video3d/trainer.py

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