def command_generation_teacher_force(self, observation_strings, task_desc_strings, target_strings):
input_target_strings = [" ".join(["[CLS]"] + item.split()) for item in target_strings]
output_target_strings = [" ".join(item.split() + ["[SEP]"]) for item in target_strings]
input_obs = self.get_word_input(observation_strings)
h_obs, obs_mask = self.encode(observation_strings, use_model="online")
h_td, td_mask = self.encode(task_desc_strings, use_model="online")
aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid
input_target = self.get_word_input(input_target_strings)
ground_truth = self.get_word_input(output_target_strings) # batch x target_length
target_mask = compute_mask(input_target) # mask of ground truth should be the same
pred = self.online_net.decode(input_target, target_mask, aggregated_obs_representation, obs_mask, None, input_obs) # batch x target_length x vocab
batch_loss = NegativeLogLoss(pred * target_mask.unsqueeze(-1), ground_truth, target_mask, smoothing_eps=self.smoothing_eps)
loss = torch.mean(batch_loss)
if loss is None:
return None
# Backpropagate
self.online_net.zero_grad()
self.optimizer.zero_grad()
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(self.online_net.parameters(), self.clip_grad_norm)
self.optimizer.step() # apply gradients
return to_np(loss)
def command_generation_greedy_generation(self, observation_feats,
task_desc_strings,
previous_dynamics):
with torch.no_grad():
batch_size = len(observation_feats)
aggregated_obs_feat = self.aggregate_feats_seq(observation_feats)
h_obs = self.online_net.vision_fc(aggregated_obs_feat)
h_td, td_mask = self.encode(task_desc_strings, use_model="online")
h_td_mean = self.online_net.masked_mean(h_td, td_mask).unsqueeze(1)
h_obs = h_obs.to(h_td_mean.device)
vision_td = torch.cat((h_obs, h_td_mean),
dim=1) # batch x k boxes x hid
vision_td_mask = torch.ones(
(batch_size,
h_obs.shape[1] + h_td_mean.shape[1])).to(h_td_mean.device)
if self.recurrent:
averaged_vision_td_representation = self.online_net.masked_mean(
vision_td, vision_td_mask)
current_dynamics = self.online_net.rnncell(
averaged_vision_td_representation, previous_dynamics
) if previous_dynamics is not None else self.online_net.rnncell(
averaged_vision_td_representation)
else:
current_dynamics = None
# greedy generation
input_target_list = [[self.word2id["[CLS]"]]
for i in range(batch_size)]
eos = np.zeros(batch_size)
for _ in range(self.max_target_length):
input_target = copy.deepcopy(input_target_list)
input_target = pad_sequences(
input_target, maxlen=max_len(input_target)).astype('int32')
input_target = to_pt(input_target, self.use_cuda)
target_mask = compute_mask(
input_target) # mask of ground truth should be the same
pred = self.online_net.vision_decode(
input_target, target_mask, vision_td, vision_td_mask,
current_dynamics) # batch x target_length x vocab
# pointer softmax
pred = to_np(pred[:, -1]) # batch x vocab
pred = np.argmax(pred, -1) # batch
for b in range(batch_size):
new_stuff = [pred[b]] if eos[b] == 0 else []
input_target_list[b] = input_target_list[b] + new_stuff
if pred[b] == self.word2id["[SEP]"]:
eos[b] = 1
if np.sum(eos) == batch_size:
break
res = [self.tokenizer.decode(item) for item in input_target_list]
res = [
item.replace("[CLS]", "").replace("[SEP]", "").strip()
for item in res
]
res = [item.replace(" in / on ", " in/on ") for item in res]
return res, current_dynamics
def encode_text(self, input_word_ids):
# input_word_ids: batch x seq_len
# text embedding / encoding
input_word_masks = compute_mask(input_word_ids)
embeddings = self.embed(input_word_ids,
input_word_masks) # batch x seq_len x emb
squared_mask = torch.bmm(
input_word_masks.unsqueeze(-1),
input_word_masks.unsqueeze(1)) # batch x seq_len x seq_len
encoding_sequence = embeddings
for i in range(self.encoder_layers):
encoding_sequence = self.encoder[i](
encoding_sequence, input_word_masks, squared_mask,
i * (self.encoder_conv_num + 2) + 1,
self.encoder_layers) # batch x time x enc
return encoding_sequence, input_word_masks
def command_generation_teacher_force(self, observation_feats,
task_desc_strings, target_strings):
input_target_strings = [
" ".join(["[CLS]"] + item.split()) for item in target_strings
]
output_target_strings = [
" ".join(item.split() + ["[SEP]"]) for item in target_strings
]
batch_size = len(observation_feats)
aggregated_obs_feat = self.aggregate_feats_seq(observation_feats)
h_obs = self.online_net.vision_fc(aggregated_obs_feat)
h_td, td_mask = self.encode(task_desc_strings, use_model="online")
h_td_mean = self.online_net.masked_mean(h_td, td_mask).unsqueeze(1)
h_obs = h_obs.to(h_td_mean.device)
vision_td = torch.cat((h_obs, h_td_mean),
dim=1) # batch x k boxes x hi
vision_td_mask = torch.ones(
(batch_size,
h_obs.shape[1] + h_td_mean.shape[1])).to(h_td_mean.device)
input_target = self.get_word_input(input_target_strings)
ground_truth = self.get_word_input(
output_target_strings) # batch x target_length
target_mask = compute_mask(
input_target) # mask of ground truth should be the same
pred = self.online_net.vision_decode(
input_target, target_mask, vision_td, vision_td_mask,
None) # batch x target_length x vocab
batch_loss = NegativeLogLoss(pred * target_mask.unsqueeze(-1),
ground_truth,
target_mask,
smoothing_eps=self.smoothing_eps)
loss = torch.mean(batch_loss)
if loss is None:
return None, None
# Backpropagate
self.online_net.zero_grad()
self.optimizer.zero_grad()
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(self.online_net.parameters(),
self.clip_grad_norm)
self.optimizer.step() # apply gradients
return to_np(pred), to_np(loss)
def command_generation_recurrent_teacher_force(self, seq_observation_strings, seq_task_desc_strings, seq_target_strings, contains_first_step=False):
loss_list = []
previous_dynamics = None
h_td, td_mask = self.encode(seq_task_desc_strings[0], use_model="online")
for step_no in range(self.dagger_replay_sample_history_length):
input_target_strings = [" ".join(["[CLS]"] + item.split()) for item in seq_target_strings[step_no]]
output_target_strings = [" ".join(item.split() + ["[SEP]"]) for item in seq_target_strings[step_no]]
input_obs = self.get_word_input(seq_observation_strings[step_no])
h_obs, obs_mask = self.encode(seq_observation_strings[step_no], use_model="online")
aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid
averaged_representation = self.online_net.masked_mean(aggregated_obs_representation, obs_mask) # batch x hid
current_dynamics = self.online_net.rnncell(averaged_representation, previous_dynamics) if previous_dynamics is not None else self.online_net.rnncell(averaged_representation)
input_target = self.get_word_input(input_target_strings)
ground_truth = self.get_word_input(output_target_strings) # batch x target_length
target_mask = compute_mask(input_target) # mask of ground truth should be the same
pred = self.online_net.decode(input_target, target_mask, aggregated_obs_representation, obs_mask, current_dynamics, input_obs) # batch x target_length x vocab
previous_dynamics = current_dynamics
if (not contains_first_step) and step_no < self.dagger_replay_sample_update_from:
previous_dynamics = previous_dynamics.detach()
continue
batch_loss = NegativeLogLoss(pred * target_mask.unsqueeze(-1), ground_truth, target_mask, smoothing_eps=self.smoothing_eps)
loss = torch.mean(batch_loss)
loss_list.append(loss)
loss = torch.stack(loss_list).mean()
if loss is None:
return None
# Backpropagate
self.online_net.zero_grad()
self.optimizer.zero_grad()
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
torch.nn.utils.clip_grad_norm_(self.online_net.parameters(), self.clip_grad_norm)
self.optimizer.step() # apply gradients
return to_np(loss)
def command_generation_beam_search_generation(self, observation_strings, task_desc_strings, previous_dynamics):
with torch.no_grad():
batch_size = len(observation_strings)
beam_width = self.beam_width
if beam_width == 1:
res, current_dynamics = self.command_generation_greedy_generation(observation_strings, task_desc_strings, previous_dynamics)
res = [[item] for item in res]
return res, current_dynamics
generate_top_k = self.generate_top_k
res = []
input_obs = self.get_word_input(observation_strings)
h_obs, obs_mask = self.encode(observation_strings, use_model="online")
h_td, td_mask = self.encode(task_desc_strings, use_model="online")
aggregated_obs_representation = self.online_net.aggretate_information(h_obs, obs_mask, h_td, td_mask) # batch x obs_length x hid
if self.recurrent:
averaged_representation = self.online_net.masked_mean(aggregated_obs_representation, obs_mask) # batch x hid
current_dynamics = self.online_net.rnncell(averaged_representation, previous_dynamics) if previous_dynamics is not None else self.online_net.rnncell(averaged_representation)
else:
current_dynamics = None
for b in range(batch_size):
# starts from CLS tokens
__input_target_list = [self.word2id["[CLS]"]]
__input_obs = input_obs[b: b + 1] # 1 x obs_len
__obs_mask = obs_mask[b: b + 1] # 1 x obs_len
__aggregated_obs_representation = aggregated_obs_representation[b: b + 1] # 1 x obs_len x hid
if current_dynamics is not None:
__current_dynamics = current_dynamics[b: b + 1] # 1 x hid
else:
__current_dynamics = None
ended_nodes = []
# starting node - previous node, input target, logp, length
node = BeamSearchNode(None, __input_target_list, 0, 1)
nodes_queue = PriorityQueue()
# start the queue
nodes_queue.put((node.val, node))
queue_size = 1
while(True):
# give up when decoding takes too long
if queue_size > 2000:
break
# fetch the best node
score, n = nodes_queue.get()
__input_target_list = n.input_target
if (n.input_target[-1] == self.word2id["[SEP]"] or n.length >= self.max_target_length) and n.previous_node != None:
ended_nodes.append((score, n))
# if we reached maximum # of sentences required
if len(ended_nodes) >= generate_top_k:
break
else:
continue
input_target = pad_sequences([__input_target_list], dtype='int32')
input_target = to_pt(input_target, self.use_cuda)
target_mask = compute_mask(input_target)
# decode for one step using decoder
pred = self.online_net.decode(input_target, target_mask, __aggregated_obs_representation, __obs_mask, __current_dynamics, __input_obs) # 1 x target_length x vocab
pred = pred[0][-1].cpu()
gt_zero = torch.gt(pred, 0.0).float() # vocab
epsilon = torch.le(pred, 0.0).float() * 1e-8 # vocab
log_pred = torch.log(pred + epsilon) * gt_zero # vocab
top_beam_width_log_probs, top_beam_width_indicies = torch.topk(log_pred, beam_width)
next_nodes = []
for new_k in range(beam_width):
pos = top_beam_width_indicies[new_k]
log_p = top_beam_width_log_probs[new_k].item()
node = BeamSearchNode(n, __input_target_list + [pos], n.log_prob + log_p, n.length + 1)
next_nodes.append((node.val, node))
# put them into queue
for i in range(len(next_nodes)):
score, nn = next_nodes[i]
nodes_queue.put((score, nn))
# increase qsize
queue_size += len(next_nodes) - 1
# choose n best paths
if len(ended_nodes) == 0:
ended_nodes = [nodes_queue.get() for _ in range(generate_top_k)]
utterances = []
for score, n in sorted(ended_nodes, key=operator.itemgetter(0)):
utte = n.input_target
utte_string = self.tokenizer.decode(utte)
utterances.append(utte_string)
utterances = [item.replace("[CLS]", "").replace("[SEP]", "").strip() for item in utterances]
utterances = [item.replace(" in / on ", " in/on " ) for item in utterances]
res.append(utterances)
return res, current_dynamics
