# 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. DHead_kwargs = None, # Options for real contrastive head. GHead_kwargs = None, # Options for fake contrastive head. no_cl_on_g = False, # Options for fake instance discrmination for generator. cl_loss_weight = {}, # Options for multiple loss weights for InsGen. 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 = 100, # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit. ada_linear = False, # Whether to linearly increase the strength of ADA. 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. __CUR_NIMG__ = torch.tensor(0, dtype=torch.long, device=device) __CUR_TICK__ = torch.tensor(0, dtype=torch.long, device=device) __BATCH_IDX__ = torch.tensor(0, dtype=torch.long, device=device) best_fid = 9999 # 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() # Construct contrastive heads. DHead = dnnlib.util.construct_class_by_name(**DHead_kwargs).train().to(device) if DHead_kwargs is not None else None GHead = dnnlib.util.construct_class_by_name(**GHead_kwargs).train().to(device) if GHead_kwargs is not None else None D_ema = copy.deepcopy(D).eval() # 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 = augment_p if ada_target is not None: ada_stats = training_stats.Collector(regex='Loss/signs/real') # Check for existing checkpoint ckpt_pkl = None if os.path.isfile(misc.get_ckpt_path(run_dir)): ckpt_pkl = resume_pkl = misc.get_ckpt_path(run_dir) # 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), ('D_ema', D_ema), ('DHead', DHead), ('GHead', GHead)]: if module is None: continue misc.copy_params_and_buffers(resume_data[name], module, require_all=False) __CUR_NIMG__ = resume_data['progress']['cur_nimg'].to(device) __CUR_TICK__ = resume_data['progress']['cur_tick'].to(device) __BATCH_IDX__ = resume_data['progress']['batch_idx'].to(device) best_fid = resume_data['progress']['best_fid'] # only needed for rank == 0 augment_pipe.p = float(resume_data['progress']['cur_p'][0]) del resume_data # 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]) t = torch.empty([batch_gpu, D.t_dim], device=device) misc.print_module_summary(D, [img, c, t]) # 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), (None, D_ema)]: 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 # Distribute Heads across GPUs. if rank == 0: print(f'Distributing Contrastive Heads across {num_gpus} GPUS...') if num_gpus > 1: if DHead is not None: DHead = torch.nn.parallel.DistributedDataParallel(DHead, device_ids=[device], broadcast_buffers=True) if GHead is not None: GHead = torch.nn.parallel.DistributedDataParallel(GHead, device_ids=[device], broadcast_buffers=True) # 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) # Setup contrastive training phases. if rank == 0: print('Setting up contrastive training phases...') cl_phases = dict() for name, module, opt_kwargs, reg_interval in [('GHead', GHead, G_opt_kwargs, G_reg_interval), ('DHead', DHead, D_opt_kwargs, D_reg_interval)]: if module is None: continue assert (reg_interval is not None) # 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 cl_phases.update({name+'main': dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)}) # 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() if num_gpus > 1: # broadcast loaded states to all torch.distributed.broadcast(__CUR_NIMG__, 0) torch.distributed.broadcast(__CUR_TICK__, 0) torch.distributed.broadcast(__BATCH_IDX__, 0) torch.distributed.barrier() # ensure all processes received this info cur_nimg = __CUR_NIMG__.item() cur_tick = __CUR_TICK__.item() 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)] # Update D_ema with torch.autograd.profiler.record_function('Dema'): momentum = 0.999 if DHead_kwargs is None else DHead_kwargs.momentum for p_ema, p in zip(D_ema.parameters(), D.parameters()): p_ema.data = p_ema.data * momentum + p.data * (1. - momentum) # 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, cl_phases=cl_phases, D_ema=D_ema, g_fake_cl=not no_cl_on_g, **cl_loss_weight) # 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 = (augment_pipe.p + adjust).clip(min=0., max=1.) # augment_pipe.p = (augment_pipe.p + adjust).clip(min=0.) augment_pipe.update_T() # 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) if augment_pipe is not None else 0):.3f}"] fields += [f"T {training_stats.report0('Progress/augment_T', float(augment_pipe.num_timesteps) if augment_pipe is not None else 0)}"] 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), ('D_ema', D_ema), ('DHead', DHead), ('GHead', GHead)]: 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 # Save Checkpoint if needed if (rank == 0) and (network_snapshot_ticks is not None) and ( done or cur_tick % network_snapshot_ticks == 0): snapshot_pkl = misc.get_ckpt_path(run_dir) # save as tensors to avoid error for multi GPU snapshot_data['progress'] = { 'cur_nimg': torch.LongTensor([cur_nimg]), 'cur_tick': torch.LongTensor([cur_tick]), 'cur_p': torch.FloatTensor([augment_pipe.p]), 'batch_idx': torch.LongTensor([batch_idx]), 'best_fid': best_fid, } if hasattr(loss, 'pl_mean'): snapshot_data['progress']['pl_mean'] = loss.pl_mean.cpu() 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) # save best fid ckpt snapshot_pkl = os.path.join(run_dir, f'best_model.pkl') cur_nimg_txt = os.path.join(run_dir, f'best_nimg.txt') if rank == 0: if 'fid50k_full' in stats_metrics and stats_metrics['fid50k_full'] < best_fid: best_fid = stats_metrics['fid50k_full'] with open(snapshot_pkl, 'wb') as f: pickle.dump(snapshot_data, f) # save curr iteration number (directly saving it to pkl leads to problems with multi GPU) with open(cur_nimg_txt, 'w') as f: f.write(f"nimg: {cur_nimg} best_fid: {best_fid}") 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...') #----------------------------------------------------------------------------