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Add fix for using cpu device #72

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Add fix for using cpu device #72

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d41e16a
Add fix for using cpu device
gutnar Nov 12, 2019
e65915a
Specify 'cuda:0' as default device
gutnar Nov 12, 2019
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11 changes: 7 additions & 4 deletions code/demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,10 +13,13 @@
from run_SiamRPN import SiamRPN_init, SiamRPN_track
from utils import get_axis_aligned_bbox, cxy_wh_2_rect

# get supported device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# load net
net = SiamRPNvot()
net.load_state_dict(torch.load(join(realpath(dirname(__file__)), 'SiamRPNVOT.model')))
net.eval().cuda()
net.load_state_dict(torch.load(join(realpath(dirname(__file__)), 'SiamRPNVOT.model'), map_location=device))
net.eval().to(device)

# image and init box
image_files = sorted(glob.glob('./bag/*.jpg'))
Expand All @@ -26,14 +29,14 @@
# tracker init
target_pos, target_sz = np.array([cx, cy]), np.array([w, h])
im = cv2.imread(image_files[0]) # HxWxC
state = SiamRPN_init(im, target_pos, target_sz, net)
state = SiamRPN_init(im, target_pos, target_sz, net, device)

# tracking and visualization
toc = 0
for f, image_file in enumerate(image_files):
im = cv2.imread(image_file)
tic = cv2.getTickCount()
state = SiamRPN_track(state, im) # track
state = SiamRPN_track(state, im, device) # track
toc += cv2.getTickCount()-tic
res = cxy_wh_2_rect(state['target_pos'], state['target_sz'])
res = [int(l) for l in res]
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8 changes: 4 additions & 4 deletions code/run_SiamRPN.py
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Original file line number Diff line number Diff line change
Expand Up @@ -114,7 +114,7 @@ def sz_wh(wh):
return target_pos, target_sz, score[best_pscore_id]


def SiamRPN_init(im, target_pos, target_sz, net):
def SiamRPN_init(im, target_pos, target_sz, net, device="cuda:0"):
state = dict()
p = TrackerConfig()
p.update(net.cfg)
Expand All @@ -140,7 +140,7 @@ def SiamRPN_init(im, target_pos, target_sz, net):
z_crop = get_subwindow_tracking(im, target_pos, p.exemplar_size, s_z, avg_chans)

z = Variable(z_crop.unsqueeze(0))
net.temple(z.cuda())
net.temple(z.to(device))

if p.windowing == 'cosine':
window = np.outer(np.hanning(p.score_size), np.hanning(p.score_size))
Expand All @@ -157,7 +157,7 @@ def SiamRPN_init(im, target_pos, target_sz, net):
return state


def SiamRPN_track(state, im):
def SiamRPN_track(state, im, device="cuda:0"):
p = state['p']
net = state['net']
avg_chans = state['avg_chans']
Expand All @@ -176,7 +176,7 @@ def SiamRPN_track(state, im):
# extract scaled crops for search region x at previous target position
x_crop = Variable(get_subwindow_tracking(im, target_pos, p.instance_size, round(s_x), avg_chans).unsqueeze(0))

target_pos, target_sz, score = tracker_eval(net, x_crop.cuda(), target_pos, target_sz * scale_z, window, scale_z, p)
target_pos, target_sz, score = tracker_eval(net, x_crop.to(device), target_pos, target_sz * scale_z, window, scale_z, p)
target_pos[0] = max(0, min(state['im_w'], target_pos[0]))
target_pos[1] = max(0, min(state['im_h'], target_pos[1]))
target_sz[0] = max(10, min(state['im_w'], target_sz[0]))
Expand Down