def train(): dataloaders_head_A, mapping_assignment_dataloader, mapping_test_dataloader = \ segmentation_create_dataloaders(config) dataloaders_head_B = dataloaders_head_A # unlike for clustering datasets net = archs.__dict__[config.arch](config) if config.restart: dict = torch.load(os.path.join(config.out_dir, dict_name), map_location=lambda storage, loc: storage) net.load_state_dict(dict["net"]) net.cuda() net = torch.nn.DataParallel(net) net.train() optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr) if config.restart: optimiser.load_state_dict(dict["optimiser"]) heads = ["A", "B"] if hasattr(config, "head_B_first") and config.head_B_first: heads = ["B", "A"] # Results # ---------------------------------------------------------------------- if config.restart: next_epoch = config.last_epoch + 1 print("starting from epoch %d" % next_epoch) config.epoch_acc = config.epoch_acc[:next_epoch] # in case we overshot config.epoch_avg_subhead_acc = config.epoch_avg_subhead_acc[: next_epoch] config.epoch_stats = config.epoch_stats[:next_epoch] config.epoch_loss_head_A = config.epoch_loss_head_A[:(next_epoch - 1)] config.epoch_loss_no_lamb_head_A = config.epoch_loss_no_lamb_head_A[:( next_epoch - 1)] config.epoch_loss_head_B = config.epoch_loss_head_B[:(next_epoch - 1)] config.epoch_loss_no_lamb_head_B = config.epoch_loss_no_lamb_head_B[:( next_epoch - 1)] else: config.epoch_acc = [] config.epoch_avg_subhead_acc = [] config.epoch_stats = [] config.epoch_loss_head_A = [] config.epoch_loss_no_lamb_head_A = [] config.epoch_loss_head_B = [] config.epoch_loss_no_lamb_head_B = [] _ = segmentation_eval( config, net, mapping_assignment_dataloader=mapping_assignment_dataloader, mapping_test_dataloader=mapping_test_dataloader, sobel=(not config.no_sobel), using_IR=config.using_IR) print("Pre: time %s: \n %s" % (datetime.now(), nice(config.epoch_stats[-1]))) sys.stdout.flush() next_epoch = 1 fig, axarr = plt.subplots(6, sharex=False, figsize=(20, 20)) if not config.use_uncollapsed_loss: print("using condensed loss (default)") loss_fn = IID_segmentation_loss else: print("using uncollapsed loss!") loss_fn = IID_segmentation_loss_uncollapsed # Train # ------------------------------------------------------------------------ for e_i in xrange(next_epoch, config.num_epochs): print("Starting e_i: %d %s" % (e_i, datetime.now())) sys.stdout.flush() if e_i in config.lr_schedule: optimiser = update_lr(optimiser, lr_mult=config.lr_mult) for head_i in range(2): head = heads[head_i] if head == "A": dataloaders = dataloaders_head_A epoch_loss = config.epoch_loss_head_A epoch_loss_no_lamb = config.epoch_loss_no_lamb_head_A lamb = config.lamb_A elif head == "B": dataloaders = dataloaders_head_B epoch_loss = config.epoch_loss_head_B epoch_loss_no_lamb = config.epoch_loss_no_lamb_head_B lamb = config.lamb_B iterators = (d for d in dataloaders) b_i = 0 avg_loss = 0. # over heads and head_epochs (and subheads) avg_loss_no_lamb = 0. avg_loss_count = 0 for tup in itertools.izip(*iterators): net.module.zero_grad() if not config.no_sobel: pre_channels = config.in_channels - 1 else: pre_channels = config.in_channels all_img1 = torch.zeros(config.batch_sz, pre_channels, config.input_sz, config.input_sz).to( torch.float32).cuda() all_img2 = torch.zeros(config.batch_sz, pre_channels, config.input_sz, config.input_sz).to( torch.float32).cuda() all_affine2_to_1 = torch.zeros(config.batch_sz, 2, 3).to(torch.float32).cuda() all_mask_img1 = torch.zeros(config.batch_sz, config.input_sz, config.input_sz).to( torch.float32).cuda() curr_batch_sz = tup[0][0].shape[0] for d_i in xrange(config.num_dataloaders): img1, img2, affine2_to_1, mask_img1 = tup[d_i] assert (img1.shape[0] == curr_batch_sz) actual_batch_start = d_i * curr_batch_sz actual_batch_end = actual_batch_start + curr_batch_sz all_img1[ actual_batch_start:actual_batch_end, :, :, :] = img1 all_img2[ actual_batch_start:actual_batch_end, :, :, :] = img2 all_affine2_to_1[actual_batch_start: actual_batch_end, :, :] = affine2_to_1 all_mask_img1[ actual_batch_start:actual_batch_end, :, :] = mask_img1 if not (curr_batch_sz == config.dataloader_batch_sz) and (e_i == next_epoch): print("last batch sz %d" % curr_batch_sz) curr_total_batch_sz = curr_batch_sz * config.num_dataloaders # times 2 all_img1 = all_img1[:curr_total_batch_sz, :, :, :] all_img2 = all_img2[:curr_total_batch_sz, :, :, :] all_affine2_to_1 = all_affine2_to_1[:curr_total_batch_sz, :, :] all_mask_img1 = all_mask_img1[:curr_total_batch_sz, :, :] if (not config.no_sobel): all_img1 = sobel_process(all_img1, config.include_rgb, using_IR=config.using_IR) all_img2 = sobel_process(all_img2, config.include_rgb, using_IR=config.using_IR) x1_outs = net(all_img1, head=head) x2_outs = net(all_img2, head=head) avg_loss_batch = None # avg over the heads avg_loss_no_lamb_batch = None for i in xrange(config.num_subheads): loss, loss_no_lamb = loss_fn( x1_outs[i], x2_outs[i], all_affine2_to_1=all_affine2_to_1, all_mask_img1=all_mask_img1, lamb=lamb, half_T_side_dense=config.half_T_side_dense, half_T_side_sparse_min=config.half_T_side_sparse_min, half_T_side_sparse_max=config.half_T_side_sparse_max) if avg_loss_batch is None: avg_loss_batch = loss avg_loss_no_lamb_batch = loss_no_lamb else: avg_loss_batch += loss avg_loss_no_lamb_batch += loss_no_lamb avg_loss_batch /= config.num_subheads avg_loss_no_lamb_batch /= config.num_subheads if ((b_i % 100) == 0) or (e_i == next_epoch): print( "Model ind %d epoch %d head %s batch: %d avg loss %f avg loss no " "lamb %f " "time %s" % \ (config.model_ind, e_i, head, b_i, avg_loss_batch.item(), avg_loss_no_lamb_batch.item(), datetime.now())) sys.stdout.flush() if not np.isfinite(avg_loss_batch.item()): print("Loss is not finite... %s:" % str(avg_loss_batch)) exit(1) avg_loss += avg_loss_batch.item() avg_loss_no_lamb += avg_loss_no_lamb_batch.item() avg_loss_count += 1 avg_loss_batch.backward() optimiser.step() torch.cuda.empty_cache() b_i += 1 if b_i == 2 and config.test_code: break avg_loss = float(avg_loss / avg_loss_count) avg_loss_no_lamb = float(avg_loss_no_lamb / avg_loss_count) epoch_loss.append(avg_loss) epoch_loss_no_lamb.append(avg_loss_no_lamb) # Eval # ----------------------------------------------------------------------- is_best = segmentation_eval( config, net, mapping_assignment_dataloader=mapping_assignment_dataloader, mapping_test_dataloader=mapping_test_dataloader, sobel=(not config.no_sobel), using_IR=config.using_IR) print("Pre: time %s: \n %s" % (datetime.now(), nice(config.epoch_stats[-1]))) sys.stdout.flush() axarr[0].clear() axarr[0].plot(config.epoch_acc) axarr[0].set_title("acc (best), top: %f" % max(config.epoch_acc)) axarr[1].clear() axarr[1].plot(config.epoch_avg_subhead_acc) axarr[1].set_title("acc (avg), top: %f" % max(config.epoch_avg_subhead_acc)) axarr[2].clear() axarr[2].plot(config.epoch_loss_head_A) axarr[2].set_title("Loss head A") axarr[3].clear() axarr[3].plot(config.epoch_loss_no_lamb_head_A) axarr[3].set_title("Loss no lamb head A") axarr[4].clear() axarr[4].plot(config.epoch_loss_head_B) axarr[4].set_title("Loss head B") axarr[5].clear() axarr[5].plot(config.epoch_loss_no_lamb_head_B) axarr[5].set_title("Loss no lamb head B") fig.canvas.draw_idle() fig.savefig(os.path.join(config.out_dir, "plots.png")) if is_best or (e_i % config.save_freq == 0): net.module.cpu() save_dict = { "net": net.module.state_dict(), "optimiser": optimiser.state_dict() } if e_i % config.save_freq == 0: torch.save(save_dict, os.path.join(config.out_dir, "latest.pytorch")) config.last_epoch = e_i # for last saved version if is_best: torch.save(save_dict, os.path.join(config.out_dir, "best.pytorch")) with open(os.path.join(config.out_dir, "best_config.pickle"), 'wb') as outfile: pickle.dump(config, outfile) with open(os.path.join(config.out_dir, "best_config.txt"), "w") as text_file: text_file.write("%s" % config) net.module.cuda() with open(os.path.join(config.out_dir, "config.pickle"), 'wb') as outfile: pickle.dump(config, outfile) with open(os.path.join(config.out_dir, "config.txt"), "w") as text_file: text_file.write("%s" % config) if config.test_code: exit(0)
sys.stdout.flush() next_epoch = 1 fig, axarr = plt.subplots(4, sharex=False, figsize=(20, 20)) # Train ------------------------------------------------------------------------ for e_i in range(next_epoch, config.num_epochs): print("Starting e_i: %d" % e_i) sys.stdout.flush() iterators = (d for d in dataloaders) b_i = 0 if e_i in config.lr_schedule: optimiser = update_lr(optimiser, lr_mult=config.lr_mult) avg_loss = 0. avg_loss_no_lamb = 0. avg_loss_count = 0 for tup in zip(*iterators): net.module.zero_grad() # one less because this is before sobel all_imgs = torch.zeros(config.batch_sz, config.in_channels - 1, config.input_sz, config.input_sz).cuda() all_imgs_tf = torch.zeros(config.batch_sz, config.in_channels - 1, config.input_sz, config.input_sz).cuda()
def main(): config = parse_config() # Setup ---------------------------------------------------------------------- config.contiguous_sz = 10 # Tencrop config.out_dir = os.path.join(config.out_root, str(config.model_ind)) if not os.path.exists(config.out_dir): os.makedirs(config.out_dir) if config.restart: given_config = config reloaded_config_path = os.path.join(given_config.out_dir, "config.pickle") print("Loading restarting config from: %s" % reloaded_config_path) with open(reloaded_config_path, "rb") as config_f: config = pickle.load(config_f) assert (config.model_ind == given_config.model_ind) config.restart = True config.num_epochs = given_config.num_epochs # train for longer config.restart_new_model_ind = given_config.restart_new_model_ind config.new_model_ind = given_config.new_model_ind start_epoch = config.last_epoch + 1 print("...restarting from epoch %d" % start_epoch) # in case we overshot without saving config.epoch_acc = config.epoch_acc[:start_epoch] config.epoch_loss = config.epoch_loss[:start_epoch] else: config.epoch_acc = [] config.epoch_loss = [] start_epoch = 0 # old config only used retrospectively for setting up model at start reloaded_config_path = os.path.join(config.out_root, str(config.old_model_ind), "best_config.pickle") print("Loading old features config from: %s" % reloaded_config_path) with open(reloaded_config_path, "rb") as config_f: old_config = pickle.load(config_f) assert (old_config.model_ind == config.old_model_ind) if config.new_batch_sz == -1: config.new_batch_sz = old_config.batch_sz fig, axarr = plt.subplots(2, sharex=False, figsize=(20, 20)) # Data ----------------------------------------------------------------------- # make supervised data: train on train, test on test, unlabelled is unused assert old_config.sobel, "Old model should have been trained with sobel being activated" tf1, tf2, tf3 = sobel_make_transforms(old_config, random_affine=config.random_affine, cutout=config.cutout, cutout_p=config.cutout_p, cutout_max_box=config.cutout_max_box, affine_p=config.affine_p) if old_config.dataset == "STL10": dataset_class = torchvision.datasets.STL10 train_data = dataset_class(root=old_config.dataset_root, transform=tf2, split="train") # also could use tf1 test_data = dataset_class(root=old_config.dataset_root, transform=None, split="test") elif old_config.dataset in HANDWRITING_DATASETS: dataset_root = os.path.join(old_config.dataset_root, old_config.dataset) train_json_path = os.path.join("train", old_config.dataset + "_train.json") train_data = HandwritingDataset([train_json_path], dataset_root, transform=tf2) test_json_path = os.path.join("test", old_config.dataset + "_test.json") test_data = HandwritingDataset([test_json_path], dataset_root, transform=None) else: raise NotImplementedError train_loader = torch.utils.data.DataLoader(train_data, batch_size=config.new_batch_sz, shuffle=True, num_workers=0, drop_last=False) test_data = TenCropAndFinish(test_data, input_sz=old_config.input_sz, include_rgb=old_config.include_rgb) test_loader = torch.utils.data.DataLoader( test_data, batch_size=config.new_batch_sz, # full batch shuffle=False, num_workers=0, drop_last=False) # Model ---------------------------------------------------------------------- net_features = archs.__dict__[old_config.arch](old_config).cuda() if not config.restart: model_path = os.path.join(old_config.out_dir, "best_net.pytorch") net_features.load_state_dict( torch.load(model_path, map_location=lambda storage, loc: storage)) dlen = get_dlen(net_features, train_loader, include_rgb=old_config.include_rgb, penultimate_features=config.penultimate_features) print("dlen: %d" % dlen) assert (config.arch == "SupHead5") net = SupHead5(net_features, dlen=dlen, gt_k=old_config.gt_k) if config.restart: print("restarting from latest net") model_path = os.path.join(config.out_dir, "latest_net.pytorch") net.load_state_dict( torch.load(model_path, map_location=lambda storage, loc: storage)) net.cuda() net = torch.nn.DataParallel(net) opt_trunk = torch.optim.Adam(net.module.trunk.parameters(), lr=config.trunk_lr) opt_head = torch.optim.Adam(net.module.head.parameters(), lr=config.head_lr) if config.restart: print("restarting from latest optimiser") optimiser_states = torch.load( os.path.join(config.out_dir, "latest_optimiser.pytorch")) opt_trunk.load_state_dict(optimiser_states["opt_trunk"]) opt_head.load_state_dict(optimiser_states["opt_head"]) else: print("using new optimiser state") criterion = nn.CrossEntropyLoss().cuda() if not config.restart: net.eval() acc = assess_acc_block( net, test_loader, gt_k=old_config.gt_k, include_rgb=old_config.include_rgb, penultimate_features=config.penultimate_features, contiguous_sz=config.contiguous_sz) print("pre: model %d old model %d, acc %f time %s" % (config.model_ind, config.old_model_ind, acc, datetime.now())) sys.stdout.flush() config.epoch_acc.append(acc) if config.restart_new_model_ind: assert config.restart config.model_ind = config.new_model_ind # old_model_ind stays same config.out_dir = os.path.join(config.out_root, str(config.model_ind)) print("restarting as model %d" % config.model_ind) if not os.path.exists(config.out_dir): os.makedirs(config.out_dir) # Train ---------------------------------------------------------------------- for e_i in xrange(start_epoch, config.num_epochs): net.train() if e_i in config.lr_schedule: print("e_i %d, multiplying lr for opt trunk and head by %f" % (e_i, config.lr_mult)) opt_trunk = update_lr(opt_trunk, lr_mult=config.lr_mult) opt_head = update_lr(opt_head, lr_mult=config.lr_mult) if not hasattr(config, "lr_changes"): config.lr_changes = [] config.lr_changes.append((e_i, config.lr_mult)) avg_loss = 0. num_batches = len(train_loader) for i, (imgs, targets) in enumerate(train_loader): imgs = sobel_process(imgs.cuda(), old_config.include_rgb) targets = targets.cuda() x_out = net(imgs, penultimate_features=config.penultimate_features) loss = criterion(x_out, targets) avg_loss += float(loss.data) opt_trunk.zero_grad() opt_head.zero_grad() loss.backward() opt_trunk.step() opt_head.step() if (i % 100 == 0) or (e_i == start_epoch): print("batch %d of %d, loss %f, time %s" % (i, num_batches, float(loss.data), datetime.now())) sys.stdout.flush() avg_loss /= num_batches # Eval ---------------------------------------------------------------------- net.eval() acc = assess_acc_block( net, test_loader, gt_k=old_config.gt_k, include_rgb=old_config.include_rgb, penultimate_features=config.penultimate_features, contiguous_sz=config.contiguous_sz) print( "model %d old model %d epoch %d acc %f time %s" % (config.model_ind, config.old_model_ind, e_i, acc, datetime.now())) sys.stdout.flush() is_best = False if acc > max(config.epoch_acc): is_best = True config.epoch_acc.append(acc) config.epoch_loss.append(avg_loss) axarr[0].clear() axarr[0].plot(config.epoch_acc) axarr[0].set_title("Acc") axarr[1].clear() axarr[1].plot(config.epoch_loss) axarr[1].set_title("Loss") fig.canvas.draw_idle() fig.savefig(os.path.join(config.out_dir, "plots.png")) if is_best or (e_i % 10 == 0): net.module.cpu() if is_best: torch.save(net.module.state_dict(), os.path.join(config.out_dir, "best_net.pytorch")) torch.save( { "opt_head": opt_head.state_dict(), "opt_trunk": opt_trunk.state_dict() }, os.path.join(config.out_dir, "best_optimiser.pytorch")) # save model sparingly for this script if e_i % 10 == 0: torch.save(net.module.state_dict(), os.path.join(config.out_dir, "latest_net.pytorch")) torch.save( { "opt_head": opt_head.state_dict(), "opt_trunk": opt_trunk.state_dict() }, os.path.join(config.out_dir, "latest_optimiser.pytorch")) net.module.cuda() config.last_epoch = e_i # for last saved version with open(os.path.join(config.out_dir, "config.pickle"), 'w') as outfile: pickle.dump(config, outfile) with open(os.path.join(config.out_dir, "config.txt"), "w") as text_file: text_file.write("%s" % config)
def train(render_count=-1): dataloaders_head_A, dataloaders_head_B, \ mapping_assignment_dataloader, mapping_test_dataloader = \ cluster_twohead_create_dataloaders(config) net = archs.__dict__[config.arch](config) if config.restart: model_path = os.path.join(config.out_dir, net_name) print("Model path: %s" % model_path) net.load_state_dict( torch.load(model_path, map_location=lambda storage, loc: storage)) net.cuda() net = torch.nn.DataParallel(net) net.train() optimiser = get_opt(config.opt)(net.module.parameters(), lr=config.lr) if config.restart: if not (given_config is not None and given_config.num_epochs == 0): print("loading latest opt") optimiser.load_state_dict( torch.load(os.path.join(config.out_dir, opt_name))) heads = ["B", "A"] if config.head_A_first: heads = ["A", "B"] head_epochs = {} head_epochs["A"] = config.head_A_epochs head_epochs["B"] = config.head_B_epochs # Results # ---------------------------------------------------------------------- if config.restart: if not config.restart_from_best: next_epoch = config.last_epoch + 1 # corresponds to last saved model else: next_epoch = np.argmax(np.array(config.epoch_acc)) + 1 print("starting from epoch %d" % next_epoch) # in case we overshot without saving config.epoch_acc = config.epoch_acc[:next_epoch] # in case we overshot config.epoch_avg_subhead_acc = config.epoch_avg_subhead_acc[: next_epoch] config.epoch_stats = config.epoch_stats[:next_epoch] if config.double_eval: config.double_eval_acc = config.double_eval_acc[:next_epoch] config.double_eval_avg_subhead_acc = config.double_eval_avg_subhead_acc[: next_epoch] config.double_eval_stats = config.double_eval_stats[:next_epoch] config.epoch_loss_head_A = config.epoch_loss_head_A[:(next_epoch - 1)] config.epoch_loss_no_lamb_head_A = config.epoch_loss_no_lamb_head_A[:( next_epoch - 1)] config.epoch_loss_head_B = config.epoch_loss_head_B[:(next_epoch - 1)] config.epoch_loss_no_lamb_head_B = config.epoch_loss_no_lamb_head_B[:( next_epoch - 1)] else: config.epoch_acc = [] config.epoch_avg_subhead_acc = [] config.epoch_stats = [] if config.double_eval: config.double_eval_acc = [] config.double_eval_avg_subhead_acc = [] config.double_eval_stats = [] config.epoch_loss_head_A = [] config.epoch_loss_no_lamb_head_A = [] config.epoch_loss_head_B = [] config.epoch_loss_no_lamb_head_B = [] subhead = None if config.select_subhead_on_loss: subhead = get_subhead_using_loss(config, dataloaders_head_B, net, sobel=False, lamb=config.lamb_B) _ = cluster_eval( config, net, mapping_assignment_dataloader=mapping_assignment_dataloader, mapping_test_dataloader=mapping_test_dataloader, sobel=False, use_subhead=subhead) print("Pre: time %s: \n %s" % (datetime.now(), nice(config.epoch_stats[-1]))) if config.double_eval: print("double eval: \n %s" % (nice(config.double_eval_stats[-1]))) sys.stdout.flush() next_epoch = 1 fig, axarr = plt.subplots(6 + 2 * int(config.double_eval), sharex=False, figsize=(20, 20)) save_progression = hasattr(config, "save_progression") and \ config.save_progression if save_progression: save_progression_count = 0 save_progress(config, net, mapping_assignment_dataloader, mapping_test_dataloader, save_progression_count, sobel=False, render_count=render_count) save_progression_count += 1 # Train # ------------------------------------------------------------------------ for e_i in xrange(next_epoch, config.num_epochs): print("Starting e_i: %d" % e_i) if e_i in config.lr_schedule: optimiser = update_lr(optimiser, lr_mult=config.lr_mult) for head_i in range(2): head = heads[head_i] if head == "A": dataloaders = dataloaders_head_A epoch_loss = config.epoch_loss_head_A epoch_loss_no_lamb = config.epoch_loss_no_lamb_head_A lamb = config.lamb_A elif head == "B": dataloaders = dataloaders_head_B epoch_loss = config.epoch_loss_head_B epoch_loss_no_lamb = config.epoch_loss_no_lamb_head_B lamb = config.lamb_B avg_loss = 0. # over heads and head_epochs (and subheads) avg_loss_no_lamb = 0. avg_loss_count = 0 for head_i_epoch in range(head_epochs[head]): sys.stdout.flush() iterators = (d for d in dataloaders) b_i = 0 for tup in itertools.izip(*iterators): net.module.zero_grad() all_imgs = torch.zeros( (config.batch_sz, config.in_channels, config.input_sz, config.input_sz)).cuda() all_imgs_tf = torch.zeros( (config.batch_sz, config.in_channels, config.input_sz, config.input_sz)).cuda() imgs_curr = tup[0][0] # always the first curr_batch_sz = imgs_curr.size(0) for d_i in xrange(config.num_dataloaders): imgs_tf_curr = tup[1 + d_i][0] # from 2nd to last assert (curr_batch_sz == imgs_tf_curr.size(0)) actual_batch_start = d_i * curr_batch_sz actual_batch_end = actual_batch_start + curr_batch_sz all_imgs[actual_batch_start:actual_batch_end, :, :, :] = \ imgs_curr.cuda() all_imgs_tf[actual_batch_start:actual_batch_end, :, :, :] = \ imgs_tf_curr.cuda() if not (curr_batch_sz == config.dataloader_batch_sz): print("last batch sz %d" % curr_batch_sz) curr_total_batch_sz = curr_batch_sz * config.num_dataloaders # # times 2 all_imgs = all_imgs[:curr_total_batch_sz, :, :, :] all_imgs_tf = all_imgs_tf[:curr_total_batch_sz, :, :, :] x_outs = net(all_imgs) x_tf_outs = net(all_imgs_tf) avg_loss_batch = None # avg over the heads avg_loss_no_lamb_batch = None for i in xrange(config.num_subheads): loss, loss_no_lamb = IID_loss(x_outs[i], x_tf_outs[i], lamb=lamb) if avg_loss_batch is None: avg_loss_batch = loss avg_loss_no_lamb_batch = loss_no_lamb else: avg_loss_batch += loss avg_loss_no_lamb_batch += loss_no_lamb avg_loss_batch /= config.num_subheads avg_loss_no_lamb_batch /= config.num_subheads if ((b_i % 100) == 0) or (e_i == next_epoch): print( "Model ind %d epoch %d head %s batch: %d avg loss %f avg loss no lamb %f time %s" % \ (config.model_ind, e_i, head, b_i, avg_loss_batch.item(), avg_loss_no_lamb_batch.item(), datetime.now())) sys.stdout.flush() if not np.isfinite(avg_loss_batch.item()): print("Loss is not finite... %s:" % avg_loss_batch.item()) exit(1) avg_loss += avg_loss_batch.item() avg_loss_no_lamb += avg_loss_no_lamb_batch.item() avg_loss_count += 1 avg_loss_batch.backward() optimiser.step() if ((b_i % 50) == 0) and save_progression: save_progress(config, net, mapping_assignment_dataloader, mapping_test_dataloader, save_progression_count, sobel=False, render_count=render_count) save_progression_count += 1 b_i += 1 if b_i == 2 and config.test_code: break avg_loss = float(avg_loss / avg_loss_count) avg_loss_no_lamb = float(avg_loss_no_lamb / avg_loss_count) epoch_loss.append(avg_loss) epoch_loss_no_lamb.append(avg_loss_no_lamb) # Eval # ----------------------------------------------------------------------- subhead = None if config.select_subhead_on_loss: subhead = get_subhead_using_loss(config, dataloaders_head_B, net, sobel=False, lamb=config.lamb_B) is_best = cluster_eval( config, net, mapping_assignment_dataloader=mapping_assignment_dataloader, mapping_test_dataloader=mapping_test_dataloader, sobel=False, use_subhead=subhead) print("Pre: time %s: \n %s" % (datetime.now(), nice(config.epoch_stats[-1]))) if config.double_eval: print("double eval: \n %s" % (nice(config.double_eval_stats[-1]))) sys.stdout.flush() axarr[0].clear() axarr[0].plot(config.epoch_acc) axarr[0].set_title("acc (best), top: %f" % max(config.epoch_acc)) axarr[1].clear() axarr[1].plot(config.epoch_avg_subhead_acc) axarr[1].set_title("acc (avg), top: %f" % max(config.epoch_avg_subhead_acc)) axarr[2].clear() axarr[2].plot(config.epoch_loss_head_A) axarr[2].set_title("Loss head A") axarr[3].clear() axarr[3].plot(config.epoch_loss_no_lamb_head_A) axarr[3].set_title("Loss no lamb head A") axarr[4].clear() axarr[4].plot(config.epoch_loss_head_B) axarr[4].set_title("Loss head B") axarr[5].clear() axarr[5].plot(config.epoch_loss_no_lamb_head_B) axarr[5].set_title("Loss no lamb head B") if config.double_eval: axarr[6].clear() axarr[6].plot(config.double_eval_acc) axarr[6].set_title("double eval acc (best), top: %f" % max(config.double_eval_acc)) axarr[7].clear() axarr[7].plot(config.double_eval_avg_subhead_acc) axarr[7].set_title("double eval acc (avg)), top: %f" % max(config.double_eval_avg_subhead_acc)) fig.tight_layout() fig.canvas.draw_idle() fig.savefig(os.path.join(config.out_dir, "plots.png")) if is_best or (e_i % config.save_freq == 0): net.module.cpu() if e_i % config.save_freq == 0: torch.save(net.module.state_dict(), os.path.join(config.out_dir, "latest_net.pytorch")) torch.save( optimiser.state_dict(), os.path.join(config.out_dir, "latest_optimiser.pytorch")) config.last_epoch = e_i # for last saved version if is_best: # also serves as backup if hardware fails - less likely to hit this torch.save(net.module.state_dict(), os.path.join(config.out_dir, "best_net.pytorch")) torch.save( optimiser.state_dict(), os.path.join(config.out_dir, "best_optimiser.pytorch")) with open(os.path.join(config.out_dir, "best_config.pickle"), 'wb') as outfile: pickle.dump(config, outfile) with open(os.path.join(config.out_dir, "best_config.txt"), "w") as text_file: text_file.write("%s" % config) net.module.cuda() with open(os.path.join(config.out_dir, "config.pickle"), 'wb') as outfile: pickle.dump(config, outfile) with open(os.path.join(config.out_dir, "config.txt"), "w") as text_file: text_file.write("%s" % config) if config.test_code: exit(0)
def training(config, net, current_epoch, next_epoch, heads, dataloaders_head_A, dataloaders_head_B, loss_fn, optimiser): """Computes loss for head A and B for the current epoch and carries out a backward pass through the net using the optimiser with lr annealing Params: config: TODO net: TODO current_epoch: TODO next_epoch: TODO heads: TODO dataloaders_head_A: TODO dataloaders_head_B: TODO loss_fn: TODO optimiser: TODO Returns: PytorchNetwork -- the trained model """ if current_epoch in config.lr_schedule: optimiser = update_lr(optimiser, lr_mult=config.lr_mult) for head_i in range(2): head = heads[head_i] if head == "A": dataloaders = dataloaders_head_A epoch_loss = config.epoch_loss_head_A epoch_loss_no_lamb = config.epoch_loss_no_lamb_head_A lamb = config.lamb_A elif head == "B": dataloaders = dataloaders_head_B epoch_loss = config.epoch_loss_head_B epoch_loss_no_lamb = config.epoch_loss_no_lamb_head_B lamb = config.lamb_B iterators = (d for d in dataloaders) b_i = 0 avg_loss = 0. # over heads and head_epochs (and sub_heads) avg_loss_no_lamb = 0. avg_loss_count = 0 for tup in zip(*iterators): net.module.zero_grad() if not config.no_sobel: pre_channels = config.in_channels - 1 else: pre_channels = config.in_channels all_img1 = torch.zeros(config.batch_sz, pre_channels, config.input_sz, config.input_sz).to( torch.float32) all_img2 = torch.zeros(config.batch_sz, pre_channels, config.input_sz, config.input_sz).to( torch.float32) all_affine2_to_1 = torch.zeros(config.batch_sz, 2, 3).to( torch.float32) all_mask_img1 = torch.zeros(config.batch_sz, config.input_sz, config.input_sz).to(torch.float32) if not config.nocuda: all_img1 = all_img1.cuda() all_img2 = all_img2.cuda() all_affine2_to_1 = all_affine2_to_1.cuda() all_mask_img1 = all_mask_img1.cuda() curr_batch_sz = tup[0][0].shape[0] for d_i in range(config.num_dataloaders): img1, img2, affine2_to_1, mask_img1 = tup[d_i] assert (img1.shape[0] == curr_batch_sz) actual_batch_start = d_i * curr_batch_sz actual_batch_end = actual_batch_start + curr_batch_sz all_img1[actual_batch_start:actual_batch_end, :, :, :] = img1 all_img2[actual_batch_start:actual_batch_end, :, :, :] = img2 all_affine2_to_1[actual_batch_start:actual_batch_end, :, :] = affine2_to_1 all_mask_img1[actual_batch_start:actual_batch_end, :, :] = mask_img1 if not (curr_batch_sz == config.dataloader_batch_sz) and ( current_epoch == next_epoch): print("last batch sz %d" % curr_batch_sz) curr_total_batch_sz = curr_batch_sz * config.num_dataloaders # times 2 all_img1 = all_img1[:curr_total_batch_sz, :, :, :] all_img2 = all_img2[:curr_total_batch_sz, :, :, :] all_affine2_to_1 = all_affine2_to_1[:curr_total_batch_sz, :, :] all_mask_img1 = all_mask_img1[:curr_total_batch_sz, :, :] if (not config.no_sobel): all_img1 = sobel_process(all_img1, config.include_rgb, using_IR=config.using_IR, cuda_enabled=not config.nocuda) all_img2 = sobel_process(all_img2, config.include_rgb, using_IR=config.using_IR, cuda_enabled=not config.nocuda) x1_outs = net(all_img1, head=head) x2_outs = net(all_img2, head=head) avg_loss_batch = None # avg over the heads avg_loss_no_lamb_batch = None for i in range(config.num_sub_heads): loss, loss_no_lamb = loss_fn(x1_outs[i], x2_outs[i], all_affine2_to_1=all_affine2_to_1, all_mask_img1=all_mask_img1, lamb=lamb, half_T_side_dense=config.half_T_side_dense, half_T_side_sparse_min=config.half_T_side_sparse_min, half_T_side_sparse_max=config.half_T_side_sparse_max) if avg_loss_batch is None: avg_loss_batch = loss avg_loss_no_lamb_batch = loss_no_lamb else: avg_loss_batch += loss avg_loss_no_lamb_batch += loss_no_lamb avg_loss_batch /= config.num_sub_heads avg_loss_no_lamb_batch /= config.num_sub_heads if ((b_i % 100) == 0) or (current_epoch == next_epoch): print( "Model ind %d epoch %d head %s batch: %d avg loss %f avg loss no " "lamb %f " "time %s" % (config.model_ind, current_epoch, head, b_i, avg_loss_batch.item(), avg_loss_no_lamb_batch.item(), datetime.now())) sys.stdout.flush() if not np.isfinite(avg_loss_batch.item()): print("Loss is not finite... %s:" % str(avg_loss_batch)) exit(1) avg_loss += avg_loss_batch.item() avg_loss_no_lamb += avg_loss_no_lamb_batch.item() avg_loss_count += 1 if not config.nocuda: avg_loss_batch = avg_loss_batch.cuda() avg_loss_batch.backward() optimiser.step() torch.cuda.empty_cache() if not config.nocuda else None b_i += 1 if b_i == 2 and config.test_code: break avg_loss = float(avg_loss / avg_loss_count) avg_loss_no_lamb = float(avg_loss_no_lamb / avg_loss_count) epoch_loss.append(avg_loss) epoch_loss_no_lamb.append(avg_loss_no_lamb) return net
def train(config, net, optimiser, render_count=-1): # TODO: center crop ok or does it remove too much information if text is aligned left dataloader_list, mapping_assignment_dataloader, mapping_test_dataloader = get_dataloader_list( config) num_heads = len(config.output_ks) # Results ---------------------------------------------------------------------- if config.restart: if not config.restart_from_best: next_epoch = config.last_epoch + 1 # corresponds to last saved model else: next_epoch = np.argmax(np.array(config.epoch_acc)) + 1 print("starting from epoch %d" % next_epoch) # in case we overshot without saving config.epoch_acc = config.epoch_acc[:next_epoch] # in case we overshot config.epoch_avg_subhead_acc = config.epoch_avg_subhead_acc[: next_epoch] config.epoch_stats = config.epoch_stats[:next_epoch] if config.double_eval: config.double_eval_acc = config.double_eval_acc[:next_epoch] config.double_eval_avg_subhead_acc = config.double_eval_avg_subhead_acc[: next_epoch] config.double_eval_stats = config.double_eval_stats[:next_epoch] for i, loss in enumerate(config.epoch_loss): config.epoch_loss[i] = loss[:(next_epoch - 1)] for i, loss_no_lamb in enumerate(config.epoch_loss_no_lamb): config.epoch_loss_no_lamb[i] = loss_no_lamb[:(next_epoch - 1)] else: config.epoch_acc = [] config.epoch_avg_subhead_acc = [] config.epoch_stats = [] if config.double_eval: config.double_eval_acc = [] config.double_eval_avg_subhead_acc = [] config.double_eval_stats = [] config.epoch_loss = [[] for _ in range(num_heads)] config.epoch_loss_no_lamb = [[] for _ in range(num_heads)] subhead = None if config.select_subhead_on_loss: assert num_heads == 2 subhead = get_subhead_using_loss(config, dataloader_list[1], net, sobel=config.sobel, lamb=config.lamb) _ = cluster_eval( config, net, mapping_assignment_dataloader=mapping_assignment_dataloader, mapping_test_dataloader=mapping_test_dataloader, sobel=config.sobel, use_subhead=subhead) print("Pre: time %s: \n %s" % (datetime.now(), nice(config.epoch_stats[-1]))) if config.double_eval: print("double eval: \n %s" % (nice(config.double_eval_stats[-1]))) sys.stdout.flush() next_epoch = 1 fig, axarr = plt.subplots(2 + 2 * num_heads + 2 * int(config.double_eval), sharex=False, figsize=(20, 20)) save_progression = hasattr(config, "save_progression") and config.save_progression if save_progression: save_progression_count = 0 save_progress(config, net, mapping_assignment_dataloader, mapping_test_dataloader, save_progression_count, sobel=config.sobel, render_count=render_count) save_progression_count += 1 # Train ------------------------------------------------------------------------ heads = range(num_heads) if config.reverse_heads: heads = reversed(heads) for e_i in xrange(next_epoch, config.num_epochs + 1): print("Starting e_i: %d" % e_i) if e_i in config.lr_schedule: optimiser = update_lr(optimiser, lr_mult=config.lr_mult) for head_idx in heads: dataloaders = dataloader_list[head_idx] epoch_loss = config.epoch_loss[head_idx] epoch_loss_no_lamb = config.epoch_loss_no_lamb[head_idx] avg_loss = 0. # over heads and head_epochs (and subheads) avg_loss_no_lamb = 0. avg_loss_count = 0 for head_i_epoch in range(config.head_epochs[head_idx]): sys.stdout.flush() iterators = (d for d in dataloaders) b_i = 0 for tup in itertools.izip(*iterators): net.module.zero_grad() in_channels = config.in_channels if config.sobel: # one less because this is before sobel in_channels -= 1 all_imgs = torch.zeros( (config.batch_sz, in_channels, config.input_sz[0], config.input_sz[1])).cuda() all_imgs_tf = torch.zeros( (config.batch_sz, in_channels, config.input_sz[0], config.input_sz[1])).cuda() imgs_curr = tup[0][0] # always the first curr_batch_sz = imgs_curr.size(0) for d_i in xrange(config.num_dataloaders): imgs_tf_curr = tup[1 + d_i][0] # from 2nd to last assert (curr_batch_sz == imgs_tf_curr.size(0)) actual_batch_start = d_i * curr_batch_sz actual_batch_end = actual_batch_start + curr_batch_sz all_imgs[actual_batch_start: actual_batch_end, :, :, :] = imgs_curr.cuda() all_imgs_tf[ actual_batch_start: actual_batch_end, :, :, :] = imgs_tf_curr.cuda() if not (curr_batch_sz == config.dataloader_batch_sz): print("last batch sz %d" % curr_batch_sz) curr_total_batch_sz = curr_batch_sz * config.num_dataloaders all_imgs = all_imgs[:curr_total_batch_sz, :, :, :] all_imgs_tf = all_imgs_tf[:curr_total_batch_sz, :, :, :] if config.sobel: all_imgs = sobel_process(all_imgs, config.include_rgb) all_imgs_tf = sobel_process(all_imgs_tf, config.include_rgb) x_outs = net(all_imgs, head_idx=head_idx) x_tf_outs = net(all_imgs_tf, head_idx=head_idx) avg_loss_batch = None # avg over the subheads avg_loss_no_lamb_batch = None for i in xrange(config.num_subheads): loss, loss_no_lamb = IID_loss(x_outs[i], x_tf_outs[i], lamb=config.lamb) if avg_loss_batch is None: avg_loss_batch = loss avg_loss_no_lamb_batch = loss_no_lamb else: avg_loss_batch += loss avg_loss_no_lamb_batch += loss_no_lamb avg_loss_batch /= config.num_subheads avg_loss_no_lamb_batch /= config.num_subheads if ((b_i % 100) == 0) or (e_i == next_epoch and b_i < 10): print( "Model ind %d epoch %d head %s head_i_epoch %d batch %d: avg loss %f avg loss no lamb %f " "time %s" % (config.model_ind, e_i, str(head_idx), head_i_epoch, b_i, avg_loss_batch.item(), avg_loss_no_lamb_batch.item(), datetime.now())) sys.stdout.flush() if not np.isfinite(avg_loss_batch.item()): print("Loss is not finite... %s:" % str(avg_loss_batch)) sys.exit(1) avg_loss += avg_loss_batch.item() avg_loss_no_lamb += avg_loss_no_lamb_batch.item() avg_loss_count += 1 avg_loss_batch.backward() optimiser.step() if ((b_i % 50) == 0) and save_progression: save_progress(config, net, mapping_assignment_dataloader, mapping_test_dataloader, save_progression_count, sobel=False, render_count=render_count) save_progression_count += 1 b_i += 1 if b_i == 2 and config.test_code: break avg_loss = float(avg_loss / avg_loss_count) avg_loss_no_lamb = float(avg_loss_no_lamb / avg_loss_count) epoch_loss.append(avg_loss) epoch_loss_no_lamb.append(avg_loss_no_lamb) # Eval ----------------------------------------------------------------------- # Can also pick the subhead using the evaluation process (to do this, set use_subhead=None) subhead = None if config.select_subhead_on_loss: assert num_heads == 2 subhead = get_subhead_using_loss(config, dataloader_list[1], net, sobel=config.sobel, lamb=config.lamb) is_best = cluster_eval( config, net, mapping_assignment_dataloader=mapping_assignment_dataloader, mapping_test_dataloader=mapping_test_dataloader, sobel=config.sobel, use_subhead=subhead) print("Pre: time %s: \n %s" % (datetime.now(), nice(config.epoch_stats[-1]))) if config.double_eval: print("double eval: \n %s" % (nice(config.double_eval_stats[-1]))) sys.stdout.flush() axarr[0].clear() axarr[0].plot(config.epoch_acc) axarr[0].set_title("acc (best), top: %f" % max(config.epoch_acc)) axarr[1].clear() axarr[1].plot(config.epoch_avg_subhead_acc) axarr[1].set_title("acc (avg), top: %f" % max(config.epoch_avg_subhead_acc)) last_ax_idx = 1 starting_ax_idx = last_ax_idx + 1 for i in range(num_heads): axarr[starting_ax_idx + i].clear() axarr[starting_ax_idx + i].plot(config.epoch_loss[i]) axarr[starting_ax_idx + i].set_title("Loss head_idx " + str(i)) axarr[starting_ax_idx + i + 1].clear() axarr[starting_ax_idx + i + 1].plot(config.epoch_loss_no_lamb[i]) axarr[starting_ax_idx + i + 1].set_title("Loss no lamb head_idx " + str(i)) if config.double_eval: next_index = starting_ax_idx + 2 * num_heads axarr[next_index].clear() axarr[next_index].plot(config.double_eval_acc) axarr[next_index].set_title("double eval acc (best), top: %f" % max(config.double_eval_acc)) axarr[next_index + 1].clear() axarr[next_index + 1].plot(config.double_eval_avg_subhead_acc) axarr[next_index + 1].set_title("double eval acc (avg), top: %f" % max(config.double_eval_avg_subhead_acc)) fig.tight_layout() fig.canvas.draw_idle() fig.savefig(os.path.join(config.out_dir, "plots.png")) if is_best or (e_i % config.save_freq == 0): net.module.cpu() if e_i % config.save_freq == 0: torch.save(net.module.state_dict(), os.path.join(config.out_dir, "latest_net.pytorch")) torch.save( optimiser.state_dict(), os.path.join(config.out_dir, "latest_optimiser.pytorch")) config.last_epoch = e_i # for last saved version if is_best: # also serves as backup if hardware fails - less likely to hit this torch.save(net.module.state_dict(), os.path.join(config.out_dir, "best_net.pytorch")) torch.save( optimiser.state_dict(), os.path.join(config.out_dir, "best_optimiser.pytorch")) with open(os.path.join(config.out_dir, "best_config.pickle"), 'wb') as outfile: pickle.dump(config, outfile) with open(os.path.join(config.out_dir, "best_config.txt"), "w") as text_file: text_file.write("%s" % config) net.module.cuda() with open(os.path.join(config.out_dir, "config.pickle"), 'wb') as outfile: pickle.dump(config, outfile) with open(os.path.join(config.out_dir, "config.txt"), "w") as text_file: text_file.write("%s" % config) if config.test_code: exit(0)