add pretrained and update launch

example/mobilenetv2_imagenet2012/launch.py

@@ -130,9 +130,11 @@ def main():
     log_files = []
     env = os.environ.copy()
     env['RANK_SIZE'] = str(args.nproc_per_node)
+    cur_path = os.getcwd()
     for rank_id in range(0, args.nproc_per_node):
+        os.chdir(cur_path)
         device_id = visible_devices[rank_id]
-        device_dir = os.path.join(os.getcwd(), 'device{}'.format(rank_id))
+        device_dir = os.path.join(cur_path, 'device{}'.format(rank_id))
         env['RANK_ID'] = str(rank_id)
         env['DEVICE_ID'] = str(device_id)
         if args.nproc_per_node > 1:
@@ -141,11 +143,12 @@ def main():
         if os.path.exists(device_dir):
             shutil.rmtree(device_dir)
         os.mkdir(device_dir)
+        os.chdir(device_dir)
         cmd = [sys.executable, '-u']
         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, env=env)
+        process = subprocess.Popen(cmd, stdout=log_file, stderr=log_file, env=env)
         processes.append(process)
         cmds.append(cmd)
         log_files.append(log_file)

example/mobilenetv2_imagenet2012/train.py

@@ -37,6 +37,7 @@ from mindspore.train.model import Model, ParallelMode
 
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
 from mindspore.train.loss_scale_manager import FixedLossScaleManager
+from mindspore.train.serialization import load_checkpoint, load_param_into_net
 import mindspore.dataset.engine as de
 from mindspore.communication.management import init
 
@@ -46,6 +47,7 @@ de.config.set_seed(1)
 
 parser = argparse.ArgumentParser(description='Image classification')
 parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
+parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
 args_opt = parser.parse_args()
 
 device_id = int(os.getenv('DEVICE_ID'))
@@ -166,6 +168,9 @@ if __name__ == '__main__':
     dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
                              repeat_num=epoch_size, batch_size=config.batch_size)
     step_size = dataset.get_dataset_size()
+    if args_opt.pre_trained:
+        param_dict = load_checkpoint(args_opt.pre_trained)
+        load_param_into_net(net, param_dict)
 
     loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
     lr = Tensor(get_lr(global_step=0, lr_init=0, lr_end=0, lr_max=config.lr,