class PVNBaselineEvaluator(Evaluator):
def __init__(self, args, filepath):
super(PVNBaselineEvaluator, self).__init__(args, filepath)
def init_models(self):
self.args.belief_downsample_factor = 1
self.mapper = Mapper(self.args).to(self.args.device)
self.navigator = UnetNavigator(self.args).to(self.args.device)
def load_model_weights(self):
self.mapper.load_state_dict(self.loader["mapper"])
self.navigator.load_state_dict(self.loader["navigator"])
def set_models_eval(self):
self.mapper.eval()
self.navigator.eval()
def get_predictions(self, seq, seq_mask, seq_lens, batch, xyzhe, simulator_next_action):
if self.args.multi_maps:
all_spatial_maps = []
all_masks = []
spatial_map, mask = self.mapper.init_map(xyzhe)
for t in range(self.args.timesteps):
rgb, depth, states = self.sim.getPanos()
spatial_map, mask, ftm = self.mapper(rgb, depth, states, spatial_map, mask)
if self.args.multi_maps:
all_spatial_maps.append(spatial_map.unsqueeze(1))
all_masks.append(mask)
if self.args.timesteps != 1:
simulator_next_action()
if self.args.multi_maps:
spatial_map = torch.cat(all_spatial_maps, dim=1).flatten(0, 1)
mask = torch.cat(all_masks, dim=1).flatten(0, 1)
seq = seq.unsqueeze(1).expand(-1, self.args.timesteps, -1).flatten(0, 1)
seq_lens = seq_lens.unsqueeze(1).expand(-1, self.args.timesteps).flatten(0, 1)
# Predict with unet
pred = self.navigator(seq, seq_lens, spatial_map)
# shape: (batch_size*timesteps, 2)
goal_pred = pred[:, 0, :, :]
path_pred = pred[:, 1, :, :]
return goal_pred, path_pred, mask
class FilterEvaluator(Evaluator):
def __init__(self, args, filepath):
super(FilterEvaluator, self).__init__(args, filepath)
def init_models(self):
self.mapper = Mapper(self.args).to(self.args.device)
self.model = Filter(self.args).to(self.args.device)
def load_model_weights(self):
self.mapper.load_state_dict(self.loader["mapper"])
self.model.load_state_dict(self.loader["filter"])
print("Loaded Mapper and Filter from: %s" % filepath)
def set_models_eval(self):
self.mapper.eval()
self.model.eval()
def viz_attention_weights(self,
act_att_weights,
obs_att_weights,
seqs,
seq_lens,
split,
it,
save_folder="viz"):
# act_att_weights: (N, T, self.args.max_steps-1, torch.max(seq_lens))
# seqs: (N, max_seq_len)
# seq_len (N,)
batch_size = act_att_weights.shape[0]
timesteps = act_att_weights.shape[1]
for n in range(batch_size):
instruction = self.dataloader.tokenizer.decode_sentence(
seqs[n]).split()
for t in range(timesteps):
act_att = act_att_weights[n][t].cpu().numpy()
obs_att = obs_att_weights[n][t].cpu().numpy()
valid_act_att = act_att[:, :seq_lens[n]].transpose()
valid_obs_att = obs_att[:, :seq_lens[n]].transpose()
fig = viz_utils.plot_att_graph(valid_act_att, valid_obs_att,
instruction, seq_lens[n].item())
if t == 0: # Currently doesn't use the map, so can just save once
fig.savefig("%s/attention-%s-it%d-n%d-t%d.png" %
(save_folder, split, it, n, t))
plt.close('all')
print("Saved Attention viz: %s/attention-%s-it%d-n-t.png" %
(save_folder, split, it))
def eval_viz(self, goal_pred, path_pred, mask, split, it):
if self.args.viz_folder == "":
save_folder = "tracker/viz/%s_%d" % (self.args.exp_name,
self.args.val_epoch)
else:
save_folder = "%s/%s_%d" % (
self.args.viz_folder, self.args.exp_name, self.args.val_epoch)
if not os.path.exists(save_folder):
os.mkdir(save_folder)
self.viz_attention_weights(self.act_att_weights, self.obs_att_weights,
self.seqs, self.seq_lens, split, it,
save_folder)
all_floorplan_images = []
for im in self.floorplan_images:
all_floorplan_images.extend(
[im for t in range(self.args.timesteps)])
self.floorplan_images = all_floorplan_images # len: batch_size * timesteps
self.goal_map = F.interpolate(
self.goal_map.unsqueeze(1),
scale_factor=self.args.debug_scale).squeeze(1)
self.path_map = F.interpolate(
self.path_map.unsqueeze(1),
scale_factor=self.args.debug_scale).squeeze(1)
goal_pred = F.interpolate(
goal_pred.unsqueeze(1),
scale_factor=self.args.debug_scale).squeeze(1)
path_pred = F.interpolate(
path_pred.unsqueeze(1),
scale_factor=self.args.debug_scale).squeeze(1)
mask = F.interpolate(mask.unsqueeze(1),
scale_factor=self.args.debug_scale).squeeze(1)
goal_pred = utils.minmax_normalize(goal_pred)
path_pred = utils.minmax_normalize(path_pred)
map_masks = viz_utils.get_masks(self.floorplan_images, mask, goal_pred)
target_images = viz_utils.get_floorplan_with_goal_path_maps(
self.floorplan_images,
self.goal_map,
self.path_map,
scale_factor=self.args.debug_scale,
target=True)
predicted_images = viz_utils.get_floorplan_with_goal_path_maps(
self.floorplan_images,
goal_pred,
None,
scale_factor=self.args.debug_scale)
path_pred = path_pred.reshape(
self.args.batch_size * self.args.timesteps,
self.args.max_steps - 1, 3, path_pred.shape[-2],
path_pred.shape[-1])
path_pred = (255 * torch.flip(path_pred, [3])).type(
torch.ByteTensor).cpu().numpy()
new_belief = path_pred[:, :, 0, :, :]
obs_likelihood = path_pred[:, :, 1, :, :]
belief = path_pred[:, :, 2, :, :]
viz_utils.save_floorplans_with_belief_maps(
[map_masks, target_images, predicted_images],
[new_belief, obs_likelihood, belief],
self.args.max_steps - 1,
self.args.batch_size,
self.args.timesteps,
split,
it,
save_folder=save_folder)
def get_predictions(self, seq, seq_mask, seq_lens, batch, xyzhe,
simulator_next_action):
all_masks = []
spatial_map, mask = self.mapper.init_map(xyzhe)
N = seq.shape[0]
T = self.args.timesteps
self.act_att_weights = torch.zeros(N, T, self.args.max_steps - 1,
torch.max(seq_lens))
self.obs_att_weights = torch.zeros(N, T, self.args.max_steps - 1,
torch.max(seq_lens))
goal_pred = torch.zeros(N,
T,
self.args.map_range_y,
self.args.map_range_x,
device=spatial_map.device)
path_pred = torch.zeros(N,
T,
self.args.max_steps - 1,
3,
self.args.map_range_y,
self.args.map_range_x,
device=spatial_map.device)
if self.args.debug_mode:
floor_maps = self.floor_maps(self.sim.getState())
for t in range(self.args.timesteps):
rgb, depth, states = self.sim.getPanos()
spatial_map, mask, ftm = self.mapper(rgb, depth, states,
spatial_map, mask)
belief = self.mapper.belief_map(
xyzhe, self.args.filter_input_sigma).log()
if t == 0:
belief_pred = torch.zeros(N,
T,
self.args.max_steps,
self.args.heading_states,
belief.shape[-2],
belief.shape[-1],
device=spatial_map.device)
belief_pred[:, t, 0, :, :, :] = belief.exp()
state = None
for k in range(self.args.max_steps - 1):
input_belief = belief
new_belief, obs_likelihood, state, act_att_weights, obs_att_weights, _ = self.model(
k, seq, seq_mask, seq_lens, belief, spatial_map, state)
belief = new_belief + obs_likelihood
self.act_att_weights[:, t, k, :] = act_att_weights
self.obs_att_weights[:, t, k, :] = obs_att_weights
# Renormalize
belief = belief - belief.reshape(
belief.shape[0],
-1).logsumexp(dim=1).unsqueeze(1).unsqueeze(1).unsqueeze(1)
path_pred[:, t, k, 0, :, :] = F.interpolate(
new_belief.exp().sum(dim=1).unsqueeze(1),
scale_factor=self.args.belief_downsample_factor).squeeze(1)
path_pred[:, t, k, 1, :, :] = F.interpolate(
obs_likelihood.exp().sum(dim=1).unsqueeze(1),
scale_factor=self.args.belief_downsample_factor).squeeze(1)
path_pred[:, t, k, 2, :, :] = F.interpolate(
belief.exp().sum(dim=1).unsqueeze(1),
scale_factor=self.args.belief_downsample_factor).squeeze(1)
belief_pred[:, t, k + 1, :, :, :] = belief.exp()
if self.args.debug_mode:
base_maps = self.overlay_mask(floor_maps, mask)
belief_maps = self.overlay_belief(base_maps, belief_pred[:, t])
cv2.imshow('belief', belief_maps[0])
cv2.waitKey(0)
goal_pred[:, t, :, :] = F.interpolate(
belief.exp().sum(dim=1).unsqueeze(1),
scale_factor=self.args.belief_downsample_factor).squeeze(1)
all_masks.append(mask)
simulator_next_action()
mask = torch.cat(all_masks, dim=1).flatten(0, 1)
self.seq_lens = seq_lens
self.seqs = seq
return goal_pred.flatten(0, 1), path_pred.flatten(0, 3), mask
class PolicyTrainer(Trainer):
""" Train a filter and a policy """
def __init__(self, args, filepath=None):
super(PolicyTrainer, self).__init__(args, filepath, load_sim=False)
self.sim = PanoSimulatorWithGraph(self.args)
# load models
self.mapper = Mapper(self.args).to(self.args.device)
self.model = Filter(self.args).to(self.args.device)
self.policy = ReactivePolicy(self.args).to(self.args.device)
if filepath:
loader = torch.load(filepath)
self.mapper.load_state_dict(loader["mapper"])
self.model.load_state_dict(loader["filter"])
self.policy.load_state_dict(loader["policy"])
print("Loaded Mapper, Filter and Policy from: %s" % filepath)
elif args:
self.model.init_weights()
self.policy.init_weights()
self.belief_criterion = LogBeliefLoss()
self.policy_criterion = torch.nn.NLLLoss(
ignore_index=self.args.action_ignore_index, reduction='none')
def validate(self, split):
"""
split: "val_seen" or "val_unseen"
"""
vln_eval = Evaluation(split)
self.sim.record_traj(True)
with torch.no_grad():
if (split == "val_seen"):
val_dataloader = self.valseendata
elif (split == "val_unseen"):
val_dataloader = self.valunseendata
elif (split == "train"):
val_dataloader = self.traindata
for it in range(self.args.validation_iterations):
# Load minibatch and simulator
seq, seq_mask, seq_lens, batch = val_dataloader.get_batch()
self.sim.newEpisode(batch)
# Initialize the mapper
xyzhe = self.sim.getXYZHE()
spatial_map, mask = self.mapper.init_map(xyzhe)
ended = torch.zeros(self.args.batch_size,
device=self.args.device).byte()
for t in range(self.args.timesteps):
if self.args.policy_gt_belief:
path_xyzhe, path_len = val_dataloader.path_xyzhe()
else:
rgb, depth, states = self.sim.getPanos()
spatial_map, mask, ftm = self.mapper(
rgb, depth, states, spatial_map, mask)
del states
del depth
del ftm
del rgb
features, _, _ = self.sim.getGraphNodes()
P = features.shape[0] # num graph nodes
belief_features = torch.empty(P,
self.args.max_steps,
device=self.args.device)
state = None
steps = self.args.max_steps - 1
belief = self.mapper.belief_map(
xyzhe, self.args.filter_input_sigma).log()
sigma = self.args.filter_heatmap_sigma
gridcellsize = self.args.gridcellsize * self.args.belief_downsample_factor
belief_features[:, 0] = belief_at_nodes(
belief.exp(), features[:, :4], sigma, gridcellsize)
for k in range(steps):
if self.args.policy_gt_belief:
target_heatmap = self.mapper.belief_map(
path_xyzhe[k + 1],
self.args.filter_heatmap_sigma)
belief_features[:, k + 1] = belief_at_nodes(
target_heatmap, features[:, :4], sigma,
gridcellsize)
else:
input_belief = belief
# Train a filter
new_belief, obs_likelihood, state, _, _, _ = self.model(
k, seq, seq_mask, seq_lens, input_belief,
spatial_map, state)
belief = new_belief + obs_likelihood
# Renormalize
belief = belief - belief.reshape(
belief.shape[0],
-1).logsumexp(dim=1).unsqueeze(1).unsqueeze(
1).unsqueeze(1)
belief_features[:, k + 1] = belief_at_nodes(
belief.exp(), features[:, :4], sigma,
gridcellsize)
# Probs from policy
aug_features = torch.cat([features, belief_features],
dim=1)
log_prob = self.policy(aug_features)
# Take argmax action in the sim
_, action_idx = log_prob.exp().max(dim=1)
ended |= self.sim.takeMultiStepAction(action_idx)
if ended.all():
break
# Eval
scores = vln_eval.score(self.sim.traj, check_all_trajs=False)
self.sim.record_traj(False)
return scores['result'][0][split]
def logging_loop(self, it):
""" Logging and checkpointing stuff """
if it % self.args.validate_every == 0:
self.mapper.eval()
self.model.eval()
self.policy.eval()
scores_train = self.validate("train")
scores_val_seen = self.validate("val_seen")
scores_val_unseen = self.validate("val_unseen")
self.prev_time, time_taken = utils.time_it(self.prev_time)
print(
"Iteration: %d Loss: %f Train Success: %f Val Seen Success: %f Val Unseen Success: %f Time: %0.2f secs"
% (it, self.loss.item(), scores_train['success'],
scores_val_seen['success'], scores_val_unseen['success'],
time_taken))
if self.visdom:
# visdom: X, Y, key, line_name, x_label, y_label, fig_title
self.visdom.line(it,
self.loss.item(),
"train_loss",
"Train Loss",
"Iterations",
"Loss",
title=" Train Phase")
units = {
'length': 'm',
'error': 'm',
'oracle success': '%',
'success': '%',
'spl': '%'
}
sub = self.args.validation_iterations * self.args.batch_size
for metric, score in scores_train.items():
m = metric.title()
self.visdom.line(it,
score,
metric,
"Train (%d)" % sub,
"Iterations",
units[metric],
title=m)
for metric, score in scores_val_seen.items():
m = metric.title()
self.visdom.line(it,
score,
metric,
"Val Seen (%d)" % sub,
"Iterations",
units[metric],
title=m)
for metric, score in scores_val_unseen.items():
m = metric.title()
self.visdom.line(it,
score,
metric,
"Val Unseen (%d)" % sub,
"Iterations",
units[metric],
title=m)
self.mapper.train()
self.model.train()
self.policy.train()
elif it % self.args.log_every == 0:
self.prev_time, time_taken = utils.time_it(self.prev_time)
print("Iteration: %d Loss: %f Time: %0.2f secs" %
(it, self.loss.item(), time_taken))
if self.visdom:
self.visdom.line(it,
self.loss.item(),
"train_loss",
"Train Loss",
"Iterations",
"Loss",
title="Train Phase")
if it % self.args.checkpoint_every == 0:
saver = {
"mapper": self.mapper.state_dict(),
"args": self.args,
"filter": self.model.state_dict(),
"policy": self.policy.state_dict()
}
dir = "%s/%s" % (self.args.snapshot_dir, self.args.exp_name)
if not os.path.exists(dir):
os.makedirs(dir)
torch.save(saver, "%s/%s_%d" % (dir, self.args.exp_name, it))
if self.visdom:
self.visdom.save()
def train(self):
"""
Supervised training of the mapper, filter and policy
"""
torch.autograd.set_detect_anomaly(True)
self.prev_time = time.time()
map_opt = self.optimizer(self.mapper.parameters())
model_opt = self.optimizer(self.model.parameters())
policy_opt = self.optimizer(self.policy.parameters())
# Set models to train phase
self.mapper.train()
self.model.train()
self.policy.train()
for it in range(1, self.args.max_iterations + 1):
map_opt.zero_grad()
model_opt.zero_grad()
policy_opt.zero_grad()
traindata = self.traindata
# Load minibatch and simulator
seq, seq_mask, seq_lens, batch = traindata.get_batch()
self.sim.newEpisode(batch)
# Initialize the mapper
xyzhe = self.sim.getXYZHE()
spatial_map, mask = self.mapper.init_map(xyzhe)
# Note Filter model is being trained to predict the GT path. This is not the path taken by Policy.
path_xyzhe, path_len = traindata.path_xyzhe()
self.loss = 0
ended = torch.zeros(self.args.batch_size,
device=self.args.device).byte()
for t in range(self.args.timesteps):
if not self.args.policy_gt_belief:
rgb, depth, states = self.sim.getPanos()
spatial_map, mask, ftm = self.mapper(
rgb, depth, states, spatial_map, mask)
del states
del depth
del ftm
del rgb
features, _, _ = self.sim.getGraphNodes()
P = features.shape[0] # num graph nodes
belief_features = torch.empty(P,
self.args.max_steps,
device=self.args.device)
state = None
steps = self.args.max_steps - 1
gt_input_belief = None
belief = self.mapper.belief_map(
path_xyzhe[0], self.args.filter_input_sigma).log()
sigma = self.args.filter_heatmap_sigma
gridcellsize = self.args.gridcellsize * self.args.belief_downsample_factor
belief_features[:,
0] = belief_at_nodes(belief.exp(),
features[:, :4], sigma,
gridcellsize)
for k in range(steps):
target_heatmap = self.mapper.belief_map(
path_xyzhe[k + 1], self.args.filter_heatmap_sigma)
if self.args.policy_gt_belief:
belief_features[:, k + 1] = belief_at_nodes(
target_heatmap, features[:, :4], sigma,
gridcellsize)
else:
input_belief = belief
# Train a filter
if self.args.teacher_force_motion_model:
gt_input_belief = self.mapper.belief_map(
path_xyzhe[k],
self.args.filter_heatmap_sigma).log()
new_belief, obs_likelihood, state, _, _, new_gt_belief = self.model(
k, seq, seq_mask, seq_lens, input_belief,
spatial_map, state, gt_input_belief)
belief = new_belief.detach(
) + obs_likelihood #Don't backprop through belief
else:
new_belief, obs_likelihood, state, _, _, new_gt_belief = self.model(
k, seq, seq_mask, seq_lens, input_belief,
spatial_map, state, gt_input_belief)
belief = new_belief + obs_likelihood
# Renormalize
belief = belief - belief.reshape(
belief.shape[0], -1).logsumexp(
dim=1).unsqueeze(1).unsqueeze(1).unsqueeze(1)
# Determine target and loss for the filter
belief_loss = self.belief_criterion(
belief, target_heatmap,
valid=~ended) / (~ended).sum()
if self.args.teacher_force_motion_model: # Separate loss for the motion model
belief_loss += self.belief_criterion(
new_gt_belief, target_heatmap,
valid=~ended) / (~ended).sum()
self.loss += belief_loss
belief_features[:, k + 1] = belief_at_nodes(
belief.exp(), features[:, :4], sigma, gridcellsize)
# Train a policy
aug_features = torch.cat([features, belief_features], dim=1)
log_prob = self.policy(aug_features)
# Train a policy
target_idx = traindata.closest_to_goal(self.sim.G)
policy_loss = self.args.policy_loss_lambda * self.policy_criterion(
log_prob, target_idx).sum() / (~ended).sum()
self.loss += policy_loss
# Take action in the sim
if self.args.supervision_prob < 0:
supervision_prob = 1.0 - float(
it) / self.args.max_iterations
else:
supervision_prob = self.args.supervision_prob
sampled_action = D.Categorical(log_prob.exp()).sample()
weights = torch.tensor(
[1.0 - supervision_prob, supervision_prob],
dtype=torch.float,
device=log_prob.device)
ix = torch.multinomial(weights,
self.args.batch_size,
replacement=True).byte()
action_idx = torch.where(ix, target_idx, sampled_action)
ended |= self.sim.takeMultiStepAction(action_idx)
trunc = self.args.truncate_after
if (t % trunc) == (
trunc -
1) or t + 1 == self.args.timesteps or ended.all():
self.loss.backward()
self.loss.detach_()
spatial_map.detach_()
if state is not None:
state = self.model.detach_state(state)
state = None # Recalc to get gradients from later in the decoding - #TODO keep this part of the graph without re-running the forward part
if ended.all():
break
del spatial_map
del mask
map_opt.step()
model_opt.step()
policy_opt.step()
self.logging_loop(it)
class FilterTrainer(Trainer):
""" Train a filter """
def __init__(self, args, filepath=None):
super(FilterTrainer, self).__init__(args, filepath)
# load models
self.mapper = Mapper(self.args).to(self.args.device)
self.model = Filter(self.args).to(self.args.device)
self.map_opt = self.optimizer(self.mapper.parameters())
self.model_opt = self.optimizer(self.model.parameters())
if filepath:
loader = torch.load(filepath)
self.mapper.load_state_dict(loader["mapper"])
self.model.load_state_dict(loader["filter"])
self.map_opt.load_state_dict(loader["mapper_optimizer"])
self.model_opt.load_state_dict(loader["filter_optimizer"])
print("Loaded Mapper and Filter from: %s" % filepath)
elif args:
self.model.init_weights()
self.criterion = LogBeliefLoss()
def validate(self, split):
"""
split: "val_seen" or "val_unseen"
"""
with torch.no_grad():
if (split == "val_seen"):
val_dataloader = self.valseendata
elif (split == "val_unseen"):
val_dataloader = self.valunseendata
total_loss = torch.tensor(0.0)
normalizer = 0
for it in range(self.args.validation_iterations):
# Load minibatch and simulator
seq, seq_mask, seq_lens, batch = val_dataloader.get_batch()
self.sim.newEpisode(batch)
# Initialize the mapper
xyzhe = self.sim.getXYZHE()
spatial_map, mask = self.mapper.init_map(xyzhe)
# Note mm is being validated on the GT path. This is not the path taken by Mapper.
path_xyzhe, path_len = val_dataloader.path_xyzhe()
for t in range(self.args.timesteps):
rgb, depth, states = self.sim.getPanos()
spatial_map, mask, ftm = self.mapper(
rgb, depth, states, spatial_map, mask)
state = None
steps = self.args.max_steps - 1
belief = self.mapper.belief_map(
path_xyzhe[0], self.args.filter_input_sigma).log()
for k in range(steps):
input_belief = belief
# Train a filter
new_belief, obs_likelihood, state, _, _, _ = self.model(
k, seq, seq_mask, seq_lens, input_belief,
spatial_map, state)
belief = new_belief + obs_likelihood
# Renormalize
belief = belief - belief.reshape(
belief.shape[0], -1).logsumexp(
dim=1).unsqueeze(1).unsqueeze(1).unsqueeze(1)
# Determine target and loss
target_heatmap = self.mapper.belief_map(
path_xyzhe[k + 1], self.args.filter_heatmap_sigma)
# To make loss independent of heading_states, we sum over all heading states (logsumexp) for validation
total_loss += self.criterion(
belief.logsumexp(dim=1).unsqueeze(1),
target_heatmap.sum(dim=1).unsqueeze(1))
normalizer += self.args.batch_size
# Take action in the sim
self.sim.takePseudoSupervisedAction(
val_dataloader.get_supervision)
return total_loss / normalizer
def logging_loop(self, it):
""" Logging and checkpointing stuff """
if it % self.args.validate_every == 0:
self.mapper.eval()
self.model.eval()
loss_val_seen = self.validate("val_seen")
loss_val_unseen = self.validate("val_unseen")
self.prev_time, time_taken = utils.time_it(self.prev_time)
print(
"Iteration: %d Loss: %f Val Seen Loss: %f Val Unseen Loss: %f Time: %0.2f secs"
% (it, self.loss.item(), loss_val_seen.item(),
loss_val_unseen.item(), time_taken))
if self.visdom:
# visdom: X, Y, key, line_name, x_label, y_label, fig_title
self.visdom.line(it,
self.loss.item(),
"train_loss",
"Train Loss",
"Iterations",
"Loss",
title=" Train Phase")
self.visdom.line(it,
loss_val_seen.item(),
"val_loss",
"Val Seen Loss",
"Iterations",
"Loss",
title="Val Phase")
self.visdom.line(it,
loss_val_unseen.item(),
"val_loss",
"Val Unseen Loss",
"Iterations",
"Loss",
title="Val Phase")
self.mapper.train()
self.model.train()
elif it % self.args.log_every == 0:
self.prev_time, time_taken = utils.time_it(self.prev_time)
print("Iteration: %d Loss: %f Time: %0.2f secs" %
(it, self.loss.item(), time_taken))
if self.visdom:
self.visdom.line(it,
self.loss.item(),
"train_loss",
"Train Loss",
"Iterations",
"Loss",
title="Train Phase")
if it % self.args.checkpoint_every == 0:
saver = {
"mapper": self.mapper.state_dict(),
"filter": self.model.state_dict(),
"mapper_optimizer": self.map_opt.state_dict(),
"filter_optimizer": self.model_opt.state_dict(),
"epoch": it,
"args": self.args,
}
dir = "%s/%s" % (self.args.snapshot_dir, self.args.exp_name)
if not os.path.exists(dir):
os.makedirs(dir)
torch.save(saver, "%s/%s_%d" % (dir, self.args.exp_name, it))
if self.visdom:
self.visdom.save()
def map_to_image(self):
""" Show where map features are found in the RGB images """
traindata = self.traindata
# Load minibatch and simulator
seq, seq_mask, seq_lens, batch = traindata.get_batch(False)
self.sim.newEpisode(batch)
# Initialize the mapper
xyzhe = self.sim.getXYZHE()
spatial_map, mask = self.mapper.init_map(xyzhe)
for t in range(self.args.timesteps):
rgb, depth, states = self.sim.getPanos()
# ims = (torch.flip(rgb, [1])).permute(0,2,3,1).cpu().detach().numpy()
# for n in range(ims.shape[0]):
# cv2.imwrite('im %d.png' % n, ims[n])
spatial_map, mask, ftm = self.mapper(rgb, depth, states,
spatial_map, mask)
im_ix = torch.arange(0, ftm.shape[0],
step=self.args.batch_size).to(ftm.device)
ims = self.feature_sources(rgb[im_ix], ftm[im_ix], 56, 48)
for n in range(ims.shape[0]):
cv2.imwrite('im %d-%d.png' % (t, n), ims[n])
# Take action in the sim
self.sim.takePseudoSupervisedAction(traindata.get_supervision)
def train(self):
"""
Supervised training of the filter
"""
torch.autograd.set_detect_anomaly(True)
self.prev_time = time.time()
# Set models to train phase
self.mapper.train()
self.model.train()
for it in range(self.args.start_epoch, self.args.max_iterations + 1):
self.map_opt.zero_grad()
self.model_opt.zero_grad()
traindata = self.traindata
# Load minibatch and simulator
seq, seq_mask, seq_lens, batch = traindata.get_batch()
self.sim.newEpisode(batch)
if self.args.debug_mode:
debug_path = traindata.get_path()
# Initialize the mapper
xyzhe = self.sim.getXYZHE()
spatial_map, mask = self.mapper.init_map(xyzhe)
# Note model is being trained on the GT path. This is not the path taken by Mapper.
path_xyzhe, path_len = traindata.path_xyzhe()
loss = 0
normalizer = 0
for t in range(self.args.timesteps):
rgb, depth, states = self.sim.getPanos()
spatial_map, mask, ftm = self.mapper(rgb, depth, states,
spatial_map, mask)
del states
del depth
del ftm
if not self.args.debug_mode:
del rgb
state = None
steps = self.args.max_steps - 1
belief = self.mapper.belief_map(
path_xyzhe[0], self.args.filter_input_sigma).log()
for k in range(steps):
input_belief = belief
# Train a filter
new_belief, obs_likelihood, state, _, _, _ = self.model(
k, seq, seq_mask, seq_lens, input_belief, spatial_map,
state)
belief = new_belief + obs_likelihood
# Renormalize
belief = belief - belief.reshape(
belief.shape[0], -1).logsumexp(
dim=1).unsqueeze(1).unsqueeze(1).unsqueeze(1)
# Determine target and loss
target_heatmap = self.mapper.belief_map(
path_xyzhe[k + 1], self.args.filter_heatmap_sigma)
loss += self.criterion(belief, target_heatmap)
normalizer += self.args.batch_size
if self.args.debug_mode:
input_belief = F.interpolate(
input_belief.exp(),
scale_factor=self.args.belief_downsample_factor,
mode='bilinear',
align_corners=False).sum(dim=1).unsqueeze(1)
belief_up = F.interpolate(
belief.exp(),
scale_factor=self.args.belief_downsample_factor,
mode='bilinear',
align_corners=False).sum(dim=1).unsqueeze(1)
target_heatmap_up = F.interpolate(
target_heatmap,
scale_factor=self.args.belief_downsample_factor,
mode='bilinear',
align_corners=False).sum(dim=1).unsqueeze(1)
debug_map = self.mapper.debug_maps(debug_path)
self.visual_debug(t, rgb, mask, debug_map,
input_belief, belief_up,
target_heatmap_up)
trunc = self.args.truncate_after
if (t % trunc) == (trunc - 1) or t + 1 == self.args.timesteps:
self.loss = loss / normalizer
self.loss.backward()
self.loss.detach_()
loss = 0
normalizer = 0
spatial_map.detach_()
if state is not None:
state = self.model.detach_state(state)
state = None # Recalc to get gradients from later in the decoding - #TODO keep this part of the graph without re-running the forward part
# Take action in the sim
self.sim.takePseudoSupervisedAction(traindata.get_supervision)
del spatial_map
del mask
self.map_opt.step()
self.model_opt.step()
self.logging_loop(it)
class PolicyEvaluator(Trainer):
def __init__(self, args, filepath):
super(PolicyEvaluator, self).__init__(args, filepath, load_sim=False)
# load models
self.mapper = Mapper(self.args).to(self.args.device)
self.model = Filter(self.args).to(self.args.device)
self.policy = ReactivePolicy(self.args).to(self.args.device)
loader = torch.load(filepath)
self.mapper.load_state_dict(loader["mapper"])
self.model.load_state_dict(loader["filter"])
self.policy.load_state_dict(loader["policy"])
print("Loaded Mapper, Filter and Policy from: %s" % filepath)
if self.args.viz_folder == "":
self.args.viz_folder = "tracker/viz/%s_%d" % (self.args.exp_name,
self.args.val_epoch)
else:
self.args.viz_folder = "%s/%s_%d" % (
self.args.viz_folder, self.args.exp_name, self.args.val_epoch)
if not os.path.exists(self.args.viz_folder) and self.args.viz_eval:
os.makedirs(self.args.viz_folder)
def evaluate(self, split):
sim = PanoSimulatorWithGraph(self.args, disable_rendering=True)
sim.record_traj(True)
self.mapper.eval()
self.model.eval()
self.policy.eval()
vln_eval = Evaluation(split)
with torch.no_grad():
if split == "val_seen":
self.dataloader = self.valseendata
elif split == "val_unseen":
self.dataloader = self.valunseendata
else:
self.dataloader = DataLoader(self.args, splits=[split])
iterations = int(
math.ceil(
len(self.dataloader.data) / float(self.args.batch_size)))
for it in tqdm(range(iterations),
desc="Evaluation Progress for %s split" % split):
# Load minibatch and simulator
seq, seq_mask, seq_lens, batch = self.dataloader.get_batch()
sim.newEpisode(batch)
# Initialize the mapper
xyzhe = sim.getXYZHE()
spatial_map, mask = self.mapper.init_map(xyzhe)
if self.args.viz_eval and it < self.args.viz_iterations:
floor_maps = self.floor_maps(sim.getState())
ended = torch.zeros(self.args.batch_size,
device=self.args.device).byte()
viz_counter = 0
for t in range(self.args.timesteps):
if self.args.policy_gt_belief:
path_xyzhe, path_len = self.dataloader.path_xyzhe()
else:
rgb, depth, states = sim.getPanos()
spatial_map, mask, ftm = self.mapper(
rgb, depth, states, spatial_map, mask)
del states
del ftm
features, _, _ = sim.getGraphNodes()
P = features.shape[0] # num graph nodes
belief_features = torch.empty(P,
self.args.max_steps,
device=self.args.device)
state = None
steps = self.args.max_steps - 1
belief = self.mapper.belief_map(
xyzhe, self.args.filter_input_sigma).log()
if t == 0:
belief_pred = torch.zeros(seq.shape[0],
self.args.max_steps,
self.args.heading_states,
belief.shape[-2],
belief.shape[-1],
device=spatial_map.device)
belief_pred[:, 0, :, :, :] = belief.exp()
sigma = self.args.filter_heatmap_sigma
gridcellsize = self.args.gridcellsize * self.args.belief_downsample_factor
belief_features[:, 0] = belief_at_nodes(
belief.exp(), features[:, :4], sigma, gridcellsize)
act_att_weights = torch.zeros(seq.shape[0], steps,
torch.max(seq_lens))
obs_att_weights = torch.zeros(seq.shape[0], steps,
torch.max(seq_lens))
for k in range(steps):
if self.args.policy_gt_belief:
target_heatmap = self.mapper.belief_map(
path_xyzhe[k + 1],
self.args.filter_heatmap_sigma)
belief_features[:, k + 1] = belief_at_nodes(
target_heatmap, features[:, :4], sigma,
gridcellsize)
belief_pred[:, k + 1, :, :, :] = target_heatmap
else:
input_belief = belief
# Train a filter
new_belief, obs_likelihood, state, act_att, obs_att, _ = self.model(
k, seq, seq_mask, seq_lens, input_belief,
spatial_map, state)
belief = new_belief + obs_likelihood
# Renormalize
belief = belief - belief.reshape(
belief.shape[0],
-1).logsumexp(dim=1).unsqueeze(1).unsqueeze(
1).unsqueeze(1)
belief_pred[:, k + 1, :, :, :] = belief.exp()
belief_features[:, k + 1] = belief_at_nodes(
belief.exp(), features[:, :4], sigma,
gridcellsize)
act_att_weights[:, k] = act_att
obs_att_weights[:, k] = obs_att
# Probs from policy
aug_features = torch.cat([features, belief_features],
dim=1)
log_prob = self.policy(aug_features)
# Take argmax action in the sim
_, action_idx = log_prob.exp().max(dim=1)
if self.args.viz_eval and it < self.args.viz_iterations:
num_cam_views = self.args.num_pano_views * self.args.num_pano_sweeps
rgb = rgb.permute(0, 2, 3,
1).reshape(num_cam_views,
self.args.batch_size,
rgb.shape[-2],
rgb.shape[-1], 3)
depth = depth.expand(-1, 3, -1, -1).permute(
0, 2, 3,
1).reshape(num_cam_views, self.args.batch_size,
depth.shape[-2], depth.shape[-1], 3)
# Save attention over instruction
if t == 0:
att_ims = []
belief_ims = [[]
for n in range(self.args.batch_size)]
for n in range(seq.shape[0]):
instruction = self.dataloader.tokenizer.decode_sentence(
seq[n]).split()
act_att = act_att_weights[n].cpu().numpy()
obs_att = obs_att_weights[n].cpu().numpy()
valid_act_att = act_att[:, :
seq_lens[n]].transpose(
)
valid_obs_att = obs_att[:, :
seq_lens[n]].transpose(
)
fig = viz_utils.plot_att_graph(
valid_act_att,
valid_obs_att,
instruction,
seq_lens[n].item(),
black_background=True)
if self.args.viz_gif:
att_ims.append(
viz_utils.figure_to_rgb(fig))
else:
fig.savefig(
"%s/attention-%s-it%d-n%d.png" %
(self.args.viz_folder, split, it, n),
facecolor=fig.get_facecolor(),
transparent=True)
plt.close('all')
for k in range(3):
if self.args.policy_gt_belief:
viz = floor_maps
else:
viz = self.overlay_mask(floor_maps, mask)
if k >= 1:
viz = self.overlay_belief(viz, belief_pred)
viz = self.overlay_goal(
viz,
self.dataloader.goal_coords() +
self.mapper.map_center[:, :2])
if k >= 2:
viz = self.overlay_local_graph(
viz, features, action_idx)
else:
viz = self.overlay_local_graph(viz, features)
for n in range(len(viz)):
if not ended[n]:
if self.args.viz_gif:
image = viz[n] * 255
# Add attention image on left
min_val = image.shape[
0] // 2 - att_ims[n].shape[0] // 2
max_val = min_val + att_ims[n].shape[0]
image = np.flip(
image[min_val:max_val,
min_val:max_val, :], 2)
image = np.concatenate(
[att_ims[n], image], axis=1)
# Add rgb images at bottom
new_width = int(image.shape[-2] /
float(rgb.shape[0]))
new_height = int(new_width *
rgb.shape[-3] /
float(rgb.shape[-2]))
rgb_ims = [
cv2.resize(
rgb[i,
n].cpu().detach().numpy(),
(new_width, new_height))
for i in range(rgb.shape[0])
]
rgb_ims = np.concatenate(rgb_ims,
axis=1)
# Add depth images at bottom
depth_ims = [
cv2.resize(
depth[i, n].cpu().detach(
).numpy() / 200.0,
(new_width, new_height))
for i in range(depth.shape[0])
]
depth_ims = np.concatenate(depth_ims,
axis=1)
image = np.concatenate(
[image, rgb_ims, depth_ims],
axis=0)
belief_ims[n].append(
image.astype(np.uint8))
else:
filename = '%s/belief-%s-it%d-n%d-t%d_%d.png' % (
self.args.viz_folder, split, it, n,
t, viz_counter)
cv2.imwrite(filename, viz[n] * 255)
viz_counter += 1
ended |= sim.takeMultiStepAction(action_idx)
if ended.all():
break
if self.args.viz_gif and self.args.viz_eval and it < self.args.viz_iterations:
import imageio
for n in range(self.args.batch_size):
filename = '%s/%s-%s-it%d-n%d' % (
self.args.viz_folder, split, batch[n]['instr_id'],
it, n)
if not os.path.exists(filename):
os.makedirs(filename)
with imageio.get_writer(filename + '.gif',
mode='I',
format='GIF-PIL',
subrectangles=True,
fps=1) as writer:
for i, image in enumerate(belief_ims[n]):
writer.append_data(image)
cv2.imwrite("%s/%04d.png" % (filename, i),
np.flip(image, 2))
# Eval
out_dir = "%s/%s" % (args.result_dir, args.exp_name)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
output_file = "%s/%s_%s_%d.json" % (out_dir, split, args.exp_name,
args.val_epoch)
with open(output_file, 'w') as f:
json.dump(sim.traj, f)
scores = vln_eval.score(output_file)
print(scores)
with open(output_file.replace('.json', '_scores.json'), 'w') as f:
json.dump(scores, f)