OpenMMlab导出yolov3模型并用onnxruntime和tensorrt推理

导出onnx文件

直接使用脚本

import torch
from mmdet.apis import init_detector, inference_detectorconfig_file = './configs/yolo/yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py'
checkpoint_file = 'yolov3_mobilenetv2_mstrain-416_300e_coco_20210718_010823-f68a07b3.pth'
model = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'
torch.onnx.export(model, (torch.zeros(1, 3, 416, 416),), "out.onnx", opset_version=11)

导出的onnx结构如下：

输出是包含三个不同层级检测头的输出。若需要合并检测结果，需要修改脚本如下：

import torch
from itertools import repeat
from mmdet.apis import init_detector, inference_detectorconfig_file = './configs/yolo/yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py'
checkpoint_file = 'yolov3_mobilenetv2_mstrain-416_300e_coco_20210718_010823-f68a07b3.pth'
model = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'class YOLOV3(torch.nn.Module):def __init__(self):super().__init__()self.model = init_detector(config_file, checkpoint_file, device='cpu')self.class_num = 80self.base_sizes = [[(116, 90), (156, 198), (373, 326)], [(30, 61), (62, 45), (59, 119)], [(10, 13), (16, 30), (33, 23)]]self.stride = [32, 16, 8]self.strides = [tuple(repeat(x, 2)) for x in self.stride]self.centers = [(x[0] / 2., x[1] / 2.) for x in self.strides]self.base_anchors=self.gen_base_anchors()def gen_base_anchors(self):multi_level_base_anchors = []for i, base_sizes_per_level in enumerate(self.base_sizes):center = self.centers[i]x_center, y_center = centerbase_anchors = []for base_size in base_sizes_per_level:w, h = base_sizebase_anchor = torch.Tensor([x_center - 0.5 * w, y_center - 0.5 * h, x_center + 0.5 * w, y_center + 0.5 * h])base_anchors.append(base_anchor)base_anchors = torch.stack(base_anchors, dim=0)multi_level_base_anchors.append(base_anchors)return multi_level_base_anchorsdef _meshgrid(self, x, y):xx = x.repeat(y.shape[0])yy = y.view(-1, 1).repeat(1, x.shape[0]).view(-1)return xx, yydef grid_priors(self, featmap_sizes):multi_level_anchors = []for i in range(len(featmap_sizes)):base_anchors = self.base_anchors[i]feat_h, feat_w = featmap_sizes[i]stride_w, stride_h = self.strides[i]shift_x = torch.arange(0, feat_w) * stride_wshift_y = torch.arange(0, feat_h) * stride_hshift_xx, shift_yy = self._meshgrid(shift_x, shift_y)shifts = torch.stack([shift_xx, shift_yy, shift_xx, shift_yy], dim=-1)anchors = base_anchors[None, :, :] + shifts[:, None, :]anchors = anchors.view(-1, 4)           multi_level_anchors.append(anchors)return multi_level_anchorsdef decode(self, bboxes, pred_bboxes, stride):xy_centers = (bboxes[..., :2] + bboxes[..., 2:]) * 0.5 + (pred_bboxes[..., :2] - 0.5) * stridewhs = (bboxes[..., 2:] - bboxes[..., :2]) * 0.5 * pred_bboxes[..., 2:].exp()decoded_bboxes = torch.stack((xy_centers[..., 0] - whs[..., 0], xy_centers[..., 1] - whs[..., 1], xy_centers[..., 0] + whs[..., 0], xy_centers[..., 1] + whs[..., 1]), dim=-1)return decoded_bboxesdef forward(self, x):x = self.model.backbone(x)x = self.model.neck(x)pred_maps = self.model.bbox_head(x)flatten_preds = []flatten_strides = []for pred, stride in zip(pred_maps[0], self.stride):pred = pred.permute(0, 2, 3, 1).reshape(1, -1, 5+self.class_num)pred[..., :2] = pred[..., :2].sigmoid()flatten_preds.append(pred)flatten_strides.append(pred.new_tensor(stride).expand(pred.size(1)))flatten_preds = torch.cat(flatten_preds, dim=1)flatten_bbox_preds = flatten_preds[..., :4]  flatten_objectness = flatten_preds[..., 4].sigmoid()flatten_preds[..., 4] = flatten_objectnessflatten_cls_scores = flatten_preds[..., 5:].sigmoid()flatten_preds[..., 5:] = flatten_cls_scoresfeatmap_sizes = [pred_map.shape[-2:] for pred_map in pred_maps[0]]mlvl_anchors = self.grid_priors(featmap_sizes)flatten_anchors = torch.cat(mlvl_anchors)flatten_strides = torch.cat(flatten_strides)flatten_bboxes = self.decode(flatten_anchors, flatten_bbox_preds, flatten_strides.unsqueeze(-1))flatten_preds[..., :4] = flatten_bboxesreturn flatten_predsmodel = YOLOV3().eval()
input = torch.zeros(1, 3, 416, 416, device='cpu')
torch.onnx.export(model, input, "out.onnx", opset_version=11)

导出的onnx结构如下：

安装mmdeploy的话，可以通过下面脚本导出onnx模型。

from mmdeploy.apis import torch2onnx
from mmdeploy.backend.sdk.export_info import export2SDKimg = 'bus.jpg'
work_dir = './work_dir/onnx/yolov3'
save_file = './end2end.onnx'
deploy_cfg = 'mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py'
model_cfg = 'mmdetection/configs/yolo/yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py'
model_checkpoint = 'checkpoints/yolov3_mobilenetv2_mstrain-416_300e_coco_20210718_010823-f68a07b3.pth'
device = 'cpu'# 1. convert model to onnx
torch2onnx(img, work_dir, save_file, deploy_cfg, model_cfg, model_checkpoint, device)# 2. extract pipeline info for sdk use (dump-info)
export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)

onnx模型的结构如下：

onnxruntime推理

手动导出的onnx模型使用onnxruntime推理：

import cv2
import numpy as np
import onnxruntimeCLASSES = ['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'] #coco80类别       
use_letterbox = True
input_shape = (416, 416)      def nms(dets, thresh):x1 = dets[:, 0]y1 = dets[:, 1]x2 = dets[:, 2]y2 = dets[:, 3]areas = (y2 - y1 + 1) * (x2 - x1 + 1)scores = dets[:, 4]keep = []index = scores.argsort()[::-1] while index.size > 0:i = index[0]keep.append(i)x11 = np.maximum(x1[i], x1[index[1:]]) y11 = np.maximum(y1[i], y1[index[1:]])x22 = np.minimum(x2[i], x2[index[1:]])y22 = np.minimum(y2[i], y2[index[1:]])w = np.maximum(0, x22 - x11 + 1)                              h = np.maximum(0, y22 - y11 + 1) overlaps = w * hious = overlaps / (areas[i] + areas[index[1:]] - overlaps)idx = np.where(ious <= thresh)[0]index = index[idx + 1]return keepdef filter_box(org_box, conf_thres, iou_thres): #过滤掉无用的框org_box = np.squeeze(org_box)conf = org_box[..., 4] > conf_thresbox = org_box[conf == True]cls_cinf = box[..., 5:]cls = []for i in range(len(cls_cinf)):cls.append(int(np.argmax(cls_cinf[i])))all_cls = list(set(cls))    output = []for i in range(len(all_cls)):curr_cls = all_cls[i]curr_cls_box = []curr_out_box = []for j in range(len(cls)):if cls[j] == curr_cls:box[j][4] *= cls_cinf[j][curr_cls]box[j][5] = curr_clscurr_cls_box.append(box[j][:6])curr_cls_box = np.array(curr_cls_box)curr_out_box = nms(curr_cls_box,iou_thres)for k in curr_out_box:output.append(curr_cls_box[k])output = np.array(output) #(4, 6)return outputdef letterbox(im, new_shape=(416, 416), color=(114, 114, 114)):# Resize and pad image while meeting stride-multiple constraintsshape = im.shape[:2]  # current shape [height, width]# Scale ratio (new / old)r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])# Compute paddingnew_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))    dw, dh = (new_shape[1] - new_unpad[0])/2, (new_shape[0] - new_unpad[1])/2  # wh padding top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))left, right = int(round(dw - 0.1)), int(round(dw + 0.1))if shape[::-1] != new_unpad:  # resizeim = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add borderreturn imdef scale_boxes(input_shape, boxes, shape):# Rescale boxes (xyxy) from input_shape to shapegain = min(input_shape[0] / shape[0], input_shape[1] / shape[1])  # gain  = old / newpad = (input_shape[1] - shape[1] * gain) / 2, (input_shape[0] - shape[0] * gain) / 2  # wh paddingboxes[..., [0, 2]] -= pad[0]  # x paddingboxes[..., [1, 3]] -= pad[1]  # y paddingboxes[..., :4] /= gainboxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2return boxesdef draw(image, box_data):box_data = scale_boxes(input_shape, box_data, image.shape)boxes = box_data[...,:4].astype(np.int32) scores = box_data[...,4]classes = box_data[...,5].astype(np.int32)for box, score, cl in zip(boxes, scores, classes):top, left, right, bottom = boxprint('class: {}, score: {}, coordinate: [{}, {}, {}, {}]'.format(CLASSES[cl], score, top, left, right, bottom))cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 1)cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score), (top, left), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)if __name__=="__main__":img = cv2.imread('bus.jpg')if use_letterbox:input = letterbox(img, input_shape)else:input = cv2.resize(img, input_shape)input = input[:, :, ::-1].transpose(2, 0, 1).astype(dtype=np.float32)  #BGR2RGB和HWC2CHWinput[0,:] = (input[0,:] - 123.675) / 58.395   input[1,:] = (input[1,:] - 116.28) / 57.12input[2,:] = (input[2,:] - 103.53) / 57.375input = np.expand_dims(input, axis=0)onnx_session = onnxruntime.InferenceSession('yolov3.onnx', providers=['CPUExecutionProvider'])input_name=[]for node in onnx_session.get_inputs():input_name.append(node.name)output_name=[]for node in onnx_session.get_outputs():output_name.append(node.name)input_feed={}for name in input_name:input_feed[name] = inputpred = onnx_session.run(None, input_feed)outbox = filter_box(pred, 0.5, 0.5)draw(img, outbox)cv2.imwrite('res.jpg', img)

mmdeploy导出的onnx模型使用onnxruntime推理：

import cv2
import numpy as np
import onnxruntimeCLASSES = ['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'] #coco80类别       
use_letterbox = True
input_shape = (416, 416)      def filter_box(org_box, conf_thres): #删除置信度小于conf_thres的BOXflag = org_box[0][..., 4] > conf_thresbox = org_box[0][flag == True] cls = org_box[1][flag == True].reshape(-1, 1) output = np.concatenate((box, cls), axis=1)  return outputdef letterbox(im, new_shape=(416, 416), color=(114, 114, 114)):# Resize and pad image while meeting stride-multiple constraintsshape = im.shape[:2]  # current shape [height, width]# Scale ratio (new / old)r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])# Compute paddingnew_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))    dw, dh = (new_shape[1] - new_unpad[0])/2, (new_shape[0] - new_unpad[1])/2  # wh padding top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))left, right = int(round(dw - 0.1)), int(round(dw + 0.1))if shape[::-1] != new_unpad:  # resizeim = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add borderreturn imdef scale_boxes(input_shape, boxes, shape):# Rescale boxes (xyxy) from input_shape to shapegain = min(input_shape[0] / shape[0], input_shape[1] / shape[1])  # gain  = old / newpad = (input_shape[1] - shape[1] * gain) / 2, (input_shape[0] - shape[0] * gain) / 2  # wh paddingboxes[..., [0, 2]] -= pad[0]  # x paddingboxes[..., [1, 3]] -= pad[1]  # y paddingboxes[..., :4] /= gainboxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2return boxesdef draw(image, box_data):box_data = scale_boxes(input_shape, box_data, image.shape)boxes = box_data[...,:4].astype(np.int32) scores = box_data[...,4]classes = box_data[...,5].astype(np.int32)for box, score, cl in zip(boxes, scores, classes):top, left, right, bottom = boxprint('class: {}, score: {}, coordinate: [{}, {}, {}, {}]'.format(CLASSES[cl], score, top, left, right, bottom))cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 1)cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score), (top, left), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)if __name__=="__main__":img = cv2.imread('bus.jpg')if use_letterbox:input = letterbox(img, input_shape)else:input = cv2.resize(img, input_shape)input = input[:, :, ::-1].transpose(2, 0, 1).astype(dtype=np.float32)  #BGR2RGB和HWC2CHWinput[0,:] = (input[0,:] - 123.675) / 58.395   input[1,:] = (input[1,:] - 116.28) / 57.12input[2,:] = (input[2,:] - 103.53) / 57.375input = np.expand_dims(input, axis=0)onnx_session = onnxruntime.InferenceSession('../work_dir/onnx/yolov3/end2end.onnx', providers=['CPUExecutionProvider'])input_name=[]for node in onnx_session.get_inputs():input_name.append(node.name)output_name=[]for node in onnx_session.get_outputs():output_name.append(node.name)input_feed={}for name in input_name:input_feed[name] = inputpred = onnx_session.run(None, input_feed)outbox = filter_box(pred, 0.5)draw(img, outbox)cv2.imwrite('res.jpg', img)

直接使用mmdeploy的api推理：

from mmdeploy.apis import inference_modelmodel_cfg = 'mmdetection/configs/yolo/yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py'
deploy_cfg = 'mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py'
img = 'bus.jpg'
backend_files = ['work_dir/onnx/yolov3/end2end.onnx']
device = 'cpu'result = inference_model(model_cfg, deploy_cfg, backend_files, img, device)
print(result)

或者

from mmdeploy_runtime import Detector
import cv2# 读取图片
img = cv2.imread('bus.jpg')# 创建检测器
detector = Detector(model_path='work_dir/onnx/yolov3', device_name='cpu')# 执行推理
bboxes, labels, _ = detector(img)
# 使用阈值过滤推理结果，并绘制到原图中
indices = [i for i in range(len(bboxes))]
for index, bbox, label_id in zip(indices, bboxes, labels):[left, top, right, bottom], score = bbox[0:4].astype(int),  bbox[4]if score < 0.3:continuecv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0))
cv2.imwrite('output_detection.png', img)

导出engine文件

这里通过trtexec转换onnx文件，LZ的版本是TensorRT-8.2.1.8。

./trtexec.exe --onnx=yolov3.onnx --saveEngine=yolov3.engine --workspace=20480

tensorrt推理

手动导出的模型使用tensorrt推理：

import cv2
import numpy as np
import tensorrt as trt
import pycuda.autoinit 
import pycuda.driver as cuda  CLASSES = ['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'] #coco80类别       
use_letterbox = True
input_shape = (416, 416)      def nms(dets, thresh):x1 = dets[:, 0]y1 = dets[:, 1]x2 = dets[:, 2]y2 = dets[:, 3]areas = (y2 - y1 + 1) * (x2 - x1 + 1)scores = dets[:, 4]keep = []index = scores.argsort()[::-1] while index.size > 0:i = index[0]keep.append(i)x11 = np.maximum(x1[i], x1[index[1:]]) y11 = np.maximum(y1[i], y1[index[1:]])x22 = np.minimum(x2[i], x2[index[1:]])y22 = np.minimum(y2[i], y2[index[1:]])w = np.maximum(0, x22 - x11 + 1)                              h = np.maximum(0, y22 - y11 + 1) overlaps = w * hious = overlaps / (areas[i] + areas[index[1:]] - overlaps)idx = np.where(ious <= thresh)[0]index = index[idx + 1]return keepdef filter_box(org_box, conf_thres, iou_thres): #过滤掉无用的框org_box = np.squeeze(org_box)conf = org_box[..., 4] > conf_thresbox = org_box[conf == True]cls_cinf = box[..., 5:]cls = []for i in range(len(cls_cinf)):cls.append(int(np.argmax(cls_cinf[i])))all_cls = list(set(cls))    output = []for i in range(len(all_cls)):curr_cls = all_cls[i]curr_cls_box = []curr_out_box = []for j in range(len(cls)):if cls[j] == curr_cls:box[j][4] *= cls_cinf[j][curr_cls]box[j][5] = curr_clscurr_cls_box.append(box[j][:6])curr_cls_box = np.array(curr_cls_box)curr_out_box = nms(curr_cls_box,iou_thres)for k in curr_out_box:output.append(curr_cls_box[k])output = np.array(output) #(4, 6)return outputdef letterbox(im, new_shape=(416, 416), color=(114, 114, 114)):# Resize and pad image while meeting stride-multiple constraintsshape = im.shape[:2]  # current shape [height, width]# Scale ratio (new / old)r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])# Compute paddingnew_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))    dw, dh = (new_shape[1] - new_unpad[0])/2, (new_shape[0] - new_unpad[1])/2  # wh padding top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))left, right = int(round(dw - 0.1)), int(round(dw + 0.1))if shape[::-1] != new_unpad:  # resizeim = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add borderreturn imdef scale_boxes(input_shape, boxes, shape):# Rescale boxes (xyxy) from input_shape to shapegain = min(input_shape[0] / shape[0], input_shape[1] / shape[1])  # gain  = old / newpad = (input_shape[1] - shape[1] * gain) / 2, (input_shape[0] - shape[0] * gain) / 2  # wh paddingboxes[..., [0, 2]] -= pad[0]  # x paddingboxes[..., [1, 3]] -= pad[1]  # y paddingboxes[..., :4] /= gainboxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2return boxesdef draw(image, box_data):box_data = scale_boxes(input_shape, box_data, image.shape)boxes = box_data[...,:4].astype(np.int32) scores = box_data[...,4]classes = box_data[...,5].astype(np.int32)for box, score, cl in zip(boxes, scores, classes):top, left, right, bottom = boxprint('class: {}, score: {}, coordinate: [{}, {}, {}, {}]'.format(CLASSES[cl], score, top, left, right, bottom))cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 1)cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score), (top, left), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)if __name__=="__main__":logger = trt.Logger(trt.Logger.WARNING)with open("yolov3.engine", "rb") as f, trt.Runtime(logger) as runtime:engine = runtime.deserialize_cuda_engine(f.read())context = engine.create_execution_context()h_input = cuda.pagelocked_empty(trt.volume(context.get_binding_shape(0)), dtype=np.float32)h_output = cuda.pagelocked_empty(trt.volume(context.get_binding_shape(1)), dtype=np.float32)d_input = cuda.mem_alloc(h_input.nbytes)d_output = cuda.mem_alloc(h_output.nbytes)stream = cuda.Stream()img = cv2.imread('bus.jpg')if use_letterbox:input = letterbox(img, input_shape)else:input = cv2.resize(img, input_shape)input = input[:, :, ::-1].transpose(2, 0, 1).astype(dtype=np.float32)  #BGR2RGB和HWC2CHWinput[0,:] = (input[0,:] - 123.675) / 58.395   input[1,:] = (input[1,:] - 116.28) / 57.12input[2,:] = (input[2,:] - 103.53) / 57.375input = np.expand_dims(input, axis=0) np.copyto(h_input, input.ravel())with engine.create_execution_context() as context:cuda.memcpy_htod_async(d_input, h_input, stream)context.execute_async_v2(bindings=[int(d_input), int(d_output)], stream_handle=stream.handle)cuda.memcpy_dtoh_async(h_output, d_output, stream)stream.synchronize()  pred = h_output.reshape(1, 10647, 85) outbox = filter_box(pred, 0.5, 0.5)draw(img, outbox)cv2.imwrite('res.jpg', img)

使用mmdeploy的api推理：

from mmdeploy.apis import inference_modelmodel_cfg ='mmdetection/configs/yolo/yolov3_mobilenetv2_8xb24-ms-416-300e_coco.py'
deploy_cfg = 'mmdeploy/configs/mmdet/detection/detection_tensorrt_static-320x320.py'
img = 'bus.jpg'
backend_files = ['work_dir/trt/yolov3/end2end.engine']
device = 'cuda'result = inference_model(model_cfg, deploy_cfg, backend_files, img, device)
print(result)

或者

from mmdeploy_runtime import Detector
import cv2# 读取图片
img = cv2.imread('bus.jpg')# 创建检测器
detector = Detector(model_path='work_dir/trt/yolox', device_name='cuda')# 执行推理
bboxes, labels, _ = detector(img)
# 使用阈值过滤推理结果，并绘制到原图中
indices = [i for i in range(len(bboxes))]
for index, bbox, label_id in zip(indices, bboxes, labels):[left, top, right, bottom], score = bbox[0:4].astype(int),  bbox[4]if score < 0.3:continuecv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0))
cv2.imwrite('output_detection.png', img)