def play_episode(self,episode:int):
state = self.env.reset()
previous_x = None
episode_actions = torch.empty(size=(0,),dtype=torch.long,device=self.device)
episode_logits = torch.empty(size=(0,self.env.action_space.n),device=self.device)
average_rewards = numpy.empty(shape=(0,), dtype=numpy.float)
episode_rewards = numpy.empty(shape=(0,), dtype=numpy.float)
while True:
#if not self.render:
# self.env.render()
current_x = self.PreProcessing(state)
x = current_x - previous_x if previous_x is not None else numpy.zeros_like(current_x)
previous_x = current_x
action_logits = self.agent(torch.tensor(x).float().unsqueeze(dim=0).to(self.device))
episode_logits = torch.cat((action_logits,episode_logits),dim=0)
action = Categorical(logits=action_logits).sample()
episode_actions = torch.cat((episode_actions,action),dim=0)
state,reward,done,_ = self.env.step(action = action.cpu().item())
episode_rewards = numpy.concatenate((episode_rewards,numpy.array([reward])),axis=0)
average_rewards = numpy.concatenate((average_rewards,numpy.expand_dims(numpy.mean(episode_rewards),axis=0)),axis=0)
if done:
episode+=1
discounted_rewards = PG_RL.get_discounted_rewards(rewards=episode_rewards,gamma=self.gamma)
discounted_rewards -= average_rewards
discounted_rewards /= numpy.std(discounted_rewards)
sum_of_rewards = numpy.sum(episode_rewards)
mask = one_hot(episode_actions,num_classes=self.env.action_space.n)
episode_log_probs = torch.sum(mask.float()*log_softmax(episode_logits,dim=1),dim=1)
episode_weighted_log_probs = episode_log_probs * torch.tensor(discounted_rewards).float().to(self.device)
sum_weighted_log_probs = torch.sum(episode_weighted_log_probs).unsqueeze(dim=0)
#show_video()
return sum_weighted_log_probs, episode_logits, sum_of_rewards, episode
def agent_step(self):
with torch.no_grad():
action_probs, log_probs, termination_probs, q_u, q_omega = self.policy(
self.state)
if self.current_option is None:
self.current_option = Categorical(
probs=self._epsilon_probs(q_omega[0])).sample()
action = Categorical(
probs=action_probs[0, self.current_option, :]).sample()
action = action.cpu().detach().numpy()
# action = self.env.action_space.sample()
action = int(action)
next_state, reward, done, info = self.env.step(action)
# self.env.render()
self.replay_buffer.add([
self.state, action, self.current_option, self.previous_option,
reward, next_state, done
])
self.state = next_state
self.previous_option = self.current_option
if done:
self.agent_reset()
elif termination_probs[0, self.current_option] >= torch.rand(1):
self.current_option = None
return reward, done
def evaluate(self, true_labels, all_preds, entropies, **kwargs):
ood_entropies = np.zeros(0)
accuracies = []
with torch.no_grad():
for batch_num, batch in enumerate(self.ds_loader):
x, y = batch
x = x.to(self.device)
if not self.ensemble:
out = self.model(x)
else:
out = 0
for model in self.ensemble:
out += model(x)
out /= len(self.ensemble)
probs = F.softmax(out, dim=-1)
preds, _ = torch.max(probs, dim=-1)
# entropy
entropy = Categorical(probs).entropy().squeeze()
entropies = np.concatenate(
(entropies, entropy.detach().cpu().numpy()))
ood_entropies = np.concatenate(
(ood_entropies, entropy.cpu().numpy()))
# accuracy
predictions = out.argmax(dim=-1, keepdim=True).view_as(y).cpu()
correct = y.eq(predictions).sum().item()
acc = correct / out.shape[0]
accuracies.append(acc)
true_labels = np.concatenate((true_labels, np.zeros(len(x))))
all_preds = np.concatenate((all_preds, preds.cpu().reshape(
(-1))))
auroc = calculate_auroc(true_labels, all_preds)
aupr = calculate_aupr(true_labels, all_preds)
auroc_entropy = calculate_auroc(1 - true_labels, entropies)
aupr_entropy = calculate_aupr(1 - true_labels, entropies)
auroc_name = f'auroc_{self.ds_dataset}'
aupr_name = f'aupr_{self.ds_dataset}'
auroc_ent_name = f'auroc_entropy_{self.ds_dataset}'
aupr_ent_name = f'aupr_entropy_{self.ds_dataset}'
entropy_name = f'entropy_{self.ds_dataset}'
acc_name = f"acc_{self.ds_dataset}"
return {
acc_name: np.mean(accuracies),
auroc_name: auroc,
aupr_name: aupr,
entropy_name: np.mean(ood_entropies),
auroc_ent_name: auroc_entropy,
aupr_ent_name: aupr_entropy
}
def play_ep(self):
# reset env state after every episode
state = self.env.reset()
prev_x = None
episode_actions = torch.empty(size=(0,), dtype=torch.long, device=self.device)
episode_logits = torch.empty(size=(0, 2),device=self.device)
average_rewards = np.empty(shape=(0,), dtype=np.float)
episode_rewards = np.empty(shape=(0,), dtype=np.float)
while True:
# render env for display
if self.render_env:
self.env.render()
# pre-preprocess current the state and subtract from previous state to add-in motion information
cur_x = prepro(state)
x = cur_x - prev_x if prev_x is not None else np.zeros(self.in_sz).astype(np.float32)
prev_x = cur_x
# get choice from network
action_logit = self.agent(torch.tensor(x).float().unsqueeze(0).to(self.device))
# add to buffer
episode_logits = torch.cat((episode_logits, action_logit), dim=0)
# sample and action and execute the action
action = Categorical(logits=action_logit).sample()
# add to buffer
episode_actions = torch.cat((episode_actions, action),dim=0)
state, reward, done, _ = self.env.step(action=action.cpu().item())
# add to buffer
episode_rewards = np.concatenate((episode_rewards, np.array([reward])), axis=0)
# like averaging from 1 to nth time step (on-average return till that time step)
average_rewards = np.concatenate((average_rewards, np.expand_dims(np.mean(episode_rewards), axis=0)), axis=0)
if reward != 0: # Pong has either +1 or -1 reward exactly when game ends.
print(('ep #: game finished, reward: %f' % (reward)) + ('' if reward == -1 else ' !!!!!!!!'))
if done: # end of episode
# get discounted rewards and normalize the return
discounted_rewards = discount_rewards(episode_rewards, gamma=self.gamma)
# subtract baseline rewards
discounted_rewards -= average_rewards
# set mask for the actions executed
mask = one_hot(episode_actions, num_classes=2)
# similar to cross-entropy for classification but with fake labels and our action confidence
weighted_ps = torch.sum(mask.float() * log_softmax(episode_logits, dim=1), dim=1)
# weight the loss with the discounted rewards to get expected reward from distribution
episode_weighted_loss = weighted_ps * torch.tensor(discounted_rewards).float().to(self.device)
return episode_weighted_loss, episode_logits, episode_rewards
def get_action(self, state_np):
state_th = torch.tensor(state_np).float()
action_th = self.forward(state_th)
if self.type == 'discrete':
action_sampled_th = Categorical(logits=action_th).sample()
else:
raise NotImplementedError
action_sampled_np = action_sampled_th.cpu().detach().numpy()
return action_sampled_np
def choose_action(self, states, buffer=True):
probs, values = self.forward(states)
# print("values:", values)
# print("probs:", probs)
actions = Categorical(probs).sample()
if buffer:
self.state_buffer.append(states)
self.value_buffer.append(values)
self.prob_buffer.append(probs)
self.action_buffer.append(torch.unsqueeze(actions, 1))
# print("actions:", actions)
actions = actions.cpu().numpy() + 1
values = values.detach().cpu().numpy()
probs = probs.detach().cpu().numpy()
return actions, values, probs
def evaluate(self, true_labels, all_preds, entropies, **kwargs):
ood_entropies = np.zeros(0)
with torch.no_grad():
for batch_num, batch in enumerate(self.ood_loader):
x, y = batch
x = x.float().to(self.device)
if not self.ensemble:
out = self.model(x)
else:
out = 0
for model in self.ensemble:
out += model(x)
out /= len(self.ensemble)
probs = F.softmax(out, dim=-1)
preds, _ = torch.max(probs, dim=-1)
entropy = Categorical(probs).entropy().squeeze()
entropies = np.concatenate(
(entropies, entropy.detach().cpu().numpy()))
ood_entropies = np.concatenate(
(ood_entropies, entropy.cpu().numpy()))
true_labels = np.concatenate((true_labels, np.zeros(len(x))))
all_preds = np.concatenate((all_preds, preds.cpu().reshape(
(-1))))
auroc = calculate_auroc(true_labels, all_preds)
aupr = calculate_aupr(true_labels, all_preds)
auroc_entropy = calculate_auroc(1 - true_labels, entropies)
aupr_entropy = calculate_aupr(1 - true_labels, entropies)
auroc_name = f'auroc_{self.ood_dataset}'
aupr_name = f'aupr_{self.ood_dataset}'
auroc_ent_name = f'auroc_entropy_{self.ood_dataset}'
aupr_ent_name = f'aupr_entropy_{self.ood_dataset}'
entropy_name = f'entropy_{self.ood_dataset}'
return {
auroc_name: auroc,
aupr_name: aupr,
entropy_name: np.mean(ood_entropies),
auroc_ent_name: auroc_entropy,
aupr_ent_name: aupr_entropy
}
def _step(self, obs, hiddens, masks):
with torch.no_grad():
values, action_probs, hiddens = self.model(obs, hiddens, masks)
actions = Categorical(action_probs.detach()).sample()
# Sample actions from the output distributions
obs, rewards, dones, infos = self.envs.step(actions.cpu().numpy())
obs = torch.from_numpy(obs)
rewards = torch.from_numpy(rewards).unsqueeze(1)
masks = torch.from_numpy(1 - (dones)).unsqueeze(1)
actions = actions.unsqueeze(1)
self.rollouts.insert(
obs, #next
hiddens, #next
actions, #now
action_probs, #now
values, #now
rewards, #now
masks) #next
def get_next_batch(self, env):
for _ in range(C.NUM_EPOCHS):
epoch_logits = torch.empty(size=(0, self.action_space_size),
device=self.DEVICE)
epoch_weighted_log_probs = torch.empty(size=(0, ),
dtype=torch.float,
device=self.DEVICE)
total_rewards = deque([], maxlen=C.BATCH_SIZE_PER_THREAD)
episode_counter = 0
while episode_counter < C.BATCH_SIZE_PER_THREAD:
episode_counter += 1
# reset the environment to a random initial state every epoch
state = env.reset()
# initialize the episode arrays
episode_actions = torch.empty(size=(0, ),
dtype=torch.long,
device=self.DEVICE)
episode_logits = torch.empty(size=(0, C.action_space_size),
device=self.DEVICE)
average_rewards = np.empty(shape=(0, ), dtype=np.float)
episode_rewards = np.empty(shape=(0, ), dtype=np.float)
# episode loop
for step_index in range(0, C.max_simulation_length):
# get the action logits from the agent - (preferences)
action_logits = self.m(
torch.tensor(state).float().unsqueeze(dim=0).to(
self.DEVICE))
# append the logits to the episode logits list
episode_logits = torch.cat((episode_logits, action_logits),
dim=0)
# sample an action according to the action distribution
action = Categorical(logits=action_logits).sample()
# append the action to the episode action list to obtain the trajectory
# we need to store the actions and logits so we could calculate the gradient of the performance
episode_actions = torch.cat((episode_actions, action),
dim=0)
# take the chosen action, observe the reward and the next state
state, reward, done, _ = env.step(
action=action.cpu().item())
# append the reward to the rewards pool that we collect during the episode
# we need the rewards so we can calculate the weights for the policy gradient
# and the baseline of average
episode_rewards = np.concatenate(
(episode_rewards, np.array([reward])), axis=0)
# here the average reward is state specific
average_rewards = np.concatenate(
(average_rewards,
np.expand_dims(np.mean(episode_rewards), axis=0)),
axis=0)
# turn the rewards we accumulated during the episode into the rewards-to-go:
# earlier actions are responsible for more rewards than the later taken actions
discounted_rewards_to_go = utils.get_discounted_rewards(
rewards=episode_rewards, gamma=C.GAMMA)
discounted_rewards_to_go -= average_rewards # baseline - state specific average
# calculate the sum of the rewards for the running average metric
sum_of_rewards = np.sum(episode_rewards)
# after each episode append the sum of total rewards to the deque
total_rewards.append(sum_of_rewards)
# set the mask for the actions taken in the episode
mask = one_hot(episode_actions,
num_classes=C.action_space_size)
# calculate the log-probabilities of the taken actions
# mask is needed to filter out log-probabilities of not related logits
episode_log_probs = torch.sum(
mask.float() * log_softmax(episode_logits, dim=1), dim=1)
# weight the episode log-probabilities by the rewards-to-go
episode_weighted_log_probs = episode_log_probs * \
torch.tensor(discounted_rewards_to_go).float().to(self.DEVICE)
# calculate the sum over trajectory of the weighted log-probabilities
sum_weighted_log_probs = torch.sum(
episode_weighted_log_probs).unsqueeze(dim=0)
# append the weighted log-probabilities of actions
epoch_weighted_log_probs = torch.cat(
(epoch_weighted_log_probs, sum_weighted_log_probs), dim=0)
# append the logits - needed for the entropy bonus calculation
epoch_logits = torch.cat((epoch_logits, episode_logits), dim=0)
# calculate the loss
loss, entropy = utils.calculate_loss(
C.BETA,
epoch_logits=epoch_logits,
weighted_log_probs=epoch_weighted_log_probs)
yield loss, total_rewards
def _update(self, states, actions, rewards, advantages, returns, masks,
epoch):
old_model = copy.deepcopy(self.model)
policy_losses = np.array([])
entropies = np.array([])
value_losses = np.array([])
losses = np.array([])
for _ in range(self.ppo_epochs):
rand_list = (torch.randperm(self.batch_num * self.batch_size).view(
-1, self.batch_size).tolist())
for ind in rand_list:
batch = states[ind]
actor_logits, vals, _ = self.model(batch)
log_probs = F.log_softmax(actor_logits, dim=1)
with torch.no_grad():
old_actor_logits, _, _ = old_model(batch)
old_log_probs = F.log_softmax(old_actor_logits, dim=1)
adv = advantages[ind].to(self.device)
advs = advantages.to(self.device)
adv = (adv - advs.mean()) / (advs.std() + 1e-8)
A = returns[ind].to(self.device) - vals
action = actions[ind].to(self.device)
old_log_probs = old_log_probs.gather(1, action)
log_probs = log_probs.gather(1, action)
r = (log_probs - old_log_probs).exp()
clip = r.clamp(min=1 - self.epsilon, max=1 + self.epsilon)
L, _ = torch.stack([r * adv.detach(),
clip * adv.detach()]).min(0)
v_l = A.pow(2).mean()
L = L.mean()
entropy = Categorical(F.softmax(actor_logits,
dim=1)).entropy().mean()
loss = -L + self.v_loss_coef * v_l - self.entropy_coef * entropy
self.optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(),
self.max_grad_norm)
self.optimizer.step()
policy_losses = np.append(policy_losses,
L.cpu().detach().numpy())
value_losses = np.append(value_losses,
v_l.cpu().detach().numpy())
losses = np.append(losses, loss.cpu().detach().numpy())
entropies = np.append(entropies,
entropy.cpu().detach().numpy())
policy_loss = policy_losses.mean()
value_loss = value_losses.mean()
loss = losses.mean()
entropy = entropies.mean()
self.writer.add_scalar("PolicyLoss", policy_loss, epoch + 1)
self.writer.add_scalar("ValueLoss", value_loss, epoch + 1)
self.writer.add_scalar("Loss", loss, epoch + 1)
self.writer.add_scalar("Entropy", entropy, epoch + 1)
del states, actions, rewards, advantages, returns, masks
def play_episode(environment, device, action_space_size, agent, gamma,
episode: int):
"""
Plays an episode of the environment.
episode: the episode counter
Returns:
sum_weighted_log_probs: the sum of the log-prob of an action multiplied by the reward-to-go from that state
episode_logits: the logits of every step of the episode - needed to compute entropy for entropy bonus
finished_rendering_this_epoch: pass-through rendering flag
sum_of_rewards: sum of the rewards for the episode - needed for the average over 200 episode statistic
"""
agent.to('cpu')
device = 'cpu'
# reset the environment to a random initial state every epoch
state = environment.reset()
# initialize the episode arrays
episode_actions = torch.empty(size=(0, ), dtype=torch.long, device=device)
episode_logits = torch.empty(size=(0, action_space_size), device=device)
average_rewards = np.empty(shape=(0, ), dtype=np.float)
episode_rewards = np.empty(shape=(0, ), dtype=np.float)
# episode loop
while True:
# get the action logits from the agent - (preferences)
action_logits = agent(
torch.tensor(state).float().unsqueeze(dim=0).to(device))
#print('action logits is',action_logits)
# append the logits to the episode logits list
episode_logits = torch.cat((episode_logits, action_logits), dim=0)
# sample an action according to the action distribution
action = Categorical(logits=action_logits).sample()
#print('the action after categorical is',action)
# append the action to the episode action list to obtain the trajectory
# we need to store the actions and logits so we could calculate the gradient of the performance
episode_actions = torch.cat((episode_actions, action), dim=0)
# take the chosen action, observe the reward and the next state
state, reward, done, _ = environment.step(action=action.cpu().item())
# append the reward to the rewards pool that we collect during the episode
# we need the rewards so we can calculate the weights for the policy gradient
# and the baseline of average
episode_rewards = np.concatenate((episode_rewards, np.array([reward])),
axis=0)
# here the average reward is state specific
average_rewards = np.concatenate(
(average_rewards, np.expand_dims(np.mean(episode_rewards),
axis=0)),
axis=0)
# the episode is over
if done:
# increment the episode
episode += 1
# turn the rewards we accumulated during the episode into the rewards-to-go:
# earlier actions are responsible for more rewards than the later taken actions
discounted_rewards_to_go = utils.get_discounted_rewards(
rewards=episode_rewards, gamma=gamma)
discounted_rewards_to_go -= average_rewards # baseline - state specific average
# # calculate the sum of the rewards for the running average metric
sum_of_rewards = np.sum(episode_rewards)
# set the mask for the actions taken in the episode
mask = one_hot(episode_actions,
num_classes=environment.action_space.n)
# calculate the log-probabilities of the taken actions
# mask is needed to filter out log-probabilities of not related logits
episode_log_probs = torch.sum(mask.float() *
log_softmax(episode_logits, dim=1),
dim=1)
# weight the episode log-probabilities by the rewards-to-go
episode_weighted_log_probs = episode_log_probs * \
torch.tensor(discounted_rewards_to_go).float().to(device)
# calculate the sum over trajectory of the weighted log-probabilities
sum_weighted_log_probs = torch.sum(
episode_weighted_log_probs).unsqueeze(dim=0)
sum_weighted_log_probs = sum_weighted_log_probs.to('cpu')
episode_logits = episode_logits.to('cpu')
sum_weighted_log_probs = sum_weighted_log_probs.to(device)
episode_logits = episode_logits.to(device)
return sum_weighted_log_probs, episode_logits, sum_of_rewards, episode
def inference(
self,
sent_memory_emb,
graph_memory_emb,
sent_memory_mask,
graph_memory_mask,
max_step,
use_sampling=False,
):
batch_size, sent_memory_seq, dim = list(sent_memory_emb.shape)
_, graph_memory_seq, _ = list(graph_memory_emb.shape)
sent_memory_mask_inv = sent_memory_mask == 0 # [batch, sent_memory_seq]
graph_memory_mask_inv = graph_memory_mask == 0 # [batch, sent_memory_seq]
target_ids = [[self.BOS
for i in range(batch_size)]] # [target_seq, batch]
target_mask = [[1.0] for i in range(batch_size)] # [batch, target_seq]
target_prob = [] # [target_seq, batch]
is_finish = [False for _ in range(batch_size)]
rows = torch.arange(batch_size).to(device)
for step in range(max_step):
cur_seq = step + 1
cur_emb = self.dec_word_embedding(
torch.tensor(target_ids).to(device)) # [cur_seq, batch, dim]
cur_emb = self.position_encoder(cur_emb) # [cur_seq, batch, dim]
cur_mask = torch.tensor(target_mask).to(device)
cur_mask_inv = cur_mask == 0.0 # [batch, cur_seq]
cur_triu_mask = torch.triu(torch.ones(cur_seq, cur_seq).to(device),
diagonal=1) # [cur_seq, cur_seq]
cur_triu_mask.masked_fill_(cur_triu_mask == 1, -1e20)
cur_emb = self.decoder(
cur_emb,
sent_memory_emb, # [batch, sent_len, dim]
graph_memory_emb, # [batch, graph_len, dim]
tgt_mask=cur_triu_mask,
tgt_key_padding_mask=cur_mask_inv,
sent_memory_key_padding_mask=sent_memory_mask_inv,
graph_memory_key_padding_mask=graph_memory_mask_inv,
) # [batch, cur_seq, dim]
assert has_nan(cur_emb) is False
# break after the first time when all items are finished
if all(is_finish) or step == max_step - 1:
cur_len = cur_mask.sum(dim=1).long()
target_vec = universal_sentence_embedding(
cur_emb, cur_mask, cur_len)
break
# generating step outputs
logits = self.projector(cur_emb[:, -1, :]).view(
batch_size, self.word_vocab_size) # [batch, vocab]
if use_sampling is False:
indices = logits.argmax(dim=1) # [batch]
else:
indices = Categorical(logits=logits).sample() # [batch]
prob = F.softmax(logits, dim=1)[rows, indices] # [batch]
target_prob.append(prob)
indices = indices.cpu().tolist()
target_ids.append(indices)
for i in range(batch_size):
target_mask[i].append(
0.0 if is_finish[i] else
1.0) # based on if is_finish in the last step
for i in range(batch_size):
is_finish[i] |= indices[i] == self.EOS
target_ids = list(map(list,
zip(*target_ids[1:]))) # [batch, target_seq]
target_mask = torch.tensor([x[1:] for x in target_mask
]).to(device) # [batch, target_seq]
target_prob = torch.stack(target_prob, dim=1) # [batch, target_seq]
return target_vec, target_ids, target_prob, target_mask
def predict_mstcn(self,
model_dir,
results_dir,
features_path,
vid_list_file,
epoch,
actions_dict,
device,
sample_rate,
bsn_result_path,
mstcn_use_lbp,
poolingLength=99):
self.model.eval()
inverse_dict = {v: k for k, v in actions_dict.items()}
lbp = LocalBarrierPooling(poolingLength)
lbp = lbp.to(device)
with torch.no_grad():
self.model.to(device)
self.model.load_state_dict(
torch.load(model_dir + "/epoch-" + str(epoch) + ".model"))
file_ptr = open(vid_list_file, 'r')
list_of_vids = file_ptr.read().split('\n')[:-1]
file_ptr.close()
for vid in list_of_vids:
print(vid)
features = np.load(features_path + vid.split('.')[0] + '.npy')
features = features[:, ::sample_rate]
if mstcn_use_lbp:
num_frames = np.shape(features)[1]
barrier_file = bsn_result_path + vid + ".csv"
barrier = np.array(pd.read_csv(barrier_file))
temporal_scale = np.shape(barrier)[0]
barrier = np.transpose(barrier)
barrier = torch.tensor(
barrier, dtype=torch.float) #size=[num_frames]
if temporal_scale <= num_frames:
resize_barrier = F.interpolate(barrier,
size=num_frames,
mode='nearest')
else:
resize_barrier = barrier
resize_barrier = resize_barrier.unsqueeze(0)
resize_barrier = resize_barrier.unsqueeze(
0) # size=[1,1,num_frames]
resize_barrier = resize_barrier.to(device)
input_x = torch.tensor(features, dtype=torch.float)
input_x.unsqueeze_(0)
input_x = input_x.to(device)
predictions = self.model(
input_x, torch.ones(input_x.size(), device=device))
predictions = predictions[-1]
if mstcn_use_lbp:
if temporal_scale <= num_frames:
predictions = lbp(predictions, resize_barrier)
else:
predictions = F.interpolate(predictions,
size=temporal_scale,
mode='linear',
align_corners=False)
predictions = lbp(predictions, resize_barrier)
predictions = F.interpolate(predictions,
size=num_frames,
mode='linear',
align_corners=False)
predictions = F.softmax(predictions, dim=1)
entropy = Categorical(
probs=predictions.squeeze(0).transpose(1, 0)).entropy()
entropy = entropy.cpu().numpy().astype(np.str)
f_name = vid.split('/')[-1].split('.')[0]
f_ptr = open(results_dir + "/entropy_" + f_name, "w")
f_ptr.write(' '.join(entropy))
f_ptr.close()
_, predicted = torch.max(predictions.data, 1)
predicted = predicted.squeeze()
recognition = []
for i in range(len(predicted)):
recognition = np.concatenate(
(recognition,
[inverse_dict[predicted[i].item()]] * sample_rate))
f_name = vid.split('/')[-1].split('.')[0]
f_ptr = open(results_dir + "/" + f_name, "w")
f_ptr.write("### Frame level recognition: ###\n")
f_ptr.write(' '.join(recognition))
f_ptr.close()
def evaluate(self, **kwargs):
true_labels = np.zeros(0)
all_preds = np.zeros(0)
all_correct = np.zeros(0)
conf_true_labels = np.zeros(0)
brier_scores = []
entropies = np.zeros(0)
acc = []
nll = []
with torch.no_grad():
for batch_num, batch in enumerate(self.test_loader):
x, y = batch
x = x.to(self.device)
if not self.ensemble:
out = self.model(x)
else:
out = 0
for model in self.ensemble:
out += model(x)
out /= len(self.ensemble)
# Logits to probability distribution
probs = F.softmax(out, dim=-1)
# Maximum softmax probability
preds, indices = torch.max(probs, dim=-1)
# Label predictions
label_preds = probs.argmax(dim=-1, keepdim=True).view_as(y)
# Compute accuracy
corrects = y.eq(label_preds.cpu())
correct = corrects.sum().item()
acc.append(correct / out.shape[0])
all_correct = np.concatenate(
(all_correct, corrects.cpu().numpy()))
# Compute entropy
entropy = Categorical(probs).entropy().squeeze()
entropies = np.concatenate((entropies, entropy.cpu().numpy()))
# Compute brier score
brier_scores.append(calculate_brier_score(probs, y))
# Compute NLL
nll.append(-np.mean(np.log(preds.cpu().numpy())))
true_labels = np.concatenate((true_labels, np.ones(len(x))))
all_preds = np.concatenate((all_preds, preds.cpu().reshape(
(-1))))
conf_true_labels = np.concatenate(
(conf_true_labels, torch.isclose(
y.cpu(),
indices.cpu()).numpy().astype(float).reshape(-1)))
conf_auroc = calculate_auroc(conf_true_labels, all_preds)
conf_aupr = calculate_aupr(conf_true_labels, all_preds)
brier_score = np.mean(np.array(brier_scores))
ece = calculate_ece(all_preds, all_correct)
return {
'conf_auroc': conf_auroc,
'conf_aupr': conf_aupr,
'brier_score': brier_score,
'entropy': np.mean(entropies),
'test_acc': np.mean(acc),
'nll': np.mean(nll),
'ece': ece,
}, true_labels, all_preds, entropies
def act(self, x):
with torch.no_grad():
logits = self(x)
m = Categorical(logits=logits).sample().squeeze()
return m.cpu().item()
def main():
# make the environments
if args.num_envs == 1:
env = [gym.make(args.env_name)]
else:
env = [gym.make(args.env_name) for i in range(args.num_envs)]
env = MultiGym(env, render=args.render)
n_states = env.observation_space.shape
n_actions = env.action_space.n
print('state shape:', n_states, 'actions:', n_actions)
policy = ConvPolicy(n_actions).to(device)
optimizer = optim.RMSprop(policy.parameters(), lr=args.lr)
if args.algo == 'ppo':
sys.path.append('../')
from algorithms.ppo import PPO
update_algo = PPO(policy=policy,
optimizer=optimizer,
num_steps=args.num_steps,
num_envs=args.num_envs,
state_size=(4, 105, 80),
entropy_coef=args.entropy,
gamma=args.gamma,
device=device,
epochs=args.ppo_epochs)
else:
sys.path.append('../')
from algorithms.a2c import A2C
update_algo = A2C(policy=policy,
optimizer=optimizer,
num_steps=args.num_steps,
num_envs=args.num_envs,
state_size=(4, 105, 80),
entropy_coef=args.entropy,
gamma=args.gamma,
device=device)
end_rewards = []
try:
print('starting episodes')
idx = 0
d = False
reward_sum = np.zeros((args.num_envs))
restart = True
frame = env.reset()
mask = torch.ones(args.num_envs)
all_start = time.time()
for update_idx in range(args.num_updates):
update_algo.policy.train()
# stack the frames
s = train_state_proc.proc_state(frame, mask=mask)
# insert state before getting actions
update_algo.states[0].copy_(s)
start = time.time()
for step in range(args.num_steps):
with torch.no_grad():
# get probability dist and values
p, v = update_algo.policy(update_algo.states[step])
a = Categorical(p).sample()
# take action get response
frame, r, d = env.step(
a.cpu().numpy() if args.num_envs > 1 else [a.item()])
s = train_state_proc.proc_state(frame, mask)
update_algo.insert_experience(step=step,
s=s,
a=a,
v=v,
r=r,
d=d)
mask = torch.tensor(1. - d).float()
reward_sum = (reward_sum + r)
# if any episode finished append episode reward to list
if d.any():
end_rewards.extend(reward_sum[d])
# reset any rewards that finished
reward_sum = reward_sum * mask.numpy()
idx += 1
with torch.no_grad():
_, next_val = update_algo.policy(update_algo.states[-1])
update_algo.update(next_val.view(1, args.num_envs).to(device),
next_mask=mask.to(device))
if args.lr_decay:
for params in update_algo.optimizer.param_groups:
params['lr'] = (
lr_min + 0.5 * (args.lr - lr_min) *
(1 + np.cos(np.pi * idx / args.num_updates)))
# update every so often by displaying results in term
if (update_idx % args.log_interval
== 0) and (len(end_rewards) > 0):
total_steps = (idx + 1) * args.num_envs * args.num_steps
end = time.time()
print(end_rewards[-10:])
print('Updates {}\t Time: {:.4f} \t FPS: {}'.format(
update_idx, end - start,
int(total_steps / (end - all_start))))
print(
'Mean Episode Rewards: {:.2f} \t Min/Max Current Rewards: {}/{}'
.format(np.mean(end_rewards[-10:]), reward_sum.min(),
reward_sum.max()))
except KeyboardInterrupt:
pass
torch.save(
update_algo.policy.state_dict(),
'../model_weights/{}_{}_conv.pth'.format(args.env_name, args.algo))
import pandas as pd
out_dict = {'avg_end_rewards': end_rewards}
out_log = pd.DataFrame(out_dict)
out_log.to_csv('../logs/{}_{}_rewards.csv'.format(args.env_name,
args.algo),
index=False)
out_dict = {
'actor losses': update_algo.actor_losses,
'critic losses': update_algo.critic_losses,
'entropy': update_algo.entropy_logs
}
out_log = pd.DataFrame(out_dict)
out_log.to_csv('../logs/{}_{}_training_behavior.csv'.format(
args.env_name, args.algo),
index=False)
plt.plot(end_rewards)
plt.show()