print('im_detect: {:d}/{:d} {:.4f}s {:.3f}s'.format(
i + 1, num_images, detect_time, nms_time))
_t['im_detect'].clear()
_t['misc'].clear()
with open(det_file, 'wb') as f:
pickle.dump(all_boxes, f, pickle.HIGHEST_PROTOCOL)
print('Evaluating detections')
testset.evaluate_detections(all_boxes, save_folder)
if __name__ == '__main__':
img_dim = (300, 512)[args.size == '512']
num_classes = (21, 81)[args.dataset == 'COCO']
net = build_net(img_dim, num_classes) # initialize detector
state_dict = torch.load(args.trained_model)
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
head = k[:7]
if head == 'module.':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
net.load_state_dict(new_state_dict)
net.eval()
print('Finished loading model!')
from models.FSSD_VGG_BN import build_net
elif args.version == 'FSSD_VGG_prune':
from models.FSSD_VGG_prune import build_net
else:
print('Unkown version!')
img_dim = (300, 512)[args.size == '512']
rgb_means = (104, 117, 123)
p = 0.6
num_classes = (21, 81)[args.dataset == 'COCO']
batch_size = args.batch_size
weight_decay = 0.0005
gamma = 0.1
momentum = 0.9
net = build_net(img_dim, num_classes)
print(net)
if not args.resume_net:
base_weights = torch.load(args.basenet)
print('Loading base network...')
net.base.load_state_dict(base_weights)
def xavier(param):
init.xavier_uniform(param)
def weights_init(m):
for key in m.state_dict():
if key.split('.')[-1] == 'weight':
if 'conv' in key:
init.kaiming_normal(m.state_dict()[key], mode='fan_out')
if 'bn' in key:
cls = int(cls)
title = "%s:%.2f" % (CLASSES[int(cls)], s)
coords = (x1,y1), x2-x1+1, y2-y1+1
color = colors[cls]
currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
currentAxis.text(x1, y1, title, bbox={'facecolor': color, 'alpha': 0.5})
plt.axis('off')
plt.savefig(name.split('.')[0]+'.eps',format='eps',bbox_inches = 'tight')
plt.show()
if __name__ == "__main__":
Image = os.listdir('image/')
for img_name in Image:
img = cv2.imread("image/"+img_name)
model = 'fssd_voc_79_74.pth'
net = build_net(300, 21)
state_dict = torch.load(model)
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict.items():
head = k[:7]
if head == 'module.':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
net.load_state_dict(new_state_dict)
net.eval()
net = net.cuda()
cudnn.benchmark = True
print("Finished loading model")