OpenMMlab导出yolox模型并用onnxruntime和tensorrt推理

导出onnx文件

直接使用脚本

python 复制代码
import torch
from mmdet.apis import init_detector, inference_detector


config_file = './configs/yolox/yolox_tiny_8xb8-300e_coco.py'
checkpoint_file = 'yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth'
model = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'
torch.onnx.export(model, (torch.zeros(1, 3, 416, 416),), "yolox.onnx", opset_version=11)

导出的onnx结构如下:

输出是包含多个检测头的输出。若需要合并检测结果,需要修改脚本如下:

python 复制代码
import torch
import cv2
import numpy as np
from mmdet.apis import init_detector, inference_detector


config_file = './configs/yolox/yolox_tiny_8xb8-300e_coco.py'
checkpoint_file = 'yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth'
model = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'


class YOLOX(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.model = init_detector(config_file, checkpoint_file, device='cpu')
        self.class_num = 80
        self.strides = [(8, 8), (16, 16), (32, 32)]
             
    def _meshgrid(self, x, y):
        yy, xx = torch.meshgrid(y, x)
        return xx.reshape(-1), yy.reshape(-1)

    def grid_priors(self, featmap_sizes):
        multi_level_priors = []
        for i in range(len(featmap_sizes)):
            feat_h, feat_w = featmap_sizes[i]
            stride_w, stride_h = self.strides[i]
            shift_x = torch.arange(0, feat_w) * stride_w
            shift_y = torch.arange(0, feat_h) * stride_h
            shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
            stride_w = shift_xx.new_full((shift_xx.shape[0], ), stride_w)
            stride_h = shift_xx.new_full((shift_yy.shape[0], ), stride_h)
            shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h], dim=-1)       
            multi_level_priors.append(shifts)
        return multi_level_priors
    
    def bbox_decode(self, priors, bbox_preds):
        xys = (bbox_preds[..., :2] * priors[:, 2:]) + priors[:, :2]
        whs = bbox_preds[..., 2:].exp() * priors[:, 2:]
        tl_x = (xys[..., 0] - whs[..., 0] / 2)
        tl_y = (xys[..., 1] - whs[..., 1] / 2)
        br_x = (xys[..., 0] + whs[..., 0] / 2)
        br_y = (xys[..., 1] + whs[..., 1] / 2)
        decoded_bboxes = torch.stack([tl_x, tl_y, br_x, br_y], -1)
        return decoded_bboxes
        
    def forward(self, x):
        x = self.model.backbone(x)
        x = self.model.neck(x)
        pred_maps = self.model.bbox_head(x)
        
        cls_scores, bbox_preds, objectnesses = pred_maps       
        featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]      
        mlvl_priors = self.grid_priors(featmap_sizes)
        
        flatten_cls_scores = [cls_score.permute(0, 2, 3, 1).reshape(1, -1, self.class_num) for cls_score in cls_scores]
        flatten_bbox_preds = [bbox_pred.permute(0, 2, 3, 1).reshape(1, -1, 4) for bbox_pred in bbox_preds]
        flatten_objectness = [objectness.permute(0, 2, 3, 1).reshape(1, -1) for objectness in objectnesses]
        flatten_cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid()
        flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
        flatten_objectness = torch.cat(flatten_objectness, dim=1).sigmoid()
        flatten_priors = torch.cat(mlvl_priors)
        flatten_bboxes = self.bbox_decode(flatten_priors, flatten_bbox_preds)
        
        return flatten_bboxes, flatten_objectness, flatten_cls_scores
    
    
model = YOLOX().eval()
input = torch.zeros(1, 3, 416, 416, device='cpu')
torch.onnx.export(model, input, "yolox.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。

python 复制代码
from mmdeploy.apis import torch2onnx
from mmdeploy.backend.sdk.export_info import export2SDK


img = 'bus.jpg'
work_dir = './work_dir/onnx/yolox'
save_file = './end2end.onnx'
deploy_cfg = 'mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py'
model_cfg = 'mmdetection/configs/yolox/yolox_tiny_8xb8-300e_coco.py'
model_checkpoint = 'checkpoints/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.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:

python 复制代码
import cv2
import numpy as np
import onnxruntime


class_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'] #coco80类别     
input_shape = (416, 416)      
score_threshold = 0.2  
nms_threshold = 0.5
confidence_threshold = 0.2   


def nms(boxes, scores, score_threshold, nms_threshold):
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    areas = (y2 - y1 + 1) * (x2 - x1 + 1)
    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 * h
        ious = overlaps / (areas[i] + areas[index[1:]] - overlaps)
        idx = np.where(ious <= nms_threshold)[0]
        index = index[idx + 1]
    return keep


def filter_box(outputs): 
    outputs0, outputs1, outputs2 = outputs
    flag = outputs1 > confidence_threshold
    output0 = outputs0[flag].reshape(-1, 4)
    output1 = outputs1[flag].reshape(-1, 1)
    classes_scores = outputs2[flag].reshape(-1, 80)
    outputs = np.concatenate((output0, output1, classes_scores), axis=1)
     
    boxes = []
    scores = []
    class_ids = []
    for i in range(len(classes_scores)):
        class_id = np.argmax(classes_scores[i])
        outputs[i][4] *= classes_scores[i][class_id]
        outputs[i][5] = class_id
        if outputs[i][4] > score_threshold:
            boxes.append(outputs[i][:6])
            scores.append(outputs[i][4])
            class_ids.append(outputs[i][5])
            
    boxes = np.array(boxes)
    scores = np.array(scores)
    indices = nms(boxes, scores, score_threshold, nms_threshold) 
    output = boxes[indices]
    return output


def letterbox(im, new_shape=(416, 416), color=(114, 114, 114)):
    # Resize and pad image while meeting stride-multiple constraints
    shape = im.shape[:2]  # current shape [height, width]

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
    
    # Compute padding
    new_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:  # resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return im


def scale_boxes(boxes, shape):
    # Rescale boxes (xyxy) from input_shape to shape
    gain = min(input_shape[0] / shape[0], input_shape[1] / shape[1])  # gain  = old / new
    pad = (input_shape[1] - shape[1] * gain) / 2, (input_shape[0] - shape[0] * gain) / 2  # wh padding
    boxes[..., [0, 2]] -= pad[0]  # x padding
    boxes[..., [1, 3]] -= pad[1]  # y padding
    boxes[..., :4] /= gain
    boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2
    boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2
    return boxes


def draw(image, box_data):
    box_data = scale_boxes(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 = box
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 1)
        cv2.putText(image, '{0} {1:.2f}'.format(class_names[cl], score), (top, left), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)


if __name__=="__main__":
    image = cv2.imread('bus.jpg')
    input = letterbox(image, input_shape)
    input = cv2.resize(image, input_shape)
    input = input[:, :, ::-1].transpose(2, 0, 1).astype(dtype=np.float32)  #BGR2RGB和HWC2CHW
    input = np.expand_dims(input, axis=0)
    
    onnx_session = onnxruntime.InferenceSession('yolox.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)

    inputs = {}
    for name in input_name:
        inputs[name] = input
        
    outputs = onnx_session.run(None, inputs)
    
    boxes = filter_box(outputs)
    draw(image, boxes)
    cv2.imwrite('result.jpg', image)

mmdeploy导出的onnx模型使用onnxruntime推理:

python 复制代码
import cv2
import numpy as np
import onnxruntime


class_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'] #coco80类别      
input_shape = (416, 416)      
confidence_threshold = 0.2


def filter_box(outputs): #删除置信度小于confidence_threshold的BOX
    flag = outputs[0][..., 4] > confidence_threshold
    boxes = outputs[0][flag] 
    class_ids = outputs[1][flag].reshape(-1, 1) 
    output = np.concatenate((boxes, class_ids), axis=1)  
    return output


def letterbox(im, new_shape=(416, 416), color=(114, 114, 114)):
    # Resize and pad image while meeting stride-multiple constraints
    shape = im.shape[:2]  # current shape [height, width]

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
    
    # Compute padding
    new_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:  # resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return im


def scale_boxes(input_shape, boxes, shape):
    # Rescale boxes (xyxy) from input_shape to shape
    gain = min(input_shape[0] / shape[0], input_shape[1] / shape[1])  # gain  = old / new
    pad = (input_shape[1] - shape[1] * gain) / 2, (input_shape[0] - shape[0] * gain) / 2  # wh padding

    boxes[..., [0, 2]] -= pad[0]  # x padding
    boxes[..., [1, 3]] -= pad[1]  # y padding
    boxes[..., :4] /= gain
    boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2
    boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2
    return boxes


def 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 = box
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 1)
        cv2.putText(image, '{0} {1:.2f}'.format(class_names[cl], score), (top, left), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 1)


if __name__=="__main__":
    images = cv2.imread('bus.jpg')
    input = letterbox(images, input_shape)
    input = input[:, :, ::-1].transpose(2, 0, 1).astype(dtype=np.float32)  #BGR2RGB和HWC2CHW
    input = np.expand_dims(input, axis=0)
    
    onnx_session = onnxruntime.InferenceSession('../work_dir/onnx/yolox/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)

    inputs = {}
    for name in input_name:
        inputs[name] = input
        
    outputs = onnx_session.run(None, inputs)
    
    boxes = filter_box(outputs)
    draw(images, boxes)
    cv2.imwrite('result.jpg', images)

直接使用mmdeploy的api推理:

python 复制代码
from mmdeploy.apis import inference_model


model_cfg = 'mmdetection/configs/yolox/yolox_tiny_8xb8-300e_coco.py'
deploy_cfg = 'mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py'
img = 'bus.jpg'
backend_files = ['work_dir/onnx/yolox/end2end.onnx']
device = 'cpu'

result = inference_model(model_cfg, deploy_cfg, backend_files, img, device)
print(result)

或者:

python 复制代码
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/yolox', 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:
      continue
  cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0))
cv2.imwrite('result.jpg', 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。

bash 复制代码
./trtexec.exe --onnx=yolox.onnx --saveEngine=yolox.engine --workspace=20480

tensorrt推理

手动导出的模型使用tensorrt推理:

python 复制代码
import cv2
import numpy as np
import tensorrt as trt
import pycuda.autoinit 
import pycuda.driver as cuda  


class_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'] #coco80类别     
input_shape = (416, 416)      
score_threshold = 0.2  
nms_threshold = 0.5
confidence_threshold = 0.2   


def nms(boxes, scores, score_threshold, nms_threshold):
    x1 = boxes[:, 0]
    y1 = boxes[:, 1]
    x2 = boxes[:, 2]
    y2 = boxes[:, 3]
    areas = (y2 - y1 + 1) * (x2 - x1 + 1)
    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 * h
        ious = overlaps / (areas[i] + areas[index[1:]] - overlaps)
        idx = np.where(ious <= nms_threshold)[0]
        index = index[idx + 1]
    return keep


def filter_box(outputs): 
    outputs0, outputs1, outputs2 = outputs
    flag = outputs1 > confidence_threshold
    output0 = outputs0[flag].reshape(-1, 4)
    output1 = outputs1[flag].reshape(-1, 1)
    classes_scores = outputs2[flag].reshape(-1, 80)
    outputs = np.concatenate((output0, output1, classes_scores), axis=1)
     
    boxes = []
    scores = []
    class_ids = []
    for i in range(len(classes_scores)):
        class_id = np.argmax(classes_scores[i])
        outputs[i][4] *= classes_scores[i][class_id]
        outputs[i][5] = class_id
        if outputs[i][4] > score_threshold:
            boxes.append(outputs[i][:6])
            scores.append(outputs[i][4])
            class_ids.append(outputs[i][5])
            
    boxes = np.array(boxes)
    scores = np.array(scores)
    indices = nms(boxes, scores, score_threshold, nms_threshold) 
    output = boxes[indices]
    return output


def letterbox(im, new_shape=(416, 416), color=(114, 114, 114)):
    # Resize and pad image while meeting stride-multiple constraints
    shape = im.shape[:2]  # current shape [height, width]

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
    
    # Compute padding
    new_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:  # resize
        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return im


def scale_boxes(boxes, shape):
    # Rescale boxes (xyxy) from input_shape to shape
    gain = min(input_shape[0] / shape[0], input_shape[1] / shape[1])  # gain  = old / new
    pad = (input_shape[1] - shape[1] * gain) / 2, (input_shape[0] - shape[0] * gain) / 2  # wh padding
    boxes[..., [0, 2]] -= pad[0]  # x padding
    boxes[..., [1, 3]] -= pad[1]  # y padding
    boxes[..., :4] /= gain
    boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1])  # x1, x2
    boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0])  # y1, y2
    return boxes


def draw(image, box_data):
    box_data = scale_boxes(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 = box
        cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 1)
        cv2.putText(image, '{0} {1:.2f}'.format(class_names[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("yolox.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_output0 = cuda.pagelocked_empty(trt.volume(context.get_binding_shape(1)), dtype=np.float32)
    h_output1 = cuda.pagelocked_empty(trt.volume(context.get_binding_shape(2)), dtype=np.float32)
    h_output2 = cuda.pagelocked_empty(trt.volume(context.get_binding_shape(3)), dtype=np.float32)
    d_input = cuda.mem_alloc(h_input.nbytes)
    d_output0 = cuda.mem_alloc(h_output0.nbytes)
    d_output1 = cuda.mem_alloc(h_output1.nbytes)
    d_output2 = cuda.mem_alloc(h_output2.nbytes)
    stream = cuda.Stream()
    
    image = cv2.imread('bus.jpg')
    input = letterbox(image, input_shape)
    input = input[:, :, ::-1].transpose(2, 0, 1).astype(dtype=np.float32)  #BGR2RGB和HWC2CHW
    input = 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_output0), int(d_output1), int(d_output2)], stream_handle=stream.handle)
        cuda.memcpy_dtoh_async(h_output0, d_output0, stream)
        cuda.memcpy_dtoh_async(h_output1, d_output1, stream)
        cuda.memcpy_dtoh_async(h_output2, d_output2, stream)
        stream.synchronize()  
        h_output = []
        h_output.append(h_output2.reshape(1, 3549, 4))
        h_output.append(h_output1.reshape(1, 3549))
        h_output.append(h_output0.reshape(1, 3549, 80))
        boxes = filter_box(h_output)
        draw(image, boxes)
        cv2.imwrite('result.jpg', image)

使用mmdeploy的api推理:

python 复制代码
from mmdeploy.apis import torch2onnx
from mmdeploy.backend.sdk.export_info import export2SDK


img = 'bus.jpg'
work_dir = './work_dir/onnx/yolox'
save_file = './end2end.onnx'
deploy_cfg = 'mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py'
model_cfg = 'mmdetection/configs/yolox/yolox_tiny_8xb8-300e_coco.py'
model_checkpoint = 'checkpoints/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.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)

或者

python 复制代码
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:
      continue
  cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0))
cv2.imwrite('result.jpg', img)
相关推荐
EasyNVR1 小时前
EasyRTC:开启智能硬件与全平台互动新时代
网络·音视频·webrtc·p2p·智能硬件·视频监控
红豆和绿豆6 小时前
如何发起http的请求,在系统中集成
网络·网络协议·http
Wlq04156 小时前
三种安全协议 IPSec & SSL & PGP
网络·安全·ssl
Liu-Eleven7 小时前
lwip和tcp/ip区别
网络·网络协议·tcp/ip
黑客Ash8 小时前
网络安全配置截图
网络·安全·web安全
xianwu54310 小时前
反向代理模块kd
开发语言·网络·数据库·c++·mysql
路由侠内网穿透10 小时前
无公网IP可实现外网访问群晖 WebDAV
网络·网络协议·tcp/ip·docker
垚垚 Securify 前沿站10 小时前
Apache Logic4j 库反序列化漏洞复现与深度剖析
linux·网络·安全·web安全·系统安全·apache
vvilkim11 小时前
TCP/IP协议
网络·网络协议·tcp/ip
SKYDROID云卓小助手12 小时前
无人设备遥控器之视频回传篇
网络·人工智能·嵌入式硬件·目标检测·计算机视觉·音视频