Before running

• install numactl：

apt-get install numactl # for Ubuntu
yum install numactl # for CentOS

• get R-50.pkl：

mkdir -p /root/.torch/models/
wget https://dl.fbaipublicfiles.com/detectron/ImageNetPretrained/MSRA/R-50.pkl
mv R-50.pkl /root/.torch/models/

• ln -s dataset：

mkdir ./dataset
ln -snf path_to_coco ./dataset/coco

• other requirements：

pip3 install torchvision==0.2.1

# other recommended requirements
apex==0.1+ascend.20220315
torch==1.5.0+ascend.post5.20220315

• source env and build：

source test/env_npu.sh

Running

• To train：

# 1p train full
bash test/train_full_1p.sh --data_path=./dataset/

# 1p train perf
bash test/train_performance_1p.sh --data_path=./dataset/

# 8p train full
bash test/train_full_8p.sh --data_path=./dataset/

# 8p train perf
bash test/train_performance_8p.sh --data_path=./dataset/

• To evaluate:

bash test/train_eval_1p.sh --data_path=./dataset/ --weight_path=./model_0044999.pth  # for example

Result

1p batch_size == 8，8p batch_size == 64

NAME	Steps	BBOX-MAP	SEGM-MAP	FPS
GPU-1p	360000	-	-	8.7
GPU-8p	20000	29.0	25.7	55.1
NPU-1p	400	-	-	4.6
NPU-8p	20000	28.8	25.7	34.8

公网地址说明

代码涉及公网地址参考 public_address_statement.md