fix device (#5450)

tutorials/mobilenetv3_prod/Step6/README.md

@@ -50,7 +50,7 @@ MobileNetV3 是 2019 年提出的一种基于 NAS 的新的轻量级网络，为
 
 | 模型      | top1/5 acc (参考精度) | top1/5 acc (复现精度) | 下载链接 |
 |:---------:|:------:|:----------:|:----------:|
-| Mo | -/-   | 0.601/0.826   | [预训练模型](https://paddle-model-ecology.bj.bcebos.com/model/mobilenetv3_reprod/mobilenet_v3_small_pretrained.pdparams) \|  [Inference模型(coming soon!)]() \| [日志](https://paddle-model-ecology.bj.bcebos.com/model/mobilenetv3_reprod/train_mobilenet_v3_small.log) |
+| Mo | -/-   | 0.601/0.826   | [预训练模型](https://paddle-model-ecology.bj.bcebos.com/model/mobilenetv3_reprod/mobilenet_v3_small_pretrained.pdparams) \|  [Inference模型](https://paddle-model-ecology.bj.bcebos.com/model/mobilenetv3_reprod/mobilenet_v3_small_infer.tar) \| [日志](https://paddle-model-ecology.bj.bcebos.com/model/mobilenetv3_reprod/train_mobilenet_v3_small.log) |
 
 <a name="3"></a>
 

tutorials/mobilenetv3_prod/Step6/train.py

@@ -19,12 +19,10 @@ import numpy as np
 from reprod_log import ReprodLogger
 
 
-def train_one_epoch(
-        model,
+def train_one_epoch(model,
                     criterion,
                     optimizer,
                     data_loader,
-        device,
                     epoch,
                     print_freq,
                     amp_level=None,
@@ -85,7 +83,7 @@ def train_one_epoch(
         reader_start = time.time()
 
 
-def evaluate(model, criterion, data_loader, device, print_freq=100, amp_level=None):
+def evaluate(model, criterion, data_loader, print_freq=100, amp_level=None):
     model.eval()
     metric_logger = utils.MetricLogger(delimiter="  ")
     header = 'Test:'
@@ -160,7 +158,10 @@ def main(args):
 
     print(args)
 
-    device = paddle.set_device(args.device)
+    try:
+        paddle.set_device(args.device)
+    except:
+        print("device set error, use default device...")
 
     # multi cards
     if paddle.distributed.get_world_size() > 1:
@@ -226,13 +227,13 @@ def main(args):
         if args.amp_level == 'O2':
             model = paddle.amp.decorate(models=model, level='O2')
 
-
     # multi cards
     if paddle.distributed.get_world_size() > 1:
         model = paddle.DataParallel(model)
 
     if args.test_only and paddle.distributed.get_rank() == 0:
-        top1 = evaluate(model, criterion, data_loader_test, device=device, amp_level=args.amp_level)
+        top1 = evaluate(
+            model, criterion, data_loader_test, amp_level=args.amp_level)
         return top1
 
     print("Start training")
@@ -240,11 +241,12 @@ def main(args):
     best_top1 = 0.0
 
     for epoch in range(args.start_epoch, args.epochs):
-        train_one_epoch(model, criterion, optimizer, data_loader, device,
-                        epoch, args.print_freq, args.amp_level, scaler)
+        train_one_epoch(model, criterion, optimizer, data_loader, epoch,
+                        args.print_freq, args.amp_level, scaler)
         lr_scheduler.step()
         if paddle.distributed.get_rank() == 0:
-            top1 = evaluate(model, criterion, data_loader_test, device=device, amp_level=args.amp_level)
+            top1 = evaluate(
+                model, criterion, data_loader_test, amp_level=args.amp_level)
             if args.output_dir:
                 paddle.save(model.state_dict(),
                             os.path.join(args.output_dir,
@@ -285,9 +287,7 @@ def get_args_parser(add_help=True):
         metavar='N',
         help='number of total epochs to run')
     parser.add_argument(
-        '--amp_level',
-        default=None,
-        help='amp level can set to be : O1/O2')
+        '--amp_level', default=None, help='amp level can set to be : O1/O2')
     parser.add_argument(
         '-j',
         '--workers',