我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用utils.timer.Timer()。
def camera_detector(self, cap, wait=10): detect_timer = Timer() ret, _ = cap.read() while ret: ret, frame = cap.read() detect_timer.tic() result = self.detect(frame) detect_timer.toc() print('Average detecting time: {:.3f}s'.format(detect_timer.average_time)) self.draw_result(frame, result) cv2.imshow('Camera', frame) cv2.waitKey(wait) ret, frame = cap.read()
def train_model(self, max_iters): """Network training loop.""" last_snapshot_iter = -1 timer = Timer() while self.solver.iter < max_iters: # Make one SGD update timer.tic() self.solver.step(1) timer.toc() if self.solver.iter % (10 * self.solver_param.display) == 0: print 'speed: {:.3f}s / iter'.format(timer.average_time) if self.solver.iter % cfg.TRAIN.SNAPSHOT_ITERS == 0: last_snapshot_iter = self.solver.iter self.snapshot() if last_snapshot_iter != self.solver.iter: self.snapshot()
def train_model(self, max_iters): """Network training loop.""" last_snapshot_iter = -1 timer = Timer() model_paths = [] while self.solver.iter < max_iters: # Make one SGD update timer.tic() self.solver.step(1) timer.toc() if self.solver.iter % (10 * self.solver_param.display) == 0: print 'speed: {:.3f}s / iter'.format(timer.average_time) if self.solver.iter % cfg.TRAIN.SNAPSHOT_ITERS == 0: last_snapshot_iter = self.solver.iter model_paths.append(self.snapshot()) if last_snapshot_iter != self.solver.iter: model_paths.append(self.snapshot()) return model_paths
def imdb_proposals(net, imdb): """Generate RPN proposals on all images in an imdb.""" _t = Timer() imdb_boxes = [[] for _ in xrange(imdb.num_images)] for i in xrange(imdb.num_images): im = cv2.imread(imdb.image_path_at(i)) _t.tic() imdb_boxes[i], scores = im_proposals(net, im) _t.toc() print 'im_proposals: {:d}/{:d} {:.3f}s' \ .format(i + 1, imdb.num_images, _t.average_time) if 0: dets = np.hstack((imdb_boxes[i], scores)) # from IPython import embed; embed() _vis_proposals(im, dets[:3, :], thresh=0.9) plt.show() return imdb_boxes
def _get_feature_scale(self, num_images=100): TARGET_NORM = 20.0 # Magic value from traditional R-CNN _t = Timer() roidb = self.imdb.roidb total_norm = 0.0 count = 0.0 inds = npr.choice(xrange(self.imdb.num_images), size=num_images, replace=False) for i_, i in enumerate(inds): im = cv2.imread(self.imdb.image_path_at(i)) if roidb[i]['flipped']: im = im[:, ::-1, :] _t.tic() scores, boxes = im_detect(self.net, im, roidb[i]['boxes']) _t.toc() feat = self.net.blobs[self.layer].data total_norm += np.sqrt((feat ** 2).sum(axis=1)).sum() count += feat.shape[0] print('{}/{}: avg feature norm: {:.3f}'.format(i_ + 1, num_images, total_norm / count)) return TARGET_NORM * 1.0 / (total_norm / count)
def train_model(self, max_iters): """Network training loop.""" last_snapshot_iter = -1 timer = Timer() model_paths = [] while self.solver.iter < max_iters: # Make one SGD update timer.tic() self.solver.step(1) timer.toc() if self.solver.iter % (10 * self.solver_param.display) == 0: sys.stderr.write('rank: {} iteration: {} speed: {:.3f}s / iter\n'.format(self.rank, self.solver.iter, timer.average_time)) if self.rank == 0 and self.solver.iter % cfg.TRAIN.SNAPSHOT_ITERS == 0: last_snapshot_iter = self.solver.iter model_paths.append(self.snapshot()) if self.rank == 0 and last_snapshot_iter != self.solver.iter: model_paths.append(self.snapshot()) return model_paths
def imdb_proposals(net, imdb, rank, count, output_dir): """Generate RPN proposals on all images in an imdb.""" _t = Timer() for i in xrange(rank, imdb.num_images, count): # imdb.num_images im = cv2.imread(imdb.image_path_at(i)) _t.tic() imdb_boxes, scores = im_proposals(net, im) with open(osp.join(output_dir, "{}.pkl".format(i)), "wb") as fp: cPickle.dump(imdb_boxes, fp, cPickle.HIGHEST_PROTOCOL) _t.toc() print 'im_proposals: {:d}/{:d} {:.3f}s' \ .format(i + 1, imdb.num_images, _t.average_time) if 0: dets = np.hstack((imdb_boxes, scores)) # from IPython import embed; embed() _vis_proposals(im, dets[:3, :], thresh=0.9) plt.show()
def imdb_proposals_det(net, imdb): """Generate RPN proposals on all images in an imdb.""" _t = Timer() imdb_boxes = [[] for _ in xrange(imdb.num_images)] for i in xrange(imdb.num_images): im = cv2.imread(imdb.image_path_at(i)) _t.tic() boxes, scores = im_proposals(net, im) _t.toc() print 'im_proposals: {:d}/{:d} {:.3f}s' \ .format(i + 1, imdb.num_images, _t.average_time) dets = np.hstack((boxes, scores)) imdb_boxes[i] = dets if 0: # from IPython import embed; embed() _vis_proposals(im, dets[:3, :], thresh=0.9) plt.show() return imdb_boxes
