def __init__(self): # Create logger self.__logger = logging.getLogger('file_crawler') self.__logger.setLevel(logging.INFO) logging_handler = logging.StreamHandler() logging_formatter = logging.Formatter(LOGGING_FORMAT) logging_handler.setFormatter(logging_formatter) self.__logger.addHandler(logging_handler) self.__manager = FileCrawlerManager() self.__manager.start() # Create process-safe args object self.__cli_args = get_cli_args(self.__manager) # FIXME: This doesn't work correctly on Windows. # According to the documentation here: https://docs.python.org/2.7/library/multiprocessing.html#logging # None of the configuration will be inherited by the child processes except for the level, but in practice # I'm seeing that even the level isn't being inherited. I tried a few different ways to pass down the level, # but none of them worked. if self.__cli_args.verbose: self.__logger.setLevel(logging.DEBUG) self.__timer = Timer( start_message="Starting to scan %s for files matching %s" % (self.__cli_args.root_dir, self.__cli_args.keyword.pattern)) self.__dir_queue = self.__manager.Queue() self.__file_queue = self.__manager.Queue() self.__results = self.__manager.FileCrawlerResults( self.__logger.getEffectiveLevel()) self.__processes = list() self._create_processes()
def mode_train(): """The main training mode entrypoint""" start_time = time.time() io = get_io() logline("using GPU?", tf.test.is_gpu_available()) logline("train") enter_group() logline("loading preprocessed data") preprocessed = load_preprocessed(io) logline("creating models") train_model = create_model(batch_size=io.get("batch_size")) logline("fitting model") enter_group() fit_model(io, train_model, preprocessed) exit_group() logline("exporting model") export_model(train_model, io) exit_group() logline("done training, runtime is {}".format( Timer.stringify_time(Timer.format_time(time.time() - start_time))))
def mode_test(): """The main testing mode entrypoint""" start_time = time.time() io = get_io() logline("test") enter_group() logline("reconstructing model") model = create_model(1) logline("applying learned weights") model = apply_weights(model, io) logline("reading testing files") test_files = read_test_files(io) logline("running testing data") enter_group() run_tests(io, model, test_files) exit_group() exit_group() logline("done training, runtime is {}".format( Timer.stringify_time(Timer.format_time(time.time() - start_time))))
def test_timer_automatic(self): timer = Timer() with freeze_time('2012-01-14 03:21:34.100'): timer.__enter__() with freeze_time('2012-01-14 03:21:34.255'): timer.__exit__() self.assertEqual(timer.interval, 155)
def test_timer_manual(self): timer = Timer() with freeze_time('2012-01-14 03:21:34.100'): timer.start() with freeze_time('2012-01-14 03:21:34.255'): timer.stop() self.assertEqual(timer.interval, 155)
def __init__(self, sess, model, data, logger): super(CTPNTrainer, self).__init__(sess, model, data, logger) self.imdb = data.load_imdb('voc_2007_trainval') self.roidb = data.get_training_roidb(self.imdb) self.pretrained_model = cfg.PRETRAINED_MODEL if cfg.PRETRAINED_MODEL else None # print('Computing bounding-box regression targets...') # if cfg.TRAIN.BBOX_REG: # self.bbox_means, self.bbox_stds = data.add_bbox_regression_targets(self.roidb) # print('done') self.timer = Timer()
def mode_preprocess(): """The main preprocessing entrypoint""" start_time = time.time() preprocessed = [] io = get_io() logline("preprocessing") enter_group() logline("reading input paths") enter_group() input_paths = collect_input_paths(io) for input_path in input_paths: logline('found path: "{}"'.format(input_path)) exit_group() logline("iterating files") enter_group() for file in get_files(input_paths): if not file: error("no files") return None features = gen_features(file) outputs = gen_outputs(file, io) feature_arr = list(map(lambda x: x.to_arr(), features)) output_arr = list(map(lambda x: x.to_arr(), outputs)) assert np.array(feature_arr).shape[1] == Features.length() assert np.array(output_arr).shape[1] == OUT_VEC_SIZE preprocessed.append({ "file_name": file.name, "features": feature_arr, "outputs": output_arr }) logline('done with file: "{}"'.format(file.name)) file.close() exit_group() logline("done iterating files") with open(io.get("output_file"), "wb+") as file: pickle.dump(preprocessed, file) logline("wrote output to file: {}".format(io.get("output_file"))) exit_group() logline("done preprocessing, runtime is {}".format( Timer.stringify_time(Timer.format_time(time.time() - start_time))))
def extract_features_batch(model, config, source_path, target_path, voxel_size, device): folders = get_folder_list(source_path) assert len( folders) > 0, f"Could not find 3DMatch folders under {source_path}" logging.info(folders) list_file = os.path.join(target_path, "list.txt") f = open(list_file, "w") timer, tmeter = Timer(), AverageMeter() num_feat = 0 model.eval() for fo in folders: if 'evaluation' in fo: continue files = get_file_list(fo, ".ply") fo_base = os.path.basename(fo) f.write("%s %d\n" % (fo_base, len(files))) for i, fi in enumerate(files): # Extract features from a file pcd = o3d.io.read_point_cloud(fi) save_fn = "%s_%03d" % (fo_base, i) if i % 100 == 0: logging.info(f"{i} / {len(files)}: {save_fn}") timer.tic() xyz_down, feature = extract_features(model, xyz=np.array(pcd.points), rgb=None, normal=None, voxel_size=voxel_size, device=device, skip_check=True) t = timer.toc() if i > 0: tmeter.update(t) num_feat += len(xyz_down) np.savez_compressed(os.path.join(target_path, save_fn), points=np.array(pcd.points), xyz=xyz_down, feature=feature.detach().cpu().numpy()) if i % 20 == 0 and i > 0: # 最后一项算的是每个点的特征提取时间 logging.info( f'Average time: {tmeter.avg}, FPS: {num_feat / tmeter.sum}, time / feat: {tmeter.sum / num_feat}, ' ) f.close()
def run_inspect_sfm_perspective(self): work_dir = os.path.abspath(self.config['work_dir']) log_file = os.path.join(work_dir, 'logs/log_inspect_sfm_perspective.txt') self.logger.set_log_file(log_file) local_timer = Timer('inspect sfm') local_timer.start() # inspect sfm perspective sfm_dir = os.path.join(work_dir, 'colmap/sfm_perspective') for subdir in ['tri', 'tri_ba']: dir = os.path.join(sfm_dir, subdir) logging.info('\ninspecting {} ...'.format(dir)) inspect_dir = os.path.join(sfm_dir, 'inspect_' + subdir) if os.path.exists(inspect_dir): shutil.rmtree(inspect_dir) db_path = os.path.join(sfm_dir, 'database.db') sfm_inspector = SparseInspector(dir, db_path, inspect_dir, camera_model='PERSPECTIVE') sfm_inspector.inspect_all() # stop local timer local_timer.mark('inspect sfm perspective done') logging.info(local_timer.summary())
def clean_data_general(self, ift): dataset_dir = self.config["dataset_dir"] work_dir = self.config["work_dir"] # set log file and timer log_file = os.path.join(work_dir, "logs/log_clean_data.txt") self.logger.set_log_file(log_file) # create a local timer local_timer = Timer("Data cleaning Module") local_timer.start() # clean data cleaned_data_dir = os.path.join(work_dir, "cleaned_data") if os.path.exists(cleaned_data_dir): # remove cleaned_data_dir shutil.rmtree(cleaned_data_dir) os.mkdir(cleaned_data_dir) # check if dataset_dir is a list or tuple if not (isinstance(dataset_dir, list) or isinstance(dataset_dir, tuple)): dataset_dir = [ dataset_dir, ] clean_data_general(dataset_dir, cleaned_data_dir, ift=ift) # stop local timer local_timer.mark("Data cleaning done") logging.info(local_timer.summary())
def run_crop_image_general( self, ift, oft, execute_parallel, remove_aux_file, apply_tone_mapping, joint_tone_mapping, ): work_dir = self.config["work_dir"] # set log file log_file = os.path.join(work_dir, "logs/log_crop_image.txt") self.logger.set_log_file(log_file) # create a local timer local_timer = Timer("Image cropping module") local_timer.start() # crop image and tone map image_crop_general( work_dir, ift, oft, execute_parallel, remove_aux_file, apply_tone_mapping, joint_tone_mapping, ) # stop local timer local_timer.mark("image cropping done") logging.info(local_timer.summary())
def clean_data(self): dataset_dir = self.config['dataset_dir'] work_dir = self.config['work_dir'] # set log file and timer log_file = os.path.join(work_dir, 'logs/log_clean_data.txt') self.logger.set_log_file(log_file) # create a local timer local_timer = Timer('Data cleaning Module') local_timer.start() # clean data cleaned_data_dir = os.path.join(work_dir, 'cleaned_data') if os.path.exists(cleaned_data_dir): # remove cleaned_data_dir shutil.rmtree(cleaned_data_dir) os.mkdir(cleaned_data_dir) # check if dataset_dir is a list or tuple if not (isinstance(dataset_dir, list) or isinstance(dataset_dir, tuple)): dataset_dir = [dataset_dir, ] clean_data(dataset_dir, cleaned_data_dir) # stop local timer local_timer.mark('Data cleaning done') logging.info(local_timer.summary())
def train(self): curr_iter = self.curr_iter data_loader = self.data_loader data_loader_iter = self.data_loader.__iter__() data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer() total_loss = 0 total_num = 0.0 while (curr_iter < self.config.opt.max_iter): curr_iter += 1 epoch = curr_iter / len(self.data_loader) batch_loss, batch_pos_loss, batch_neg_loss = self._train_iter( data_loader_iter, [data_meter, data_timer, total_timer]) total_loss += batch_loss total_num += 1 if curr_iter % self.lr_update_freq == 0 or curr_iter == 1: lr = self.scheduler.get_last_lr() self.scheduler.step() if self.is_master: logging.info(f" Epoch: {epoch}, LR: {lr}") self._save_checkpoint(curr_iter, 'checkpoint_' + str(curr_iter)) if curr_iter % self.config.trainer.stat_freq == 0 and self.is_master: self.writer.add_scalar('train/loss', batch_loss, curr_iter) self.writer.add_scalar('train/pos_loss', batch_pos_loss, curr_iter) self.writer.add_scalar('train/neg_loss', batch_neg_loss, curr_iter) logging.info( "Train Epoch: {:.3f} [{}/{}], Current Loss: {:.3e}".format( epoch, curr_iter, len(self.data_loader), batch_loss) + "\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}, LR: {}" .format(data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg, self.scheduler.get_last_lr())) data_meter.reset() total_timer.reset()
def run_aggregate_3d(self): work_dir = self.config['work_dir'] # set log file log_file = os.path.join(work_dir, 'logs/log_aggregate_3d.txt') self.logger.set_log_file(log_file) # create a local timer local_timer = Timer('3D aggregation module') local_timer.start() aggregate_3d.run_fuse(work_dir) # stop local timer local_timer.mark('3D aggregation done') logging.info(local_timer.summary())
def run_crop_image(self): work_dir = self.config['work_dir'] # set log file log_file = os.path.join(work_dir, 'logs/log_crop_image.txt') self.logger.set_log_file(log_file) # create a local timer local_timer = Timer('Image cropping module') local_timer.start() # crop image and tone map image_crop(work_dir) # stop local timer local_timer.mark('image cropping done') logging.info(local_timer.summary())
def run_aggregate_2p5d(self): work_dir = self.config['work_dir'] # set log file log_file = os.path.join(work_dir, 'logs/log_aggregate_2p5d.txt') self.logger.set_log_file(log_file) # create a local timer local_timer = Timer('2.5D aggregation module') local_timer.start() max_processes = -1 if 'aggregate_max_processes' in self.config: max_processes = self.config['aggregate_max_processes'] aggregate_2p5d.run_fuse(work_dir, max_processes=max_processes) # stop local timer local_timer.mark('2.5D aggregation done') logging.info(local_timer.summary())
def run_derive_approx(self): work_dir = self.config['work_dir'] # set log file to 'logs/log_derive_approx.txt' log_file = os.path.join(work_dir, 'logs/log_derive_approx.txt') self.logger.set_log_file(log_file) # create a local timer local_timer = Timer('Derive Approximation Module') local_timer.start() # derive approximations for later uses appr = CameraApprox(work_dir) appr.approx_affine_latlonalt() appr.approx_perspective_enu() # stop local timer local_timer.mark('Derive approximation done') logging.info(local_timer.summary())
def run_colmap_mvs(self, window_radius=3): work_dir = self.config['work_dir'] mvs_dir = os.path.join(work_dir, 'colmap/mvs') # set log file log_file = os.path.join(work_dir, 'logs/log_mvs.txt') self.logger.set_log_file(log_file) # create a local timer local_timer = Timer('Colmap MVS Module') local_timer.start() # first run PMVS without filtering run_photometric_mvs(mvs_dir, window_radius) # next do forward-backward checking and filtering run_consistency_check(mvs_dir, window_radius) # stop local timer local_timer.mark('Colmap MVS done') logging.info(local_timer.summary())
def run_colmap_sfm_perspective(self, weight=0.01): work_dir = os.path.abspath(self.config['work_dir']) sfm_dir = os.path.join(work_dir, 'colmap/sfm_perspective') if not os.path.exists(sfm_dir): os.mkdir(sfm_dir) log_file = os.path.join(work_dir, 'logs/log_sfm_perspective.txt') self.logger.set_log_file(log_file) # create a local timer local_timer = Timer('Colmap SfM Module, perspective camera') local_timer.start() # create a hard link to avoid copying of images if os.path.exists(os.path.join(sfm_dir, 'images')): os.unlink(os.path.join(sfm_dir, 'images')) os.symlink(os.path.relpath(os.path.join(work_dir, 'colmap/subset_for_sfm/images'), sfm_dir), os.path.join(sfm_dir, 'images')) init_camera_file = os.path.join(work_dir, 'colmap/subset_for_sfm/perspective_dict.json') colmap_sfm_perspective.run_sfm(work_dir, sfm_dir, init_camera_file, weight) # stop local timer local_timer.mark('Colmap SfM done') logging.info(local_timer.summary())
def calibrate_neighbors(dataset, config, collate_fn, keep_ratio=0.8, samples_threshold=2000): timer = Timer() last_display = timer.total_time # From config parameter, compute higher bound of neighbors number in a neighborhood hist_n = int(np.ceil(4 / 3 * np.pi * (config.deform_radius + 1)**3)) neighb_hists = np.zeros((config.num_layers, hist_n), dtype=np.int32) # Get histogram of neighborhood sizes i in 1 epoch max. for i in range(len(dataset)): timer.tic() batched_input = collate_fn([dataset[i]], config, neighborhood_limits=[hist_n] * 5) # update histogram counts = [ torch.sum(neighb_mat < neighb_mat.shape[0], dim=1).numpy() for neighb_mat in batched_input['neighbors'] ] hists = [np.bincount(c, minlength=hist_n)[:hist_n] for c in counts] neighb_hists += np.vstack(hists) timer.toc() if timer.total_time - last_display > 0.1: last_display = timer.total_time print(f"Calib Neighbors {i:08d}: timings {timer.total_time:4.2f}s") if np.min(np.sum(neighb_hists, axis=1)) > samples_threshold: break cumsum = np.cumsum(neighb_hists.T, axis=0) percentiles = np.sum(cumsum < (keep_ratio * cumsum[hist_n - 1, :]), axis=0) neighborhood_limits = percentiles print('\n') return neighborhood_limits
def ctpn(self, sess, net, image_name): """ :param sess: 会话 :param net: 创建的测试网络 :param image_name: 所要测试的单张图片的目录 :return: """ timer = Timer() timer.tic() # 读取图片 image = cv2.imread(image_name) shape = image.shape[:2] # 获取高,宽 # resize_im,返回缩放后的图片和相应的缩放比。缩放比定义为 修改后的图/原图 img, scale = TestClass.resize_im(image, scale=self._cfg.TEST.SCALE, max_scale=self._cfg.TEST.MAX_SCALE) # 将图片去均值化 im_orig = img.astype(np.float32, copy=True) im_orig -= self._cfg.TRAIN.PIXEL_MEANS # 将缩放和去均值化以后的图片,放入网络进行前向计算,获取分数和对应的文本片段,该片段为映射到最原始图片的坐标 scores, boxes = TestClass.test_ctpn(sess, net, im_orig, scale) # 此处调用了一个文本检测器 textdetector = TextDetector(self._cfg) """ 输入参数分别为: N×4矩阵,每行为一个已经映射回最初的图片的文字片段坐标 N维向量,对应的分数 两维向量,分别为最原始图片的高宽 返回: 一个N×9的矩阵,表示N个拼接以后的完整的文本框。每一行,前八个元素一次是左上,右上,左下,右下的坐标,最后一个元素是文本框的分数 """ boxes = textdetector.detect(boxes, scores, shape) self.draw_boxes(image, image_name, boxes, scale) timer.toc() print(('Detection took {:.3f}s for ' '{:d} object proposals').format(timer.total_time, boxes.shape[0]))
def _train_epoch(self, epoch): config = self.config gc.collect() self.model.train() # Epoch starts from 1 total_loss = 0 total_num = 0.0 data_loader = self.data_loader data_loader_iter = self.data_loader.__iter__() iter_size = self.iter_size data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer() pos_dist_meter, neg_dist_meter = AverageMeter(), AverageMeter() start_iter = (epoch - 1) * (len(data_loader) // iter_size) for curr_iter in range(len(data_loader) // iter_size): self.optimizer.zero_grad() batch_loss = 0 data_time = 0 total_timer.tic() for iter_idx in range(iter_size): data_timer.tic() input_dict = data_loader_iter.next() data_time += data_timer.toc(average=False) # pairs consist of (xyz1 index, xyz0 index) sinput0 = ME.SparseTensor( input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device) F0 = self.model(sinput0).F sinput1 = ME.SparseTensor( input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device) F1 = self.model(sinput1).F pos_pairs = input_dict['correspondences'] loss, pos_dist, neg_dist = self.triplet_loss( F0, F1, pos_pairs, num_pos=config.triplet_num_pos * config.batch_size, num_hn_samples=config.triplet_num_hn * config.batch_size, num_rand_triplet=config.triplet_num_rand * config.batch_size) loss /= iter_size loss.backward() batch_loss += loss.item() pos_dist_meter.update(pos_dist) neg_dist_meter.update(neg_dist) self.optimizer.step() gc.collect() torch.cuda.empty_cache() total_loss += batch_loss total_num += 1.0 total_timer.toc() data_meter.update(data_time) if curr_iter % self.config.stat_freq == 0: self.writer.add_scalar('train/loss', batch_loss, start_iter + curr_iter) logging.info( "Train Epoch: {} [{}/{}], Current Loss: {:.3e}, Pos dist: {:.3e}, Neg dist: {:.3e}" .format(epoch, curr_iter, len(self.data_loader) // iter_size, batch_loss, pos_dist_meter.avg, neg_dist_meter.avg) + "\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}".format( data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg)) pos_dist_meter.reset() neg_dist_meter.reset() data_meter.reset() total_timer.reset()
def _train_epoch(self, epoch): gc.collect() self.model.train() # Epoch starts from 1 total_loss = 0 total_num = 0.0 data_loader = self.data_loader data_loader_iter = self.data_loader.__iter__() iter_size = self.iter_size data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer() start_iter = (epoch - 1) * (len(data_loader) // iter_size) for curr_iter in range(len(data_loader) // iter_size): self.optimizer.zero_grad() batch_pos_loss, batch_neg_loss, batch_loss = 0, 0, 0 data_time = 0 total_timer.tic() for iter_idx in range(iter_size): data_timer.tic() input_dict = data_loader_iter.next() data_time += data_timer.toc(average=False) sinput0 = ME.SparseTensor( input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device) F0 = self.model(sinput0).F sinput1 = ME.SparseTensor( input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device) F1 = self.model(sinput1).F pos_pairs = input_dict['correspondences'] pos_loss, neg_loss = self.contrastive_hardest_negative_loss( F0, F1, pos_pairs, num_pos=self.config.num_pos_per_batch * self.config.batch_size, num_hn_samples=self.config.num_hn_samples_per_batch * self.config.batch_size) pos_loss /= iter_size neg_loss /= iter_size loss = pos_loss + self.neg_weight * neg_loss loss.backward() batch_loss += loss.item() batch_pos_loss += pos_loss.item() batch_neg_loss += neg_loss.item() self.optimizer.step() gc.collect() torch.cuda.empty_cache() total_loss += batch_loss total_num += 1.0 total_timer.toc() data_meter.update(data_time) if curr_iter % self.config.stat_freq == 0: self.writer.add_scalar('train/loss', batch_loss, start_iter + curr_iter) self.writer.add_scalar('train/pos_loss', batch_pos_loss, start_iter + curr_iter) self.writer.add_scalar('train/neg_loss', batch_neg_loss, start_iter + curr_iter) logging.info( "Train Epoch: {} [{}/{}], Current Loss: {:.3e} Pos: {:.3f} Neg: {:.3f}" .format(epoch, curr_iter, len(self.data_loader) // iter_size, batch_loss, batch_pos_loss, batch_neg_loss) + "\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}".format( data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg)) data_meter.reset() total_timer.reset()
def _valid_epoch(self): # Change the network to evaluation mode self.model.eval() self.val_data_loader.dataset.reset_seed(0) num_data = 0 hit_ratio_meter, feat_match_ratio, loss_meter, rte_meter, rre_meter = AverageMeter( ), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter() data_timer, feat_timer, matching_timer = Timer(), Timer(), Timer() tot_num_data = len(self.val_data_loader.dataset) if self.val_max_iter > 0: tot_num_data = min(self.val_max_iter, tot_num_data) data_loader_iter = self.val_data_loader.__iter__() for batch_idx in range(tot_num_data): data_timer.tic() input_dict = data_loader_iter.next() data_timer.toc() # pairs consist of (xyz1 index, xyz0 index) feat_timer.tic() sinput0 = ME.SparseTensor( input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device) F0 = self.model(sinput0).F sinput1 = ME.SparseTensor( input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device) F1 = self.model(sinput1).F feat_timer.toc() matching_timer.tic() xyz0, xyz1, T_gt = input_dict['pcd0'], input_dict['pcd1'], input_dict['T_gt'] xyz0_corr, xyz1_corr = self.find_corr(xyz0, xyz1, F0, F1, subsample_size=5000) T_est = te.est_quad_linear_robust(xyz0_corr, xyz1_corr) loss = corr_dist(T_est, T_gt, xyz0, xyz1, weight=None) loss_meter.update(loss) rte = np.linalg.norm(T_est[:3, 3] - T_gt[:3, 3]) rte_meter.update(rte) rre = np.arccos((np.trace(T_est[:3, :3].t() @ T_gt[:3, :3]) - 1) / 2) if not np.isnan(rre): rre_meter.update(rre) hit_ratio = self.evaluate_hit_ratio( xyz0_corr, xyz1_corr, T_gt, thresh=self.config.hit_ratio_thresh) hit_ratio_meter.update(hit_ratio) feat_match_ratio.update(hit_ratio > 0.05) matching_timer.toc() num_data += 1 torch.cuda.empty_cache() if batch_idx % 100 == 0 and batch_idx > 0: logging.info(' '.join([ f"Validation iter {num_data} / {tot_num_data} : Data Loading Time: {data_timer.avg:.3f},", f"Feature Extraction Time: {feat_timer.avg:.3f}, Matching Time: {matching_timer.avg:.3f},", f"Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},", f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}" ])) data_timer.reset() logging.info(' '.join([ f"Final Loss: {loss_meter.avg:.3f}, RTE: {rte_meter.avg:.3f}, RRE: {rre_meter.avg:.3f},", f"Hit Ratio: {hit_ratio_meter.avg:.3f}, Feat Match Ratio: {feat_match_ratio.avg:.3f}" ])) return { "loss": loss_meter.avg, "rre": rre_meter.avg, "rte": rte_meter.avg, 'feat_match_ratio': feat_match_ratio.avg, 'hit_ratio': hit_ratio_meter.avg }
def _train_epoch(self, epoch): gc.collect() self.model.train() # Epoch starts from 1 total_loss = 0 total_num = 0.0 data_loader = self.data_loader data_loader_iter = self.data_loader.__iter__() iter_size = self.iter_size start_iter = (epoch - 1) * (len(data_loader) // iter_size) data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer() # Main training for curr_iter in range(len(data_loader) // iter_size): self.optimizer.zero_grad() batch_pos_loss, batch_neg_loss, batch_loss = 0, 0, 0 data_time = 0 total_timer.tic() for iter_idx in range(iter_size): # Caffe iter size data_timer.tic() input_dict = data_loader_iter.next() data_time += data_timer.toc(average=False) # pairs consist of (xyz1 index, xyz0 index) sinput0 = ME.SparseTensor( input_dict['sinput0_F'], coords=input_dict['sinput0_C']).to(self.device) F0 = self.model(sinput0).F sinput1 = ME.SparseTensor( input_dict['sinput1_F'], coords=input_dict['sinput1_C']).to(self.device) F1 = self.model(sinput1).F N0, N1 = len(sinput0), len(sinput1) pos_pairs = input_dict['correspondences'] neg_pairs = self.generate_rand_negative_pairs(pos_pairs, max(N0, N1), N0, N1) pos_pairs = pos_pairs.long().to(self.device) neg_pairs = torch.from_numpy(neg_pairs).long().to(self.device) neg0 = F0.index_select(0, neg_pairs[:, 0]) neg1 = F1.index_select(0, neg_pairs[:, 1]) pos0 = F0.index_select(0, pos_pairs[:, 0]) pos1 = F1.index_select(0, pos_pairs[:, 1]) # Positive loss pos_loss = (pos0 - pos1).pow(2).sum(1) # Negative loss neg_loss = F.relu(self.neg_thresh - ((neg0 - neg1).pow(2).sum(1) + 1e-4).sqrt()).pow(2) pos_loss_mean = pos_loss.mean() / iter_size neg_loss_mean = neg_loss.mean() / iter_size # Weighted loss loss = pos_loss_mean + self.neg_weight * neg_loss_mean loss.backward( ) # To accumulate gradient, zero gradients only at the begining of iter_size batch_loss += loss.item() batch_pos_loss += pos_loss_mean.item() batch_neg_loss += neg_loss_mean.item() self.optimizer.step() torch.cuda.empty_cache() total_loss += batch_loss total_num += 1.0 total_timer.toc() data_meter.update(data_time) # Print logs if curr_iter % self.config.stat_freq == 0: self.writer.add_scalar('train/loss', batch_loss, start_iter + curr_iter) self.writer.add_scalar('train/pos_loss', batch_pos_loss, start_iter + curr_iter) self.writer.add_scalar('train/neg_loss', batch_neg_loss, start_iter + curr_iter) logging.info( "Train Epoch: {} [{}/{}], Current Loss: {:.3e} Pos: {:.3f} Neg: {:.3f}" .format(epoch, curr_iter, len(self.data_loader) // iter_size, batch_loss, batch_pos_loss, batch_neg_loss) + "\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}".format( data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg)) data_meter.reset() total_timer.reset()
def _valid_epoch(self, data_loader_iter): # Change the network to evaluation mode self.model.eval() num_data = 0 hit_ratio_meter, reciprocity_ratio_meter = AverageMeter( ), AverageMeter() reciprocity_hit_ratio_meter = AverageMeter() data_timer, feat_timer = Timer(), Timer() tot_num_data = len(self.val_data_loader.dataset) if self.val_max_iter > 0: tot_num_data = min(self.val_max_iter, tot_num_data) for curr_iter in range(tot_num_data): data_timer.tic() input_dict = self.get_data(data_loader_iter) data_timer.toc() # pairs consist of (xyz1 index, xyz0 index) feat_timer.tic() with torch.no_grad(): F0 = self.model(input_dict['img0'].to(self.device)) F1 = self.model(input_dict['img1'].to(self.device)) feat_timer.toc() # Test self.num_pos_per_batch * self.batch_size features only. _, _, H0, W0 = F0.shape _, _, H1, W1 = F1.shape for batch_idx, pair in enumerate(input_dict['pairs']): N = len(pair) sel = np.random.choice(N, min(N, self.config.num_pos_per_batch), replace=False) curr_pair = pair[sel] w0, h0, w1, h1 = torch.floor(curr_pair.t() / self.out_tensor_stride).long() feats0 = F0[batch_idx, :, h0, w0] nn_inds1 = find_nn_gpu(feats0, F1[batch_idx, :].view(F1.shape[1], -1), nn_max_n=self.config.nn_max_n, transposed=True) # Convert the index to coordinate: BxCxHxW xs1 = nn_inds1 % W1 ys1 = nn_inds1 // W1 # Test reciprocity nn_inds0 = find_nn_gpu(F1[batch_idx, :, ys1, xs1], F0[batch_idx, :].view(F0.shape[1], -1), nn_max_n=self.config.nn_max_n, transposed=True) # Convert the index to coordinate: BxCxHxW xs0 = nn_inds0 % W0 ys0 = nn_inds0 // W0 dist_sq = (w1 - xs1)**2 + (h1 - ys1)**2 is_correct = dist_sq < (self.config.ucn_inlier_threshold_pixel / self.out_tensor_stride)**2 hit_ratio_meter.update(is_correct.sum().item() / len(is_correct)) # Recipocity test result dist_sq_nn = (w0 - xs0)**2 + (h0 - ys0)**2 mask = dist_sq_nn < (self.config.ucn_inlier_threshold_pixel / self.out_tensor_stride)**2 reciprocity_ratio_meter.update(mask.sum().item() / float(len(mask))) reciprocity_hit_ratio_meter.update( is_correct[mask].sum().item() / (mask.sum().item() + eps)) torch.cuda.empty_cache() # visualize_image_correspondence(input_dict['img0'][batch_idx, 0].numpy() + 0.5, # input_dict['img1'][batch_idx, 0].numpy() + 0.5, # F0[batch_idx], F1[batch_idx], curr_iter, # self.config) num_data += 1 if num_data % 100 == 0: logging.info(', '.join([ f"Validation iter {num_data} / {tot_num_data} : Data Loading Time: {data_timer.avg:.3f}", f"Feature Extraction Time: {feat_timer.avg:.3f}, Hit Ratio: {hit_ratio_meter.avg}", f"Reciprocity Ratio: {reciprocity_ratio_meter.avg}, Reci Filtered Hit Ratio: {reciprocity_hit_ratio_meter.avg}" ])) data_timer.reset() logging.info(', '.join([ f"Validation : Data Loading Time: {data_timer.avg:.3f}", f"Feature Extraction Time: {feat_timer.avg:.3f}, Hit Ratio: {hit_ratio_meter.avg}", f"Reciprocity Ratio: {reciprocity_ratio_meter.avg}, Reci Filtered Hit Ratio: {reciprocity_hit_ratio_meter.avg}" ])) return { 'hit_ratio': hit_ratio_meter.avg, 'reciprocity_ratio': reciprocity_ratio_meter.avg, 'reciprocity_hit_ratio': reciprocity_hit_ratio_meter.avg, }
def _train_epoch(self, epoch, data_loader_iter): # Epoch starts from 1 total_loss = 0 total_num = 0.0 iter_size = self.iter_size data_meter, data_timer, total_timer = AverageMeter(), Timer(), Timer() for curr_iter in range(self.train_max_iter): self.optimizer.zero_grad() batch_pos_loss, batch_neg_loss, batch_loss = 0, 0, 0 data_time = 0 total_timer.tic() for iter_idx in range(iter_size): data_timer.tic() input_dict = self.get_data(data_loader_iter) data_time += data_timer.toc(average=False) F0 = self.model(input_dict['img0'].to(self.device)) F1 = self.model(input_dict['img1'].to(self.device)) pos_loss, neg_loss = self.contrastive_loss( input_dict['img0'].numpy() + 0.5, input_dict['img1'].numpy() + 0.5, F0, F1, input_dict['pairs'], num_pos=self.config.num_pos_per_batch, num_hn_samples=self.config.num_hn_samples_per_batch) pos_loss /= iter_size neg_loss /= iter_size loss = pos_loss + self.neg_weight * neg_loss loss.backward() batch_loss += loss.item() batch_pos_loss += pos_loss.item() batch_neg_loss += neg_loss.item() self.optimizer.step() gc.collect() torch.cuda.empty_cache() total_loss += batch_loss total_num += 1.0 total_timer.toc() data_meter.update(data_time) torch.cuda.empty_cache() if curr_iter % self.config.stat_freq == 0: self.writer.add_scalar('train/loss', batch_loss, curr_iter) self.writer.add_scalar('train/pos_loss', batch_pos_loss, curr_iter) self.writer.add_scalar('train/neg_loss', batch_neg_loss, curr_iter) logging.info( "Train epoch {}, iter {}, Current Loss: {:.3e} Pos: {:.3f} Neg: {:.3f}" .format(epoch, curr_iter, batch_loss, batch_pos_loss, batch_neg_loss) + "\tData time: {:.4f}, Train time: {:.4f}, Iter time: {:.4f}" .format(data_meter.avg, total_timer.avg - data_meter.avg, total_timer.avg)) data_meter.reset() total_timer.reset()
class CTPNTrainer(BaseTrain): def __init__(self, sess, model, data, logger): super(CTPNTrainer, self).__init__(sess, model, data, logger) self.imdb = data.load_imdb('voc_2007_trainval') self.roidb = data.get_training_roidb(self.imdb) self.pretrained_model = cfg.PRETRAINED_MODEL if cfg.PRETRAINED_MODEL else None # print('Computing bounding-box regression targets...') # if cfg.TRAIN.BBOX_REG: # self.bbox_means, self.bbox_stds = data.add_bbox_regression_targets(self.roidb) # print('done') self.timer = Timer() def get_train_op(self, loss): lr = tf.Variable(cfg.TRAIN.LEARNING_RATE, trainable=False) if cfg.TRAIN.SOLVER == 'Adam': opt = tf.train.AdamOptimizer(cfg.TRAIN.LEARNING_RATE) elif cfg.TRAIN.SOLVER == 'RMS': opt = tf.train.RMSPropOptimizer(cfg.TRAIN.LEARNING_RATE) else: momentum = cfg.TRAIN.MOMENTUM opt = tf.train.MomentumOptimizer(lr, momentum) if cfg.TRAIN.WITH_CLIP: tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), 10.0) train_op = opt.apply_gradients(list(zip(grads, tvars)), global_step=self.global_step) else: train_op = opt.minimize(loss, global_step=self.global_step) return train_op, lr def load_model(self, restore): restore_iter = 0 if self.pretrained_model is not None and not restore: try: print(('Loading pretrained model weights from {:s}').format( self.pretrained_model)) self.model.load_npz(self.pretrained_model, self.sess, True) except: raise 'Check your pretrained model {:s}'.format( self.pretrained_model) # resuming a trainer if restore: ckpt_path = self.model.load_ckpt(self.sess) stem = os.path.splitext(os.path.basename(ckpt_path))[0] restore_iter = int(stem.split('_')[-1]) self.sess.run(self.global_step.assign(restore_iter)) return restore_iter def train(self, max_iters, restore=False): """Network training loop.""" data_layer = DataGenerator(self.roidb, self.imdb.nrof_classes, self.data) total_loss, model_loss, rpn_cross_entropy, rpn_loss_box = self.model.build_loss( ohem=cfg.TRAIN.OHEM) summary_op, log_image, log_image_data, log_image_name = self.logger.init_summary( rpn_reg_loss=rpn_loss_box, rpn_cls_loss=rpn_cross_entropy, model_loss=model_loss, total_loss=total_loss) train_op, lr = self.get_train_op(total_loss) # intialize variables self.sess.run(tf.global_variables_initializer()) restore_iter = self.load_model(restore) fetch_list = [ total_loss, model_loss, rpn_cross_entropy, rpn_loss_box, summary_op, train_op ] print(restore_iter, max_iters) for _iter in range(restore_iter, max_iters, cfg.TRAIN.EPOCH_SIZE): losses = self.train_epoch(_iter, lr, data_layer, fetch_list) print('iter: %d / %d, total loss: %.4f, model loss: %.4f, rpn_loss_cls: %.4f, rpn_loss_box: %.4f, lr: %f' % \ (_iter+cfg.TRAIN.EPOCH_SIZE, max_iters, losses[0], losses[1], losses[2], losses[3], losses[5].eval())) self.logger.summarize(losses[4], self.global_step.eval()) self.save(_iter + cfg.TRAIN.EPOCH_SIZE) def train_epoch(self, tm_iter, lr, data_layer, fetch_list): loop = tqdm(range(cfg.TRAIN.EPOCH_SIZE)) for _iter in loop: tm_iter += _iter if tm_iter != 0 and tm_iter % cfg.TRAIN.STEPSIZE == 0: self.sess.run(tf.assign(lr, lr.eval() * cfg.TRAIN.GAMMA)) self.timer.tic() total_loss_val, model_loss_val, rpn_loss_cls_val, rpn_loss_box_val, summary_str, _ = \ self.train_step(data_layer,fetch_list) _diff_time = self.timer.toc(average=False) print('speed: {:.3f}s / iter'.format(_diff_time)) return total_loss_val, model_loss_val, rpn_loss_cls_val, rpn_loss_box_val, summary_str, lr def train_step(self, data_layer, fetch_list): blobs = data_layer.forward() feed_dict = { self.model.data: blobs['data'], self.model.im_info: blobs['im_info'], self.model.keep_prob: 0.5, self.model.gt_boxes: blobs['gt_boxes'], self.model.gt_ishard: blobs['gt_ishard'], self.model.dontcare_areas: blobs['dontcare_areas'] } return self.sess.run(fetches=fetch_list, feed_dict=feed_dict)
from lib.spark import spark, sc from lib.plotly import py from lib.timer import Timer import plotly.graph_objs as go from pyspark.ml.feature import VectorAssembler from scipy.spatial import ConvexHull with Timer('read', 'Reading data'): df = df_base = spark.read.csv('data/yellow_tripdata_2016-01.csv', header=True, inferSchema=True) with Timer('sample', 'Sampling data'): df = df.sample(False, 0.005) from lib.process import process with Timer('process', 'Cleaning invalid data'): df = process(df) from pyspark.sql.functions import col, udf from pyspark.sql.types import IntegerType from pyspark import StorageLevel K = 6 N = 24//K groups = {i: range(i*N, i*N+N) for i in range(K)} @udf(returnType=IntegerType()) def get_group(d):
import numpy as np from lib.timer import Timer import breakout_detection import runners.data_loader as data_loader import matplotlib.pyplot as plt SAMPLE_FILE_PATH = '../data/demo7.csv' if __name__ == '__main__': sw = Timer() data = data_loader.load_data(SAMPLE_FILE_PATH) sw.start() edm_multi = breakout_detection.EdmMulti() max_snp = max(max(data.values), 1) # Z = [x/float(max_snp) for x in data.values] Z = [x for x in data.values] edm_multi.evaluate(Z, min_size=24, beta=0.001, degree=1) print(sw.elapsed(f'data length: {len(data.values)}, using time:')) plt.plot(np.asarray(data.index).tolist(), Z) result = edm_multi.getLoc() print(result) for i in result: plt.axvline(np.asarray(data.index).tolist()[i], color='#FF4E24') # plt.plot(np.asarray(data.index).tolist()[i], np.asarray(data.values).tolist()[i], 'ro') plt.show()
from lib.spark import spark, sc from lib.plotly import py from lib.timer import Timer from lib.process import process import plotly.graph_objs as go from pyspark.ml.feature import VectorAssembler from scipy.spatial import ConvexHull import pickle import numpy as np with Timer('read', 'Reading data'): df = df_base = spark.read.csv('data/yellow_tripdata_2016-01.csv', header=True, inferSchema=True) with Timer('process', 'Cleaning invalid data'): df = process(df) from pyspark.sql.functions import col, udf from pyspark.sql.types import IntegerType from pyspark import StorageLevel from pyspark.ml.clustering import GaussianMixture gmm = GaussianMixture(k=1000) result = [] with Timer('clustering', 'Computing clusters'): for weekday in range(7): for hour in range(24): with Timer('clustering',
def main(config): test_loader = make_data_loader( config, config.test_phase, 1, num_threads=config.test_num_workers, shuffle=True) num_feats = 1 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') Model = load_model(config.model) model = Model( num_feats, config.model_n_out, bn_momentum=config.bn_momentum, conv1_kernel_size=config.conv1_kernel_size, normalize_feature=config.normalize_feature) checkpoint = torch.load(config.save_dir + '/checkpoint.pth') model.load_state_dict(checkpoint['state_dict']) model = model.to(device) model.eval() success_meter, rte_meter, rre_meter = AverageMeter(), AverageMeter(), AverageMeter() data_timer, feat_timer, reg_timer = Timer(), Timer(), Timer() test_iter = test_loader.__iter__() N = len(test_iter) n_gpu_failures = 0 # downsample_voxel_size = 2 * config.voxel_size for i in range(len(test_iter)): data_timer.tic() try: data_dict = test_iter.next() except ValueError: n_gpu_failures += 1 logging.info(f"# Erroneous GPU Pair {n_gpu_failures}") continue data_timer.toc() xyz0, xyz1 = data_dict['pcd0'], data_dict['pcd1'] T_gth = data_dict['T_gt'] xyz0np, xyz1np = xyz0.numpy(), xyz1.numpy() pcd0 = make_open3d_point_cloud(xyz0np) pcd1 = make_open3d_point_cloud(xyz1np) with torch.no_grad(): feat_timer.tic() sinput0 = ME.SparseTensor( data_dict['sinput0_F'].to(device), coordinates=data_dict['sinput0_C'].to(device)) F0 = model(sinput0).F.detach() sinput1 = ME.SparseTensor( data_dict['sinput1_F'].to(device), coordinates=data_dict['sinput1_C'].to(device)) F1 = model(sinput1).F.detach() feat_timer.toc() feat0 = make_open3d_feature(F0, 32, F0.shape[0]) feat1 = make_open3d_feature(F1, 32, F1.shape[0]) reg_timer.tic() distance_threshold = config.voxel_size * 1.0 ransac_result = o3d.registration.registration_ransac_based_on_feature_matching( pcd0, pcd1, feat0, feat1, distance_threshold, o3d.registration.TransformationEstimationPointToPoint(False), 4, [ o3d.registration.CorrespondenceCheckerBasedOnEdgeLength(0.9), o3d.registration.CorrespondenceCheckerBasedOnDistance( distance_threshold) ], o3d.registration.RANSACConvergenceCriteria(4000000, 10000)) T_ransac = torch.from_numpy( ransac_result.transformation.astype(np.float32)) reg_timer.toc() # Translation error rte = np.linalg.norm(T_ransac[:3, 3] - T_gth[:3, 3]) rre = np.arccos((np.trace(T_ransac[:3, :3].t() @ T_gth[:3, :3]) - 1) / 2) # Check if the ransac was successful. successful if rte < 2m and rre < 5◦ # http://openaccess.thecvf.com/content_ECCV_2018/papers/Zi_Jian_Yew_3DFeat-Net_Weakly_Supervised_ECCV_2018_paper.pdf if rte < 2: rte_meter.update(rte) if not np.isnan(rre) and rre < np.pi / 180 * 5: rre_meter.update(rre) if rte < 2 and not np.isnan(rre) and rre < np.pi / 180 * 5: success_meter.update(1) else: success_meter.update(0) logging.info(f"Failed with RTE: {rte}, RRE: {rre}") if i % 10 == 0: logging.info( f"{i} / {N}: Data time: {data_timer.avg}, Feat time: {feat_timer.avg}," + f" Reg time: {reg_timer.avg}, RTE: {rte_meter.avg}," + f" RRE: {rre_meter.avg}, Success: {success_meter.sum} / {success_meter.count}" + f" ({success_meter.avg * 100} %)") data_timer.reset() feat_timer.reset() reg_timer.reset() logging.info( f"RTE: {rte_meter.avg}, var: {rte_meter.var}," + f" RRE: {rre_meter.avg}, var: {rre_meter.var}, Success: {success_meter.sum} " + f"/ {success_meter.count} ({success_meter.avg * 100} %)")