def forward_sum_symbolic(self, landmark_r_theta_dict_list):
x_list = []
for landmark_r_theta_dict in landmark_r_theta_dict_list:
x = cuda_var(torch.zeros(1, self.image_emb_size))
for landmark, (r, theta) in landmark_r_theta_dict.iteritems():
if theta == -1:
# not visible
continue
# get landmark embedding
landmark_id = self.landmark_names.index(landmark)
landmark_var = cuda_var(
torch.from_numpy(np.array([landmark_id])))
landmark_embedding = self.landmark_embedding(landmark_var)
# get r embedding
r_var = cuda_var(torch.from_numpy(np.array([r])))
r_embedding = self.r_embedding(r_var)
# get theta embedding
theta_var = cuda_var(torch.from_numpy(np.array([theta])))
theta_embedding = self.theta_embedding(theta_var)
embedding = torch.cat(
[landmark_embedding, r_embedding, theta_embedding], dim=1)
#embedding = F.relu(self.dense(embedding))
x = x + embedding
x_list.append(x)
return torch.cat(x_list)
def calc_loss_entropy(self, batch_replay_items):
agent_observation_state_ls = []
immediate_rewards = []
action_batch = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
immediate_rewards.append(replay_item.get_reward())
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
immediate_rewards = cuda_var(
torch.from_numpy(np.array(immediate_rewards)).float())
num_states = int(action_batch.size()[0])
model_prob_batch = self.model.get_probs_batch(
agent_observation_state_ls)
chosen_log_probs = model_prob_batch.gather(1, action_batch.view(-1, 1))
reward_log_probs = immediate_rewards * chosen_log_probs.view(-1)
entropy = -torch.mean(
torch.sum(model_prob_batch * torch.exp(model_prob_batch), 1))
objective = torch.sum(reward_log_probs) / num_states
loss = -(objective + self.entropy_coef * entropy)
self.entropy = entropy
return loss
def calc_loss(self, batch_replay_items):
log_probabilities = []
rewards = []
action_batch = []
for replay_item in batch_replay_items:
log_probabilities.append(replay_item.get_log_prob())
action_batch.append(replay_item.get_action())
rewards.append(replay_item.get_reward())
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
rewards = cuda_var(torch.from_numpy(np.array(rewards))).float()
num_states = int(action_batch.size()[0])
model_prob_batch = torch.cat(log_probabilities, dim=0)
chosen_log_probs = model_prob_batch.gather(1, action_batch.view(-1, 1))
reward_log_probs = rewards * chosen_log_probs.view(-1)
entropy = -torch.mean(
torch.sum(model_prob_batch * torch.exp(model_prob_batch), 1))
objective = torch.sum(reward_log_probs) / num_states
loss = -(objective + self.entropy_coef * entropy)
self.entropy = entropy
return loss
def get_probs(self,
agent_observed_state,
model_state,
mode=None,
volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
# Image list is already padded with zero-images if <5 images are available
images = agent_observed_state.get_image()[-5:]
image_batch = cuda_var(
torch.from_numpy(np.array(images)).float(), volatile)
# Flatten them? TODO: maybe don't hardcode this later on? batch size is 1 ;)
image_batch = image_batch.view(1, 15, self.config["image_height"],
self.config["image_width"])
# List of instructions. False is there because it expects a second argument. TODO: figure out what this is
instructions_batch = ([agent_observed_state.get_instruction()], False)
# Previous action
prev_actions_raw = [agent_observed_state.get_previous_action()]
# If previous action is non-existant then encode that as a stop?
prev_actions = [
self.none_action if a is None else a for a in prev_actions_raw
]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
# Get probabilities
probs_batch, new_model_state = self.final_module(
image_batch, instructions_batch, prev_actions_batch, model_state)
# last two we don't really need...
return probs_batch, new_model_state, None, None
def get_probs(self,
agent_observed_state,
model_state,
mode=None,
volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
# Extract the last 4 images or add dummy paddings
image_seqs = [[aos.get_image()] for aos in agent_observed_state_list]
image_batch = cuda_var(
torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [
aos.get_instruction() for aos in agent_observed_state_list
]
instructions_batch = cuda_var(
torch.from_numpy(np.array(instructions)).long())
time = agent_observed_state.time_step
time = cuda_var(torch.from_numpy(np.array([time])).long())
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, instructions_batch, time, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
def calc_loss(self, batch):
curr_obs = cuda_var(
torch.cat([
torch.from_numpy(np.array(point[0])).view(1, -1)
for point in batch
],
dim=0)).float()
actions = cuda_var(
torch.cat([
torch.from_numpy(np.array(point[1])).view(1, -1)
for point in batch
],
dim=0)).float()
next_obs = cuda_var(
torch.cat([
torch.from_numpy(np.array(point[2])).view(1, -1)
for point in batch
],
dim=0)).float()
gold_labels = cuda_var(
torch.cat([
torch.from_numpy(np.array(point[3])).view(1, -1)
for point in batch
],
dim=0)).long()
log_probs = self.forward(curr_obs, actions, next_obs)
classification_loss = -torch.mean(
log_probs.gather(1, gold_labels.view(-1, 1)))
return classification_loss
def calc_loss_old(self, batch_replay_items):
angle_batch = []
distance_batch = []
batch_next_state_feature = []
for replay_item in batch_replay_items:
angle, distance = replay_item.get_goal()
angle_batch.append(angle)
distance_batch.append(distance)
batch_next_state_feature.append(replay_item.get_state_feature())
angle_batch = cuda_var(torch.from_numpy(np.array(angle_batch)))
distance_batch = cuda_var(torch.from_numpy(np.array(distance_batch)))
batch_next_state_feature = torch.cat(batch_next_state_feature)
# Compute the negative log probability loss
goal_angle_log_probability, goal_distance_log_probability = self.model.predict_goal_result(
batch_next_state_feature)
chosen_angle_log_probs = goal_angle_log_probability.gather(
1, angle_batch.view(-1, 1))
chosen_distance_log_probs = goal_distance_log_probability.gather(
1, distance_batch.view(-1, 1))
goal_probability_loss = -torch.sum(chosen_angle_log_probs) - torch.sum(
chosen_distance_log_probs)
num_states = float(len(batch_replay_items))
goal_probability_loss = goal_probability_loss / num_states
return goal_probability_loss
def get_probs(self, agent_observed_state, model_state, mode=None, volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seq_lens = [1]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
# max_len = max(image_seq_lens)
# image_seqs = [aos.get_image()[:max_len]
# for aos in agent_observed_state_list]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
read_pointers = [aos.get_read_pointers()
for aos in agent_observed_state_list]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)), volatile)
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, image_seq_lens_batch, instructions_batch, prev_actions_batch, mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
def calc_loss(self, batch_replay_items):
""" Given a set of replay items this function calculates the loss variable """
agent_observation_state_ls = []
immediate_rewards = []
action_batch = []
log_probabilities = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
immediate_rewards.append(replay_item.get_reward())
log_probabilities.append(replay_item.get_log_prob())
log_probabilities = torch.cat(log_probabilities)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
immediate_rewards = cuda_var(
torch.from_numpy(np.array(immediate_rewards)).float())
model_log_prob_batch = log_probabilities
chosen_log_probs = model_log_prob_batch.gather(
1, action_batch.view(-1, 1))
reward_log_probs = immediate_rewards * chosen_log_probs.view(-1)
model_prob_batch = torch.exp(model_log_prob_batch)
self.entropy = -torch.sum(
torch.sum(model_log_prob_batch * model_prob_batch, 1))
objective = torch.sum(reward_log_probs)
loss = -objective - self.entropy_coef * self.entropy
return loss
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
immediate_rewards = []
action_batch = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
action_batch.append(replay_item.get_action())
immediate_rewards.append(replay_item.get_reward())
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
immediate_rewards = cuda_var(
torch.from_numpy(np.array(immediate_rewards)).float())
num_states = int(action_batch.size()[0])
model_prob_batch = self.model.get_probs_batch(
agent_observation_state_ls)
chosen_log_probs = model_prob_batch.gather(1, action_batch.view(-1, 1))
reward_log_probs = immediate_rewards * chosen_log_probs.view(-1)
gold_distribution = cuda_var(
torch.FloatTensor([0.6719, 0.1457, 0.1435, 0.0387]))
cross_entropy = -torch.mean(
torch.sum(gold_distribution * model_prob_batch, 1))
objective = torch.sum(reward_log_probs) / num_states
loss = -(objective - self.entropy_coef * cross_entropy)
self.cross_entropy = cross_entropy
return loss
def get_probs(self,
agent_observed_state,
model_state,
mode=None,
volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(
torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [
aos.get_instruction() for aos in agent_observed_state_list
]
instructions_batch = cuda_var(
torch.from_numpy(np.array(instructions)).long())
time = agent_observed_state.time_step
time = cuda_var(torch.from_numpy(np.array([time])).long())
previous_action = agent_observed_state.previous_action
if previous_action is None:
previous_action = 4 # num_actions + 1
previous_action = cuda_var(
torch.from_numpy(np.array([previous_action])).long())
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(
image_batch, instructions_batch, time, previous_action, mode,
model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
def get_probs_symbolic_text(self, agent_observed_state, symbolic_text, model_state, mode=None, volatile=False):
""" Same as get_probs instead forces the model to use the given symbolic text """
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seq_lens = [1]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions_batch = [symbolic_text]
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)), volatile)
probs_batch, new_model_state, image_emb_seq, state_feature = self.final_module(image_batch, image_seq_lens_batch,
instructions_batch, prev_actions_batch,
mode, model_state)
return probs_batch, new_model_state, image_emb_seq, state_feature
def get_probs_batch(self, agent_observed_state_list, mode=None):
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True
)
images = [aos.get_image() for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(images)).float())
instructions = [aos.get_instruction()
for aos in agent_observed_state_list]
read_pointers = [aos.get_read_pointers()
for aos in agent_observed_state_list]
instructions_batch = (instructions, read_pointers)
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch = self.final_module(image_batch, instructions_batch,
prev_actions_batch, mode)
return probs_batch
def get_attention_prob(self,
agent_observed_state,
model_state,
mode=None,
volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(
torch.from_numpy(np.array(image_seqs)).float(), volatile)
instructions = [
aos.get_instruction() for aos in agent_observed_state_list
]
instructions_batch = cuda_var(
torch.from_numpy(np.array(instructions)).long())
time = agent_observed_state.time_step
time = cuda_var(torch.from_numpy(np.array([time])).long())
instruction_string = instruction_to_string(
agent_observed_state.instruction, self.config)
state_feature = self.final_module.get_attention_prob(
image_batch, instructions_batch, instruction_string,
agent_observed_state.goal)
return state_feature
def get_probs(self, agent_observed_state, model_state, mode=None):
assert isinstance(agent_observed_state, AgentObservedState)
agent_observed_state_list = [agent_observed_state]
image_seq_lens = [1]
image_seq_lens_batch = cuda_tensor(
torch.from_numpy(np.array(image_seq_lens)))
image_seqs = [[aos.get_last_image()]
for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
goal_image_seqs = [[aos.get_goal_image()] for aos in agent_observed_state_list]
goal_image_batch = cuda_var(torch.from_numpy(np.array(goal_image_seqs)).float())
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch, new_model_state, image_emb_seq = self.final_module(image_batch, image_seq_lens_batch,
goal_image_batch, prev_actions_batch,
mode, model_state)
return probs_batch, new_model_state, image_emb_seq
def calc_loss(self, batch_replay_items):
agent_observation_state_ls = []
landmark = []
theta_1 = []
theta_2 = []
r = []
for replay_item in batch_replay_items:
agent_observation_state_ls.append(
replay_item.get_agent_observed_state())
landmark_, theta_1_, theta_2_, r_ = replay_item.get_symbolic_text()
landmark.append(landmark_)
theta_1.append(theta_1_)
theta_2.append(theta_2_)
r.append(r_)
num_states = len(agent_observation_state_ls)
landmark_batch = cuda_var(torch.from_numpy(np.array(landmark)))
theta_1_batch = cuda_var(torch.from_numpy(np.array(theta_1)))
theta_2_batch = cuda_var(torch.from_numpy(np.array(theta_2)))
r_batch = cuda_var(torch.from_numpy(np.array(r)))
model_prob_landmark, model_prob_theta_1, model_prob_theta_2, model_prob_r \
= self.model.get_symbolic_text_batch(agent_observation_state_ls)
# compute expected theta
model_prob_theta_1_ = torch.exp(model_prob_theta_1)
model_prob_theta_2_ = torch.exp(model_prob_theta_2)
expected_theta_1 = torch.matmul(model_prob_theta_1_,
self.theta_values) # batch
expected_theta_2 = torch.matmul(model_prob_theta_2_,
self.theta_values) # batch
gold_theta_1 = self.theta_values.gather(0, theta_1_batch.view(-1, 1))
gold_theta_2 = self.theta_values.gather(0, theta_2_batch.view(-1, 1))
theta_1_diff_1 = torch.remainder(gold_theta_1 - expected_theta_1, 360)
theta_1_diff_2 = torch.remainder(expected_theta_1 - gold_theta_1, 360)
theta_1_diff = torch.min(theta_1_diff_1, theta_1_diff_2)
theta_1_loss = torch.mean(theta_1_diff**2)
theta_2_diff_1 = torch.remainder(gold_theta_2 - expected_theta_2, 360)
theta_2_diff_2 = torch.remainder(expected_theta_2 - gold_theta_2, 360)
theta_2_diff = torch.min(theta_2_diff_1, theta_2_diff_2)
theta_2_loss = torch.mean(theta_2_diff**2)
chosen_log_probs_landmark = model_prob_landmark.gather(
1, landmark_batch.view(-1, 1))
# chosen_log_probs_theta_1 = model_prob_theta_1.gather(1, theta_1_batch.view(-1, 1))
# chosen_log_probs_theta_2 = model_prob_theta_2.gather(1, theta_2_batch.view(-1, 1))
chosen_log_probs_r = model_prob_r.gather(1, r_batch.view(-1, 1))
cross_entropy_loss_objective = torch.sum(chosen_log_probs_landmark) / num_states \
+ torch.sum(chosen_log_probs_r) / num_states
loss = -cross_entropy_loss_objective + 0.0002 * theta_1_loss + 0.0002 * theta_2_loss
return loss
def get_loss_and_prob(volatile_features, goal, final_height, final_width):
attention_probs = volatile_features["attention_probs"]
attention_logits = volatile_features["attention_logits"]
attention_log_prob = F.log_softmax(attention_logits, dim=0)
row, col, row_real, col_real = goal
gold_prob = GoalPrediction.generate_gold_prob(goal, final_height,
final_width)
if row is None:
cross_entropy_loss = -torch.sum(
gold_prob * attention_log_prob) # cross entropy loss
meta = {"cross_entropy": cross_entropy_loss, "dist_loss": None}
return cross_entropy_loss, attention_log_prob[final_height *
final_width], meta
row_, col_ = row + 0.5, col + 0.5
position_height = cuda_var(
torch.from_numpy(np.array(list(range(
0, final_height))))).float().view(-1, 1) + 0.5
position_width = cuda_var(
torch.from_numpy(np.array(list(range(
0, final_width))))).float().view(-1, 1) + 0.5
attention_prob = attention_probs[:-1].view(final_height, final_width)
expected_row = torch.sum(position_height * attention_prob)
expected_col = torch.sum(position_width.view(1, -1) * attention_prob)
dist_loss = torch.sqrt((expected_row - row_) * (expected_row - row_) +
(expected_col - col_) * (expected_col - col_))
cross_entropy_loss = -torch.sum(
gold_prob * attention_log_prob) # cross entropy loss
if row is None or col is None:
ix = final_height * final_width
else:
ix = row * final_width + col
if GoalPrediction.loss_type == GoalPrediction.LOGLOSS:
loss = -attention_log_prob[ix]
elif GoalPrediction.loss_type == GoalPrediction.LOGLOSS_DIST:
loss = -attention_log_prob[ix] + dist_loss
elif GoalPrediction.loss_type == GoalPrediction.CROSS_ENTROPY:
loss = cross_entropy_loss
elif GoalPrediction.loss_type == GoalPrediction.DIST_LOSS:
loss = dist_loss
elif GoalPrediction.loss_type == GoalPrediction.CROSS_ENTROPY_AND_DIST_LOSS:
loss = cross_entropy_loss + dist_loss
else:
raise AssertionError("Unhandled loss type ",
GoalPrediction.loss_type)
prob = attention_log_prob[ix]
meta = {"cross_entropy": cross_entropy_loss, "dist_loss": dist_loss}
return loss, prob, meta
def forward(self, instructions_batch):
token_lists, text_pointers = instructions_batch
batch_size = len(token_lists)
text_lengths = np.array([len(tokens) for tokens in token_lists])
dims = (self.num_layers, batch_size, self.hidden_dim)
hidden_f = (Variable(cuda_tensor(torch.zeros(*dims)),
requires_grad=False),
Variable(cuda_tensor(torch.zeros(*dims)),
requires_grad=False))
hidden_b = (Variable(cuda_tensor(torch.zeros(*dims)),
requires_grad=False),
Variable(cuda_tensor(torch.zeros(*dims)),
requires_grad=False))
# pad text tokens with 0's
tokens_batch_f = [[] for _ in xrange(batch_size)]
tokens_batch_b = [[] for _ in xrange(batch_size)]
for i in xrange(batch_size):
num_zeros = text_lengths[0] - text_lengths[i]
tokens_batch_f[i] = token_lists[i] + [0] * num_zeros
tokens_batch_b[i] = token_lists[i][::-1] + [0] * num_zeros
tokens_batch_f = cuda_var(torch.from_numpy(np.array(tokens_batch_f)))
tokens_batch_b = cuda_var(torch.from_numpy(np.array(tokens_batch_b)))
# swap so batch dimension is second, sequence dimension is first
tokens_batch_f = tokens_batch_f.transpose(0, 1)
tokens_batch_b = tokens_batch_b.transpose(0, 1)
emb_sentence_f = self.embedding(tokens_batch_f)
emb_sentence_b = self.embedding(tokens_batch_b)
packed_input_f = pack_padded_sequence(emb_sentence_f, text_lengths)
packed_input_b = pack_padded_sequence(emb_sentence_b, text_lengths)
lstm_out_packed_f, _ = self.lstm_f(packed_input_f, hidden_f)
lstm_out_packed_b, _ = self.lstm_b(packed_input_b, hidden_b)
# return average output embedding
lstm_out_f, _ = pad_packed_sequence(lstm_out_packed_f)
lstm_out_b, _ = pad_packed_sequence(lstm_out_packed_b)
lstm_out_f = lstm_out_f.transpose(0, 1)
lstm_out_b = lstm_out_b.transpose(0, 1)
embeddings_list = []
for i, (start_i, end_i) in enumerate(text_pointers):
embeddings = []
if start_i > 0:
embeddings.append(lstm_out_f[i][start_i - 1])
else:
embeddings.append(cuda_var(torch.zeros(self.hidden_dim)))
embeddings.append(lstm_out_f[i][end_i - 1])
embeddings.append(lstm_out_b[i][start_i])
if end_i < text_lengths[i]:
embeddings.append(lstm_out_b[i][end_i])
else:
embeddings.append(cuda_var(torch.zeros(self.hidden_dim)))
embeddings_list.append(torch.cat(embeddings).view(1, -1))
embeddings_batch = torch.cat(embeddings_list)
return embeddings_batch
def save_correlation_figure_(num_homing_policies, model, test_batches,
exp_name):
correlation_stats = {}
for batch in test_batches:
prev_observations = cuda_var(
torch.cat([
torch.from_numpy(np.array(point.get_curr_obs())).view(1, -1)
for point in batch
],
dim=0)).float()
actions = cuda_var(
torch.cat([
torch.from_numpy(np.array(point.get_action())).view(1, -1)
for point in batch
],
dim=0)).long()
observations = cuda_var(
torch.cat([
torch.from_numpy(np.array(point.get_next_obs())).view(1, -1)
for point in batch
],
dim=0)).float()
# Compute loss
_, info_dict = model.gen_prob(prev_observations, actions,
observations) # batch x 2
assigned_states = info_dict["assigned_states"]
for i, point in enumerate(batch):
assigned_state = int(assigned_states[i])
if point.get_next_state() in correlation_stats:
correlation_stats[
point.get_next_state()][assigned_state] += 1.0
else:
vec = np.zeros(num_homing_policies, dtype=np.float32)
vec[assigned_state] = 1.0
correlation_stats[point.get_next_state()] = vec
num_states = 0
image = []
for key in sorted(correlation_stats):
vec = correlation_stats[key]
vec = vec / max(1.0, vec.sum())
image.append(vec)
num_states += 1
image = np.vstack(image)
image = scipy.misc.imresize(image,
(num_states * 100, num_homing_policies * 100))
filelist = os.listdir('./%s' % exp_name)
num_images = len(filelist)
scipy.misc.imsave("./%s/image_%d.png" % (exp_name, num_images + 1), image)
def get_log_prob(self, replay_item):
image_batch, instruction = replay_item
image_seqs = [image_batch]
image_batch = cuda_var(torch.from_numpy(np.array(image_seqs)).float())
instructions = [instruction]
instructions_batch = cuda_var(
torch.from_numpy(np.array(instructions)).long())
return self.final_module(image_batch, instructions_batch)
def get_probs(self,
agent_observed_state,
model_state,
mode=None,
volatile=False):
assert isinstance(agent_observed_state, AgentObservedState)
#supposedly this is already padded with zeros, but i need to double check that code
images = agent_observed_state.get_image()[-5:]
# image_seqs = [[aos.get_last_image()]
# for aos in agent_observed_state_list]
image_batch = cuda_var(
torch.from_numpy(np.array(images)).float(), volatile)
#flatten them? TODO: maybe don't hardcode this later on? batch size is 1 ;)
image_batch = image_batch.view(1, 15, 128, 128)
# list of list :)
instructions_batch = ([agent_observed_state.get_instruction()], False)
#instructions_batch = (cuda_var(torch.from_numpy(np.array(instructions)).long()), False)
#print("instructions", instructions)
#print("instructins_batch", instructions_batch)
prev_actions_raw = agent_observed_state.get_previous_action()
prev_actions_raw = self.none_action if prev_actions_raw is None else prev_actions_raw
if prev_actions_raw == 81:
previous_direction_id = [4]
else:
previous_direction_id = [prev_actions_raw % 4]
#this input is is over the space 81 things :)
previous_block_id = [int(prev_actions_raw / 4)]
prev_block_id_batch = cuda_var(
torch.from_numpy(np.array(previous_block_id)))
prev_direction_id_batch = cuda_var(
torch.from_numpy(np.array(previous_direction_id)))
# prev_actions = [self.none_action if a is None else a
# for a in prev_actions_raw]
#prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch, new_model_state = self.final_module(
image_batch, instructions_batch, prev_block_id_batch,
prev_direction_id_batch, model_state)
# last two we don't really need...
return probs_batch, new_model_state, None, None
def calc_loss(self, batch_replay_items):
if len(batch_replay_items) <= 1:
return None
action_batch = []
batch_image_feature = []
batch_next_image_feature = []
for replay_item in batch_replay_items:
next_image_emb = replay_item.get_next_image_emb()
if next_image_emb is None: # sometimes it can None for the last item in a rollout
continue
action_batch.append(replay_item.get_action())
batch_image_feature.append(replay_item.get_image_emb())
batch_next_image_feature.append(next_image_emb)
action_batch = cuda_var(torch.from_numpy(np.array(action_batch)))
batch_image_feature = torch.cat(batch_image_feature)
batch_next_image_feature = torch.cat(batch_next_image_feature)
# Predict the feature of next image
batch_predicted_next_image_feature = self.model.predict_action_result(
batch_image_feature, action_batch)
# Compute the squared mean loss
diff = (batch_predicted_next_image_feature - batch_next_image_feature)
temporal_autoencoding_loss = torch.mean(diff**2)
return temporal_autoencoding_loss
def calc_loss(self, batch_replay_items):
images = []
visible_objects = []
for replay_item in batch_replay_items:
image, visible_objects_ = replay_item
visible_objects.append(visible_objects_)
images.append([image])
theta_logits = self.model.get_probs(images) # batch x 67 x 12
num_states = int(theta_logits.size()[0])
one_hot_vector = torch.zeros(theta_logits.size())
for i in range(0, num_states):
visible_objects_example = visible_objects[i]
for landmark in range(0, self.num_landmark):
# See if the landmark is present and visible in the agent's field of view
if landmark in visible_objects_example and visible_objects_example[
landmark][1] != -1:
r, theta = visible_objects_example[landmark]
one_hot_vector[i, landmark, theta] = 1.0
loss = F.binary_cross_entropy_with_logits(
theta_logits,
cuda_var(one_hot_vector).float())
return loss
def _gather_fqi_samples(replay_dataset, step, horizon, reward_func, learned_policy):
dataset = []
for replay_item in replay_dataset[step]:
assert type(replay_item) == TransitionDatapoint and \
replay_item.get_timestep() == step and \
replay_item.is_valid() == 1
current_obs = replay_item.get_curr_obs()
next_obs = replay_item.get_next_obs()
if reward_func is None:
total_reward = replay_item.get_reward()
else:
total_reward = reward_func(current_obs, step)
if step < horizon:
obs_var = cuda_var(torch.from_numpy(next_obs)).float().view(1, -1)
q_val = learned_policy[step + 1].gen_q_val(obs_var).view(-1) # num_actions
total_reward += float(q_val.max(0)[0].data.cpu()) # Predict reward and take max
datapoint = (current_obs,
replay_item.get_action_prob(),
replay_item.get_action(),
total_reward,
replay_item.get_curr_state(),
replay_item.get_next_state(),
replay_item.get_policy_index())
dataset.append(datapoint)
return dataset
def get_probs_batch(self, agent_observed_state_list, mode=None):
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True
)
symbolic_image_list = []
for aos in agent_observed_state_list:
x_pos, z_pos, y_angle = aos.get_position_orientation()
landmark_pos_dict = aos.get_landmark_pos_dict()
symbolic_image = get_visible_landmark_r_theta(
x_pos, z_pos, y_angle, landmark_pos_dict)
symbolic_image_list.append(symbolic_image)
image_batch = symbolic_image_list
instructions_batch = [aos.get_symbolic_instruction()
for aos in agent_observed_state_list]
prev_actions_raw = [aos.get_previous_action()
for aos in agent_observed_state_list]
prev_actions = [self.none_action if a is None else a
for a in prev_actions_raw]
prev_actions_batch = cuda_var(torch.from_numpy(np.array(prev_actions)))
probs_batch = self.final_module(image_batch, instructions_batch,
prev_actions_batch, mode)
return probs_batch
def get_probs_and_visible_objects(self, agent_observed_state_list):
for aos in agent_observed_state_list:
assert isinstance(aos, AgentObservedState)
# print "batch size:", len(agent_observed_state_list)
# sort list by instruction length
agent_observed_state_list = sorted(
agent_observed_state_list,
key=lambda aos_: len(aos_.get_instruction()),
reverse=True)
images = [[aos.get_last_image()] for aos in agent_observed_state_list]
image_batch = cuda_var(torch.from_numpy(np.array(images)).float())
landmarks_visible = []
for aos in agent_observed_state_list:
x_pos, z_pos, y_angle = aos.get_position_orientation()
landmark_pos_dict = aos.get_landmark_pos_dict()
visible_landmarks = get_visible_landmark_r_theta(
x_pos, z_pos, y_angle, landmark_pos_dict, self.landmark_names)
landmarks_visible.append(visible_landmarks)
# shape is BATCH_SIZE x 63 x 2
probs_batch = self.final_module(image_batch)
# landmarks_visible is list of length BATCH_SIZE, each item is a set containing landmark indices
return probs_batch, landmarks_visible
def log_homing_policy_reward(self, env, homing_policies, step, logger):
num_samples = self.constants["eval_homing_policy_sample_size"]
all_total_reward = 0.0
for ix, policy in enumerate(homing_policies[step]):
total_reward = 0.0
for _ in range(0, num_samples):
# Rollin for steps
obs, meta = env.reset()
for step_ in range(1, step + 1):
obs_var = cuda_var(torch.from_numpy(obs)).float().view(1, -1)
action = policy[step_].sample_action(obs_var)
obs, reward, done, meta = env.step(action)
total_reward = total_reward + reward
total_reward = total_reward / float(max(1, num_samples))
all_total_reward = all_total_reward + total_reward
logger.log("After horizon %r. Policy Number %r receives mean reward %r" % (step, ix + 1, total_reward))
all_total_reward = all_total_reward / float(max(1, len(homing_policies[step])))
logger.log("After horizon %r. Random Policy receives reward %r" % (step, all_total_reward))
def generate_gold_prob(goal, final_height, final_width, sigma2=0.5):
row, col, row_real, col_real = goal
gold_prob = cuda_var(torch.zeros(final_height * final_width +
1)).float()
if row is None or col is None:
gold_prob[final_height *
final_width] = 1.0 # last value indicates not present
return gold_prob
row_ = float(round(row_real)) + 0.5
col_ = float(round(col_real)) + 0.5
for i in range(0, final_height):
for j in range(0, final_width):
ix = i * final_width + j
center = (i + 0.5, j + 0.5)
# dist2 = (center[0] - row_real) * (center[0] - row_real) + \
# (center[1] - col_real) * (center[1] - col_real)
dist2 = (center[0] - row_) * (center[0] - row_) + \
(center[1] - col_) * (center[1] - col_)
gold_prob[ix] = -dist2 / (2.0 * sigma2)
gold_prob = torch.exp(gold_prob).float()
gold_prob[final_height * final_width] = 0.0
gold_prob = gold_prob / (gold_prob.sum() + 0.00001)
return gold_prob
def _gather_last_observation(env, actions, step, homing_policies,
selection_weights):
start_obs, meta = env.reset()
if step > 1:
if selection_weights is None:
# Select a homing policy for the previous time step randomly uniformly
policy = random.choice(homing_policies[step - 1])
else:
# Select a homing policy for the previous time step using the given weights
# policy = random.choices(homing_policies[step - 1], weights=selection_weights, k=1)[0]
ix = gp.sample_action_from_prob(selection_weights)
policy = homing_policies[step - 1][ix]
obs = start_obs
for step_ in range(1, step):
obs_var = cuda_var(torch.from_numpy(obs)).float().view(1, -1)
action = policy[step_].sample_action(obs_var)
obs, reward, done, meta = env.step(action)
action = random.choice(actions)
new_obs, reward, done, meta = env.step(action)
return new_obs, meta
def forward(self, instructions_batch):
token_lists, _ = instructions_batch
batch_size = len(token_lists)
dims = (self.num_layers, batch_size, self.hidden_dim)
hidden = (Variable(cuda_tensor(torch.zeros(*dims)), requires_grad=False),
Variable(cuda_tensor(torch.zeros(*dims)), requires_grad=False))
# pad text tokens with 0's
text_lengths = np.array([len(tokens) for tokens in token_lists])
tokens_batch = [[] for _ in range(batch_size)]
for i in range(batch_size):
num_zeros = text_lengths[0] - text_lengths[i]
tokens_batch[i] = token_lists[i] + [0] * num_zeros
tokens_batch = cuda_var(torch.from_numpy(np.array(tokens_batch)))
# swap so batch dimension is second, sequence dimension is first
tokens_batch = tokens_batch.transpose(0, 1)
emb_sentence = self.embedding(tokens_batch)
packed_input = pack_padded_sequence(emb_sentence, text_lengths)
lstm_out_packed, _ = self.lstm(packed_input, hidden)
# return average output embedding
lstm_out, seq_lengths = pad_packed_sequence(lstm_out_packed)
lstm_out = lstm_out.transpose(0, 1)
sum_emb_list = []
for i, seq_out in enumerate(lstm_out):
seq_len = seq_lengths[i]
sum_emb = torch.sum(seq_out[:seq_len], 0) / seq_len
sum_emb_list.append(sum_emb.view(1, -1))
return torch.cat(sum_emb_list)
