Add PaddleClas infer.py (#107)

* Update README.md

* Update README.md

* Update README.md

* Create README.md

* Update README.md

* Update README.md

* Update README.md

* Update README.md

* Add evaluation calculate time and fix some bugs

* Update classification __init__

* Move to ppseg

* Add segmentation doc

* Add PaddleClas infer.py

* Update PaddleClas infer.py

* Delete .infer.py.swp

Co-authored-by: Jason <jiangjiajun@baidu.com>

examples/vision/segmentation/paddleseg/python/infer.py

@@ -0,0 +1,56 @@
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleClas model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+    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()
+        option.set_trt_input_shape("inputs", [1, 3, 224, 224],
+                                   [1, 3, 224, 224], [1, 3, 224, 224])
+    return option
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+model_file = os.path.join(args.model, "inference.pdmodel")
+params_file = os.path.join(args.model, "inference.pdiparams")
+config_file = os.path.join(args.model, "inference_cls.yaml")
+#model = fd.vision.classification.PaddleClasModel(model_file, params_file, config_file, runtime_option=runtime_option)
+model = fd.vision.classification.ResNet50vd(
+    model_file, params_file, config_file, runtime_option=runtime_option)
+
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im, args.topk)
+print(result)