def train():
# Prepare gym
env = create_env()
h, w, c = env.observation_space.shape
# Prepare models
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_dir, fn = "./policy_grad", '{}.pth'
model = Baseline(h, w).to(device)
model.train()
optimizer = optim.RMSprop(model.parameters(),
lr=LEARN_RATE,
weight_decay=WEIGHT_DECAY)
# Train
steps_done = 0
num_episodes = 2000
episode_rewards = []
for i_episode in tqdm(range(num_episodes)):
# Complete 1 episode
print("Episode {}".format(i_episode + 1))
i_rewards, i_states, i_actions, steps_done = generate_episode(
env, model, device, steps_done, episode_rewards)
# Update model
optimize_model(device, model, optimizer, i_rewards, i_actions,
i_states)
# Save model every couple episodes
if (i_episode + 1) % SAVE_EPI == 0:
path = os.path.join(model_dir, fn.format(episode_rewards[-1]))
torch.save(model.state_dict(), path)
print('Complete')
np.save('./rewards_policy_grad.npy', episode_rewards)
env.close()
plt.ioff()
plt.show()
print(model_name)
model = EfficientNet.from_name(model_name)
#model = EfficientNet.from_pretrained(model_name, num_classes=350)
#summary(model,input_size=(3,224,224))
else:
model = Baseline(args.hidden_size, args.output_size)
optimizer = optim.Adam(model.parameters(), args.learning_rate)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=1,
verbose=True)
criterion = nn.CrossEntropyLoss() #multi-class classification task
model = model.to(device)
model.train()
# DONOTCHANGE: They are reserved for nsml
bind_model(model)
# below the nsml load
nsml.load(checkpoint='15', session='team_62/airush1/40')
nsml.save('stillgoing')
if args.pause:
nsml.paused(scope=locals())
if args.mode == "train":
# Warning: Do not load data before this line
dataloader = train_dataloader(args.input_size, args.batch_size,
args.num_workers)
for epoch_idx in range(1, args.epochs + 1):
total_loss = 0
def train(args):
start_time = time.time()
device = torch.device('cuda' if args.cuda else 'cpu')
pprint(args.__dict__)
interface = FileInterface(**args.__dict__)
piqa_model = Baseline(**args.__dict__).to(device)
loss_model = Loss().to(device)
optimizer = torch.optim.Adam(p for p in piqa_model.parameters()
if p.requires_grad)
batch_size = args.batch_size
char_vocab_size = args.char_vocab_size
glove_vocab_size = args.glove_vocab_size
word_vocab_size = args.word_vocab_size
glove_size = args.glove_size
elmo = args.elmo
draft = args.draft
def preprocess(interface_):
# get data
print('Loading train and dev data')
train_examples = load_squad(interface_.train_path, draft=draft)
dev_examples = load_squad(interface_.test_path, draft=draft)
# iff creating processor
print('Loading GloVe')
glove_words, glove_emb_mat = load_glove(
glove_size,
vocab_size=args.glove_vocab_size - 2,
glove_dir=interface_.glove_dir,
draft=draft)
print('Constructing processor')
processor = SquadProcessor(char_vocab_size,
glove_vocab_size,
word_vocab_size,
elmo=elmo)
processor.construct(train_examples, glove_words)
# data loader
print('Preprocessing datasets')
train_dataset = tuple(
processor.preprocess(example) for example in train_examples)
dev_dataset = tuple(
processor.preprocess(example) for example in dev_examples)
print('Creating data loaders')
train_sampler = SquadSampler(train_dataset,
max_context_size=256,
max_question_size=32,
bucket=True,
shuffle=True)
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
collate_fn=processor.collate,
sampler=train_sampler)
dev_sampler = SquadSampler(dev_dataset, bucket=True)
dev_loader = DataLoader(dev_dataset,
batch_size=batch_size,
collate_fn=processor.collate,
sampler=dev_sampler)
if args.preload:
train_loader = tuple(train_loader)
dev_loader = tuple(dev_loader)
out = {
'glove_emb_mat': glove_emb_mat,
'processor': processor,
'train_dataset': train_dataset,
'dev_dataset': dev_dataset,
'train_loader': train_loader,
'dev_loader': dev_loader
}
return out
out = interface.cache(
preprocess,
interface_=interface) if args.cache else preprocess(interface)
glove_emb_mat = out['glove_emb_mat']
processor = out['processor']
train_dataset = out['train_dataset']
dev_dataset = out['dev_dataset']
train_loader = out['train_loader']
dev_loader = out['dev_loader']
print("Initializing model weights")
piqa_model.load_glove(torch.tensor(glove_emb_mat))
bind_model(interface, processor, piqa_model, optimizer=optimizer)
step = 0
best_report = None
print('Training')
piqa_model.train()
for epoch_idx in range(args.epochs):
for i, train_batch in enumerate(train_loader):
train_batch = {
key: val.to(device)
for key, val in train_batch.items()
}
model_output = piqa_model(step=step, **train_batch)
train_results = processor.postprocess_batch(
train_dataset, train_batch, model_output)
train_loss = loss_model(step=step, **model_output, **train_batch)
train_f1 = float(
np.mean([result['f1'] for result in train_results]))
train_em = float(
np.mean([result['em'] for result in train_results]))
# optimize
optimizer.zero_grad()
train_loss.backward()
optimizer.step()
step += 1
# report & eval & save
if step % args.report_period == 1:
report = OrderedDict(step=step,
train_loss=train_loss.item(),
train_f1=train_f1,
train_em=train_em,
time=time.time() - start_time)
interface.report(**report)
print(', '.join('%s=%.5r' % (s, r) for s, r in report.items()))
if step % args.eval_save_period == 1:
with torch.no_grad():
piqa_model.eval()
loss_model.eval()
pred = {}
dev_losses, dev_results = [], []
for dev_batch, _ in zip(dev_loader,
range(args.eval_steps)):
dev_batch = {
key: val.to(device)
for key, val in dev_batch.items()
}
model_output = piqa_model(**dev_batch)
results = processor.postprocess_batch(
dev_dataset, dev_batch, model_output)
dev_loss = loss_model(step=step,
**dev_batch,
**model_output)
for result in results:
pred[result['id']] = result['pred']
dev_results.extend(results)
dev_losses.append(dev_loss.item())
dev_loss = float(np.mean(dev_losses))
dev_f1 = float(
np.mean([result['f1'] for result in dev_results]))
dev_em = float(
np.mean([result['em'] for result in dev_results]))
report = OrderedDict(step=step,
dev_loss=dev_loss,
dev_f1=dev_f1,
dev_em=dev_em,
time=time.time() - start_time)
summary = False
if best_report is None or report['dev_f1'] > best_report[
'dev_f1']:
best_report = report
summary = True
interface.save(iteration=step)
interface.pred(pred)
interface.report(summary=summary, **report)
print(
', '.join('%s=%.5r' % (s, r)
for s, r in report.items()),
'(dev_f1_best=%.5r @%d)' %
(best_report['dev_f1'], best_report['step']))
piqa_model.train()
loss_model.train()
if step == args.train_steps:
break
if step == args.train_steps:
break
