def main(): args = parse_args() logging.info(args) demo = BaseDemo(args) if args.train: demo.train() if args.test: demo.test() if args.test_gt: demo.test_gt()
def main(): args = parse_args() logging.info(args) demo = Demo(args) if args.train: demo.train_unsupervised() if args.test: demo.test_unsupervised() if args.test_gt: demo.test_gt_unsupervised()
def main(): logging.info('----------------------------------------------------------------') logging.info('****************************************************************') args = parse_args() logging.info(args) if args.data == 'kitti': data = KittiData(args.data_path, args.train_proportion, args.test_proportion) train_meta = data.train_meta train_data = KittiDataLoader(train_meta, args.batch_size, args.image_heights, args.image_widths, args.output_heights, args.output_widths, args.num_scale, data_augment=False, shuffle=False) test_meta = data.test_meta test_data = KittiDataLoader(test_meta, args.batch_size, args.image_heights, args.image_widths, args.output_heights, args.output_widths, args.num_scale) else: print('Data not implemented yet') return if args.model == 'base': model = BaseNet(args.image_channel, args.num_class) elif args.model == 'base_3d': model = Base3DNet(args.image_channel, args.depth_channel, args.num_class) elif args.model == 'base_2stream': model = Base2StreamNet(args.image_channel, args.depth_channel, args.num_class) elif args.model == 'seg_3d': model = Seg3DNet(args.image_channel, args.depth_channel, args.num_class) else: print('Model not implemented yet') return interface = DetectInterface(data, train_data, test_data, model, args.learning_rate, args.train_epoch, args.test_interval, args.test_iteration, args.save_interval, args.init_model_path, args.save_model_path, args.tensorboard_path) if args.train: logging.info('Experiment: %s, training', args.exp_name) interface.train() elif args.test: logging.info('Experiment: %s, testing all', args.exp_name) interface.test_all() elif args.visualize: logging.info('Experiment: %s, visualizing', args.exp_name) interface.visualize(args.image_name, args.depth_name, args.flow_name, args.box_name, args.figure_path) elif args.visualize_all: logging.info('Experiment: %s, visualizing all', args.exp_name) interface.visualize_all(args.image_list, args.figure_path) else: print('Unknown command') return
def main(): logging.info( '----------------------------------------------------------------') logging.info( '****************************************************************') args = parse_args() logging.info(args) if args.data == 'vdrift': data = VDriftData(args.data_path, args.batch_size, args.image_heights, args.image_widths, args.output_height, args.output_width, args.num_scale, args.train_proportion, args.test_proportion, args.show_statistics) else: print('Data not implemented yet') return if args.model == 'base': model = BaseNet(args.image_channel, args.num_class) elif args.model == 'base_3d': model = Base3DNet(args.image_channel, args.depth_channel, args.num_class) elif args.model == 'base_2stream': model = Base2StreamNet(args.image_channel, args.depth_channel, args.num_class) else: print('Model not implemented yet') return interface = SegmentInterface(data, model, args.learning_rate, args.train_iteration, args.test_iteration, args.test_interval, args.save_interval, args.init_model_path, args.save_model_path, args.tensorboard_path) if args.train: logging.info('Experiment: %s, training', args.exp_name) interface.train() elif args.test: logging.info('Experiment: %s, testing all', args.exp_name) interface.test_all() elif args.visualize: logging.info('Experiment: %s, visualizing', args.exp_name) interface.visualize(args.image_name, args.depth_name, args.flow_x_name, args.flow_y_name, args.seg_name, args.figure_path) elif args.visualize_all: logging.info('Experiment: %s, visualizing all', args.exp_name) interface.visualize_all(args.image_list, args.figure_path) else: print('Unknown command') return
def main(): args = parse_args() logging.info(args) if args.data == 'mlt': data = MLTData(args.batch_size, args.image_size, args.direction_type, args.train_proportion, args.test_proportion, args.show_statistics) elif args.data == 'viper': print('Not Implemented Yet') return data_test = DataTest(data) data_test.test()
def main(): args = parse_args() logging.info(args) if args.data == 'kitti': data = KittiData(args.data_path, args.train_proportion, args.test_proportion) train_meta = data.train_meta train_data = KittiDataLoader(train_meta, args.batch_size, args.image_heights, args.image_widths, args.output_heights, args.output_widths, args.num_scale, data_augment=True, shuffle=True) data_test = DataTest(train_data) data_test.test() test_meta = data.test_meta test_data = KittiDataLoader(test_meta, args.batch_size, args.image_heights, args.image_widths, args.output_heights, args.output_widths, args.num_scale) data_test = DataTest(test_data) data_test.test() meta = { 'image': [args.image_name], 'depth': [args.depth_name], 'flow': [args.flow_name], 'box': [read_box(args.box_name)] } data = KittiDataLoader(meta, args.batch_size, args.image_heights, args.image_widths, args.output_heights, args.output_widths, args.num_scale) data_test = DataTest(data) data_test.test() else: print('Not Implemented Yet') return
def main(): args = parse_args() logging.info(args) # args.data_path = '/media/yi/DATA/data-orig/kitti/training' # args.image_name = '/media/yi/DATA/data-orig/kitti/training/image_2/007480.png' # args.depth_name = '/media/yi/DATA/data-orig/kitti/training/disp_unsup/007480.png' # args.flow_name = '/media/yi/DATA/data-orig/kitti/training/flow_unsup/007480.png' # args.box_name = '/media/yi/DATA/data-orig/kitti/training/label_2/007480.txt' if args.data == 'kitti': data = KittiData(args.data_path, args.batch_size, args.image_heights, args.image_widths, args.output_heights, args.output_widths, args.num_scale, args.train_proportion, args.test_proportion, args.show_statistics) else: print('Not Implemented Yet') return data_test = DataTest(data) data_test.test() data_test.test_one_image(args.image_name, args.depth_name, args.flow_name, args.box_name)
def unit_test(): args = learning_args.parse_args() logging.info(args) data = MpiiData(args) im = data.get_next_batch(data.train_images) data.display(im)
def main(): logging.info('----------------------------------------------------------------') logging.info('****************************************************************') args = parse_args() logging.info(args) if args.data == 'mlt': data = MLTData(args.batch_size, args.image_size, args.direction_type, args.train_proportion, args.test_proportion, args.show_statistics) elif args.data == 'viper': print('Not Implemented Yet') return if args.model == 'base': model = BaseNet(args.image_channel, args.num_class) elif args.model == 'base_direct': model = BaseDirectNet(args.image_channel, args.direction_dim, args.num_class) elif args.model == 'base_3d': model = Base3DNet(args.image_channel, args.depth_channel, args.num_class) elif args.model == 'base_direct_3d': model = BaseDirect3DNet(args.image_channel, args.depth_channel, args.direction_dim, args.num_class) elif args.model == 'base_2stream': model = Base2StreamNet(args.image_channel, args.depth_channel, args.num_class) elif args.model == 'base_direct_2stream': model = BaseDirect2StreamNet(args.image_channel, args.depth_channel, args.direction_dim, args.num_class) elif args.model == 'hard_gt_attn': model = HardGtAttnNet(args.image_size[0], args.image_channel, args.num_class) elif args.model == 'hard_direct': model = HardDirectNet(args.image_size[0], args.image_channel, args.direction_dim, args.num_class) elif args.model == 'hard_gt_attn_3d': model = HardGtAttn3DNet(args.image_size[0], args.image_channel, args.depth_channel, args.num_class) elif args.model == 'hard_gt_attn_2stream': model = HardGtAttn2StreamNet(args.image_size[0], args.image_channel, args.depth_channel, args.num_class) elif args.model == 'soft_attn': model = SoftAttnNet(args.attention_size, args.image_channel, args.num_class) elif args.model == 'soft_direct': model = SoftDirectNet(args.attention_size, args.image_channel, args.direction_dim, args.num_class) elif args.model == 'soft_comb': model = SoftCombNet(args.attention_size, args.image_channel, args.direction_dim, args.num_class) elif args.model == 'soft_attn_3d': model = SoftAttn3DNet(args.attention_size, args.image_channel, args.depth_channel, args.num_class) elif args.model == 'soft_direct_3d': model = SoftDirect3DNet(args.attention_size, args.image_channel, args.depth_channel, args.direction_dim, args.num_class) elif args.model == 'soft_comb_3d': model = SoftComb3DNet(args.attention_size, args.image_channel, args.depth_channel, args.direction_dim, args.num_class) elif args.model == 'soft_attn_2stream': model = SoftAttn2StreamNet(args.attention_size, args.image_channel, args.depth_channel, args.num_class) elif args.model == 'soft_direct_2stream': model = SoftDirect2StreamNet(args.attention_size, args.image_channel, args.depth_channel, args.direction_dim, args.num_class) elif args.model == 'soft_comb_2stream': model = SoftComb2StreamNet(args.attention_size, args.image_channel, args.depth_channel, args.direction_dim, args.num_class) if args.attention_type == 'soft': interface = SoftAttnInterface(data, model, args.learning_rate, args.train_iteration, args.test_iteration, args.test_interval, args.save_interval, args.init_model_path, args.save_model_path, args.tensorboard_path) elif args.attention_type == 'hard': interface = HardAttnInterface(data, model, args.learning_rate, args.train_iteration, args.test_iteration, args.test_interval, args.save_interval, args.init_model_path, args.save_model_path, args.tensorboard_path) if args.train: logging.info('Experiment: %s, training', args.exp_name) interface.train() elif args.test: logging.info('Experiment: %s, testing all', args.exp_name) interface.test_all() elif args.visualize: logging.info('Experiment: %s, visualizing', args.exp_name) interface.visualize(args.image_name, args.depth_name, args.box_name, args.figure_path) elif args.visualize_all: logging.info('Experiment: %s, visualizing all', args.exp_name) interface.visualize_all(args.image_list, args.figure_path)
def unit_test(): args = learning_args.parse_args() logging.info(args) data = BoxDataComplex(args) im, motion, motion_r, motion_label, motion_label_r, seg_layer = data.get_next_batch() data.display(im, motion, motion_r, seg_layer)
def unit_test(): args = learning_args.parse_args() logging.info(args) data = Kitti128Sample(args) im = data.get_next_batch(data.test_images) data.display(im)
def unit_test(): args = learning_args.parse_args() logging.info(args) data = ChairData(args) im, motion, motion_label, seg_layer = data.get_next_batch(data.train_images) data.display(im, motion, seg_layer)
def main(): args = parse_args() logging.info(args) demo = BaseDemo(args) demo.compare()
def unit_test(): args = learning_args.parse_args() logging.info(args) data = Robot64Data(args) im = data.get_next_batch(data.train_meta) data.display(im)
def unit_test(): args = learning_args.parse_args() logging.info(args) data = MnistDataBidirect(args) im, motion, motion_r, motion_label, motion_label_r, seg_layer = data.get_next_batch(data.train_images) data.display(im, motion, motion_r, seg_layer)