def main():
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
transform = transforms.Compose([
transforms.Resize((SETTING.imsize, SETTING.imsize)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
if args.dataset == 'coco':
val_dset = CocoDset(root=SETTING.root_path, img_dir='val2017', ann_dir='annotations/captions_val2017.json', transform=transform)
val_loader = DataLoader(val_dset, batch_size=SETTING.batch_size, shuffle=False, num_workers=SETTING.n_cpu, collate_fn=collater)
vocab = Vocabulary(max_len=SETTING.max_len)
vocab.load_vocab(args.vocab_path)
imenc = ImageEncoder(SETTING.out_size, SETTING.cnn_type)
capenc = CaptionEncoder(len(vocab), SETTING.emb_size, SETTING.out_size, SETTING.rnn_type)
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
imenc = imenc.to(device)
capenc = capenc.to(device)
assert args.checkpoint is not None
print("loading model and optimizer checkpoint from {} ...".format(args.checkpoint), flush=True)
ckpt = torch.load(args.checkpoint, map_location=device)
imenc.load_state_dict(ckpt["encoder_state"])
capenc.load_state_dict(ckpt["decoder_state"])
begin = time.time()
dset = EmbedDset(val_loader, imenc, capenc, vocab, args)
print("database created | {} ".format(sec2str(time.time()-begin)), flush=True)
savedir = os.path.join("out", args.config_name)
if not os.path.exists(savedir):
os.makedirs(savedir, 0o777)
image = dset.embedded["image"]
caption = dset.embedded["caption"]
n_i = image.shape[0]
n_c = caption.shape[0]
all = np.concatenate([image, caption], axis=0)
emb_file = os.path.join(savedir, "embedding_{}.npy".format(n_i))
save_file = os.path.join(savedir, "{}.npy".format(SETTING.method))
vis_file = os.path.join(savedir, "{}.png".format(SETTING.method))
np.save(emb_file, all)
print("saved embeddings to {}".format(emb_file), flush=True)
dimension_reduction(emb_file, save_file, method=SETTING.method)
plot_embeddings(save_file, n_i, vis_file, method=SETTING.method)
def main():
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)
transform = transforms.Compose([
transforms.Resize((SETTING.imsize, SETTING.imsize)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
if args.dataset == 'coco':
val_dset = CocoDset(root=SETTING.root_path,
img_dir='val2017',
ann_dir='annotations/captions_val2017.json',
transform=transform)
val_loader = DataLoader(val_dset,
batch_size=SETTING.batch_size,
shuffle=False,
num_workers=SETTING.n_cpu,
collate_fn=collater)
vocab = Vocabulary(max_len=SETTING.max_len)
vocab.load_vocab(args.vocab_path)
imenc = ImageEncoder(SETTING.out_size, SETTING.cnn_type)
capenc = CaptionEncoder(len(vocab), SETTING.emb_size, SETTING.out_size,
SETTING.rnn_type)
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
imenc = imenc.to(device)
capenc = capenc.to(device)
assert SETTING.checkpoint is not None
print("loading model and optimizer checkpoint from {} ...".format(
SETTING.checkpoint),
flush=True)
ckpt = torch.load(SETTING.checkpoint)
imenc.load_state_dict(ckpt["encoder_state"])
capenc.load_state_dict(ckpt["decoder_state"])
begin = time.time()
dset = EmbedDset(val_loader, imenc, capenc, vocab, args)
print("database created | {} ".format(sec2str(time.time() - begin)),
flush=True)
retrieve_i2c(dset, val_dset, args.image_path, imenc, transform)
retrieve_c2i(dset, val_dset, args.output_dir, args.caption, capenc, vocab)
def main():
args = parse_args()
transform = transforms.Compose([
transforms.Resize((args.imsize, args.imsize)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
if args.dataset == 'coco':
val_dset = CocoDataset(root=args.root_path,
imgdir='val2017',
jsonfile='annotations/captions_val2017.json',
transform=transform,
mode='all')
val_loader = DataLoader(val_dset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.n_cpu,
collate_fn=collater_eval)
vocab = Vocabulary(max_len=args.max_len)
vocab.load_vocab(args.vocab_path)
imenc = ImageEncoder(args.out_size, args.cnn_type)
capenc = CaptionEncoder(len(vocab), args.emb_size, args.out_size,
args.rnn_type)
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
imenc = imenc.to(device)
capenc = capenc.to(device)
assert 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"])
begin = time.time()
dset = EmbedDataset(val_loader, imenc, capenc, vocab, args)
print("database created | {} ".format(sec2str(time.time() - begin)),
flush=True)
retrieve_i2c(dset, val_dset, imenc, vocab, args)
retrieve_c2i(dset, val_dset, capenc, vocab, args)
def main():
args = parse_args()
# Prepare dataset (for vocab)
dataset = ImageCaptionDataset('', args.caption_path)
# Load image
img = dataset.parse_image(args.image)
# Load model
encoder = ImageEncoder('cpu', pretrained=args.use_pretrained)
decoder = CaptionDecoder('cpu', len(dataset.vocab), embedding_dim=args.embedding_dim,
enc_hidden_dim=encoder.hidden_dim, dec_hidden_dim=args.dec_hidden_dim,
word_to_int=dataset.word_to_int)
encoder.load_state_dict(torch.load(args.encoder))
decoder.load_state_dict(torch.load(args.decoder))
# Generate caption
gen_caption(encoder, decoder, img, dataset, args)
def build_models(self):
# ###################encoders######################################## #
image_encoder = ImageEncoder(output_channels=cfg.hidden_dim)
if cfg.text_encoder_path != '':
img_encoder_path = cfg.text_encoder_path.replace('text_encoder', 'image_encoder')
print('Load image encoder from:', img_encoder_path)
state_dict = torch.load(img_encoder_path, map_location='cpu')
if 'model' in state_dict.keys():
image_encoder.load_state_dict(state_dict['model'])
else:
image_encoder.load_state_dict(state_dict)
for p in image_encoder.parameters(): # make image encoder grad on
p.requires_grad = True
# image_encoder.eval()
epoch = 0
###################################################################
text_encoder = TextEncoder(bert_config = self.bert_config)
if cfg.text_encoder_path != '':
epoch = cfg.text_encoder_path[istart:iend]
epoch = int(epoch) + 1
text_encoder_path = cfg.text_encoder_path
print('Load text encoder from:', text_encoder_path)
state_dict = torch.load(text_encoder_path, map_location='cpu')
if 'model' in state_dict.keys():
text_encoder.load_state_dict(state_dict['model'])
else:
text_encoder.load_state_dict(state_dict)
for p in text_encoder.parameters(): # make text encoder grad on
p.requires_grad = True
# ########################################################### #
if cfg.CUDA:
text_encoder = text_encoder.cuda()
image_encoder = image_encoder.cuda()
return [text_encoder, image_encoder, epoch]
def train(task: int, model_file_name: str):
"""Train model.
Args:
task (int): Task.
model_file_name (str): Model file name (saved or to be saved).
"""
# Check if data exists.
if not isfile(DatasetConfig.common_raw_data_file):
raise ValueError('No common raw data.')
# Load extracted common data.
common_data: CommonData = load_pkl(DatasetConfig.common_raw_data_file)
# Dialog data files.
train_dialog_data_file = DatasetConfig.get_dialog_filename(
task, TRAIN_MODE)
valid_dialog_data_file = DatasetConfig.get_dialog_filename(
task, VALID_MODE)
test_dialog_data_file = DatasetConfig.get_dialog_filename(task, TEST_MODE)
if not isfile(train_dialog_data_file):
raise ValueError('No train dialog data file.')
if not isfile(valid_dialog_data_file):
raise ValueError('No valid dialog data file.')
# Load extracted dialogs.
train_dialogs: List[TidyDialog] = load_pkl(train_dialog_data_file)
valid_dialogs: List[TidyDialog] = load_pkl(valid_dialog_data_file)
test_dialogs: List[TidyDialog] = load_pkl(test_dialog_data_file)
if task in {KNOWLEDGE_TASK}:
knowledge_data = KnowledgeData()
# Dataset wrap.
train_dataset = Dataset(
task,
common_data.dialog_vocab,
None, #common_data.obj_id,
train_dialogs,
knowledge_data if task == KNOWLEDGE_TASK else None)
valid_dataset = Dataset(
task,
common_data.dialog_vocab,
None, #common_data.obj_id,
valid_dialogs,
knowledge_data if task == KNOWLEDGE_TASK else None)
test_dataset = Dataset(
task,
common_data.dialog_vocab,
None, #common_data.obj_id,
test_dialogs,
knowledge_data if task == KNOWLEDGE_TASK else None)
print('Train dataset size:', len(train_dataset))
print('Valid dataset size:', len(valid_dataset))
print('Test dataset size:', len(test_dataset))
# Get initial embedding.
vocab_size = len(common_data.dialog_vocab)
embed_init = get_embed_init(common_data.glove,
vocab_size).to(GlobalConfig.device)
# Context model configurations.
context_text_encoder_config = ContextTextEncoderConfig(
vocab_size, embed_init)
context_image_encoder_config = ContextImageEncoderConfig()
context_encoder_config = ContextEncoderConfig()
# Context models.
context_text_encoder = TextEncoder(context_text_encoder_config)
context_text_encoder = context_text_encoder.to(GlobalConfig.device)
context_image_encoder = ImageEncoder(context_image_encoder_config)
context_image_encoder = context_image_encoder.to(GlobalConfig.device)
context_encoder = ContextEncoder(context_encoder_config)
context_encoder = context_encoder.to(GlobalConfig.device)
# Load model file.
model_file = join(DatasetConfig.dump_dir, model_file_name)
if isfile(model_file):
state = torch.load(model_file)
# if task != state['task']:
# raise ValueError("Task doesn't match.")
context_text_encoder.load_state_dict(state['context_text_encoder'])
context_image_encoder.load_state_dict(state['context_image_encoder'])
context_encoder.load_state_dict(state['context_encoder'])
# Task-specific parts.
if task == INTENTION_TASK:
intention_train(context_text_encoder, context_image_encoder,
context_encoder, train_dataset, valid_dataset,
test_dataset, model_file)
elif task == TEXT_TASK:
text_train(context_text_encoder, context_image_encoder,
context_encoder, train_dataset, valid_dataset, test_dataset,
model_file, common_data.dialog_vocab, embed_init)
elif task == RECOMMEND_TASK:
recommend_train(context_text_encoder, context_image_encoder,
context_encoder, train_dataset, valid_dataset,
test_dataset, model_file, vocab_size, embed_init)
elif task == KNOWLEDGE_TASK:
knowledge_attribute_train(context_text_encoder, context_image_encoder,
context_encoder, train_dataset,
valid_dataset, test_dataset, model_file,
knowledge_data.attribute_data,
common_data.dialog_vocab, embed_init)
image_encoder = ImageEncoder(input_dim=2048, output_dim=1024, nhead=nhead)
image_encoder.load_pretrained_weights(
path='../user_data/image_encoder_large.pth')
image_encoder = image_encoder.cuda()
# text_generator = TextGenerator(score_model.embed.num_embeddings).cuda()
# score_model = ScoreModel(30522, 256, num_heads=1).cuda()
# category_embedding = CategoryEmbedding(256).cuda()
optimizer = Adam(score_model.get_params() + image_encoder.get_params())
if start_epoch > 0 and local_rank == 0:
checkpoints = torch.load(
os.path.join(checkpoints_dir,
'model-epoch{}.pth'.format(start_epoch)))
score_model.load_state_dict(checkpoints['score'])
image_encoder.load_state_dict(checkpoints['item'])
# text_generator.load_state_dict(checkpoints['generator'])
optimizer.load_state_dict(checkpoints['optimizer'])
print("load checkpoints")
# generator = iterate_minibatches(iters=(30 - start_epoch) * len(loader), batch_size=256, num_workers=8, root_dir='/home/dingyuhui/dataset/kdd-data', use_bert=use_bert)
scheduler = ExponentialLR(optimizer, 0.95, last_epoch=start_epoch - 1)
score_model = nn.parallel.DistributedDataParallel(
score_model,
find_unused_parameters=True,
device_ids=[local_rank],
output_device=local_rank)
# image_encoder = nn.parallel.DistributedDataParallel(image_encoder, find_unused_parameters=True, device_ids=[local_rank], output_device=local_rank)
# contrastive_loss = ContrastiveLoss(0.9, max_violation=True, reduction='mean')
# contrastive_loss = ExponentialLoss()
for epoch in range(start_epoch, 30):
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
def load_network(self):
image_generator = ImageGenerator()
image_generator.apply(weights_init)
disc_image = DiscriminatorImage()
disc_image.apply(weights_init)
emb_dim = 300
text_encoder = TextEncoder(emb_dim, self.txt_emb,
1, dropout=0.0)
attn_model = 'general'
text_generator = TextGenerator(attn_model, emb_dim, len(self.txt_dico.id2word),
self.txt_emb,
n_layers=1, dropout=0.0)
image_encoder = ImageEncoder()
image_encoder.apply(weights_init)
disc_latent = DiscriminatorLatent(emb_dim)
if cfg.NET_G != '':
state_dict = \
torch.load(cfg.NET_G,
map_location=lambda storage, loc: storage)
netG.load_state_dict(state_dict)
print('Load from: ', cfg.NET_G)
if cfg.NET_D != '':
state_dict = \
torch.load(cfg.NET_D,
map_location=lambda storage, loc: storage)
netD.load_state_dict(state_dict)
print('Load from: ', cfg.NET_D)
if cfg.ENCODER != '':
state_dict = \
torch.load(cfg.ENCODER,
map_location=lambda storage, loc: storage)
encoder.load_state_dict(state_dict)
print('Load from: ', cfg.ENCODER)
if cfg.DECODER != '':
state_dict = \
torch.load(cfg.DECODER,
map_location=lambda storage, loc: storage)
decoder.load_state_dict(state_dict)
print('Load from: ', cfg.DECODER)
if cfg.IMAGE_ENCODER != '':
state_dict = \
torch.load(cfg.IMAGE_ENCODER,
map_location=lambda storage, loc: storage)
image_encoder.load_state_dict(state_dict)
print('Load from: ', cfg.IMAGE_ENCODER)
if cfg.CUDA:
image_encoder.cuda()
image_generator.cuda()
text_encoder.cuda()
text_generator.cuda()
disc_image.cuda()
disc_latent.cuda()
return image_encoder, image_generator, text_encoder, text_generator, disc_image, disc_latent
image_decoder = CaptionRNN(num_classes=VOCAB_SIZE,
word_emb_dim=WORD_EMB_DIM,
img_emb_dim=IMAGE_EMB_DIM,
hidden_dim=HIDDEN_DIM)
word_embedding.eval()
image_encoder.eval()
image_decoder.eval()
""" 9) Load Weights """
LOAD_WEIGHTS = True
EMBEDDING_WEIGHT_FILE = 'checkpoints/BIGDATASET-weights-embedding-epoch-3.pt'
ENCODER_WEIGHT_FILE = 'checkpoints/BIGDATASET-weights-encoder-epoch-3.pt'
DECODER_WEIGHT_FILE = 'checkpoints/BIGDATASET-weights-decoder-epoch-3.pt'
if LOAD_WEIGHTS:
print("Loading pretrained weights...")
word_embedding.load_state_dict(torch.load(EMBEDDING_WEIGHT_FILE))
image_encoder.load_state_dict(torch.load(ENCODER_WEIGHT_FILE))
image_decoder.load_state_dict(torch.load(DECODER_WEIGHT_FILE))
""" 10) Device Setup"""
device = 'cuda:1'
device = torch.device(device)
word_embedding = word_embedding.to(device)
image_encoder = image_encoder.to(device)
image_decoder = image_decoder.to(device)
print(vocab.word_to_index('yooooo'))
for i, batch in enumerate(val_loader):
image_batch, word_ids_batch = batch[0].to(device), batch[1].to(device)
for image in image_batch:
def valid(epoch=1,
checkpoints_dir='./checkpoints',
use_bert=False,
data_path=None,
out_path='../prediction_result/valid_pred.json',
output_ndcg=True):
print("valid epoch{}".format(epoch))
if data_path is not None:
kdd_dataset = ValidDataset(data_path, use_bert=use_bert)
else:
kdd_dataset = ValidDataset(data_path, use_bert=use_bert)
loader = DataLoader(kdd_dataset,
collate_fn=collate_fn_valid,
batch_size=128,
shuffle=False,
num_workers=8)
tbar = tqdm(loader)
text_encoder = TextEncoder(kdd_dataset.unknown_token + 1,
1024,
256,
use_bert=use_bert).cuda()
image_encoder = ImageEncoder(input_dim=2048, output_dim=1024,
nhead=4).cuda()
score_model = ScoreModel(1024, 256).cuda()
# category_embedding = model.CategoryEmbedding(768).cuda()
checkpoints = torch.load(
os.path.join(checkpoints_dir, 'model-epoch{}.pth'.format(epoch)))
text_encoder.load_state_dict(checkpoints['query'])
image_encoder.load_state_dict(checkpoints['item'])
score_model.load_state_dict(checkpoints['score'])
# score_model.load_state_dict(checkpoints['score'])
outputs = {}
image_encoder.eval()
text_encoder.eval()
score_model.eval()
for query_id, product_id, query, query_len, features, boxes, category, obj_len in tbar:
query, query_len = query.cuda(), query_len.cuda()
query, hidden = text_encoder(query, query_len)
features, boxes, obj_len = features.cuda(), boxes.cuda(), obj_len.cuda(
)
features = image_encoder(features, boxes, obj_len)
score = score_model(query, hidden, query_len, features)
score = score.data.cpu().numpy()
# print(score2)
for q_id, p_id, s in zip(query_id.data.numpy(),
product_id.data.numpy(), score):
outputs.setdefault(str(q_id), [])
outputs[str(q_id)].append((p_id, s))
for k, v in outputs.items():
v = sorted(v, key=lambda x: x[1], reverse=True)
v = [(str(x[0]), float(x[1])) for x in v]
outputs[k] = v
with open(out_path, 'w') as f:
json.dump(outputs, f)
if output_ndcg:
pred = read_json(out_path)
gt = read_json('../data/valid/valid_answer.json')
score = 0
k = 5
for key, val in gt.items():
ground_truth_ids = [str(x) for x in val]
predictions = [x[0] for x in pred[key][:k]]
ref_vec = [1.0] * len(ground_truth_ids)
pred_vec = [
1.0 if pid in ground_truth_ids else 0.0 for pid in predictions
]
score += get_ndcg(pred_vec, ref_vec, k)
# print(key)
# print([pid for pid in predictions if pid not in ground_truth_ids])
# print('========')
# score += len(set(predictions).intersection(ground_truth_ids)) / len(ground_truth_ids)
score = score / len(gt)
print('ndcg@%d: %.4f' % (k, score))
return score
else:
return None
def main():
args = parse_args()
transform = transforms.Compose([
transforms.Resize((args.imsize, args.imsize)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
if args.dataset == "coco":
val_dset = CocoDataset(
root=args.root_path,
split="val",
transform=transform,
)
val_loader = DataLoader(
val_dset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.n_cpu,
collate_fn=collater,
)
vocab = Vocabulary(max_len=args.max_len)
vocab.load_vocab(args.vocab_path)
imenc = ImageEncoder(args.out_size, args.cnn_type)
capenc = CaptionEncoder(len(vocab), args.emb_size, args.out_size,
args.rnn_type)
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
imenc = imenc.to(device)
capenc = capenc.to(device)
assert args.checkpoint is not None
print("loading model and optimizer checkpoint from {} ...".format(
args.checkpoint),
flush=True)
ckpt = torch.load(args.checkpoint, map_location=device)
imenc.load_state_dict(ckpt["encoder_state"])
capenc.load_state_dict(ckpt["decoder_state"])
begin = time.time()
dset = EmbedDataset(val_loader, imenc, capenc, vocab, args)
print("database created | {} ".format(sec2str(time.time() - begin)),
flush=True)
savedir = os.path.join("out", args.config_name)
if not os.path.exists(savedir):
os.makedirs(savedir, 0o777)
image = dset.embedded["image"]
caption = dset.embedded["caption"]
n_i = image.shape[0]
n_c = caption.shape[0]
all = np.concatenate([image, caption], axis=0)
emb_file = os.path.join(savedir, "embedding_{}.npy".format(n_i))
save_file = os.path.join(savedir, "{}.npy".format(args.method))
vis_file = os.path.join(savedir, "{}.png".format(args.method))
np.save(emb_file, all)
print("saved embeddings to {}".format(emb_file), flush=True)
dimension_reduction(emb_file, save_file, method=args.method)
plot_embeddings(save_file, n_i, vis_file, method=args.method)
def main():
args = parse_args()
transform = transforms.Compose([
transforms.Resize((args.imsize, args.imsize)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
if args.dataset == 'coco':
train_dset = CocoDataset(root=args.root_path,
transform=transform,
mode='one')
val_dset = CocoDataset(root=args.root_path,
imgdir='val2017',
jsonfile='annotations/captions_val2017.json',
transform=transform,
mode='all')
train_loader = DataLoader(train_dset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.n_cpu,
collate_fn=collater_train)
val_loader = DataLoader(val_dset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.n_cpu,
collate_fn=collater_eval)
vocab = Vocabulary(max_len=args.max_len)
vocab.load_vocab(args.vocab_path)
imenc = ImageEncoder(args.out_size, args.cnn_type)
capenc = CaptionEncoder(len(vocab), args.emb_size, args.out_size,
args.rnn_type)
device = torch.device(
"cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
imenc = imenc.to(device)
capenc = capenc.to(device)
optimizer = optim.SGD([{
'params': imenc.parameters(),
'lr': args.lr_cnn,
'momentum': args.mom_cnn
}, {
'params': capenc.parameters(),
'lr': args.lr_rnn,
'momentum': args.mom_rnn
}])
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='max',
factor=0.1,
patience=args.patience,
verbose=True)
lossfunc = PairwiseRankingLoss(margin=args.margin,
method=args.method,
improved=args.improved,
intra=args.intra)
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"])
scheduler.load_state_dict(ckpt["scheduler_state"])
offset = ckpt["epoch"]
else:
offset = 0
imenc = nn.DataParallel(imenc)
capenc = nn.DataParallel(capenc)
metrics = {}
assert offset < args.max_epochs
for ep in range(offset, args.max_epochs):
imenc, capenc, optimizer = train(ep + 1, train_loader, imenc, capenc,
optimizer, lossfunc, vocab, args)
data = validate(ep + 1, val_loader, imenc, capenc, vocab, args)
totalscore = 0
for rank in [1, 5, 10, 20]:
totalscore += data["i2c_recall@{}".format(rank)] + data[
"c2i_recall@{}".format(rank)]
scheduler.step(totalscore)
# save checkpoint
ckpt = {
"stats": data,
"epoch": ep + 1,
"encoder_state": imenc.module.state_dict(),
"decoder_state": capenc.module.state_dict(),
"optimizer_state": optimizer.state_dict(),
"scheduler_state": scheduler.state_dict()
}
if not os.path.exists(args.model_save_path):
os.makedirs(args.model_save_path)
savepath = os.path.join(
args.model_save_path,
"epoch_{:04d}_score_{:05d}.ckpt".format(ep + 1,
int(100 * totalscore)))
print(
"saving model and optimizer checkpoint to {} ...".format(savepath),
flush=True)
torch.save(ckpt, savepath)
print("done for epoch {}".format(ep + 1), flush=True)
for k, v in data.items():
if k not in metrics.keys():
metrics[k] = [v]
else:
metrics[k].append(v)
visualize(metrics, args)
