apoorva/greenhouse
2
1
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

minor fix

Browse Source
This commit is contained in:
AlexeyAB84 2022-08-02 00:45:17 +03:00
parent cc42a206ef
commit 0a58f6d1cb
1 changed files with 14 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
detect.py
View File

@ -63,6 +63,9 @@ def detect(save_img=False):
# Run inference # Run inference
if device.type != 'cpu': if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once
old_img_w = old_img_h = imgsz
old_img_b = 1
t0 = time.time() t0 = time.time()
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)
@ -71,13 +74,22 @@ def detect(save_img=False):
if img.ndimension() == 3: if img.ndimension() == 3:
img = img.unsqueeze(0) img = img.unsqueeze(0)
# Warmup
if device.type != 'cpu' and (old_img_b != img.shape[0] or old_img_h != img.shape[2] or old_img_w != img.shape[3]):
old_img_b = img.shape[0]
old_img_h = img.shape[2]
old_img_w = img.shape[3]
for i in range(3):
model(img, augment=opt.augment)[0]
# Inference # Inference
t1 = time_synchronized() t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0] pred = model(img, augment=opt.augment)[0]
t2 = time_synchronized()
# Apply NMS # Apply NMS
pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms)
t2 = time_synchronized() t3 = time_synchronized()
# Apply Classifier # Apply Classifier
if classify: if classify:
@ -93,7 +105,6 @@ def detect(save_img=False):
p = Path(p) # to Path p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg save_path = str(save_dir / p.name) # img.jpg
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det): if len(det):
# Rescale boxes from img_size to im0 size # Rescale boxes from img_size to im0 size
@ -117,7 +128,7 @@ def detect(save_img=False):
plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3)
# Print time (inference + NMS) # Print time (inference + NMS)
#print(f'{s}Done. ({t2 - t1:.3f}s)') #print(f'{s}Done. ({(1E3 * (t2 - t1)):.1f}ms) Inference, ({(1E3 * (t3 - t2)):.1f}s) NMS')
# Stream results # Stream results
if view_img: if view_img: