if args.cuda: cudnn.fastest = True torch.set_default_tensor_type('torch.cuda.FloatTensor') else: torch.set_default_tensor_type('torch.FloatTensor') if args.resume and not args.display: with open(args.ap_data_file, 'rb') as f: ap_data = pickle.load(f) calc_map(ap_data) exit() if args.image is None and args.video is None and args.images is None: dataset = COCODetection(cfg.dataset.valid_images, cfg.dataset.valid_info, transform=BaseTransform(), has_gt=cfg.dataset.has_gt) prep_coco_cats() else: dataset = None print('Loading model...', end='') net = Yolact() net.load_weights(args.trained_model) net.eval() print(' Done.') if args.cuda: net = net.cuda() evaluate(net, dataset)
def train(): if not os.path.exists(args.save_folder): os.mkdir(args.save_folder) dataset = COCODetection(image_path=cfg.dataset.train_images, info_file=cfg.dataset.train_info, transform=SSDAugmentation(MEANS)) print("--------------") if args.validation_epoch > 0: setup_eval() val_dataset = COCODetection(image_path=cfg.dataset.valid_images, info_file=cfg.dataset.valid_info, transform=BaseTransform(MEANS)) print("============") # Parallel wraps the underlying module, but when saving and loading we don't want that yolact_net = Yolact() net = yolact_net net.train() if args.log: log = Log(cfg.name, args.log_folder, dict(args._get_kwargs()), overwrite=(args.resume is None), log_gpu_stats=args.log_gpu) # I don't use the timer during training (I use a different timing method). # Apparently there's a race condition with multiple GPUs, so disable it just to be safe. timer.disable_all() # Both of these can set args.resume to None, so do them before the check if args.resume == 'interrupt': args.resume = SavePath.get_interrupt(args.save_folder) elif args.resume == 'latest': args.resume = SavePath.get_latest(args.save_folder, cfg.name) if args.resume is not None: print('Resuming training, loading {}...'.format(args.resume)) yolact_net.load_weights(args.resume) if args.start_iter == -1: args.start_iter = SavePath.from_str(args.resume).iteration else: print('Initializing weights...') yolact_net.init_weights(backbone_path=args.save_folder + cfg.backbone.path) optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.decay) criterion = MultiBoxLoss(num_classes=cfg.num_classes, pos_threshold=cfg.positive_iou_threshold, neg_threshold=cfg.negative_iou_threshold, negpos_ratio=cfg.ohem_negpos_ratio) if args.batch_alloc is not None: args.batch_alloc = [int(x) for x in args.batch_alloc.split(',')] if sum(args.batch_alloc) != args.batch_size: print( 'Error: Batch allocation (%s) does not sum to batch size (%s).' % (args.batch_alloc, args.batch_size)) exit(-1) net = CustomDataParallel(NetLoss(net, criterion)) if args.cuda: net = net.cuda() # Initialize everything if not cfg.freeze_bn: yolact_net.freeze_bn() # Freeze bn so we don't kill our means yolact_net(torch.zeros(1, 3, cfg.max_size, cfg.max_size).cuda()) if not cfg.freeze_bn: yolact_net.freeze_bn(True) # loss counters loc_loss = 0 conf_loss = 0 iteration = max(args.start_iter, 0) last_time = time.time() epoch_size = len(dataset) // args.batch_size num_epochs = math.ceil(cfg.max_iter / epoch_size) # Which learning rate adjustment step are we on? lr' = lr * gamma ^ step_index step_index = 0 data_loader = data.DataLoader(dataset, args.batch_size, num_workers=args.num_workers, shuffle=True, collate_fn=detection_collate, pin_memory=True) save_path = lambda epoch, iteration: SavePath( cfg.name, epoch, iteration).get_path(root=args.save_folder) time_avg = MovingAverage() global loss_types # Forms the print order loss_avgs = {k: MovingAverage(100) for k in loss_types} print('Begin training!') print() # try-except so you can use ctrl+c to save early and stop training try: for epoch in range(num_epochs): # Resume from start_iter if (epoch + 1) * epoch_size < iteration: continue for datum in data_loader: # Stop if we've reached an epoch if we're resuming from start_iter if iteration == (epoch + 1) * epoch_size: break # Stop at the configured number of iterations even if mid-epoch if iteration == cfg.max_iter: break # Change a config setting if we've reached the specified iteration changed = False for change in cfg.delayed_settings: if iteration >= change[0]: changed = True cfg.replace(change[1]) # Reset the loss averages because things might have changed for avg in loss_avgs: avg.reset() # If a config setting was changed, remove it from the list so we don't keep checking if changed: cfg.delayed_settings = [ x for x in cfg.delayed_settings if x[0] > iteration ] # Warm up by linearly interpolating the learning rate from some smaller value if cfg.lr_warmup_until > 0 and iteration <= cfg.lr_warmup_until: set_lr(optimizer, (args.lr - cfg.lr_warmup_init) * (iteration / cfg.lr_warmup_until) + cfg.lr_warmup_init) # Adjust the learning rate at the given iterations, but also if we resume from past that iteration while step_index < len( cfg.lr_steps ) and iteration >= cfg.lr_steps[step_index]: step_index += 1 set_lr(optimizer, args.lr * (args.gamma**step_index)) # Zero the grad to get ready to compute gradients optimizer.zero_grad() # Forward Pass + Compute loss at the same time (see CustomDataParallel and NetLoss) losses = net(datum) losses = {k: (v).mean() for k, v in losses.items() } # Mean here because Dataparallel loss = sum([losses[k] for k in losses]) # no_inf_mean removes some components from the loss, so make sure to backward through all of it # all_loss = sum([v.mean() for v in losses.values()]) # Backprop loss.backward( ) # Do this to free up vram even if loss is not finite if torch.isfinite(loss).item(): optimizer.step() # Add the loss to the moving average for bookkeeping for k in losses: loss_avgs[k].add(losses[k].item()) cur_time = time.time() elapsed = cur_time - last_time last_time = cur_time # Exclude graph setup from the timing information if iteration != args.start_iter: time_avg.add(elapsed) if iteration % 10 == 0: eta_str = str( datetime.timedelta(seconds=(cfg.max_iter - iteration) * time_avg.get_avg())).split('.')[0] total = sum([loss_avgs[k].get_avg() for k in losses]) loss_labels = sum([[k, loss_avgs[k].get_avg()] for k in loss_types if k in losses], []) print(('[%3d] %7d ||' + (' %s: %.3f |' * len(losses)) + ' T: %.3f || ETA: %s || timer: %.3f') % tuple([epoch, iteration] + loss_labels + [total, eta_str, elapsed]), flush=True) if args.log: precision = 5 loss_info = { k: round(losses[k].item(), precision) for k in losses } loss_info['T'] = round(loss.item(), precision) if args.log_gpu: log.log_gpu_stats = (iteration % 10 == 0 ) # nvidia-smi is sloooow log.log('train', loss=loss_info, epoch=epoch, iter=iteration, lr=round(cur_lr, 10), elapsed=elapsed) log.log_gpu_stats = args.log_gpu iteration += 1 if iteration % args.save_interval == 0 and iteration != args.start_iter: if args.keep_latest: latest = SavePath.get_latest(args.save_folder, cfg.name) print('Saving state, iter:', iteration) yolact_net.save_weights(save_path(epoch, iteration)) if args.keep_latest and latest is not None: if args.keep_latest_interval <= 0 or iteration % args.keep_latest_interval != args.save_interval: print('Deleting old save...') os.remove(latest) # This is done per epoch if args.validation_epoch > 0: if epoch % args.validation_epoch == 0 and epoch > 0: compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None) # Compute validation mAP after training is finished compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None) except KeyboardInterrupt: if args.interrupt: print('Stopping early. Saving network...') # Delete previous copy of the interrupted network so we don't spam the weights folder SavePath.remove_interrupt(args.save_folder) yolact_net.save_weights( save_path(epoch, repr(iteration) + '_interrupt')) exit() yolact_net.save_weights(save_path(epoch, iteration))
def train(rank, args): if args.num_gpus > 1: multi_gpu_rescale(args) if rank == 0: if not os.path.exists(args.save_folder): os.mkdir(args.save_folder) # fix the seed for reproducibility seed = args.random_seed + rank torch.manual_seed(seed) np.random.seed(seed) random.seed(seed) # set up logger setup_logger(output=os.path.join(args.log_folder, cfg.name), distributed_rank=rank) logger = logging.getLogger("yolact.train") w = SummaryHelper(distributed_rank=rank, log_dir=os.path.join(args.log_folder, cfg.name)) w.add_text("argv", " ".join(sys.argv)) logger.info("Args: {}".format(" ".join(sys.argv))) import git with git.Repo(search_parent_directories=True) as repo: w.add_text("git_hash", repo.head.object.hexsha) logger.info("git hash: {}".format(repo.head.object.hexsha)) if args.num_gpus > 1: try: logger.info("Initializing torch.distributed backend...") dist.init_process_group(backend='nccl', init_method=args.dist_url, world_size=args.num_gpus, rank=rank) except Exception as e: logger.error("Process group URL: {}".format(args.dist_url)) raise e misc.barrier() if torch.cuda.device_count() > 1: logger.info('Multiple GPUs detected! Turning off JIT.') collate_fn = detection_collate if cfg.dataset.name == 'YouTube VIS': dataset = YoutubeVIS(image_path=cfg.dataset.train_images, info_file=cfg.dataset.train_info, configs=cfg.dataset, transform=SSDAugmentationVideo(MEANS)) if cfg.dataset.joint == 'coco': joint_dataset = COCODetection( image_path=cfg.joint_dataset.train_images, info_file=cfg.joint_dataset.train_info, transform=SSDAugmentation(MEANS)) joint_collate_fn = detection_collate if args.validation_epoch > 0: setup_eval() val_dataset = YoutubeVIS(image_path=cfg.dataset.valid_images, info_file=cfg.dataset.valid_info, configs=cfg.dataset, transform=BaseTransformVideo(MEANS)) collate_fn = collate_fn_youtube_vis elif cfg.dataset.name == 'FlyingChairs': dataset = FlyingChairs(image_path=cfg.dataset.trainval_images, info_file=cfg.dataset.trainval_info) collate_fn = collate_fn_flying_chairs else: dataset = COCODetection(image_path=cfg.dataset.train_images, info_file=cfg.dataset.train_info, transform=SSDAugmentation(MEANS)) if args.validation_epoch > 0: setup_eval() val_dataset = COCODetection(image_path=cfg.dataset.valid_images, info_file=cfg.dataset.valid_info, transform=BaseTransform(MEANS)) # Set cuda device early to avoid duplicate model in master GPU if args.cuda: torch.cuda.set_device(rank) # Parallel wraps the underlying module, but when saving and loading we don't want that yolact_net = Yolact() net = yolact_net net.train() # I don't use the timer during training (I use a different timing method). # Apparently there's a race condition with multiple GPUs. # use timer for experiments timer.disable_all() # Both of these can set args.resume to None, so do them before the check if args.resume == 'interrupt': args.resume = SavePath.get_interrupt(args.save_folder) elif args.resume == 'latest': args.resume = SavePath.get_latest(args.save_folder, cfg.name) if args.resume is not None: logger.info('Resuming training, loading {}...'.format(args.resume)) yolact_net.load_weights(args.resume, args=args) if args.start_iter == -1: args.start_iter = SavePath.from_str(args.resume).iteration else: logger.info('Initializing weights...') yolact_net.init_weights(backbone_path=args.save_folder + cfg.backbone.path) if cfg.flow.train_flow: criterion = OpticalFlowLoss() else: criterion = MultiBoxLoss(num_classes=cfg.num_classes, pos_threshold=cfg.positive_iou_threshold, neg_threshold=cfg.negative_iou_threshold, negpos_ratio=3) if args.cuda: net.cuda(rank) if misc.is_distributed_initialized(): net = nn.parallel.DistributedDataParallel( net, device_ids=[rank], output_device=rank, broadcast_buffers=False, find_unused_parameters=True) optimizer = optim.SGD(filter(lambda x: x.requires_grad, net.parameters()), lr=args.lr, momentum=args.momentum, weight_decay=args.decay) # loss counters iteration = max(args.start_iter, 0) w.set_step(iteration) last_time = time.time() epoch_size = len(dataset) // args.batch_size // args.num_gpus num_epochs = math.ceil(cfg.max_iter / epoch_size) # Which learning rate adjustment step are we on? lr' = lr * gamma ^ step_index step_index = 0 from data.sampler_utils import InfiniteSampler, build_batch_data_sampler infinite_sampler = InfiniteSampler(dataset, seed=args.random_seed, num_replicas=args.num_gpus, rank=rank, shuffle=True) train_sampler = build_batch_data_sampler(infinite_sampler, images_per_batch=args.batch_size) data_loader = data.DataLoader( dataset, num_workers=args.num_workers, collate_fn=collate_fn, multiprocessing_context="fork" if args.num_workers > 1 else None, batch_sampler=train_sampler) data_loader_iter = iter(data_loader) if cfg.dataset.joint: joint_infinite_sampler = InfiniteSampler(joint_dataset, seed=args.random_seed, num_replicas=args.num_gpus, rank=rank, shuffle=True) joint_train_sampler = build_batch_data_sampler( joint_infinite_sampler, images_per_batch=args.batch_size) joint_data_loader = data.DataLoader( joint_dataset, num_workers=args.num_workers, collate_fn=joint_collate_fn, multiprocessing_context="fork" if args.num_workers > 1 else None, batch_sampler=joint_train_sampler) joint_data_loader_iter = iter(joint_data_loader) save_path = lambda epoch, iteration: SavePath( cfg.name, epoch, iteration).get_path(root=args.save_folder) time_avg = MovingAverage() data_time_avg = MovingAverage(10) global loss_types # Forms the print order loss_avgs = {k: MovingAverage(100) for k in loss_types} def backward_and_log(prefix, net_outs, targets, masks, num_crowds, extra_loss=None): optimizer.zero_grad() out = net_outs["pred_outs"] losses = criterion(out, targets, masks, num_crowds) losses = {k: v.mean() for k, v in losses.items()} # Mean here because Dataparallel if extra_loss is not None: assert type(extra_loss) == dict losses.update(extra_loss) loss = sum([losses[k] for k in losses]) # Backprop loss.backward() # Do this to free up vram even if loss is not finite if torch.isfinite(loss).item(): optimizer.step() # Add the loss to the moving average for bookkeeping for k in losses: loss_avgs[k].add(losses[k].item()) w.add_scalar('{prefix}/{key}'.format(prefix=prefix, key=k), losses[k].item()) return losses logger.info('Begin training!') # try-except so you can use ctrl+c to save early and stop training try: for epoch in range(num_epochs): # Resume from start_iter if (epoch + 1) * epoch_size < iteration: continue while True: data_start_time = time.perf_counter() datum = next(data_loader_iter) data_end_time = time.perf_counter() data_time = data_end_time - data_start_time if iteration != args.start_iter: data_time_avg.add(data_time) # Stop if we've reached an epoch if we're resuming from start_iter if iteration == (epoch + 1) * epoch_size: break # Stop at the configured number of iterations even if mid-epoch if iteration == cfg.max_iter: break # Change a config setting if we've reached the specified iteration changed = False for change in cfg.delayed_settings: if iteration >= change[0]: changed = True cfg.replace(change[1]) # Reset the loss averages because things might have changed for avg in loss_avgs: avg.reset() # If a config setting was changed, remove it from the list so we don't keep checking if changed: cfg.delayed_settings = [ x for x in cfg.delayed_settings if x[0] > iteration ] # Warm up by linearly interpolating the learning rate from some smaller value if cfg.lr_warmup_until > 0 and iteration <= cfg.lr_warmup_until and cfg.lr_warmup_init < args.lr: set_lr(optimizer, (args.lr - cfg.lr_warmup_init) * (iteration / cfg.lr_warmup_until) + cfg.lr_warmup_init) elif cfg.lr_schedule == 'cosine': set_lr( optimizer, args.lr * ((math.cos(math.pi * iteration / cfg.max_iter) + 1.) * .5)) # Adjust the learning rate at the given iterations, but also if we resume from past that iteration while cfg.lr_schedule == 'step' and step_index < len( cfg.lr_steps ) and iteration >= cfg.lr_steps[step_index]: step_index += 1 set_lr(optimizer, args.lr * (args.gamma**step_index)) global lr w.add_scalar('meta/lr', lr) if cfg.dataset.name == "FlyingChairs": imgs_1, imgs_2, flows = prepare_flow_data(datum) net_outs = net(None, extras=(imgs_1, imgs_2)) # Compute Loss optimizer.zero_grad() losses = criterion(net_outs, flows) losses = {k: v.mean() for k, v in losses.items() } # Mean here because Dataparallel loss = sum([losses[k] for k in losses]) # Backprop loss.backward( ) # Do this to free up vram even if loss is not finite if torch.isfinite(loss).item(): optimizer.step() # Add the loss to the moving average for bookkeeping for k in losses: loss_avgs[k].add(losses[k].item()) w.add_scalar('loss/%s' % k, losses[k].item()) elif cfg.dataset.joint or not cfg.dataset.is_video: if cfg.dataset.joint: joint_datum = next(joint_data_loader_iter) # Load training data # Note, for training on multiple gpus this will use the custom replicate and gather I wrote up there images, targets, masks, num_crowds = prepare_data( joint_datum) else: images, targets, masks, num_crowds = prepare_data( datum) extras = { "backbone": "full", "interrupt": False, "moving_statistics": { "aligned_feats": [] } } net_outs = net(images, extras=extras) run_name = "joint" if cfg.dataset.joint else "compute" losses = backward_and_log(run_name, net_outs, targets, masks, num_crowds) # Forward Pass if cfg.dataset.is_video: # reference frames references = [] moving_statistics = {"aligned_feats": [], "conf_hist": []} for idx, frame in enumerate(datum[:0:-1]): images, annots = frame extras = { "backbone": "full", "interrupt": True, "keep_statistics": True, "moving_statistics": moving_statistics } with torch.no_grad(): net_outs = net(images, extras=extras) moving_statistics["feats"] = net_outs["feats"] moving_statistics["lateral"] = net_outs["lateral"] keys_to_save = ("outs_phase_1", "outs_phase_2") for key in set(net_outs.keys()) - set(keys_to_save): del net_outs[key] references.append(net_outs) # key frame with annotation, but not compute full backbone frame = datum[0] images, annots = frame frame = ( images, annots, ) images, targets, masks, num_crowds = prepare_data(frame) extras = { "backbone": "full", "interrupt": not cfg.flow.base_backward, "moving_statistics": moving_statistics } gt_net_outs = net(images, extras=extras) if cfg.flow.base_backward: losses = backward_and_log("compute", gt_net_outs, targets, masks, num_crowds) keys_to_save = ("outs_phase_1", "outs_phase_2") for key in set(gt_net_outs.keys()) - set(keys_to_save): del gt_net_outs[key] # now do the warp if len(references) > 0: reference_frame = references[0] extras = { "backbone": "partial", "moving_statistics": moving_statistics } net_outs = net(images, extras=extras) extra_loss = yolact_net.extra_loss( net_outs, gt_net_outs) losses = backward_and_log("warp", net_outs, targets, masks, num_crowds, extra_loss=extra_loss) cur_time = time.time() elapsed = cur_time - last_time last_time = cur_time w.add_scalar('meta/data_time', data_time) w.add_scalar('meta/iter_time', elapsed) # Exclude graph setup from the timing information if iteration != args.start_iter: time_avg.add(elapsed) if iteration % 10 == 0: eta_str = str( datetime.timedelta(seconds=(cfg.max_iter - iteration) * time_avg.get_avg())).split('.')[0] if torch.cuda.is_available(): max_mem_mb = torch.cuda.max_memory_allocated( ) / 1024.0 / 1024.0 # torch.cuda.reset_max_memory_allocated() else: max_mem_mb = None logger.info("""\ eta: {eta} epoch: {epoch} iter: {iter} \ {losses} {loss_total} \ time: {time} data_time: {data_time} lr: {lr} {memory}\ """.format(eta=eta_str, epoch=epoch, iter=iteration, losses=" ".join([ "{}: {:.3f}".format(k, loss_avgs[k].get_avg()) for k in losses ]), loss_total="T: {:.3f}".format( sum([loss_avgs[k].get_avg() for k in losses])), data_time="{:.3f}".format(data_time_avg.get_avg()), time="{:.3f}".format(elapsed), lr="{:.6f}".format(lr), memory="max_mem: {:.0f}M".format(max_mem_mb))) if rank == 0 and iteration % 100 == 0: if cfg.flow.train_flow: import flowiz as fz from layers.warp_utils import deform_op tgt_size = (64, 64) flow_size = flows.size()[2:] vis_data = [] for pred_flow in net_outs: vis_data.append(pred_flow) deform_gt = deform_op(imgs_2, flows) flows_pred = [ F.interpolate(x, size=flow_size, mode='bilinear', align_corners=False) for x in net_outs ] deform_preds = [ deform_op(imgs_2, x) for x in flows_pred ] vis_data.append( F.interpolate(flows, size=tgt_size, mode='area')) vis_data = [ F.interpolate(flow[:1], size=tgt_size) for flow in vis_data ] vis_data = [ fz.convert_from_flow( flow[0].data.cpu().numpy().transpose( 1, 2, 0)).transpose( 2, 0, 1).astype('float32') / 255 for flow in vis_data ] def convert_image(image): image = F.interpolate(image, size=tgt_size, mode='area') image = image[0] image = image.data.cpu().numpy() image = image[::-1] image = image.transpose(1, 2, 0) image = image * np.array(STD) + np.array(MEANS) image = image.transpose(2, 0, 1) image = image / 255 image = np.clip(image, -1, 1) image = image[::-1] return image vis_data.append(convert_image(imgs_1)) vis_data.append(convert_image(imgs_2)) vis_data.append(convert_image(deform_gt)) vis_data.extend( [convert_image(x) for x in deform_preds]) vis_data_stack = np.stack(vis_data, axis=0) w.add_images("preds_flow", vis_data_stack) elif cfg.flow.warp_mode == "flow": import flowiz as fz tgt_size = (64, 64) vis_data = [] for pred_flow, _, _ in net_outs["preds_flow"]: vis_data.append(pred_flow) vis_data = [ F.interpolate(flow[:1], size=tgt_size) for flow in vis_data ] vis_data = [ fz.convert_from_flow( flow[0].data.cpu().numpy().transpose( 1, 2, 0)).transpose( 2, 0, 1).astype('float32') / 255 for flow in vis_data ] input_image = F.interpolate(images, size=tgt_size, mode='area') input_image = input_image[0] input_image = input_image.data.cpu().numpy() input_image = input_image.transpose(1, 2, 0) input_image = input_image * np.array( STD[::-1]) + np.array(MEANS[::-1]) input_image = input_image.transpose(2, 0, 1) input_image = input_image / 255 input_image = np.clip(input_image, -1, 1) vis_data.append(input_image) vis_data_stack = np.stack(vis_data, axis=0) w.add_images("preds_flow", vis_data_stack) iteration += 1 w.set_step(iteration) if rank == 0 and iteration % args.save_interval == 0 and iteration != args.start_iter: if args.keep_latest: latest = SavePath.get_latest(args.save_folder, cfg.name) logger.info('Saving state, iter: {}'.format(iteration)) yolact_net.save_weights(save_path(epoch, iteration)) if args.keep_latest and latest is not None: if args.keep_latest_interval <= 0 or iteration % args.keep_latest_interval != args.save_interval: logger.info('Deleting old save...') os.remove(latest) misc.barrier() # This is done per epoch if args.validation_epoch > 0: if epoch % args.validation_epoch == 0 and epoch > 0: if rank == 0: compute_validation_map(yolact_net, val_dataset) misc.barrier() except KeyboardInterrupt: misc.barrier() if args.interrupt_no_save: logger.info('No save on interrupt, just exiting...') elif rank == 0: print('Stopping early. Saving network...') # Delete previous copy of the interrupted network so we don't spam the weights folder SavePath.remove_interrupt(args.save_folder) yolact_net.save_weights( save_path(epoch, repr(iteration) + '_interrupt')) return if rank == 0: yolact_net.save_weights(save_path(epoch, iteration))
def train(): if not os.path.exists(args.save_folder): os.mkdir(args.save_folder) dataset = COCODetection(image_path=cfg.dataset.train_images, info_file=cfg.dataset.train_info, transform=SSDAugmentation(MEANS)) if args.validation_epoch > 0: setup_eval() val_dataset = COCODetection(image_path=cfg.dataset.valid_images, info_file=cfg.dataset.valid_info, transform=BaseTransform(MEANS)) # Parallel wraps the underlying module, but when saving and loading we don't want that yolact_net = Yolact() net = yolact_net net.train() # I don't use the timer during training (I use a different timing method). # Apparently there's a race condition with multiple GPUs. timer.disable_all() # Both of these can set args.resume to None, so do them before the check if args.resume == 'interrupt': args.resume = SavePath.get_interrupt(args.save_folder) elif args.resume == 'latest': args.resume = SavePath.get_latest(args.save_folder, cfg.name) if args.resume is not None: print('Resuming training, loading {}...'.format(args.resume)) yolact_net.load_weights(args.resume) if args.start_iter == -1: args.start_iter = SavePath.from_str(args.resume).iteration else: print('Initializing weights...') yolact_net.init_weights(backbone_path=args.save_folder + cfg.backbone.path) optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.decay) criterion = MultiBoxLoss(num_classes=cfg.num_classes, pos_threshold=cfg.positive_iou_threshold, neg_threshold=cfg.negative_iou_threshold, negpos_ratio=3) if args.cuda: cudnn.benchmark = True net = nn.DataParallel(net).cuda() criterion = nn.DataParallel(criterion).cuda() # net = net.cuda() # criterion = criterion.cuda() # criterion = criterion.cuda() # loss counters loc_loss = 0 conf_loss = 0 iteration = max(args.start_iter, 0) last_time = time.time() epoch_size = len(dataset) // args.batch_size num_epochs = math.ceil(cfg.max_iter / epoch_size) # Which learning rate adjustment step are we on? lr' = lr * gamma ^ step_index step_index = 0 data_loader = data.DataLoader(dataset, args.batch_size, num_workers=args.num_workers, shuffle=True, collate_fn=detection_collate, pin_memory=True) save_path = lambda epoch, iteration: SavePath( cfg.name, epoch, iteration).get_path(root=args.save_folder) time_avg = MovingAverage() global loss_types # Forms the print order loss_avgs = {k: MovingAverage(100) for k in loss_types} print('Begin training!') print() # try-except so you can use ctrl+c to save early and stop training try: for epoch in range(num_epochs): # Resume from start_iter if (epoch + 1) * epoch_size < iteration: continue for datum in data_loader: # Stop if we've reached an epoch if we're resuming from start_iter if iteration == (epoch + 1) * epoch_size: break # Stop at the configured number of iterations even if mid-epoch if iteration == cfg.max_iter: break # Change a config setting if we've reached the specified iteration changed = False for change in cfg.delayed_settings: if iteration >= change[0]: changed = True cfg.replace(change[1]) # Reset the loss averages because things might have changed for avg in loss_avgs: avg.reset() # If a config setting was changed, remove it from the list so we don't keep checking if changed: cfg.delayed_settings = [ x for x in cfg.delayed_settings if x[0] > iteration ] # Warm up by linearly interpolating the learning rate from some smaller value if cfg.lr_warmup_until > 0 and iteration <= cfg.lr_warmup_until: set_lr(optimizer, (args.lr - cfg.lr_warmup_init) * (iteration / cfg.lr_warmup_until) + cfg.lr_warmup_init) # Adjust the learning rate at the given iterations, but also if we resume from past that iteration while step_index < len( cfg.lr_steps ) and iteration >= cfg.lr_steps[step_index]: step_index += 1 set_lr(optimizer, args.lr * (args.gamma**step_index)) # Load training data # Note, for training on multiple gpus this will use the custom replicate and gather I wrote up there images, targets, masks, num_crowds = prepare_data(datum) # Forward Pass out = net(images) # Compute Loss optimizer.zero_grad() wrapper = ScatterWrapper(targets, masks, num_crowds) losses = criterion(out, wrapper, wrapper.make_mask()) losses = {k: v.mean() for k, v in losses.items() } # Mean here because Dataparallel # original # loss = sum([losses[k] for k in losses]) loss = sum([losses[k] for k in losses]) + losses['S'] * 10 # Huan # Backprop loss.backward( ) # Do this to free up vram even if loss is not finite if torch.isfinite(loss).item(): optimizer.step() # Add the loss to the moving average for bookkeeping for k in losses: loss_avgs[k].add(losses[k].item()) cur_time = time.time() elapsed = cur_time - last_time last_time = cur_time # Exclude graph setup from the timing information if iteration != args.start_iter: time_avg.add(elapsed) if iteration % 100 == 0: eta_str = \ str(datetime.timedelta(seconds=(cfg.max_iter - iteration) * time_avg.get_avg())).split('.')[0] total = sum([loss_avgs[k].get_avg() for k in losses]) loss_labels = sum([[k, loss_avgs[k].get_avg()] for k in loss_types if k in losses], []) print(('[%3d] %7d ||' + (' %s: %.3f |' * len(losses)) + ' T: %.3f || ETA: %s || timer: %.3f') % tuple([epoch, iteration] + loss_labels + [total, eta_str, elapsed]), flush=True) iteration += 1 if iteration % args.save_interval == 0 and iteration != args.start_iter: if args.keep_latest: latest = SavePath.get_latest(args.save_folder, cfg.name) print('Saving state, iter:', iteration) yolact_net.save_weights(save_path(epoch, iteration)) if args.keep_latest and latest is not None: if args.keep_latest_interval <= 0 or iteration % args.keep_latest_interval != args.save_interval: print('Deleting old save...') os.remove(latest) # This is done per epoch if args.validation_epoch > 0: if epoch % args.validation_epoch == 0 and epoch > 0: compute_validation_map(yolact_net, val_dataset) except KeyboardInterrupt: print('Stopping early. Saving network...') # Delete previous copy of the interrupted network so we don't spam the weights folder SavePath.remove_interrupt(args.save_folder) yolact_net.save_weights( save_path(epoch, repr(iteration) + '_interrupt')) exit() yolact_net.save_weights(save_path(epoch, iteration))
if args.config is None: piece = args.trained_model.split('/')[1].split('_') name = f'{piece[0]}_{piece[1]}_config' print( f'\nConfig not specified. Parsed \'{name}\' from the checkpoint name.\n' ) set_cfg(name) with torch.no_grad(): if cuda: cudnn.benchmark = True cudnn.fastest = True torch.set_default_tensor_type('torch.cuda.FloatTensor') else: torch.set_default_tensor_type('torch.FloatTensor') dataset = COCODetection(cfg.dataset.valid_images, cfg.dataset.valid_info, augmentation=BaseTransform()) print('Loading model...') net = Yolact() net.load_weights(args.trained_model) net.eval() if cuda: net = net.cuda() evaluate(net, dataset, args.max_num, False, args.benchmark, args.cocoapi, args.traditional_nms)
def train(): if not os.path.exists(args.save_folder): os.mkdir(args.save_folder) dataset = COCODetection(image_path=cfg.dataset.train_images, info_file=cfg.dataset.train_info, transform=SSDAugmentation(MEANS)) if args.validation_epoch > 0: setup_eval() val_dataset = COCODetection(image_path=cfg.dataset.valid_images, info_file=cfg.dataset.valid_info, transform=BaseTransform(MEANS)) # Parallel wraps the underlying module, but when saving and loading we don't want that yolact_net = Yolact() net = yolact_net net.train() print('\n--- Generator created! ---') # NOTE # I maunally set the original image size and seg size as 138 # might change in the future, for example 550 if cfg.pred_seg: dis_size = 138 dis_net = Discriminator_Dcgan(i_size=dis_size, s_size=dis_size) # set the dis net's initial parameter values dis_net.apply(gan_init) dis_net.train() print('--- Discriminator created! ---\n') if args.log: log = Log(cfg.name, args.log_folder, dict(args._get_kwargs()), overwrite=(args.resume is None), log_gpu_stats=args.log_gpu) # I don't use the timer during training (I use a different timing method). # Apparently there's a race condition with multiple GPUs, so disable it just to be safe. timer.disable_all() # Both of these can set args.resume to None, so do them before the check if args.resume == 'interrupt': args.resume = SavePath.get_interrupt(args.save_folder) elif args.resume == 'latest': args.resume = SavePath.get_latest(args.save_folder, cfg.name) if args.resume is not None: print('Resuming training, loading {}...'.format(args.resume)) yolact_net.load_weights(args.resume) if args.start_iter == -1: args.start_iter = SavePath.from_str(args.resume).iteration else: print('Initializing weights...') yolact_net.init_weights(backbone_path=args.save_folder + cfg.backbone.path) # optimizer_gen = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum, # weight_decay=args.decay) # if cfg.pred_seg: # optimizer_dis = optim.SGD(dis_net.parameters(), lr=cfg.dis_lr, momentum=args.momentum, # weight_decay=args.decay) # schedule_dis = ReduceLROnPlateau(optimizer_dis, mode = 'min', patience=6, min_lr=1E-6) # NOTE: Using the Ranger Optimizer for the generator optimizer_gen = Ranger(net.parameters(), lr=args.lr, weight_decay=args.decay) if cfg.pred_seg: optimizer_dis = optim.SGD(dis_net.parameters(), lr=cfg.dis_lr) schedule_dis = ReduceLROnPlateau(optimizer_dis, mode='min', patience=6, min_lr=1E-6) criterion = MultiBoxLoss(num_classes=cfg.num_classes, pos_threshold=cfg.positive_iou_threshold, neg_threshold=cfg.negative_iou_threshold, negpos_ratio=cfg.ohem_negpos_ratio, pred_seg=cfg.pred_seg) criterion_dis = nn.BCELoss() if args.batch_alloc is not None: args.batch_alloc = [int(x) for x in args.batch_alloc.split(',')] if sum(args.batch_alloc) != args.batch_size: print( 'Error: Batch allocation (%s) does not sum to batch size (%s).' % (args.batch_alloc, args.batch_size)) exit(-1) net = CustomDataParallel(NetLoss(net, criterion, pred_seg=cfg.pred_seg)) if args.cuda: net = net.cuda() if cfg.pred_seg: dis_net = dis_net.cuda() # Initialize everything if not cfg.freeze_bn: yolact_net.freeze_bn() # Freeze bn so we don't kill our means yolact_net(torch.zeros(1, 3, cfg.max_size, cfg.max_size).cuda()) if not cfg.freeze_bn: yolact_net.freeze_bn(True) # loss counters loc_loss = 0 conf_loss = 0 iteration = max(args.start_iter, 0) last_time = time.time() epoch_size = len(dataset) // args.batch_size num_epochs = math.ceil(cfg.max_iter / epoch_size) # Which learning rate adjustment step are we on? lr' = lr * gamma ^ step_index step_index = 0 data_loader = data.DataLoader(dataset, args.batch_size, num_workers=args.num_workers, shuffle=True, collate_fn=detection_collate, pin_memory=True) # NOTE val_loader = data.DataLoader(val_dataset, args.batch_size, num_workers=args.num_workers * 2, shuffle=True, collate_fn=detection_collate, pin_memory=True) save_path = lambda epoch, iteration: SavePath( cfg.name, epoch, iteration).get_path(root=args.save_folder) time_avg = MovingAverage() global loss_types # Forms the print order # TODO: global command can modify global variable inside of the function. loss_avgs = {k: MovingAverage(100) for k in loss_types} print('Begin training!') print() # try-except so you can use ctrl+c to save early and stop training try: for epoch in range(num_epochs): # Resume from start_iter if (epoch + 1) * epoch_size < iteration: continue for datum in data_loader: # Stop if we've reached an epoch if we're resuming from start_iter if iteration == (epoch + 1) * epoch_size: break # Stop at the configured number of iterations even if mid-epoch if iteration == cfg.max_iter: break # Change a config setting if we've reached the specified iteration changed = False for change in cfg.delayed_settings: if iteration >= change[0]: changed = True cfg.replace(change[1]) # Reset the loss averages because things might have changed for avg in loss_avgs: avg.reset() # If a config setting was changed, remove it from the list so we don't keep checking if changed: cfg.delayed_settings = [ x for x in cfg.delayed_settings if x[0] > iteration ] # Warm up by linearly interpolating the learning rate from some smaller value if cfg.lr_warmup_until > 0 and iteration <= cfg.lr_warmup_until: set_lr(optimizer_gen, (args.lr - cfg.lr_warmup_init) * (iteration / cfg.lr_warmup_until) + cfg.lr_warmup_init) # Adjust the learning rate at the given iterations, but also if we resume from past that iteration while step_index < len( cfg.lr_steps ) and iteration >= cfg.lr_steps[step_index]: step_index += 1 set_lr(optimizer_gen, args.lr * (args.gamma**step_index)) # Zero the grad to get ready to compute gradients optimizer_gen.zero_grad() # NOTE if cfg.pred_seg: # ====== GAN Train ====== optimizer_dis.zero_grad() dis_net.zero_grad() # train the gen and dis in different iteration count it_alter_period = iteration % (cfg.gen_iter + cfg.dis_iter) if it_alter_period >= cfg.gen_iter: freeze_pretrain(yolact_net, freeze=True) freeze_pretrain(net, freeze=True) freeze_pretrain(dis_net, freeze=False) if it_alter_period == (cfg.gen_iter + 1): print('--- Generator freeze ---') print('--- Discriminator training ---') # ----- Discriminator part ----- # seg_list is the prediction mask # can be regarded as generated images from YOLACT # pred_list is the prediction label # seg_list dim: list of (138,138,instances) # pred_list dim: list of (instances) losses, seg_list, pred_list = net(datum) seg_clas, mask_clas, b, seg_size = seg_mask_clas( seg_list, pred_list, datum) # input image size is [b, 3, 550, 550] # downsample to [b, 3, seg_h, seg_w] image = interpolate(torch.stack(datum[0]), size=seg_size, mode='bilinear', align_corners=False) # Because in the discriminator training, we do not # want the gradient flow back to the generator part # we detach seg_clas (mask_clas come the data, does not have grad) seg_input = seg_clas.clone().detach() output_pred = dis_net(img=image, seg=seg_input) output_grou = dis_net(img=image, seg=mask_clas) if iteration % (cfg.gen_iter + cfg.dis_iter) == 0: print( f'Probability of fake is fake: {output_pred.mean().item():.2f}' ) print( f'Probability of real is real: {output_grou.mean().item():.2f}' ) # 0 for Fake/Generated # 1 for True/Ground Truth fake_label = torch.zeros(b) real_label = torch.ones(b) # Advice of practical implementation # from https://arxiv.org/abs/1611.08408 # loss_pred = -criterion_dis(output_pred,target=real_label) loss_pred = criterion_dis(output_pred, target=fake_label) loss_grou = criterion_dis(output_grou, target=real_label) loss_dis = loss_pred + loss_grou # TODO: Grid Search this one lambda_dis = cfg.lambda_dis loss_dis = lambda_dis * loss_dis # Backprop of the discriminator loss_dis.backward() optimizer_dis.step() else: freeze_pretrain(yolact_net, freeze=False) freeze_pretrain(net, freeze=False) freeze_pretrain(dis_net, freeze=True) if it_alter_period == 0: print('--- Generator training ---') print('--- Discriminator freeze ---') # ----- Generator part ----- net.zero_grad() losses, seg_list, pred_list = net(datum) seg_clas, mask_clas, b, seg_size = seg_mask_clas( seg_list, pred_list, datum) image = interpolate(torch.stack(datum[0]), size=seg_size, mode='bilinear', align_corners=False) # Perform forward pass of all-fake batch through D # NOTE that seg_clas CANNOT detach, in order to flow the # gradient back to the generator output = dis_net(img=image, seg=seg_clas) # Since the log(1-D(G(x))) not provide sufficient gradients # We want log(D(G(x)) instead, this can be achieve by # use the real_label as target. # This step is crucial for the information of discriminator # to go into the generator. # Calculate G's loss based on this output real_label = torch.ones(b) loss_gen = criterion_dis(output, target=real_label) if not it_alter_period >= cfg.gen_iter: # since the dis is already freeze, the gradients will only # record the YOLACT loss_gen.backward() # Do this to free up vram even if loss is not finite losses = { k: (v).mean() for k, v in losses.items() } # Mean here because Dataparallel loss = sum([losses[k] for k in losses]) if torch.isfinite(loss).item(): # since the optimizer_gen is for YOLACT only # only the gen will be updated optimizer_gen.step() else: # ====== Normal YOLACT Train ====== # Forward Pass + Compute loss at the same time (see CustomDataParallel and NetLoss) losses = net(datum) losses = {k: (v).mean() for k, v in losses.items() } # Mean here because Dataparallel loss = sum([losses[k] for k in losses]) # no_inf_mean removes some components from the loss, so make sure to backward through all of it # all_loss = sum([v.mean() for v in losses.values()]) # Backprop loss.backward( ) # Do this to free up vram even if loss is not finite if torch.isfinite(loss).item(): optimizer_gen.step() # Add the loss to the moving average for bookkeeping for k in losses: loss_avgs[k].add(losses[k].item()) cur_time = time.time() elapsed = cur_time - last_time last_time = cur_time # Exclude graph setup from the timing information if iteration != args.start_iter: time_avg.add(elapsed) if iteration % 10 == 0: eta_str = str( datetime.timedelta(seconds=(cfg.max_iter - iteration) * time_avg.get_avg())).split('.')[0] total = sum([loss_avgs[k].get_avg() for k in losses]) loss_labels = sum([[k, loss_avgs[k].get_avg()] for k in loss_types if k in losses], []) if cfg.pred_seg and (it_alter_period >= cfg.gen_iter): print( ('[%3d] %7d ||' + (' %s: %.3f |' * len(losses)) + ' T: %.3f || Dis: %.2f || ETA: %s || timer: %.3f') % tuple([epoch, iteration] + loss_labels + [total, loss_dis, eta_str, elapsed]), flush=True) else: print(('[%3d] %7d ||' + (' %s: %.3f |' * len(losses)) + ' T: %.3f || ETA: %s || timer: %.3f') % tuple([epoch, iteration] + loss_labels + [total, eta_str, elapsed]), flush=True) # Loss Key: # - B: Box Localization Loss # - C: Class Confidence Loss # - M: Mask Loss # - P: Prototype Loss # - D: Coefficient Diversity Loss # - E: Class Existence Loss # - S: Semantic Segmentation Loss # - T: Total loss # -Dis:Discriminator Loss if args.log: precision = 5 loss_info = { k: round(losses[k].item(), precision) for k in losses } loss_info['T'] = round(loss.item(), precision) if args.log_gpu: log.log_gpu_stats = (iteration % 10 == 0 ) # nvidia-smi is sloooow log.log('train', loss=loss_info, epoch=epoch, iter=iteration, lr=round(cur_lr, 10), elapsed=elapsed) log.log_gpu_stats = args.log_gpu iteration += 1 if iteration % args.save_interval == 0 and iteration != args.start_iter: if args.keep_latest: latest = SavePath.get_latest(args.save_folder, cfg.name) print('Saving state, iter:', iteration) yolact_net.save_weights(save_path(epoch, iteration)) if args.keep_latest and latest is not None: if args.keep_latest_interval <= 0 or iteration % args.keep_latest_interval != args.save_interval: print('Deleting old save...') os.remove(latest) # This is done per epoch if args.validation_epoch > 0: # NOTE: Validation loss # if cfg.pred_seg: # net.eval() # dis_net.eval() # cfg.gan_eval = True # with torch.no_grad(): # for datum in tqdm(val_loader, desc='GAN Validation'): # losses, seg_list, pred_list = net(datum) # losses, seg_list, pred_list = net(datum) # # TODO: warp below as a function # seg_list = [v.permute(2,1,0).contiguous() for v in seg_list] # b = len(seg_list) # batch size # _, seg_h, seg_w = seg_list[0].size() # seg_clas = torch.zeros(b, cfg.num_classes-1, seg_h, seg_w) # mask_clas = torch.zeros(b, cfg.num_classes-1, seg_h, seg_w) # target_list = [target for target in datum[1][0]] # mask_list = [interpolate(mask.unsqueeze(0), size = (seg_h,seg_w),mode='bilinear', \ # align_corners=False).squeeze() for mask in datum[1][1]] # for idx in range(b): # for i, (pred, i_target) in enumerate(zip(pred_list[idx], target_list[idx])): # seg_clas[idx, pred, ...] += seg_list[idx][i,...] # mask_clas[idx, i_target[-1].long(), ...] += mask_list[idx][i,...] # seg_clas = torch.clamp(seg_clas, 0, 1) # image = interpolate(torch.stack(datum[0]), size = (seg_h,seg_w), # mode='bilinear',align_corners=False) # real_label = torch.ones(b) # output_pred = dis_net(img = image, seg = seg_clas) # output_grou = dis_net(img = image, seg = mask_clas) # loss_pred = -criterion_dis(output_pred,target=real_label) # loss_grou = criterion_dis(output_grou,target=real_label) # loss_dis = loss_pred + loss_grou # losses = { k: (v).mean() for k,v in losses.items() } # loss = sum([losses[k] for k in losses]) # val_loss = loss - cfg.lambda_dis*loss_dis # schedule_dis.step(loss_dis) # lr = [group['lr'] for group in optimizer_dis.param_groups] # print(f'Discriminator lr: {lr[0]}') # net.train() if epoch % args.validation_epoch == 0 and epoch > 0: cfg.gan_eval = False dis_net.eval() compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None) # Compute validation mAP after training is finished compute_validation_map(epoch, iteration, yolact_net, val_dataset, log if args.log else None) except KeyboardInterrupt: if args.interrupt: print('Stopping early. Saving network...') # Delete previous copy of the interrupted network so we don't spam the weights folder SavePath.remove_interrupt(args.save_folder) yolact_net.save_weights( save_path(epoch, repr(iteration) + '_interrupt')) exit() yolact_net.save_weights(save_path(epoch, iteration))
class empyt_dataset(object): def __init__(self): self.ids = [] if __name__ == '__main__': # load net num_classes = len(VOC_CLASSES) + 1 # +1 background net = build_ssd('test', size=args.input_dim, num_classes=num_classes) net.load_state_dict(torch.load(args.trained_model)) net.eval() print('Finished loading model: ', args.trained_model) if args.cuda: net = net.cuda() cudnn.benchmark = True # load data dataset = empyt_dataset() # evaluation test_net(args.save_folder, net, args.cuda, dataset, BaseTransform(input_dim), args.top_k, 300, thresh=args.confidence_threshold, remove_ignored=args.remove_ignored)
# eval_fnames = all_xmlfnames[int(n_data*0.8):][:100] eval_fnames = [ idx.split('.')[0] + '_label.xml' for idx in read_fname(args.eval_path) ] all_xml = os.listdir('/home/xav/project/DR/data/newxml/') eval_fnames = list(set(eval_fnames).intersection(set(all_xml))) print('eval_fnames len: {}'.format(len(eval_fnames))) # args.means = [0, 0, 0] # use val dataset to eval dataset_eval = DetectionDataset(args.img_root, args.xml_root, eval_fnames, args.crop_size, args.shift_rate, args.pad_value, BaseTransform(args.input_size, args.means), True) dataloader_eval = data.DataLoader(dataset_eval, args.batch_size, num_workers=args.num_workers, shuffle=False, collate_fn=detection_collate, pin_memory=True) print('eval dataset len: {}'.format(len(dataset_eval))) model = DetModel(args) model.init_model() # model.load_weights() model.eval(dataset_eval, writer, plot_which=args.plot_which,
if args.cuda: cudnn.benchmark = True cudnn.fastest = True torch.set_default_tensor_type('torch.cuda.FloatTensor') else: torch.set_default_tensor_type('torch.FloatTensor') if args.resume and not args.display: with open(args.ap_data_file, 'rb') as f: ap_data = pickle.load(f) calc_map(ap_data) exit() if args.image is None and args.video is None and args.images is None: dataset = COCODetection(cfg.dataset.valid_images, cfg.dataset.valid_info, transform=BaseTransform()) prep_coco_cats(dataset.coco.cats) else: dataset = None print('Loading model...', end='') net = Yolact() net.load_weights(args.trained_model) net.eval() print(' Done.') if args.cuda: net = net.cuda() evaluate(net, dataset)