def evaluate_df():
ap_tst_1006, _, _ = evaluate(iterator_tst,
[img_ids_tst, features_tst, labels_1006_tst],
model.features, model.gzs_logits, sess, model)
ap_tst_81, _, _ = evaluate(iterator_tst,
[img_ids_tst, features_tst, labels_81_tst],
model.features, model.zs_logits, sess, model)
print('mAP 1006', np.mean(ap_tst_1006))
print('mAP 81', np.mean(ap_tst_81))
g_F1_3_tst, g_P_3_tst, g_R_3_tst = evaluate_k(
3, iterator_tst, [img_ids_tst, features_tst, labels_1006_tst],
model.features, model.gzs_logits, sess, model)
g_F1_5_tst, g_P_5_tst, g_R_5_tst = evaluate_k(
5, iterator_tst, [img_ids_tst, features_tst, labels_1006_tst],
model.features, model.gzs_logits, sess, model)
F1_3_tst, P_3_tst, R_3_tst = evaluate_k(
3, iterator_tst, [img_ids_tst, features_tst, labels_81_tst],
model.features, model.zs_logits, sess, model)
F1_5_tst, P_5_tst, R_5_tst = evaluate_k(
5, iterator_tst, [img_ids_tst, features_tst, labels_81_tst],
model.features, model.zs_logits, sess, model)
print('k=3', np.mean(F1_3_tst), np.mean(P_3_tst), np.mean(R_3_tst))
print('k=5', np.mean(F1_5_tst), np.mean(P_5_tst), np.mean(R_5_tst))
print('g_k=10', np.mean(g_F1_3_tst), np.mean(g_P_3_tst),
np.mean(g_R_3_tst))
print('g_k=20', np.mean(g_F1_5_tst), np.mean(g_P_5_tst),
np.mean(g_R_5_tst))
df_81 = pd.DataFrame()
df_81['classes'] = tag81
df_81['F1_3'] = F1_3_tst
df_81['P_3'] = P_3_tst
df_81['R_3'] = R_3_tst
df_81['F1_5'] = F1_5_tst
df_81['P_5'] = P_5_tst
df_81['R_5'] = R_5_tst
df_81['ap'] = ap_tst_81
df_1k = pd.DataFrame()
df_1k['g_F1_3'] = g_F1_3_tst
df_1k['g_P_3'] = g_P_3_tst
df_1k['g_R_3'] = g_R_3_tst
df_1k['g_F1_5'] = g_F1_5_tst
df_1k['g_P_5'] = g_P_5_tst
df_1k['g_R_5'] = g_R_5_tst
df_1k['aP'] = ap_tst_1006
return df_81, df_1k
def evaluate_df():
ap_tst, predictions_tst_v, labels_tst_v = evaluate(
iterator_test, [img_ids_test, features_test, seen_labels_test],
model.features, model.logits, sess, model)
print('mAP', np.mean(ap_tst))
norm_b = np.linalg.norm(predictions_tst_v)
F1_3_tst, P_3_tst, R_3_tst = evaluate_k(
3, iterator_test, [img_ids_test, features_test, seen_labels_test],
model.features, model.logits, sess, model, predictions_tst_v,
labels_tst_v)
F1_5_tst, P_5_tst, R_5_tst = evaluate_k(
5, iterator_test, [img_ids_test, features_test, seen_labels_test],
model.features, model.logits, sess, model, predictions_tst_v,
labels_tst_v)
print('sanity check {}'.format(np.linalg.norm(predictions_tst_v) - norm_b))
## reload best model
print(np.mean(F1_3_tst), np.mean(P_3_tst), np.mean(R_3_tst))
print(np.mean(F1_5_tst), np.mean(P_5_tst), np.mean(R_5_tst))
df = pd.DataFrame()
df['classes'] = seen_classes
df['F1_10'] = F1_3_tst
df['P_10'] = P_3_tst
df['R_10'] = R_3_tst
df['F1_20'] = F1_5_tst
df['P_20'] = P_5_tst
df['R_20'] = R_5_tst
df['ap'] = ap_tst
return df
def evaluate_df():
F1_3_tst, P_3_tst, R_3_tst = evaluate_k(
3, iterator_tst, [img_ids_tst, features_tst, labels_81_tst],
model.features, model.logits, sess, model)
F1_5_tst, P_5_tst, R_5_tst = evaluate_k(
5, iterator_tst, [img_ids_tst, features_tst, labels_81_tst],
model.features, model.logits, sess, model)
ap_tst, _, _ = evaluate(iterator_tst,
[img_ids_tst, features_tst, labels_81_tst],
model.features, model.logits, sess, model)
## reload best model
print('mAP', np.mean(ap_tst))
print('k=3', np.mean(F1_3_tst), np.mean(P_3_tst), np.mean(R_3_tst))
print('k=5', np.mean(F1_5_tst), np.mean(P_5_tst), np.mean(R_5_tst))
df = pd.DataFrame()
df['classes'] = tag81
df['F1_3'] = F1_3_tst
df['P_3'] = P_3_tst
df['R_3'] = R_3_tst
df['F1_5'] = F1_5_tst
df['P_5'] = P_5_tst
df['R_5'] = R_5_tst
df['ap'] = ap_tst
return df
def evaluate_df():
ap_tst, predictions_mll, labels_mll = evaluate(
iterator_test, [img_ids_test, features_test, seen_labels_test],
model.features, model.logits, sess, model)
F1_3_tst, P_3_tst, R_3_tst = evaluate_k(
3, iterator_test, [img_ids_test, features_test, seen_labels_test],
model.features, model.logits, sess, model, predictions_mll, labels_mll)
F1_5_tst, P_5_tst, R_5_tst = evaluate_k(
5, iterator_test, [img_ids_test, features_test, seen_labels_test],
model.features, model.logits, sess, model, predictions_mll, labels_mll)
## reload best model
print('mAP', np.mean(ap_tst))
print('k=3', np.mean(F1_3_tst), np.mean(P_3_tst), np.mean(R_3_tst))
print('k=5', np.mean(F1_5_tst), np.mean(P_5_tst), np.mean(R_5_tst))
df = pd.DataFrame()
df['classes'] = seen_classes
df['F1_3'] = F1_3_tst
df['P_3'] = P_3_tst
df['R_3'] = R_3_tst
df['F1_5'] = F1_5_tst
df['P_5'] = P_5_tst
df['R_5'] = R_5_tst
df['ap'] = ap_tst
return df
t1 = time() - t0
elbo = np.mean(elbo)
cat_mean = np.mean(cat_mean)
kl = np.mean(kls)
accuracy = np.mean(accuracy)
logger.add(epoch,
kl=kl,
tr_elbo=elbo,
tr_acc=accuracy,
tr_ll=cat_mean,
tr_time=t1)
model.eval()
t_dvi = time()
test_acc_dvi = evaluate(model, test_loader, mode='dvi', args=args)
t_dvi = time() - t_dvi
if not args.no_mc:
t_mc = time()
test_acc_mcvi = evaluate(model,
test_loader,
mode='mcvi',
args=args)
t_mc = time() - t_mc
logger.add(epoch, te_acc_mcvi=test_acc_mcvi, te_time_mcvi=t_mc)
test_acc_samples = evaluate(model,
test_loader,
mode='samples_dvi',
args=args)
inputs, labels = Variable(inputs.cuda(async=True)), Variable(labels.cuda(async=True))
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
accs.append(metrics.logit2acc(outputs.data, labels)) # probably a bad way to calculate accuracy
training_loss += loss.cpu().data.numpy()[0]
logger.add(epoch, tr_loss=training_loss/steps, tr_acc=np.mean(accs))
# Deterministic test
net.eval()
acc, nll = utils.evaluate(net, testloader, num_ens=1)
logger.add(epoch, te_nll_det=nll, te_acc_det=acc)
# Stochastic test
net.train()
acc, nll = utils.evaluate(net, testloader, num_ens=1)
logger.add(epoch, te_nll_stoch=nll, te_acc_stoch=acc)
# Test-time averaging
net.train()
acc, nll = utils.evaluate(net, testloader, num_ens=20)
logger.add(epoch, te_nll_ens=nll, te_acc_ens=acc)
logger.add(epoch, time=time()-t0)
logger.iter_info()
logger.save(silent=True)
def do_testing(env,
model,
target_model=None,
dpaths=None,
render=False,
num_episodes=100):
#print("Is on test mode ?", not env.is_training)
tf.reset_default_graph()
# Create placeholders
states_pl = tf.placeholder(tf.float32,
shape=(None, FRAME_WIDTH, FRAME_HEIGHT,
FRAME_BUFFER_SIZE),
name='states')
actions_pl = tf.placeholder(tf.int32, shape=(None), name='actions')
targets_pl = tf.placeholder(tf.float32, shape=(None), name='targets')
# Value function approximator network
q_output = model.graph(states_pl)
# Build target network
q_target_net = target_model.graph(states_pl)
# Compute Q from current q_output and one hot actions
Q = tf.reduce_sum(tf.multiply(
q_output, tf.one_hot(actions_pl, env.action_space.n,
dtype=tf.float32)),
axis=1)
# Loss operation
loss_op = tf.reduce_mean(tf.square(targets_pl - Q) / 2)
# Prediction Op
prediction = tf.argmax(q_output, 1)
#prediction = q_output
# Model Saver
saver = tf.train.Saver()
# init all variables
init_op = tf.global_variables_initializer()
# Limit memory usage for multiple training at same time
config = tf.ConfigProto(allow_soft_placement=True)
#config.gpu_options.per_process_gpu_memory_fraction = 0.33
# Start Session
with tf.Session(config=config) as sess:
if dpaths is not None:
new_saver = tf.train.import_meta_graph(dpaths[1])
new_saver.restore(sess, tf.train.latest_checkpoint(dpaths[0]))
data_stats = None
if env.usdqn_sim.data_testing:
for i in range(len(env.usdqn_sim.wheel_data)):
print('i:%s/%s' % (i, len(env.usdqn_sim.wheel_data)))
means, stds, vmin, vmax = evaluate(
env, sess, prediction, states_pl,
env.usdqn_sim.data_testing, GAMMA, False, render)
stats = np.concatenate(
[np.array([i]), means, stds, vmin, vmax],
axis=-1).reshape([1, -1])
if data_stats is None:
data_stats = stats
else:
data_stats = np.concatenate([data_stats, stats],
axis=0)
np.savetxt(os.path.join(dpaths[0], 'testing.csv'),
data_stats,
delimiter=',')
else:
means, stds = evaluate(env, sess, prediction, states_pl,
num_episodes, GAMMA, False, render)
#env.usdqn_sim.save_history()
# Save means
print(means)
def train(args):
# Verify algorithm and config
global env_options, trainer_options
algo = args.algo
if algo == "PPO":
config = ppo_config
else:
raise ValueError("args.algo must in [PPO]")
config.num_envs = args.num_envs
config.activation = nn.ReLU
if args.trainopt is not None:
f = open(args.trainopt)
trainer_options = json.load(f)
if args.opt is not None:
opt = json.load(open(args.opt))
env_options = opt['env']
trainer_options = opt['trainer']
# Seed the environments and setup torch
seed = args.seed
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.set_num_threads(1)
# Clean log directory
log_dir = verify_log_dir('work_dirs', args.log_dir)
# Create vectorized environments
num_envs = args.num_envs
env_id = args.env_id
main_envs = make_envs(
env_id='Humanoid-v3',
seed=seed,
log_dir=log_dir,
num_envs=num_envs,
asynchronous=True,
)
aux_envs = make_envs(
env_id='Walker2d-v3',
seed=seed,
log_dir=log_dir,
num_envs=num_envs,
asynchronous=True,
)
envs = [main_envs, aux_envs]
# eval_env is main_env
healthy_z_range = (1.0, 2.0)
eval_env = gym.make(env_id,
healthy_z_range=healthy_z_range,
healthy_reward=0)
main_obs_dim = 376
main_act_dim = 17
main_reduce_obs_dim = 46
main_reduce_act_dim = 11
aux_obs_dim = 17
aux_act_dim = 6
obs_dims = [main_reduce_obs_dim, aux_obs_dim]
act_dims = [main_act_dim, aux_act_dim]
dim_dict = dict(obs_a=main_reduce_obs_dim,
act_a=main_reduce_act_dim,
obs_b=aux_obs_dim,
act_b=aux_act_dim,
coeff_a=0.4,
coeff_b=1)
dim_dict['act_dim'] = 17
dim_dict['real_obs_dim'] = 46
# Setup trainer
if algo == "PPO":
trainer = PPOTrainerMTMT(config, dim_dict)
else:
raise NotImplementedError
frame_stack_tensors = [
FrameStackTensor(num_envs, main_envs.observation_space.shape,
config.device),
FrameStackTensor(num_envs, aux_envs.observation_space.shape,
config.device)
]
# Setup some stats helpers
episode_rewards = [
np.zeros([num_envs, 1], dtype=np.float),
np.zeros([num_envs, 1], dtype=np.float)
]
total_episodes = total_steps = iteration = 0
reward_recorders = [deque(maxlen=100), deque(maxlen=100)]
episode_length_recorders = [deque(maxlen=100), deque(maxlen=100)]
sample_timer = Timer()
process_timer = Timer()
update_timer = Timer()
total_timer = Timer()
progress = []
evaluate_stat = {}
# Start training
print("Start training!")
obs = [envs[i].reset() for i in range(2)]
_ = [frame_stack_tensors[i].update(obs[i]) for i in range(2)]
# first update
for i in range(2):
trainer.rollouts[i].observations[0].copy_(
reduce_shape(frame_stack_tensors[i].get(), obs_dims[i]))
branch_names = ['a', 'b']
while True: # Break when total_steps exceeds maximum value
with sample_timer:
# prepare rollout a
for ind in range(2):
for index in range(config.num_steps):
trainer.model.eval()
values, actions, action_log_prob = trainer.model.step(
reduce_shape(frame_stack_tensors[ind].get(),
obs_dims[ind]),
deterministic=False,
branch=branch_names[ind])
cpu_actions = actions.cpu().numpy()
cpu_actions = enlarge_shape(cpu_actions, act_dims[ind])
# obs, done, info not needed, we have masks & obs in frame_stack_tensors
_, reward, _, _, masks, new_total_episodes, new_total_steps, episode_rewards[ind] = \
step_envs(cpu_actions, envs[ind], episode_rewards[ind], frame_stack_tensors[ind],
reward_recorders[ind], episode_length_recorders[ind],
total_steps, total_episodes, config.device)
if ind == 0:
total_episodes = new_total_episodes
total_steps = new_total_steps
rewards = torch.from_numpy(reward.astype(np.float32)).view(
-1, 1).to(config.device)
trainer.rollouts[ind].insert(
reduce_shape(frame_stack_tensors[ind].get(),
obs_dims[ind]), actions, action_log_prob,
values, rewards, masks)
# ===== Process Samples =====
with process_timer:
with torch.no_grad():
for i in range(2):
next_value = trainer.compute_values(
trainer.rollouts[i].observations[-1], branch_names[i])
trainer.rollouts[i].compute_returns(
next_value, config.GAMMA)
trainer.model.train()
# ===== Update Policy =====
with update_timer:
losses = trainer.update(trainer.rollouts[0], trainer.rollouts[1])
policy_loss, value_loss, total_loss = list(zip(*losses))
trainer.rollouts[0].after_update()
trainer.rollouts[1].after_update()
# ===== Evaluate Current Policy =====
if iteration % config.eval_freq == 0:
eval_timer = Timer()
# seems ok, by default model is dealing with task1
rewards, eplens = evaluate(trainer, eval_env, 1, dim_dict=dim_dict)
evaluate_stat = summary(rewards, "episode_reward")
evaluate_stat.update(summary(eplens, "episode_length"))
evaluate_stat.update(
dict(evaluate_time=eval_timer.now,
evaluate_iteration=iteration))
# ===== Log information =====
if iteration % config.log_freq == 0:
stats = dict(
log_dir=log_dir,
frame_per_second=int(total_steps / total_timer.now),
training_episode_reward_a=summary(reward_recorders[0],
"episode_reward"),
training_episode_length_a=summary(episode_length_recorders[0],
"episode_length"),
training_episode_reward_b=summary(reward_recorders[1],
"episode_reward"),
training_episode_length_b=summary(episode_length_recorders[1],
"episode_length"),
evaluate_stats=evaluate_stat,
learning_stats_a=dict(policy_loss=policy_loss[0],
value_loss=value_loss[0],
total_loss=total_loss[0]),
learning_stats_b=dict(policy_loss=policy_loss[1],
value_loss=value_loss[1],
total_loss=total_loss[1]),
total_steps=total_steps,
total_episodes=total_episodes,
time_stats=dict(sample_time=sample_timer.avg,
process_time=process_timer.avg,
update_time=update_timer.avg,
total_time=total_timer.now,
episode_time=sample_timer.avg +
process_timer.avg + update_timer.avg),
iteration=iteration)
progress.append(stats)
pretty_print({
"===== {} Training Iteration {} =====".format(algo, iteration):
stats
})
if iteration % config.save_freq == 0:
trainer_path = trainer.save_w(log_dir, "iter{}".format(iteration))
progress_path = save_progress(log_dir, progress)
print(
"Saved trainer state at <{}>. Saved progress at <{}>.".format(
trainer_path, progress_path))
# [TODO] Stop training when total_steps is greater than args.max_steps
if total_steps > args.max_steps:
break
pass
iteration += 1
trainer.save_w(log_dir, "final")
envs.close()
dataset = dataset.map(utils.parser(anchors, num_classes).parser_example,
num_parallel_calls=10)
dataset = dataset.repeat().shuffle(SHUFFLE_SIZE).batch(BATCH_SIZE).prefetch(
BATCH_SIZE)
iterator = dataset.make_one_shot_iterator()
example = iterator.get_next()
images, *y_true = example
model = yolov3.yolov3(num_classes)
with tf.variable_scope('yolov3'):
y_pred = model.forward(images, is_training=False)
loss = model.compute_loss(y_pred, y_true)
y_pred = model.predict(y_pred)
load_ops = utils.load_weights(tf.global_variables(scope='yolov3'),
weights_path)
sess.run(load_ops)
for epoch in range(EPOCHS):
run_items = sess.run([y_pred, y_true] + loss)
rec, prec, mAP = utils.evaluate(run_items[0],
run_items[1],
num_classes,
score_thresh=0.3,
iou_thresh=0.5)
print(
"=> EPOCH: %2d\ttotal_loss:%7.4f\tloss_coord:%7.4f\tloss_sizes:%7.4f\tloss_confs:%7.4f\tloss_class:%7.4f"
"\trec:%7.4f\tprec:%7.4f\tmAP:%7.4f" %
(epoch, run_items[2], run_items[3], run_items[4], run_items[5],
run_items[6], rec, prec, mAP))
def train(args):
# Verify algorithm and config
global env_options, trainer_options
algo = args.algo
if algo == "PPO":
config = ppo_config
else:
raise ValueError("args.algo must in [PPO]")
config.num_envs = args.num_envs
if args.envopt is not None:
f = open(args.envopt)
env_options = json.load(f)
if args.trainopt is not None:
f = open(args.trainopt)
trainer_options = json.load(f)
if args.opt is not None:
opt = json.load(open(args.opt))
env_options = opt['env']
trainer_options = opt['trainer']
# Seed the environments and setup torch
seed = args.seed
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.set_num_threads(1)
# Clean log directory
log_dir = verify_log_dir('work_dirs', args.log_dir)
# Create vectorized environments
num_envs = args.num_envs
env_id = args.env_id
envs = make_envs(
env_id=env_id,
seed=seed,
log_dir=log_dir,
num_envs=num_envs,
asynchronous=True,
options=env_options,
)
if env_id == "Walker2d-v3":
healthy_z_range = (0.8, 2.0)
elif env_id == 'Humanoid-v3':
healthy_z_range = (1.0, 2.0)
if 'healthy_z_range' in env_options:
healthy_z_range = env_options['healthy_z_range']
eval_env = gym.make(env_id,
healthy_z_range=healthy_z_range,
healthy_reward=0)
if env_id == "Walker2d-v3":
eval_env = Walker2d_wrapper(eval_env, env_options)
obs_dim = envs.observation_space.shape[0]
act_dim = envs.action_space.shape[0]
real_obs_dim = obs_dim
real_act_dim = act_dim
if 'real_obs_dim' in trainer_options:
real_obs_dim = trainer_options['real_obs_dim']
if 'real_act_dim' in trainer_options:
real_act_dim = trainer_options['real_act_dim']
dim_dict = dict(obs_dim=obs_dim,
act_dim=act_dim,
real_obs_dim=real_obs_dim,
real_act_dim=real_act_dim)
# Setup trainer
if algo == "PPO":
trainer = PPOTrainer(envs, config, trainer_options)
else:
raise NotImplementedError
# Create a placeholder tensor to help stack frames in 2nd dimension
# That is turn the observation from shape [num_envs, 1, 84, 84] to
# [num_envs, 4, 84, 84].
frame_stack_tensor = FrameStackTensor(num_envs,
envs.observation_space.shape,
config.device)
# Setup some stats helpers
episode_rewards = np.zeros([num_envs, 1], dtype=np.float)
total_episodes = total_steps = iteration = 0
reward_recorder = deque(maxlen=100)
episode_length_recorder = deque(maxlen=100)
sample_timer = Timer()
process_timer = Timer()
update_timer = Timer()
total_timer = Timer()
progress = []
evaluate_stat = {}
# Start training
print("Start training!")
obs = envs.reset()
frame_stack_tensor.update(obs)
trainer.rollouts.observations[0].copy_(
reduce_shape(frame_stack_tensor.get(), real_obs_dim))
while True: # Break when total_steps exceeds maximum value
with sample_timer:
for index in range(config.num_steps):
trainer.model.eval()
values, actions, action_log_prob = trainer.model.step(
reduce_shape(frame_stack_tensor.get(), real_obs_dim))
cpu_actions = actions.cpu().numpy()
cpu_actions = enlarge_shape(cpu_actions, act_dim)
obs, reward, done, info, masks, total_episodes, \
total_steps, episode_rewards = step_envs(
cpu_actions, envs, episode_rewards, frame_stack_tensor,
reward_recorder, episode_length_recorder, total_steps,
total_episodes, config.device)
rewards = torch.from_numpy(reward.astype(np.float32)).view(
-1, 1).to(config.device)
# Store samples
trainer.rollouts.insert(
reduce_shape(frame_stack_tensor.get(), real_obs_dim),
actions, action_log_prob, values, rewards, masks)
# ===== Process Samples =====
with process_timer:
with torch.no_grad():
next_value = trainer.compute_values(
trainer.rollouts.observations[-1])
trainer.rollouts.compute_returns(next_value, config.GAMMA)
trainer.model.train()
# ===== Update Policy =====
with update_timer:
policy_loss, value_loss, total_loss = trainer.update(
trainer.rollouts)
trainer.rollouts.after_update()
# ===== Evaluate Current Policy =====
if iteration % config.eval_freq == 0:
eval_timer = Timer()
rewards, eplens = evaluate(trainer, eval_env, 1, dim_dict=dim_dict)
evaluate_stat = summary(rewards, "episode_reward")
evaluate_stat.update(summary(eplens, "episode_length"))
evaluate_stat.update(
dict(evaluate_time=eval_timer.now,
evaluate_iteration=iteration))
# ===== Log information =====
if iteration % config.log_freq == 0:
stats = dict(
log_dir=log_dir,
frame_per_second=int(total_steps / total_timer.now),
training_episode_reward=summary(reward_recorder,
"episode_reward"),
training_episode_length=summary(episode_length_recorder,
"episode_length"),
evaluate_stats=evaluate_stat,
learning_stats=dict(policy_loss=policy_loss,
value_loss=value_loss,
total_loss=total_loss),
total_steps=total_steps,
total_episodes=total_episodes,
time_stats=dict(sample_time=sample_timer.avg,
process_time=process_timer.avg,
update_time=update_timer.avg,
total_time=total_timer.now,
episode_time=sample_timer.avg +
process_timer.avg + update_timer.avg),
iteration=iteration)
progress.append(stats)
pretty_print({
"===== {} Training Iteration {} =====".format(algo, iteration):
stats
})
if iteration % config.save_freq == 0:
trainer_path = trainer.save_w(log_dir, "iter{}".format(iteration))
progress_path = save_progress(log_dir, progress)
print(
"Saved trainer state at <{}>. Saved progress at <{}>.".format(
trainer_path, progress_path))
# [TODO] Stop training when total_steps is greater than args.max_steps
if total_steps > args.max_steps:
break
pass
iteration += 1
trainer.save_w(log_dir, "final")
envs.close()
t1 = time() - t0
elbo = np.mean(elbo)
cat_mean = np.mean(cat_mean)
kl = np.mean(kls)
accuracy = np.mean(accuracy)
logger.add(epoch,
kl=kl,
tr_elbo=elbo,
tr_acc=accuracy,
tr_ll=cat_mean,
tr_time=t1)
model.eval()
t_dvi = time()
test_acc_dvi = evaluate(model, test_loader, mode='dvi', args=args)
t_dvi = time() - t_dvi
if not args.no_mc:
t_mc = time()
test_acc_mcvi = evaluate(model,
test_loader,
mode='mcvi',
args=args)
t_mc = time() - t_mc
logger.add(epoch, te_acc_mcvi=test_acc_mcvi, te_time_mcvi=t_mc)
logger.add(epoch, te_acc_dvi=test_acc_dvi, te_time_dvi=t_dvi)
logger.iter_info()
logger.save(silent=True)
], feed_dict)
accum_l = l * (1 - alpha) + alpha * accum_l
if np.isnan(l):
pdb.set_trace()
# if i%1000 == 0 and is_save:
# summary = sess.run(summary_op, feed_dict)
# summary_writer.add_summary(summary, i)
if i % eval_interval == 0 or i == NUS_WIDE_zs_n_iters - 1:
print('Time elapse: ', time.clock() - tic)
tic = time.clock()
F1_val, P_val, R_val = evaluate_k(
k, iterator_val, [img_ids_val, features_val, labels_925_val],
model.features, model.logits, sess, model)
ap_val, _, _ = evaluate(iterator_val,
[img_ids_val, features_val, labels_925_val],
model.features, model.logits, sess, model)
F1_u_val, P_u_val, R_u_val = evaluate_k(
k, iterator_val, [img_ids_val, features_val, labels_81_val],
model.features, model.zs_logits, sess, model)
mF1_val, mP_val, mR_val, mAP_val = [
np.mean(F1_val),
np.mean(P_val),
np.mean(R_val),
np.mean(ap_val)
]
mF1_u_val, mP_u_val, mR_u_val = [
np.mean(F1_u_val),
np.mean(P_u_val),
np.mean(R_u_val)
def train(args):
# Verify algorithm and config
algo = args.algo
if algo == "PPO":
config = ppo_config
elif algo == "A2C":
config = a2c_config
else:
raise ValueError("args.algo must in [PPO, A2C]")
config.num_envs = args.num_envs
assert args.env_id in ["cPong-v0", "CartPole-v0",
"cPongTournament-v0"]
# Seed the environments and setup torch
seed = args.seed
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.set_num_threads(1)
# Clean log directory
log_dir = verify_log_dir(args.log_dir, algo)
# Create vectorized environments
num_envs = args.num_envs
env_id = args.env_id
envs = make_envs(
env_id=env_id,
seed=seed,
log_dir=log_dir,
num_envs=num_envs,
asynchronous=True,
resized_dim=config.resized_dim
)
eval_envs = make_envs(
env_id=env_id,
seed=seed,
log_dir=log_dir,
num_envs=num_envs,
asynchronous=False,
resized_dim=config.resized_dim
)
test = env_id == "CartPole-v0"
tournament = env_id == "cPongTournament-v0"
frame_stack = 4 if not test else 1
if tournament:
assert algo == "PPO", "Using PPO in tournament is a good idea, " \
"because of its efficiency compared to A2C."
# Setup trainer
if algo == "PPO":
trainer = PPOTrainer(envs, config, frame_stack, _test=test)
else:
trainer = A2CTrainer(envs, config, frame_stack, _test=test)
# Create a placeholder tensor to help stack frames in 2nd dimension
# That is turn the observation from shape [num_envs, 1, 84, 84] to
# [num_envs, 4, 84, 84].
frame_stack_tensor = FrameStackTensor(
num_envs, envs.observation_space.shape, frame_stack, config.device)
# Setup some stats helpers
episode_rewards = np.zeros([num_envs, 1], dtype=np.float)
total_episodes = total_steps = iteration = 0
reward_recorder = deque(maxlen=100)
episode_length_recorder = deque(maxlen=100)
sample_timer = Timer()
process_timer = Timer()
update_timer = Timer()
total_timer = Timer()
progress = []
evaluate_stat = {}
# Start training
print("Start training!")
obs = envs.reset()
frame_stack_tensor.update(obs)
trainer.rollouts.observations[0].copy_(frame_stack_tensor.get())
while True: # Break when total_steps exceeds maximum value
# ===== Sample Data =====
with sample_timer:
for index in range(config.num_steps):
# Get action
# [TODO] Get the action
# Hint:
# 1. Remember to disable gradient computing
# 2. trainer.rollouts is a storage containing all data
# 3. What observation is needed for trainer.compute_action?
with torch.no_grad():
values, actions, action_log_prob = trainer.compute_action(trainer.rollouts.observations[index])
cpu_actions = actions.view(-1).cpu().numpy()
# Step the environment
# (Check step_envs function, you need to implement it)
obs, reward, done, info, masks, total_episodes, \
total_steps, episode_rewards = step_envs(
cpu_actions, envs, episode_rewards, frame_stack_tensor,
reward_recorder, episode_length_recorder, total_steps,
total_episodes, config.device, test)
rewards = torch.from_numpy(
reward.astype(np.float32)).view(-1, 1).to(config.device)
# Store samples
trainer.rollouts.insert(
frame_stack_tensor.get(), actions.view(-1, 1),
action_log_prob, values, rewards, masks)
# ===== Process Samples =====
with process_timer:
with torch.no_grad():
next_value = trainer.compute_values(
trainer.rollouts.observations[-1])
trainer.rollouts.compute_returns(next_value, config.GAMMA)
# ===== Update Policy =====
with update_timer:
policy_loss, value_loss, dist_entropy, total_loss = \
trainer.update(trainer.rollouts)
trainer.rollouts.after_update()
# ===== Reset opponent if in tournament mode =====
if tournament and iteration % config.num_steps == 0:
# Randomly choose one agent in each iteration
envs.reset_opponent()
# ===== Evaluate Current Policy =====
if iteration % config.eval_freq == 0:
eval_timer = Timer()
evaluate_rewards, evaluate_lengths = evaluate(
trainer, eval_envs, frame_stack, 20)
evaluate_stat = summary(evaluate_rewards, "episode_reward")
if evaluate_lengths:
evaluate_stat.update(
summary(evaluate_lengths, "episode_length"))
evaluate_stat.update(dict(
win_rate=float(
sum(np.array(evaluate_rewards) >= 0) / len(
evaluate_rewards)),
evaluate_time=eval_timer.now,
evaluate_iteration=iteration
))
# ===== Log information =====
if iteration % config.log_freq == 0:
stats = dict(
log_dir=log_dir,
frame_per_second=int(total_steps / total_timer.now),
training_episode_reward=summary(reward_recorder,
"episode_reward"),
training_episode_length=summary(episode_length_recorder,
"episode_length"),
evaluate_stats=evaluate_stat,
learning_stats=dict(
policy_loss=policy_loss,
entropy=dist_entropy,
value_loss=value_loss,
total_loss=total_loss
),
total_steps=total_steps,
total_episodes=total_episodes,
time_stats=dict(
sample_time=sample_timer.avg,
process_time=process_timer.avg,
update_time=update_timer.avg,
total_time=total_timer.now,
episode_time=sample_timer.avg + process_timer.avg +
update_timer.avg
),
iteration=iteration
)
if tournament:
stats["opponent"] = envs.current_agent_name
progress.append(stats)
pretty_print({
"===== {} Training Iteration {} =====".format(
algo, iteration): stats
})
if iteration % config.save_freq == 0:
trainer_path = trainer.save_w(log_dir, "iter{}".format(iteration))
progress_path = save_progress(log_dir, progress)
print("Saved trainer state at <{}>. Saved progress at <{}>.".format(
trainer_path, progress_path
))
# [TODO] Stop training when total_steps is greater than args.max_steps
if total_steps > args.max_steps:
break
iteration += 1
trainer.save_w(log_dir, "final")
envs.close()
def _train(trainer, envs, eval_envs, config, num_envs, algo, log_dir,
tournament, test):
# Setup some stats helpers
episode_rewards = np.zeros([num_envs, 1], dtype=np.float)
total_episodes = total_steps = iteration = 0
reward_recorder = deque(maxlen=100)
episode_length_recorder = deque(maxlen=100)
sample_timer = Timer()
process_timer = Timer()
update_timer = Timer()
total_timer = Timer()
progress = []
evaluate_stat = {}
while True: # Break when total_steps exceeds maximum value
# ===== Sample Data =====
with sample_timer:
for index in range(config.num_steps):
# Get action
if hasattr(trainer.model, 'reset_state'):
trainer.model.reset_state()
with torch.no_grad():
values, actions, action_log_prob = trainer.compute_action(
trainer.rollouts.processed_observations[index])
trainer.model.update_hidden(actions)
if trainer.discrete:
cpu_actions = actions.view(-1).cpu().numpy()
else:
cpu_actions = actions.cpu().numpy()
# Step the environment
# (Check step_envs function, you need to implement it)
obs, reward, done, info, masks, total_episodes, total_steps, episode_rewards = step_envs(
cpu_actions, envs, episode_rewards, reward_recorder,
episode_length_recorder, total_steps, total_episodes,
config.device)
rewards = torch.from_numpy(reward.astype(np.float32)).view(
-1, 1).to(config.device)
# Store samples
if trainer.discrete:
actions = actions.view(-1, 1)
with torch.no_grad():
raw_obs = trainer.process_obs(obs)
processed_obs = trainer.model.world_model(raw_obs).detach()
trainer.rollouts.insert(obs, actions, action_log_prob, values,
rewards, masks, processed_obs)
# trainer.rollouts.insert(obs, actions, action_log_prob, values, rewards, masks)
# ===== Process Samples =====
with process_timer:
with torch.no_grad():
next_value = trainer.compute_values(
trainer.rollouts.processed_observations[-1])
trainer.rollouts.compute_returns(next_value, config.gamma)
# ===== Update Policy =====
with update_timer:
policy_loss, value_loss, dist_entropy, total_loss = trainer.update(
trainer.rollouts)
# vae_loss, mdrnn_loss\
# = trainer.update(trainer.rollouts)
trainer.model.reset_state()
trainer.rollouts.after_update()
# ===== Reset opponent if in tournament mode =====
if tournament and iteration % config.num_steps == 0:
# Randomly choose one agent in each iteration
envs.reset_opponent()
# ===== Evaluate Current Policy =====
if eval_envs is not None and iteration % config.eval_freq == 0:
eval_timer = Timer()
evaluate_rewards, evaluate_lengths = evaluate(
trainer, eval_envs, 20)
evaluate_stat = summary(evaluate_rewards, "episode_reward")
if evaluate_lengths:
evaluate_stat.update(
summary(evaluate_lengths, "episode_length"))
evaluate_stat.update(
dict(win_rate=float(
sum(np.array(evaluate_rewards) >= 0) /
len(evaluate_rewards)),
evaluate_time=eval_timer.now,
evaluate_iteration=iteration))
# ===== Log information =====
if iteration % config.log_freq == 0:
stats = dict(
log_dir=log_dir,
frame_per_second=int(total_steps / total_timer.now),
training_episode_reward=summary(reward_recorder,
"episode_reward"),
training_episode_length=summary(episode_length_recorder,
"episode_length"),
evaluate_stats=evaluate_stat,
learning_stats=dict(
policy_loss=policy_loss,
entropy=dist_entropy,
value_loss=value_loss,
# vae_loss= vae_loss,
# mdrnn_loss=mdrnn_loss,
total_loss=total_loss),
total_steps=total_steps,
total_episodes=total_episodes,
time_stats=dict(sample_time=sample_timer.avg,
process_time=process_timer.avg,
update_time=update_timer.avg,
total_time=total_timer.now,
episode_time=sample_timer.avg +
process_timer.avg + update_timer.avg),
iteration=iteration)
if tournament:
stats["opponent"] = envs.current_agent_name
progress.append(stats)
from IPython.display import clear_output
clear_output()
pretty_print({
"===== {} Training Iteration {} =====".format(algo, iteration):
stats
})
progress_path = save_progress(log_dir, progress)
if iteration % config.save_freq == 0:
trainer_path = trainer.save_w(log_dir, "iter{}".format(iteration))
progress_path = save_progress(log_dir, progress)
print(
"Saved trainer state at <{}>. Saved progress at <{}>.".format(
trainer_path, progress_path))
if total_steps > int(args.max_steps):
break
iteration += 1
def train(self):
ANCHORS = utils.get_anchors(self.anchors_path, self.img_h, self.img_w)
parser = Parser(image_h=self.img_h,
image_w=self.img_w,
anchors=ANCHORS,
num_classes=self.num_classes)
trainset = dataset(parser,
self.train_records,
self.batch_size,
shuffle=self.shuffle_size)
testset = dataset(parser,
self.test_records,
self.batch_size,
shuffle=None)
is_training = tf.placeholder(tf.bool)
example = tf.cond(is_training, lambda: trainset.get_next(),
lambda: testset.get_next())
images, y_true = example
model = yolov3.yolov3(self.num_classes, ANCHORS)
with tf.variable_scope('yolov3'):
# Give the images to the network, and receive a prediction
# feature map
pred_feature_map = model.forward(images,
is_training=is_training,
n_filters_dn=self.n_filters_dn,
n_strides_dn=self.n_strides_dn,
n_ksizes_dn=self.n_ksizes_dn)
loss = model.compute_loss(pred_feature_map, y_true,
self.iou_threshold)
y_pred = model.predict(pred_feature_map)
tf.summary.scalar("loss/coord_loss", loss[1])
tf.summary.scalar("loss/sizes_loss", loss[2])
tf.summary.scalar("loss/confs_loss", loss[3])
tf.summary.scalar("loss/class_loss", loss[4])
global_step = tf.Variable(0,
trainable=True,
collections=[tf.GraphKeys.LOCAL_VARIABLES])
write_op = tf.summary.merge_all()
writer_train = tf.summary.FileWriter("../../data/train_summary",
sess.graph)
writer_test = tf.summary.FileWriter("../../data/test_summary")
update_vars = tf.contrib.framework.get_variables_to_restore(
include=["yolov3/yolo-v3"])
lr = tf.train.exponential_decay(self.learning_rate,
global_step,
decay_steps=self.decay_steps,
decay_rate=self.decay_rate,
staircase=True)
optimizer = tf.train.AdamOptimizer(lr)
# set dependencies for BN ops
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss[0],
var_list=update_vars,
global_step=global_step)
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer()
])
saver = tf.train.Saver(max_to_keep=2)
for step in range(self.steps):
run_items = sess.run([train_op, write_op, y_pred, y_true] + loss,
feed_dict={is_training: True})
if (step + 1) % self.eval_internal == 0:
train_rec_value, train_prec_value = utils.evaluate(
run_items[2], run_items[3])
writer_train.add_summary(run_items[1], global_step=step)
writer_train.flush() # Flushes the event file to disk
if (step + 1) % self.save_internal == 0:
saver.save(sess,
save_path=self.checkpoint_path,
global_step=step + 1)
if (step + 1) % self.print_every_n == 0:
print(f"=> STEP {step+1} [TRAIN]:\tloss_xy: " +
f"{run_items[5]:.4f} \tloss_wh:{run_items[6]:.4f} \t" +
f"loss_conf:{run_items[7]:.4f} \tloss_class:" +
f"{run_items[8]:.4f}")
run_items = sess.run([write_op, y_pred, y_true] + loss,
feed_dict={is_training: False})
if (step + 1) % self.eval_internal == 0:
test_rec_value, test_prec_value = utils.evaluate(
run_items[1], run_items[2])
print(f"\n{20*'='}> evaluation result <{20*'='}\n")
print(f"=> STEP {step+1} [TRAIN]:\trecall:" +
f"{train_rec_value:.2f} \tprecision:" +
f"{train_prec_value:.4f}")
print(f"=> STEP {step+1} [VALID]:\trecall:" +
f"{test_rec_value:.2f} \tprecision:" +
f"{test_prec_value:.4f}")
print(f"\n{20*'='}> evaluation result <{20*'='}\n")
writer_test.add_summary(run_items[0], global_step=step)
writer_test.flush() # Flushes the event file to disk
df_f_zs, df_f_gzs = evaluate_zs_df_OpenImage(
iterator_tst=iterator_test,
tensors_zs=tensors_zs,
tensors_gzs=tensors_gzs,
unseen_classes=unseen_classes,
classes=classes,
sess=sess,
model=model,
k_zs=k_zs,
k_gzs=k_gzs)
print('-' * 30)
if i % eval_interval == 0 or i == n_iters - 1:
print('Time elapse: ', time.clock() - tic)
tic = time.clock()
ap_val, predictions_mll, labels_mll = evaluate(
iterator_val, [img_ids_val, features_val, labels_val],
model.features, model.logits, sess, model)
F1_val, P_val, R_val = evaluate_k(
k, iterator_val, [img_ids_val, features_val, labels_val],
model.features, model.logits, sess, model, predictions_mll,
labels_mll)
mF1_val, mP_val, mR_val, mAP_val = [
np.mean(F1_val),
np.mean(P_val),
np.mean(R_val),
np.mean(ap_val)
]
learning_rate = lr.adapt(mAP_val)
values = [
i, l, l_rank, l_att_span, l_att_global, l_att_dist, mF1_val,
def main():
fmt = {
'tr_loss': '3.1e',
'tr_acc': '.4f',
'te_acc_det': '.4f',
'te_acc_stoch': '.4f',
'te_acc_ens': '.4f',
'te_acc_perm_sigma': '.4f',
'te_acc_zero_mean': '.4f',
'te_acc_perm_sigma_ens': '.4f',
'te_acc_zero_mean_ens': '.4f',
'te_nll_det': '.4f',
'te_nll_stoch': '.4f',
'te_nll_ens': '.4f',
'te_nll_perm_sigma': '.4f',
'te_nll_zero_mean': '.4f',
'te_nll_perm_sigma_ens': '.4f',
'te_nll_zero_mean_ens': '.4f',
'time': '.3f'
}
fmt = {**fmt, **{'la%d' % i: '.4f' for i in range(4)}}
args = get_args()
logger = Logger("lenet5-VDO", fmt=fmt)
trainset = torchvision.datasets.MNIST(root='./data',
train=True,
download=True,
transform=transforms.ToTensor())
train_sampler = torch.utils.data.BatchSampler(
torch.utils.data.RandomSampler(trainset),
batch_size=args.batch_size,
drop_last=False)
trainloader = torch.utils.data.DataLoader(trainset,
batch_sampler=train_sampler,
num_workers=args.workers,
pin_memory=True)
testset = torchvision.datasets.MNIST(root='./data',
train=False,
download=True,
transform=transforms.ToTensor())
test_sampler = torch.utils.data.BatchSampler(
torch.utils.data.SequentialSampler(testset),
batch_size=args.batch_size,
drop_last=False)
testloader = torch.utils.data.DataLoader(testset,
batch_sampler=test_sampler,
num_workers=args.workers,
pin_memory=True)
net = LeNet5()
net = net.to(device=args.device, dtype=args.dtype)
if args.print_model:
logger.print(net)
criterion = metrics.SGVLB(net, len(trainset)).to(device=args.device,
dtype=args.dtype)
optimizer = optim.Adam(net.parameters(), lr=args.learning_rate)
epochs = args.epochs
lr_start = args.learning_rate
for epoch in trange(epochs): # loop over the dataset multiple times
t0 = time()
utils.adjust_learning_rate(
optimizer, metrics.lr_linear(epoch, 0, epochs, lr_start))
net.train()
training_loss = 0
accs = []
steps = 0
for i, (inputs, labels) in enumerate(tqdm(trainloader), 0):
steps += 1
inputs, labels = inputs.to(
device=args.device,
dtype=args.dtype), labels.to(device=args.device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
accs.append(metrics.logit2acc(
outputs.data,
labels)) # probably a bad way to calculate accuracy
training_loss += loss.item()
logger.add(epoch, tr_loss=training_loss / steps, tr_acc=np.mean(accs))
# Deterministic test
net.eval()
acc, nll = utils.evaluate(net,
testloader,
device=args.device,
num_ens=1)
logger.add(epoch, te_nll_det=nll, te_acc_det=acc)
# Stochastic test
net.train()
acc, nll = utils.evaluate(net,
testloader,
device=args.device,
num_ens=1)
logger.add(epoch, te_nll_stoch=nll, te_acc_stoch=acc)
# Test-time averaging
net.train()
acc, nll = utils.evaluate(net,
testloader,
device=args.device,
num_ens=20)
logger.add(epoch, te_nll_ens=nll, te_acc_ens=acc)
# Zero-mean
net.train()
net.dense1.set_flag('zero_mean', True)
acc, nll = utils.evaluate(net,
testloader,
device=args.device,
num_ens=1)
net.dense1.set_flag('zero_mean', False)
logger.add(epoch, te_nll_zero_mean=nll, te_acc_zero_mean=acc)
# Permuted sigmas
net.train()
net.dense1.set_flag('permute_sigma', True)
acc, nll = utils.evaluate(net,
testloader,
device=args.device,
num_ens=1)
net.dense1.set_flag('permute_sigma', False)
logger.add(epoch, te_nll_perm_sigma=nll, te_acc_perm_sigma=acc)
# Zero-mean test-time averaging
net.train()
net.dense1.set_flag('zero_mean', True)
acc, nll = utils.evaluate(net,
testloader,
device=args.device,
num_ens=20)
net.dense1.set_flag('zero_mean', False)
logger.add(epoch, te_nll_zero_mean_ens=nll, te_acc_zero_mean_ens=acc)
# Permuted sigmas test-time averaging
net.train()
net.dense1.set_flag('permute_sigma', True)
acc, nll = utils.evaluate(net,
testloader,
device=args.device,
num_ens=20)
net.dense1.set_flag('permute_sigma', False)
logger.add(epoch, te_nll_perm_sigma_ens=nll, te_acc_perm_sigma_ens=acc)
logger.add(epoch, time=time() - t0)
las = [
np.mean(net.conv1.log_alpha.data.cpu().numpy()),
np.mean(net.conv2.log_alpha.data.cpu().numpy()),
np.mean(net.dense1.log_alpha.data.cpu().numpy()),
np.mean(net.dense2.log_alpha.data.cpu().numpy())
]
logger.add(epoch, **{'la%d' % i: las[i] for i in range(4)})
logger.iter_info()
logger.save(silent=True)
torch.save(net.state_dict(), logger.checkpoint)
logger.save()
tf.summary.scalar("yolov3/recall", rec_tensor)
tf.summary.scalar("yolov3/precision", prec_tensor)
tf.summary.scalar("yolov3/mAP", mAP_tensor)
tf.summary.scalar("yolov3/total_loss", loss[0])
tf.summary.scalar("loss/coord_loss", loss[1])
tf.summary.scalar("loss/sizes_loss", loss[2])
tf.summary.scalar("loss/confs_loss", loss[3])
tf.summary.scalar("loss/class_loss", loss[4])
write_op = tf.summary.merge_all()
writer_train = tf.summary.FileWriter("./data/log/train", graph=sess.graph)
sess.run(tf.global_variables_initializer())
for epoch in range(EPOCHS):
run_items = sess.run([train_op, y_pred, y_true] + loss, feed_dict={is_training:True})
rec, prec, mAP = utils.evaluate(run_items[1], run_items[2], num_classes)
_, _, _, summary = sess.run([tf.assign(rec_tensor, rec),
tf.assign(prec_tensor, prec),
tf.assign(mAP_tensor, mAP), write_op], feed_dict={is_training:True})
writer_train.add_summary(summary, global_step=epoch)
writer_train.flush() # Flushes the event file to disk
if epoch%1000 == 0: saver.save(sess, save_path="./checkpoint/yolov3.ckpt", global_step=epoch)
print("=> EPOCH:%10d\ttotal_loss:%7.4f\tloss_coord:%7.4f\tloss_sizes:%7.4f\tloss_confs:%7.4f\tloss_class:%7.4f"
"\trec:%.2f\tprec:%.2f\tmAP:%.2f"
%(epoch, run_items[3], run_items[4], run_items[5], run_items[6], run_items[7], rec, prec, mAP))
for j in range(flags.train_ens):
outputs[:, :, j] = F.log_softmax(net(inputs), dim=1)
log_outputs = utils.logmeanexp(outputs, dim=2)
loss = criterion(log_outputs, labels)
loss.backward()
optimizer.step()
accs.append(metrics.logit2acc(log_outputs.data, labels))
training_loss += loss.cpu().data.numpy()[0]
logger.add(epoch, tr_loss=training_loss/steps, tr_acc=np.mean(accs))
# Ens 100 test
net.train()
acc, nll = utils.evaluate(net, testloader, num_ens=100)
logger.add(epoch, te_nll_ens100=nll, te_acc_ens100=acc)
# Stochastic test
net.train()
acc, nll = utils.evaluate(net, testloader, num_ens=1)
logger.add(epoch, te_nll_stoch=nll, te_acc_stoch=acc)
# Test-time averaging
net.train()
acc, nll = utils.evaluate(net, testloader, num_ens=10)
logger.add(epoch, te_nll_ens10=nll, te_acc_ens10=acc)
logger.add(epoch, time=time()-t0)
logger.iter_info()
logger.save(silent=True)
def train(args):
# Verify algorithm and config
algo = args.algo
if algo == "PPO":
config = ppo_config
elif algo == "A2C":
config = a2c_config
else:
raise ValueError("args.algo must in [PPO, A2C]")
config.num_envs = args.num_envs
# Seed the environments and setup torch
seed = args.seed
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed(seed)
torch.set_num_threads(1)
# Clean log directory
log_dir = verify_log_dir(args.log_dir, algo)
# Create vectorized environments
num_envs = args.num_envs
env_name = args.env_name
# Prepare tensorboard file
args.save_log = 'Pairtrding-{}'.format(time.strftime("%Y%m%d-%H%M%S"))
generate_date = str(datetime.now().date())
writer = SummaryWriter(args.log_dir + '/runs/' + generate_date + '/' +
args.save_log)
# download stock price data from yahoo finance
stocklist = [
'0700.hk', '2318.hk', '3988.hk', '0998.hk', '1398.hk', '3968.hk',
'0981.hk', '0005.hk'
]
# 腾讯，平安，中银，中信，工商，招商，中芯国际，汇丰
stocktickers = ' '.join(stocklist)
data = yf.download(tickers=stocktickers,
start="2010-01-01",
end="2019-12-31")
data = data['Close']
columnchange = []
for stock in data.columns:
name = stock + 'change'
columnchange.append(name)
data[name] = data[stock] - data[stock].shift(1)
CorrDict = {}
for i in columnchange:
for j in columnchange:
if i != j and (i, j) not in CorrDict:
CorrDict[(i, j)] = data[i].corr(data[j])
pair = list(max(CorrDict))
pair.append(pair[0][:7])
pair.append(pair[1][:7])
dataremain = data[pair]
from sklearn import linear_model
import numpy as np
model = linear_model.LinearRegression()
model.fit(dataremain[pair[0]][1:-250].to_numpy().reshape(-1, 1),
y=dataremain[pair[1]][1:-250])
beta = model.coef_[0]
dataremain['Spread'] = beta * data[pair[0]] - data[pair[1]]
Spreadmean = dataremain['Spread'].mean()
Spreadstd = dataremain['Spread'].std()
dataremain['Z-score'] = (dataremain['Spread'] - Spreadmean) / Spreadstd
envs = PairtradingEnv(stock1=dataremain[pair[2]][:-250],
stock2=dataremain[pair[3]][:-250])
eval_envs = PairtradingEnv(stock1=dataremain[pair[2]][-250:],
stock2=dataremain[pair[3]][-250:])
baseline_config = baselineConfig(mean=Spreadmean, std=Spreadstd, beta=beta)
baseline_trainer = baseline(env=envs, config=baseline_config)
baseline_eval = baseline(env=eval_envs, config=baseline_config)
test = env_name == "CartPole-v0"
frame_stack = args.input_length if not test else 1
# Setup trainer
if algo == "PPO":
trainer = PPOTrainer(envs, config, frame_stack, _test=test)
else:
trainer = A2CTrainer(envs, config, frame_stack, _test=test)
# Create a placeholder tensor to help stack frames in 2nd dimension
# That is turn the observation from shape [num_envs, 1, 84, 84] to
# [num_envs, 4, 84, 84].
frame_stack_tensor = FrameStackTensor(
num_envs, envs.observation_space.shape, frame_stack,
config.device) # envs.observation_space.shape: 1,42,42
# Setup some stats helpers
episode_rewards = np.zeros([num_envs, 1], dtype=np.float)
total_episodes = total_steps = iteration = 0
reward_recorder = deque(maxlen=100)
episode_length_recorder = deque(maxlen=100)
episode_values = deque(maxlen=100)
sample_timer = Timer()
process_timer = Timer()
update_timer = Timer()
total_timer = Timer()
progress = []
evaluate_stat = {}
# Start training
print("Start training!")
while True: # Break when total_steps exceeds maximum value
# ===== Sample Data =====
# episode_values = []
episode_rewards = np.zeros([num_envs, 1], dtype=np.float)
for env_id in range(num_envs):
obs = envs.reset() # obs.shape: 15,1,42,42
frame_stack_tensor.update(obs, env_id)
trainer.rollouts.observations[0, env_id].copy_(
frame_stack_tensor.get(env_id)
) #trainer.rollouts.observations.shape: torch.Size([201, 15, 4, 42, 42])
with sample_timer:
for index in range(config.num_steps):
# Get action
# [TODO] Get the action
# Hint:
# 1. Remember to disable gradient computing
# 2. trainer.rollouts is a storage containing all data
# 3. What observation is needed for trainer.compute_action?
with torch.no_grad():
values, actions_cash, action_log_prob_cash, actions_beta, action_log_prob_beta = trainer.compute_action(
trainer.rollouts.observations[index, env_id])
act = baseline_trainer.compute_action(
actions_cash.view(-1), actions_beta.view(-1))
cpu_actions = act
# Step the environment
# (Check step_envs function, you need to implement it)
obs, reward, done, masks, total_episodes, \
total_steps, episode_rewards, episode_values = step_envs(
cpu_actions, envs, env_id, episode_rewards, episode_values, frame_stack_tensor,
reward_recorder, episode_length_recorder, total_steps,
total_episodes, config.device, test)
rewards = torch.from_numpy(
np.array(reward).astype(np.float32)).view(-1).to(
config.device)
# Store samples
trainer.rollouts.insert(frame_stack_tensor.get(env_id),
actions_cash.view(-1),
action_log_prob_cash.view(-1),
actions_beta.view(-1),
action_log_prob_beta.view(-1),
values.view(-1), rewards,
masks.view(-1), env_id)
# ===== Process Samples =====
with process_timer:
with torch.no_grad():
next_value = trainer.compute_values(
trainer.rollouts.observations[-1])
trainer.rollouts.compute_returns(next_value, config.GAMMA)
# ===== Update Policy =====
with update_timer:
policy_loss, value_loss, dist_entropy, total_loss = \
trainer.update(trainer.rollouts)
trainer.rollouts.after_update()
# Add training statistics to tensorboard log file
writer.add_scalar('train_policy_loss', policy_loss, iteration)
writer.add_scalar('train_value_loss', value_loss, iteration)
writer.add_scalar('train_dist_entropy', dist_entropy, iteration)
writer.add_scalar('train_total_loss', total_loss, iteration)
writer.add_scalar('train_episode_rewards',
np.mean(episode_rewards), iteration)
writer.add_scalar('train_episode_values',
np.array(episode_values).mean(), iteration)
# ===== Evaluate Current Policy =====
if iteration % config.eval_freq == 0:
eval_timer = Timer()
evaluate_rewards, evaluate_lengths, evaluate_values = evaluate(
trainer, eval_envs, baseline_eval, frame_stack, 5)
evaluate_stat = summary(evaluate_rewards, "episode_reward")
if evaluate_lengths:
evaluate_stat.update(
summary(evaluate_lengths, "episode_length"))
evaluate_stat.update(
dict(win_rate=float(
sum(np.array(evaluate_rewards) >= 0) /
len(evaluate_rewards)),
evaluate_time=eval_timer.now,
evaluate_iteration=iteration,
evaluate_values=float(np.array(evaluate_values).mean())))
# Add evaluation statistics to tensorboard log file
writer.add_scalar('eval_episode_rewards',
np.array(evaluate_rewards).mean(),
iteration // config.eval_freq)
writer.add_scalar('eval_episode_values',
np.array(evaluate_values).mean(),
iteration // config.eval_freq)
# ===== Log information =====
if iteration % config.log_freq == 0:
stats = dict(
log_dir=log_dir,
frame_per_second=int(total_steps / total_timer.now),
training_episode_reward=summary(reward_recorder,
"episode_reward"),
training_episode_values=summary(episode_values,
"episode_value"),
training_episode_length=summary(episode_length_recorder,
"episode_length"),
evaluate_stats=evaluate_stat,
learning_stats=dict(policy_loss=policy_loss,
entropy=dist_entropy,
value_loss=value_loss,
total_loss=total_loss),
total_steps=total_steps,
total_episodes=total_episodes,
time_stats=dict(sample_time=sample_timer.avg,
process_time=process_timer.avg,
update_time=update_timer.avg,
total_time=total_timer.now,
episode_time=sample_timer.avg +
process_timer.avg + update_timer.avg),
iteration=iteration)
progress.append(stats)
pretty_print({
"===== {} Training Iteration {} =====".format(algo, iteration):
stats
})
if iteration % config.save_freq == 0:
trainer_path = trainer.save_w(log_dir, "iter{}".format(iteration))
progress_path = save_progress(log_dir, progress)
print(
"Saved trainer state at <{}>. Saved progress at <{}>.".format(
trainer_path, progress_path))
if iteration >= args.max_steps:
break
iteration += 1
trainer.save_w(log_dir, "final")
envs.close()
# set dependencies for BN ops
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(loss[0], var_list=update_vars, global_step=global_step)
sess.run([tf.global_variables_initializer(), tf.local_variables_initializer()])
saver_to_restore.restore(sess, "./checkpoint/yolov3.ckpt")
saver = tf.train.Saver(max_to_keep=2)
for step in range(STEPS):
run_items = sess.run([train_op, write_op, y_pred, y_true] + loss, feed_dict={is_training:True})
if (step+1) % EVAL_INTERNAL == 0:
train_rec_value, train_prec_value = utils.evaluate(run_items[2], run_items[3])
writer_train.add_summary(run_items[1], global_step=step)
writer_train.flush() # Flushes the event file to disk
if (step+1) % SAVE_INTERNAL == 0: saver.save(sess, save_path="./checkpoint/yolov3_"+dataname+".ckpt", global_step=step+1)
print("=> STEP %10d [TRAIN]:\tloss_xy:%7.4f \tloss_wh:%7.4f \tloss_conf:%7.4f \tloss_class:%7.4f"
%(step+1, run_items[5], run_items[6], run_items[7], run_items[8]))
run_items = sess.run([write_op, y_pred, y_true] + loss, feed_dict={is_training:False})
if (step+1) % EVAL_INTERNAL == 0:
test_rec_value, test_prec_value = utils.evaluate(run_items[1], run_items[2])
print("\n=======================> evaluation result <================================\n")
print("=> STEP %10d [TRAIN]:\trecall:%7.4f \tprecision:%7.4f" %(step+1, train_rec_value, train_prec_value))
def do_online_qlearning(env,
test_env,
model,
params,
learning_rate,
epsilon_s,
gpu_device,
target_model=None,
replay_buffer=None,
dpaths=None,
training=True):
tf.reset_default_graph()
with tf.device(gpu_device):
# Create placeholders
states_pl = tf.placeholder(tf.float32,
shape=(None, FRAME_WIDTH, FRAME_HEIGHT,
FRAME_BUFFER_SIZE),
name='states')
actions_pl = tf.placeholder(tf.int32, shape=(None), name='actions')
targets_pl = tf.placeholder(tf.float32, shape=(None), name='targets')
# Value function approximator network
q_output = model.graph(states_pl)
# Build target network
q_target_net = target_model.graph(states_pl)
tf_train = tf.trainable_variables()
num_tf_train = len(tf_train)
target_net_vars = []
for i, var in enumerate(tf_train[0:num_tf_train // 2]):
target_net_vars.append(tf_train[i + num_tf_train // 2].assign(
var.value()))
# Compute Q from current q_output and one hot actions
# Q = tf.reduce_sum(
# tf.multiply(q_output, actions_pl), axis=1)
Q = tf.reduce_sum(tf.multiply(
q_output,
tf.one_hot(actions_pl, env.action_space.n, dtype=tf.float32)),
axis=1)
# Loss operation
loss_op = tf.reduce_mean(tf.square(targets_pl - Q) / 2)
# Optimizer Op
#optimizer = tf.train.RMSPropOptimizer(learning_rate=learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
# Training Op
train_op = optimizer.minimize(loss_op)
# Prediction Op
prediction = tf.argmax(q_output, 1)
# Model Saver
saver = tf.train.Saver()
# init all variables
init_op = tf.global_variables_initializer()
# Limit memory usage for multiple training at same time
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.49
#config.gpu_options.allow_growth = True
# Start Session
with tf.Session(config=config) as sess:
sess.run(init_op)
sess.graph.finalize()
# Performance from untrained Q-learning
if not training:
return evaluate(test_env, sess, prediction, states_pl,
params['EVAL_EPISODES'], GAMMA, False, False)
start_time = time.time()
# Load env and get observations
observation = env.reset()
# Observation Buffer
observation_buffer = list()
# Save results
losses = list()
means = list()
stds = list()
#init epsilon
epsilon = epsilon_s['start']
for step in range(params['TRAINING_STEPS']):
loss = 0
# Stack observations in buffer of 4
if len(observation_buffer) < FRAME_BUFFER_SIZE:
observation_buffer.append(observation)
# Collect next observation with uniformly random action
a_rnd = env.action_space.sample()
observation, _, done, _ = env.step(a_rnd)
# Observations buffer is ready
else:
# Stack observation buffer
state = np.stack(observation_buffer, axis=-1)
# Epsilon greedy policy
if epsilon > np.random.rand(1):
# Exploration
# Use uniformly sampled action from env
action = np.array(env.action_space.sample(),
dtype=np.float32).reshape((-1))
else:
# Exploitation
# Use model predicted action
action = sess.run(prediction,
feed_dict={
states_pl:
state.reshape([
-1, FRAME_WIDTH, FRAME_HEIGHT,
FRAME_BUFFER_SIZE
]).astype('float32')
})
# action for next observation
action = action.reshape([-1]).astype('int32')
observation, reward, done, info = env.step(action[0])
# Clip reward
r = reward_clip(reward)
# Update observation buffer
observation_buffer.append(observation)
observation_buffer[0:1] = []
next_state = np.stack(observation_buffer, axis=-1)
# Add transition to replay buffer
# Store state as uint8 for memory optim
replay_buffer.add(((state * 255).astype('uint8'), action, r,
(next_state * 255).astype('uint8'), done))
# If replay buffer is ready to be sampled
if replay_buffer.ready:
# Train model on replay buffer
b_states, b_actions, b_reward, b_next_state, b_term_state = replay_buffer.next_transitions(
)
q_out, q_out_target = sess.run(
[q_output, q_target_net],
feed_dict={states_pl: b_next_state})
#q_out_max = np.amax(q_out, axis=1)
#q_target = b_reward + GAMMA * (1 - b_term_state) * q_out_max
q_out_argmax = np.unravel_index(np.argmax(q_out, axis=1),
q_out.shape)
q_target = b_reward + GAMMA * (
1 - b_term_state) * q_out_target[q_out_argmax]
# Run training Op on batch of replay experience
loss, _ = sess.run(
[loss_op, train_op],
feed_dict={
states_pl: b_states,
actions_pl: b_actions,
targets_pl: q_target.astype('float32')
})
if done:
observation = env.reset()
# Update epsilon greedy policy
if epsilon > epsilon_s['end']:
epsilon -= (epsilon_s['start'] -
epsilon_s['end']) / epsilon_s['decay']
# Copy variables target network
if (step + 1) % params['TARGET_UPDATE'] == 0:
for var in target_net_vars:
sess.run(var)
if step % params['LOSS_STEPS'] == 0:
# Save loss
losses.append(loss)
if step % params['LOG_STEPS'] == 0:
print('\n', time.strftime('%Y-%m-%d %H:%M:%S',
time.localtime()))
print('Episodes %d -> %d Done (%.3fs) ... ' %
(max(1, step + 1 - params['LOG_STEPS']), step + 1,
time.time() - start_time))
print('- Training loss: %.4f' % loss)
start_time = time.time()
# Force flush for nohup
sys.stdout.flush()
if 'EVAL_STEPS_START' in params:
if (step % params['EVAL_STEPS_START'] == 0
and step <= params['EVAL_STEPS_STOP'] and step != 0):
silent = (step % params['LOG_STEPS'] != 0)
cur_means, cur_stds = evaluate(
test_env,
sess,
prediction,
#states_pl, 1, GAMMA, silent)
states_pl,
params['EVAL_EPISODES'],
GAMMA,
silent)
means.append(cur_means)
stds.append(cur_stds)
if step % params['EVAL_STEPS'] == 0:
silent = (step % params['LOG_STEPS'] != 0)
cur_means, cur_stds = evaluate(
test_env,
sess,
prediction,
#states_pl, 1, GAMMA, silent)
states_pl,
params['EVAL_EPISODES'],
GAMMA,
silent)
# Save means
means.append(cur_means)
stds.append(cur_stds)
# Save models
if dpaths is not None and step % params['SAVE_STEPS'] == 0:
saver.save(sess, dpaths, global_step=step)
# Save models
if dpaths is not None:
saver.save(sess, dpaths)
# Return Q-learning Experience results
return losses, means, stds
