fix model zoo error for pynative

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

@@ -14,7 +14,7 @@
 # ============================================================================
 
 import argparse
-
+import ast
 
 def launch_parse_args():
 
@@ -43,6 +43,7 @@ def train_parse_args():
         help='run platform, only support CPU, GPU and Ascend')
     train_parser.add_argument('--pretrain_ckpt', type=str, default=None, help='Pretrained checkpoint path \
         for fine tune or incremental learning')
+    train_parser.add_argument('--run_distribute', type=ast.literal_eval, default=True, help='Run distribute')
     train_parser.add_argument('--train_method', type=str, choices=("train", "fine_tune", "incremental_learn"), \
         help="\"fine_tune\"or \"incremental_learn\" if to fine tune the net  after loading the ckpt, \"train\" to \
         train from initialization model")

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

@@ -59,6 +59,7 @@ def set_config(args):
         "save_checkpoint_path": "./checkpoint",
         "platform": args.platform,
         "ccl": "nccl",
+        "run_distribute": args.run_distribute
     })
     config_ascend = ed({
         "num_classes": 1000,

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

@@ -51,11 +51,14 @@ def create_dataset(dataset_path, do_train, config, repeat_num=1):
                                        num_shards=rank_size, shard_id=rank_id)
     elif config.platform == "GPU":
         if do_train:
+            if config.run_distribute:
                 from mindspore.communication.management import get_rank, get_group_size
                 ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
                                            num_shards=get_group_size(), shard_id=get_rank())
             else:
                 ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
+        else:
+            ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
     elif config.platform == "CPU":
         ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
 

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

@@ -22,6 +22,7 @@ from mindspore.communication.management import get_rank, init, get_group_size
 
 from src.models import Monitor
 
+
 def switch_precision(net, data_type, config):
     if config.platform == "Ascend":
         net.to_float(data_type)
@@ -29,13 +30,14 @@ def switch_precision(net, data_type, config):
             if isinstance(cell, nn.Dense):
                 cell.to_float(mstype.float32)
 
-def context_device_init(config):
 
+def context_device_init(config):
     if config.platform == "CPU":
         context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, save_graphs=False)
 
     elif config.platform == "GPU":
         context.set_context(mode=context.GRAPH_MODE, device_target=config.platform, save_graphs=False)
+        if config.run_distribute:
             init("nccl")
             context.set_auto_parallel_context(device_num=get_group_size(),
                                               parallel_mode=ParallelMode.DATA_PARALLEL,
@@ -53,6 +55,7 @@ def context_device_init(config):
     else:
         raise ValueError("Only support CPU, GPU and Ascend.")
 
+
 def set_context(config):
     if config.platform == "CPU":
         context.set_context(mode=context.GRAPH_MODE, device_target=config.platform,
@@ -64,6 +67,7 @@ def set_context(config):
         context.set_context(mode=context.GRAPH_MODE,
                             device_target=config.platform, save_graphs=False)
 
+
 def config_ckpoint(config, lr, step_size):
     cb = None
     if config.platform in ("CPU", "GPU") or config.rank_id == 0:
@@ -75,7 +79,10 @@ def config_ckpoint(config, lr, step_size):
             ckpt_save_dir = config.save_checkpoint_path
 
             if config.platform == "GPU":
+                if config.run_distribute:
                     ckpt_save_dir += "ckpt_" + str(get_rank()) + "/"
+                else:
+                    ckpt_save_dir += "ckpt_" + "/"
 
             ckpt_cb = ModelCheckpoint(prefix="mobilenetV2", directory=ckpt_save_dir, config=config_ck)
             cb += [ckpt_cb]

model_zoo/official/cv/mobilenetv3/src/dataset.py

@@ -21,7 +21,7 @@ import mindspore.dataset.vision.c_transforms as C
 import mindspore.dataset.transforms.c_transforms as C2
 
 
-def create_dataset(dataset_path, do_train, config, device_target, repeat_num=1, batch_size=32):
+def create_dataset(dataset_path, do_train, config, device_target, repeat_num=1, batch_size=32, run_distribute=False):
     """
     create a train or eval dataset
 
@@ -36,11 +36,14 @@ def create_dataset(dataset_path, do_train, config, device_target, repeat_num=1,
     """
     if device_target == "GPU":
         if do_train:
+            if run_distribute:
                 from mindspore.communication.management import get_rank, get_group_size
                 ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True,
                                            num_shards=get_group_size(), shard_id=get_rank())
             else:
                 ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
+        else:
+            ds = de.ImageFolderDataset(dataset_path, num_parallel_workers=8, shuffle=True)
     else:
         raise ValueError("Unsupported device_target.")
 
@@ -56,7 +59,8 @@ def create_dataset(dataset_path, do_train, config, device_target, repeat_num=1,
     resize_op = C.Resize(256)
     center_crop = C.CenterCrop(resize_width)
     rescale_op = C.RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
-    normalize_op = C.Normalize(mean=[0.485*255, 0.456*255, 0.406*255], std=[0.229*255, 0.224*255, 0.225*255])
+    normalize_op = C.Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
+                               std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
     change_swap_op = C.HWC2CHW()
 
     if do_train:

model_zoo/official/cv/mobilenetv3/train.py

@@ -16,6 +16,7 @@
 
 import time
 import argparse
+import ast
 import numpy as np
 
 from mindspore import context
@@ -46,12 +47,14 @@ 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')
 parser.add_argument('--device_target', type=str, default="GPU", help='run device_target')
+parser.add_argument('--run_distribute', type=ast.literal_eval, default=True, help='Run distribute')
 args_opt = parser.parse_args()
 
 if args_opt.device_target == "GPU":
     context.set_context(mode=context.GRAPH_MODE,
                         device_target="GPU",
                         save_graphs=False)
+    if args_opt.run_distribute:
         init()
         context.set_auto_parallel_context(device_num=get_group_size(),
                                           parallel_mode=ParallelMode.DATA_PARALLEL,
@@ -168,7 +171,8 @@ if __name__ == '__main__':
                                  config=config_gpu,
                                  device_target=args_opt.device_target,
                                  repeat_num=1,
-                                 batch_size=config_gpu.batch_size)
+                                 batch_size=config_gpu.batch_size,
+                                 run_distribute=args_opt.run_distribute)
         step_size = dataset.get_dataset_size()
         # resume
         if args_opt.pre_trained:
@@ -191,7 +195,10 @@ if __name__ == '__main__':
                       loss_scale_manager=loss_scale)
 
         cb = [Monitor(lr_init=lr.asnumpy())]
+        if args_opt.run_distribute:
             ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
+        else:
+            ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + "/"
         if config_gpu.save_checkpoint:
             config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
                                          keep_checkpoint_max=config_gpu.keep_checkpoint_max)

model_zoo/official/recommend/wide_and_deep/src/wide_and_deep.py

@@ -399,6 +399,6 @@ class PredictWithSigmoid(nn.Cell):
         self.sigmoid = P.Sigmoid()
 
     def construct(self, batch_ids, batch_wts, labels):
-        logits, _, _, = self.network(batch_ids, batch_wts)
+        logits, _, = self.network(batch_ids, batch_wts)
         pred_probs = self.sigmoid(logits)
         return logits, pred_probs, labels