def demo():
args = parse_args()
print('call with args: {}'.format(args))
# input images
images_dir = 'images'
images_names = ['image1.jpg', 'image2.jpg']
classes = ('aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor')
model = Yolov2()
weight_loader = WeightLoader()
weight_loader.load(model, 'yolo-voc.weights')
print('loaded')
# model_path = os.path.join(args.output_dir, args.model_name + '.pth')
# print('loading model from {}'.format(model_path))
# if torch.cuda.is_available():
# checkpoint = torch.load(model_path)
# else:
# checkpoint = torch.load(model_path, map_location='cpu')
# model.load_state_dict(checkpoint['model'])
if args.use_cuda:
model.cuda()
model.eval()
print('model loaded')
for image_name in images_names:
image_path = os.path.join(images_dir, image_name)
img = Image.open(image_path)
im_data, im_info = prepare_im_data(img)
if args.use_cuda:
im_data_variable = Variable(im_data).cuda()
else:
im_data_variable = Variable(im_data)
tic = time.time()
yolo_output = model(im_data_variable)
yolo_output = [item[0].data for item in yolo_output]
detections = yolo_eval(yolo_output, im_info, conf_threshold=0.6, nms_threshold=0.4)
toc = time.time()
cost_time = toc - tic
print('im detect, cost time {:4f}, FPS: {}'.format(
toc-tic, int(1 / cost_time)))
det_boxes = detections[:, :5].cpu().numpy()
det_classes = detections[:, -1].long().cpu().numpy()
im2show = draw_detection_boxes(img, det_boxes, det_classes, class_names=classes)
plt.figure()
plt.imshow(im2show)
plt.show()
def demo():
args = parse_args()
print('call with args: {}'.format(args))
# input images
images_dir = 'images'
images_names = ['trainval1.jpg', 'trainval2.jpg', 'test1.jpg', 'test2.jpg']
classes = ("car", "bus", "truck", "svehicle", "pedestrian", "motorbike", "bicycle", "train", "signal", "signs")
model = Yolov2(arch=args.arch)
#weight_loader = WeightLoader()
#weight_loader.load(model, 'yolo-voc.weights')
#print('loaded')
model_path = os.path.join(args.output_dir, args.model_name + '.pth')
print('loading model from {}'.format(model_path))
if torch.cuda.is_available():
checkpoint = torch.load(model_path)
else:
checkpoint = torch.load(model_path, map_location='cpu')
model.load_state_dict(checkpoint['model'])
if args.use_cuda:
model.cuda()
model.eval()
print('model loaded')
print(model)
## generate weight
idx = 0
for ii, module in enumerate(model.trunk.features):
#print("ii",ii)
#print("module",module)
#print(type(module))
#print(module.__dict__)
if isinstance( module, torch.nn.modules.conv.Conv2d):
print("conv2d layer_%d" % idx)
#print("weight",module.__dict__['_parameters']['weight'])
weight = module.__dict__['_parameters']['weight']
weight = weight.detach().numpy() # nn.tensor -> numpy
#print(weight)
#print(weight.shape)
if ii == 0:
header_w = float_dtype+' w_%s[%d][%d][%d][%d]=\n' % ((str(ii),)+(weight.shape)) + arr2header(weight)
save_header('./weight_l0.h', header_w)
#print("bias",module.__dict__['_parameters']['bias'])
bias = module.__dict__['_parameters']['bias']
bias = bias.detach().numpy() # nn.tensor -> numpy
#print(bias)
if ii == 0:
coef = bias.reshape(-1,).astype(float_np_dtype)
header = float_dtype+(' b_%s[%d]=\n' % ((str(ii)),len(coef))) + arr2header(coef)
save_header('./bias_l0.h', header)
idx += 1
exit()
for image_name in images_names:
image_path = os.path.join(images_dir, image_name)
img = Image.open(image_path)
im_data, im_info = prepare_im_data(img)
if args.use_cuda:
im_data_variable = Variable(im_data).cuda()
else:
im_data_variable = Variable(im_data)
tic = time.time()
yolo_output = model(im_data_variable)
yolo_output = [item[0].data for item in yolo_output]
detections = yolo_eval(yolo_output, im_info, conf_threshold=0.2, nms_threshold=0.4)
##print(detections)
toc = time.time()
cost_time = toc - tic
print('im detect, cost time {:4f}, FPS: {}'.format(
toc-tic, int(1 / cost_time)))
det_boxes = detections[:, :5].cpu().numpy()
det_classes = detections[:, -1].long().cpu().numpy()
im2show = draw_detection_boxes(img, det_boxes, det_classes, class_names=classes)
plt.figure()
plt.imshow(im2show)
#plt.show()
save_image_path = os.path.join(images_dir, image_name + "_detect.jpg")
print("save -> " + save_image_path)
plt.savefig(save_image_path)
def demo():
args = parse_args()
print('call with args: {}'.format(args))
# input images
images_dir = 'images'
images_names = ['trainval1.jpg', 'trainval2.jpg', 'test1.jpg', 'test2.jpg']
classes = ("car", "bus", "truck", "svehicle", "pedestrian", "motorbike",
"bicycle", "train", "signal", "signs")
model = Yolov2(arch=args.arch)
#weight_loader = WeightLoader()
#weight_loader.load(model, 'yolo-voc.weights')
#print('loaded')
model_path = os.path.join(args.output_dir, args.model_name + '.pth')
print('loading model from {}'.format(model_path))
if torch.cuda.is_available():
checkpoint = torch.load(model_path)
else:
checkpoint = torch.load(model_path, map_location='cpu')
model.load_state_dict(checkpoint['model'])
if args.use_cuda:
model.cuda()
model.eval()
print('model loaded')
for image_name in images_names:
image_path = os.path.join(images_dir, image_name)
img = Image.open(image_path)
im_data, im_info = prepare_im_data(img)
if args.use_cuda:
im_data_variable = Variable(im_data).cuda()
else:
im_data_variable = Variable(im_data)
tic = time.time()
yolo_output = model(im_data_variable)
yolo_output = [item[0].data for item in yolo_output]
detections = yolo_eval(yolo_output,
im_info,
conf_threshold=0.2,
nms_threshold=0.4)
##print(detections)
toc = time.time()
cost_time = toc - tic
print('im detect, cost time {:4f}, FPS: {}'.format(
toc - tic, int(1 / cost_time)))
det_boxes = detections[:, :5].cpu().numpy()
det_classes = detections[:, -1].long().cpu().numpy()
im2show = draw_detection_boxes(img,
det_boxes,
det_classes,
class_names=classes)
plt.figure()
plt.imshow(im2show)
#plt.show()
save_image_path = os.path.join(images_dir, image_name + "_detect.jpg")
print("save -> " + save_image_path)
plt.savefig(save_image_path)