import torch import torch.nn as nn import onnxruntime import numpy as np import argparse from utils import ( LoadImages, non_max_suppression, plot_images, output_to_target, ) import sys import pathlib CURRENT_DIR = pathlib.Path(__file__).parent sys.path.append(str(CURRENT_DIR)) def preprocess(img): img = torch.from_numpy(img) img = img.float() # uint8 to fp16/32 img /= 255 # 0 - 255 to 0.0 - 1.0 return img class DFL(nn.Module): # Integral module of Distribution Focal Loss (DFL) proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391 def __init__(self, c1=16): super().__init__() self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False) x = torch.arange(c1, dtype=torch.float) self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1)) self.c1 = c1 def forward(self, x): b, c, a = x.shape # batch, channels, anchors return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view( b, 4, a ) def dist2bbox(distance, anchor_points, xywh=True, dim=-1): """Transform distance(ltrb) to box(xywh or xyxy).""" lt, rb = torch.split(distance, 2, dim) x1y1 = anchor_points - lt x2y2 = anchor_points + rb if xywh: c_xy = (x1y1 + x2y2) / 2 wh = x2y2 - x1y1 return torch.cat((c_xy, wh), dim) # xywh bbox return torch.cat((x1y1, x2y2), dim) # xyxy bbox def post_process(x): dfl = DFL(16) anchors = torch.tensor( np.load( "./anchors.npy", allow_pickle=True, ) ) strides = torch.tensor( np.load( "./strides.npy", allow_pickle=True, ) ) box, cls = torch.cat([xi.view(x[0].shape[0], 144, -1) for xi in x], 2).split( (16 * 4, 80), 1 ) dbox = dist2bbox(dfl(box), anchors.unsqueeze(0), xywh=True, dim=1) * strides y = torch.cat((dbox, cls.sigmoid()), 1) return y, x def make_parser(): parser = argparse.ArgumentParser("onnxruntime inference sample") parser.add_argument( "-m", "--onnx_model", type=str, default="./yolov8m.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 classnames = ['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'] names = {k: classnames[k] for k in range(80)} imgsz = [640, 640] if __name__ == '__main__': args = make_parser().parse_args() source = args.image_path dataset = LoadImages( source, imgsz=imgsz, stride=32, auto=False, transforms=None, vid_stride=1 ) onnx_weight = args.onnx_model if args.ipu: providers = ["VitisAIExecutionProvider"] provider_options = [{"config_file": args.provider_config}] onnx_model = onnxruntime.InferenceSession(onnx_weight, providers=providers, provider_options=provider_options) else: onnx_model = onnxruntime.InferenceSession(onnx_weight) for batch in dataset: path, im, im0s, vid_cap, s = batch im = preprocess(im) if len(im.shape) == 3: im = im[None] # outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: im.cpu().numpy()}) # outputs = [torch.tensor(item) for item in outputs] outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: im.permute(0, 2, 3, 1).cpu().numpy()}) outputs = [torch.tensor(item).permute(0, 3, 1, 2) for item in outputs] preds = post_process(outputs) preds = non_max_suppression( preds, 0.25, 0.7, agnostic=False, max_det=300, classes=None ) plot_images( im, *output_to_target(preds, max_det=15), source, fname=args.output_path, names=names, )