train with ascend, modify api and debug

model_zoo/official/cv/mobilenetv2/eval.py

@@ -32,7 +32,7 @@ if __name__ == '__main__':
 
     backbone_net = MobileNetV2Backbone(platform=args_opt.platform)
     head_net = MobileNetV2Head(input_channel=backbone_net.out_channels, num_classes=config.num_classes)
-    net = mobilenet_v2(feature_net, head_net)
+    net = mobilenet_v2(backbone_net, head_net)
 
     #load the trained checkpoint file to the net for evaluation
     if args_opt.head_ckpt:
@@ -51,7 +51,7 @@ if __name__ == '__main__':
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
     model = Model(net, loss_fn=loss, metrics={'acc'})
 
-    res = model.eval(dataset)
+    res = model.eval(dataset, dataset_sink_mode=False)
     print(f"result:{res}\npretrain_ckpt={args_opt.pretrain_ckpt}")
     if args_opt.head_ckpt:
         print(f"head_ckpt={args_opt.head_ckpt}")

model_zoo/official/cv/mobilenetv2/scripts/run_train.sh

@@ -84,9 +84,9 @@ run_gpu()
 run_cpu()
 {
 
-    if [ ! -d $4 ]
+    if [ ! -d $2 ]
     then
-        echo "error: DATASET_PATH=$4 is not a directory"
+        echo "error: DATASET_PATH=$2 is not a directory"
     exit 1
     fi
 

model_zoo/official/cv/mobilenetv2/src/args.py

@@ -22,7 +22,7 @@ def launch_parse_args():
         that will spawn up multiple distributed processes")
     launch_parser.add_argument('--platform', type=str, default="Ascend", choices=("Ascend", "GPU", "CPU"), \
         help='run platform, only support GPU, CPU and Ascend')
-    launch_parser.add_argument("--nproc_per_node", type=int, default=1, choices=(0, 1, 2, 3, 4, 5, 6, 7), \
+    launch_parser.add_argument("--nproc_per_node", type=int, default=1, choices=(1, 2, 3, 4, 5, 6, 7, 8), \
         help="The number of processes to launch on each node, for D training, this is recommended to be set \
             to the number of D in your system so that each process can be bound to a single D.")
     launch_parser.add_argument("--visible_devices", type=str, default="0,1,2,3,4,5,6,7", help="will use the \
@@ -32,7 +32,7 @@ def launch_parse_args():
             the training script")
 
     launch_args, unknown = launch_parser.parse_known_args()
-    launch_args.train_script_args = unknown
+    launch_args.training_script_args = unknown
     launch_args.training_script_args += ["--platform", launch_args.platform]
     return launch_args
 

model_zoo/official/cv/mobilenetv2/src/launch.py

@@ -46,8 +46,8 @@ def main():
         os.mkdir(device_dir)
         os.chdir(device_dir)
         cmd = [sys.executable, '-u']
-        cmd.append(args.train_script)
-        cmd.extend(args.train_script_args)
+        cmd.append(args.training_script)
+        cmd.extend(args.training_script_args)
         log_file = open('{dir}/log{id}.log'.format(dir=device_dir, id=rank_id), 'w')
         process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
         processes.append(process)

model_zoo/official/cv/mobilenetv2/src/utils.py

@@ -17,8 +17,9 @@ from mindspore import context
 from mindspore import nn
 from mindspore.common import dtype as mstype
 from mindspore.train.model import ParallelMode
+from mindspore.parallel._auto_parallel_context import auto_parallel_context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
-from mindspore.communication.management import get_rank, init
+from mindspore.communication.management import get_rank, init, get_group_size
 
 from src.models import Monitor
 

model_zoo/official/cv/mobilenetv2/train.py

@@ -26,7 +26,7 @@ from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 from mindspore.common import dtype as mstype
 from mindspore.train.model import Model
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
-from mindspore.train.serialization import _exec_save_checkpoint
+from mindspore.train.serialization import save_checkpoint
 from mindspore.common import set_seed
 
 from src.dataset import create_dataset, extract_features
@@ -88,7 +88,7 @@ if __name__ == '__main__':
         opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay)
 
         network = WithLossCell(net, loss)
-        network = TrainOneStepCell(net, opt)
+        network = TrainOneStepCell(network, opt)
         network.set_train()
 
         features_path = args_opt.dataset_path + '_features'
@@ -116,7 +116,7 @@ if __name__ == '__main__':
             .format(epoch + 1, step_size, epoch_mseconds, per_step_mseconds, np.mean(np.array(losses))), \
                 end="")
             if (epoch + 1) % config.save_checkpoint_epochs == 0:
-                _exec_save_checkpoint(network, os.path.join(config.save_checkpoint_path, \
+                save_checkpoint(network, os.path.join(config.save_checkpoint_path, \
                     f"mobilenetv2_head_{epoch+1}.ckpt"))
         print("total cost {:5.4f} s".format(time.time() - start))