p04896132
/
mindspore
forked from mindspore-Ecosystem/mindspore
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!22767 bug fix 

Merge pull request !22767 from wukesong/wks_1.3_squ1_1
This commit is contained in:
i-robot 2021-09-02 03:22:10 +00:00 committed by Gitee
parent b29d94eece 2bab9ab1a9
commit c5a1f5b855
3 changed files with 16 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
model_zoo/research/cv/squeezenet1_1/README.md
View File

@ -149,6 +149,13 @@ For more configuration details, please refer the script `config.py`.
Usage: sh scripts/run_standalone_train.sh [DEVICE_ID] [DATASET_PATH] [PRETRAINED_CKPT_PATH](optional)
```
```shell
# standalone training example
sh scripts/run_standalone_train.sh 0 /data/imagenet/train
```
checkpoint can be produced in training process and be saved in the folder ./train/ckpt_squeezenet.
For distributed training, a hccl configuration file with JSON format needs to be created in advance.
Please follow the instructions in the link [hccl_tools](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/utils/hccl_tools).
@ -182,11 +189,9 @@ Usage: sh scripts/run_eval.sh [DEVICE_ID] [DATASET_PATH] [CHECKPOINT_PATH]
```shell
# evaluation example
sh scripts/run_eval.sh 0 ~/data/imagenet/train ckpt_squeezenet/squeezenet_imagenet-200_40036.ckpt
sh scripts/run_eval.sh 0 /data/imagenet/val ./train/ckpt_squeezenet/squeezenet_imagenet-200_40036.ckpt
```
checkpoint can be produced in training process.
### Result
Evaluation result will be stored in the example path, whose folder name is "eval". Under this, you can find result like the followings in log.

9
model_zoo/research/cv/squeezenet1_1/eval.py
View File

@ -25,7 +25,6 @@ from src.CrossEntropySmooth import CrossEntropySmooth
from src.squeezenet import SqueezeNet as squeezenet
from src.dataset import create_dataset_imagenet as create_dataset
from src.config import config
import moxing as mox
local_data_url = '/cache/data'
local_ckpt_url = '/cache/ckpt.ckpt'
@ -33,7 +32,7 @@ local_ckpt_url = '/cache/ckpt.ckpt'
parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--dataset', type=str, default='imagenet', help='Dataset.')
parser.add_argument('--net', type=str, default='squeezenet', help='Model.')
parser.add_argument('--run_cloudbrain', type=ast.literal_eval, default=True,
parser.add_argument('--run_cloudbrain', type=ast.literal_eval, default=False,
help='Whether it is running on CloudBrain platform.')
parser.add_argument('--checkpoint_path', type=str, default=None, help='Checkpoint file path')
parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
@ -60,6 +59,7 @@ if __name__ == '__main__':
# create dataset
if args_opt.run_cloudbrain:
import moxing as mox
mox.file.copy_parallel(args_opt.checkpoint_path, local_ckpt_url)
mox.file.copy_parallel(args_opt.data_url, local_data_url)
dataset = create_dataset(dataset_path=local_data_url,
@ -81,7 +81,10 @@ if __name__ == '__main__':
net = squeezenet(num_classes=config.class_num)
# load checkpoint
param_dict = load_checkpoint(local_ckpt_url)
if args_opt.run_cloudbrain:
param_dict = load_checkpoint(local_ckpt_url)
else:
param_dict = load_checkpoint(args_opt.checkpoint_path)
load_param_into_net(net, param_dict)
net.set_train(False)

4
model_zoo/research/cv/squeezenet1_1/train.py
View File

@ -37,9 +37,9 @@ from src.dataset import create_dataset_imagenet as create_dataset
parser = argparse.ArgumentParser(description='SqueezeNet1_1')
parser.add_argument('--net', type=str, default='squeezenet', help='Model.')
parser.add_argument('--dataset', type=str, default='imagenet', help='Dataset.')
parser.add_argument('--run_cloudbrain', type=ast.literal_eval, default=True,
parser.add_argument('--run_cloudbrain', type=ast.literal_eval, default=False,
help='Whether it is running on CloudBrain platform.')
parser.add_argument('--run_distribute', type=bool, default=True, help='Run distribute')
parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
parser.add_argument('--device_num', type=int, default=1, help='Device num.')
parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')