Merge branch 'dygraph' of https://github.com/wuyefeilin/PaddleSeg into dygraph

dygraph/datasets/dataset.py

@@ -15,6 +15,8 @@
 import os
 
 import paddle.fluid as fluid
+import numpy as np
+from PIL import Image
 
 
 class Dataset(fluid.io.Dataset):
@@ -85,12 +87,18 @@ class Dataset(fluid.io.Dataset):
 
     def __getitem__(self, idx):
         image_path, grt_path = self.file_list[idx]
-        im, im_info, label = self.transforms(im=image_path, label=grt_path)
         if self.mode == 'train':
+            im, im_info, label = self.transforms(im=image_path, label=grt_path)
             return im, label
         elif self.mode == 'eval':
-            return im, label
+            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
 
     def __len__(self):

dygraph/infer.py

@@ -98,19 +98,20 @@ def infer(model, test_dataset=None, model_dir=None, save_dir='output'):
 
     logging.info("Start to predict...")
     for im, im_info, im_path in tqdm.tqdm(test_dataset):
-        im = im[np.newaxis, ...]
         im = to_variable(im)
         pred, _ = model(im, mode='test')
         pred = pred.numpy()
         pred = np.squeeze(pred).astype('uint8')
-        keys = list(im_info.keys())
-        for k in keys[::-1]:
-            if k == 'shape_before_resize':
-                h, w = im_info[k][0], im_info[k][1]
+        for info in im_info[::-1]:
+            if info[0] == 'resize':
+                h, w = info[1][0], info[1][1]
                 pred = cv2.resize(pred, (w, h), cv2.INTER_NEAREST)
-            elif k == 'shape_before_padding':
-                h, w = im_info[k][0], im_info[k][1]
+            elif info[0] == 'padding':
+                h, w = info[1][0], info[1][1]
                 pred = pred[0:h, 0:w]
+            else:
+                raise Exception("Unexpected info '{}' in im_info".format(
+                    info[0]))
 
         im_file = im_path.replace(test_dataset.data_dir, '')
         if im_file[0] == '/':

dygraph/train.py

@@ -230,10 +230,8 @@ def train(model,
                 mean_iou, mean_acc = evaluate(
                     model,
                     eval_dataset,
-                    places=places,
                     model_dir=current_save_dir,
                     num_classes=num_classes,
-                    batch_size=batch_size,
                     ignore_index=ignore_index,
                     epoch_id=epoch + 1)
                 if mean_iou > best_mean_iou:

dygraph/val.py

@@ -16,8 +16,10 @@ import argparse
 import os
 import math
 
-from paddle.fluid.dygraph.base import to_variable
 import numpy as np
+import tqdm
+import cv2
+from paddle.fluid.dygraph.base import to_variable
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.parallel import ParallelEnv
 from paddle.fluid.io import DataLoader
@@ -61,12 +63,6 @@ def parse_args():
         nargs=2,
         default=[512, 512],
         type=int)
-    parser.add_argument(
-        '--batch_size',
-        dest='batch_size',
-        help='Mini batch size',
-        type=int,
-        default=2)
     parser.add_argument(
         '--model_dir',
         dest='model_dir',
@@ -79,10 +75,8 @@ def parse_args():
 
 def evaluate(model,
              eval_dataset=None,
-             places=None,
              model_dir=None,
              num_classes=None,
-             batch_size=2,
              ignore_index=255,
              epoch_id=None):
     ckpt_path = os.path.join(model_dir, 'model')
@@ -90,15 +84,7 @@ def evaluate(model,
     model.set_dict(para_state_dict)
     model.eval()
 
-    batch_sampler = BatchSampler(
-        eval_dataset, batch_size=batch_size, shuffle=False, drop_last=False)
-    loader = DataLoader(
-        eval_dataset,
-        batch_sampler=batch_sampler,
-        places=places,
-        return_list=True,
-    )
-    total_steps = len(batch_sampler)
+    total_steps = len(eval_dataset)
     conf_mat = ConfusionMatrix(num_classes, streaming=True)
 
     logging.info(
@@ -106,15 +92,26 @@ def evaluate(model,
             len(eval_dataset), total_steps))
     timer = Timer()
     timer.start()
-    for step, data in enumerate(loader):
-        images = data[0]
-        labels = data[1].astype('int64')
-        pred, _ = model(images, mode='eval')
-
-        pred = pred.numpy()
-        labels = labels.numpy()
-        mask = labels != ignore_index
-        conf_mat.calculate(pred=pred, label=labels, ignore=mask)
+    for step, (im, im_info, label) in enumerate(eval_dataset):
+        im = to_variable(im)
+        pred, _ = model(im, mode='eval')
+        pred = pred.numpy().astype('float32')
+        pred = np.squeeze(pred)
+        for info in im_info[::-1]:
+            if info[0] == 'resize':
+                h, w = info[1][0], info[1][1]
+                pred = cv2.resize(pred, (w, h), cv2.INTER_NEAREST)
+            elif info[0] == 'padding':
+                h, w = info[1][0], info[1][1]
+                pred = pred[0:h, 0:w]
+            else:
+                raise Exception("Unexpected info '{}' in im_info".format(
+                    info[0]))
+        pred = pred[np.newaxis, :, :, np.newaxis]
+        pred = pred.astype('int64')
+        mask = label != ignore_index
+
+        conf_mat.calculate(pred=pred, label=label, ignore=mask)
         _, iou = conf_mat.mean_iou()
 
         time_step = timer.elapsed_time()
@@ -163,10 +160,8 @@ def main(args):
         evaluate(
             model,
             eval_dataset,
-            places=places,
             model_dir=args.model_dir,
-            num_classes=eval_dataset.num_classes,
-            batch_size=args.batch_size)
+            num_classes=eval_dataset.num_classes)
 
 
 if __name__ == '__main__':