update train/eval judge

dygraph/infer.py

@@ -99,7 +99,7 @@ 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 = to_variable(im)
-        pred, _ = model(im, mode='test')
+        pred, _ = model(im)
         pred = pred.numpy()
         pred = np.squeeze(pred).astype('uint8')
         for info in im_info[::-1]:

dygraph/models/hrnet.py

@@ -171,7 +171,7 @@ class HRNet(fluid.dygraph.Layer):
         logit = self.conv_last_1(x)
         logit = fluid.layers.resize_bilinear(logit, input_shape)
 
-        if mode == 'train':
+        if self.training:
             if label is None:
                 raise Exception('Label is need during training')
             return self._get_loss(logit, label)

dygraph/models/unet.py

@@ -29,11 +29,11 @@ class UNet(fluid.dygraph.Layer):
         self.ignore_index = ignore_index
         self.EPS = 1e-5
 
-    def forward(self, x, label=None, mode='train'):
+    def forward(self, x, label=None):
         encode_data, short_cuts = self.encode(x)
         decode_data = self.decode(encode_data, short_cuts)
         logit = self.get_logit(decode_data)
-        if mode == 'train':
+        if self.training:
             return self._get_loss(logit, label)
         else:
             score_map = fluid.layers.softmax(logit, axis=1)

dygraph/train.py

@@ -188,12 +188,12 @@ def train(model,
             images = data[0]
             labels = data[1].astype('int64')
             if nranks > 1:
-                loss = model_parallel(images, labels, mode='train')
+                loss = model_parallel(images, labels)
                 loss = model_parallel.scale_loss(loss)
                 loss.backward()
                 model_parallel.apply_collective_grads()
             else:
-                loss = model(images, labels, mode='train')
+                loss = model(images, labels)
                 loss.backward()
             optimizer.minimize(loss)
             model.clear_gradients()

dygraph/val.py

@@ -94,7 +94,7 @@ def evaluate(model,
     timer.start()
     for step, (im, im_info, label) in enumerate(eval_dataset):
         im = to_variable(im)
-        pred, _ = model(im, mode='eval')
+        pred, _ = model(im)
         pred = pred.numpy().astype('float32')
         pred = np.squeeze(pred)
         for info in im_info[::-1]: