mirrors/yolov5
1
0
Fork 0
mirror of https://github.com/ultralytics/yolov5.git synced 2025-06-03 14:49:29 +08:00
Code Issues Projects Releases Wiki Activity

Update run-once lines (#2058)

Browse Source
This commit is contained in:
Glenn Jocher 2021-01-27 16:01:24 -08:00 committed by GitHub
parent f59f80114c
commit 2a835c79a9
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 4 additions and 4 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
detect.py
View File

@ -56,9 +56,9 @@ def detect(save_img=False):
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference # Run inference
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
t0 = time.time() t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap in dataset: for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device) img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32 img = img.half() if half else img.float() # uint8 to fp16/32

4
test.py
View File

@ -82,8 +82,8 @@ def test(data,
# Dataloader # Dataloader
if not training: if not training:
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img if device.type != 'cpu':
_ = model(img.half() if half else img) if device.type != 'cpu' else None # run once model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True, dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True,
prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))[0] prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))[0]