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Remove if True: in train.py #28

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Remove if True: in train.py #28

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61 changes: 30 additions & 31 deletions train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -161,40 +161,39 @@ def train(opt):
current_score = - val_loss

best_flag = False
if True: # if true
if best_val_score is None or current_score > best_val_score:
best_val_score = current_score
best_flag = True
checkpoint_path = os.path.join(opt.checkpoint_path, 'model.pth')
if best_val_score is None or current_score > best_val_score:
best_val_score = current_score
best_flag = True
checkpoint_path = os.path.join(opt.checkpoint_path, 'model.pth')
torch.save(model.state_dict(), checkpoint_path)
print("model saved to {}".format(checkpoint_path))
optimizer_path = os.path.join(opt.checkpoint_path, 'optimizer.pth')
torch.save(optimizer.state_dict(), optimizer_path)

# Dump miscalleous informations
infos['iter'] = iteration
infos['epoch'] = epoch
infos['iterators'] = loader.iterators
infos['split_ix'] = loader.split_ix
infos['best_val_score'] = best_val_score
infos['opt'] = opt
infos['vocab'] = loader.get_vocab()

histories['val_result_history'] = val_result_history
histories['loss_history'] = loss_history
histories['lr_history'] = lr_history
histories['ss_prob_history'] = ss_prob_history
with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'.pkl'), 'wb') as f:
cPickle.dump(infos, f)
with open(os.path.join(opt.checkpoint_path, 'histories_'+opt.id+'.pkl'), 'wb') as f:
cPickle.dump(histories, f)

if best_flag:
checkpoint_path = os.path.join(opt.checkpoint_path, 'model-best.pth')
torch.save(model.state_dict(), checkpoint_path)
print("model saved to {}".format(checkpoint_path))
optimizer_path = os.path.join(opt.checkpoint_path, 'optimizer.pth')
torch.save(optimizer.state_dict(), optimizer_path)

# Dump miscalleous informations
infos['iter'] = iteration
infos['epoch'] = epoch
infos['iterators'] = loader.iterators
infos['split_ix'] = loader.split_ix
infos['best_val_score'] = best_val_score
infos['opt'] = opt
infos['vocab'] = loader.get_vocab()

histories['val_result_history'] = val_result_history
histories['loss_history'] = loss_history
histories['lr_history'] = lr_history
histories['ss_prob_history'] = ss_prob_history
with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'.pkl'), 'wb') as f:
with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'-best.pkl'), 'wb') as f:
cPickle.dump(infos, f)
with open(os.path.join(opt.checkpoint_path, 'histories_'+opt.id+'.pkl'), 'wb') as f:
cPickle.dump(histories, f)

if best_flag:
checkpoint_path = os.path.join(opt.checkpoint_path, 'model-best.pth')
torch.save(model.state_dict(), checkpoint_path)
print("model saved to {}".format(checkpoint_path))
with open(os.path.join(opt.checkpoint_path, 'infos_'+opt.id+'-best.pkl'), 'wb') as f:
cPickle.dump(infos, f)

# Stop if reaching max epochs
if epoch >= opt.max_epochs and opt.max_epochs != -1:
Expand Down