update train,py for resent

model_zoo/official/cv/resnet/train.py

@@ -177,40 +177,20 @@ if __name__ == '__main__':
                     {'params': no_decayed_params},
                     {'order_params': net.trainable_params()}]
     opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
-    # define loss, model
-    if target == "Ascend":
-        if args_opt.dataset == "imagenet2012":
-            if not config.use_label_smooth:
-                config.label_smooth_factor = 0.0
-            loss = CrossEntropySmooth(sparse=True, reduction="mean",
-                                      smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
-        else:
-            loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-        loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
-        model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
-                      amp_level="O2", keep_batchnorm_fp32=False)
+    if args_opt.dataset == "imagenet2012":
+        if not config.use_label_smooth:
+            config.label_smooth_factor = 0.0
+        loss = CrossEntropySmooth(sparse=True, reduction="mean",
+                                  smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
     else:
-        # GPU and CPU target
-        if args_opt.dataset == "imagenet2012":
-            if not config.use_label_smooth:
-                config.label_smooth_factor = 0.0
-            loss = CrossEntropySmooth(sparse=True, reduction="mean",
-                                      smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
-        else:
-            loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
-
-        if (args_opt.net == "resnet101" or args_opt.net == "resnet50") and \
-            not args_opt.parameter_server and target != "CPU":
-            opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay,
-                           config.loss_scale)
-            loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
-            # Mixed precision
-            model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
-                          amp_level="O2", keep_batchnorm_fp32=False)
-        else:
-            ## fp32 training
-            opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay)
-            model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
+        loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
+    model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
+                  amp_level="O2", keep_batchnorm_fp32=False)
+    if (args_opt.net != "resnet101" and args_opt.net != "resnet50") or \
+        args_opt.parameter_server or target == "CPU":
+        ## fp32 training
+        model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
     if cfg.optimizer == "Thor" and args_opt.dataset == "imagenet2012":
         from src.lr_generator import get_thor_damping
         damping = get_thor_damping(0, config.damping_init, config.damping_decay, 70, step_size)