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train.py
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train.py
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import shutil
import pickle
from socket import NI_MAXHOST
import time
from utils.utils import *
from utils.data import get_train_loader
from utils.opt import parse_opt
from sklearn.cluster import KMeans
from sklearn.cluster import MiniBatchKMeans
# from spherecluster import SphericalKMeans
import models
from models.encoder import Encoder
from models.decoder import Decoder
from models.capmodel import CapModel
import torch
import h5py
import os
import torch.nn as nn
import numpy as np
from evaluate import evaluate, convert_data_to_coco_scorer_format
from tensorboard_logger import configure, log_value
from torchtext.data import *
import torchtext
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def main(opt):
configure(opt.log_environment, flush_secs=10)
# load vocabulary
filed = torchtext.legacy.data.Field(sequential=True, tokenize="spacy",
eos_token="<eos>",
include_lengths=True,
batch_first=True,
fix_length=opt.max_words,
lower=True,
unk_token="<unk>"
)
if opt.min_freq ==4:
filed.vocab = pickle.load(open(opt.vocab_pkl_path, 'rb'))
elif opt.min_freq == 2:
filed.vocab = pickle.load(open(opt.vocab_pkl_path, 'rb'))
vocab_size = len(filed.vocab)
# if opt.dataset == 'msr-vtt':
# filed.vocab = np.array(filed.vocab)
print(vocab_size)
# print parameters
print('Learning rate: %.5f' % opt.learning_rate)
print('Learning rate decay: ', opt.learning_rate_decay)
print('Batch size: %d' % opt.train_batch_size)
print('results directory: ', opt.result_dir)
# build model
encoder = Encoder(opt)
decoder = Decoder(opt, filed)
net = CapModel(encoder, decoder)
if opt.use_multi_gpu:
net = torch.nn.DataParallel(net)
print('Total parameters:', sum(param.numel() for param in net.parameters()))
if os.path.exists(opt.model_pth_path) and opt.use_checkpoint:
print('asfsafsdfsdf')
net.load_state_dict(torch.load(opt.model_pth_path))
net.to(DEVICE)
# initialize loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=opt.learning_rate)
if os.path.exists(opt.optimizer_pth_path) and opt.use_checkpoint:
optimizer.load_state_dict(torch.load(opt.optimizer_pth_path))
# initialize data loader
train_loader = get_train_loader(opt.train_caption_pkl_path, opt.feature_h5_path, filed,
opt.train_batch_size)
total_step = len(train_loader)
# prepare groundtruth
reference = convert_data_to_coco_scorer_format(opt.test_reference_txt_path)
# start training
best_meteor = 0
best_meteor_epoch = 0
best_cider = 0
best_cider_epoch = 0
loss_count = 0
count = 0
saving_schedule = [int(x * total_step / opt.save_per_epoch) for x in list(range(opt.save_per_epoch, opt.save_per_epoch + 1))]
print('total: ', total_step)
print('saving_schedule: ', saving_schedule)
cat_feat = []
for epoch in range(opt.max_epoch):
start_time = time.time()
if opt.learning_rate_decay and epoch % opt.learning_rate_decay_every == 0 and epoch > 0:
opt.learning_rate /= opt.learning_rate_decay_rate
epsilon = max(0.6, opt.ss_factor / (opt.ss_factor + np.exp(epoch / opt.ss_factor)))
print('epoch:%d\tepsilon:%.8f' % (epoch, epsilon))
log_value('epsilon', epsilon, epoch)
# cat_feat = None
for i, (frames, captions, cap_lens, video_ids) in enumerate(train_loader, start=1):
# convert data to DEVICE mode
frames = frames.to(DEVICE)
targets = captions.to(DEVICE)
# print("iter {}/{}".format(i,len(train_loader)))
# cat_feat.append(frames.cpu())
# compute results of the model
optimizer.zero_grad()
outputs, _ = net(frames, targets, epsilon)
tokens = outputs
bsz = len(captions)
# remove pad and flatten outputs
outputs = torch.cat([outputs[j][:cap_lens[j]] for j in range(bsz)], 0)
outputs = outputs.view(-1, vocab_size)
# remove pad and flatten targets
targets = torch.cat([targets[j][:cap_lens[j]] for j in range(bsz)], 0)
targets = targets.view(-1)
# compute captioning loss
cap_loss = criterion(outputs, targets)
total_loss = cap_loss
log_value('cap_loss', cap_loss.item(), epoch * total_step + i)
# log_value('lin_loss', lin_loss.item(), epoch * total_step + i)
log_value('total_loss', total_loss.item(), epoch * total_step + i)
loss_count += total_loss.item()
total_loss.backward()
clip_gradient(optimizer, opt.grad_clip)
optimizer.step()
if i % 100 == 0 or bsz < opt.train_batch_size:
loss_count /= 100.0 if bsz == opt.train_batch_size else i % 100
print('Epoch [%d/%d], Step [%d/%d], Loss: %.4f, Perplexity: %5.4f' %
(epoch, opt.max_epoch, i, total_step, loss_count,
np.exp(loss_count)))
loss_count = 0
tokens = tokens.max(2)[1]
tokens = tokens.data[0].squeeze()
if opt.use_multi_gpu:
we = net.module.decoder.decode_tokens(tokens)
gt = net.module.decoder.decode_tokens(captions[0].squeeze())
else:
we = net.decoder.decode_tokens(tokens)
gt = net.decoder.decode_tokens(captions[0].squeeze())
# print('[vid:%d]' % video_ids[0])
# print('WE: %s\nGT: %s' % (we, gt))
if i in saving_schedule:
torch.save(net.state_dict(), opt.model_pth_path)
torch.save(optimizer.state_dict(), opt.optimizer_pth_path)
# blockPrint()
start_time_eval = time.time()
net.eval()
# use opt.val_range to find the best hyperparameters
metrics = evaluate(opt, net, opt.test_range, opt.test_prediction_txt_path, reference)
end_time_eval = time.time()
enablePrint()
print('evaluate time: %.3fs' % (end_time_eval - start_time_eval))
for k, v in metrics.items():
log_value(k, v, epoch * len(saving_schedule) + count)
print('%s: %.6f' % (k, v))
if k == 'METEOR' and v > best_meteor:
shutil.copy2(opt.model_pth_path, opt.best_meteor_pth_path)
shutil.copy2(opt.optimizer_pth_path, opt.best_meteor_optimizer_pth_path)
best_meteor = v
best_meteor_epoch = epoch
if k == 'CIDEr' and v > best_cider:
shutil.copy2(opt.model_pth_path, opt.best_cider_pth_path)
shutil.copy2(opt.optimizer_pth_path, opt.best_cider_optimizer_pth_path)
best_cider = v
best_cider_epoch = epoch
print('Step: %d, Learning rate: %.8f' % (epoch * len(saving_schedule) + count, opt.learning_rate))
optimizer = torch.optim.Adam(net.parameters(), lr=opt.learning_rate)
log_value('Learning rate', opt.learning_rate, epoch * len(saving_schedule) + count)
count += 1
count %= 4
net.train()
# cat_feat = torch.cat(cat_feat,dim=0)
# print("begin kmeans...{}".format(cat_feat.view(-1,4096).size()))
# # kmeans = KMeans(n_clusters=300, random_state=0).fit(cat_feat.view(-1,4096))
# kmeans = MiniBatchKMeans(n_clusters=1000, random_state=0, batch_size = 2048).fit(cat_feat.view(-1,4096))
# # kmeans = SphericalKMeans(n_clusters=300).fit(cat_feat.view(-1,4096))
# print(kmeans.cluster_centers_.shape)
# end_time = time.time()
# print("*******One epoch time: %.3fs*******\n" % (end_time - start_time))
# print('best cider: %.3f' % best_cider)
# f = h5py.File('data/VATEX/vatex_concept1000_feat.h5','w')
# f['concept_features'] = kmeans.cluster_centers_
# f.close()
# exit()
with open(opt.test_score_txt_path, 'w') as f:
f.write('MODEL: {}\n'.format(opt.model))
f.write('best meteor epoch: {}\n'.format(best_meteor_epoch))
f.write('best cider epoch: {}\n'.format(best_cider_epoch))
f.write('best cider score: {}\n'.format(best_cider))
f.write('Learning rate: {:6f}\n'.format(opt.learning_rate))
f.write('Learning rate decay: {}\n'.format(opt.learning_rate_decay))
f.write('Batch size: {}\n'.format(opt.train_batch_size))
f.write('results directory: {}\n'.format(opt.result_dir))
if __name__ == '__main__':
opt = parse_opt()
main(opt)