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train.py
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train.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import json
import torch
import numpy as np
import random
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
import sys
import time
import argparse
from src.models.models import TAILOR
from src.models.optimization import BertAdam
from src.utils.eval import get_metrics
from src.utils.eval_gap import *
from torch.utils.data import DataLoader, WeightedRandomSampler
import torch.utils.data as data
from util import parallel_apply, get_logger
from src.dataloaders.cmu_dataloader import AlignedMoseiDataset, UnAlignedMoseiDataset
#torch.distributed.init_process_group(backend="nccl")
global logger
def get_args(description='Multi-modal Multi-label Emotion Recognition'):
parser = argparse.ArgumentParser(description=description)
parser.add_argument("--do_train", action='store_true', help="Whether to run training.")
parser.add_argument("--do_test", action='store_true', help="whether to run test")
parser.add_argument("--aligned", action='store_true', help="whether train align of unalign dataset")
parser.add_argument("--data_path", type=str, help='cmu_mosei data_path')
parser.add_argument("--output_dir", default=None, type=str, required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument('--num_thread_reader', type=int, default=1, help='')
parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate')
parser.add_argument('--epochs', type=int, default=20, help='upper epoch limit')
parser.add_argument('--unaligned_data_path', type=str, default='/amax/cmy/mosei_senti_data_noalign.pkl', help='load unaligned dataset')
parser.add_argument('--batch_size', type=int, default=256, help='batch size')
parser.add_argument('--lr_decay', type=float, default=0.9, help='Learning rate exp epoch decay')
parser.add_argument('--n_display', type=int, default=100, help='Information display frequence')
parser.add_argument('--text_dim', type=int, default=300, help='text_feature_dimension')
parser.add_argument('--video_dim', type=int, default=35, help='video feature dimension')
parser.add_argument('--audio_dim', type=int, default=74, help='audio_feature_dimension')
parser.add_argument('--seed', type=int, default=42, help='random seed')
parser.add_argument('--max_words', type=int, default=60, help='')
parser.add_argument('--max_frames', type=int, default=60, help='')
parser.add_argument('--max_sequence', type=int, default=60, help='')
parser.add_argument('--max_label', type=int, default=6, help='')
parser.add_argument("--bert_model", default="bert-base", type=str, required=False, help="Bert module")
parser.add_argument("--visual_model", default="visual-base", type=str, required=False, help="Visual module")
parser.add_argument("--audio_model", default="audio-base", type=str, required=False, help="Audio module")
parser.add_argument("--cross_model", default="cross-base", type=str, required=False, help="Cross module")
parser.add_argument("--decoder_model", default="decoder-base", type=str, required=False, help="Decoder module")
parser.add_argument("--init_model", default=None, type=str, required=False, help="Initial model.")
parser.add_argument("--warmup_proportion", default=0.1, type=float,
help="Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10%% of training.")
parser.add_argument('--gradient_accumulation_steps', type=int, default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument('--n_gpu', type=int, default=1, help="Changed in the execute process.")
parser.add_argument("--world_size", default=0, type=int, help="distribted training")
parser.add_argument("--local_rank", default=0, type=int, help="distribted training")
parser.add_argument('--coef_lr', type=float, default=0.1, help='coefficient for bert branch.')
parser.add_argument('--bert_num_hidden_layers', type=int, default=6, help="Layer NO. of visual.")
parser.add_argument('--visual_num_hidden_layers', type=int, default=3, help="Layer NO. of visual.")
parser.add_argument('--audio_num_hidden_layers', type=int, default=3, help="Layer No. of audio")
parser.add_argument('--cross_num_hidden_layers', type=int, default=3, help="Layer NO. of cross.")
parser.add_argument('--decoder_num_hidden_layers', type=int, default=1, help="Layer NO. of decoder.")
parser.add_argument("--num_classes", default=6, type=int, required=False)
parser.add_argument("--hidden_size",type=int, default=256)
args = parser.parse_args()
# Check paramenters
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
if not args.do_train and not args.do_test:
raise ValueError("At least one of `do_train` or `do_test` must be True.")
args.batch_size = int(args.batch_size / args.gradient_accumulation_steps)
return args
def set_seed_logger(args):
global logger
# predefining random initial seeds
random.seed(args.seed)
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed) # if you are using multi-GPU.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.cuda.set_device(args.local_rank)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir, exist_ok=True)
logger = get_logger(os.path.join(args.output_dir, "log.txt"))
if args.local_rank == 0:
logger.info("Effective parameters:")
for key in sorted(args.__dict__):
logger.info(" <<< {}: {}".format(key, args.__dict__[key]))
return args
def init_device(args, local_rank):
global logger
device = torch.device("cuda" if torch.cuda.is_available() else "cpu", local_rank)
n_gpu = 1
logger.info("device: {} n_gpu: {}".format(device, n_gpu))
args.n_gpu = n_gpu
if args.batch_size % args.n_gpu != 0:
raise ValueError("Invalid batch_size/batch_size_val and n_gpu parameter: {}%{} and {}%{}, should be == 0".format(
args.batch_size, args.n_gpu, args.batch_size_val, args.n_gpu))
return device, n_gpu
def init_model(args, device, n_gpu, local_rank):
if args.init_model:
model_state_dict = torch.load(args.init_model, map_location='cpu')
else:
model_state_dict = None
# Prepare model
model = TAILOR.from_pretrained(args.bert_model, args.visual_model, args.audio_model, args.cross_model, args.decoder_model, task_config=args)
return model
def prep_optimizer(args, model, num_train_optimization_steps, device, n_gpu, local_rank, coef_lr=1.):
if hasattr(model, 'module'):
model = model.module
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
no_decay_param_tp = [(n, p) for n, p in param_optimizer if not any(nd in n for nd in no_decay)]
decay_param_tp = [(n, p) for n, p in param_optimizer if any(nd in n for nd in no_decay)]
no_decay_bert_param_tp = [(n, p) for n, p in no_decay_param_tp if "audio." in n]
no_decay_nobert_param_tp = [(n, p) for n, p in no_decay_param_tp if "audio." not in n]
decay_bert_param_tp = [(n, p) for n, p in decay_param_tp if "audio." in n]
decay_nobert_param_tp = [(n, p) for n, p in decay_param_tp if "audio." not in n]
optimizer_grouped_parameters = [
{'params': [p for n, p in no_decay_bert_param_tp], 'weight_decay': 0.01, 'lr': args.lr * 1.0},
{'params': [p for n, p in no_decay_nobert_param_tp], 'weight_decay': 0.01},
{'params': [p for n, p in decay_bert_param_tp], 'weight_decay': 0.0, 'lr': args.lr * 1.0},
{'params': [p for n, p in decay_nobert_param_tp], 'weight_decay': 0.0}
]
scheduler = None
optimizer = BertAdam(optimizer_grouped_parameters, lr=args.lr, warmup=args.warmup_proportion,
schedule='warmup_linear', t_total=num_train_optimization_steps, weight_decay=0.01,
max_grad_norm=1.0)
return optimizer, scheduler, model
def prep_dataloader(args):
Dataset = AlignedMoseiDataset if args.aligned else UnAlignedMoseiDataset
train_dataset = Dataset(
args.data_path,
'train'
)
val_dataset = Dataset(
args.data_path,
'valid'
)
test_dataset = Dataset(
args.data_path,
'test'
)
label_input, label_mask = train_dataset._get_label_input()
train_dataloader = DataLoader(
train_dataset,
batch_size=args.batch_size // args.n_gpu,
num_workers=args.num_thread_reader,
pin_memory=False,
shuffle=True,
drop_last=True
)
val_dataloader = DataLoader(
val_dataset,
batch_size=args.batch_size // args.n_gpu,
num_workers=args.num_thread_reader,
pin_memory=False,
shuffle=True,
drop_last=True
)
test_dataloader = DataLoader(
test_dataset,
batch_size=args.batch_size // args.n_gpu,
num_workers=args.num_thread_reader,
pin_memory=False,
shuffle=True,
drop_last=True
)
train_length = len(train_dataset)
val_length = len(val_dataset)
test_length = len(test_dataset)
return train_dataloader, val_dataloader, test_dataloader, train_length, val_length, test_length, label_input, label_mask
def save_model(args, model, epoch):
# Only save the model it-self
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(
args.output_dir, "pytorch_model_{}.bin.".format(epoch))
torch.save(model_to_save.state_dict(), output_model_file)
logger.info("Model saved to %s", output_model_file)
return output_model_file
def load_model(epoch, args, n_gpu, device, model_file=None):
if model_file is None or len(model_file) == 0:
model_file = os.path.join(args.output_dir, "pytorch_model.bin.{}".format(epoch))
if os.path.exists(model_file):
model_state_dict = torch.load(model_file, map_location='cpu')
if args.local_rank == 0:
logger.info("Model loaded from %s", model_file)
cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed')
model = TAILOR.from_pretrained(args.bert_model, args.visual_model, args.audio_model, args.cross_model,
cache_dir=cache_dir, state_dict=model_state_dict, task_config=args)
model.to(device)
else:
model = None
return model
def train_epoch(epoch, args, model, train_dataloader, device, n_gpu, optimizer, scheduler, global_step, local_rank=0, label_input=None, label_mask=None):
global logger
model.train()
log_step = args.n_display
start_time = time.time()
total_loss = 0
total_pred = []
total_true_label = []
total_pred_scores = []
for step, batch in enumerate(train_dataloader):
# torch.cuda.empty_cache()
if n_gpu == 1:
# multi-gpu does scattering it-self
batch = tuple(t.to(device=device, non_blocking=True) for t in batch)
pairs_text, pairs_mask, video, video_mask,audio, audio_mask, ground_label = batch
model_loss, batch_pred, true_label, pred_scores = model(pairs_text, pairs_mask, video, video_mask, audio, audio_mask, label_input, label_mask, groundTruth_labels=ground_label, training=True)
if n_gpu > 1:
model_loss = model_loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
model_loss = model_loss / args.gradient_accumulation_steps
model_loss.backward()
total_loss += float(model_loss)
total_pred.append(batch_pred)
total_true_label.append(true_label)
total_pred_scores.append(pred_scores)
if (step + 1) % args.gradient_accumulation_steps == 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
if scheduler is not None:
scheduler.step() # Update learning rate schedule
optimizer.step()
optimizer.zero_grad()
global_step += 1
if global_step % log_step == 0 and local_rank == 0:
logger.info("Epoch: %d/%d, Step: %d/%d, Lr: %s, loss: %f, Time/step: %f", epoch + 1,
args.epochs, step + 1,
len(train_dataloader), "-".join([str('%.6f'%itm) for itm in sorted(list(set(optimizer.get_lr())))]),float(model_loss),
(time.time() - start_time) / (log_step * args.gradient_accumulation_steps))
start_time = time.time()
total_loss = total_loss / len(train_dataloader)
total_pred=torch.cat(total_pred,0)
total_true_label = torch.cat(total_true_label, 0)
total_pred_scores = torch.cat(total_pred_scores, 0)
return total_loss, total_pred, total_true_label, total_pred_scores
def eval_epoch(args, model, val_dataloader, device, n_gpu, label_input, label_mask):
if hasattr(model, 'module'):
model = model.module.to(device)
else:
model = model.to(device)
model.eval()
with torch.no_grad():
total_pred = []
total_true_label = []
total_pred_scores = []
for _, batch in enumerate(val_dataloader):
batch = tuple(t.to(device) for t in batch)
text, text_mask, video, video_mask, audio, audio_mask, groundTruth_labels = batch
batch_pred, true_label, pred_scores = model(text, text_mask, video, video_mask, audio, audio_mask, label_input, label_mask, groundTruth_labels=groundTruth_labels, training=False)
total_pred.append(batch_pred)
total_true_label.append(true_label)
total_pred_scores.append(pred_scores)
total_pred=torch.cat(total_pred,0)
total_true_label = torch.cat(total_true_label, 0)
total_pred_scores = torch.cat(total_pred_scores, 0)
return total_pred, total_true_label, total_pred_scores
def main():
global logger
train_time = time.time()
args = get_args()
args = set_seed_logger(args)
device, n_gpu = init_device(args, args.local_rank)
model = init_model(args, device, n_gpu, args.local_rank)
model = model.to(device)
if args.aligned == False:
logger.warning("!!!!!!!!!!!!!! you start train unaligned dataset")
else:
logger.warning("!!!!!!!!!!!!!! you start train aligned dataset")
print('***** dataloder preping ... *****')
if args.do_train:
train_dataloader, val_dataloader, test_dataloader, train_length, val_length, test_length, label_input, label_mask = prep_dataloader(args)
label_input = label_input.to(device)
label_mask = label_mask.to(device)
num_train_optimization_steps = (int(len(train_dataloader) + args.gradient_accumulation_steps - 1)
/ args.gradient_accumulation_steps) * args.epochs
coef_lr = args.coef_lr
if args.init_model:
coef_lr = 1.0
optimizer, scheduler, model = prep_optimizer(args, model, num_train_optimization_steps, device, n_gpu, args.local_rank, coef_lr=coef_lr)
if args.local_rank == 0:
logger.info("***** Running training *****")
logger.info(" Num examples = %d", train_length)
logger.info(" Batch size = %d", args.batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps * args.gradient_accumulation_steps)
best_score = 0.000
best_output_model_file = None
global_step = 0
best_model = None
for epoch in range(args.epochs):
total_loss, total_pred, total_label, total_pred_scores= train_epoch(epoch, args, model, train_dataloader, device, n_gpu, optimizer,
scheduler, global_step, local_rank=args.local_rank, label_input=label_input, label_mask=label_mask)
total_micro_f1, total_micro_precision, total_micro_recall, total_acc = get_metrics(total_pred, total_label)
total_pred_scores = total_pred_scores.data.cpu().numpy()
total_label = total_label.data.cpu().numpy()
train_gap = calculate_gap(total_pred_scores, total_label)
if args.local_rank == 0:
logger.info("Epoch %d/%d Finished, Train Loss: %f, Train_micro_f1: %f, Train_micro_precision: %f, Train_micro_recall: %f, Train_acc: %f, train_gap: %f", \
epoch + 1, args.epochs, total_loss, total_micro_f1, total_micro_precision, total_micro_recall, total_acc, train_gap)
if args.local_rank == 0:
logger.info("***** Running valing *****")
logger.info(" Num examples = %d", val_length)
logger.info(" Batch_size = %d", args.batch_size)
val_pred, val_label, val_pred_scores = eval_epoch(args, model, val_dataloader, device, n_gpu, label_input, label_mask)
val_micro_f1, val_micro_precision, val_micro_recall, val_acc = get_metrics(val_pred, val_label)
val_pred_scores = val_pred_scores.data.cpu().numpy()
val_label = val_label.data.cpu().numpy()
val_gap = calculate_gap(val_pred_scores, val_label)
logger.info("----- micro_f1: %f, micro_precision: %f, micro_recall: %f, acc: %f, val_gap: %f", \
val_micro_f1, val_micro_precision, val_micro_recall, val_acc, val_gap)
output_model_file = save_model(args, model, epoch)
if best_score <= val_micro_f1:
best_score = val_micro_f1
best_model = model
best_output_model_file = output_model_file
logger.info("The best model is: {}, the f1 is: {:.4f}".format(best_output_model_file, best_score))
if args.local_rank == 0:
logger.info('***** Running testing *****')
logger.info(' Num examples = %d', test_length)
logger.info(" Batch_size = %d", args.batch_size)
test_pred, test_label, test_pred_scores = eval_epoch(args, best_model, test_dataloader, device, n_gpu, label_input, label_mask)
test_micro_f1, test_micro_precision, test_micro_recall, test_acc = get_metrics(test_pred, test_label)
test_pred_scores = test_pred_scores.data.cpu().numpy()
test_label = test_label.data.cpu().numpy()
test_gap = calculate_gap(test_pred_scores, test_label)
logger.info("----- micro_f1: %f, micro_precision: %f, micro_recall: %f, acc: %f, test_gap: %f", \
test_micro_f1, test_micro_precision, test_micro_recall, test_acc, test_gap)
if __name__ == "__main__":
main()