def test(cfg): """ Perform multi-view testing on the pretrained video model. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ # Set up environment. du.init_distributed_training(cfg) # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Print config. logger.info("Test with config:") logger.info(cfg) # Build the video model and print model statistics. model = build_model(cfg) if du.is_master_proc() and cfg.LOG_MODEL_INFO: misc.log_model_info(model, cfg, use_train_input=False) cu.load_test_checkpoint(cfg, model) # Create video testing loaders. test_loader = loader.construct_loader(cfg, "test") logger.info("Testing model for {} iterations".format(len(test_loader))) if cfg.DETECTION.ENABLE: assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE or cfg.NUM_GPUS == 0 test_meter = AVAMeter(len(test_loader), cfg, mode="test") else: assert ( test_loader.dataset.num_videos % (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS) == 0) # Create meters for multi-view testing. test_meter = TestMeter( test_loader.dataset.num_videos // (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS), cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS, cfg.MODEL.NUM_CLASSES, len(test_loader), cfg.DATA.MULTI_LABEL, cfg.DATA.ENSEMBLE_METHOD, ) # Set up writer for logging to Tensorboard format. if cfg.TENSORBOARD.ENABLE and du.is_master_proc( cfg.NUM_GPUS * cfg.NUM_SHARDS): writer = tb.TensorboardWriter(cfg) else: writer = None # # Perform multi-view test on the entire dataset. test_meter = perform_test(test_loader, model, test_meter, cfg, writer) if writer is not None: writer.close()
def visualize(cfg): """ Perform layer weights and activations visualization on the model. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ if cfg.TENSORBOARD.ENABLE and cfg.TENSORBOARD.MODEL_VIS.ENABLE: # Set up environment. du.init_distributed_training(cfg) # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Print config. logger.info("Model Visualization with config:") logger.info(cfg) # Build the video model and print model statistics. model = build_model(cfg) if du.is_master_proc() and cfg.LOG_MODEL_INFO: misc.log_model_info(model, cfg, is_train=False) cu.load_test_checkpoint(cfg, model) # Create video testing loaders. vis_loader = loader.construct_loader(cfg, "test") logger.info( "Visualize model for {} data points".format(len(vis_loader)) ) if cfg.DETECTION.ENABLE: assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE # Set up writer for logging to Tensorboard format. if du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS): writer = tb.TensorboardWriter(cfg) else: writer = None # Run visualization on the model run_visualization(vis_loader, model, cfg, writer) if writer is not None: writer.close()
def test(cfg): """ Perform multi-view testing on the pretrained video model. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ # Set up environment. du.init_distributed_training(cfg) # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Print config. logger.info("Test with config:") logger.info(cfg) # Build the video model and print model statistics. model = build_model(cfg) cu.load_test_checkpoint(cfg, model) # Create video testing loaders. test_loader = loader.construct_loader(cfg, "test") logger.info("Testing model for {} iterations".format(len(test_loader))) # Create meters for loss tracking test_meter = TrainMeter(test_loader.dataset.num_videos, cfg) # Set up writer for logging to Tensorboard format. if cfg.TENSORBOARD.ENABLE and du.is_master_proc( cfg.NUM_GPUS * cfg.NUM_SHARDS ): writer = tb.TensorboardWriter(cfg) else: writer = None # # Perform multi-view test on the entire dataset. test_meter = perform_test(test_loader, model, test_meter, cfg, writer) if writer is not None: writer.close()
def __init__(self, cfg): """ Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ self.cfg = cfg self.class_names, _, self.subset = get_class_names( cfg.TENSORBOARD.CLASS_NAMES_PATH, subset_path=cfg.TENSORBOARD.WRONG_PRED_VIS.SUBSET_PATH, ) if self.subset is not None: self.subset = set(self.subset) self.num_class = cfg.MODEL.NUM_CLASSES self.video_vis = VideoVisualizer( cfg.MODEL.NUM_CLASSES, cfg.TENSORBOARD.CLASS_NAMES_PATH, 1, cfg.TENSORBOARD.MODEL_VIS.COLORMAP, ) self.tag = cfg.TENSORBOARD.WRONG_PRED_VIS.TAG self.writer = tb.TensorboardWriter(cfg) self.model_incorrect_classes = set()
def train(cfg): """ Train a video model for many epochs on train set and evaluate it on val set. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ # Set up environment. du.init_distributed_training(cfg) # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Init multigrid. multigrid = None if cfg.MULTIGRID.LONG_CYCLE or cfg.MULTIGRID.SHORT_CYCLE: multigrid = MultigridSchedule() cfg = multigrid.init_multigrid(cfg) if cfg.MULTIGRID.LONG_CYCLE: cfg, _ = multigrid.update_long_cycle(cfg, cur_epoch=0) # Print config. logger.info("Train with config:") logger.info(pprint.pformat(cfg)) # Build the video model and print model statistics. model = build_model(cfg) if du.is_master_proc() and cfg.LOG_MODEL_INFO: misc.log_model_info(model, cfg, use_train_input=True) # Construct the optimizer. optimizer = optim.construct_optimizer(model, cfg) # Load a checkpoint to resume training if applicable. start_epoch = cu.load_train_checkpoint(cfg, model, optimizer) # Create the video train and val loaders. train_loader = loader.construct_loader(cfg, "train") val_loader = loader.construct_loader(cfg, "val") precise_bn_loader = (loader.construct_loader( cfg, "train", is_precise_bn=True) if cfg.BN.USE_PRECISE_STATS else None) # Create meters. if cfg.DETECTION.ENABLE: train_meter = AVAMeter(len(train_loader), cfg, mode="train") val_meter = AVAMeter(len(val_loader), cfg, mode="val") else: train_meter = TrainMeter(len(train_loader), cfg) val_meter = ValMeter(len(val_loader), cfg) # set up writer for logging to Tensorboard format. if cfg.TENSORBOARD.ENABLE and du.is_master_proc( cfg.NUM_GPUS * cfg.NUM_SHARDS): writer = tb.TensorboardWriter(cfg) else: writer = None # Perform the training loop. logger.info("Start epoch: {}".format(start_epoch + 1)) for cur_epoch in range(start_epoch, cfg.SOLVER.MAX_EPOCH): if cfg.MULTIGRID.LONG_CYCLE: cfg, changed = multigrid.update_long_cycle(cfg, cur_epoch) if changed: ( model, optimizer, train_loader, val_loader, precise_bn_loader, train_meter, val_meter, ) = build_trainer(cfg) # Load checkpoint. if cu.has_checkpoint(cfg.OUTPUT_DIR): last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR) assert "{:05d}.pyth".format(cur_epoch) in last_checkpoint else: last_checkpoint = cfg.TRAIN.CHECKPOINT_FILE_PATH logger.info("Load from {}".format(last_checkpoint)) cu.load_checkpoint(last_checkpoint, model, cfg.NUM_GPUS > 1, optimizer) # Shuffle the dataset. loader.shuffle_dataset(train_loader, cur_epoch) # Train for one epoch. train_epoch(train_loader, model, optimizer, train_meter, cur_epoch, cfg, writer) is_checkp_epoch = (cu.is_checkpoint_epoch( cfg, cur_epoch, None if multigrid is None else multigrid.schedule, )) is_eval_epoch = misc.is_eval_epoch( cfg, cur_epoch, None if multigrid is None else multigrid.schedule) # Compute precise BN stats. if ((is_checkp_epoch or is_eval_epoch) and cfg.BN.USE_PRECISE_STATS and len(get_bn_modules(model)) > 0): calculate_and_update_precise_bn( precise_bn_loader, model, min(cfg.BN.NUM_BATCHES_PRECISE, len(precise_bn_loader)), cfg.NUM_GPUS > 0, ) _ = misc.aggregate_sub_bn_stats(model) # Save a checkpoint. if is_checkp_epoch: cu.save_checkpoint(cfg.OUTPUT_DIR, model, optimizer, cur_epoch, cfg) # Evaluate the model on validation set. if is_eval_epoch: eval_epoch(val_loader, model, val_meter, cur_epoch, cfg, writer) if writer is not None: writer.close()
def visualize(cfg): """ Perform layer weights and activations visualization on the model. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ if cfg.TENSORBOARD.ENABLE and (cfg.TENSORBOARD.MODEL_VIS.ENABLE or cfg.TENSORBOARD.WRONG_PRED_VIS.ENABLE): # Set up environment. du.init_distributed_training(cfg) # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Print config. logger.info("Model Visualization with config:") logger.info(cfg) # Build the video model and print model statistics. model = build_model(cfg) model.eval() if du.is_master_proc() and cfg.LOG_MODEL_INFO: misc.log_model_info(model, cfg, use_train_input=False) cu.load_test_checkpoint(cfg, model) # Create video testing loaders. vis_loader = loader.construct_loader(cfg, "test") if cfg.DETECTION.ENABLE: assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE or cfg.NUM_GPUS == 0 # Set up writer for logging to Tensorboard format. if du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS): writer = tb.TensorboardWriter(cfg) else: writer = None if cfg.TENSORBOARD.PREDICTIONS_PATH != "": assert not cfg.DETECTION.ENABLE, "Detection is not supported." logger.info( "Visualizing class-level performance from saved results...") if writer is not None: with g_pathmgr.open(cfg.TENSORBOARD.PREDICTIONS_PATH, "rb") as f: preds, labels = pickle.load(f, encoding="latin1") writer.plot_eval(preds, labels) if cfg.TENSORBOARD.MODEL_VIS.ENABLE: if cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.ENABLE: assert ( not cfg.DETECTION.ENABLE ), "Detection task is currently not supported for Grad-CAM visualization." if cfg.MODEL.ARCH in cfg.MODEL.SINGLE_PATHWAY_ARCH: assert ( len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST) == 1 ), "The number of chosen CNN layers must be equal to the number of pathway(s), given {} layer(s).".format( len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST)) elif cfg.MODEL.ARCH in cfg.MODEL.MULTI_PATHWAY_ARCH: assert ( len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST) == 2 ), "The number of chosen CNN layers must be equal to the number of pathway(s), given {} layer(s).".format( len(cfg.TENSORBOARD.MODEL_VIS.GRAD_CAM.LAYER_LIST)) else: raise NotImplementedError( "Model arch {} is not in {}".format( cfg.MODEL.ARCH, cfg.MODEL.SINGLE_PATHWAY_ARCH + cfg.MODEL.MULTI_PATHWAY_ARCH, )) logger.info("Visualize model analysis for {} iterations".format( len(vis_loader))) # Run visualization on the model run_visualization(vis_loader, model, cfg, writer) if cfg.TENSORBOARD.WRONG_PRED_VIS.ENABLE: logger.info("Visualize Wrong Predictions for {} iterations".format( len(vis_loader))) perform_wrong_prediction_vis(vis_loader, model, cfg) if writer is not None: writer.close()
def test(cfg): """ Perform multi-view testing on the pretrained video model. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ # Set up environment. du.init_distributed_training(cfg) # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Print config. logger.info("Test with config:") logger.info(cfg) # Build the video model and print model statistics. model = build_model(cfg) out_str_prefix = "lin" if cfg.MODEL.DETACH_FINAL_FC else "" if du.is_master_proc() and cfg.LOG_MODEL_INFO: misc.log_model_info(model, cfg, use_train_input=False) if (cfg.TASK == "ssl" and cfg.MODEL.MODEL_NAME == "ContrastiveModel" and cfg.CONTRASTIVE.KNN_ON): train_loader = loader.construct_loader(cfg, "train") out_str_prefix = "knn" if hasattr(model, "module"): model.module.init_knn_labels(train_loader) else: model.init_knn_labels(train_loader) cu.load_test_checkpoint(cfg, model) # Create video testing loaders. test_loader = loader.construct_loader(cfg, "test") logger.info("Testing model for {} iterations".format(len(test_loader))) if cfg.DETECTION.ENABLE: assert cfg.NUM_GPUS == cfg.TEST.BATCH_SIZE or cfg.NUM_GPUS == 0 test_meter = AVAMeter(len(test_loader), cfg, mode="test") else: assert ( test_loader.dataset.num_videos % (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS) == 0) # Create meters for multi-view testing. test_meter = TestMeter( test_loader.dataset.num_videos // (cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS), cfg.TEST.NUM_ENSEMBLE_VIEWS * cfg.TEST.NUM_SPATIAL_CROPS, cfg.MODEL.NUM_CLASSES if not cfg.TASK == "ssl" else cfg.CONTRASTIVE.NUM_CLASSES_DOWNSTREAM, len(test_loader), cfg.DATA.MULTI_LABEL, cfg.DATA.ENSEMBLE_METHOD, ) # Set up writer for logging to Tensorboard format. if cfg.TENSORBOARD.ENABLE and du.is_master_proc( cfg.NUM_GPUS * cfg.NUM_SHARDS): writer = tb.TensorboardWriter(cfg) else: writer = None # # Perform multi-view test on the entire dataset. test_meter = perform_test(test_loader, model, test_meter, cfg, writer) if writer is not None: writer.close() result_string = ( "_a{}{}{} Top1 Acc: {} Top5 Acc: {} MEM: {:.2f} dataset: {}{}" "".format( out_str_prefix, cfg.TEST.DATASET[0], test_meter.stats["top1_acc"], test_meter.stats["top1_acc"], test_meter.stats["top5_acc"], misc.gpu_mem_usage(), cfg.TEST.DATASET[0], cfg.MODEL.NUM_CLASSES, )) logger.info("testing done: {}".format(result_string)) return result_string
def test(cfg): """ Perform multi-view testing on the pretrained audio model. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ # Set up environment. du.init_distributed_training(cfg) # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Print config. logger.info("Test with config:") logger.info(cfg) # Build the audio model and print model statistics. model = build_model(cfg) if du.is_master_proc() and cfg.LOG_MODEL_INFO: misc.log_model_info(model, cfg) cu.load_test_checkpoint(cfg, model) # Create audio testing loaders. test_loader = loader.construct_loader(cfg, "test") logger.info("Testing model for {} iterations".format(len(test_loader))) assert ( len(test_loader.dataset) % cfg.TEST.NUM_ENSEMBLE_VIEWS == 0 ) # Create meters for multi-view testing. if cfg.TEST.DATASET == 'epickitchens': test_meter = EPICTestMeter( len(test_loader.dataset) // cfg.TEST.NUM_ENSEMBLE_VIEWS, cfg.TEST.NUM_ENSEMBLE_VIEWS, cfg.MODEL.NUM_CLASSES, len(test_loader), cfg.DATA.ENSEMBLE_METHOD, ) else: test_meter = TestMeter( len(test_loader.dataset) // cfg.TEST.NUM_ENSEMBLE_VIEWS, cfg.TEST.NUM_ENSEMBLE_VIEWS, cfg.MODEL.NUM_CLASSES[0], len(test_loader), cfg.DATA.MULTI_LABEL, cfg.DATA.ENSEMBLE_METHOD, ) # Set up writer for logging to Tensorboard format. if cfg.TENSORBOARD.ENABLE and du.is_master_proc( cfg.NUM_GPUS * cfg.NUM_SHARDS ): writer = tb.TensorboardWriter(cfg) else: writer = None # # Perform multi-view test on the entire dataset. test_meter, preds, preds_clips, labels, metadata = perform_test(test_loader, model, test_meter, cfg, writer) if du.is_master_proc(): if cfg.TEST.DATASET == 'epickitchens': results = {'verb_output': preds[0], 'noun_output': preds[1], 'narration_id': metadata} scores_path = os.path.join(cfg.OUTPUT_DIR, 'scores') if not os.path.exists(scores_path): os.makedirs(scores_path) file_path = os.path.join(scores_path, cfg.EPICKITCHENS.TEST_SPLIT+'.pkl') pickle.dump(results, open(file_path, 'wb')) else: if cfg.TEST.DATASET == 'vggsound': get_stats(preds, labels) results = {'scores': preds, 'labels': labels} scores_path = os.path.join(cfg.OUTPUT_DIR, 'scores') if not os.path.exists(scores_path): os.makedirs(scores_path) file_path = os.path.join(scores_path, 'test.pkl') pickle.dump(results, open(file_path, 'wb')) if writer is not None: writer.close()
def train(cfg): """ Train an audio model for many epochs on train set and evaluate it on val set. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ # Set up environment. du.init_distributed_training(cfg) # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Print config. logger.info("Train with config:") logger.info(pprint.pformat(cfg)) # Build the audio model and print model statistics. model = build_model(cfg) if du.is_master_proc() and cfg.LOG_MODEL_INFO: misc.log_model_info(model, cfg) if cfg.BN.FREEZE: model.module.freeze_fn( 'bn_parameters') if cfg.NUM_GPUS > 1 else model.freeze_fn( 'bn_parameters') # Construct the optimizer. optimizer = optim.construct_optimizer(model, cfg) # Load a checkpoint to resume training if applicable. start_epoch = cu.load_train_checkpoint(cfg, model, optimizer) # Create the audio train and val loaders. if cfg.TRAIN.DATASET != 'epickitchens' or not cfg.EPICKITCHENS.TRAIN_PLUS_VAL: train_loader = loader.construct_loader(cfg, "train") val_loader = loader.construct_loader(cfg, "val") precise_bn_loader = (loader.construct_loader(cfg, "train") if cfg.BN.USE_PRECISE_STATS else None) else: train_loader = loader.construct_loader(cfg, "train+val") val_loader = loader.construct_loader(cfg, "val") precise_bn_loader = (loader.construct_loader(cfg, "train+val") if cfg.BN.USE_PRECISE_STATS else None) # Create meters. if cfg.TRAIN.DATASET == 'epickitchens': train_meter = EPICTrainMeter(len(train_loader), cfg) val_meter = EPICValMeter(len(val_loader), cfg) else: train_meter = TrainMeter(len(train_loader), cfg) val_meter = ValMeter(len(val_loader), cfg) # set up writer for logging to Tensorboard format. if cfg.TENSORBOARD.ENABLE and du.is_master_proc( cfg.NUM_GPUS * cfg.NUM_SHARDS): writer = tb.TensorboardWriter(cfg) else: writer = None if cfg.WANDB.ENABLE and du.is_master_proc(cfg.NUM_GPUS * cfg.NUM_SHARDS): wandb_log = True if cfg.TRAIN.AUTO_RESUME and cfg.WANDB.RUN_ID != "": wandb.init(project='slowfast', config=cfg, sync_tensorboard=True, resume=cfg.WANDB.RUN_ID) else: wandb.init(project='slowfast', config=cfg, sync_tensorboard=True) wandb.watch(model) else: wandb_log = False # Perform the training loop. logger.info("Start epoch: {}".format(start_epoch + 1)) for cur_epoch in range(start_epoch, cfg.SOLVER.MAX_EPOCH): # Shuffle the dataset. loader.shuffle_dataset(train_loader, cur_epoch) # Train for one epoch. train_epoch(train_loader, model, optimizer, train_meter, cur_epoch, cfg, writer, wandb_log) is_checkp_epoch = cu.is_checkpoint_epoch( cfg, cur_epoch, ) is_eval_epoch = misc.is_eval_epoch( cfg, cur_epoch, ) # Compute precise BN stats. if ((is_checkp_epoch or is_eval_epoch) and cfg.BN.USE_PRECISE_STATS and len(get_bn_modules(model)) > 0): calculate_and_update_precise_bn( precise_bn_loader, model, min(cfg.BN.NUM_BATCHES_PRECISE, len(precise_bn_loader)), cfg.NUM_GPUS > 0, ) _ = misc.aggregate_sub_bn_stats(model) # Save a checkpoint. if is_checkp_epoch: cu.save_checkpoint(cfg.OUTPUT_DIR, model, optimizer, cur_epoch, cfg) # Evaluate the model on validation set. if is_eval_epoch: is_best_epoch, _ = eval_epoch(val_loader, model, val_meter, cur_epoch, cfg, writer, wandb_log) if is_best_epoch: cu.save_checkpoint(cfg.OUTPUT_DIR, model, optimizer, cur_epoch, cfg, is_best_epoch=is_best_epoch) if writer is not None: writer.close()
def train(cfg): """ Train a video model for many epochs on train set and evaluate it on val set. Args: cfg (CfgNode): configs. Details can be found in slowfast/config/defaults.py """ # Set up environment. du.init_distributed_training(cfg) # Set random seed from configs. np.random.seed(cfg.RNG_SEED) torch.manual_seed(cfg.RNG_SEED) # Setup logging format. logging.setup_logging(cfg.OUTPUT_DIR) # Init multigrid. multigrid = None if cfg.MULTIGRID.LONG_CYCLE or cfg.MULTIGRID.SHORT_CYCLE: multigrid = MultigridSchedule() cfg = multigrid.init_multigrid(cfg) if cfg.MULTIGRID.LONG_CYCLE: cfg, _ = multigrid.update_long_cycle(cfg, cur_epoch=0) # Print config. logger.info("Train with config:") logger.info(pprint.pformat(cfg)) # Build the video model and print model statistics. model = build_model(cfg) if du.is_master_proc() and cfg.LOG_MODEL_INFO: misc.log_model_info(model, cfg, use_train_input=True) # Construct the optimizer. optimizer = optim.construct_optimizer(model, cfg) # Create a GradScaler for mixed precision training scaler = torch.cuda.amp.GradScaler(enabled=cfg.TRAIN.MIXED_PRECISION) # Load a checkpoint to resume training if applicable. if cfg.TRAIN.AUTO_RESUME and cu.has_checkpoint(cfg.OUTPUT_DIR): logger.info("Load from last checkpoint.") last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR, task=cfg.TASK) if last_checkpoint is not None: checkpoint_epoch = cu.load_checkpoint( last_checkpoint, model, cfg.NUM_GPUS > 1, optimizer, scaler if cfg.TRAIN.MIXED_PRECISION else None, ) start_epoch = checkpoint_epoch + 1 elif "ssl_eval" in cfg.TASK: last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR, task="ssl") checkpoint_epoch = cu.load_checkpoint( last_checkpoint, model, cfg.NUM_GPUS > 1, optimizer, scaler if cfg.TRAIN.MIXED_PRECISION else None, epoch_reset=True, clear_name_pattern=cfg.TRAIN.CHECKPOINT_CLEAR_NAME_PATTERN, ) start_epoch = checkpoint_epoch + 1 else: start_epoch = 0 elif cfg.TRAIN.CHECKPOINT_FILE_PATH != "": logger.info("Load from given checkpoint file.") checkpoint_epoch = cu.load_checkpoint( cfg.TRAIN.CHECKPOINT_FILE_PATH, model, cfg.NUM_GPUS > 1, optimizer, scaler if cfg.TRAIN.MIXED_PRECISION else None, inflation=cfg.TRAIN.CHECKPOINT_INFLATE, convert_from_caffe2=cfg.TRAIN.CHECKPOINT_TYPE == "caffe2", epoch_reset=cfg.TRAIN.CHECKPOINT_EPOCH_RESET, clear_name_pattern=cfg.TRAIN.CHECKPOINT_CLEAR_NAME_PATTERN, ) start_epoch = checkpoint_epoch + 1 else: start_epoch = 0 # Create the video train and val loaders. train_loader = loader.construct_loader(cfg, "train") val_loader = loader.construct_loader(cfg, "val") precise_bn_loader = (loader.construct_loader( cfg, "train", is_precise_bn=True) if cfg.BN.USE_PRECISE_STATS else None) # if ( # cfg.TASK == "ssl" # and cfg.MODEL.MODEL_NAME == "ContrastiveModel" # and cfg.CONTRASTIVE.KNN_ON # ): # if hasattr(model, "module"): # model.module.init_knn_labels(train_loader) # else: # model.init_knn_labels(train_loader) # Create meters. if cfg.DETECTION.ENABLE: train_meter = AVAMeter(len(train_loader), cfg, mode="train") val_meter = AVAMeter(len(val_loader), cfg, mode="val") else: train_meter = TrainMeter(1e6, cfg) val_meter = ValMeter(1e6, cfg) # set up writer for logging to Tensorboard format. if cfg.TENSORBOARD.ENABLE and du.is_master_proc( cfg.NUM_GPUS * cfg.NUM_SHARDS): writer = tb.TensorboardWriter(cfg) else: writer = None # Perform the training loop. logger.info("Start epoch: {}".format(start_epoch + 1)) epoch_timer = EpochTimer() for cur_epoch in range(start_epoch, cfg.SOLVER.MAX_EPOCH): if cur_epoch > 0 and cfg.DATA.LOADER_CHUNK_SIZE > 0: num_chunks = math.ceil(cfg.DATA.LOADER_CHUNK_OVERALL_SIZE / cfg.DATA.LOADER_CHUNK_SIZE) skip_rows = (cur_epoch) % num_chunks * cfg.DATA.LOADER_CHUNK_SIZE logger.info( f"=================+++ num_chunks {num_chunks} skip_rows {skip_rows}" ) cfg.DATA.SKIP_ROWS = skip_rows logger.info(f"|===========| skip_rows {skip_rows}") train_loader = loader.construct_loader(cfg, "train") loader.shuffle_dataset(train_loader, cur_epoch) if cfg.MULTIGRID.LONG_CYCLE: cfg, changed = multigrid.update_long_cycle(cfg, cur_epoch) if changed: ( model, optimizer, train_loader, val_loader, precise_bn_loader, train_meter, val_meter, ) = build_trainer(cfg) # Load checkpoint. if cu.has_checkpoint(cfg.OUTPUT_DIR): last_checkpoint = cu.get_last_checkpoint(cfg.OUTPUT_DIR, task=cfg.TASK) assert "{:05d}.pyth".format(cur_epoch) in last_checkpoint else: last_checkpoint = cfg.TRAIN.CHECKPOINT_FILE_PATH logger.info("Load from {}".format(last_checkpoint)) cu.load_checkpoint(last_checkpoint, model, cfg.NUM_GPUS > 1, optimizer) # Shuffle the dataset. loader.shuffle_dataset(train_loader, cur_epoch) if hasattr(train_loader.dataset, "_set_epoch_num"): train_loader.dataset._set_epoch_num(cur_epoch) # Train for one epoch. epoch_timer.epoch_tic() train_epoch( train_loader, model, optimizer, scaler, train_meter, cur_epoch, cfg, writer, ) epoch_timer.epoch_toc() logger.info( f"Epoch {cur_epoch} takes {epoch_timer.last_epoch_time():.2f}s. Epochs " f"from {start_epoch} to {cur_epoch} take " f"{epoch_timer.avg_epoch_time():.2f}s in average and " f"{epoch_timer.median_epoch_time():.2f}s in median.") logger.info( f"For epoch {cur_epoch}, each iteraction takes " f"{epoch_timer.last_epoch_time()/len(train_loader):.2f}s in average. " f"From epoch {start_epoch} to {cur_epoch}, each iteraction takes " f"{epoch_timer.avg_epoch_time()/len(train_loader):.2f}s in average." ) is_checkp_epoch = (cu.is_checkpoint_epoch( cfg, cur_epoch, None if multigrid is None else multigrid.schedule, ) or cur_epoch == cfg.SOLVER.MAX_EPOCH - 1) is_eval_epoch = misc.is_eval_epoch( cfg, cur_epoch, None if multigrid is None else multigrid.schedule) # Compute precise BN stats. if ((is_checkp_epoch or is_eval_epoch) and cfg.BN.USE_PRECISE_STATS and len(get_bn_modules(model)) > 0): calculate_and_update_precise_bn( precise_bn_loader, model, min(cfg.BN.NUM_BATCHES_PRECISE, len(precise_bn_loader)), cfg.NUM_GPUS > 0, ) _ = misc.aggregate_sub_bn_stats(model) # Save a checkpoint. if is_checkp_epoch: cu.save_checkpoint( cfg.OUTPUT_DIR, model, optimizer, cur_epoch, cfg, scaler if cfg.TRAIN.MIXED_PRECISION else None, ) # Evaluate the model on validation set. if is_eval_epoch: eval_epoch( val_loader, model, val_meter, cur_epoch, cfg, train_loader, writer, ) if writer is not None: writer.close() result_string = "Top1 Acc: {:.2f} Top5 Acc: {:.2f} MEM: {:.2f}" "".format( 100 - val_meter.min_top1_err, 100 - val_meter.min_top5_err, misc.gpu_mem_usage(), ) logger.info("training done: {}".format(result_string)) return result_string