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import numpy as np
import cv2
import torch
import onnxruntime
import sys
import pathlib
CURRENT_DIR = pathlib.Path(__file__).parent
sys.path.append(str(CURRENT_DIR))
import argparse
from utils import (
letterbox,
non_max_suppression,
scale_coords,
Annotator,
Colors,
)
def pre_process(img):
img = letterbox(img, [640, 640], stride=32, auto=False)[0]
# Convert
img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
img = np.ascontiguousarray(img)
img = img.astype("float32")
img = img / 255.0
img = img[np.newaxis, :]
return img
def post_process(x):
x = list(x)
z = [] # inference output
stride = [8, 16, 32]
for i in range(3):
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
x[i] = (
torch.tensor(x[i])
.view(bs, 3, 85, ny, nx)
.permute(0, 1, 3, 4, 2)
.contiguous()
)
y = x[i].sigmoid()
xy = (y[..., 0:2] * 2.0 - 0.5 + grid[i]) * stride[i]
wh = (y[..., 2:4] * 2) ** 2 * anchor_grid[i]
y = torch.cat((xy, wh, y[..., 4:]), -1)
z.append(y.view(bs, -1, 85))
return (torch.cat(z, 1), x)
def make_parser():
parser = argparse.ArgumentParser("onnxruntime inference sample")
parser.add_argument(
"-m",
"--onnx_model",
type=str,
default="./yolov5s.onnx",
help="input your onnx model.",
)
parser.add_argument(
"-i",
"--image_path",
type=str,
default='./demo.jpg',
help="path to your input image.",
)
parser.add_argument(
"-o",
"--output_path",
type=str,
default='./demo_infer.jpg',
help="path to your output directory.",
)
parser.add_argument(
'--ipu',
action='store_true',
help='flag for ryzen ai'
)
parser.add_argument(
'--provider_config',
default='',
type=str,
help='provider config for ryzen ai'
)
return parser
names = ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush']
if __name__ == '__main__':
args = make_parser().parse_args()
onnx_path = args.onnx_model
if args.ipu:
providers = ["VitisAIExecutionProvider"]
provider_options = [{"config_file": args.provider_config}]
onnx_weight = onnxruntime.InferenceSession(onnx_path, providers=providers, provider_options=provider_options)
else:
onnx_weight = onnxruntime.InferenceSession(onnx_path)
grid = np.load("./grid.npy", allow_pickle=True)
anchor_grid = np.load("./anchor_grid.npy", allow_pickle=True)
path = args.image_path
new_path = args.output_path
conf_thres, iou_thres, classes, agnostic_nms, max_det = 0.25, 0.45, None, False, 1000
img0 = cv2.imread(path)
img = pre_process(img0)
onnx_input = {onnx_weight.get_inputs()[0].name: img.transpose(0, 2, 3, 1)}
onnx_output = onnx_weight.run(None, onnx_input)
onnx_output = [torch.tensor(item).permute(0, 3, 1, 2) for item in onnx_output]
onnx_output = post_process(onnx_output)
pred = non_max_suppression(
onnx_output[0], conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det
)
colors = Colors()
det = pred[0]
im0 = img0.copy()
annotator = Annotator(im0, line_width=2, example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Write results
for *xyxy, conf, cls in reversed(det):
c = int(cls) # integer class
label = f"{names[c]} {conf:.2f}"
annotator.box_label(xyxy, label, color=colors(c, True))
# Stream results
im0 = annotator.result()
cv2.imwrite(new_path, im0)
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