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agent.py
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agent.py
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import json
import os
import sys
import numpy as np
import random
import math
import time
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch import optim
import torch.nn.functional as F
from env import R2RBatch
from utils import padding_idx, add_idx, Tokenizer
import utils
import model
import param
from param import args
from collections import defaultdict
class BaseAgent(object):
''' Base class for an R2R agent to generate and save trajectories. '''
def __init__(self, env, results_path):
self.env = env
self.results_path = results_path
random.seed(1)
self.results = {}
self.losses = [] # For learning agents
def write_results(self):
output = [{'instr_id':k, 'trajectory': v} for k,v in self.results.items()]
with open(self.results_path, 'w') as f:
json.dump(output, f)
def get_results(self):
output = [{'instr_id': k, 'trajectory': v} for k, v in self.results.items()]
return output
def rollout(self, **args):
''' Return a list of dicts containing instr_id:'xx', path:[(viewpointId, heading_rad, elevation_rad)] '''
raise NotImplementedError
@staticmethod
def get_agent(name):
return globals()[name+"Agent"]
def test(self, iters=None, **kwargs):
self.env.reset_epoch(shuffle=(iters is not None)) # If iters is not none, shuffle the env batch
self.losses = []
self.results = {}
# We rely on env showing the entire batch before repeating anything
looped = False
self.loss = 0
if iters is not None:
# For each time, it will run the first 'iters' iterations. (It was shuffled before)
for i in range(iters):
for traj in self.rollout(**kwargs):
self.loss = 0
self.results[traj['instr_id']] = traj['path']
else: # Do a full round
while True:
for traj in self.rollout(**kwargs):
if traj['instr_id'] in self.results:
looped = True
else:
self.loss = 0
self.results[traj['instr_id']] = traj['path']
if looped:
break
class Seq2SeqAgent(BaseAgent):
''' An agent based on an LSTM seq2seq model with attention. '''
# For now, the agent can't pick which forward move to make - just the one in the middle
env_actions = {
'left': (0,-1, 0), # left
'right': (0, 1, 0), # right
'up': (0, 0, 1), # up
'down': (0, 0,-1), # down
'forward': (1, 0, 0), # forward
'<end>': (0, 0, 0), # <end>
'<start>': (0, 0, 0), # <start>
'<ignore>': (0, 0, 0) # <ignore>
}
def __init__(self, env, results_path, tok, episode_len=20):
super(Seq2SeqAgent, self).__init__(env, results_path)
self.tok = tok
self.episode_len = episode_len
self.feature_size = self.env.feature_size
# Models
enc_hidden_size = args.rnn_dim//2 if args.bidir else args.rnn_dim
self.encoder = model.EncoderLSTM(tok.vocab_size(), args.wemb, enc_hidden_size, padding_idx,
args.dropout, bidirectional=args.bidir).cuda()
self.decoder = model.AttnDecoderLSTM(args.aemb, args.rnn_dim, args.dropout, feature_size=self.feature_size + args.angle_feat_size).cuda()
self.critic = model.Critic().cuda()
self.models = (self.encoder, self.decoder, self.critic)
# Optimizers
self.encoder_optimizer = args.optimizer(self.encoder.parameters(), lr=args.lr)
self.decoder_optimizer = args.optimizer(self.decoder.parameters(), lr=args.lr)
self.critic_optimizer = args.optimizer(self.critic.parameters(), lr=args.lr)
self.optimizers = (self.encoder_optimizer, self.decoder_optimizer, self.critic_optimizer)
# Evaluations
self.losses = []
self.criterion = nn.CrossEntropyLoss(ignore_index=args.ignoreid, size_average=False)
# Logs
sys.stdout.flush()
self.logs = defaultdict(list)
def _sort_batch(self, obs):
''' Extract instructions from a list of observations and sort by descending
sequence length (to enable PyTorch packing). '''
seq_tensor = np.array([ob['instr_encoding'] for ob in obs])
seq_lengths = np.argmax(seq_tensor == padding_idx, axis=1)
seq_lengths[seq_lengths == 0] = seq_tensor.shape[1] # Full length
seq_tensor = torch.from_numpy(seq_tensor)
seq_lengths = torch.from_numpy(seq_lengths)
# Sort sequences by lengths
seq_lengths, perm_idx = seq_lengths.sort(0, True) # True -> descending
sorted_tensor = seq_tensor[perm_idx]
mask = (sorted_tensor == padding_idx)[:,:seq_lengths[0]] # seq_lengths[0] is the Maximum length
return Variable(sorted_tensor, requires_grad=False).long().cuda(), \
mask.byte().cuda(), \
list(seq_lengths), list(perm_idx)
def _feature_variable(self, obs):
''' Extract precomputed features into variable. '''
features = np.empty((len(obs), args.views, self.feature_size + args.angle_feat_size), dtype=np.float32)
for i, ob in enumerate(obs):
features[i, :, :] = ob['feature'] # Image feat
return Variable(torch.from_numpy(features), requires_grad=False).cuda()
def _candidate_variable(self, obs):
candidate_leng = [len(ob['candidate']) + 1 for ob in obs] # +1 is for the end
candidate_feat = np.zeros((len(obs), max(candidate_leng), self.feature_size + args.angle_feat_size), dtype=np.float32)
# Note: The candidate_feat at len(ob['candidate']) is the feature for the END
# which is zero in my implementation
for i, ob in enumerate(obs):
for j, c in enumerate(ob['candidate']):
candidate_feat[i, j, :] = c['feature'] # Image feat
return torch.from_numpy(candidate_feat).cuda(), candidate_leng
def get_input_feat(self, obs):
input_a_t = np.zeros((len(obs), args.angle_feat_size), np.float32)
for i, ob in enumerate(obs):
input_a_t[i] = utils.angle_feature(ob['heading'], ob['elevation'])
input_a_t = torch.from_numpy(input_a_t).cuda()
f_t = self._feature_variable(obs) # Image features from obs
candidate_feat, candidate_leng = self._candidate_variable(obs)
return input_a_t, f_t, candidate_feat, candidate_leng
def _teacher_action(self, obs, ended):
"""
Extract teacher actions into variable.
:param obs: The observation.
:param ended: Whether the action seq is ended
:return:
"""
a = np.zeros(len(obs), dtype=np.int64)
for i, ob in enumerate(obs):
if ended[i]: # Just ignore this index
a[i] = args.ignoreid
else:
for k, candidate in enumerate(ob['candidate']):
if candidate['viewpointId'] == ob['teacher']: # Next view point
a[i] = k
break
else: # Stop here
assert ob['teacher'] == ob['viewpoint'] # The teacher action should be "STAY HERE"
a[i] = len(ob['candidate'])
return torch.from_numpy(a).cuda()
def make_equiv_action(self, a_t, perm_obs, perm_idx=None, traj=None):
"""
Interface between Panoramic view and Egocentric view
It will convert the action panoramic view action a_t to equivalent egocentric view actions for the simulator
"""
def take_action(i, idx, name):
if type(name) is int: # Go to the next view
self.env.env.sims[idx].makeAction(name, 0, 0)
else: # Adjust
self.env.env.sims[idx].makeAction(*self.env_actions[name])
state = self.env.env.sims[idx].getState()
if traj is not None:
traj[i]['path'].append((state.location.viewpointId, state.heading, state.elevation))
if perm_idx is None:
perm_idx = range(len(perm_obs))
for i, idx in enumerate(perm_idx):
action = a_t[i]
if action != -1: # -1 is the <stop> action
select_candidate = perm_obs[i]['candidate'][action]
src_point = perm_obs[i]['viewIndex']
trg_point = select_candidate['pointId']
src_level = (src_point ) // 12 # The point idx started from 0
trg_level = (trg_point ) // 12
while src_level < trg_level: # Tune up
take_action(i, idx, 'up')
src_level += 1
while src_level > trg_level: # Tune down
take_action(i, idx, 'down')
src_level -= 1
while self.env.env.sims[idx].getState().viewIndex != trg_point: # Turn right until the target
take_action(i, idx, 'right')
assert select_candidate['viewpointId'] == \
self.env.env.sims[idx].getState().navigableLocations[select_candidate['idx']].viewpointId
take_action(i, idx, select_candidate['idx'])
def rollout(self, train_ml=None, train_rl=True, reset=True, speaker=None):
"""
:param train_ml: The weight to train with maximum likelihood
:param train_rl: whether use RL in training
:param reset: Reset the environment
:param speaker: Speaker used in back translation.
If the speaker is not None, use back translation.
O.w., normal training
:return:
"""
if self.feedback == 'teacher' or self.feedback == 'argmax':
train_rl = False
if reset:
# Reset env
obs = np.array(self.env.reset())
else:
obs = np.array(self.env._get_obs())
batch_size = len(obs)
if speaker is not None: # Trigger the self_train mode!
noise = self.decoder.drop_env(torch.ones(self.feature_size).cuda())
batch = self.env.batch.copy()
speaker.env = self.env
insts = speaker.infer_batch(featdropmask=noise) # Use the same drop mask in speaker
# Create fake environments with the generated instruction
boss = np.ones((batch_size, 1), np.int64) * self.tok.word_to_index['<BOS>'] # First word is <BOS>
insts = np.concatenate((boss, insts), 1)
for i, (datum, inst) in enumerate(zip(batch, insts)):
if inst[-1] != self.tok.word_to_index['<PAD>']: # The inst is not ended!
inst[-1] = self.tok.word_to_index['<EOS>']
datum.pop('instructions')
datum.pop('instr_encoding')
datum['instructions'] = self.tok.decode_sentence(inst)
datum['instr_encoding'] = inst
obs = np.array(self.env.reset(batch))
# Reorder the language input for the encoder (do not ruin the original code)
seq, seq_mask, seq_lengths, perm_idx = self._sort_batch(obs)
perm_obs = obs[perm_idx]
ctx, h_t, c_t = self.encoder(seq, seq_lengths)
ctx_mask = seq_mask
# Init the reward shaping
last_dist = np.zeros(batch_size, np.float32)
for i, ob in enumerate(perm_obs): # The init distance from the view point to the target
last_dist[i] = ob['distance']
# Record starting point
traj = [{
'instr_id': ob['instr_id'],
'path': [(ob['viewpoint'], ob['heading'], ob['elevation'])]
} for ob in perm_obs]
# For test result submission
visited = [set() for _ in perm_obs]
# Initialization the tracking state
ended = np.array([False] * batch_size) # Indices match permuation of the model, not env
# Init the logs
rewards = []
hidden_states = []
policy_log_probs = []
masks = []
entropys = []
ml_loss = 0.
h1 = h_t
for t in range(self.episode_len):
input_a_t, f_t, candidate_feat, candidate_leng = self.get_input_feat(perm_obs)
if speaker is not None: # Apply the env drop mask to the feat
candidate_feat[..., :-args.angle_feat_size] *= noise
f_t[..., :-args.angle_feat_size] *= noise
h_t, c_t, logit, h1 = self.decoder(input_a_t, f_t, candidate_feat,
h_t, h1, c_t,
ctx, ctx_mask,
already_dropfeat=(speaker is not None))
hidden_states.append(h_t)
# Mask outputs where agent can't move forward
# Here the logit is [b, max_candidate]
candidate_mask = utils.length2mask(candidate_leng)
if args.submit: # Avoding cyclic path
for ob_id, ob in enumerate(perm_obs):
visited[ob_id].add(ob['viewpoint'])
for c_id, c in enumerate(ob['candidate']):
if c['viewpointId'] in visited[ob_id]:
candidate_mask[ob_id][c_id] = 1
logit.masked_fill_(candidate_mask, -float('inf'))
# Supervised training
target = self._teacher_action(perm_obs, ended)
ml_loss += self.criterion(logit, target)
# Determine next model inputs
if self.feedback == 'teacher':
a_t = target # teacher forcing
elif self.feedback == 'argmax':
_, a_t = logit.max(1) # student forcing - argmax
a_t = a_t.detach()
log_probs = F.log_softmax(logit, 1) # Calculate the log_prob here
policy_log_probs.append(log_probs.gather(1, a_t.unsqueeze(1))) # Gather the log_prob for each batch
elif self.feedback == 'sample':
probs = F.softmax(logit, 1) # sampling an action from model
c = torch.distributions.Categorical(probs)
self.logs['entropy'].append(c.entropy().sum().item()) # For log
entropys.append(c.entropy()) # For optimization
a_t = c.sample().detach()
policy_log_probs.append(c.log_prob(a_t))
else:
print(self.feedback)
sys.exit('Invalid feedback option')
# Prepare environment action
# NOTE: Env action is in the perm_obs space
cpu_a_t = a_t.cpu().numpy()
for i, next_id in enumerate(cpu_a_t):
if next_id == (candidate_leng[i]-1) or next_id == args.ignoreid: # The last action is <end>
cpu_a_t[i] = -1 # Change the <end> and ignore action to -1
# Make action and get the new state
self.make_equiv_action(cpu_a_t, perm_obs, perm_idx, traj)
obs = np.array(self.env._get_obs())
perm_obs = obs[perm_idx] # Perm the obs for the resu
# Calculate the mask and reward
dist = np.zeros(batch_size, np.float32)
reward = np.zeros(batch_size, np.float32)
mask = np.ones(batch_size, np.float32)
for i, ob in enumerate(perm_obs):
dist[i] = ob['distance']
if ended[i]: # If the action is already finished BEFORE THIS ACTION.
reward[i] = 0.
mask[i] = 0.
else: # Calculate the reward
action_idx = cpu_a_t[i]
if action_idx == -1: # If the action now is end
if dist[i] < 3: # Correct
reward[i] = 2.
else: # Incorrect
reward[i] = -2.
else: # The action is not end
reward[i] = - (dist[i] - last_dist[i]) # Change of distance
if reward[i] > 0: # Quantification
reward[i] = 1
elif reward[i] < 0:
reward[i] = -1
else:
raise NameError("The action doesn't change the move")
rewards.append(reward)
masks.append(mask)
last_dist[:] = dist
# Update the finished actions
# -1 means ended or ignored (already ended)
ended[:] = np.logical_or(ended, (cpu_a_t == -1))
# Early exit if all ended
if ended.all():
break
if train_rl:
# Last action in A2C
input_a_t, f_t, candidate_feat, candidate_leng = self.get_input_feat(perm_obs)
if speaker is not None:
candidate_feat[..., :-args.angle_feat_size] *= noise
f_t[..., :-args.angle_feat_size] *= noise
last_h_, _, _, _ = self.decoder(input_a_t, f_t, candidate_feat,
h_t, h1, c_t,
ctx, ctx_mask,
speaker is not None)
rl_loss = 0.
# NOW, A2C!!!
# Calculate the final discounted reward
last_value__ = self.critic(last_h_).detach() # The value esti of the last state, remove the grad for safety
discount_reward = np.zeros(batch_size, np.float32) # The inital reward is zero
for i in range(batch_size):
if not ended[i]: # If the action is not ended, use the value function as the last reward
discount_reward[i] = last_value__[i]
length = len(rewards)
total = 0
for t in range(length-1, -1, -1):
discount_reward = discount_reward * args.gamma + rewards[t] # If it ended, the reward will be 0
mask_ = Variable(torch.from_numpy(masks[t]), requires_grad=False).cuda()
clip_reward = discount_reward.copy()
r_ = Variable(torch.from_numpy(clip_reward), requires_grad=False).cuda()
v_ = self.critic(hidden_states[t])
a_ = (r_ - v_).detach()
# r_: The higher, the better. -ln(p(action)) * (discount_reward - value)
rl_loss += (-policy_log_probs[t] * a_ * mask_).sum()
rl_loss += (((r_ - v_) ** 2) * mask_).sum() * 0.5 # 1/2 L2 loss
if self.feedback == 'sample':
rl_loss += (- 0.01 * entropys[t] * mask_).sum()
self.logs['critic_loss'].append((((r_ - v_) ** 2) * mask_).sum().item())
total = total + np.sum(masks[t])
self.logs['total'].append(total)
# Normalize the loss function
if args.normalize_loss == 'total':
rl_loss /= total
elif args.normalize_loss == 'batch':
rl_loss /= batch_size
else:
assert args.normalize_loss == 'none'
self.loss += rl_loss
if train_ml is not None:
self.loss += ml_loss * train_ml / batch_size
if type(self.loss) is int: # For safety, it will be activated if no losses are added
self.losses.append(0.)
else:
self.losses.append(self.loss.item() / self.episode_len) # This argument is useless.
return traj
def _dijkstra(self):
"""
The dijkstra algorithm.
Was called beam search to be consistent with existing work.
But it actually finds the Exact K paths with smallest listener log_prob.
:return:
[{
"scan": XXX
"instr_id":XXX,
'instr_encoding": XXX
'dijk_path': [v1, v2, ..., vn] (The path used for find all the candidates)
"paths": {
"trajectory": [viewpoint_id1, viewpoint_id2, ..., ],
"action": [act_1, act_2, ..., ],
"listener_scores": [log_prob_act1, log_prob_act2, ..., ],
"visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)
}
}]
"""
def make_state_id(viewpoint, action): # Make state id
return "%s_%s" % (viewpoint, str(action))
def decompose_state_id(state_id): # Make state id
viewpoint, action = state_id.split("_")
action = int(action)
return viewpoint, action
# Get first obs
obs = self.env._get_obs()
# Prepare the state id
batch_size = len(obs)
results = [{"scan": ob['scan'],
"instr_id": ob['instr_id'],
"instr_encoding": ob["instr_encoding"],
"dijk_path": [ob['viewpoint']],
"paths": []} for ob in obs]
# Encoder
seq, seq_mask, seq_lengths, perm_idx = self._sort_batch(obs)
recover_idx = np.zeros_like(perm_idx)
for i, idx in enumerate(perm_idx):
recover_idx[idx] = i
ctx, h_t, c_t = self.encoder(seq, seq_lengths)
ctx, h_t, c_t, ctx_mask = ctx[recover_idx], h_t[recover_idx], c_t[recover_idx], seq_mask[recover_idx] # Recover the original order
# Dijk Graph States:
id2state = [
{make_state_id(ob['viewpoint'], -95):
{"next_viewpoint": ob['viewpoint'],
"running_state": (h_t[i], h_t[i], c_t[i]),
"location": (ob['viewpoint'], ob['heading'], ob['elevation']),
"feature": None,
"from_state_id": None,
"score": 0,
"scores": [],
"actions": [],
}
}
for i, ob in enumerate(obs)
] # -95 is the start point
visited = [set() for _ in range(batch_size)]
finished = [set() for _ in range(batch_size)]
graphs = [utils.FloydGraph() for _ in range(batch_size)] # For the navigation path
ended = np.array([False] * batch_size)
# Dijk Algorithm
for _ in range(300):
# Get the state with smallest score for each batch
# If the batch is not ended, find the smallest item.
# Else use a random item from the dict (It always exists)
smallest_idXstate = [
max(((state_id, state) for state_id, state in id2state[i].items() if state_id not in visited[i]),
key=lambda item: item[1]['score'])
if not ended[i]
else
next(iter(id2state[i].items()))
for i in range(batch_size)
]
# Set the visited and the end seqs
for i, (state_id, state) in enumerate(smallest_idXstate):
assert (ended[i]) or (state_id not in visited[i])
if not ended[i]:
viewpoint, action = decompose_state_id(state_id)
visited[i].add(state_id)
if action == -1:
finished[i].add(state_id)
if len(finished[i]) >= args.candidates: # Get enough candidates
ended[i] = True
# Gather the running state in the batch
h_ts, h1s, c_ts = zip(*(idXstate[1]['running_state'] for idXstate in smallest_idXstate))
h_t, h1, c_t = torch.stack(h_ts), torch.stack(h1s), torch.stack(c_ts)
# Recover the env and gather the feature
for i, (state_id, state) in enumerate(smallest_idXstate):
next_viewpoint = state['next_viewpoint']
scan = results[i]['scan']
from_viewpoint, heading, elevation = state['location']
self.env.env.sims[i].newEpisode(scan, next_viewpoint, heading, elevation) # Heading, elevation is not used in panoramic
obs = self.env._get_obs()
# Update the floyd graph
# Only used to shorten the navigation length
# Will not effect the result
for i, ob in enumerate(obs):
viewpoint = ob['viewpoint']
if not graphs[i].visited(viewpoint): # Update the Graph
for c in ob['candidate']:
next_viewpoint = c['viewpointId']
dis = self.env.distances[ob['scan']][viewpoint][next_viewpoint]
graphs[i].add_edge(viewpoint, next_viewpoint, dis)
graphs[i].update(viewpoint)
results[i]['dijk_path'].extend(graphs[i].path(results[i]['dijk_path'][-1], viewpoint))
input_a_t, f_t, candidate_feat, candidate_leng = self.get_input_feat(obs)
# Run one decoding step
h_t, c_t, alpha, logit, h1 = self.decoder(input_a_t, f_t, candidate_feat,
h_t, h1, c_t,
ctx, ctx_mask,
False)
# Update the dijk graph's states with the newly visited viewpoint
candidate_mask = utils.length2mask(candidate_leng)
logit.masked_fill_(candidate_mask, -float('inf'))
log_probs = F.log_softmax(logit, 1) # Calculate the log_prob here
_, max_act = log_probs.max(1)
for i, ob in enumerate(obs):
current_viewpoint = ob['viewpoint']
candidate = ob['candidate']
current_state_id, current_state = smallest_idXstate[i]
old_viewpoint, from_action = decompose_state_id(current_state_id)
assert ob['viewpoint'] == current_state['next_viewpoint']
if from_action == -1 or ended[i]: # If the action is <end> or the batch is ended, skip it
continue
for j in range(len(ob['candidate']) + 1): # +1 to include the <end> action
# score + log_prob[action]
modified_log_prob = log_probs[i][j].detach().cpu().item()
new_score = current_state['score'] + modified_log_prob
if j < len(candidate): # A normal action
next_id = make_state_id(current_viewpoint, j)
next_viewpoint = candidate[j]['viewpointId']
trg_point = candidate[j]['pointId']
heading = (trg_point % 12) * math.pi / 6
elevation = (trg_point // 12 - 1) * math.pi / 6
location = (next_viewpoint, heading, elevation)
else: # The end action
next_id = make_state_id(current_viewpoint, -1) # action is -1
next_viewpoint = current_viewpoint # next viewpoint is still here
location = (current_viewpoint, ob['heading'], ob['elevation'])
if next_id not in id2state[i] or new_score > id2state[i][next_id]['score']:
id2state[i][next_id] = {
"next_viewpoint": next_viewpoint,
"location": location,
"running_state": (h_t[i], h1[i], c_t[i]),
"from_state_id": current_state_id,
"feature": (f_t[i].detach().cpu(), candidate_feat[i][j].detach().cpu()),
"score": new_score,
"scores": current_state['scores'] + [modified_log_prob],
"actions": current_state['actions'] + [len(candidate)+1],
}
# The active state is zero after the updating, then setting the ended to True
for i in range(batch_size):
if len(visited[i]) == len(id2state[i]): # It's the last active state
ended[i] = True
# End?
if ended.all():
break
# Move back to the start point
for i in range(batch_size):
results[i]['dijk_path'].extend(graphs[i].path(results[i]['dijk_path'][-1], results[i]['dijk_path'][0]))
"""
"paths": {
"trajectory": [viewpoint_id1, viewpoint_id2, ..., ],
"action": [act_1, act_2, ..., ],
"listener_scores": [log_prob_act1, log_prob_act2, ..., ],
"visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)
}
"""
# Gather the Path
for i, result in enumerate(results):
assert len(finished[i]) <= args.candidates
for state_id in finished[i]:
path_info = {
"trajectory": [],
"action": [],
"listener_scores": id2state[i][state_id]['scores'],
"listener_actions": id2state[i][state_id]['actions'],
"visual_feature": []
}
viewpoint, action = decompose_state_id(state_id)
while action != -95:
state = id2state[i][state_id]
path_info['trajectory'].append(state['location'])
path_info['action'].append(action)
path_info['visual_feature'].append(state['feature'])
state_id = id2state[i][state_id]['from_state_id']
viewpoint, action = decompose_state_id(state_id)
state = id2state[i][state_id]
path_info['trajectory'].append(state['location'])
for need_reverse_key in ["trajectory", "action", "visual_feature"]:
path_info[need_reverse_key] = path_info[need_reverse_key][::-1]
result['paths'].append(path_info)
return results
def beam_search(self, speaker):
"""
:param speaker: The speaker to be used in searching.
:return:
{
"scan": XXX
"instr_id":XXX,
"instr_encoding": XXX
"dijk_path": [v1, v2, ...., vn]
"paths": [{
"trajectory": [viewoint_id0, viewpoint_id1, viewpoint_id2, ..., ],
"action": [act_1, act_2, ..., ],
"listener_scores": [log_prob_act1, log_prob_act2, ..., ],
"speaker_scores": [log_prob_word1, log_prob_word2, ..., ],
}]
}
"""
self.env.reset()
results = self._dijkstra()
"""
return from self._dijkstra()
[{
"scan": XXX
"instr_id":XXX,
"instr_encoding": XXX
"dijk_path": [v1, v2, ...., vn]
"paths": [{
"trajectory": [viewoint_id0, viewpoint_id1, viewpoint_id2, ..., ],
"action": [act_1, act_2, ..., ],
"listener_scores": [log_prob_act1, log_prob_act2, ..., ],
"visual_feature": [(f1_step1, f2_step2, ...), (f1_step2, f2_step2, ...)
}]
}]
"""
# Compute the speaker scores:
for result in results:
lengths = []
num_paths = len(result['paths'])
for path in result['paths']:
assert len(path['trajectory']) == (len(path['visual_feature']) + 1)
lengths.append(len(path['visual_feature']))
max_len = max(lengths)
img_feats = torch.zeros(num_paths, max_len, 36, self.feature_size + args.angle_feat_size)
can_feats = torch.zeros(num_paths, max_len, self.feature_size + args.angle_feat_size)
for j, path in enumerate(result['paths']):
for k, feat in enumerate(path['visual_feature']):
img_feat, can_feat = feat
img_feats[j][k] = img_feat
can_feats[j][k] = can_feat
img_feats, can_feats = img_feats.cuda(), can_feats.cuda()
features = ((img_feats, can_feats), lengths)
insts = np.array([result['instr_encoding'] for _ in range(num_paths)])
seq_lengths = np.argmax(insts == self.tok.word_to_index['<EOS>'], axis=1) # len(seq + 'BOS') == len(seq + 'EOS')
insts = torch.from_numpy(insts).cuda()
speaker_scores = speaker.teacher_forcing(train=True, features=features, insts=insts, for_listener=True)
for j, path in enumerate(result['paths']):
path.pop("visual_feature")
path['speaker_scores'] = -speaker_scores[j].detach().cpu().numpy()[:seq_lengths[j]]
return results
def beam_search_test(self, speaker):
self.encoder.eval()
self.decoder.eval()
self.critic.eval()
looped = False
self.results = {}
while True:
for traj in self.beam_search(speaker):
if traj['instr_id'] in self.results:
looped = True
else:
self.results[traj['instr_id']] = traj
if looped:
break
def test(self, use_dropout=False, feedback='argmax', allow_cheat=False, iters=None):
''' Evaluate once on each instruction in the current environment '''
self.feedback = feedback
if use_dropout:
self.encoder.train()
self.decoder.train()
self.critic.train()
else:
self.encoder.eval()
self.decoder.eval()
self.critic.eval()
super(Seq2SeqAgent, self).test(iters)
def zero_grad(self):
self.loss = 0.
self.losses = []
for model, optimizer in zip(self.models, self.optimizers):
model.train()
optimizer.zero_grad()
def accumulate_gradient(self, feedback='teacher', **kwargs):
if feedback == 'teacher':
self.feedback = 'teacher'
self.rollout(train_ml=args.teacher_weight, train_rl=False, **kwargs)
elif feedback == 'sample':
self.feedback = 'teacher'
self.rollout(train_ml=args.ml_weight, train_rl=False, **kwargs)
self.feedback = 'sample'
self.rollout(train_ml=None, train_rl=True, **kwargs)
else:
assert False
def optim_step(self):
self.loss.backward()
torch.nn.utils.clip_grad_norm(self.encoder.parameters(), 40.)
torch.nn.utils.clip_grad_norm(self.decoder.parameters(), 40.)
self.encoder_optimizer.step()
self.decoder_optimizer.step()
self.critic_optimizer.step()
def train(self, n_iters, feedback='teacher', **kwargs):
''' Train for a given number of iterations '''
self.feedback = feedback
self.encoder.train()
self.decoder.train()
self.critic.train()
self.losses = []
for iter in range(1, n_iters + 1):
self.encoder_optimizer.zero_grad()
self.decoder_optimizer.zero_grad()
self.critic_optimizer.zero_grad()
self.loss = 0
if feedback == 'teacher':
self.feedback = 'teacher'
self.rollout(train_ml=args.teacher_weight, train_rl=False, **kwargs)
elif feedback == 'sample':
if args.ml_weight != 0:
self.feedback = 'teacher'
self.rollout(train_ml=args.ml_weight, train_rl=False, **kwargs)
self.feedback = 'sample'
self.rollout(train_ml=None, train_rl=True, **kwargs)
else:
assert False
self.loss.backward()
torch.nn.utils.clip_grad_norm(self.encoder.parameters(), 40.)
torch.nn.utils.clip_grad_norm(self.decoder.parameters(), 40.)
self.encoder_optimizer.step()
self.decoder_optimizer.step()
self.critic_optimizer.step()
def save(self, epoch, path):
''' Snapshot models '''
the_dir, _ = os.path.split(path)
os.makedirs(the_dir, exist_ok=True)
states = {}
def create_state(name, model, optimizer):
states[name] = {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}
all_tuple = [("encoder", self.encoder, self.encoder_optimizer),
("decoder", self.decoder, self.decoder_optimizer),
("critic", self.critic, self.critic_optimizer)]
for param in all_tuple:
create_state(*param)
torch.save(states, path)
def load(self, path):
''' Loads parameters (but not training state) '''
states = torch.load(path)
def recover_state(name, model, optimizer):
state = model.state_dict()
model_keys = set(state.keys())
load_keys = set(states[name]['state_dict'].keys())
if model_keys != load_keys:
print("NOTICE: DIFFERENT KEYS IN THE LISTEREN")
state.update(states[name]['state_dict'])
model.load_state_dict(state)
if args.loadOptim:
optimizer.load_state_dict(states[name]['optimizer'])
all_tuple = [("encoder", self.encoder, self.encoder_optimizer),
("decoder", self.decoder, self.decoder_optimizer),
("critic", self.critic, self.critic_optimizer)]
for param in all_tuple:
recover_state(*param)
return states['encoder']['epoch'] - 1