def recommend_train(context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder,
train_dataset: Dataset,
valid_dataset: Dataset,
test_dataset: Dataset,
model_file: str,
vocab_size: int,
embed_init=None):
"""Recommend train.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
train_dataset (Dataset): Train dataset.
valid_dataset (Dataset): Valid dataset.
test_dataset (Dataset): Test dataset.
model_file (str): Saved model file.
vocab_size (int): Vocabulary size.
embed_init: Initial embedding (vocab_size, embed_size).
"""
# Data loader.
train_data_loader = DataLoader(
dataset=train_dataset,
batch_size=RecommendTrainConfig.batch_size,
shuffle=True,
num_workers=RecommendTrainConfig.num_data_loader_workers)
# Model.
similarity_config = SimilarityConfig(vocab_size, embed_init)
similarity = Similarity(similarity_config).to(GlobalConfig.device)
# Model parameters.
params = list(
chain.from_iterable([
list(model.parameters()) for model in [
context_text_encoder, context_image_encoder, context_encoder,
similarity
]
]))
optimizer = Adam(params, lr=RecommendTrainConfig.learning_rate)
epoch_id = 0
min_valid_loss = None
# Load saved state.
if isfile(model_file):
state = torch.load(model_file)
similarity.load_state_dict(state['similarity'])
optimizer.load_state_dict(state['optimizer'])
epoch_id = state['epoch_id']
min_valid_loss = state['min_valid_loss']
# Loss.
sum_loss = 0
bad_loss_cnt = 0
# Switch to train mode.
context_text_encoder.train()
context_image_encoder.train()
context_encoder.train()
similarity.train()
finished = False
for epoch_id in range(epoch_id, RecommendTrainConfig.num_iterations):
for batch_id, train_data in enumerate(train_data_loader):
# Sets gradients to 0.
optimizer.zero_grad()
context_dialog, pos_products, neg_products = train_data
texts, text_lengths, images, utter_types = context_dialog
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
batch_size = texts.size(0)
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
# utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, _ = encode_context(context_text_encoder,
context_image_encoder, context_encoder,
texts, text_lengths, images)
# (batch_size, context_vector_size)
loss = recommend_loss(similarity, batch_size, context,
pos_products, neg_products)
sum_loss += loss
loss.backward()
optimizer.step()
# Print loss every `TrainConfig.print_freq` batches.
if (batch_id + 1) % RecommendTrainConfig.print_freq == 0:
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
sum_loss /= RecommendTrainConfig.print_freq
print('epoch: {} \tbatch: {} \tloss: {} \ttime: {}'.format(
epoch_id + 1, batch_id + 1, sum_loss, cur_time))
sum_loss = 0
# Valid every `TrainConfig.valid_freq` batches.
if (batch_id + 1) % RecommendTrainConfig.valid_freq == 0:
valid_loss = recommend_valid(context_text_encoder,
context_image_encoder,
context_encoder, similarity,
valid_dataset)
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('valid_loss: {} \ttime: {}'.format(valid_loss, cur_time))
# Save current best model.
if min_valid_loss is None or valid_loss < min_valid_loss:
min_valid_loss = valid_loss
bad_loss_cnt = 0
save_dict = {
'task': RECOMMEND_TASK,
'epoch_id': epoch_id,
'min_valid_loss': min_valid_loss,
'optimizer': optimizer.state_dict(),
'context_text_encoder':
context_text_encoder.state_dict(),
'context_image_encoder':
context_image_encoder.state_dict(),
'context_encoder': context_encoder.state_dict(),
'similarity': similarity.state_dict()
}
torch.save(save_dict, model_file)
print('Best model saved.')
else:
bad_loss_cnt += 1
if bad_loss_cnt > RecommendTrainConfig.patience:
recommend_test(context_text_encoder, context_image_encoder,
context_encoder, similarity, test_dataset)
finished = True
break
if finished:
break
def knowledge_attribute_train(context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder,
train_dataset: Dataset,
valid_dataset: Dataset,
test_dataset: Dataset,
model_file: str,
attribute_data: AttributeData,
vocab: Dict[str, int],
embed_init=None):
"""Knowledge styletip train.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
train_dataset (Dataset): Train dataset.
valid_dataset (Dataset): Valid dataset.
test_dataset (Dataset): Test dataset.
model_file (str): Saved model file.
attribute_data (AttributeData): Attribute data.
vocab (Dict[str, int]): Vocabulary.
embed_init: Initial embedding (vocab_size, embed_size).
"""
# Data loader.
train_data_loader = DataLoader(
dataset=train_dataset,
batch_size=KnowledgeAttributeTrainConfig.batch_size,
shuffle=True,
num_workers=KnowledgeAttributeTrainConfig.num_data_loader_workers)
# Model.
vocab_size = len(vocab)
attribute_kv_memory_config = AttributeKVMemoryConfig(
len(attribute_data.key_vocab), len(attribute_data.value_vocab))
text_decoder_config = KnowledgeTextDecoderConfig(vocab_size,
MemoryConfig.memory_size,
MemoryConfig.output_size,
embed_init)
to_hidden = ToHidden(text_decoder_config)
to_hidden = to_hidden.to(GlobalConfig.device)
attribute_kv_memory = KVMemory(attribute_kv_memory_config)
attribute_kv_memory = attribute_kv_memory.to(GlobalConfig.device)
text_decoder = TextDecoder(text_decoder_config)
text_decoder = text_decoder.to(GlobalConfig.device)
# Model parameters.
params = list(
chain.from_iterable([
list(model.parameters()) for model in [
context_text_encoder, context_image_encoder, context_encoder,
to_hidden, attribute_kv_memory, text_decoder
]
]))
optimizer = Adam(params, lr=KnowledgeAttributeTrainConfig.learning_rate)
epoch_id = 0
min_valid_loss = None
# Load saved state.
if isfile(model_file):
state = torch.load(model_file)
to_hidden.load_state_dict(state['to_hidden'])
attribute_kv_memory.load_state_dict(state['attribute_kv_memory'])
text_decoder.load_state_dict(state['text_decoder'])
optimizer.load_state_dict(state['optimizer'])
epoch_id = state['epoch_id']
min_valid_loss = state['min_valid_loss']
# Loss.
sum_loss = 0
bad_loss_cnt = 0
# Switch to train mode.
context_text_encoder.train()
context_image_encoder.train()
context_encoder.train()
to_hidden.train()
attribute_kv_memory.train()
text_decoder.train()
finished = False
for epoch_id in range(epoch_id,
KnowledgeAttributeTrainConfig.num_iterations):
for batch_id, train_data in enumerate(train_data_loader):
# Set gradients to 0.
optimizer.zero_grad()
train_data, products = train_data
keys, values, pair_length = products
keys = keys.to(GlobalConfig.device)
values = values.to(GlobalConfig.device)
pair_length = pair_length.to(GlobalConfig.device)
texts, text_lengths, images, utter_types = train_data
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, hiddens = encode_context(context_text_encoder,
context_image_encoder,
context_encoder, texts,
text_lengths, images)
# (batch_size, context_vector_size)
encode_knowledge_func = partial(attribute_kv_memory, keys, values,
pair_length)
loss, n_totals = text_loss(to_hidden, text_decoder,
text_decoder_config.text_length,
context, texts[-1], text_lengths[-1],
hiddens, encode_knowledge_func)
sum_loss += loss / text_decoder_config.text_length
loss.backward()
optimizer.step()
# Print loss every `TrainConfig.print_freq` batches.
if (batch_id + 1) % KnowledgeAttributeTrainConfig.print_freq == 0:
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
sum_loss /= KnowledgeAttributeTrainConfig.print_freq
print('epoch: {} \tbatch: {} \tloss: {} \ttime: {}'.format(
epoch_id + 1, batch_id + 1, sum_loss, cur_time))
sum_loss = 0
# Valid every `TrainConfig.valid_freq` batches.
if (batch_id + 1) % KnowledgeAttributeTrainConfig.valid_freq == 0:
valid_loss = knowledge_attribute_valid(
context_text_encoder, context_image_encoder,
context_encoder, to_hidden, attribute_kv_memory,
text_decoder, valid_dataset,
text_decoder_config.text_length)
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('valid_loss: {} \ttime: {}'.format(valid_loss, cur_time))
# Save current best model.
if min_valid_loss is None or valid_loss < min_valid_loss:
min_valid_loss = valid_loss
bad_loss_cnt = 0
save_dict = {
'task': KNOWLEDGE_ATTRIBUTE_SUBTASK,
'epoch_id': epoch_id,
'min_valid_loss': min_valid_loss,
'optimizer': optimizer.state_dict(),
'context_text_encoder':
context_text_encoder.state_dict(),
'context_image_encoder':
context_image_encoder.state_dict(),
'context_encoder': context_encoder.state_dict(),
'to_hidden': to_hidden.state_dict(),
'attribute_kv_memory':
attribute_kv_memory.state_dict(),
'text_decoder': text_decoder.state_dict()
}
torch.save(save_dict, model_file)
print('Best model saved.')
else:
bad_loss_cnt += 1
if bad_loss_cnt > KnowledgeAttributeTrainConfig.patience:
knowledge_attribute_test(
context_text_encoder, context_image_encoder,
context_encoder, to_hidden, attribute_kv_memory,
text_decoder, test_dataset,
text_decoder_config.text_length, vocab)
finished = True
break
if finished:
break
loss.backward()
optimizer.step()
distributed.all_reduce(loss_manual_mining.data)
losses_manual_mining += loss_manual_mining.data.item() / len(
args.devices)
if local_rank == 0:
total = i + 1
tbar.set_description('epoch: %d, loss manual mining: %.3f' %
(epoch + 1, losses_manual_mining / total))
# tbar.set_description('epoch: %d, loss1: %.3f, loss2: %.3f'
# % (epoch + 1, losses_manual_mining / (i + 1), losses_hard_mining / (i + 1)))
scheduler.step(epoch)
if local_rank == 0:
checkpoints = {
'score': score_model.module.state_dict(),
'item': image_encoder.state_dict(),
'optimizer': optimizer.state_dict()
}
torch.save(
checkpoints,
os.path.join(checkpoints_dir,
'model-epoch{}.pth'.format(epoch + 1)))
# score_model.eval()
# score_model.eval()
with torch.no_grad():
valid(epoch + 1, checkpoints_dir, use_bert=use_bert)
distributed.barrier()
def main():
# ignore warnings
#warnings.simplefilter('ignore')
args = get_arguments()
SETTING = Dict(yaml.safe_load(open(os.path.join('arguments',args.arg+'.yaml'), encoding='utf8')))
print(args)
args.device = list (map(str,args.device))
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(args.device)
#image transformer
train_transform = transforms.Compose([
transforms.Resize(SETTING.imsize_pre),
transforms.RandomCrop(SETTING.imsize),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
val_transform = transforms.Compose([
transforms.Resize(SETTING.imsize_pre),
transforms.CenterCrop(SETTING.imsize),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# data load
if args.dataset == 'coco':
train_dset = CocoDset(root=SETTING.root_path,img_dir='train2017', ann_dir='annotations/captions_train2017.json', transform=train_transform)
val_dset = CocoDset(root=SETTING.root_path, img_dir='val2017', ann_dir='annotations/captions_val2017.json', transform=val_transform)
train_loader = DataLoader(train_dset, batch_size=SETTING.batch_size, shuffle=True, num_workers=SETTING.n_cpu, collate_fn=collater)
val_loader = DataLoader(val_dset, batch_size=SETTING.batch_size, shuffle=False, num_workers=SETTING.n_cpu, collate_fn=collater)
# setup vocab dict
vocab = Vocabulary(max_len=SETTING.max_len)
vocab.load_vocab(args.vocab_path)
# setup encoder
imenc = ImageEncoder(SETTING.out_size, SETTING.cnn_type)
capenc = CaptionEncoder(len(vocab), SETTING.emb_size, SETTING.out_size, SETTING.rnn_type, vocab.padidx)
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
imenc = imenc.to(device)
capenc = capenc.to(device)
# learning rate
cfgs = [{'params' : imenc.fc.parameters(), 'lr' : float(SETTING.lr_cnn)},
{'params' : capenc.parameters(), 'lr' : float(SETTING.lr_rnn)}]
# optimizer
if SETTING.optimizer == 'SGD':
optimizer = optim.SGD(cfgs, momentum=SETTING.momentum, weight_decay=SETTING.weight_decay)
elif SETTING.optimizer == 'Adam':
optimizer = optim.Adam(cfgs, betas=(SETTING.beta1, SETTING.beta2), weight_decay=SETTING.weight_decay)
elif SETTING.optimizer == 'RMSprop':
optimizer = optim.RMSprop(cfgs, alpha=SETTING.alpha, weight_decay=SETTING.weight_decay)
if SETTING.scheduler == 'Plateau':
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=SETTING.dampen_factor, patience=SETTING.patience, verbose=True)
elif SETTING.scheduler == 'Step':
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=SETTING.patience, gamma=SETTING.dampen_factor)
# loss
lossfunc = PairwiseRankingLoss(margin=SETTING.margin, method=SETTING.method, improved=args.improved, intra=SETTING.intra, lamb=SETTING.imp_weight)
# if start from checkpoint
if args.checkpoint is not None:
print("loading model and optimizer checkpoint from {} ...".format(args.checkpoint), flush=True)
ckpt = torch.load(args.checkpoint)
imenc.load_state_dict(ckpt["encoder_state"])
capenc.load_state_dict(ckpt["decoder_state"])
optimizer.load_state_dict(ckpt["optimizer_state"])
if SETTING.scheduler != 'None':
scheduler.load_state_dict(ckpt["scheduler_state"])
offset = ckpt["epoch"]
data = ckpt["stats"]
bestscore = 0
for rank in [1, 5, 10, 20]:
bestscore += data["i2c_recall@{}".format(rank)] + data["c2i_recall@{}".format(rank)]
bestscore = int(bestscore)
# start new training
else:
offset = 0
bestscore = -1
if args.dataparallel:
print("Using Multiple GPU . . . ")
imenc = nn.DataParallel(imenc)
capenc = nn.DataParallel(capenc)
metrics = {}
es_cnt = 0
# training
assert offset < SETTING.max_epochs
for ep in range(offset, SETTING.max_epochs):
epoch = ep+1
# unfreeze cnn parameters
if epoch == SETTING.freeze_epoch:
if args.dataparallel:
optimizer.add_param_group({'params': imenc.module.cnn.parameters(), 'lr': float(SETTING.lr_cnn)})
else:
optimizer.add_param_group({'params': imenc.cnn.parameters(), 'lr': float(SETTING.lr_cnn)})
#train(1epoch)
train(epoch, train_loader, imenc, capenc, optimizer, lossfunc, vocab, args, SETTING)
#validate
data = validate(epoch, val_loader, imenc, capenc, vocab, args, SETTING)
totalscore = 0
for rank in [1, 5, 10, 20]:
totalscore += data["i2c_recall@{}".format(rank)] + data["c2i_recall@{}".format(rank)]
totalscore = int(totalscore)
#scheduler update
if SETTING.scheduler == 'Plateau':
scheduler.step(totalscore)
if SETTING.scheduler == 'Step':
scheduler.step()
# update checkpoint
if args.dataparallel:
ckpt = {
"stats": data,
"epoch": epoch,
"encoder_state": imenc.module.state_dict(),
"decoder_state": capenc.module.state_dict(),
"optimizer_state": optimizer.state_dict()
}
else:
ckpt = {
"stats": data,
"epoch": epoch,
"encoder_state": imenc.state_dict(),
"decoder_state": capenc.state_dict(),
"optimizer_state": optimizer.state_dict()
}
if SETTING.scheduler != 'None':
ckpt['scheduler_state'] = scheduler.state_dict()
# make savedir
savedir = os.path.join("models", args.arg)
if not os.path.exists(savedir):
os.makedirs(savedir)
#
for k, v in data.items():
if k not in metrics.keys():
metrics[k] = [v]
else:
metrics[k].append(v)
# save checkpoint
savepath = os.path.join(savedir, "epoch_{:04d}_score_{:03d}.ckpt".format(epoch, totalscore))
if int(totalscore) > int(bestscore):
print("score: {:03d}, saving model and optimizer checkpoint to {} ...".format(totalscore, savepath), flush=True)
bestscore = totalscore
torch.save(ckpt, savepath)
es_cnt = 0
else:
print("score: {:03d}, no improvement from best score of {:03d}, not saving".format(totalscore, bestscore), flush=True)
es_cnt += 1
# early stopping
if es_cnt == SETTING.es_cnt:
print("early stopping at epoch {} because of no improvement for {} epochs".format(epoch, SETTING.es_cnt))
break
print("done for epoch {:04d}".format(epoch), flush=True)
visualize(metrics, args, SETTING)
print("complete training")
def main(args):
# Create model directory
if not os.path.exists(args.model_path):
os.makedirs(args.model_path)
# Image preprocessing
train_transform = transforms.Compose([
transforms.RandomCrop(args.image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
# val_transform = transforms.Compose([
# transforms.Resize(args.image_size, interpolation=Image.LANCZOS),
# transforms.ToTensor(),
# transforms.Normalize((0.485, 0.456, 0.406),
# (0.229, 0.224, 0.225))])
# Load vocabulary wrapper.
with open(args.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# Build data loader
train_data_loader = get_loader(args.train_image_dir, args.train_vqa_path, args.ix_to_ans_file, args.train_description_file, vocab, train_transform, args.batch_size, shuffle=True, num_workers=args.num_workers)
#val_data_loader = get_loader(args.val_image_dir, args.val_vqa_path, args.ix_to_ans_file, vocab, val_transform, args.batch_size, shuffle=False, num_workers=args.num_workers)
image_encoder = ImageEncoder(args.img_feature_size)
question_emb_size = 1024
# description_emb_size = 512
no_ans = 1000
question_encoder = BertEncoder(question_emb_size)
# ques_description_encoder = BertEncoder(description_emb_size)
# vqa_decoder = VQA_Model(args.img_feature_size, question_emb_size, description_emb_size, no_ans)
vqa_decoder = VQA_Model(args.img_feature_size, question_emb_size, no_ans)
pretrained_epoch = 0
if args.pretrained_epoch > 0:
pretrained_epoch = args.pretrained_epoch
image_encoder.load_state_dict(torch.load('./models/image_encoder-' + str(pretrained_epoch) + '.pkl'))
question_encoder.load_state_dict(torch.load('./models/question_encoder-' + str(pretrained_epoch) + '.pkl'))
# ques_description_encoder.load_state_dict(torch.load('./models/ques_description_encoder-' + str(pretrained_epoch) + '.pkl'))
vqa_decoder.load_state_dict(torch.load('./models/vqa_decoder-' + str(pretrained_epoch) + '.pkl'))
if torch.cuda.is_available():
image_encoder.cuda()
question_encoder.cuda()
# ques_description_encoder.cuda()
vqa_decoder.cuda()
print("Cuda is enabled...")
criterion = nn.CrossEntropyLoss()
# params = image_encoder.get_params() + question_encoder.get_params() + ques_description_encoder.get_params() + vqa_decoder.get_params()
params = list(image_encoder.parameters()) + list(question_encoder.parameters()) + list(vqa_decoder.parameters())
#print("params: ", params)
optimizer = torch.optim.Adam(params, lr=args.learning_rate, weight_decay=args.weight_decay)
total_train_step = len(train_data_loader)
min_avg_loss = float("inf")
overfit_warn = 0
for epoch in range(args.num_epochs):
if epoch < pretrained_epoch:
continue
image_encoder.train()
question_encoder.train()
#ques_description_encoder.train()
vqa_decoder.train()
avg_loss = 0.0
avg_acc = 0.0
for bi, (question_arr, image_vqa, target_answer, answer_str) in enumerate(train_data_loader):
loss = 0
image_encoder.zero_grad()
question_encoder.zero_grad()
#ques_description_encoder.zero_grad()
vqa_decoder.zero_grad()
images = to_var(torch.stack(image_vqa))
question_arr = to_var(torch.stack(question_arr))
#ques_desc_arr = to_var(torch.stack(ques_desc_arr))
target_answer = to_var(torch.tensor(target_answer))
image_emb = image_encoder(images)
question_emb = question_encoder(question_arr)
#ques_desc_emb = ques_description_encoder(ques_desc_arr)
#output = vqa_decoder(image_emb, question_emb, ques_desc_emb)
output = vqa_decoder(image_emb, question_emb)
loss = criterion(output, target_answer)
_, prediction = torch.max(output,1)
no_correct_prediction = prediction.eq(target_answer).sum().item()
accuracy = no_correct_prediction * 100/ args.batch_size
####
target_answer_no = target_answer.tolist()
prediction_no = prediction.tolist()
####
loss_num = loss.item()
avg_loss += loss.item()
avg_acc += no_correct_prediction
#loss /= (args.batch_size)
loss.backward()
optimizer.step()
# Print log info
if bi % args.log_step == 0:
print('Epoch [%d/%d], Train Step [%d/%d], Loss: %.4f, Acc: %.4f'
%(epoch + 1, args.num_epochs, bi, total_train_step, loss.item(), accuracy))
avg_loss /= (args.batch_size * total_train_step)
avg_acc /= (args.batch_size * total_train_step)
print('Epoch [%d/%d], Average Train Loss: %.4f, Average Train acc: %.4f' %(epoch + 1, args.num_epochs, avg_loss, avg_acc))
# Save the models
torch.save(image_encoder.state_dict(), os.path.join(args.model_path, 'image_encoder-%d.pkl' %(epoch+1)))
torch.save(question_encoder.state_dict(), os.path.join(args.model_path, 'question_encoder-%d.pkl' %(epoch+1)))
#torch.save(ques_description_encoder.state_dict(), os.path.join(args.model_path, 'ques_description_encoder-%d.pkl' %(epoch+1)))
torch.save(vqa_decoder.state_dict(), os.path.join(args.model_path, 'vqa_decoder-%d.pkl' %(epoch+1)))
overfit_warn = overfit_warn + 1 if (min_avg_loss < avg_loss) else 0
min_avg_loss = min(min_avg_loss, avg_loss)
lossFileName = "result/result_"+str(epoch)+".txt"
test_fd = open(lossFileName, 'w')
test_fd.write('Epoch: '+ str(epoch) + ' avg_loss: ' + str(avg_loss)+ " avg_acc: "+ str(avg_acc)+"\n")
test_fd.close()
if overfit_warn >= 5:
print("terminated as overfitted")
break
word_embedding = word_embedding.to(device)
image_encoder = image_encoder.to(device)
image_decoder = image_decoder.to(device)
""" 11) text file logging """
log_filename = f"logs/training_log-BIGDATASET-BIGMODEL.log"
logging.basicConfig(filename=log_filename, level=logging.DEBUG)
EPOCHS = 100
START_EPOCH = 0
print("Beginning Training")
for epoch in range(START_EPOCH, EPOCHS):
# TRAIN
results = run_epoch(epoch, train_loader, image_encoder, image_decoder, word_embedding, loss_fn, optim, device,
train=True)
print(results.to_string(-1))
logging.debug(results.to_string(-1))
# VAL
results = run_epoch(epoch, val_loader, image_encoder, image_decoder, word_embedding, loss_fn, optim, device,
train=False)
print('Val ' + results.to_string(-1))
logging.debug('Val ' + results.to_string(-1))
# SAVE
torch.save(word_embedding.state_dict(), f"checkpoints/BIGMODEL-BIGDATASET-weights-embedding-epoch-{epoch}.pt")
torch.save(image_encoder.state_dict(), f"checkpoints/BIGMODEL-BIGDATASET-weights-encoder-epoch-{epoch}.pt")
torch.save(image_decoder.state_dict(), f"checkpoints/BIGMODEL-BIGDATASET-weights-decoder-epoch-{epoch}.pt")
def intention_train(context_text_encoder: TextEncoder,
context_image_encoder: ImageEncoder,
context_encoder: ContextEncoder, train_dataset: Dataset,
valid_dataset: Dataset, test_dataset: Dataset,
model_file: str):
"""Intention train.
Args:
context_text_encoder (TextEncoder): Context text encoder.
context_image_encoder (ImageEncoder): Context image encoder.
context_encoder (ContextEncoder): Context encoder.
train_dataset (Dataset): Train dataset.
valid_dataset (Dataset): Valid dataset.
test_dataset (Dataset): Test dataset.
model_file (str): Saved model file.
"""
# Data loader.
train_data_loader = DataLoader(
dataset=train_dataset,
batch_size=IntentionTrainConfig.batch_size,
shuffle=True,
num_workers=IntentionTrainConfig.num_data_loader_workers)
# Model.
intention_config = IntentionConfig()
intention = Intention(intention_config).to(GlobalConfig.device)
# Model parameters.
params = list(
chain.from_iterable([
list(model.parameters()) for model in [
context_text_encoder, context_image_encoder, context_encoder,
intention
]
]))
optimizer = Adam(params, lr=IntentionTrainConfig.learning_rate)
epoch_id = 0
min_valid_loss = None
# Load saved state.
if isfile(model_file):
state = torch.load(model_file)
intention.load_state_dict(state['intention'])
optimizer.load_state_dict(state['optimizer'])
epoch_id = state['epoch_id']
min_valid_loss = state['min_valid_loss']
# Loss.
sum_loss = 0
bad_loss_cnt = 0
# Switch to train mode.
context_text_encoder.train()
context_image_encoder.train()
context_encoder.train()
intention.train()
finished = False
for epoch_id in range(epoch_id, IntentionTrainConfig.num_iterations):
for batch_id, train_data in enumerate(train_data_loader):
# Sets gradients to 0.
optimizer.zero_grad()
texts, text_lengths, images, utter_types = train_data
# Sizes:
# texts: (batch_size, dialog_context_size + 1, dialog_text_max_len)
# text_lengths: (batch_size, dialog_context_size + 1)
# images: (batch_size, dialog_context_size + 1,
# pos_images_max_num, 3, image_size, image_size)
# utter_types: (batch_size, )
# To device.
texts = texts.to(GlobalConfig.device)
text_lengths = text_lengths.to(GlobalConfig.device)
images = images.to(GlobalConfig.device)
utter_types = utter_types.to(GlobalConfig.device)
texts.transpose_(0, 1)
# (dialog_context_size + 1, batch_size, dialog_text_max_len)
text_lengths.transpose_(0, 1)
# (dialog_context_size + 1, batch_size)
images.transpose_(0, 1)
images.transpose_(1, 2)
# (dialog_context_size + 1, pos_images_max_num, batch_size, 3,
# image_size, image_size)
# Encode context.
context, _ = encode_context(context_text_encoder,
context_image_encoder, context_encoder,
texts, text_lengths, images)
# (batch_size, context_vector_size)
intent_prob = intention(context)
# (batch_size, utterance_type_size)
loss = nll_loss(intent_prob, utter_types)
sum_loss += loss
loss.backward()
optimizer.step()
# Print loss every `TrainConfig.print_freq` batches.
if (batch_id + 1) % IntentionTrainConfig.print_freq == 0:
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
sum_loss /= IntentionTrainConfig.print_freq
print('epoch: {} \tbatch: {} \tloss: {} \ttime: {}'.format(
epoch_id + 1, batch_id + 1, sum_loss, cur_time))
sum_loss = 0
# Valid every `TrainConfig.valid_freq` batches.
if (batch_id + 1) % IntentionTrainConfig.valid_freq == 0:
valid_loss, accuracy = intention_valid(context_text_encoder,
context_image_encoder,
context_encoder,
intention,
valid_dataset)
cur_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print('valid_loss: {} \taccuracy: {} \ttime: {}'.format(
valid_loss, accuracy, cur_time))
# Save current best model.
if min_valid_loss is None or valid_loss < min_valid_loss:
min_valid_loss = valid_loss
bad_loss_cnt = 0
save_dict = {
'task': INTENTION_TASK,
'epoch_id': epoch_id,
'min_valid_loss': min_valid_loss,
'optimizer': optimizer.state_dict(),
'context_text_encoder':
context_text_encoder.state_dict(),
'context_image_encoder':
context_image_encoder.state_dict(),
'context_encoder': context_encoder.state_dict(),
'intention': intention.state_dict()
}
torch.save(save_dict, model_file)
print('Best model saved.')
else:
bad_loss_cnt += 1
if bad_loss_cnt > IntentionTrainConfig.patience:
intention_test(context_text_encoder,
context_image_encoder, context_encoder,
intention, test_dataset)
finished = True
break
if finished:
break
