# coding=utf-8 # copied from hugginface github # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. # team. # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """BERT pre-training runner.""" import argparse import json import os from os.path import exists, join import random from time import time import torch from torch.nn import functional as F from torch.nn.utils import clip_grad_norm_ from torch.optim import Adam, Adamax from torch.utils.data import DataLoader from data.data import ConcatDetectFeatBertTokDataset as ConcatDataset from apex import amp from horovod import torch as hvd import numpy as np from tqdm import tqdm from data import (DistributedTokenBucketSampler, DetectFeatLmdb, MlmDatasetForVCR, mlm_collate_for_vcr, MrmDatasetForVCR, mrm_collate_for_vcr, MrcDatasetForVCR, mrc_collate_for_vcr, MetaLoader, PrefetchLoader) from model import BertForImageTextPretrainingForVCR from optim import warmup_linear, noam_schedule, vqa_schedule, AdamW from utils.logger import LOGGER, TB_LOGGER, RunningMeter, add_log_to_file from utils.distributed import (all_reduce_and_rescale_tensors, all_gather_list, broadcast_tensors) from utils.save import ModelSaver, save_training_meta from utils.misc import NoOp, parse_with_config NUM_SPECIAL_TOKENS = 81 IMG_DIM = 2048 IMG_LABEL_DIM = 1601 def parse_tasks(datasets): task_names = [] dset_paths = [] mix_ratio = [] for i, dset in enumerate(datasets): assert len(dset['db']) == len(dset['img']) if 'mix_ratio' in dset: assert len(dset['tasks']) == len(dset['mix_ratio']) mix_ratio.extend(dset['mix_ratio']) task_names.extend(f'{t}_{dset["name"]}' for t in dset['tasks']) n_task = len(dset['tasks']) dset_paths.extend([(dset['db'], dset['img'])] * n_task) assert len(task_names) == len(set(task_names)) == len(dset_paths) if mix_ratio: assert len(task_names) == len(mix_ratio) return task_names, dset_paths, mix_ratio else: return task_names, dset_paths def build_sampler(lens, batch_size, eval_, bucket_size=8192): droplast = not eval_ sampler = DistributedTokenBucketSampler( hvd.size(), hvd.rank(), lens, bucket_size=bucket_size, batch_size=batch_size, droplast=droplast) return sampler def build_mlm_train_dataloader(txt_db, img_dir_gt, img_dir, n_gpu, opts): LOGGER.info(f"Loading MLM Train Dataset {txt_db}, " f"{[i.img_dir for i in img_dir]}" f"{[i.img_dir for i in img_dir_gt]}") train_datasets = [MlmDatasetForVCR( db, dir_gt_, dir_, opts.max_txt_len, task=t) for db, dir_gt_, dir_ in zip(txt_db, img_dir_gt, img_dir) for t in opts.vcr_task] train_dataset = ConcatDataset(train_datasets) train_sampler = build_sampler(train_dataset.lens, opts.train_batch_size, eval_=False) train_dataloader = DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=opts.n_workers, pin_memory=opts.pin_mem, collate_fn=mlm_collate_for_vcr) LOGGER.info(f"{len(train_dataset)} samples loaded") return train_dataloader def build_mrm_train_dataloader(txt_db, img_dir_gt, img_dir, n_gpu, opts): LOGGER.info(f"Loading MRM Train Dataset {txt_db}, " f"{[i.img_dir for i in img_dir]}" f"{[i.img_dir for i in img_dir_gt]}") train_datasets = [MrmDatasetForVCR( opts.mrm_prob, db, dir_gt_, dir_, opts.max_txt_len, task=t) for db, dir_gt_, dir_ in zip(txt_db, img_dir_gt, img_dir) for t in opts.vcr_task] train_dataset = ConcatDataset(train_datasets) train_sampler = build_sampler(train_dataset.lens, opts.train_batch_size, eval_=False) train_dataloader = DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=opts.n_workers, pin_memory=opts.pin_mem, collate_fn=mrm_collate_for_vcr) LOGGER.info(f"{len(train_dataset)} samples loaded") return train_dataloader def build_mrc_train_dataloader(txt_db, img_dir_gt, img_dir, n_gpu, opts): LOGGER.info(f"Loading MRC Train Dataset {txt_db}, " f"{[i.img_dir for i in img_dir]}" f"{[i.img_dir for i in img_dir_gt]}") train_datasets = [MrcDatasetForVCR( opts.mrc_prob, db, dir_gt_, dir_, opts.max_txt_len, task=t) for db, dir_gt_, dir_ in zip(txt_db, img_dir_gt, img_dir) for t in opts.vcr_task] train_dataset = ConcatDataset(train_datasets) train_sampler = build_sampler(train_dataset.lens, opts.train_batch_size, eval_=False) train_dataloader = DataLoader(train_dataset, batch_sampler=train_sampler, num_workers=opts.n_workers, pin_memory=opts.pin_mem, collate_fn=mrc_collate_for_vcr) LOGGER.info(f"{len(train_dataset)} samples loaded") return train_dataloader def build_mlm_val_dataloader(txt_db, img_dir_gt, img_dir, n_gpu, opts): LOGGER.info(f"Loading MLM Val Dataset {txt_db}, " f"{img_dir_gt.img_dir}, {img_dir.img_dir}") val_datasets = [MlmDatasetForVCR( txt_db, img_dir_gt, img_dir, -1, task=t) for t in opts.vcr_task] val_dataset = ConcatDataset(val_datasets) val_sampler = build_sampler(val_dataset.lens, opts.val_batch_size, eval_=True) val_dataloader = DataLoader(val_dataset, batch_sampler=val_sampler, num_workers=opts.n_workers, pin_memory=opts.pin_mem, collate_fn=mlm_collate_for_vcr) LOGGER.info(f"{len(val_dataset)} samples loaded") return val_dataloader def build_mrm_val_dataloader(txt_db, img_dir_gt, img_dir, n_gpu, opts): LOGGER.info(f"Loading MRM Val Dataset {txt_db}, " f"{img_dir_gt.img_dir}, {img_dir.img_dir}") val_datasets = [MrmDatasetForVCR( opts.mrm_prob, txt_db, img_dir_gt, img_dir, -1, task=t) for t in opts.vcr_task] val_dataset = ConcatDataset(val_datasets) val_sampler = build_sampler(val_dataset.lens, opts.val_batch_size, eval_=True) val_dataloader = DataLoader(val_dataset, batch_sampler=val_sampler, num_workers=opts.n_workers, pin_memory=opts.pin_mem, collate_fn=mrm_collate_for_vcr) LOGGER.info(f"{len(val_dataset)} samples loaded") return val_dataloader def build_mrc_val_dataloader(txt_db, img_dir_gt, img_dir, n_gpu, opts): LOGGER.info(f"Loading MRC Val Dataset {txt_db}, " f"{img_dir_gt.img_dir}, {img_dir.img_dir}") val_datasets = [MrcDatasetForVCR( opts.mrc_prob, txt_db, img_dir_gt, img_dir, -1, task=t) for t in opts.vcr_task] val_dataset = ConcatDataset(val_datasets) val_sampler = build_sampler(val_dataset.lens, opts.val_batch_size, eval_=True) val_dataloader = DataLoader(val_dataset, batch_sampler=val_sampler, num_workers=opts.n_workers, pin_memory=opts.pin_mem, collate_fn=mrc_collate_for_vcr) LOGGER.info(f"{len(val_dataset)} samples loaded") return val_dataloader def load_img_feat(dir_list, path2imgdir, opts): dir_ = dir_list.split(";") assert len(dir_) <= 2, "More than two img_dirs found" img_dir_gt, img_dir = None, None gt_dir_path, dir_path = "", "" for d in dir_: if "gt" in d: gt_dir_path = d else: dir_path = d if gt_dir_path != "": img_dir_gt = path2imgdir.get(gt_dir_path, None) if img_dir_gt is None: img_dir_gt = DetectFeatLmdb(gt_dir_path, -1, opts.max_bb, opts.min_bb, 100, opts.compressed_db) path2imgdir[gt_dir_path] = img_dir_gt if dir_path != "": img_dir = path2imgdir.get(dir_path, None) if img_dir is None: img_dir = DetectFeatLmdb(dir_path, opts.conf_th, opts.max_bb, opts.min_bb, opts.num_bb, opts.compressed_db) path2imgdir[dir_path] = img_dir return img_dir, img_dir_gt, path2imgdir def main(opts): hvd.init() n_gpu = hvd.size() device = torch.device("cuda", hvd.local_rank()) torch.cuda.set_device(hvd.local_rank()) rank = hvd.rank() opts.rank = rank LOGGER.info("device: {} n_gpu: {}, rank: {}, " "16-bits training: {}".format( device, n_gpu, hvd.rank(), opts.fp16)) if opts.gradient_accumulation_steps < 1: raise ValueError("Invalid gradient_accumulation_steps parameter: {}, " "should be >= 1".format( opts.gradient_accumulation_steps)) random.seed(opts.seed) np.random.seed(opts.seed) torch.manual_seed(opts.seed) if n_gpu > 0: torch.cuda.manual_seed_all(opts.seed) if rank == 0: save_training_meta(opts) TB_LOGGER.create(join(opts.output_dir, 'log')) pbar = tqdm(total=opts.num_train_steps) model_saver = ModelSaver(join(args.output_dir, 'ckpt')) add_log_to_file(join(opts.output_dir, 'log', 'log.txt')) else: LOGGER.disabled = True pbar = NoOp() model_saver = NoOp() all_dbs = [db for datasets in [opts.train_datasets, opts.val_datasets] for dset in datasets for db in dset['db']] bert_model = json.load(open(f'{all_dbs[0]}/meta.json'))['bert'] assert all(bert_model == json.load(open(f'{db}/meta.json'))['bert'] for db in all_dbs) train_tasks, train_data_paths, mix_ratio = parse_tasks(opts.train_datasets) train_dataloaders = [] path2imgdir = {} for (dbs, dirs), task in zip(train_data_paths, train_tasks): img_dirs = [] img_gt_dirs = [] for db, dir_list in zip(dbs, dirs): img_dir, img_dir_gt, path2imgdir = load_img_feat( dir_list, path2imgdir, opts) img_dirs.append(img_dir) img_gt_dirs.append(img_dir_gt) if task.startswith('mlm'): loader = build_mlm_train_dataloader(dbs, img_gt_dirs, img_dirs, n_gpu, opts) elif task.startswith('mrm'): loader = build_mrm_train_dataloader(dbs, img_gt_dirs, img_dirs, n_gpu, opts) elif task.startswith('mrc'): loader = build_mrc_train_dataloader(dbs, img_gt_dirs, img_dirs, n_gpu, opts) else: raise ValueError(f'Undefined task {task}') train_dataloaders.append(loader) val_tasks, val_data_paths = parse_tasks(opts.val_datasets) val_dataloaders = [] for (db, dir_), task in zip(val_data_paths, val_tasks): assert len(db) == len(dir_) == 1 db = db[0] dir_ = dir_[0] img_dir, img_dir_gt, path2imgdir = load_img_feat( dir_, path2imgdir, opts) if task.startswith('mlm'): loader = build_mlm_val_dataloader(db, img_dir_gt, img_dir, n_gpu, opts) elif task.startswith('mrm'): loader = build_mrm_val_dataloader(db, img_dir_gt, img_dir, n_gpu, opts) elif task.startswith('mrc'): loader = build_mrc_val_dataloader(db, img_dir_gt, img_dir, n_gpu, opts) else: raise ValueError(f'Undefined task {task}') val_dataloaders.append(PrefetchLoader(loader)) meta_loader = MetaLoader(train_dataloaders, mix_ratio=mix_ratio, names=train_tasks, accum_steps=opts.gradient_accumulation_steps, distributed=n_gpu > 1) meta_loader = PrefetchLoader(meta_loader) named_val_loaders = list(zip(val_tasks, val_dataloaders)) # Prepare model if opts.checkpoint: if opts.checkpoint == 'google-bert': checkpoint = None else: checkpoint = torch.load(opts.checkpoint) else: checkpoint = {} model = BertForImageTextPretrainingForVCR.from_pretrained( bert_model, img_dim=IMG_DIM, img_label_dim=IMG_LABEL_DIM, state_dict=checkpoint) model.init_type_embedding() model.init_word_embedding(NUM_SPECIAL_TOKENS) model.pad_vocab() # tensor core padding for vocabulary if opts.cut_bert != -1: # cut some layers of BERT model.bert.encoder.layer = torch.nn.ModuleList( model.bert.encoder.layer[:opts.cut_bert]) for name, module in model.named_modules(): # we might want to tune dropout for smaller dataset if isinstance(module, torch.nn.Dropout): if module.p != opts.dropout: module.p = opts.dropout LOGGER.info(f'{name} set to {opts.dropout}') model.to(device) # make sure every process has same model parameters in the beginning broadcast_tensors([p.data for p in model.parameters()], 0) # Prepare optimizer param_optimizer = list(model.named_parameters()) no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight'] optimizer_grouped_parameters = [ {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': opts.weight_decay}, {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0} ] if opts.optim == 'adam': OptimCls = Adam elif opts.optim == 'adamax': OptimCls = Adamax elif opts.optim == 'adamw': OptimCls = AdamW else: raise ValueError('invalid optimizer') optimizer = OptimCls(optimizer_grouped_parameters, lr=opts.learning_rate, betas=opts.betas) model, optimizer = amp.initialize(model, optimizer, enabled=opts.fp16, opt_level='O2') global_step = 0 if rank == 0: save_training_meta(opts) TB_LOGGER.create(join(opts.output_dir, 'log')) pbar = tqdm(total=opts.num_train_steps) model_saver = ModelSaver(join(opts.output_dir, 'ckpt')) os.makedirs(join(opts.output_dir, 'results')) # store VQA predictions add_log_to_file(join(opts.output_dir, 'log', 'log.txt')) else: LOGGER.disabled = True pbar = NoOp() model_saver = NoOp() LOGGER.info(f"***** Running training with {n_gpu} GPUs *****") LOGGER.info(" Batch size = %d", opts.train_batch_size) LOGGER.info(" Accumulate steps = %d", opts.gradient_accumulation_steps) LOGGER.info(" Num steps = %d", opts.num_train_steps) task2loss = {task: RunningMeter(f'loss/{task}') for task in train_tasks} model.train() n_examples = 0 n_epoch = 0 start = time() # quick hack for amp delay_unscale bug optimizer.zero_grad() optimizer.step() while True: for step, (name, batch) in enumerate(meta_loader): input_ids, *_ = batch n_examples += input_ids.size(0) task = name.split('_')[0] loss = model(*batch, task=task, compute_loss=True) loss = loss.mean() # loss is not normalized if task == 'mrckl': # MRCkl normalization; safeguard fp16 overflow loss = loss.float() * IMG_LABEL_DIM delay_unscale = (step+1) % opts.gradient_accumulation_steps != 0 with amp.scale_loss(loss, optimizer, delay_unscale=delay_unscale ) as scaled_loss: scaled_loss.backward() if not delay_unscale: # gather gradients from every processes # do this before unscaling to make sure every process uses # the same gradient scale grads = [p.grad.data for p in model.parameters() if p.requires_grad and p.grad is not None] all_reduce_and_rescale_tensors(grads, float(1)) task2loss[name](loss.item()) if (step + 1) % opts.gradient_accumulation_steps == 0: global_step += 1 # learning rate scheduling if opts.decay == 'linear': lr_this_step = opts.learning_rate * warmup_linear( global_step, opts.warmup_steps, opts.num_train_steps) elif opts.decay == 'invsqrt': lr_this_step = opts.learning_rate * noam_schedule( global_step, opts.warmup_steps) elif opts.decay == 'constant': lr_this_step = opts.learning_rate elif opts.decay == 'vqa': lr_this_step = opts.learning_rate * vqa_schedule( global_step, opts.warm_int, opts.decay_int, opts.decay_st, opts.decay_rate) if lr_this_step < 0: # save guard for possible miscalculation of train steps lr_this_step = 1e-8 for param_group in optimizer.param_groups: param_group['lr'] = lr_this_step TB_LOGGER.add_scalar('lr', lr_this_step, global_step) # log loss for t, l in task2loss.items(): loss = sum(v for v in all_gather_list(l.val) if v is not None) / hvd.size() task2loss[t] = RunningMeter(f'loss/{t}', loss) TB_LOGGER.log_scaler_dict({l.name: l.val for l in task2loss.values() if l.val is not None}) TB_LOGGER.step() # update model params if opts.grad_norm != -1: grad_norm = clip_grad_norm_(amp.master_params(optimizer), opts.grad_norm) TB_LOGGER.add_scalar('grad_norm', grad_norm, global_step) optimizer.step() optimizer.zero_grad() pbar.update(1) if global_step % 5 == 0: torch.cuda.empty_cache() if global_step % 100 == 0: # monitor training throughput tot_ex = sum(all_gather_list(n_examples)) ex_per_sec = int(tot_ex / (time()-start)) LOGGER.info(f'{tot_ex} examples trained at ' f'{ex_per_sec} ex/s') TB_LOGGER.add_scalar('perf/ex_per_s', ex_per_sec, global_step) if global_step % opts.valid_steps == 0: validate(model, named_val_loaders) model_saver.save(model, global_step) if global_step >= opts.num_train_steps: break if global_step % opts.valid_steps != 0: validate(model, named_val_loaders) model_saver.save(model, global_step) def validate(model, named_val_loaders): model.eval() for task, loader in named_val_loaders: LOGGER.info(f"validate on {task} task") if task.startswith('mlm'): val_log = validate_mlm(model, loader) elif task.startswith('mrm'): val_log = validate_mrm(model, loader) elif task.startswith('mrc'): val_log = validate_mrc(model, loader, task) else: raise ValueError(f'Undefined task {task}') val_log = {f'{task}_{k}': v for k, v in val_log.items()} TB_LOGGER.log_scaler_dict( {f'valid_{task}/{k}': v for k, v in val_log.items()}) model.train() @torch.no_grad() def validate_mrc(model, val_loader, task): LOGGER.info("start running MRC validation...") val_loss = 0 n_feat = 0 st = time() tot_score = 0 for i, batch in enumerate(val_loader): *_, label = batch feat_mask, label_targets = label prediction_soft_label = model( *batch, task=task, compute_loss=False) if "kl" in task: prediction_soft_label = F.log_softmax( prediction_soft_label, dim=-1) loss = F.kl_div( prediction_soft_label, label_targets, reduction='sum') tot_score += compute_accuracy_for_mrc( prediction_soft_label, label_targets) else: cls_label_targets = label_targets.max(dim=-1)[1] # argmax loss = F.cross_entropy( prediction_soft_label, cls_label_targets, ignore_index=0, reduction='sum') tot_score += compute_accuracy_for_mrc( prediction_soft_label[:, 1:], label_targets[:, 1:]) val_loss += loss.item() n_feat += feat_mask.sum().item() val_loss = sum(all_gather_list(val_loss)) tot_score = sum(all_gather_list(tot_score)) n_feat = sum(all_gather_list(n_feat)) tot_time = time()-st val_loss /= n_feat val_acc = tot_score / n_feat val_log = {'loss': val_loss, 'acc': val_acc, 'feat_per_s': n_feat/tot_time} LOGGER.info(f"validation finished in {int(tot_time)} seconds, " f"score: {val_acc*100:.2f}") return val_log @torch.no_grad() def validate_mrm(model, val_loader): LOGGER.info("start running MRM validation...") val_loss = 0 n_feat = 0 st = time() for i, batch in enumerate(val_loader): *_, feat_mask = batch loss = model(*batch, task='mrm', compute_loss=True) val_loss += loss.sum().item() n_feat += feat_mask.sum().item() val_loss = sum(all_gather_list(val_loss)) n_feat = sum(all_gather_list(n_feat)) tot_time = time()-st val_loss /= (n_feat * IMG_DIM) val_log = {'loss': val_loss, 'feat_per_s': n_feat/tot_time} LOGGER.info(f"validation finished in {int(tot_time)} seconds, " f"loss: {val_loss:.2f}") return val_log @torch.no_grad() def validate_mlm(model, val_loader): LOGGER.info(f"start running MLM validation ...") val_loss = 0 n_correct = 0 n_word = 0 st = time() for i, batch in enumerate(val_loader): *inputs, txt_labels = batch loss = model.forward(*batch, task='mlm', compute_loss=True) # loss_fct = torch.nn.CrossEntropyLoss(ignore_index=-1, # reduction='sum') # loss = loss_fct(scores, txt_labels) loss = loss.mean() val_loss += loss.item() # n_correct += accuracy_count(scores, txt_labels) n_word += txt_labels.numel() val_loss = sum(all_gather_list(val_loss)) n_correct = sum(all_gather_list(n_correct)) n_word = sum(all_gather_list(n_word)) tot_time = time()-st val_loss /= n_word acc = n_correct / n_word val_log = {'loss': val_loss, 'acc': acc, 'tok_per_s': n_word/tot_time} LOGGER.info(f"validation finished in {int(tot_time)} seconds, " f"acc: {acc*100:.2f}" f"loss: {val_loss}") return val_log def compute_accuracy_for_mrc(out, labels): outputs = out.max(dim=-1)[1] labels = labels.max(dim=-1)[1] # argmax n_correct = (outputs == labels).sum().item() return n_correct def accuracy_count(out, labels): outputs = out.max(dim=-1)[1] mask = labels != -1 n_correct = (outputs == labels).masked_select(mask).sum().item() return n_correct if __name__ == "__main__": parser = argparse.ArgumentParser() # Required parameters # NOTE: train tasks and val tasks cannot take command line arguments parser.add_argument('--compressed_db', action='store_true', help='use compressed LMDB') parser.add_argument("--vcr_task", default=["qar"], type=str, nargs='+', choices=['qa', 'qar'], help="VCR tasks: qa or qar") parser.add_argument('--tasks', default=None, type=str, nargs='+', help="specify pretraining tasks") parser.add_argument('--mrm_prob', default=0.15, type=float, help='probability to mask in MRM training') parser.add_argument('--mrc_prob', default=0.15, type=float, help='probability to mask in MRC training') parser.add_argument("--checkpoint", default=None, type=str, help="pretrained model (can take 'google-bert') ") parser.add_argument("--cut_bert", default=-1, type=int, help="reduce BERT layers (-1 for original depth)") parser.add_argument( "--output_dir", default=None, type=str, help="The output directory where the model checkpoints will be " "written.") # Prepro parameters parser.add_argument('--max_txt_len', type=int, default=60, help='max number of tokens in text (BERT BPE)') parser.add_argument('--conf_th', type=float, default=0.2, help='threshold for dynamic bounding boxes ' '(-1 for fixed)') parser.add_argument('--max_bb', type=int, default=100, help='max number of bounding boxes') parser.add_argument('--min_bb', type=int, default=10, help='min number of bounding boxes') parser.add_argument('--num_bb', type=int, default=36, help='static number of bounding boxes') # training parameters parser.add_argument("--train_batch_size", default=4096, type=int, help="Total batch size for training. " "(batch by tokens)") parser.add_argument("--val_batch_size", default=4096, type=int, help="Total batch size for validation. " "(batch by tokens)") parser.add_argument('--gradient_accumulation_steps', type=int, default=16, help="Number of updates steps to accumualte before " "performing a backward/update pass.") parser.add_argument("--learning_rate", default=3e-5, type=float, help="The initial learning rate for Adam.") parser.add_argument("--valid_steps", default=1000, type=int, help="Run validation every X steps") parser.add_argument("--num_train_steps", default=100000, type=int, help="Total number of training updates to perform.") parser.add_argument('--mask_prob', default=0.15, type=float, help='probability to mask in MRC training') parser.add_argument("--optim", default='adam', choices=['adam', 'adamax', 'adamw'], help="optimizer") parser.add_argument("--betas", default=[0.9, 0.98], nargs='+', help="beta for adam optimizer") parser.add_argument("--decay", default='linear', choices=['linear', 'invsqrt', 'constant', 'vqa'], help="learning rate decay method") parser.add_argument("--decay_int", default=2000, type=int, help="interval between VQA lr decy") parser.add_argument("--warm_int", default=2000, type=int, help="interval for VQA lr warmup") parser.add_argument("--decay_st", default=20000, type=int, help="when to start decay") parser.add_argument("--decay_rate", default=0.2, type=float, help="ratio of lr decay") parser.add_argument("--dropout", default=0.1, type=float, help="tune dropout regularization") parser.add_argument("--weight_decay", default=0.0, type=float, help="weight decay (L2) regularization") parser.add_argument("--grad_norm", default=0.25, type=float, help="gradient clipping (-1 for no clipping)") parser.add_argument("--warmup_steps", default=4000, type=int, help="Number of training steps to perform linear " "learning rate warmup for. (invsqrt decay)") # device parameters parser.add_argument('--seed', type=int, default=42, help="random seed for initialization") parser.add_argument('--fp16', action='store_true', help="Whether to use 16-bit float precision instead " "of 32-bit") parser.add_argument('--n_workers', type=int, default=4, help="number of data workers") parser.add_argument('--pin_mem', action='store_true', help="pin memory") # can use config files parser.add_argument('--config', help='JSON config files') args = parse_with_config(parser) if exists(args.output_dir) and os.listdir(args.output_dir): raise ValueError("Output directory ({}) already exists and is not " "empty.".format(args.output_dir)) # options safe guard # TODO if args.conf_th == -1: assert args.max_bb + args.max_txt_len + 2 <= 512 else: assert args.num_bb + args.max_txt_len + 2 <= 512 assert len(args.vcr_task) > 0, "Must choose at least one vcr task" main(args)