使用mmlab导出onnx模型：
from mmdeploy.apis import torch2onnx
from mmdeploy.backend.sdk.export_info import export2SDKimg = 'demo.JPEG'
work_dir = './work_dir/onnx/detr'
save_file = './end2end.onnx'
deploy_cfg = 'mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py'
model_cfg = 'mmdetection/configs/detr/detr_r50_8xb2-150e_coco.py'
model_checkpoint = 'checkpoints/detr_r50_8xb2-150e_coco_20221023_153551-436d03e8.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模型过于复杂无法通过netron可视化（强行打开会巨卡），因此通过onnx的python包来解析onnx模型，只需确定模型的输入输出即可：
import onnxmodel = onnx.load("./work_dir/onnx/detr/end2end.onnx")
print(model.graph.input)
print(model.graph.output)
打印如下：
[name: "input"
type {tensor_type {elem_type: 1shape {dim {dim_param: "batch"}dim {dim_value: 3}dim {dim_param: "height"}dim {dim_param: "width"}}}
}
]
[name: "dets"
type {tensor_type {elem_type: 1shape {dim {dim_param: "batch"}dim {dim_param: "num_dets"}dim {dim_value: 5}}}
}
, name: "labels"
type {tensor_type {elem_type: 7shape {dim {dim_param: "batch"}dim {dim_param: "num_dets"}}}
}
]
手动编写onnxruntime推理脚本：
import cv2
import numpy as np
import onnxruntimeclass_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 = (800, 1333)
confidence_threshold = 0.2def filter_box(outputs): #删除置信度小于confidence_threshold的BOXflag = outputs[0][..., 4] > confidence_thresholdboxes = outputs[0][flag] class_ids = outputs[1][flag].reshape(-1, 1) output = np.concatenate((boxes, class_ids), 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 = boxcv2.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 = 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/detr/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] = inputoutputs = onnx_session.run(None, inputs)boxes = filter_box(outputs)draw(image, boxes)cv2.imwrite('result.jpg', image)
使用mmlab的推理接口：
from mmdeploy.apis import inference_modelmodel_cfg = 'mmdetection/configs/detr/detr_r50_8xb2-150e_coco.py'
deploy_cfg = 'mmdeploy/configs/mmdet/detection/detection_onnxruntime_dynamic.py'
img = 'mmdetection/demo/bus.jpg'
backend_files = ['work_dir/onnx/detr/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('mmdetection/demo/demo.jpg')# 创建检测器
detector = Detector(model_path='work_dir/onnx/detr', 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)
