video3d/trainer_ddp.py

import os
import os.path as osp
import glob
from datetime import datetime
import random
import torch
import video3d.utils.meters as meters
import video3d.utils.misc as misc

from video3d.dataloaders_ddp import get_sequence_loader_quadrupeds

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

def indefinite_generator_from_list(loaders):
 while True:
 random_idx = random.randint(0, len(loaders)-1)
 for x in loaders[random_idx]:
 yield x
 break

def definite_generator(loader):
 for x in loader:
 yield x
 while True:
 yield None


class TrainerDDP:
 def __init__(self, cfgs, model):
 self.cfgs = cfgs
 self.is_dry_run = cfgs.get('is_dry_run', False)

 self.rank = cfgs.get('rank', 0)
 self.world_size = cfgs.get('world_size', 1)
 self.use_ddp = cfgs.get('use_ddp', True)

 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.reset_epoch = cfgs.get('reset_epoch', False)
 self.finetune_ckpt = cfgs.get('finetune_ckpt', None)
 # print('!!!!!!!!!!!!!!!!!!!!!!!!!!')
 print(f'reset epoch: {self.reset_epoch}')
 # print('!!!!!!!!!!!!!!!!!!!!!!!!!!')
 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.flow_bool = cfgs.get('flow_bool', 0)

 if len(self.train_data_dir) <= 10 and len(self.val_data_dir) <= 10:
 self.train_loader, self.val_loader, self.test_loader = model.get_data_loaders_ddp(cfgs, self.dataset, self.rank, self.world_size, 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, flow_bool=self.flow_bool)
 else:
 # for 128 categories specific training
 self.train_loader, self.val_loader, self.test_loader = self.get_efficient_data_loaders_ddp(
 cfgs,
 self.batch_size,
 self.num_workers,
 self.in_image_size,
 self.out_image_size
 )

 print(self.train_loader, self.val_loader, self.test_loader)
 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_ddp(cfgs, 'cub', self.rank, self.world_size, 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_ddp(cfgs, 'kaggle', self.rank, self.world_size, 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
 
 dataloader_length = max([len(loader) for loader in self.train_loader]) * len(self.train_loader)
 print("Total length of data loader is: ", dataloader_length)
 
 total_epoch = int(self.num_iterations / dataloader_length) + 1

 print(f'run for {total_epoch} epochs')
 
 print('is_main_process()?', misc.is_main_process())

 for k, v in cfgs.items():
 if 'epoch' in k:
 if isinstance(v, list):
 new_v = [int(total_epoch * x / 120) + 1 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')

 self.use_wandb = cfgs.get('use_wandb', False)

 def get_efficient_data_loaders_ddp(self, cfgs, batch_size, num_workers, in_image_size, out_image_size):
 train_loader = val_loader = test_loader = None
 color_jitter_train = cfgs.get('color_jitter_train', None)
 color_jitter_val = cfgs.get('color_jitter_val', None)
 random_flip_train = cfgs.get('random_flip_train', False)

 data_loader_mode = cfgs.get('data_loader_mode', 'n_frame')
 skip_beginning = cfgs.get('skip_beginning', 4)
 skip_end = cfgs.get('skip_end', 4)
 num_sample_frames = cfgs.get('num_sample_frames', 2)
 min_seq_len = cfgs.get('min_seq_len', 10)
 max_seq_len = cfgs.get('max_seq_len', 10)
 debug_seq = cfgs.get('debug_seq', False)
 random_sample_train_frames = cfgs.get('random_sample_train_frames', False)
 shuffle_train_seqs = cfgs.get('shuffle_train_seqs', False)
 random_sample_val_frames = cfgs.get('random_sample_val_frames', False)
 load_background = cfgs.get('background_mode', 'none') == 'background'
 rgb_suffix = cfgs.get('rgb_suffix', '.png')
 load_dino_feature = cfgs.get('load_dino_feature', False)
 load_dino_cluster = cfgs.get('load_dino_cluster', False)
 dino_feature_dim = cfgs.get('dino_feature_dim', 64)

 enhance_back_view = cfgs.get('enhance_back_view', False)
 enhance_back_view_path = cfgs.get('enhance_back_view_path', None)

 override_categories = None

 get_loader_ddp = lambda **kwargs: get_sequence_loader_quadrupeds(
 mode=data_loader_mode,
 num_workers=num_workers,
 in_image_size=in_image_size,
 out_image_size=out_image_size,
 debug_seq=debug_seq,
 skip_beginning=skip_beginning,
 skip_end=skip_end,
 num_sample_frames=num_sample_frames,
 min_seq_len=min_seq_len,
 max_seq_len=max_seq_len,
 load_background=load_background,
 rgb_suffix=rgb_suffix,
 load_dino_feature=load_dino_feature,
 load_dino_cluster=load_dino_cluster,
 dino_feature_dim=dino_feature_dim,
 flow_bool=0,
 enhance_back_view=enhance_back_view,
 enhance_back_view_path=enhance_back_view_path,
 override_categories=override_categories,
 **kwargs)
 
 # just the train now
 print(f"Loading training data...")
 val_image_num = cfgs.get('few_shot_val_image_num', 5)
 # the train_data_dir is a dict and will go into the original dataset type

 #TODO: very hack here, directly assign first 7 as original categories
 o_class = ["horse", "elephant", "zebra", "cow", "giraffe", "sheep", "bear"]
 self.original_categories_paths = {}
 self.few_shot_categories_paths = {}
 self.original_val_data_path = {}
 
 for k,v in self.train_data_dir.items():
 if k in o_class:
 self.original_categories_paths.update({k: v})
 self.original_val_data_path.update({k: self.val_data_dir[k]})
 else:
 self.few_shot_categories_paths.update({k:v})
 self.new_classes_num = len(self.few_shot_categories_paths)
 self.original_classes_num = len(self.original_categories_paths)

 train_loader = get_loader_ddp(
 original_data_dirs=self.original_categories_paths, 
 few_shot_data_dirs=self.few_shot_categories_paths, 
 original_num=self.original_classes_num, 
 few_shot_num=self.new_classes_num, 
 rank=self.rank, 
 world_size=self.world_size, 
 batch_size=batch_size, 
 is_validation=False, 
 val_image_num=val_image_num, 
 shuffle=shuffle_train_seqs, 
 dense_sample=True, 
 color_jitter=color_jitter_train, 
 random_flip=random_flip_train
 )
 val_loader = get_loader_ddp(
 original_data_dirs=self.original_val_data_path, 
 few_shot_data_dirs=self.few_shot_categories_paths, 
 original_num=self.original_classes_num, 
 few_shot_num=self.new_classes_num, 
 rank=self.rank, 
 world_size=self.world_size, 
 batch_size=1, 
 is_validation=True, 
 val_image_num=val_image_num, 
 shuffle=False, 
 dense_sample=True, 
 color_jitter=color_jitter_val, 
 random_flip=False
 )

 test_loader = None

 return train_loader, val_loader, test_loader
 
 
 def load_checkpoint(self, optim=True, ckpt_path=None):
 """Search the specified/latest checkpoint in checkpoint_dir and load the model and optimizer."""
 if ckpt_path is not None:
 checkpoint_path = ckpt_path
 self.checkpoint_name = osp.basename(checkpoint_path)
 elif 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)
 # print(cp)
 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']
 
 if 'classes_vectors' in cp:
 self.model.classes_vectors = cp['classes_vectors']
 
 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_last_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, 'last.pth')
 if os.path.exists(checkpoint_path):
 os.remove(checkpoint_path)
 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)

 def save_clean_checkpoint(self, path):
 """Save model state only to specified path."""
 torch.save(self.model.get_model_state(), path)

 def test(self):
 """Perform testing."""
 self.model.to(self.device)
 epoch, self.total_iter = self.load_checkpoint(optim=False)

 if self.use_ddp:
 self.model.ddp(self.rank, self.world_size)
 self.model.set_eval()

 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
 # from IPython import embed; embed()
 if self.resume:
 start_epoch, self.total_iter = self.load_checkpoint(optim=True)
 
 if self.reset_epoch:
 start_epoch = 0
 self.total_iter = 0

 if start_epoch == 0 and self.total_iter ==0 and self.finetune_ckpt is not None:
 _, _ = self.load_checkpoint(optim=True, ckpt_path=self.finetune_ckpt)

 # distribute model
 if self.use_ddp:
 self.model.ddp(self.rank, self.world_size)

 # train with cub
 if self.train_with_cub:
 self.cub_train_data_iterator = indefinite_generator(self.train_loader_cub)

 # initialize tensorboard logger
 if misc.is_main_process() and self.use_logger:
 if self.use_wandb:
 import wandb
 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")
 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_from_list(self.val_loader) if self.visualize_validation else indefinite_generator_from_list(self.train_loader)
 # self.viz_data_iterator = iter(self.viz_data_iterator)
 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):
 torch.distributed.barrier()
 metrics = self.run_epoch(epoch)
 if self.rank == 0:
 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'))
 if self.rank == 0:
 print(f"Training completed for all {epoch+1} epochs.")

 def dry_run(self):
 print(f'rank: {self.rank}, dry_run!!!!!')
 self.dry_run_iters = self.cfgs.get('dr_iters', 2)
 self.resume = self.cfgs.get('dr_resume', True)
 self.use_logger = self.cfgs.get('dr_use_logger', True)
 self.log_freq_losses = self.cfgs.get('dr_log_freq_losses', 1)
 self.save_result_freq = self.cfgs.get('dr_save_result_freq', 1)
 self.log_freq_images = self.cfgs.get('dr_log_freq_images', 1)
 self.log_train_images = self.cfgs.get('dr_log_train_images', True)
 self.visualize_validation = self.cfgs.get('dr_visualize_validation', True)
 self.num_epochs = self.cfgs.get('dr_num_epochs', 1)
 self.train()

 def run_epoch(self, epoch):
 metrics = self.make_metrics()

 self.model.set_train()

 max_loader_len = max([len(loader) for loader in self.train_loader])
 train_generators = [indefinite_generator(loader) for loader in self.train_loader]
 
 iteration = 0
 while iteration < max_loader_len * len(self.train_loader):
 for generator in train_generators:
 batch = next(generator)

 self.total_iter += 1

 if self.total_iter % 4000 == 0:
 self.save_last_checkpoint(epoch+1, self.total_iter, optim=True)

 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()

 if self.model.enable_disc and (self.model.mask_discriminator_iter[0] < self.total_iter) and (self.model.mask_discriminator_iter[1] > self.total_iter):
 # the discriminator training
 discriminator_loss_dict, grad_loss = self.model.discriminator_step()
 m.update(
 {
 'mask_disc_loss_discriminator': discriminator_loss_dict['discriminator_loss'] - grad_loss, 
 'mask_disc_loss_discriminator_grad': grad_loss,
 'mask_disc_loss_discriminator_rv': discriminator_loss_dict['discriminator_loss_rv'],
 'mask_disc_loss_discriminator_iv': discriminator_loss_dict['discriminator_loss_iv'],
 'mask_disc_loss_discriminator_gt': discriminator_loss_dict['discriminator_loss_gt']
 }
 )
 self.logger.add_histogram('train_'+'discriminator_logits/random_view', discriminator_loss_dict['d_rv'], self.total_iter)
 if discriminator_loss_dict['d_iv'] is not None:
 self.logger.add_histogram('train_'+'discriminator_logits/input_view', discriminator_loss_dict['d_iv'], self.total_iter)
 if discriminator_loss_dict['d_gt'] is not None:
 self.logger.add_histogram('train_'+'discriminator_logits/gt_view', discriminator_loss_dict['d_gt'], self.total_iter)

 metrics.update(m, total_im_num)
 if self.rank == 0:
 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 misc.is_main_process() and self.use_logger:
 if self.rank == 0 and 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.rank == 0 and 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.rank == 0 and 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=False)
 if self.fix_viz_batch:
 print(f'fix_viz_batch:{self.fix_viz_batch}')
 batch = self.viz_batch
 else:
 batch = next(self.viz_data_iterator)
 if self.visualize_validation:
 import time
 vis_start = time.time()
 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)
 vis_end = time.time()
 print(f"vis time: {vis_end - vis_start}")
 for name, loss in m.items():
 if self.rank == 0:
 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.rank == 0 and 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)
 if self.rank == 0:
 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()
 if self.is_dry_run and iteration >= self.dry_run_iters:
 break

 iteration += 1

 self.model.scheduler_step()
 return metrics