode.lower() not in ['train', 'eval', 'test']:
-            raise Exception(
-                "mode should be 'train', 'eval' or 'test', but got {}.".format(
-                    mode))
+                "mode should be one of ('train', 'trainval', 'trainaug', 'val') in PascalVOC dataset, but got {}."
+                .format(mode))
 
         if self.transforms is None:
             raise Exception("transforms is necessary, but it is None.")
 
         if self.data_dir is None:
             if not download:
-                raise Exception("data_file not set and auto download disabled.")
+                raise Exception("data_dir not set and auto download disabled.")
             self.data_dir = download_file_and_uncompress(
                 url=URL,
                 savepath=DATA_HOME,
@@ -68,19 +61,19 @@ class PascalVOC(Dataset):
 
         image_set_dir = os.path.join(self.data_dir, 'VOC2012', 'ImageSets',
                                      'Segmentation')
-        if image_set == 'train':
+        if mode == 'train':
             file_list = os.path.join(image_set_dir, 'train.txt')
-        elif image_set == 'val':
+        elif mode == 'val':
             file_list = os.path.join(image_set_dir, 'val.txt')
-        elif image_set == 'trainval':
+        elif mode == 'trainval':
             file_list = os.path.join(image_set_dir, 'trainval.txt')
-        elif image_set == 'trainaug':
+        elif mode == 'trainaug':
             file_list = os.path.join(image_set_dir, 'train.txt')
             file_list_aug = os.path.join(image_set_dir, 'aug.txt')
 
             if not os.path.exists(file_list_aug):
                 raise Exception(
-                    "When image_set is 'trainaug', Pascal Voc dataset should be augmented, "
+                    "When mode is 'trainaug', Pascal Voc dataset should be augmented, "
                     "Please make sure voc_augment.py has been properly run when using this mode."
                 )
 
@@ -95,10 +88,11 @@ class PascalVOC(Dataset):
                 image_path = os.path.join(img_dir, ''.join([line, '.jpg']))
                 grt_path = os.path.join(grt_dir, ''.join([line, '.png']))
                 self.file_list.append([image_path, grt_path])
-        if image_set == 'trainaug':
+        if mode == 'trainaug':
             with open(file_list_aug, 'r') as f:
                 for line in f:
                     line = line.strip()
                     image_path = os.path.join(img_dir, ''.join([line, '.jpg']))
-                    grt_path = os.path.join(grt_dir, ''.join([line, '.png']))
+                    grt_path = os.path.join(grt_dir_aug, ''.join([line,
+                                                                  '.png']))
                     self.file_list.append([image_path, grt_path])

dygraph/infer.py

@@ -13,20 +13,13 @@
 # limitations under the License.
 
 import argparse
-import os
 
-from paddle.fluid.dygraph.base import to_variable
-import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
-import cv2
-import tqdm
 
 from datasets import DATASETS
 import transforms as T
 from models import MODELS
-import utils
-import utils.logging as logging
 from utils import get_environ_info
 from core import infer
 
@@ -43,7 +36,7 @@ def parse_args():
         type=str,
         default='UNet')
 
-    # params of dataset
+    # params of infer
     parser.add_argument(
         '--dataset',
         dest='dataset',

dygraph/train.py

@@ -153,7 +153,7 @@ def main(args):
             eval_transforms = T.Compose(
                 [T.Resize(args.input_size),
                  T.Normalize()])
-            eval_dataset = dataset(transforms=eval_transforms, mode='eval')
+            eval_dataset = dataset(transforms=eval_transforms, mode='val')
 
         if args.model_name not in MODELS:
             raise Exception(

dygraph/val.py

@@ -87,7 +87,7 @@ def main(args):
 
     with fluid.dygraph.guard(places):
         eval_transforms = T.Compose([T.Resize(args.input_size), T.Normalize()])
-        eval_dataset = dataset(transforms=eval_transforms, mode='eval')
+        eval_dataset = dataset(transforms=eval_transforms, mode='val')
 
         if args.model_name not in MODELS:
             raise Exception(