def inference_onnx(src, cfg):
predictor = DetPredictorTRT(cfg)
if isinstance(src, (str, list)): # 如果是list，则认为是图片预测
for result in predictor(src):
vis_all_opencv(*result, class_names=get_classes('coco'), score_thr=0.5)
if isinstance(src, int):
vis_cam(src, predictor, class_names=get_classes('coco'), score_thr=0.2)
load_device='cuda')
if task == 'load': # 已有数据集评估文件，重新载入进行评估
eval_dataset_det(
cfg_path=cfg_path,
load_from='/home/ubuntu/mytrain/ssd_vgg_voc/epoch_11.pth',
load_device='cuda',
result_file=
'/home/ubuntu/mytrain/ssd_vgg_voc/20190928_084133_eval_result.pkl')
if task == 'test': # 测试单张图或多张图的结果： cpu上0.649 sec， gpu上0.388 sec
img = cv2.imread('/home/ubuntu/MyDatasets/misc/test13.jpg')
predictor = DetPredictor(
cfg_path,
load_from=
'/home/ubuntu/mytrain/ssd_vgg_voc/old weights/epoch_61.pth',
load_device='cpu')
for results in predictor([img]):
vis_all_pyplot(*results,
class_names=get_classes('voc'),
score_thr=0.5)
if task == 'video': # 测试视频预测结果：注意方法稍有不同，vis_cam需要传入一个predictor
src = 0 # src可以等于int(cam_id), str(video path), list(img_list)
predictor = DetPredictor(
cfg_path,
load_from=
'/home/ubuntu/mytrain/ssd_vgg_voc/old weights/epoch_61.pth',
load_device='cpu')
vis_cam(src, predictor, class_names=get_classes('voc'), score_thr=0.2)
if task == 'integrate':
labels = get_classes('imagenet')
model_path = '/home/ubuntu/mytrain/onnx_to_trt/serialized.engine'
input_size = (224, 224) # (w, h)
predictor = ClsTRTPredictor(model_path, input_size, labels)
img = cv2.imread(cfg.imgs_path[0])
# 也可以先打开所有img
for result in predictor([img]):
cv2.imshow('result', result[0])
if task == 'cam':
labels = get_classes('imagenet')
model_path = '/home/ubuntu/mytrain/onnx_to_trt/serialized.engine'
input_size = (224, 224) # (w, h)
predictor = ClsTRTPredictor(model_path, input_size, labels)
vis_cam(0, predictor, labels, resolution=(800, 800))
if task == 'infe_pt': # 用pytorch推理，对比时间消耗
from utils.evaluation import ClsPredictor
labels = get_classes('imagenet')
cfg_path = 'cfg_classifier_resnet50_imagenet.py'
img_id = 1
gt_label = cfg.gt_labels[img_id]
img = cv2.imread(cfg.imgs_path[img_id])
predictor = ClsPredictor(cfg_path, load_from=None, load_device='cuda')
pred, score = list(predictor([img]))[0]
pred = labels[pred]
print('pred: %s [%.4f], gt: %s' % (pred, score, gt_label))
"""
比对结果：只考虑计算模型计算时间，其他图片预处理等时间都不考虑，即trt的do_inference()，和pytorch的model forward()
1. tensorRT inference时间: 0.061s
cfg_path=cfg_path,
load_from='/home/ubuntu/mytrain/ssd_vgg_widerface/epoch_9.pth',
load_device='cuda',
result_file=
'/home/ubuntu/mytrain/ssd_vgg_voc/20190928_084133_eval_result.pkl')
if task == 'test': # 测试单张图或多张图的结果
img = cv2.imread('/home/ubuntu/MyDatasets/misc/26.jpg')
predictor = DetPredictor(
cfg_path,
load_from='/home/ubuntu/mytrain/ssd_vgg_widerface/epoch_9.pth',
load_device='cpu')
for results in predictor([img]):
vis_all_opencv(*results,
class_names=get_classes('widerface'),
score_thr=0.25,
show=['img', 'bbox'])
# 针对widerface的训练结果模型，对小脸比较有效，但对大脸效果很差，因为数据集里边大部分都是小脸，大脸数据太少。
# 所以靠近摄像头反而检不出来，远离摄像头就能检出来。
if task == 'video':
src = 0 # src可以等于int(cam_id), str(video path), list(img_list)
predictor = DetPredictor(
cfg_path,
load_from='/home/ubuntu/mytrain/ssd_vgg_widerface/epoch_9.pth',
load_device='cuda')
vis_cam(src,
predictor,
class_names=get_classes('widerface'),
score_thr=0.2)