text: # Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
  #
  # NVIDIA CORPORATION and its licensors retain all intellectual property
  # and proprietary rights in and to this software, related documentation
  # and any modifications thereto.  Any use, reproduction, disclosure or
  # distribution of this software and related documentation without an express
  # license agreement from NVIDIA CORPORATION is strictly prohibited.
  
  import os
  import time
  import copy
  import json
  import pickle
  import psutil
  import PIL.Image
  import numpy as np
  import torch
  import dnnlib
  from torch_utils import misc
  from torch_utils import training_stats
  from torch_utils.ops import conv2d_gradfix
  from torch_utils.ops import grid_sample_gradfix
  
  import legacy
  from metrics import metric_main
  
  #----------------------------------------------------------------------------
  
  def setup_snapshot_image_grid(training_set, random_seed=0):
      rnd = np.random.RandomState(random_seed)
      gw = np.clip(7680 // training_set.image_shape[2], 7, 32)
      gh = np.clip(4320 // training_set.image_shape[1], 4, 32)
  
      # No labels => show random subset of training samples.
      if not training_set.has_labels:
          all_indices = list(range(len(training_set)))
          rnd.shuffle(all_indices)
          grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)]
  
      else:
          # Group training samples by label.
          label_groups = dict() # label => [idx, ...]
          for idx in range(len(training_set)):
              label = tuple(training_set.get_details(idx).raw_label.flat[::-1])
              if label not in label_groups:
                  label_groups[label] = []
              label_groups[label].append(idx)
  
          # Reorder.
          label_order = sorted(label_groups.keys())
          for label in label_order:
              rnd.shuffle(label_groups[label])
  
          # Organize into grid.
          grid_indices = []
          for y in range(gh):
              label = label_order[y % len(label_order)]
              indices = label_groups[label]
              grid_indices += [indices[x % len(indices)] for x in range(gw)]
              label_groups[label] = [indices[(i + gw) % len(indices)] for i in range(len(indices))]
  
      # Load data.
      images, labels = zip(*[training_set[i] for i in grid_indices])
      return (gw, gh), np.stack(images), np.stack(labels)
  
  #----------------------------------------------------------------------------
  
  def save_image_grid(img, fname, drange, grid_size):
      lo, hi = drange
      img = np.asarray(img, dtype=np.float32)
      img = (img - lo) * (255 / (hi - lo))
      img = np.rint(img).clip(0, 255).astype(np.uint8)
  
      gw, gh = grid_size
      _N, C, H, W = img.shape
      img = img.reshape(gh, gw, C, H, W)
      img = img.transpose(0, 3, 1, 4, 2)
      img = img.reshape(gh * H, gw * W, C)
  
      assert C in [1, 3]
      if C == 1:
          PIL.Image.fromarray(img[:, :, 0], 'L').save(fname)
      if C == 3:
          PIL.Image.fromarray(img, 'RGB').save(fname)
  
  #----------------------------------------------------------------------------
  
  def training_loop(
      run_dir                 = '.',      # Output directory.
      training_set_kwargs     = {},       # Options for training set.
      data_loader_kwargs      = {},       # Options for torch.utils.data.DataLoader.
      G_kwargs                = {},       # Options for generator network.
      D_kwargs                = {},       # Options for discriminator network.
      G_opt_kwargs            = {},       # Options for generator optimizer.
      D_opt_kwargs            = {},       # Options for discriminator optimizer.
      augment_kwargs          = None,     # Options for augmentation pipeline. None = disable.
      loss_kwargs             = {},       # Options for loss function.
      metrics                 = [],       # Metrics to evaluate during training.
      random_seed             = 0,        # Global random seed.
      num_gpus                = 1,        # Number of GPUs participating in the training.
      rank                    = 0,        # Rank of the current process in [0, num_gpus[.
      batch_size              = 4,        # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus.
      batch_gpu               = 4,        # Number of samples processed at a time by one GPU.
      ema_kimg                = 10,       # Half-life of the exponential moving average (EMA) of generator weights.
      ema_rampup              = None,     # EMA ramp-up coefficient.
      G_reg_interval          = 4,        # How often to perform regularization for G? None = disable lazy regularization.
      D_reg_interval          = 16,       # How often to perform regularization for D? None = disable lazy regularization.
      augment_p               = 0,        # Initial value of augmentation probability.
      ada_target              = None,     # ADA target value. None = fixed p.
      ada_interval            = 4,        # How often to perform ADA adjustment?
      ada_kimg                = 500,      # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit.
      total_kimg              = 25000,    # Total length of the training, measured in thousands of real images.
      kimg_per_tick           = 4,        # Progress snapshot interval.
      image_snapshot_ticks    = 50,       # How often to save image snapshots? None = disable.
      network_snapshot_ticks  = 50,       # How often to save network snapshots? None = disable.
      resume_pkl              = None,     # Network pickle to resume training from.
      cudnn_benchmark         = True,     # Enable torch.backends.cudnn.benchmark?
      allow_tf32              = False,    # Enable torch.backends.cuda.matmul.allow_tf32 and torch.backends.cudnn.allow_tf32?
      abort_fn                = None,     # Callback function for determining whether to abort training. Must return consistent results across ranks.
      progress_fn             = None,     # Callback function for updating training progress. Called for all ranks.
  ):
      # Initialize.
      start_time = time.time()
      device = torch.device('cuda', rank)
      np.random.seed(random_seed * num_gpus + rank)
      torch.manual_seed(random_seed * num_gpus + rank)
      torch.backends.cudnn.benchmark = cudnn_benchmark    # Improves training speed.
      torch.backends.cuda.matmul.allow_tf32 = allow_tf32  # Allow PyTorch to internally use tf32 for matmul
      torch.backends.cudnn.allow_tf32 = allow_tf32        # Allow PyTorch to internally use tf32 for convolutions
      conv2d_gradfix.enabled = True                       # Improves training speed.
      grid_sample_gradfix.enabled = True                  # Avoids errors with the augmentation pipe.
  
      # Load training set.
      if rank == 0:
          print('Loading training set...')
      training_set = dnnlib.util.construct_class_by_name(**training_set_kwargs) # subclass of training.dataset.Dataset
      training_set_sampler = misc.InfiniteSampler(dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed)
      training_set_iterator = iter(torch.utils.data.DataLoader(dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs))
      if rank == 0:
          print()
          print('Num images: ', len(training_set))
          print('Image shape:', training_set.image_shape)
          print('Label shape:', training_set.label_shape)
          print()
  
      # Construct networks.
      if rank == 0:
          print('Constructing networks...')
      common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=training_set.resolution, img_channels=training_set.num_channels)
      G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
      D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
      G_ema = copy.deepcopy(G).eval()
  
      # Resume from existing pickle.
      if (resume_pkl is not None) and (rank == 0):
          print(f'Resuming from "{resume_pkl}"')
          with dnnlib.util.open_url(resume_pkl) as f:
              resume_data = legacy.load_network_pkl(f)
          for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]:
              misc.copy_params_and_buffers(resume_data[name], module, require_all=False)
  
      # Print network summary tables.
      if rank == 0:
          z = torch.empty([batch_gpu, G.z_dim], device=device)
          c = torch.empty([batch_gpu, G.c_dim], device=device)
          img = misc.print_module_summary(G, [z, c])
          misc.print_module_summary(D, [img, c])
  
      # Setup augmentation.
      if rank == 0:
          print('Setting up augmentation...')
      augment_pipe = None
      ada_stats = None
      if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None):
          augment_pipe = dnnlib.util.construct_class_by_name(**augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
          augment_pipe.p.copy_(torch.as_tensor(augment_p))
          if ada_target is not None:
              ada_stats = training_stats.Collector(regex='Loss/signs/real')
  
      # Distribute across GPUs.
      if rank == 0:
          print(f'Distributing across {num_gpus} GPUs...')
      ddp_modules = dict()
      for name, module in [('G_mapping', G.mapping), ('G_synthesis', G.synthesis), ('D', D), (None, G_ema), ('augment_pipe', augment_pipe)]:
          if (num_gpus > 1) and (module is not None) and len(list(module.parameters())) != 0:
              module.requires_grad_(True)
              module = torch.nn.parallel.DistributedDataParallel(module, device_ids=[device], broadcast_buffers=False)
              module.requires_grad_(False)
          if name is not None:
              ddp_modules[name] = module
  
      # Setup training phases.
      if rank == 0:
          print('Setting up training phases...')
      loss = dnnlib.util.construct_class_by_name(device=device, **ddp_modules, **loss_kwargs) # subclass of training.loss.Loss
      phases = []
      for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]:
          if reg_interval is None:
              opt = dnnlib.util.construct_class_by_name(params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
              phases += [dnnlib.EasyDict(name=name+'both', module=module, opt=opt, interval=1)]
          else: # Lazy regularization.
              mb_ratio = reg_interval / (reg_interval + 1)
              opt_kwargs = dnnlib.EasyDict(opt_kwargs)
              opt_kwargs.lr = opt_kwargs.lr * mb_ratio
              opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas]
              opt = dnnlib.util.construct_class_by_name(module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
              phases += [dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)]
              phases += [dnnlib.EasyDict(name=name+'reg', module=module, opt=opt, interval=reg_interval)]
      for phase in phases:
          phase.start_event = None
          phase.end_event = None
          if rank == 0:
              phase.start_event = torch.cuda.Event(enable_timing=True)
              phase.end_event = torch.cuda.Event(enable_timing=True)
  
      # Export sample images.
      grid_size = None
      grid_z = None
      grid_c = None
      if rank == 0:
          print('Exporting sample images...')
          grid_size, images, labels = setup_snapshot_image_grid(training_set=training_set)
          save_image_grid(images, os.path.join(run_dir, 'reals.png'), drange=[0,255], grid_size=grid_size)
          grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu)
          grid_c = torch.from_numpy(labels).to(device).split(batch_gpu)
          images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
          save_image_grid(images, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size)
  
      # Initialize logs.
      if rank == 0:
          print('Initializing logs...')
      stats_collector = training_stats.Collector(regex='.*')
      stats_metrics = dict()
      stats_jsonl = None
      stats_tfevents = None
      if rank == 0:
          stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'wt')
          try:
              import torch.utils.tensorboard as tensorboard
              stats_tfevents = tensorboard.SummaryWriter(run_dir)
          except ImportError as err:
              print('Skipping tfevents export:', err)
  
      # Train.
      if rank == 0:
          print(f'Training for {total_kimg} kimg...')
          print()
      cur_nimg = 0
      cur_tick = 0
      tick_start_nimg = cur_nimg
      tick_start_time = time.time()
      maintenance_time = tick_start_time - start_time
      batch_idx = 0
      if progress_fn is not None:
          progress_fn(0, total_kimg)
      while True:
  
          # Fetch training data.
          with torch.autograd.profiler.record_function('data_fetch'):
              phase_real_img, phase_real_c = next(training_set_iterator)
              phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu)
              phase_real_c = phase_real_c.to(device).split(batch_gpu)
              all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device)
              all_gen_z = [phase_gen_z.split(batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)]
              all_gen_c = [training_set.get_label(np.random.randint(len(training_set))) for _ in range(len(phases) * batch_size)]
              all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device)
              all_gen_c = [phase_gen_c.split(batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)]
  
          # Execute training phases.
          for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c):
              if batch_idx % phase.interval != 0:
                  continue
  
              # Initialize gradient accumulation.
              if phase.start_event is not None:
                  phase.start_event.record(torch.cuda.current_stream(device))
              phase.opt.zero_grad(set_to_none=True)
              phase.module.requires_grad_(True)
  
              # Accumulate gradients over multiple rounds.
              for round_idx, (real_img, real_c, gen_z, gen_c) in enumerate(zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c)):
                  sync = (round_idx == batch_size // (batch_gpu * num_gpus) - 1)
                  gain = phase.interval
                  loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, sync=sync, gain=gain)
  
              # Update weights.
              phase.module.requires_grad_(False)
              with torch.autograd.profiler.record_function(phase.name + '_opt'):
                  for param in phase.module.parameters():
                      if param.grad is not None:
                          misc.nan_to_num(param.grad, nan=0, posinf=1e5, neginf=-1e5, out=param.grad)
                  phase.opt.step()
              if phase.end_event is not None:
                  phase.end_event.record(torch.cuda.current_stream(device))
  
          # Update G_ema.
          with torch.autograd.profiler.record_function('Gema'):
              ema_nimg = ema_kimg * 1000
              if ema_rampup is not None:
                  ema_nimg = min(ema_nimg, cur_nimg * ema_rampup)
              ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8))
              for p_ema, p in zip(G_ema.parameters(), G.parameters()):
                  p_ema.copy_(p.lerp(p_ema, ema_beta))
              for b_ema, b in zip(G_ema.buffers(), G.buffers()):
                  b_ema.copy_(b)
  
          # Update state.
          cur_nimg += batch_size
          batch_idx += 1
  
          # Execute ADA heuristic.
          if (ada_stats is not None) and (batch_idx % ada_interval == 0):
              ada_stats.update()
              adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * (batch_size * ada_interval) / (ada_kimg * 1000)
              augment_pipe.p.copy_((augment_pipe.p + adjust).max(misc.constant(0, device=device)))
  
          # Perform maintenance tasks once per tick.
          done = (cur_nimg >= total_kimg * 1000)
          if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000):
              continue
  
          # Print status line, accumulating the same information in stats_collector.
          tick_end_time = time.time()
          fields = []
          fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"]
          fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"]
          fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"]
          fields += [f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"]
          fields += [f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"]
          fields += [f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"]
          fields += [f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"]
          fields += [f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"]
          torch.cuda.reset_peak_memory_stats()
          fields += [f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"]
          training_stats.report0('Timing/total_hours', (tick_end_time - start_time) / (60 * 60))
          training_stats.report0('Timing/total_days', (tick_end_time - start_time) / (24 * 60 * 60))
          if rank == 0:
              print(' '.join(fields))
  
          # Check for abort.
          if (not done) and (abort_fn is not None) and abort_fn():
              done = True
              if rank == 0:
                  print()
                  print('Aborting...')
  
          # Save image snapshot.
          if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0):
              images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
              save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1,1], grid_size=grid_size)
  
          # Save network snapshot.
          snapshot_pkl = None
          snapshot_data = None
          if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0):
              snapshot_data = dict(training_set_kwargs=dict(training_set_kwargs))
              for name, module in [('G', G), ('D', D), ('G_ema', G_ema), ('augment_pipe', augment_pipe)]:
                  if module is not None:
                      if num_gpus > 1:
                          misc.check_ddp_consistency(module, ignore_regex=r'.*\.w_avg')
                      module = copy.deepcopy(module).eval().requires_grad_(False).cpu()
                  snapshot_data[name] = module
                  del module # conserve memory
              snapshot_pkl = os.path.join(run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl')
              if rank == 0:
                  with open(snapshot_pkl, 'wb') as f:
                      pickle.dump(snapshot_data, f)
  
          # Evaluate metrics.
          if (snapshot_data is not None) and (len(metrics) > 0):
              if rank == 0:
                  print('Evaluating metrics...')
              for metric in metrics:
                  result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'],
                      dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device)
                  if rank == 0:
                      metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl)
                  stats_metrics.update(result_dict.results)
          del snapshot_data # conserve memory
  
          # Collect statistics.
          for phase in phases:
              value = []
              if (phase.start_event is not None) and (phase.end_event is not None):
                  phase.end_event.synchronize()
                  value = phase.start_event.elapsed_time(phase.end_event)
              training_stats.report0('Timing/' + phase.name, value)
          stats_collector.update()
          stats_dict = stats_collector.as_dict()
  
          # Update logs.
          timestamp = time.time()
          if stats_jsonl is not None:
              fields = dict(stats_dict, timestamp=timestamp)
              stats_jsonl.write(json.dumps(fields) + '\n')
              stats_jsonl.flush()
          if stats_tfevents is not None:
              global_step = int(cur_nimg / 1e3)
              walltime = timestamp - start_time
              for name, value in stats_dict.items():
                  stats_tfevents.add_scalar(name, value.mean, global_step=global_step, walltime=walltime)
              for name, value in stats_metrics.items():
                  stats_tfevents.add_scalar(f'Metrics/{name}', value, global_step=global_step, walltime=walltime)
              stats_tfevents.flush()
          if progress_fn is not None:
              progress_fn(cur_nimg // 1000, total_kimg)
  
          # Update state.
          cur_tick += 1
          tick_start_nimg = cur_nimg
          tick_start_time = time.time()
          maintenance_time = tick_start_time - tick_end_time
          if done:
              break
  
      # Done.
      if rank == 0:
          print()
          print('Exiting...')
  
  #----------------------------------------------------------------------------
  