move argmax from host to device

model_zoo/official/cv/deeplabv3/src/deeplabv3.py

@@ -381,6 +381,7 @@ class DeepLabV3(nn.Cell):
         self.concat = P.Concat(axis=2)
         self.expand_dims = P.ExpandDims()
         self.reduce_mean = P.ReduceMean()
+        self.argmax = P.Argmax(axis=1)
         self.sample_common = P.ResizeBilinear((int(feature_shape[2]),
                                                int(feature_shape[3])),
                                               align_corners=True)
@@ -419,6 +420,8 @@ class DeepLabV3(nn.Cell):
                 logits_i = self.expand_dims(logits_i, 2)
                 logits = self.concat((logits, logits_i))
         logits = self.reduce_mean(logits, 2)
+        if not self.training:
+            logits = self.argmax(logits)
         return logits
 
 

model_zoo/official/cv/deeplabv3/src/losses.py

@@ -42,6 +42,8 @@ class OhemLoss(nn.Cell):
         self.loss_weight = 1.0
 
     def construct(self, logits, labels):
+        if not self.training:
+            return 0
         logits = self.transpose(logits, (0, 2, 3, 1))
         logits = self.reshape(logits, (-1, self.num))
         labels = F.cast(labels, mstype.int32)

model_zoo/official/cv/deeplabv3/src/miou_precision.py

@@ -50,10 +50,7 @@ class MiouPrecision(Metric):
             raise ValueError('Need 2 inputs (y_pred, y), but got {}'.format(len(inputs)))
         predict_in = self._convert_data(inputs[0])
         label_in = self._convert_data(inputs[1])
-        if predict_in.shape[1] != self._num_class:
-            raise ValueError('Class number not match, last input data contain {} classes, but current data contain {} '
-                             'classes'.format(self._num_class, predict_in.shape[1]))
-        pred = np.argmax(predict_in, axis=1)
+        pred = predict_in
         label = label_in
         if len(label.flatten()) != len(pred.flatten()):
             print('Skipping: len(gt) = {:d}, len(pred) = {:d}'.format(len(label.flatten()), len(pred.flatten())))