Add YOLOv5-cls Model (#335)

* add yolov5cls

* fixed bugs

* fixed bugs

* fixed preprocess bug

* add yolov5cls readme

* deal with comments

* Add YOLOv5Cls Note

* add yolov5cls test

Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
WJJ1995
2022-10-12 15:57:26 +08:00
committed by GitHub
parent 945e197bd1
commit b557dbc2d8
19 changed files with 719 additions and 4 deletions

View File

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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 YOLOv5Cls 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("images", [1, 3, 224, 224])
return option
args = parse_arguments()
# 配置runtime加载模型
runtime_option = build_option(args)
model = fd.vision.classification.YOLOv5Cls(
args.model, runtime_option=runtime_option)
# 预测图片分类结果
im = cv2.imread(args.image)
result = model.predict(im.copy(), args.topk)
print(result)