def main(exp_const,data_const,model_const):
io.mkdir_if_not_exists(exp_const.exp_dir,recursive=True)
io.mkdir_if_not_exists(exp_const.log_dir)
io.mkdir_if_not_exists(exp_const.model_dir)
configure(exp_const.log_dir)
save_constants({
'exp': exp_const,
'data': data_const,
'model': model_const},
exp_const.exp_dir)
print('Creating network ...')
model = Model()
model.const = model_const
model.encoder = Encoder(model.const.encoder).cuda()
model.decoder = Decoder(model.const.decoder).cuda()
encoder_path = os.path.join(
exp_const.model_dir,
f'encoder_{-1}')
torch.save(model.encoder.state_dict(),encoder_path)
decoder_path = os.path.join(
exp_const.model_dir,
f'decoder_{-1}')
torch.save(model.decoder.state_dict(),decoder_path)
print('Creating dataloader ...')
dataset = VisualFeaturesDataset(data_const)
dataloader = DataLoader(
dataset,
batch_size=exp_const.batch_size,
shuffle=True)
train_model(model,dataloader,exp_const)
def main(exp_const, data_const, model_const):
print('Creating network ...')
model = Model()
model.const = model_const
model.encoder = Encoder(model.const.encoder).cuda()
encoder_path = os.path.join(exp_const.model_dir,
'encoder_' + str(model.const.model_num))
model.encoder.load_state_dict(torch.load(encoder_path))
print('Creating dataloader ...')
dataset = VisualFeaturesDataset(data_const)
dataloader = DataLoader(dataset,
batch_size=exp_const.batch_size,
shuffle=True)
print('Get features ...')
features = get_visual_features(model, dataloader, exp_const)
print('Save features h5py ...')
word_features_h5py = h5py.File(
os.path.join(exp_const.exp_dir, 'word_features.h5py'), 'w')
word_features_h5py.create_dataset('features',
data=features,
chunks=(1, features.shape[1]))
word_features_h5py.create_dataset('mean', data=np.mean(features, axis=0))
word_features_h5py.close()
print('Save features word idx json ...')
word_to_idx_json = os.path.join(exp_const.exp_dir, 'word_to_idx.json')
io.dump_json_object(dataloader.dataset.word_to_idx, word_to_idx_json)