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| import os | |
| import os.path as osp | |
| import math | |
| import glob | |
| from datetime import datetime | |
| import imageio | |
| import torch | |
| import video3d.utils.meters as meters | |
| import video3d.utils.misc as misc | |
| import wandb | |
| def sample_frames(batch, num_sample_frames, iteration, stride=1): | |
| ## window slicing sampling | |
| images, masks, flows, bboxs, bg_image, seq_idx, frame_idx = batch | |
| num_seqs, total_num_frames = images.shape[:2] | |
| # start_frame_idx = iteration % (total_num_frames - num_sample_frames +1) | |
| ## forward and backward | |
| num_windows = total_num_frames - num_sample_frames +1 | |
| start_frame_idx = (iteration * stride) % (2*num_windows) | |
| ## x' = (2n-1)/2 - |(2n-1)/2 - x| : 0,1,2,3,4,5 -> 0,1,2,2,1,0 | |
| mid_val = (2*num_windows -1) /2 | |
| start_frame_idx = int(mid_val - abs(mid_val -start_frame_idx)) | |
| new_batch = images[:, start_frame_idx:start_frame_idx+num_sample_frames], \ | |
| masks[:, start_frame_idx:start_frame_idx+num_sample_frames], \ | |
| flows[:, start_frame_idx:start_frame_idx+num_sample_frames-1], \ | |
| bboxs[:, start_frame_idx:start_frame_idx+num_sample_frames], \ | |
| bg_image, \ | |
| seq_idx, \ | |
| frame_idx[:, start_frame_idx:start_frame_idx+num_sample_frames] | |
| return new_batch | |
| def indefinite_generator(loader): | |
| while True: | |
| for x in loader: | |
| yield x | |
| class Trainer: | |
| def __init__(self, cfgs, model): | |
| self.cfgs = cfgs | |
| self.device = cfgs.get('device', 'cpu') | |
| self.num_epochs = cfgs.get('num_epochs', 1) | |
| # The logic is, if the num_iterations is set in the cfg | |
| # for any 'epoch' in cfg, I rescale it to (epoch / 120) * epoch_now, as in horse exp | |
| # for any 'iter' in cfg, I just keep them the same | |
| self.num_iterations = cfgs.get('num_iterations', 0) | |
| if self.num_iterations != 0: | |
| self.use_total_iterations = True | |
| else: | |
| self.use_total_iterations = False | |
| self.num_sample_frames = cfgs.get('num_sample_frames', 100) | |
| self.sample_frame_stride = cfgs.get('sample_frame_stride', 1) | |
| self.checkpoint_dir = cfgs.get('checkpoint_dir', 'results') | |
| self.save_checkpoint_freq = cfgs.get('save_checkpoint_freq', 1) | |
| self.keep_num_checkpoint = cfgs.get('keep_num_checkpoint', 2) # -1 for keeping all checkpoints | |
| self.resume = cfgs.get('resume', True) | |
| self.use_logger = cfgs.get('use_logger', True) | |
| self.log_freq_images = cfgs.get('log_freq_images', 1000) | |
| self.log_train_images = cfgs.get('log_train_images', False) | |
| self.log_freq_losses = cfgs.get('log_freq_losses', 100) | |
| self.visualize_validation = cfgs.get('visualize_validation', False) | |
| self.fix_viz_batch = cfgs.get('fix_viz_batch', False) | |
| self.archive_code = cfgs.get('archive_code', True) | |
| self.checkpoint_name = cfgs.get('checkpoint_name', None) | |
| self.test_result_dir = cfgs.get('test_result_dir', None) | |
| self.validate = cfgs.get('validate', False) | |
| self.current_epoch = 0 | |
| self.logger = None | |
| self.viz_input = None | |
| self.dataset = cfgs.get('dataset', 'video') | |
| self.train_with_cub = cfgs.get('train_with_cub', False) | |
| self.train_with_kaggle = cfgs.get('train_with_kaggle', False) | |
| self.cub_start_epoch = cfgs.get('cub_start_epoch', 0) | |
| self.metrics_trace = meters.MetricsTrace() | |
| self.make_metrics = lambda m=None: meters.StandardMetrics(m) | |
| self.batch_size = cfgs.get('batch_size', 64) | |
| self.in_image_size = cfgs.get('in_image_size', 256) | |
| self.out_image_size = cfgs.get('out_image_size', 256) | |
| self.num_workers = cfgs.get('num_workers', 4) | |
| self.run_train = cfgs.get('run_train', False) | |
| self.train_data_dir = cfgs.get('train_data_dir', None) | |
| self.val_data_dir = cfgs.get('val_data_dir', None) | |
| self.run_test = cfgs.get('run_test', False) | |
| self.test_data_dir = cfgs.get('test_data_dir', None) | |
| self.train_loader, self.val_loader, self.test_loader = model.get_data_loaders(cfgs, self.dataset, in_image_size=self.in_image_size, out_image_size=self.out_image_size, batch_size=self.batch_size, num_workers=self.num_workers, run_train=self.run_train, run_test=self.run_test, train_data_dir=self.train_data_dir, val_data_dir=self.val_data_dir, test_data_dir=self.test_data_dir) | |
| if self.train_with_cub: | |
| self.batch_size_cub = cfgs.get('batch_size_cub', 64) | |
| self.data_dir_cub = cfgs.get('data_dir_cub', None) | |
| self.train_loader_cub, self.val_loader_cub, self.test_loader_cub = model.get_data_loaders(cfgs, 'cub', in_image_size=self.in_image_size, batch_size=self.batch_size_cub, num_workers=self.num_workers, run_train=self.run_train, run_test=self.run_test, train_data_dir=self.data_dir_cub, val_data_dir=self.data_dir_cub, test_data_dir=self.data_dir_cub) | |
| if self.train_with_kaggle: | |
| self.batch_size_kaggle = cfgs.get('batch_size_kaggle', 64) | |
| self.data_dir_kaggle = cfgs.get('data_dir_kaggle', None) | |
| self.train_loader_kaggle, self.val_loader_kaggle, self.test_loader_kaggle = model.get_data_loaders(cfgs, 'kaggle', in_image_size=self.in_image_size, batch_size=self.batch_size_kaggle, num_workers=self.num_workers, run_train=self.run_train, run_test=self.run_test, train_data_dir=self.data_dir_kaggle, val_data_dir=self.data_dir_kaggle, test_data_dir=self.data_dir_kaggle) | |
| if self.use_total_iterations: | |
| # reset the epoch related cfgs | |
| train_data_dir = cfgs.get("train_data_dir", None) | |
| if isinstance(train_data_dir, str): | |
| num_of_classes = 1 | |
| elif isinstance(train_data_dir, dict): | |
| num_of_classes = len(train_data_dir) | |
| dataloader_length = 0 | |
| for class_idx in range(num_of_classes): | |
| dataloader_length += len(self.train_loader[class_idx]) | |
| total_epoch = int(self.num_iterations / dataloader_length) + 1 | |
| print(f'run for {total_epoch} epochs') | |
| for k, v in cfgs.items(): | |
| if 'epoch' in k: | |
| if isinstance(v, list): | |
| new_v = [int(total_epoch * x / 120) for x in v] | |
| cfgs[k] = new_v | |
| elif isinstance(v, int): | |
| new_v = int(total_epoch * v / 120) + 1 | |
| cfgs[k] = new_v | |
| else: | |
| continue | |
| self.num_epochs = total_epoch | |
| self.cub_start_epoch = cfgs.get('cub_start_epoch', 0) | |
| self.cfgs = cfgs | |
| self.model = model(cfgs) | |
| self.model.trainer = self | |
| self.save_result_freq = cfgs.get('save_result_freq', None) | |
| self.train_result_dir = osp.join(self.checkpoint_dir, 'results') | |
| def load_checkpoint(self, optim=True): | |
| """Search the specified/latest checkpoint in checkpoint_dir and load the model and optimizer.""" | |
| if self.checkpoint_name is not None: | |
| checkpoint_path = osp.join(self.checkpoint_dir, self.checkpoint_name) | |
| else: | |
| checkpoints = sorted(glob.glob(osp.join(self.checkpoint_dir, '*.pth'))) | |
| if len(checkpoints) == 0: | |
| return 0, 0 | |
| checkpoint_path = checkpoints[-1] | |
| self.checkpoint_name = osp.basename(checkpoint_path) | |
| print(f"Loading checkpoint from {checkpoint_path}") | |
| cp = torch.load(checkpoint_path, map_location=self.device) | |
| self.model.load_model_state(cp) | |
| if optim: | |
| self.model.load_optimizer_state(cp) | |
| self.metrics_trace = cp['metrics_trace'] | |
| epoch = cp['epoch'] | |
| total_iter = cp['total_iter'] | |
| return epoch, total_iter | |
| def save_checkpoint(self, epoch, total_iter=0, optim=True): | |
| """Save model, optimizer, and metrics state to a checkpoint in checkpoint_dir for the specified epoch.""" | |
| misc.xmkdir(self.checkpoint_dir) | |
| checkpoint_path = osp.join(self.checkpoint_dir, f'checkpoint{epoch:03}.pth') | |
| state_dict = self.model.get_model_state() | |
| if optim: | |
| optimizer_state = self.model.get_optimizer_state() | |
| state_dict = {**state_dict, **optimizer_state} | |
| state_dict['metrics_trace'] = self.metrics_trace | |
| state_dict['epoch'] = epoch | |
| state_dict['total_iter'] = total_iter | |
| print(f"Saving checkpoint to {checkpoint_path}") | |
| torch.save(state_dict, checkpoint_path) | |
| if self.keep_num_checkpoint > 0: | |
| misc.clean_checkpoint(self.checkpoint_dir, keep_num=self.keep_num_checkpoint) | |
| def save_clean_checkpoint(self, path): | |
| """Save model state only to specified path.""" | |
| torch.save(self.model.get_model_state(), path) | |
| def reset_viz_data_iterator(self): | |
| self.viz_data_iterator = iter(self.val_loader) if self.visualize_validation else iter(self.train_loader) | |
| def reset_cub_train_data_iterator(self): | |
| self.cub_train_data_iterator = iter(self.train_loader_cub) | |
| def reset_cub_viz_data_iterator(self): | |
| self.cub_viz_data_iterator = iter(self.val_loader_cub) if self.visualize_validation else iter(self.train_loader_cub) | |
| def test(self): | |
| """Perform testing.""" | |
| self.model.to(self.device) | |
| self.model.set_eval() | |
| epoch, self.total_iter = self.load_checkpoint(optim=False) | |
| if self.test_result_dir is None: | |
| self.test_result_dir = osp.join(self.checkpoint_dir, f'test_results_{self.checkpoint_name}'.replace('.pth', '')) | |
| print(f"Saving testing results to {self.test_result_dir}") | |
| with torch.no_grad(): | |
| for iteration, batch in enumerate(self.test_loader): | |
| m = self.model.forward(batch, epoch=epoch, iter=iteration, total_iter=self.total_iter, save_results=True, save_dir=self.test_result_dir, which_data=self.dataset, is_training=False) | |
| print(f"T{epoch:04}/{iteration:05}") | |
| score_path = osp.join(self.test_result_dir, 'all_metrics.txt') | |
| # self.model.save_scores(score_path) | |
| def train(self): | |
| """Perform training.""" | |
| # archive code and configs | |
| if self.archive_code: | |
| misc.archive_code(osp.join(self.checkpoint_dir, 'archived_code.zip'), filetypes=['.py']) | |
| misc.dump_yaml(osp.join(self.checkpoint_dir, 'configs.yml'), self.cfgs) | |
| # initialize | |
| start_epoch = 0 | |
| self.total_iter = 0 | |
| self.metrics_trace.reset() | |
| self.model.to(self.device) | |
| self.model.reset_optimizers() | |
| # resume from checkpoint | |
| if self.resume: | |
| start_epoch, self.total_iter = self.load_checkpoint(optim=True) | |
| # train with cub | |
| if self.train_with_cub: | |
| self.cub_train_data_iterator = indefinite_generator(self.train_loader_cub) | |
| # initialize tensorboard logger | |
| if self.use_logger: | |
| wandb.tensorboard.patch(root_logdir=osp.join(self.checkpoint_dir, 'logs', datetime.now().strftime("%Y%m%d-%H%M%S"))) | |
| wandb.init(name=self.checkpoint_dir.split("/")[-1], project="APT36K") | |
| #wandb.tensorboard.patch(save=False, tensorboard_x=True) | |
| from torch.utils.tensorboard import SummaryWriter | |
| self.logger = SummaryWriter(osp.join(self.checkpoint_dir, 'logs', datetime.now().strftime("%Y%m%d-%H%M%S")), flush_secs=10) | |
| self.viz_data_iterator = indefinite_generator(self.val_loader) if self.visualize_validation else indefinite_generator(self.train_loader) | |
| if self.fix_viz_batch: | |
| self.viz_batch = next(self.viz_data_iterator) | |
| # train with cub | |
| if self.train_with_cub: | |
| self.cub_viz_data_iterator = indefinite_generator(self.val_loader_cub) if self.visualize_validation else indefinite_generator(self.train_loader_cub) | |
| if self.fix_viz_batch: | |
| self.viz_batch_cub = next(self.cub_viz_data_iterator) | |
| # run epochs | |
| epoch = 0 | |
| for epoch in range(start_epoch, self.num_epochs): | |
| metrics = self.run_epoch(epoch) | |
| self.metrics_trace.append("train", metrics) | |
| if (epoch+1) % self.save_checkpoint_freq == 0: | |
| self.save_checkpoint(epoch+1, total_iter=self.total_iter, optim=True) | |
| if self.cfgs.get('pyplot_metrics', True): | |
| self.metrics_trace.plot(pdf_path=osp.join(self.checkpoint_dir, 'metrics.pdf')) | |
| self.metrics_trace.save(osp.join(self.checkpoint_dir, 'metrics.json')) | |
| wandb.finish() | |
| print(f"Training completed for all {epoch+1} epochs.") | |
| def run_epoch(self, epoch): | |
| metrics = self.make_metrics() | |
| self.model.set_train() | |
| for iteration, batch in enumerate(self.train_loader): | |
| self.total_iter += 1 | |
| num_seqs, num_frames = batch[0].shape[:2] | |
| total_im_num = num_seqs*num_frames | |
| m = self.model.forward(batch, epoch=epoch, iter=iteration, total_iter=self.total_iter, which_data=self.dataset, is_training=True) | |
| if self.train_with_cub and epoch >= self.cub_start_epoch: | |
| batch_cub = next(self.cub_train_data_iterator) | |
| num_seqs, num_frames = batch_cub[0].shape[:2] | |
| total_im_num += num_seqs*num_frames | |
| m_cub = self.model.forward(batch_cub, epoch=epoch, iter=iteration, total_iter=self.total_iter, which_data='cub', is_training=True) | |
| m.update({'cub_'+k: v for k,v in m_cub.items()}) | |
| m['total_loss'] = self.model.total_loss | |
| self.model.backward() | |
| metrics.update(m, total_im_num) | |
| print(f"T{epoch:04}/{iteration:05}/{metrics}") | |
| ## reset optimizers | |
| if self.cfgs.get('opt_reset_every_iter', 0) > 0 and self.total_iter < self.cfgs.get('opt_reset_end_iter', 0): | |
| if self.total_iter % self.cfgs.get('opt_reset_every_iter', 0) == 0: | |
| self.model.reset_optimizers() | |
| if self.use_logger: | |
| if self.total_iter % self.log_freq_losses == 0: | |
| for name, loss in m.items(): | |
| label = f'cub_loss_train/{name[4:]}' if 'cub' in name else f'loss_train/{name}' | |
| self.logger.add_scalar(label, loss, self.total_iter) | |
| if self.save_result_freq is not None and self.total_iter % self.save_result_freq == 0: | |
| with torch.no_grad(): | |
| m = self.model.forward(batch, epoch=epoch, iter=iteration, total_iter=self.total_iter, save_results=True, save_dir=self.train_result_dir, which_data=self.dataset, is_training=False) | |
| torch.cuda.empty_cache() | |
| if self.total_iter % self.log_freq_images == 0: | |
| with torch.no_grad(): | |
| if self.log_train_images: | |
| m = self.model.forward(batch, epoch=epoch, iter=iteration, viz_logger=self.logger, total_iter=self.total_iter, which_data=self.dataset, logger_prefix='train_', is_training=True) | |
| if self.fix_viz_batch: | |
| batch = self.viz_batch | |
| elif self.visualize_validation: | |
| batch = next(self.viz_data_iterator) | |
| # try: | |
| # batch = next(self.viz_data_iterator) | |
| # except: # iterator exhausted | |
| # self.reset_viz_data_iterator() | |
| # batch = next(self.viz_data_iterator) | |
| m = self.model.forward(batch, epoch=epoch, iter=iteration, viz_logger=self.logger, total_iter=self.total_iter, which_data=self.dataset, logger_prefix='val_', is_training=False) | |
| for name, loss in m.items(): | |
| self.logger.add_scalar(f'loss_val/{name}', loss, self.total_iter) | |
| if self.train_with_cub and epoch >= self.cub_start_epoch: | |
| if self.log_train_images: | |
| m = self.model.forward(batch_cub, epoch=epoch, iter=iteration, viz_logger=self.logger, total_iter=self.total_iter, which_data='cub', logger_prefix='cub_train_', is_training=True) | |
| if self.fix_viz_batch: | |
| batch_cub = self.viz_batch_cub | |
| elif self.visualize_validation: | |
| batch_cub = next(self.cub_viz_data_iterator) | |
| # try: | |
| # batch = next(self.viz_data_iterator) | |
| # except: # iterator exhausted | |
| # self.reset_viz_data_iterator() | |
| # batch = next(self.viz_data_iterator) | |
| m = self.model.forward(batch_cub, epoch=epoch, iter=iteration, viz_logger=self.logger, total_iter=self.total_iter, which_data='cub', logger_prefix='cub_val_', is_training=False) | |
| for name, loss in m.items(): | |
| self.logger.add_scalar(f'cub_loss_val/{name}', loss, self.total_iter) | |
| torch.cuda.empty_cache() | |
| self.model.scheduler_step() | |
| return metrics | |