def inference(config,
checkpoint,
img_path,
device='cuda:0',
half=False,
augment=False,
scores_thr=0.3,
merge=False,
save_json=False,
save_file=False,
save_path='',
show=False):
device = select_device(device)
model = init_detector(config, checkpoint=checkpoint, device=device)
t0 = time.time()
if os.path.isdir(img_path):
imgs_paths = get_file_realpath(
img_path, *[
".jpg", ".JPG", ".jpeg", ".JPEG", ".png", ".PNG", ".bmp",
".BMP"
])
for i, img in enumerate(imgs_paths):
basename = os.path.basename(img)
result, t = inference_detector(model,
img=img,
scores_thr=scores_thr,
augment=augment,
half=half,
merge=merge)
#print(result)
# Print time (inference + NMS)
print('%s Done , object num : %s .time:(%.3fs)' %
(basename, len(result), t))
show_result(img,
result,
model.CLASSES,
show=show,
save_json=save_json,
save_file=save_file,
out_path=save_path,
file_name=basename)
else:
basename = os.path.basename(img_path)
t1 = torch_utils.time_synchronized()
result = inference_detector(model,
img=img_path,
scores_thr=scores_thr,
augment=augment,
half=half,
merge=merge)
t2 = torch_utils.time_synchronized()
# print(result)
# Print time (inference + NMS)
print('%s Done , object num : %s .time:(%.3fs)' %
(basename, len(result), t2 - t1))
show_result(img_path,
result,
model.CLASSES,
show=show,
save_json=save_json,
save_file=save_file,
out_path=save_path,
file_name=basename)
print('Done. (%.3fs)' % (time.time() - t0))
stds = [0.229, 0.224, 0.225]
size = 608
transform = transforms.Compose([
transforms.Resize(size),
transforms.CenterCrop(size),
transforms.ToTensor(),
transforms.Normalize(means, stds)
])
tensor = transform(I).unsqueeze(0).requires_grad_()
# model = models.alexnet(pretrained=True)
config = '/disk2/project/pytorch-YOLOv4/cfg/yolov4_hand_gpu.py'
checkpoint = '/disk2/project/pytorch-YOLOv4/work_dirs/test/epoch_6.pth'
model = init_detector(config, checkpoint=checkpoint, device='cuda:0')
visualisation = {}
def hook_fn(m, i, o):
visualisation[m] = o
def get_all_layers(model):
for name, layer in model._modules.items():
# If it is a sequential, don't register a hook on it
# but recursively register hook on all it's module children
if isinstance(layer, nn.Sequential):
get_all_layers(layer)
else:
# it's a non sequential. Register a hook
parser.add_argument('--half',
action='store_true',
help='fp16 half precision')
opt = parser.parse_args()
print(opt)
# config = '/disk2/project/mmdetection/mount/pytorch-YOLOv4/cfg/yolov5_coco_gpu.py'
# checkpoint = '/disk2/project/pytorch-YOLOv4/work_dirs/yolov5-l_epoch_24.pth'
cfg = Config.fromfile(opt.config)
cfg.data.val.train = False
val_dataset = build_from_dict(cfg.data.val, DATASET)
val_dataloader = build_dataloader(val_dataset,
data=cfg.data,
shuffle=False)
device = select_device(opt.device)
# model = init_detector(opt.config, checkpoint=opt.checkpoint, device=device)
model = init_detector(opt.config, checkpoint=opt.checkpoint, device=device)
result = single_gpu_test(model,
val_dataloader,
half=opt.half,
conf_thres=opt.conf_thres,
iou_thres=opt.iou_thres,
merge=opt.merge,
save_json=opt.save_json,
augment=opt.augment,
verbose=opt.verbose,
coco_val_path=opt.coco_val_path)
print(result)