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train_ITV.py
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train_ITV.py
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from __future__ import print_function
import pickle
import os
import sys
sys.path.append('./util')
# os.environ["CUDA_VISIBLE_DEVICES"] = "3"
import json
import numpy as np
import torch
import evaluation
import util.data_provider as data
import logging
import time
import argparse
import wandb
from util.util import Progbar
from util.vocab import Vocabulary,Concept
from util.text2vec import get_text_encoder
from model import ITV, get_we_parameter
from util.bigfile import BigFile
from util.util import read_dict, AverageMeter, LogCollector,makedirsforfile,checkToSkip
from util.constant import ROOT_PATH
INFO = __file__
def parse_args():
# Hyper Parameters
parser = argparse.ArgumentParser()
parser.add_argument('--rootpath', type=str, default=ROOT_PATH,help='path to datasets')
parser.add_argument('--savepath', type=str, default=ROOT_PATH, help='path to save.')
parser.add_argument('trainCollection', type=str, help='train collection')
parser.add_argument('valCollection', type=str, help='validation collection')
parser.add_argument('testCollection', type=str, help='test collection')
parser.add_argument('--n_caption', type=int, default=20, help='number of captions of each image/video (default: 1)')
parser.add_argument('--overwrite', type=int, default=0, choices=[0,1], help='overwrite existed file. (default: 0)')
parser.add_argument('--concept_bank', type=str, default='concept_word', help='concept_bank filename')
parser.add_argument('--concept_fre_threshold', type=int, default=5, help='concept frequence threshold')
# model
parser.add_argument('--model', type=str, default='ITV', help='model name. (default: dual_task)')
parser.add_argument('--vconcate', type=str, default='full',
help='visual feature concatenation style. (full|reduced) full=level 1+2+3; reduced=level 2+3')
parser.add_argument('--tconcate', type=str, default='full',
help='textual feature concatenation style. (full|reduced) full=level 1+2+3; reduced=level 2+3')
parser.add_argument('--measure', type=str, default='cosine', help='measure method. (default: cosine)')
parser.add_argument('--dropout', default=0.2, type=float, help='dropout rate (default: 0.2)')
# text-side multi-level encoding
parser.add_argument('--vocab', type=str, default='word_vocab_5', help='word vocabulary. (default: word_vocab_5)')
parser.add_argument('--word_dim', type=int, default=500, help='word embedding dimension')
parser.add_argument('--text_rnn_size', type=int, default=512, help='text rnn encoder size. (default: 1024)')
parser.add_argument('--text_kernel_num', default=512, type=int, help='number of each kind of text kernel')
parser.add_argument('--text_kernel_sizes', default='2-3-4', type=str, help='dash-separated kernel size to use for text convolution')
parser.add_argument('--text_norm', action='store_true', help='normalize the text embeddings at last layer')
# video-side multi-level encoding
parser.add_argument('--visual_feature', type=str, default='pyresnext-101_rbps13k,flatten0_output,os+pyresnet-152_imagenet11k,flatten0_output,os', help='visual feature.')
parser.add_argument('--motion_feature', type=str, default='mean_slowfast+mean_swintrans', help='motion feature.')
parser.add_argument('--visual_rnn_size', type=int, default=1024, help='visual rnn encoder size')
parser.add_argument('--visual_kernel_num', default=512, type=int, help='number of each kind of visual kernel')
parser.add_argument('--visual_kernel_sizes', default='2-3-4-5', type=str, help='dash-separated kernel size to use for visual convolution')
parser.add_argument('--visual_norm', action='store_true', help='normalize the visual embeddings at last layer')
##unify_decoder
parser.add_argument('--decoder_layers', type=str, default='0-2048', help='decoder FC layers.')
# common space learning
parser.add_argument('--text_mapping_layers', type=str, default='0-2048', help='text fully connected layers for common space learning. (default: 0-2048)')
parser.add_argument('--visual_mapping_layers', type=str, default='0-2048', help='visual fully connected layers for common space learning. (default: 0-2048)')
# loss
parser.add_argument('--loss_fun', type=str, default='mrl', help='loss function')
parser.add_argument('--loss_type', type=str, default='favorBCEloss', help='loss function for the classification loss')
parser.add_argument('--margin', type=float, default=0.2, help='rank loss margin')
parser.add_argument('--direction', type=str, default='all', help='retrieval direction (all|t2i|i2t)')
parser.add_argument('--max_violation', action='store_true', help='use max instead of sum in the rank loss')
parser.add_argument('--cost_style', type=str, default='sum', help='cost style (sum, mean). (default: sum)')
parser.add_argument('--decoder_loss_fun', type=str, default='BCEloss', help='loss function')
parser.add_argument('--multiclass_loss_lamda', type=float, default='0.1', help='how many favor positive loss function')
parser.add_argument('--ul_alpha', type=float, default='0.01',help='hyperparmeter in unlikelyhood training')
# optimizer
parser.add_argument('--optimizer', type=str, default='adam', help='optimizer. (default: rmsprop)')
parser.add_argument('--learning_rate', type=float, default=0.001, help='initial learning rate')
parser.add_argument('--decoder_learning_rate', type=float, default=0.001, help='initial learning rate')
parser.add_argument('--lr_decay_rate', default=0.99, type=float, help='learning rate decay rate. (default: 0.99)')
parser.add_argument('--grad_clip', type=float, default=2, help='gradient clipping threshold')
parser.add_argument('--unlikelihood', action='store_true', help='use unlikelihood in training')
parser.add_argument('--resume', action='store_true', help='use it to resume the model parameters')
parser.add_argument('--resume_path',default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)')
parser.add_argument('--checkpoint_name', default='model_best.pth.match.tar', type=str,
help='name of checkpoint (default: model_best.pth.tar)')
parser.add_argument('--val_metric', default='recall', type=str, help='performance metric for validation (mir|recall)')
# misc
parser.add_argument('--num_epochs', default=100, type=int, help='Number of training epochs.')
parser.add_argument('--decoder_num_epochs', default=30, type=int, help='Number of training epochs.')
parser.add_argument('--batch_size', default=128, type=int, help='Size of a training mini-batch.')
parser.add_argument('--workers', default=10, type=int, help='Number of data loader workers.')
parser.add_argument('--postfix', default='runs_ITV', help='Path to save the model and Tensorboard log.')
parser.add_argument('--log_step', default=10, type=int, help='Number of steps to print and record the log.')
parser.add_argument('--cv_name', default='ACMTOIS', type=str, help='')
parser.add_argument('--project_name', default='ITV', type=str, help='')
args = parser.parse_args()
return args
def main():
opt = parse_args()
print(json.dumps(vars(opt), indent = 2))
rootpath = opt.rootpath
trainCollection = opt.trainCollection
valCollection = opt.valCollection
testCollection = opt.testCollection
opt.project_name = 'ITV_'+trainCollection
wandb.init(project=opt.project_name)
if opt.loss_fun == "mrl" and opt.measure == "cosine":
assert opt.text_norm is True
assert opt.visual_norm is True
# checkpoint path
model_info = '%s_word_only_dp_%.1f_measure_%s_lambda_%.1f' % (opt.model, opt.dropout, opt.measure,opt.multiclass_loss_lamda)
# text encoder info
text_encode_info = 'vocab_%s_word_dim_%s_text_rnn_size_%s_text_norm_%s' % \
(opt.vocab, opt.word_dim, opt.text_rnn_size, opt.text_norm)
text_encode_info += "_kernel_sizes_%s_num_%s" % (opt.text_kernel_sizes, opt.text_kernel_num)
#video encoder encoding info
visual_encode_info = 'visual_feature_%s_visual_rnn_size_%d_visual_norm_%s' % \
(opt.visual_feature, opt.visual_rnn_size, opt.visual_norm)
visual_encode_info += "_kernel_sizes_%s_num_%s" % (opt.visual_kernel_sizes, opt.visual_kernel_num)
# joint space learning info
mapping_info = "mapping_text_%s_img_%s_decoder_%s" % (opt.text_mapping_layers, opt.visual_mapping_layers,opt.decoder_layers)
loss_info = 'loss_func_%s_margin_%s_direction_%s_max_violation_%s_cost_style_%s' % \
(opt.loss_fun, opt.margin, opt.direction, opt.max_violation, opt.cost_style)
optimizer_info = 'optimizer_%s_lr_%s_decay_%.2f_grad_clip_%.1f_val_metric_%s' % \
(opt.optimizer, opt.learning_rate, opt.lr_decay_rate, opt.grad_clip, opt.val_metric)
opt.logger_name = os.path.join(opt.savepath, trainCollection, opt.cv_name, valCollection, model_info, text_encode_info,
visual_encode_info, mapping_info, loss_info, optimizer_info, opt.postfix)
print(opt.logger_name)
if checkToSkip(os.path.join(opt.logger_name, 'model_best.pth.match.tar'), opt.overwrite):
sys.exit(0)
if checkToSkip(os.path.join(opt.logger_name, 'val_metric.txt'), opt.overwrite):
sys.exit(0)
makedirsforfile(os.path.join(opt.logger_name, 'val_metric.txt'))
logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
opt.text_kernel_sizes = list(map(int, opt.text_kernel_sizes.split('-')))
opt.visual_kernel_sizes = list(map(int, opt.visual_kernel_sizes.split('-')))
# collections: trian, val
collections = {'train': trainCollection, 'val': valCollection}
cap_file = {'train': trainCollection + '.caption.txt',
'val': valCollection + '.caption.txt'}
# caption
caption_files = { x: os.path.join(rootpath, collections[x], 'TextData', cap_file[x])
for x in collections }
# Load visual features
visual_feat_path = {x: os.path.join(rootpath, collections[x], 'FeatureData', opt.visual_feature)
for x in collections }
visual_feats = {x: BigFile(visual_feat_path[x]) for x in visual_feat_path}
motion_feat_path = {x: os.path.join(rootpath, collections[x], 'FeatureData', opt.motion_feature)
for x in collections}
motion_feats = {x: BigFile(motion_feat_path[x]) for x in motion_feat_path}
opt.visual_feat_dim = visual_feats['train'].ndims
opt.motion_feat_dim = motion_feats['train'].ndims
bow_vocab_file = os.path.join(rootpath, opt.trainCollection, 'TextData', 'vocabulary', 'bow', opt.vocab+'.pkl')
bow_vocab = pickle.load(open(bow_vocab_file, 'rb'))
bow2vec = get_text_encoder('bow')(bow_vocab)
opt.bow_vocab_size = len(bow_vocab)
# set rnn vocabulary
rnn_vocab_file = os.path.join(rootpath, opt.trainCollection, 'TextData', 'vocabulary', 'rnn', opt.vocab + '.pkl')
rnn_vocab = pickle.load(open(rnn_vocab_file, 'rb'))
opt.vocab_size = len(rnn_vocab)
# initialize word embedding
opt.we_parameter = None
if opt.word_dim == 500:
w2v_data_path = os.path.join(rootpath, "word2vec", 'flickr', 'vec500flickr30m')
opt.we_parameter = get_we_parameter(rnn_vocab, w2v_data_path)
# mapping layer structure
opt.text_mapping_layers = list(map(int, opt.text_mapping_layers.split('-')))
opt.visual_mapping_layers = list(map(int, opt.visual_mapping_layers.split('-')))
# visual concatenation
if opt.vconcate == 'full': # level 1+2+3+4
opt.visual_mapping_layers[0] = opt.visual_feat_dim + opt.visual_rnn_size * 2 + opt.visual_kernel_num * len(
opt.visual_kernel_sizes)+opt.motion_feat_dim
else:
raise NotImplementedError('Model %s not implemented' % opt.model)
# texutal concatenation
if opt.tconcate == 'full': # level 1+2+3
opt.text_mapping_layers[0] = opt.bow_vocab_size + opt.text_rnn_size * 2 + opt.text_kernel_num * len(
opt.text_kernel_sizes)
else:
raise NotImplementedError('Model %s not implemented' % opt.model)
cap_prefixs = {'train': '.caption', 'val': '.caption'}
concept_videolevel_paths = {
'train': os.path.join(rootpath, collections['train'], 'TextData', collections['train'] + cap_prefixs[
'train'] + '.txt.concept_videolevel.'+opt.concept_bank),
'val': os.path.join(rootpath, collections['val'], 'TextData', collections['val'] + cap_prefixs[
'val'] + '.txt.concept_videolevel.'+opt.concept_bank)
}
concept_file = os.path.join(rootpath, opt.trainCollection, 'TextData', 'concept', 'concept_frequency_count_gt' + str(
opt.concept_fre_threshold)+'.'+opt.concept_bank+'.txt')
opt.concept_file = concept_file
print("concept file: %s"%concept_file)
with open(concept_file, 'r') as reader:
concept_lines = reader.readlines()
##create the concept structure
concept= Concept()
for iconcept in concept_lines:
iconcept = iconcept.strip().split()
concept.add_concept(' '.join(iconcept[0:-1]))
##add contraction pairs
contradiction_file = concept_file+'.contradict.contradict_pairs'
with open(contradiction_file, 'r') as reader:
lines = reader.readlines()
for line in lines:
if line.find('//') < 0:
concept.add_contradict(line)
concept2vec = get_text_encoder('bow')(concept, istimes=0)
opt.concept_list_size = len(concept)
opt.concept = concept
#construct contradicted matrix for training. Matirx is [len(concept_list)*len(concept_list)]
contradicted_matrix_np = np.zeros([opt.concept_list_size, opt.concept_list_size])
for key in concept2vec.vocab.idx2contractIdx.keys():
values = concept2vec.vocab.idx2contractIdx[key]
contradicted_matrix_np[key, values] = 1
print("@contray pairs:%d"%contradicted_matrix_np.sum())
contradicted_matrix_sp = torch.from_numpy(contradicted_matrix_np).to_sparse()
if torch.cuda.is_available():
contradicted_matrix_sp = contradicted_matrix_sp.cuda()
opt.contradicted_matrix_sp = contradicted_matrix_sp
del contradicted_matrix_np
# Construct the model
decoder_layers=opt.decoder_layers.split('-')
del decoder_layers[0]
decoder_layers.append(opt.concept_list_size)
opt.decoder_mapping_layers = [int(ilayer) for ilayer in decoder_layers]
model = ITV(opt)
opt.we_parameter = None
# set data loader
video2frames = {x: read_dict(os.path.join(rootpath, collections[x], 'FeatureData', opt.visual_feature,'video2frames.txt'))
for x in collections }
# optionally resume from a checkpoint to the encoder
if opt.resume:
resume_path = os.path.join(opt.resume_path, opt.checkpoint_name)
if os.path.isfile(resume_path):
print("=> loading checkpoint '{}'".format(resume_path))
checkpoint = torch.load(resume_path)
start_epoch = checkpoint['epoch']
matching_best_rsum = checkpoint['matching_best_rsum']
classification_best_rsum = checkpoint['classification_best_rsum']
model.load_state_dict(checkpoint['model'])
# Eiters is used to show logs as the continuation of another
# training
model.Eiters = checkpoint['classification_Eiters']
print("=> loaded checkpoint '{}' (epoch {}, matching_best_rsum {},classification_best_rsum {})"
.format(resume_path, start_epoch, matching_best_rsum, classification_best_rsum))
else:
print("=> no checkpoint found at '{}'".format(resume_path))
data_loaders = data.get_vid_txt_data_loaders(
caption_files, visual_feats, motion_feats, rnn_vocab, bow2vec, concept2vec, opt.batch_size, opt.workers,
opt.n_caption, video2frames=video2frames,concept_video_level=concept_videolevel_paths)
# Train the Model
best_matching_currscore = 0
best_classification_currscore= 0
no_impr_counter = 0
lr_counter = 0
best_epoch = None
best_recall = 0
matching_best_epoch = None
classification_best_epoch = None
fout_val_metric_hist = open(os.path.join(opt.logger_name, 'val_metric_hist.txt'), 'w')
for epoch in range(opt.num_epochs):
print('Epoch[{0} / {1}] R: {2}'.format(epoch, opt.num_epochs, get_learning_rate(model.optimizer)[0]))
print('-'*10)
# train for one epoch
model.vid_encoder.train()
model.text_encoder.train()
model.unify_decoder.train()
train_dual_task(opt, data_loaders['train'], model,epoch)
model.vid_encoder.eval()
model.text_encoder.eval()
model.unify_decoder.eval()
# evaluate on validation set
matching_currscore, classification_vid_cur_recall,classification_text_cur_recall = evaluation.eval_ITV(opt, data_loaders['val'], model,concept2vec,opt.measure)
classification_currscore = (classification_vid_cur_recall+classification_text_cur_recall)/2
# remember best R@ sum and save checkpoint
matching_is_best = matching_currscore > best_matching_currscore
classification_is_best = classification_currscore > best_classification_currscore
if matching_is_best:
matching_best_epoch = epoch
if classification_is_best:
classification_best_epoch = epoch
best_matching_currscore = max(matching_currscore, best_matching_currscore)
best_classification_currscore = max(classification_currscore, best_classification_currscore)
print(' * matching Current perf: {}'.format(matching_currscore))
print(' * matching Best perf: {}'.format(best_matching_currscore))
print(' * classification Current perf: {}'.format(classification_currscore))
print(' * classification Best perf: {}'.format(best_classification_currscore))
print('')
fout_val_metric_hist.write(
'epoch_%d(matching,classification): %f,%f\n' % (epoch, matching_currscore, classification_currscore))
fout_val_metric_hist.flush()
# is_best = (matching_is_best | classification_is_best)
is_best = matching_is_best
if is_best:
if matching_is_best:
best_epoch = None
filename = 'model_best.pth.match.tar'
epoch_best_classification_score = classification_currscore
save_checkpoint({
'epoch': epoch + 1,
'model': model.state_dict(),
'matching_best_rsum': matching_currscore,
'classification_best_rsum': classification_currscore,
'opt': opt,
'classification_Eiters': model.Eiters,
}, matching_is_best, filename=filename, prefix=opt.logger_name + '/',
best_epoch=best_epoch)
best_epoch = epoch
lr_counter += 1
decay_learning_rate(opt, model.optimizer, opt.lr_decay_rate)
early_stop=2
if not is_best:
# Early stop occurs if the validation performance does not improve in consecutive epochs
# and loss does not decrease
no_impr_counter += 1
if (no_impr_counter > early_stop):
print('Early stopping happended.\n')
break
# When the validation performance decreased after an epoch,
# we divide the learning rate by 2 and continue training;
# but we use each learning rate for at least 3 epochs.
if lr_counter > 2:
decay_learning_rate(opt, model.optimizer, 0.5)
lr_counter = 0
else:
no_impr_counter = 0
print('best performance on validation: matching{}\n'.format(best_matching_currscore))
print('classification{}\n'.format(best_classification_currscore))
print('@matching_best epoch:{}\n'.format(matching_best_epoch))
print('@classification best epoch:{}\n'.format(classification_best_epoch))
print('best_recall:{}\n'.format(best_recall))
with open(os.path.join(opt.logger_name, 'val_metric.txt'), 'w') as fout:
fout.write('best performance on validation: epoch ' + str(best_epoch)+
'matching' + str(best_matching_currscore) +
', classification' + str(epoch_best_classification_score))
fout_val_metric_hist.close()
def train_dual_task(opt, train_loader, model, epoch):
# average meters to record the training statistics
batch_time = AverageMeter()
data_time = AverageMeter()
train_logger = LogCollector()
progbar = Progbar(len(train_loader.dataset))
end = time.time()
for i, train_data in enumerate(train_loader):
# video_data, text_data,concept_bows, caption_ori,idxs, cap_ids, video_ids= train_data
# measure data loading time
wandb.log({"train/lr": get_learning_rate(model.optimizer)[0]}, step=model.Eiters)
data_time.update(time.time() - end)
# make sure train logger is used
model.logger = train_logger
if opt.unlikelihood:
b_size, loss,loss_matching, likelihoodLoss,likelihoodLoss_vid, likelihoodLoss_text,unlikelihoodLoss, unlikelihoodLoss_vid,unlikelihoodLoss_text = model.train_dualtask(*train_data)
progbar.add(b_size, values=[('loss', loss),('matching_loss', loss_matching),
('likelihoodLoss',likelihoodLoss),('unlikelihoodLoss', unlikelihoodLoss),
('likelihoodLoss_vid', likelihoodLoss_vid), ('likelihoodLoss_text', likelihoodLoss_text),
('unlikelihoodLoss_vid', unlikelihoodLoss_vid),('unlikelihoodLoss_text', unlikelihoodLoss_text)])
else:
b_size, loss,loss_matching, likelihoodLoss,likelihoodLoss_vid, likelihoodLoss_text = model.train_dualtask(*train_data)
progbar.add(b_size, values=[('loss', loss),('matching_loss', loss_matching),
('likelihoodLoss',likelihoodLoss),
('likelihoodLoss_vid', likelihoodLoss_vid), ('likelihoodLoss_text', likelihoodLoss_text)])
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# Record logs in wandb
wandb.log({"train/eiters": epoch}, step=model.Eiters)
wandb.log({"train/step": i}, step=model.Eiters)
wandb.log({"train/batch_time": batch_time.val}, step=model.Eiters)
wandb.log({"train/data_time": data_time.val}, step=model.Eiters)
wandb.log({"train/loss": loss}, step=model.Eiters)
wandb.log({"train/loss_matching": loss_matching}, step=model.Eiters)
wandb.log({"train/likelihoodLoss": likelihoodLoss}, step=model.Eiters)
wandb.log({"train/likelihoodLoss_vid": likelihoodLoss_vid}, step=model.Eiters)
wandb.log({"train/likelihoodLoss_text": likelihoodLoss_text}, step=model.Eiters)
if opt.unlikelihood:
wandb.log({"train/unlikelihoodLoss": unlikelihoodLoss}, step=model.Eiters)
wandb.log({"train/unlikelihoodLoss_vid": unlikelihoodLoss_vid}, step=model.Eiters)
wandb.log({"train/unlikelihoodLoss_text": unlikelihoodLoss_text}, step=model.Eiters)
def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', prefix='', best_epoch=None):
"""save checkpoint at specific path"""
torch.save(state, prefix + filename)
# if is_best:
# shutil.copyfile(prefix + filename, prefix + 'model_best.pth.tar')
if best_epoch is not None:
os.remove(prefix + 'checkpoint_epoch_%s.pth.tar'%best_epoch)
def decay_learning_rate(opt, optimizer, decay):
"""decay learning rate to the last LR"""
for param_group in optimizer.param_groups:
param_group['lr'] = param_group['lr']*decay
def get_learning_rate(optimizer):
"""Return learning rate"""
lr_list = []
for param_group in optimizer.param_groups:
lr_list.append(param_group['lr'])
return lr_list
if __name__ == '__main__':
main()