!16205 lenet test

From: @huchunmei
Reviewed-by: @oacjiewen,@c_34
Signed-off-by: @c_34

model_zoo/official/cv/lenet/default_config.yaml

@@ -6,13 +6,12 @@ checkpoint_url: ""
 data_path: "/cache/data"
 output_path: "/cache/train"
 load_path: "/cache/checkpoint_path"
-checkpoint_path: './checkpoint/'
-checkpoint_file: './checkpoint/checkpoint_lenet-10_1875.ckpt'
 device_target: Ascend
 enable_profiling: False
 
-data_path_local: '/data/hcm/data/MNIST_Data/'
-ckpt_path_local: '/data/hcm/data/ckpt_lenet/checkpoint_lenet-10_1875.ckpt'
+ckpt_path: '/cache/data/'
+ckpt_file: '/cache/data/checkpoint_lenet-10_1875.ckpt'
+
 # ==============================================================================
 # Training options
 num_classes: 10

model_zoo/official/cv/lenet/eval.py

@@ -19,27 +19,20 @@ python eval.py --data_path /YourDataPath --ckpt_path Your.ckpt
 """
 
 import os
-# import sys
-# sys.path.append(os.path.join(os.getcwd(), 'utils'))
-from utils.config import config
-from utils.moxing_adapter import moxing_wrapper
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
+from src.dataset import create_dataset
+from src.lenet import LeNet5
 
 import mindspore.nn as nn
 from mindspore import context
 from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train import Model
 from mindspore.nn.metrics import Accuracy
-from src.dataset import create_dataset
-from src.lenet import LeNet5
 
-if os.path.exists(config.data_path_local):
-    config.data_path = config.data_path_local
-    ckpt_path = config.ckpt_path_local
-else:
-    ckpt_path = os.path.join(config.data_path, 'checkpoint_lenet-10_1875.ckpt')
 
 def modelarts_process():
-    pass
+    config.ckpt_path = config.ckpt_file
 
 @moxing_wrapper(pre_process=modelarts_process)
 def eval_lenet():
@@ -53,7 +46,7 @@ def eval_lenet():
     model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
 
     print("============== Starting Testing ==============")
-    param_dict = load_checkpoint(ckpt_path)
+    param_dict = load_checkpoint(config.ckpt_path)
     load_param_into_net(network, param_dict)
     ds_eval = create_dataset(os.path.join(config.data_path, "test"),
                              config.batch_size,

model_zoo/official/cv/lenet/export.py

@@ -14,23 +14,15 @@
 # ============================================================================
 """export checkpoint file into air, onnx, mindir models"""
 
-import os
-# import sys
-# sys.path.append(os.path.join(os.getcwd(), 'utils'))
-from utils.config import config
-from utils.device_adapter import get_device_id
+from src.model_utils.config import config
+from src.model_utils.device_adapter import get_device_id
+from src.lenet import LeNet5
 
 import numpy as np
 import mindspore
 from mindspore import Tensor, context, load_checkpoint, load_param_into_net, export
-from src.lenet import LeNet5
 
 
-if os.path.exists(config.data_path_local):
-    ckpt_file = config.ckpt_path_local
-else:
-    ckpt_file = os.path.join(config.data_path, 'checkpoint_lenet-10_1875.ckpt')
-
 context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
 if config.device_target == "Ascend":
     context.set_context(device_id=get_device_id())
@@ -40,7 +32,7 @@ if __name__ == "__main__":
     # define fusion network
     network = LeNet5(config.num_classes)
     # load network checkpoint
-    param_dict = load_checkpoint(ckpt_file)
+    param_dict = load_checkpoint(config.ckpt_file)
     load_param_into_net(network, param_dict)
 
     # export network

model_zoo/official/cv/lenet/scripts/run_standalone_eval_ascend.sh

@@ -17,7 +17,6 @@
 # an simple tutorial as follows, more parameters can be setting
 script_self=$(readlink -f "$0")
 self_path=$(dirname "${script_self}")
-# DATA_PATH=$1
-# CKPT_PATH=$2
-# --data_path=$DATA_PATH --device_target="Ascend" --ckpt_path=$CKPT_PATH
-python -s ${self_path}/../eval.py > log_eval.txt 2>&1 &
+DATA_PATH=$1
+CKPT_PATH=$2
+python -s ${self_path}/../eval.py --data_path=$DATA_PATH --device_target="Ascend" --ckpt_path=$CKPT_PATH > log_eval.txt 2>&1 &

model_zoo/official/cv/lenet/scripts/run_standalone_train_ascend.sh

@@ -17,7 +17,6 @@
 # an simple tutorial as follows, more parameters can be setting
 script_self=$(readlink -f "$0")
 self_path=$(dirname "${script_self}")
-# DATA_PATH=$1
-# CKPT_PATH=$2
-# --data_path=$DATA_PATH --device_target="Ascend" --ckpt_path=$CKPT_PATH 
-python -s ${self_path}/../train.py > log.txt 2>&1 &
+DATA_PATH=$1
+CKPT_PATH=$2
+python -s ${self_path}/../train.py --data_path=$DATA_PATH --device_target="Ascend" --ckpt_path=$CKPT_PATH > log.txt 2>&1 &

model_zoo/official/cv/lenet/src/model_utils/config.py

@@ -115,7 +115,7 @@ def get_config():
     """
     parser = argparse.ArgumentParser(description="default name", add_help=False)
     current_dir = os.path.dirname(os.path.abspath(__file__))
-    parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../default_config.yaml"),
+    parser.add_argument("--config_path", type=str, default=os.path.join(current_dir, "../../default_config.yaml"),
                         help="Config file path")
     path_args, _ = parser.parse_known_args()
     default, helper, choices = parse_yaml(path_args.config_path)

model_zoo/official/cv/lenet/train.py

@@ -19,14 +19,11 @@ python train.py --data_path /YourDataPath
 """
 
 import os
-# import sys
-# sys.path.append(os.path.join(os.getcwd(), 'utils'))
-from utils.config import config
-from utils.moxing_adapter import moxing_wrapper
-from utils.device_adapter import get_rank_id
-
+from src.model_utils.config import config
+from src.model_utils.moxing_adapter import moxing_wrapper
 from src.dataset import create_dataset
 from src.lenet import LeNet5
+
 import mindspore.nn as nn
 from mindspore import context
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
@@ -36,12 +33,6 @@ from mindspore.common import set_seed
 
 set_seed(1)
 
-if os.path.exists(config.data_path_local):
-    config.data_path = config.data_path_local
-    config.checkpoint_path = os.path.join(config.checkpoint_path, str(get_rank_id()))
-else:
-    config.checkpoint_path = os.path.join(config.output_path, config.checkpoint_path, str(get_rank_id()))
-
 def modelarts_pre_process():
     pass
 
@@ -59,7 +50,7 @@ def train_lenet():
     time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
     config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
                                  keep_checkpoint_max=config.keep_checkpoint_max)
-    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=config.checkpoint_path, config=config_ck)
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", directory=config.ckpt_path, config=config_ck)
 
     if config.device_target != "Ascend":
         model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})