fix: typo in edgenext yaml & ema only when no overflow (#703)

mindspore-lab · Jul 19, 2023 · 0ca6d08 · 0ca6d08
1 parent a1c5e20
commit 0ca6d08
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Showing 5 changed files with 14 additions and 2 deletions.
diff --git a/configs/edgenext/edgenext_base_ascend.yaml b/configs/edgenext/edgenext_base_ascend.yaml
@@ -3,6 +3,7 @@ mode: 0
 distribute: True
 num_parallel_workers: 8
 val_while_train: True
+seed: 1
 
 # dataset
 dataset: 'imagenet'
@@ -60,4 +61,5 @@ filter_bias_and_bn: True
 momentum: 0.9
 weight_decay: 0.05
 loss_scale: 1024
+drop_overflow_update: True
 use_nesterov: False
diff --git a/configs/edgenext/edgenext_small_ascend.yaml b/configs/edgenext/edgenext_small_ascend.yaml
@@ -3,6 +3,7 @@ mode: 0
 distribute: True
 num_parallel_workers: 8
 val_while_train: True
+seed: 1
 
 # dataset
 dataset: 'imagenet'
@@ -59,4 +60,5 @@ filter_bias_and_bn: True
 momentum: 0.9
 weight_decay: 0.05
 loss_scale: 1024
+drop_overflow_update: True
 use_nesterov: False
diff --git a/configs/edgenext/edgenext_x_small_ascend.yaml b/configs/edgenext/edgenext_x_small_ascend.yaml
@@ -3,6 +3,7 @@ mode: 0
 distribute: True
 num_parallel_workers: 8
 val_while_train: True
+seed: 1
 
 # dataset
 dataset: 'imagenet'
@@ -59,4 +60,5 @@ filter_bias_and_bn: True
 momentum: 0.9
 weight_decay: 0.05
 loss_scale: 1024
+drop_overflow_update: True
 use_nesterov: False
diff --git a/configs/edgenext/edgenext_xx_small_ascend.yaml b/configs/edgenext/edgenext_xx_small_ascend.yaml
@@ -3,6 +3,7 @@ mode: 0
 distribute: True
 num_parallel_workers: 8
 val_while_train: True
+seed: 1
 
 # dataset
 dataset: 'imagenet'
@@ -58,4 +59,5 @@ filter_bias_and_bn: True
 momentum: 0.9
 weight_decay: 0.05
 loss_scale: 1024
+drop_overflow_update: True
 use_nesterov: False
diff --git a/mindcv/utils/train_step.py b/mindcv/utils/train_step.py
@@ -152,6 +152,8 @@ def construct(self, *inputs):
                 # if there is no overflow, do optimize
                 if not overflow:
                     loss = self.gradient_accumulation(loss, grads)
+                    if self.ema:
+                        loss = F.depend(loss, self.ema_update())
             else:
                 # apply grad reducer on grads
                 grads = self.grad_reducer(grads)
@@ -161,14 +163,16 @@ def construct(self, *inputs):
                 # if there is no overflow, do optimize
                 if not overflow:
                     loss = F.depend(loss, self.optimizer(grads))
+                    if self.ema:
+                        loss = F.depend(loss, self.ema_update())
         else:  # scale_sense = loss_scale: Tensor --> TrainOneStepCell.construct
             if self.accumulate_grad:
                 loss = self.gradient_accumulation(loss, grads)
             else:
                 grads = self.grad_reducer(grads)
                 loss = F.depend(loss, self.optimizer(grads))
 
-        if self.ema:
-            loss = F.depend(loss, self.ema_update())
+            if self.ema:
+                loss = F.depend(loss, self.ema_update())
 
         return loss