def main(args):
# set up model
checkpoint_path = osp.join(args.checkpoint_dir, '%s.pth' % args.id)
checkpoint = torch.load(checkpoint_path)
opt = checkpoint['opt']
model = Navigator(checkpoint['opt'])
model.load_state_dict(checkpoint['model_state'])
model.cuda()
print('model set up.')
# set up criterions
nll_crit = SeqModelCriterion().cuda()
mse_crit = MaskedMSELoss().cuda()
# set up loader
loader_kwargs = {
'data_json': args.data_json,
'data_h5': args.data_h5,
'path_feats_dir': args.path_feats_dir,
'path_images_dir': args.path_images_dir,
'split': args.split,
'max_seq_length': 100,
'requires_imgs': False,
'nav_types': ['room'],
'question_types': ['all'],
}
dataset = NavImitationDataset(**loader_kwargs)
# evaluate
predictions, overall_nll, overall_teacher_forcing_acc, overall_mse, Nav_nll, Nav_teacher_forcing_acc, Nav_cnt = \
evaluate(dataset, model, nll_crit, mse_crit, opt)
# summarize
results_str = 'id[%s] ' % args.id
if opt['use_action']: results_str += '[use action]'
if opt['use_curriculum']: results_str += '[use curriculum]'
if opt['use_next']: results_str += '[use next]'
if opt['use_residual']: results_str += '[use residual]'
results_str += '\nsplit[%s]\n' % args.split
results_str += ' nll_loss: %.3f\n' % overall_nll
results_str += ' teacher-forcing acc (%s): %.2f%%,' % (
len(predictions), overall_teacher_forcing_acc * 100.)
results_str += ' on %s objects: %.2f%%,' % (
Nav_cnt['object'], Nav_teacher_forcing_acc['object'] * 100.)
results_str += ' on %s rooms: %.2f%%\n' % (
Nav_cnt['room'], Nav_teacher_forcing_acc['room'] * 100.)
# save
with open(args.result_json, 'w') as f:
json.dump(predictions, f)
f = open(args.report_txt, 'a')
f.write(results_str)
f.write('\n')
f.close()
def eval(rank, args, shared_nav_model, counter, split):
# metric_names
metric_names = [
'd_0_5', 'd_0_10', 'd_0_15', 'd_T_5', 'd_T_10', 'd_T_15', 'd_D_5',
'd_D_10', 'd_D_15', 'd_min_5', 'd_min_10', 'd_min_15', 'h_T_5',
'h_T_10', 'h_T_15', 'r_T_5', 'r_T_10', 'r_T_15', 'r_e_5', 'r_e_10',
'r_e_15', 'stop_5', 'stop_10', 'stop_15', 'ep_len_5', 'ep_len_10',
'ep_len_15'
]
# set up dataset
cfg = {
'colorFile':
osp.join(args.house_meta_dir, 'colormap_fine.csv'),
'roomTargetFile':
osp.join(args.house_meta_dir, 'room_target_object_map.csv'),
'modelCategoryFile':
osp.join(args.house_meta_dir, 'ModelCategoryMapping.csv'),
'prefix':
args.house_data_dir,
}
loader_kwargs = {
'data_json': args.data_json,
'data_h5': args.data_h5,
'path_feats_dir': args.path_feats_dir,
'path_images_dir': args.path_images_dir,
'split': split,
'max_seq_length': args.max_seq_length,
'nav_types': ['room'],
'gpu_id': 0,
'cfg': cfg,
'max_threads_per_gpu': args.max_threads_per_gpu,
'target_obj_conn_map_dir': args.target_obj_conn_map_dir,
'map_resolution': 500,
'pretrained_cnn_path': args.pretrained_cnn_path,
'requires_imgs': False,
'question_types': ['all'],
}
dataset = NavReinforceDataset(**loader_kwargs)
eval_loader = DataLoader(dataset, batch_size=1, num_workers=0)
print('eval_loader set up.')
# set up model
opt = vars(args)
opt['act_to_ix'] = dataset.act_to_ix
opt['num_actions'] = len(opt['act_to_ix'])
model = Navigator(opt)
print('eval navigator set up.')
while True:
# run evaluation for this epoch
invalids = []
# metrics
nav_metrics = NavMetric(
info={
'split': split,
'thread': rank
},
metric_names=metric_names,
log_json=args.results_json,
)
# sync model (fixed since now on!)
model.load_state_dict(shared_nav_model.state_dict())
model.eval()
model.cuda()
# reset envs
eval_loader.dataset._load_envs(start_idx=0, in_order=True)
# run
done = False # current epoch is not done yet
predictions = []
while not done:
for batch in tqdm(eval_loader):
# load target_paths from available_idx (of some envs)
# batch = {idx, qid, path_ix, house, id, type, phrase, phrase_emb, ego_feats,
# action_inputs, action_outputs, action_masks, action_length}
idx = batch['idx'][0].item()
qid = batch['qid'][0].item()
phrase_emb = batch['phrase_emb'].cuda() # (1, 300) float
raw_ego_feats = batch['ego_feats'] # (1, L, 3200) float
action_inputs = batch['action_inputs'] # (1, L) int
action_outputs = batch['action_outputs'] # (1, L) int
action_length = batch['action_length'][0].item()
tgt_id = batch['id'][0]
tgt_type = batch['type'][0]
pred = {
'qid': qid,
'house': batch['house'][0],
'id': batch['id'][0],
'type': batch['type'][0],
'path_name': batch['path_name'][0],
'path_ix': batch['path_ix'][0].item(),
'start_ix': batch['start_ix'][0].item(),
'key_ix': batch['key_ix'][0].item(),
'action_length': action_length,
'phrase': batch['phrase'][0],
}
metrics_slug = {}
# evaluate at multiple initializations
for i in [5, 10, 15]:
if action_length - i < 0:
invalids.append((idx, i))
continue
h3d = eval_loader.dataset.episode_house
episode_length = 0
episode_done = True
dists_to_target = []
pos_queue = []
actions = []
# forward through lstm till spawn
if len(eval_loader.dataset.episode_pos_queue[:-i]):
prev_pos_queue = eval_loader.dataset.episode_pos_queue[:
-i] # till spawned position
# ego_imgs = eval_loader.dataset.get_frames(h3d, prev_pos_queue, preprocess=True)
# ego_imgs = torch.from_numpy(ego_imgs).cuda() # (l, 3, 224, 224)
# ego_feats = eval_loader.dataset.cnn.extract_feats(ego_imgs, conv4_only=True) # (l, 3200)
# ego_feats = ego_feats.view(1, len(prev_pos_queue), 3200) # (1, l, 3200)
ego_feats_pruned = raw_ego_feats[:, :len(
prev_pos_queue), :].cuda() # (1, l, 3200)
action_inputs_pruned = action_inputs[:, :len(
prev_pos_queue)].cuda() # (1, l)
_, _, _, state = model(
ego_feats_pruned, phrase_emb,
action_inputs_pruned) # (1, l, rnn_size)
action = action_inputs[0, len(prev_pos_queue)].view(
-1).cuda() # (1, )
init_pos = eval_loader.dataset.episode_pos_queue[-i]
else:
state = None
action = torch.LongTensor([
eval_loader.dataset.act_to_ix['dummy']
]).cuda() # (1, )
init_pos = eval_loader.dataset.episode_pos_queue[-i]
# spawn
h3d.env.reset(x=init_pos[0],
y=init_pos[2],
yaw=init_pos[3])
init_dist_to_target = h3d.get_dist_to_target(
h3d.env.cam.pos)
if init_dist_to_target < 0: # unreachable
invalids.append([idx, i])
continue
dists_to_target += [init_dist_to_target]
pos_queue += [init_pos]
# act
ego_img = h3d.env.render()
ego_img = (
torch.from_numpy(ego_img.transpose(2, 0, 1)).float() /
255.).cuda()
ego_feat = eval_loader.dataset.cnn.extract_feats(
ego_img.unsqueeze(0), conv4_only=True) # (1, 3200)
collision = False
rot_act = None
rot_act_cnt = 0
for step in range(args.max_episode_length):
# forward model
episode_length += 1
logprobs, state = model.forward_step(
ego_feat, phrase_emb, action,
state) # (1, 4), (1, rnn_size)
# special case 1:
# if previous action is "forward" and collision happend and this action is still "forward", suppress it.
if action.item() == 0 and collision and torch.exp(
logprobs[0]).argmax().item() == 0:
logprobs[0][0] = -1e5
# special case 2:
# if spinned around 6 times for same rotation action, we suppress it
if torch.exp(logprobs[0]).argmax().item(
) == rot_act and rot_act_cnt > 5:
logprobs[0][torch.exp(
logprobs[0]).argmax().item()] = -1e5
# sample action
action = torch.exp(logprobs[0]).argmax().item()
actions += [action]
# accumulate rot_act
if action == 0:
rot_act = None
rot_act_cnt = 0
elif action in [1, 2]:
if rot_act == action:
rot_act_cnt += 1
else:
rot_act = action
rot_act_cnt = 1
# interact with environment
ego_img, _, episode_done, collision = h3d.step(action)
# prepare state for next action
ego_img = (torch.from_numpy(ego_img.transpose(
2, 0, 1)).float() / 255.).cuda()
ego_feat = eval_loader.dataset.cnn.extract_feats(
ego_img.unsqueeze(0), conv4_only=True) # (1, 3200)
action = torch.LongTensor([action]).cuda() # (1, )
# add to result
dists_to_target.append(
h3d.get_dist_to_target(h3d.env.cam.pos))
pos_queue.append([
h3d.env.cam.pos.x, h3d.env.cam.pos.y,
h3d.env.cam.pos.z, h3d.env.cam.yaw
])
if episode_done:
break
# add to predictions
pred['d_%s' % str(i)] = {
'actions':
actions,
'pos_queue':
pos_queue,
'gd_actions':
action_outputs[
0, len(prev_pos_queue):len(prev_pos_queue) +
i].tolist(),
'gd_pos_queue':
eval_loader.dataset.episode_pos_queue[-i:] +
[eval_loader.dataset.episode_pos_queue[-1]]
}
# is final view looking at target object?
if tgt_type == 'object':
iou = h3d.compute_target_iou(tgt_id)
R = 1 if h3d.compute_target_iou(tgt_id) >= 0.1 else 0
pred['iou_%s' % str(i)] = iou
# compute stats
metrics_slug['d_0_' + str(i)] = dists_to_target[0]
metrics_slug['ep_len_' + str(i)] = episode_length
metrics_slug['stop_' + str(i)] = 1 if action == 3 else 0
if tgt_type == 'object':
metrics_slug['d_T_' + str(i)] = dists_to_target[-1]
metrics_slug[
'd_D_' +
str(i)] = dists_to_target[0] - dists_to_target[-1]
metrics_slug['d_min_' + str(i)] = float(
np.array(dists_to_target).min())
metrics_slug['h_T_' + str(i)] = R
else:
inside_room = []
for p in pos_queue:
inside_room.append(
h3d.is_inside_room(
p, eval_loader.dataset.target_room))
if inside_room[-1] == True:
metrics_slug['r_T_' + str(i)] = 1
else:
metrics_slug['r_T_' + str(i)] = 0
if any([x == True for x in inside_room]) == True:
metrics_slug['r_e_' + str(i)] = 1
else:
metrics_slug['r_e_' + str(i)] = 0
# collate and update metrics
metrics_list = []
for i in nav_metrics.metric_names:
if i not in metrics_slug:
metrics_list.append(nav_metrics.metrics[
nav_metrics.metric_names.index(i)][0])
else:
metrics_list.append(metrics_slug[i])
# update metrics
if len(metrics_slug) > 0:
nav_metrics.update(metrics_list)
predictions.append(pred)
print(nav_metrics.get_stat_string(mode=0))
print('invalids', len(invalids))
logging.info("EVAL: init_steps: {} metrics: {}".format(
i, nav_metrics.get_stat_string(mode=0)))
logging.info("EVAL: init_steps: {} invalids: {}".format(
i, len(invalids)))
# next environments
eval_loader.dataset._load_envs()
print("eval_loader pruned_env_set len: {}".format(
len(eval_loader.dataset.pruned_env_set)))
logging.info("eval_loader pruned_env_set len: {}".format(
len(eval_loader.dataset.pruned_env_set)))
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
# save results
nav_metrics.dump_log(predictions)
# write summary
results_str = 'id[%s] split[%s]\n' % (args.id, args.split)
for n, r in zip(metric_names, nav_metrics.stats[-1]):
if r:
results_str += '%10s: %6.2f,' % (n, r[0])
if '_15' in n:
results_str += '\n'
f = open(args.report_txt, 'a')
f.write(results_str)
f.write('\n')
f.close()
# break
break
def imitation(rank, args, shared_nav_model, counter):
# set up tensorboard
writer = tb.SummaryWriter(args.tb_dir, filename_suffix=str(rank))
# set up cuda device
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
# set up random seeds
random.seed(args.seed + rank)
np.random.randn(args.seed + rank)
torch.manual_seed(args.seed + rank)
# set up loader
train_loader_kwargs = {
'data_json': args.imitation_data_json,
'data_h5': args.imitation_data_h5,
'path_feats_dir': args.path_feats_dir,
'path_images_dir': args.path_images_dir,
'split': 'train',
'max_seq_length': args.max_seq_length,
'requires_imgs': False,
'nav_types': args.nav_types,
'question_types': ['all'],
}
train_dataset = NavImitationDataset(**train_loader_kwargs)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=1)
print('train_loader set up.')
# set up optimizer on shared_nav_model
# lr = 5e-5
lr = args.learning_rate
optimizer = torch.optim.Adam(shared_nav_model.parameters(),
lr=lr,
betas=(args.optim_alpha, args.optim_beta),
eps=args.optim_epsilon,
weight_decay=args.weight_decay)
# set up models
opt = vars(args)
opt['act_to_ix'] = train_dataset.act_to_ix
opt['num_actions'] = len(opt['act_to_ix'])
model = Navigator(opt)
model.cuda()
print('navigator set up.')
# set up criterions
nll_crit = SeqModelCriterion().cuda()
# -
epoch = 0
iters = 0
# train
while True:
for batch in train_loader:
# sync model
model.load_state_dict(shared_nav_model.state_dict())
model.train()
model.cuda()
# batch = {qid, path_ix, house, id, type, phrase, phrase_emb, ego_feats, next_feats, res_feats,
# action_inputs, action_outputs, action_masks, ego_imgs}
ego_feats = batch['ego_feats'].cuda() # (n, L, 3200)
phrase_embs = batch['phrase_emb'].cuda() # (n, 300)
action_inputs = batch['action_inputs'].cuda() # (n, L)
action_outputs = batch['action_outputs'].cuda() # (n, L)
action_masks = batch['action_masks'].cuda() # (n, L)
# forward
# - logprobs (n, L, #actions)
# - output_feats (n, L, rnn_size)
# - pred_feats (n, L, 3200) or None
logprobs, _, pred_feats, _ = model(ego_feats, phrase_embs,
action_inputs)
nll_loss = nll_crit(logprobs, action_outputs, action_masks)
# backward
optimizer.zero_grad()
nll_loss.backward()
clip_model_gradient(model.parameters(), args.grad_clip)
ensure_shared_grads(model.cpu(), shared_nav_model)
optimizer.step()
if iters % 25 == 0:
print('imitation-r%s(ep%s it%s lr%.2E loss%.4f)' %
(rank, epoch, iters, lr, nll_loss))
# write to tensorboard
writer.add_scalar('imitation_rank/nll_loss',
float(nll_loss.item()), counter.value)
# increate iters
iters += 1
# decay learning rate
if args.lr_decay > 0:
if args.im_learning_rate_decay_start > 0 and iters > args.im_learning_rate_decay_start:
frac = (iters - args.im_learning_rate_decay_start
) / args.im_learning_rate_decay_every
decay_factor = 0.1**frac
lr = args.learning_rate * decay_factor
model_utils.set_lr(optimizer, lr)
epoch += 1
def eval(rank, args, shared_nav_model, counter, split='val'):
# set up cuda device
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
# tensorboard
# writer = tb.SummaryWriter(log_dir=args.tb_dir, filename_suffix=str(rank))
writer = tb.SummaryWriter(osp.join(args.tb_dir, str(rank)))
# set up dataset
cfg = {
'colorFile':
osp.join(args.house_meta_dir, 'colormap_fine.csv'),
'roomTargetFile':
osp.join(args.house_meta_dir, 'room_target_object_map.csv'),
'modelCategoryFile':
osp.join(args.house_meta_dir, 'ModelCategoryMapping.csv'),
'prefix':
args.house_data_dir,
}
loader_kwargs = {
'data_json': args.data_json,
'data_h5': args.data_h5,
'path_feats_dir': args.path_feats_dir,
'path_images_dir': args.path_images_dir,
'split': split,
'max_seq_length': args.max_seq_length,
'nav_types': args.nav_types,
'gpu_id': 0,
'cfg': cfg,
'max_threads_per_gpu': args.max_threads_per_gpu,
'target_obj_conn_map_dir': args.target_obj_conn_map_dir,
'map_resolution': 500,
'pretrained_cnn_path': args.pretrained_cnn_path,
'requires_imgs': False,
'question_types': ['all'],
}
dataset = NavReinforceDataset(**loader_kwargs)
eval_loader = DataLoader(dataset, batch_size=1, num_workers=0)
print('eval_loader set up.')
# set up model
opt = vars(args)
opt['act_to_ix'] = dataset.act_to_ix
opt['num_actions'] = len(opt['act_to_ix'])
model = Navigator(opt)
print('eval navigator set up.')
# evaluate
epoch = 0
best_eval_score = None
while epoch < int(args.max_epochs):
# wait till counter.value >= epoch * num_iters_per_epoch
cur_counter_value = counter.value
if counter.value / args.num_iters_per_epoch >= epoch:
epoch += 1
else:
continue
# run evaluation for this epoch
invalids = []
# metrics
nav_metrics = NavMetric(
info={
'split': split,
'thread': rank
},
metric_names=metric_names,
log_json=osp.join(args.log_dir, 'nav_eval_val.json'),
)
# update model (fixed since now on!)
model.load_state_dict(shared_nav_model.state_dict())
model.eval()
model.cuda()
# reset envs
eval_loader.dataset._load_envs(start_idx=0, in_order=True)
eval_loader.dataset.visited_envs = set()
# run
done = False
predictions = []
while not done:
for batch in tqdm(eval_loader):
# load target_paths from available_idx (of some envs)
# batch = {idx, qid, path_ix, house, id, type, phrase, phrase_emb, ego_feats,
# action_inputs, action_outputs, action_masks, action_length}
idx = batch['idx'][0].item()
qid = batch['qid'][0].item()
phrase_emb = batch['phrase_emb'].cuda() # (1, 300) cuda
raw_ego_feats = batch['ego_feats'] # (1, L, 3200) float
action_inputs = batch['action_inputs'] # (1, L) int
action_outputs = batch['action_outputs'] # (1, L) int
action_length = batch['action_length'][0].item()
tgt_type = batch['type'][0]
tgt_id = batch['id'][0]
pred = {
'qid': qid,
'house': batch['house'][0],
'id': batch['id'][0],
'type': batch['type'][0],
'path_name': batch['path_name'][0],
'path_ix': batch['path_ix'][0].item(),
'start_ix': batch['start_ix'][0].item(),
'key_ix': batch['key_ix'][0].item(),
'action_length': action_length,
'phrase': batch['phrase'][0],
}
metrics_slug = {}
# evaluate at multiple initializations
# for i in [5, 10, 15]:
for i in [15]: # for saving evaluation time
if action_length - i < 0:
invalids.append((idx, i))
continue
h3d = eval_loader.dataset.episode_house
episode_length = 0
episode_done = True
dists_to_target = []
pos_queue = []
actions = []
# forward through navigator till spawn
if len(eval_loader.dataset.episode_pos_queue[:-i]):
prev_pos_queue = eval_loader.dataset.episode_pos_queue[:
-i] # till spawned position
# ego_imgs = eval_loader.dataset.get_frames(h3d, prev_pos_queue, preprocess=True)
# ego_imgs = torch.from_numpy(ego_imgs).cuda() # (l, 3, 224, 224)
# ego_feats = eval_loader.dataset.cnn.extract_feats(ego_imgs, conv4_only=True) # (l, 3200)
# ego_feats = ego_feats.view(1, len(prev_pos_queue), 3200) # (1, l, 3200)
ego_feats_pruned = raw_ego_feats[:, :len(
prev_pos_queue), :].cuda() # (1, l, 3200)
action_inputs_pruned = action_inputs[:, :len(
prev_pos_queue)].cuda() # (1, l)
_, _, _, state = model(
ego_feats_pruned, phrase_emb,
action_inputs_pruned) # (1, l, rnn_size)
action = action_inputs[0, len(prev_pos_queue)].view(
-1).cuda() # (1, )
init_pos = eval_loader.dataset.episode_pos_queue[-i]
else:
state = None
action = torch.LongTensor([
eval_loader.dataset.act_to_ix['dummy']
]).cuda() # (1, )
init_pos = eval_loader.dataset.episode_pos_queue[
0] # use first position instead
# spawn
h3d.env.reset(x=init_pos[0],
y=init_pos[2],
yaw=init_pos[3])
init_dist_to_target = h3d.get_dist_to_target(
h3d.env.cam.pos)
if init_dist_to_target < 0: # unreachable
invalids.append([idx, i])
continue
dists_to_target += [init_dist_to_target]
pos_queue += [init_pos]
# act
ego_img = h3d.env.render()
ego_img = (
torch.from_numpy(ego_img.transpose(2, 0, 1)).float() /
255.).cuda()
ego_feat = eval_loader.dataset.cnn.extract_feats(
ego_img.unsqueeze(0), conv4_only=True) # (1, 3200)
collision = False
rot_act = None
rot_act_cnt = 0
for step in range(args.max_episode_length):
# forward model
episode_length += 1
with torch.no_grad():
logprobs, state = model.forward_step(
ego_feat, phrase_emb, action,
state) # (1, 4), (1, rnn_size)
# special case 1: if previous action is "forward" and collision happend and this action
# is still "forward", suppress it.
if action.item() == 0 and collision and torch.exp(
logprobs[0]).argmax().item() == 0:
logprobs[0][0] = -1e5
# special case 2:
# if spinned around 6 times for same rotation action, we suppress it
if torch.exp(logprobs[0]).argmax().item(
) == rot_act and rot_act_cnt > 5:
logprobs[0][torch.exp(
logprobs[0]).argmax().item()] = -1e5
# sample action
action = torch.exp(logprobs[0]).argmax().item()
actions += [action]
# accumulate rot_act
if action == 0:
rot_act = None
rot_act_cnt = 0
elif action in [1, 2]:
if rot_act == action:
rot_act_cnt += 1
else:
rot_act = action
rot_act_cnt = 1
# interact with environment
ego_img, _, episode_done, collision = h3d.step(action)
episode_done = episode_done or episode_length >= args.max_episode_length # no need actually
# prepare state for next action
ego_img = (torch.from_numpy(ego_img.transpose(
2, 0, 1)).float() / 255.).cuda()
ego_feat = eval_loader.dataset.cnn.extract_feats(
ego_img.unsqueeze(0), conv4_only=True) # (1, 3200)
action = torch.LongTensor([action]).cuda() # (1, )
# add to result
dists_to_target.append(
h3d.get_dist_to_target(h3d.env.cam.pos))
pos_queue.append([
h3d.env.cam.pos.x, h3d.env.cam.pos.y,
h3d.env.cam.pos.z, h3d.env.cam.yaw
])
if episode_done:
break
# add to predictions
pred['d_%s' % str(i)] = {
'actions':
actions,
'pos_queue':
pos_queue,
'gd_actions':
action_outputs[
0, len(prev_pos_queue):len(prev_pos_queue) +
i].tolist(),
'gd_pos_queue':
eval_loader.dataset.episode_pos_queue[-i:] +
[eval_loader.dataset.episode_pos_queue[-1]]
}
# compute stats
metrics_slug['d_0_' + str(i)] = dists_to_target[0]
metrics_slug['ep_len_' + str(i)] = episode_length
metrics_slug['stop_' + str(i)] = 1 if action == 3 else 0
if tgt_type == 'object':
metrics_slug['d_T_' + str(i)] = dists_to_target[-1]
metrics_slug[
'd_D_' +
str(i)] = dists_to_target[0] - dists_to_target[-1]
metrics_slug['d_min_' + str(i)] = float(
np.array(dists_to_target).min())
iou = h3d.compute_target_iou(tgt_id)
metrics_slug['h_T_' + str(i)] = 1 if iou >= 0.1 else 0
else:
inside_room = []
for p in pos_queue:
inside_room.append(
h3d.is_inside_room(
p, eval_loader.dataset.target_room))
metrics_slug['r_T_' +
str(i)] = 1 if inside_room[-1] else 0
metrics_slug['r_e_' + str(i)] = 1 if any(
[x == True for x in inside_room]) else 0
# collate and update metrics
metrics_list = []
for name in nav_metrics.metric_names:
if name not in metrics_slug:
metrics_list.append(nav_metrics.metrics[
nav_metrics.metric_names.index(name)][0])
else:
metrics_list.append(metrics_slug[name])
# update metrics
if len(metrics_slug) > 0:
nav_metrics.update(metrics_list)
predictions.append(pred)
print(nav_metrics.get_stat_string(mode=0))
print('invalids', len(invalids))
logging.info("EVAL: init_steps: {} metrics: {}".format(
i, nav_metrics.get_stat_string(mode=0)))
logging.info("EVAL: init_steps: {} invalids: {}".format(
i, len(invalids)))
print('%s/%s envs visited.' %
(len(eval_loader.dataset.visited_envs),
len(eval_loader.dataset.env_set)))
# next environments
eval_loader.dataset._load_envs()
print("eval_loader pruned_env_set len: {}".format(
len(eval_loader.dataset.pruned_env_set)))
logging.info("eval_loader pruned_env_set len: {}".format(
len(eval_loader.dataset.pruned_env_set)))
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
# write to tensorboard
for metric_name in [
'd_T_5', 'd_T_10', 'd_T_15', 'd_D_5', 'd_D_10', 'd_D_15',
'h_T_5', 'h_T_10', 'h_T_15', 'r_T_5', 'r_T_10', 'r_T_15',
'r_e_5', 'r_e_10', 'r_e_15'
]:
value = nav_metrics.metrics[nav_metrics.metric_names.index(
metric_name)][0]
if value:
# instead of counter.value (as we started eval at cur_counter_value)
writer.add_scalar('eval/%s' % metric_name, value,
cur_counter_value)
# save if best
# best_score = d_D_15 + h_T_15 + r_T_15
cur_score = 0
if nav_metrics.metrics[nav_metrics.metric_names.index('d_D_15')][0]:
cur_score += nav_metrics.metrics[nav_metrics.metric_names.index(
'd_D_15')][0]
if nav_metrics.metrics[nav_metrics.metric_names.index('h_T_15')][0]:
cur_score += nav_metrics.metrics[nav_metrics.metric_names.index(
'h_T_15')][0]
if nav_metrics.metrics[nav_metrics.metric_names.index('r_T_15')][0]:
cur_score += nav_metrics.metrics[nav_metrics.metric_names.index(
'r_T_15')][0]
if (best_eval_score is None
or cur_score > best_eval_score) and cur_counter_value > 50000:
best_eval_score = cur_score
nav_metrics.dump_log(predictions)
checkpoint_path = osp.join(args.checkpoint_dir, '%s.pth' % args.id)
checkpoint = {}
checkpoint['model_state'] = model.cpu().state_dict()
checkpoint['opt'] = vars(args)
torch.save(checkpoint, checkpoint_path)
print('model saved to %s.' % checkpoint_path)
def main(args):
# make output directory
if args.checkpoint_dir is None:
args.checkpoint_dir = 'output/nav_object'
if not osp.isdir(args.checkpoint_dir):
os.makedirs(args.checkpoint_dir)
# set random seed
random.seed(args.seed)
np.random.randn(args.seed)
torch.manual_seed(args.seed)
# set up loaders
train_loader_kwargs = {
'data_json': args.data_json,
'data_h5': args.data_h5,
'path_feats_dir': args.path_feats_dir,
'path_images_dir': args.path_images_dir,
'split': 'train',
'max_seq_length': args.max_seq_length,
'requires_imgs': False,
'nav_types': ['object'],
'question_types': ['all'],
}
train_dataset = NavImitationDataset(**train_loader_kwargs)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
num_workers=args.num_workers)
val_loader_kwargs = {
'data_json': args.data_json,
'data_h5': args.data_h5,
'path_feats_dir': args.path_feats_dir,
'path_images_dir': args.path_images_dir,
'split': 'val',
'max_seq_length': args.max_seq_length,
'requires_imgs': False,
'nav_types': ['object'],
'question_types': ['all'],
}
val_dataset = NavImitationDataset(**val_loader_kwargs)
# set up models
opt = vars(args)
opt['act_to_ix'] = train_dataset.act_to_ix
opt['num_actions'] = len(opt['act_to_ix'])
model = Navigator(opt)
model.cuda()
print('navigator set up.')
# set up criterions
nll_crit = SeqModelCriterion().cuda()
mse_crit = MaskedMSELoss().cuda()
# set up optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=args.learning_rate,
betas=(args.optim_alpha, args.optim_beta),
eps=args.optim_epsilon,
weight_decay=args.weight_decay)
# resume from checkpoint
infos = {}
iters = infos.get('iters', 0)
epoch = infos.get('epoch', 0)
val_nll_history = infos.get('val_nll_history', {})
val_mse_history = infos.get('val_mse_history', {})
val_teacher_forcing_acc_history = infos.get(
'val_teacher_forcing_acc_history', {})
val_nav_object_nll_history = infos.get('val_nav_object_nll_history', {})
val_nav_object_teacher_forcing_acc_history = infos.get(
'val_nav_object_teacher_forcing_acc_history', {})
val_nav_room_nll_history = infos.get('val_nav_room_nll_history', {})
val_nav_room_teacher_forcing_acc_history = infos.get(
'val_nav_room_teacher_forcing_acc_history', {})
loss_history = infos.get('loss_history', {})
nll_loss_history = infos.get('nll_loss_history', {})
mse_loss_history = infos.get('mse_loss_history', {})
lr = infos.get('lr', args.learning_rate)
best_val_score, best_val_acc, best_predictions = None, None, None
# start training
while iters <= args.max_iters:
print('Starting epoch %d' % epoch)
# reset seq_length
if args.use_curriculum:
# assume we need 4 epochs to get full seq_length
seq_length = min((args.max_seq_length // 4)**(epoch + 1),
args.max_seq_length)
train_dataset.reset_seq_length(seq_length)
else:
seq_length = args.max_seq_length
# train
for batch in train_loader:
# set mode
model.train()
# zero gradient
optimizer.zero_grad()
# batch = {qid, path_ix, house, id, type, phrase, phrase_emb, ego_feats, next_feats, res_feats,
# action_inputs, action_outputs, action_masks, ego_imgs}
ego_feats = batch['ego_feats'].cuda() # (n, L, 3200)
phrase_embs = batch['phrase_emb'].cuda() # (n, 300)
action_inputs = batch['action_inputs'].cuda() # (n, L)
action_outputs = batch['action_outputs'].cuda() # (n, L)
action_masks = batch['action_masks'].cuda() # (n, L)
# forward
# - logprobs (n, L, #actions)
# - output_feats (n, L, rnn_size)
# - pred_feats (n, L, 3200) or None
logprobs, _, pred_feats, _ = model(ego_feats, phrase_embs,
action_inputs)
nll_loss = nll_crit(logprobs, action_outputs, action_masks)
mse_loss = 0
if args.use_next:
next_feats = batch['next_feats'].cuda() # (n, L, 3200)
mse_loss = mse_crit(pred_feats, next_feats, action_masks)
if args.use_residual:
res_feats = batch['res_feats'].cuda() # (n, L, 3200)
mse_loss = mse_crit(pred_feats, res_feats, action_masks)
loss = nll_loss + args.mse_weight * mse_loss
# backward
loss.backward()
model_utils.clip_gradient(optimizer, args.grad_clip)
optimizer.step()
# training log
if iters % args.losses_log_every == 0:
loss_history[iters] = loss.item()
nll_loss_history[iters] = nll_loss.item()
mse_loss_history[iters] = mse_loss.item() if (
args.use_next or args.use_residual) else 0
print('iters[%s]epoch[%s], train_loss=%.3f (nll_loss=%.3f, mse_loss=%.3f) lr=%.2E, cur_seq_length=%s' % \
(iters, epoch, loss_history[iters], nll_loss_history[iters], mse_loss_history[iters], lr, train_loader.dataset.cur_seq_length))
# decay learning rate
if args.learning_rate_decay_start > 0 and iters > args.learning_rate_decay_start:
frac = (iters - args.learning_rate_decay_start
) / args.learning_rate_decay_every
decay_factor = 0.1**frac
lr = args.learning_rate * decay_factor
model_utils.set_lr(optimizer, lr)
# evaluate
if iters % args.save_checkpoint_every == 0:
print('Checking validation ...')
predictions, overall_nll, overall_teacher_forcing_acc, overall_mse, Nav_nll, Nav_teacher_forcing_acc = \
evaluate(val_dataset, model, nll_crit, mse_crit, opt)
val_nll_history[iters] = overall_nll
val_teacher_forcing_acc_history[
iters] = overall_teacher_forcing_acc
val_mse_history[iters] = overall_mse
val_nav_object_nll_history[iters] = Nav_nll['object']
val_nav_object_teacher_forcing_acc_history[
iters] = Nav_teacher_forcing_acc['object']
val_nav_room_nll_history[iters] = Nav_nll['room']
val_nav_room_teacher_forcing_acc_history[
iters] = Nav_teacher_forcing_acc['room']
# save model if best
# consider all three accuracy, perhaps a better weighting is needed.
current_score = -overall_nll
if best_val_score is None or current_score > best_val_score:
best_val_score = current_score
best_val_acc = overall_teacher_forcing_acc
best_predictions = predictions
checkpoint_path = osp.join(args.checkpoint_dir,
'%s.pth' % args.id)
checkpoint = {}
checkpoint['model_state'] = model.state_dict()
checkpoint['opt'] = vars(args)
torch.save(checkpoint, checkpoint_path)
print('model saved to %s.' % checkpoint_path)
# write to json report
infos['iters'] = iters
infos['epoch'] = epoch
infos['loss_history'] = loss_history
infos['nll_loss_history'] = nll_loss_history
infos['mse_loss_history'] = mse_loss_history
infos['val_nll_history'] = val_nll_history
infos[
'val_teacher_forcing_acc_history'] = val_teacher_forcing_acc_history
infos['val_mse_history'] = val_mse_history
infos[
'val_nav_object_nll_history'] = val_nav_object_nll_history
infos[
'val_nav_object_teacher_forcing_acc_history'] = val_nav_object_teacher_forcing_acc_history
infos['val_nav_room_nll_history'] = val_nav_room_nll_history
infos[
'val_nav_room_teacher_forcing_acc_history'] = val_nav_room_teacher_forcing_acc_history
infos['best_val_score'] = best_val_score
infos['best_val_acc'] = best_val_acc
infos[
'best_predictions'] = predictions if best_predictions is None else best_predictions
infos['opt'] = vars(args)
infos['act_to_ix'] = train_dataset.act_to_ix
infos_json = osp.join(args.checkpoint_dir, '%s.json' % args.id)
with open(infos_json, 'w') as f:
json.dump(infos, f)
print('infos saved to %s.' % infos_json)
# update iters
iters += 1
# update epoch
epoch += 1
def train(rank, args, shared_nav_model, counter, lock):
# set up tensorboard
# writer = tb.SummaryWriter(args.tb_dir, filename_suffix=str(rank))
writer = tb.SummaryWriter(osp.join(args.tb_dir, str(rank)))
# set up cuda device
gpu_id = args.gpus.index(args.gpus[rank % len(args.gpus)])
torch.cuda.set_device(gpu_id)
# set up random seeds
random.seed(args.seed + rank)
np.random.randn(args.seed + rank)
torch.manual_seed(args.seed + rank)
# set up dataset
cfg = {
'colorFile':
osp.join(args.house_meta_dir, 'colormap_fine.csv'),
'roomTargetFile':
osp.join(args.house_meta_dir, 'room_target_object_map.csv'),
'modelCategoryFile':
osp.join(args.house_meta_dir, 'ModelCategoryMapping.csv'),
'prefix':
args.house_data_dir,
}
loader_kwargs = {
'data_json': args.data_json,
'data_h5': args.data_h5,
'path_feats_dir': args.path_feats_dir,
'path_images_dir': args.path_images_dir,
'split': 'train',
'max_seq_length': args.max_seq_length,
'nav_types': args.nav_types,
'gpu_id': args.gpus[rank % len(args.gpus)],
'cfg': cfg,
'max_threads_per_gpu': args.max_threads_per_gpu,
'target_obj_conn_map_dir': args.target_obj_conn_map_dir,
'map_resolution': 500,
'pretrained_cnn_path': args.pretrained_cnn_path,
'requires_imgs': False,
'question_types': ['all'],
'ratio': [rank / args.num_processes, (rank + 1) / args.num_processes]
}
dataset = NavReinforceDataset(**loader_kwargs)
train_loader = DataLoader(dataset, batch_size=1, num_workers=0)
print('train_loader set up.')
# set up optimizer on shared_nav_model
lr = args.learning_rate
optimizer = torch.optim.Adam(shared_nav_model.parameters(),
lr=lr,
betas=(args.optim_alpha, args.optim_beta),
eps=args.optim_epsilon,
weight_decay=args.weight_decay)
# set up model
opt = vars(args)
opt['act_to_ix'] = dataset.act_to_ix
opt['num_actions'] = len(opt['act_to_ix'])
model = Navigator(opt)
print('navigator[%s] set up.' % rank)
# set up metrics outside epoch, as we use running rewards through whole training
nav_metrics = NavMetric(
info={
'split': 'train',
'thread': rank
},
metric_names=['reward', 'episode_length'],
log_json=osp.join(args.log_dir, 'nav_train_' + str(rank) + '.json'),
)
nav_metrics.update([0, 100])
reward_list, episode_length_list = [], []
rwd_stats = Statistics() # computing running mean and std of rewards
# path length multiplier
min_dist = 5 if args.nav_types == [
'object'
] else 15 # 15 for rl3 and rl5, 10 for rl4
# max_dist = 25 if args.nav_types == ['object'] else 40
max_dist = 35 if args.nav_types == ['object'] else 50
mult = 0.1 if args.nav_types == ['object'] else 0.15
rwd_thresh = 0.1 if args.nav_types == ['object'] else 0.00
epoch = 0
iters = 0
# train
while True:
# reset envs
train_loader.dataset._load_envs(start_idx=0, in_order=True)
done = False # current epoch is not done yet
while not done:
for batch in train_loader:
# sync model
model.load_state_dict(shared_nav_model.state_dict())
model.train()
model.cuda()
# load target_paths from available_idx (of some envs)
# batch = {idx, qid, path_ix, house, id, type, phrase, phrase_emb, ego_feats,
# action_inputs, action_outputs, action_masks, action_length}
idx = batch['idx'][0].item()
qid = batch['qid'][0].item()
phrase_emb = batch['phrase_emb'].cuda() # (1, 300) float
raw_ego_feats = batch['ego_feats'] # (1, L, 3200) float
action_inputs = batch['action_inputs'] # (1, L) int
action_outputs = batch['action_outputs'] # (1, L) int
action_length = batch['action_length'][0].item()
tgt_type = batch['type'][0]
tgt_id = batch['id'][0]
tgt_phrase = batch['phrase'][0]
# to be recorded
episode_length = 0
episode_done = True
dists_to_target = []
pos_queue = []
actions = []
rewards = []
nav_log_probs = []
# spawn agent
h3d = train_loader.dataset.episode_house
if np.random.uniform(0, 1, 1)[0] <= args.shortest_path_ratio:
# half chance we use shortest path to spawn the agent (if vlen > 0, i.e., shortest path long enough)
use_shortest_path = True
vlen = min(max(min_dist, int(mult * action_length)),
action_length)
# forward throught navigator till spawn
if len(train_loader.dataset.episode_pos_queue) > vlen:
prev_pos_queue = train_loader.dataset.episode_pos_queue[:
-vlen] # till spawned position
ego_feats_pruned = raw_ego_feats[:, :len(
prev_pos_queue), :].cuda() # (1, l, 3200)
action_inputs_pruned = action_inputs[:, :len(
prev_pos_queue)].cuda() # (1, l)
_, _, _, state = model(
ego_feats_pruned, phrase_emb,
action_inputs_pruned) # (1, l, rnn_size)
action = action_inputs[0, len(prev_pos_queue)].view(
-1).cuda() # (1, )
init_pos = train_loader.dataset.episode_pos_queue[
-vlen]
else:
state = None
action = torch.LongTensor([
train_loader.dataset.act_to_ix['dummy']
]).cuda() # (1, )
init_pos = train_loader.dataset.episode_pos_queue[
0] # use first position of the path
else:
# half chance we randomly spawn agent
use_shortest_path = False
state = None
action = torch.LongTensor([
train_loader.dataset.act_to_ix['dummy']
]).cuda() # (1, )
init_pos, vlen = train_loader.dataset.spawn_agent(
min_dist, max_dist)
if init_pos is None: # init_pos not found
continue
# initiate
h3d.env.reset(x=init_pos[0], y=init_pos[2], yaw=init_pos[3])
init_dist_to_target = h3d.get_dist_to_target(h3d.env.cam.pos)
if init_dist_to_target < 0: # unreachable
continue
dists_to_target += [init_dist_to_target]
pos_queue += [init_pos]
# act
ego_img = h3d.env.render()
ego_img = (
torch.from_numpy(ego_img.transpose(2, 0, 1)).float() /
255.).cuda()
ego_feat = train_loader.dataset.cnn.extract_feats(
ego_img.unsqueeze(0), conv4_only=True) # (1, 3200)
prev_action, collision = None, False
for step in range(args.max_episode_length):
# forward model one step
episode_length += 1
logprobs, state = model.forward_step(
ego_feat, phrase_emb, action,
state) # (1, 4), (1, rnn_size)
# sample action
probs = torch.exp(logprobs) # (1, 4)
action = probs.multinomial(
num_samples=1).detach() # (1, 1)
if prev_action == 0 and collision and action[0][0].item(
) == 0:
# special case: prev_action == "forward" && collision && cur_action == "forward"
# we sample from {'left', 'right', 'stop'} only
action = probs[0:1, 1:].multinomial(
num_samples=1).detach() + 1 # (1, 1)
if len(pos_queue) < min_dist:
# special case: our room navigator tends to stop early, let's push it to explore longer
action = probs[0:1, :3].multinomial(
num_samples=1).detach() # (1, 1)
nav_log_probs.append(logprobs.gather(1, action)) # (1, 1)
action = action.view(-1) # (1, )
actions.append(action)
# interact with environment
ego_img, reward, episode_done, collision = h3d.step(
action[0].item(), step_reward=True)
if not episode_done:
reward -= 0.01 # we don't wanna too long trajectory
episode_done = episode_done or episode_length >= args.max_episode_length # no need actually
reward = max(min(reward, 1), -1)
rewards.append(reward)
prev_action = action[0].item()
# prepare state for next action
ego_img = (
torch.from_numpy(ego_img.transpose(2, 0, 1)).float() /
255.).cuda()
ego_feat = train_loader.dataset.cnn.extract_feats(
ego_img.unsqueeze(0), conv4_only=True) # (1, 3200)
# add to result
dists_to_target.append(
h3d.get_dist_to_target(h3d.env.cam.pos))
pos_queue.append([
h3d.env.cam.pos.x, h3d.env.cam.pos.y,
h3d.env.cam.pos.z, h3d.env.cam.yaw
])
if episode_done:
break
# final reward
R = 0
if tgt_type == 'object':
R = 1.0 if h3d.compute_target_iou(tgt_id) >= 0.1 else -1.0
else:
R = 0.2 if h3d.is_inside_room(
pos_queue[-1],
train_loader.dataset.target_room) else -0.2
if R > 0 and h3d.compute_room_targets_iou(
room_to_objects[tgt_phrase]) > 0.1:
R += 1.0 # encourage agent to move to room-specific targets
# backward
nav_loss = 0
new_rewards = [
] # recording reshaped rewards, to be computed as moving average
advantages = []
for i in reversed(range(len(rewards))):
R = 0.99 * R + rewards[i]
new_rewards.insert(0, R)
rwd_stats.push(R)
for nav_log_prob, R in zip(nav_log_probs, new_rewards):
advantage = (R - rwd_stats.mean()) / (
rwd_stats.stddev() + 1e-5) # rl2, rl3
# advantage = R - rwd_stats.mean() # rl1
nav_loss = nav_loss - nav_log_prob * advantage
advantages.insert(0, advantage)
nav_loss /= max(1, len(nav_log_probs))
optimizer.zero_grad()
nav_loss.backward()
clip_model_gradient(model.parameters(), args.grad_clip)
# Till this point, we have grads on model's parameters!
# but shared_nav_model's grads is still not updated
ensure_shared_grads(model.cpu(), shared_nav_model)
optimizer.step()
if iters % 5 == 0:
log_info = 'train-r%2s(ep%2s it%5s lr%.2E mult%.2f, run_rwd%6.3f, bs_rwd%6.3f, bs_std%2.2f), vlen(%s)=%2s, rwd=%6.3f, ep_len=%3s, tgt:%s' % \
(rank, epoch, iters, lr, mult, nav_metrics.metrics[0][1], rwd_stats.mean(), rwd_stats.stddev(), 'short' if use_shortest_path else 'spawn', vlen, np.mean(new_rewards), len(new_rewards), tgt_phrase)
print(log_info)
# update metrics
reward_list += new_rewards
episode_length_list.append(episode_length)
if len(episode_length_list) > 50:
nav_metrics.update([reward_list, episode_length_list])
nav_metrics.dump_log()
reward_list, episode_length_list = [], []
# write to tensorboard
writer.add_scalar('train_rank%s/nav_loss' % rank,
nav_loss.item(), counter.value)
writer.add_scalar('train_rank%s/steps_avg_rwd' % rank,
float(np.mean(new_rewards)), counter.value)
writer.add_scalar('train_rank%s/steps_sum_rwd' % rank,
float(np.sum(new_rewards)), counter.value)
writer.add_scalar('train_rank%s/advantage' % rank,
float(np.mean(advantages)), counter.value)
# increase counter as this episode ends
with lock:
counter.value += 1
# increase mult
iters += 1
if nav_metrics.metrics[0][
1] > rwd_thresh: # baseline = nav_metrics.metrics[0][1]
mult = min(mult + 0.1, 1.0)
rwd_thresh += 0.01
else:
mult = max(mult - 0.1, 0.1)
rwd_thresh -= 0.01
rwd_thresh = max(0.1, min(rwd_thresh, 0.2))
# decay learning rate
if args.learning_rate_decay_start > 0 and iters > args.learning_rate_decay_start:
frac = (iters - args.learning_rate_decay_start
) / args.learning_rate_decay_every
decay_factor = 0.1**frac
lr = args.learning_rate * decay_factor
model_utils.set_lr(optimizer, lr)
# next environments
train_loader.dataset._load_envs(in_order=True)
print("train_loader pruned_env_set len: {}".format(
len(train_loader.dataset.pruned_env_set)))
logging.info("train_loader pruned_env_set len: {}".format(
len(train_loader.dataset.pruned_env_set)))
if len(train_loader.dataset.pruned_env_set) == 0:
done = True
epoch += 1