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[Model] Support PaddleDetection SSD Model (#630)

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Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
This commit is contained in:
totorolin
2022-11-28 19:13:02 +08:00
committed by GitHub
parent 56c39b5f96
commit 941057888a
7 changed files with 202 additions and 4 deletions
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1
examples/vision/detection/paddledetection/python/README.md
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@@ -40,6 +40,7 @@ fastdeploy.vision.detection.YOLOv3(model_file, params_file, config_file, runtime
fastdeploy.vision.detection.PPYOLO(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
fastdeploy.vision.detection.FasterRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
fastdeploy.vision.detection.MaskRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
fastdeploy.vision.detection.SSD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
```
PaddleDetection模型加载和初始化，其中model_file， params_file为导出的Paddle部署模型格式, config_file为PaddleDetection同时导出的部署配置yaml文件

50
examples/vision/detection/paddledetection/python/infer_ssd.py Normal file
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@@ -0,0 +1,50 @@
import fastdeploy as fd
import cv2
import os
def parse_arguments():
import argparse
import ast
parser = argparse.ArgumentParser()
parser.add_argument(
"--model_dir",
required=True,
help="Path of PaddleDetection model directory")
parser.add_argument(
"--image", required=True, help="Path of test image file.")
parser.add_argument(
"--device",
type=str,
default='cpu',
help="Type of inference device, support 'cpu' or 'gpu'.")
return parser.parse_args()
def build_option(args):
option = fd.RuntimeOption()
if args.device.lower() == "gpu":
option.use_gpu()
return option
args = parse_arguments()
model_file = os.path.join(args.model_dir, "model.pdmodel")
params_file = os.path.join(args.model_dir, "model.pdiparams")
config_file = os.path.join(args.model_dir, "infer_cfg.yml")
# 配置runtime，加载模型
runtime_option = build_option(args)
model = fd.vision.detection.SSD(
model_file, params_file, config_file, runtime_option=runtime_option)
# 预测图片检测结果
im = cv2.imread(args.image)
result = model.predict(im.copy())
print(result)
# 预测结果可视化
vis_im = fd.vision.vis_detection(im, result, score_threshold=0.5)
cv2.imwrite("visualized_result.jpg", vis_im)
print("Visualized result save in ./visualized_result.jpg")