[Model] Support New PaddleDetection Models (#959)

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Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

examples/vision/detection/paddledetection/python/README.md

@@ -49,6 +49,14 @@ fastdeploy.vision.detection.PaddleYOLOv5(model_file, params_file, config_file, r
 fastdeploy.vision.detection.PaddleYOLOv6(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
 fastdeploy.vision.detection.PaddleYOLOv7(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
 fastdeploy.vision.detection.RTMDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.CascadeRCNN(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PSSDet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.RetinaNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.PPYOLOESOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.FCOS(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.TTFNet(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.TOOD(model_file, params_file, config_file, runtime_option=None, model_format=ModelFormat.PADDLE)
+fastdeploy.vision.detection.GFL(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

examples/vision/detection/paddledetection/python/infer_cascadercnn.py

@@ -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.CascadeRCNN(
+    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)
+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")

examples/vision/detection/paddledetection/python/infer_fcos.py

@@ -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.FCOS(
+    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)
+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")

examples/vision/detection/paddledetection/python/infer_gfl.py

@@ -0,0 +1,51 @@
+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.GFL(
+    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")

examples/vision/detection/paddledetection/python/infer_ppyoloesod.py

@@ -0,0 +1,59 @@
+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'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")    
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+    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.PPYOLOESOD(
+    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")

examples/vision/detection/paddledetection/python/infer_pssdet.py

@@ -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.PSSDet(
+    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)
+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")

examples/vision/detection/paddledetection/python/infer_retinanet.py

@@ -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.RetinaNet(
+    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)
+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")

examples/vision/detection/paddledetection/python/infer_tood.py

@@ -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.TOOD(
+    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)
+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")

examples/vision/detection/paddledetection/python/infer_ttfnet.py

@@ -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.TTFNet(
+    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)
+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")