!13591 use same network in the TrainOneStepCell

From: @huangbingjian
Reviewed-by: @ginfung,@zh_qh
Signed-off-by: @zh_qh

model_zoo/official/cv/ctpn/src/network_define.py

@@ -46,8 +46,6 @@ class LossCallBack(Callback):
         self._per_print_times = per_print_times
         self.count = 0
         self.rpn_loss_sum = 0
-        self.rpn_cls_loss_sum = 0
-        self.rpn_reg_loss_sum = 0
         self.rank_id = rank_id
 
         global time_stamp_init, time_stamp_first
@@ -57,14 +55,10 @@ class LossCallBack(Callback):
 
     def step_end(self, run_context):
         cb_params = run_context.original_args()
-        rpn_loss = cb_params.net_outputs[0].asnumpy()
-        rpn_cls_loss = cb_params.net_outputs[1].asnumpy()
-        rpn_reg_loss = cb_params.net_outputs[2].asnumpy()
+        rpn_loss = cb_params.net_outputs.asnumpy()
 
         self.count += 1
         self.rpn_loss_sum += float(rpn_loss)
-        self.rpn_cls_loss_sum += float(rpn_cls_loss)
-        self.rpn_reg_loss_sum += float(rpn_reg_loss)
 
         cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1
 
@@ -72,12 +66,10 @@ class LossCallBack(Callback):
             global time_stamp_first
             time_stamp_current = time.time()
             rpn_loss = self.rpn_loss_sum / self.count
-            rpn_cls_loss = self.rpn_cls_loss_sum / self.count
-            rpn_reg_loss = self.rpn_reg_loss_sum / self.count
             loss_file = open("./loss_{}.log".format(self.rank_id), "a+")
-            loss_file.write("%lu epoch: %s step: %s ,rpn_loss: %.5f, rpn_cls_loss: %.5f, rpn_reg_loss: %.5f"%
+            loss_file.write("%lu epoch: %s step: %s rpn_loss: %.5f"%
                             (time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cur_step_in_epoch,
-                             rpn_loss, rpn_cls_loss, rpn_reg_loss))
+                             rpn_loss))
             loss_file.write("\n")
             loss_file.close()
 
@@ -123,18 +115,16 @@ class TrainOneStepCell(nn.Cell):
 
     Args:
         network (Cell): The training network.
-        network_backbone (Cell): The forward network.
         optimizer (Cell): Optimizer for updating the weights.
         sens (Number): The adjust parameter. Default value is 1.0.
         reduce_flag (bool): The reduce flag. Default value is False.
         mean (bool): Allreduce method. Default value is False.
         degree (int): Device number. Default value is None.
     """
-    def __init__(self, network, network_backbone, optimizer, sens=1.0, reduce_flag=False, mean=True, degree=None):
+    def __init__(self, network, optimizer, sens=1.0, reduce_flag=False, mean=True, degree=None):
         super(TrainOneStepCell, self).__init__(auto_prefix=False)
         self.network = network
         self.network.set_grad()
-        self.backbone = network_backbone
         self.weights = ParameterTuple(network.trainable_params())
         self.optimizer = optimizer
         self.grad = C.GradOperation(get_by_list=True,
@@ -146,8 +136,8 @@ class TrainOneStepCell(nn.Cell):
 
     def construct(self, x, gt_bbox, gt_label, gt_num, img_shape=None):
         weights = self.weights
-        rpn_loss, _, _, rpn_cls_loss, rpn_reg_loss = self.backbone(x, gt_bbox, gt_label, gt_num, img_shape)
+        loss = self.network(x, gt_bbox, gt_label, gt_num, img_shape)
         grads = self.grad(self.network, weights)(x, gt_bbox, gt_label, gt_num, img_shape, self.sens)
         if self.reduce_flag:
             grads = self.grad_reducer(grads)
-        return F.depend(rpn_loss, self.optimizer(grads)), rpn_cls_loss, rpn_reg_loss
+        return F.depend(loss, self.optimizer(grads))

model_zoo/official/cv/ctpn/train.py

@@ -100,10 +100,10 @@ if __name__ == '__main__':
         weight_decay=config.weight_decay, loss_scale=config.loss_scale)
     net_with_loss = WithLossCell(net, loss)
     if args_opt.run_distribute:
-        net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale, reduce_flag=True,
+        net = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale, reduce_flag=True,
                                mean=True, degree=device_num)
     else:
-        net = TrainOneStepCell(net_with_loss, net, opt, sens=config.loss_scale)
+        net = TrainOneStepCell(net_with_loss, opt, sens=config.loss_scale)
 
     time_cb = TimeMonitor(data_size=dataset_size)
     loss_cb = LossCallBack(rank_id=rank)

model_zoo/official/cv/deeptext/src/network_define.py

@@ -69,7 +69,7 @@ class LossCallBack(Callback):
             total_loss = self.loss_sum / self.count
 
             loss_file = open("./loss_{}.log".format(self.rank_id), "a+")
-            loss_file.write("%lu epoch: %s step: %s ,total_loss: %.5f" %
+            loss_file.write("%lu epoch: %s step: %s total_loss: %.5f" %
                             (time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cur_step_in_epoch,
                              total_loss))
             loss_file.write("\n")

model_zoo/official/cv/faster_rcnn/src/network_define.py

@@ -69,7 +69,7 @@ class LossCallBack(Callback):
             total_loss = self.loss_sum / self.count
 
             loss_file = open("./loss_{}.log".format(self.rank_id), "a+")
-            loss_file.write("%lu epoch: %s step: %s ,total_loss: %.5f" %
+            loss_file.write("%lu epoch: %s step: %s total_loss: %.5f" %
                             (time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cur_step_in_epoch,
                              total_loss))
             loss_file.write("\n")

model_zoo/official/cv/maskrcnn/src/network_define.py

@@ -68,7 +68,7 @@ class LossCallBack(Callback):
             total_loss = self.loss_sum / self.count
 
             loss_file = open("./loss_{}.log".format(self.rank_id), "a+")
-            loss_file.write("%lu epoch: %s step: %s ,total_loss: %.5f" %
+            loss_file.write("%lu epoch: %s step: %s total_loss: %.5f" %
                             (time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cur_step_in_epoch,
                              total_loss))
             loss_file.write("\n")

model_zoo/official/cv/maskrcnn_mobilenetv1/src/network_define.py

@@ -97,7 +97,7 @@ class LossCallBack(Callback):
             total_loss = self.loss_sum/self.count
 
             loss_file = open("./loss_{}.log".format(self.rank_id), "a+")
-            loss_file.write("%lu epoch: %s step: %s ,total_loss: %.5f" %
+            loss_file.write("%lu epoch: %s step: %s total_loss: %.5f" %
                             (time_stamp_current - time_stamp_first, cb_params.cur_epoch_num, cur_step_in_epoch,
                              total_loss))
             loss_file.write("\n")