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np.random.seed(cfg.RNG_SEED) |
format(args.dataset, args.net) |
cfg_from_file(args.cfg_file) |
cfg_from_list(args.set_cfgs) |
print('Using config:') |
pprint.pprint(cfg) |
combined_roidb(args.imdbval_name, False) |
imdb.competition_mode(on=True) |
print('{:d} roidb entries'.format(len(roidb) |
os.path.exists(input_dir) |
Exception('There is no input directory for loading network from ' + input_dir) |
format(args.checksession, args.checkepoch, args.checkpoint) |
vgg16(imdb.classes, pretrained=False, class_agnostic=args.class_agnostic) |
resnet(imdb.classes, 101, pretrained=False, class_agnostic=args.class_agnostic) |
resnet(imdb.classes, 50, pretrained=False, class_agnostic=args.class_agnostic) |
resnet(imdb.classes, 152, pretrained=False, class_agnostic=args.class_agnostic) |
print("network is not defined") |
pdb.set_trace() |
fasterRCNN.create_architecture() |
print("load checkpoint %s" % (load_name) |
torch.load(load_name) |
fasterRCNN.load_state_dict(checkpoint['model']) |
checkpoint.keys() |
print('load model successfully!') |
torch.FloatTensor(1) |
torch.FloatTensor(1) |
torch.LongTensor(1) |
torch.FloatTensor(1) |
im_data.cuda() |
im_info.cuda() |
num_boxes.cuda() |
gt_boxes.cuda() |
Variable(im_data) |
Variable(im_info) |
Variable(num_boxes) |
Variable(gt_boxes) |
fasterRCNN.cuda() |
time.time() |
len(imdb.image_index) |
xrange(num_images) |
xrange(imdb.num_classes) |
get_output_dir(imdb, save_name) |
iter(dataloader) |
time.time() |
time.time() |
os.path.join(output_dir, 'detections.pkl') |
fasterRCNN.eval() |
np.transpose(np.array([[],[],[],[],[]]) |
range(num_images) |
next(data_iter) |
im_data.data.resize_(data[0].size() |
copy_(data[0]) |
im_info.data.resize_(data[1].size() |
copy_(data[1]) |
gt_boxes.data.resize_(data[2].size() |
copy_(data[2]) |
num_boxes.data.resize_(data[3].size() |
copy_(data[3]) |
time.time() |
fasterRCNN(im_data, im_info, gt_boxes, num_boxes) |
box_deltas.view(-1, 4) |
torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS) |
cuda() |
torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS) |
cuda() |
box_deltas.view(1, -1, 4) |
box_deltas.view(-1, 4) |
torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_STDS) |
cuda() |
torch.FloatTensor(cfg.TRAIN.BBOX_NORMALIZE_MEANS) |
cuda() |
box_deltas.view(1, -1, 4 * len(imdb.classes) |
bbox_transform_inv(boxes, box_deltas, 1) |
clip_boxes(pred_boxes, im_info.data, 1) |
torch.from_numpy(np.tile(boxes, (1, scores.shape[1]) |
_.cuda() |
item() |
scores.squeeze() |
pred_boxes.squeeze() |
time.time() |
time.time() |
cv2.imread(imdb.image_path_at(i) |
np.copy(im) |
xrange(1, imdb.num_classes) |
torch.nonzero(scores[:,j]>thresh) |
view(-1) |
inds.numel() |
torch.sort(cls_scores, 0, True) |
torch.cat((cls_boxes, cls_scores.unsqueeze(1) |
torch.cat((cls_boxes, cls_scores) |
nms(cls_dets, cfg.TEST.NMS) |
keep.view(-1) |
long() |
vis_detections(im2show, imdb.classes[j], cls_dets.cpu() |
numpy() |
cls_dets.cpu() |
numpy() |
xrange(1, imdb.num_classes) |
len(image_scores) |
np.sort(image_scores) |
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