def eval(rank, args, shared_nav_model, shared_ans_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.model_type == 'pacman':
model_kwargs = {'question_vocab': load_vocab(args.vocab_json)}
nav_model = NavPlannerControllerModel(**model_kwargs)
else:
exit()
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
ans_model = VqaLstmCnnAttentionModel(**model_kwargs)
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'data_json': args.data_json,
'vocab': args.vocab_json,
'target_obj_conn_map_dir': args.target_obj_conn_map_dir,
'map_resolution': args.map_resolution,
'batch_size': 1,
'input_type': args.model_type,
'num_frames': 5,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': False
}
eval_loader = EqaDataLoader(**eval_loader_kwargs)
print('eval_loader has %d samples' % len(eval_loader.dataset))
args.output_nav_log_path = os.path.join(args.log_dir,
'nav_eval_' + str(rank) + '.json')
args.output_ans_log_path = os.path.join(args.log_dir,
'ans_eval_' + str(rank) + '.json')
t, epoch, best_eval_acc = 0, 0, 0.0
while epoch < int(args.max_epochs):
start_time = time.time()
invalids = []
nav_model.load_state_dict(shared_nav_model.state_dict())
nav_model.eval()
ans_model.load_state_dict(shared_ans_model.state_dict())
ans_model.eval()
ans_model.cuda()
# that's a lot of numbers
nav_metrics = NavMetric(
info={
'split': args.eval_split,
'thread': rank
},
metric_names=[
'd_0_10', 'd_0_30', 'd_0_50', 'd_T_10', 'd_T_30', 'd_T_50',
'd_D_10', 'd_D_30', 'd_D_50', 'd_min_10', 'd_min_30',
'd_min_50', 'r_T_10', 'r_T_30', 'r_T_50', 'r_e_10', 'r_e_30',
'r_e_50', 'stop_10', 'stop_30', 'stop_50', 'ep_len_10',
'ep_len_30', 'ep_len_50'
],
log_json=args.output_nav_log_path)
vqa_metrics = VqaMetric(
info={
'split': args.eval_split,
'thread': rank
},
metric_names=[
'accuracy_10', 'accuracy_30', 'accuracy_50', 'mean_rank_10',
'mean_rank_30', 'mean_rank_50', 'mean_reciprocal_rank_10',
'mean_reciprocal_rank_30', 'mean_reciprocal_rank_50'
],
log_json=args.output_ans_log_path)
if 'pacman' in args.model_type:
done = False
while done == False:
for batch in tqdm(eval_loader):
nav_model.load_state_dict(shared_nav_model.state_dict())
nav_model.eval()
nav_model.cuda()
idx, question, answer, actions, action_length = batch
metrics_slug = {}
h3d = eval_loader.dataset.episode_house
# evaluate at multiple initializations
for i in [10, 30, 50]:
t += 1
if i > action_length[0]:
invalids.append([idx[0], i])
continue
question_var = Variable(question.cuda())
controller_step = False
planner_hidden = nav_model.planner_nav_rnn.init_hidden(
1)
# forward through planner till spawn
planner_actions_in, planner_img_feats, controller_step, controller_action_in, controller_img_feat, init_pos = eval_loader.dataset.get_hierarchical_features_till_spawn(
actions[0, :action_length[0] + 1].numpy(), i)
planner_actions_in_var = Variable(
planner_actions_in.cuda())
planner_img_feats_var = Variable(
planner_img_feats.cuda())
for step in range(planner_actions_in.size(0)):
planner_scores, planner_hidden = nav_model.planner_step(
question_var,
planner_img_feats_var[step].view(1, 1, 3200),
planner_actions_in_var[step].view(1, 1),
planner_hidden)
if controller_step == True:
controller_img_feat_var = Variable(
controller_img_feat.cuda())
controller_action_in_var = Variable(
torch.LongTensor(1, 1).fill_(
int(controller_action_in)).cuda())
controller_scores = nav_model.controller_step(
controller_img_feat_var.view(1, 1, 3200),
controller_action_in_var.view(1, 1),
planner_hidden[0])
prob = F.softmax(controller_scores, dim=1)
controller_action = int(
prob.max(1)[1].data.cpu().numpy()[0])
if controller_action == 1:
controller_step = True
else:
controller_step = False
action = int(controller_action_in)
action_in = torch.LongTensor(1, 1).fill_(action +
1).cuda()
else:
prob = F.softmax(planner_scores, dim=1)
action = int(prob.max(1)[1].data.cpu().numpy()[0])
action_in = torch.LongTensor(1, 1).fill_(action +
1).cuda()
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[0], i])
continue
episode_length = 0
episode_done = True
controller_action_counter = 0
dists_to_target, pos_queue, pred_actions = [
init_dist_to_target
], [init_pos], []
planner_actions, controller_actions = [], []
if action != 3:
# take the first step
img, _, _ = h3d.step(action)
img = torch.from_numpy(img.transpose(
2, 0, 1)).float() / 255.0
img_feat_var = eval_loader.dataset.cnn(
Variable(img.view(1, 3, 224,
224).cuda())).view(
1, 1, 3200)
for step in range(args.max_episode_length):
episode_length += 1
if controller_step == False:
planner_scores, planner_hidden = nav_model.planner_step(
question_var, img_feat_var,
Variable(action_in), planner_hidden)
prob = F.softmax(planner_scores, dim=1)
action = int(
prob.max(1)[1].data.cpu().numpy()[0])
planner_actions.append(action)
pred_actions.append(action)
img, _, episode_done = h3d.step(action)
episode_done = episode_done or episode_length >= args.max_episode_length
img = torch.from_numpy(img.transpose(
2, 0, 1)).float() / 255.0
img_feat_var = eval_loader.dataset.cnn(
Variable(img.view(1, 3, 224,
224).cuda())).view(
1, 1, 3200)
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 == True:
break
# query controller to continue or not
controller_action_in = Variable(
torch.LongTensor(1,
1).fill_(action).cuda())
controller_scores = nav_model.controller_step(
img_feat_var, controller_action_in,
planner_hidden[0])
prob = F.softmax(controller_scores, dim=1)
controller_action = int(
prob.max(1)[1].data.cpu().numpy()[0])
if controller_action == 1 and controller_action_counter < 4:
controller_action_counter += 1
controller_step = True
else:
controller_action_counter = 0
controller_step = False
controller_action = 0
controller_actions.append(controller_action)
action_in = torch.LongTensor(
1, 1).fill_(action + 1).cuda()
# run answerer here
if len(pos_queue) < 5:
pos_queue = eval_loader.dataset.episode_pos_queue[
len(pos_queue) - 5:] + pos_queue
images = eval_loader.dataset.get_frames(
h3d, pos_queue[-5:], preprocess=True)
images_var = Variable(
torch.from_numpy(images).cuda()).view(
1, 5, 3, 224, 224)
scores, att_probs = ans_model(images_var, question_var)
ans_acc, ans_rank = vqa_metrics.compute_ranks(
scores.data.cpu(), answer)
pred_answer = scores.max(1)[1].data[0]
print(
'[Q_GT]', ' '.join([
eval_loader.dataset.vocab['questionIdxToToken']
[x] for x in question[0] if x != 0
]))
print(
'[A_GT]',
eval_loader.dataset.vocab['answerIdxToToken'][
answer[0]])
print(
'[A_PRED]',
eval_loader.dataset.vocab['answerIdxToToken']
[pred_answer])
# compute stats
metrics_slug['accuracy_' + str(i)] = ans_acc[0]
metrics_slug['mean_rank_' + str(i)] = ans_rank[0]
metrics_slug['mean_reciprocal_rank_' +
str(i)] = 1.0 / ans_rank[0]
metrics_slug['d_0_' + str(i)] = dists_to_target[0]
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)] = np.array(dists_to_target).min()
metrics_slug['ep_len_' + str(i)] = episode_length
if action == 3:
metrics_slug['stop_' + str(i)] = 1
else:
metrics_slug['stop_' + str(i)] = 0
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
# navigation 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])
nav_metrics.update(metrics_list)
# vqa metrics
metrics_list = []
for i in vqa_metrics.metric_names:
if i not in metrics_slug:
metrics_list.append(vqa_metrics.metrics[
vqa_metrics.metric_names.index(i)][0])
else:
metrics_list.append(metrics_slug[i])
vqa_metrics.update(metrics_list)
try:
print(nav_metrics.get_stat_string(mode=0))
print(vqa_metrics.get_stat_string(mode=0))
except:
pass
print('epoch', epoch)
print('invalids', len(invalids))
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
epoch += 1
# checkpoint if best val accuracy
if vqa_metrics.metrics[2][0] > best_eval_acc: # ans_acc_50
best_eval_acc = vqa_metrics.metrics[2][0]
if epoch % args.eval_every == 0 and args.to_log == 1:
vqa_metrics.dump_log()
nav_metrics.dump_log()
model_state = get_state(nav_model)
aad = dict(args.__dict__)
ad = {}
for i in aad:
if i[0] != '_':
ad[i] = aad[i]
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_ans_50_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_ans_acc_50:%.04f]' % best_eval_acc)
eval_loader.dataset._load_envs(start_idx=0, in_order=True)
def train(self):
"""
-- Train Model Process
"""
torch.cuda.set_device(args.gpus.index(args.gpus[0 % len(args.gpus)]))
self.eval_net.train()
self.eval_net.cuda()
self.target_net.eval()
self.target_net.cuda()
self.update_count = 0
task_total_reward, self.task_total_loss, self.task_total_q = 0., 0., 0.
group_num_list = list(range(self.train_group_num))
random.shuffle(group_num_list)
scene_num_list = list(range(1, 10))
random.shuffle(scene_num_list)
task_num = 0
print(group_num_list)
print(scene_num_list)
for group_num in group_num_list: # one eposide
for scene_num in scene_num_list:
rgb_image_raw, depth_image_raw, all_ques, all_encode_ques = self.env.new_scene(
group_num=group_num, scene_num=scene_num) #new scene
ques_num_list = np.random.choice(a=40,
size=10,
replace=False,
p=None)
task_num += 1
for ques_index in ques_num_list: #one task
single_encode_ques = all_encode_ques[ques_index]
single_ques = all_ques[ques_index]
task_total_reward = 0
task_total_reward_e = 0
task_total_reward_q = 0
self.task_total_loss = 0
self.task_total_q = 0
self.update_count = 0
task_act_num = 0
print("target:", single_ques['obj'])
for act_step in range(self.max_step):
if self.learn_step_counter > self.epsilon_update_step:
reward_weight = self.reward_weight_end
else:
reward_weight = self.reward_weight_start - (
self.learn_step_counter /
float(self.reward_update_step)) * (
self.reward_weight_start -
self.reward_weight_end)
# 1. predict
depth_image_raw, rgb_image_raw = self.env.camera.get_camera_data(
)
rgb_image = self.rgb_norm(rgb_image_raw)
depth_image = self.depth_norm(depth_image_raw)
rgb_image_var = Variable(
torch.FloatTensor(rgb_image).cuda())
rgb_image_var = rgb_image_var.unsqueeze(0)
depth_image_var = Variable(
torch.FloatTensor(depth_image).cuda())
depth_image_var = depth_image_var.unsqueeze(0)
question_var = Variable(
torch.LongTensor(single_encode_ques).cuda())
question_var = question_var.unsqueeze(0)
action = self.choose_action(rgb_image_var,
depth_image_var,
question_var)
# 2. act
# notice the action is in [0, 18*18*8-1]
rgb_1_image_raw, depth_1_image_raw, reward, terminal, reward_e, reward_q = self.env.act(
action, single_ques['obj'], single_ques['type'],
self.reward_type, reward_weight)
rgb_1_image = self.rgb_norm(rgb_1_image_raw)
depth_1_image = self.depth_norm(depth_1_image_raw)
# 3. observe & store
self.memory.add(rgb_image, depth_image, rgb_1_image,
depth_1_image, single_encode_ques,
action, reward, terminal)
# 4. learn
self.learn()
task_total_reward += reward
task_total_reward_e += reward_e
task_total_reward_q += reward_q
task_act_num += 1
if terminal:
break
avg_reward = task_total_reward / task_act_num # caculate the average reward after one task
avg_reward_e = task_total_reward_e / task_act_num
avg_reward_q = task_total_reward_q / task_act_num
avg_loss = self.task_total_loss / self.update_count
avg_q = self.task_total_q / self.update_count
print("avg_loss:", avg_loss)
print("avg_reward:", avg_reward)
print("avg_reward_e:", avg_reward_e)
print("avg_reward_q:", avg_reward_q)
logging.info("avg_reward:{}".format(avg_reward))
logging.info("avg_reward_e:{}".format(avg_reward_e))
logging.info("avg_reward_q:{}".format(avg_reward_q))
logging.info("avg_loss:{}".format(avg_loss))
logging.info("avg_q:{}".format(avg_q))
if avg_reward > self.max_avg_act_reward: #avg_reward相当于在新的场景测试集上的test score
checkpoint = {
'state': get_state(self.eval_net),
'optimizer': self.optimizer.state_dict()
}
checkpoint_path = '%s/step_%d.pt' % (
self.args.checkpoint_dir, self.learn_step_counter)
torch.save(checkpoint, checkpoint_path)
print('Saving checkpoint to %s' % checkpoint_path)
self.max_avg_act_reward = max(self.max_avg_act_reward,
avg_reward)
print(
'\n [#] Up-to-now, the max action reward is %.4f \n --------------- '
% (self.max_avg_act_reward))
logging.info("max action reward:{}".format(
self.max_avg_act_reward))
self.memory.save()
elif task_num % self.save_every == 0:
task_num += 1
checkpoint = {
'state': get_state(self.eval_net),
'optimizer': self.optimizer.state_dict()
}
checkpoint_path = '%s/step_f_%d.pt' % (
self.args.checkpoint_dir, self.learn_step_counter)
torch.save(checkpoint, checkpoint_path)
print('Saving checkpoint to %s' % checkpoint_path)
def eval(rank, args, shared_model):
print('eval start...')
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'vocab': args.vocab_json,
'batch_size': 1,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
}
# print(eval_loader_kwargs)
eval_loader = EqaDataLoader(**eval_loader_kwargs)
print('eval_loader has %d samples' % len(eval_loader.dataset))
args.output_log_path = os.path.join(args.log_dir,
'eval_' + str(rank) + '.json')
t, epoch, best_eval_acc = 0, 0, 0
while epoch < int(args.max_epochs):
model.load_state_dict(shared_model.state_dict())
model.eval()
metrics = VqaMetric(info={'split': args.eval_split},
metric_names=[
'loss', 'accuracy', 'mean_rank',
'mean_reciprocal_rank'
],
log_json=args.output_log_path)
if args.input_type == 'ques':
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
print(scores)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = True
#all_envs_loaded = eval_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers, images, _, _, _ = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update(
[loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
if all_envs_loaded == False:
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
epoch += 1
# checkpoint if best val accuracy
if metrics.metrics[1][0] > best_eval_acc:
best_eval_acc = metrics.metrics[1][0]
if epoch % args.eval_every == 0 and args.log == True:
metrics.dump_log()
model_state = get_state(model)
if args.checkpoint_path != False:
ad = checkpoint['args']
else:
ad = args.__dict__
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_accuracy_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_accuracy:%.04f]' % best_eval_acc)
def eval(rank, args, shared_model, use_vision, use_language):
gpu_idx = args.gpus.index(args.gpus[rank % len(args.gpus)])
torch.cuda.set_device(gpu_idx)
print("eval gpu:" + str(gpu_idx) + " assigned")
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': 1,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.to_cache
}
eval_loader = EqaDataLoader(**eval_loader_kwargs)
print('eval_loader has %d samples' % len(eval_loader.dataset))
args.output_log_path = os.path.join(args.log_dir,
'eval_' + str(rank) + '.json')
t, epoch, best_eval_acc = 0, 0, 0
print(epoch, args.max_epochs) # DEBUG
while epoch < int(args.max_epochs):
print("eval gpu:" + str(gpu_idx) + " running epoch " + str(epoch))
model.load_state_dict(shared_model.state_dict())
model.eval()
metrics = VqaMetric(info={'split': args.eval_split},
metric_names=[
'loss', 'accuracy', 'mean_rank',
'mean_reciprocal_rank'
],
log_json=args.output_log_path)
if args.input_type == 'ques':
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers = batch
# If not using language, replace each question with a start and end token back to back.
if not use_language:
questions = torch.zeros_like(questions)
questions.fill_(
model_kwargs['vocab']['questionTokenToIdx']['<NULL>'])
questions[:, 0] = model_kwargs['vocab'][
'questionTokenToIdx']['<START>']
questions[:, 1] = model_kwargs['vocab'][
'questionTokenToIdx']['<END>']
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = eval_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers, images, _, _, _ = batch
# If not using language, replace each question with a start and end token back to back.
if not use_language:
questions = torch.zeros_like(questions)
questions.fill_(model_kwargs['vocab']
['questionTokenToIdx']['<NULL>'])
questions[:, 0] = model_kwargs['vocab'][
'questionTokenToIdx']['<START>']
questions[:, 1] = model_kwargs['vocab'][
'questionTokenToIdx']['<END>']
# If not using vision, replace all image feature data with zeros.
if not use_vision:
images = torch.zeros_like(images)
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update(
[loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
if all_envs_loaded == False:
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
read_epoch = None
while read_epoch is None or epoch >= read_epoch:
try:
with open(args.identifier + '.shared_epoch.tmp', 'r') as f:
read_epoch = int(f.read().strip())
except (IOError, ValueError):
pass
if read_epoch is None:
# TODO: since merger, this no longer works (hanging); might need to undo changes re: threading that we
# TODO: made or debug them.
print("eval gpu:" + str(gpu_idx) +
" waiting for train thread to finish epoch " +
str(epoch))
time.sleep(
10
) # sleep until the training thread finishes another iteration
epoch = read_epoch
# checkpoint if best val accuracy
if metrics.metrics[1][0] > best_eval_acc:
best_eval_acc = metrics.metrics[1][0]
if epoch % args.eval_every == 0 and args.to_log == 1:
metrics.dump_log()
model_state = get_state(model)
if args.checkpoint_path != False:
ad = checkpoint['args']
else:
ad = args.__dict__
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_accuracy_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_accuracy:%.04f]' % best_eval_acc)
def train(rank, args, shared_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.model_type == 'pacman':
model_kwargs = {'question_vocab': load_vocab(args.vocab_json)}
model = NavPlannerControllerModel(**model_kwargs)
else:
exit()
lossFn = torch.nn.CrossEntropyLoss().cuda()
optim = torch.optim.Adam(
filter(lambda p: p.requires_grad, shared_model.parameters()),
lr=args.learning_rate)
train_loader_kwargs = {
'questions_h5': args.train_h5,
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': args.batch_size,
'input_type': args.model_type,
'num_frames': 5,
'split': 'train',
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.to_cache
}
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'data_json': args.data_json,
'vocab': args.vocab_json,
'target_obj_conn_map_dir': args.target_obj_conn_map_dir,
'map_resolution': args.map_resolution,
'batch_size': 1,
'input_type': args.model_type,
'num_frames': 5,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': False
}
args.output_log_path = os.path.join(args.log_dir,
'train_' + str(rank) + '.json')
if 'pacman' in args.model_type:
metrics = NavMetric(
info={'split': args.eval_split,
'thread': rank},
metric_names=[
'd_0_10', 'd_0_30', 'd_0_50', 'd_T_10', 'd_T_30', 'd_T_50',
'd_D_10', 'd_D_30', 'd_D_50', 'd_min_10', 'd_min_30',
'd_min_50', 'r_T_10', 'r_T_30', 'r_T_50', 'r_e_10', 'r_e_30',
'r_e_50', 'stop_10', 'stop_30', 'stop_50', 'ep_len_10',
'ep_len_30', 'ep_len_50'
],
log_json=args.output_log_path)
else:
metrics = NavMetric(
info={'split': 'train',
'thread': rank},
metric_names=['loss'],
log_json=args.output_log_path)
train_loader = EqaDataLoader(**train_loader_kwargs)
eval_loader = EqaDataLoader(**eval_loader_kwargs)
print('train_loader has %d samples' % len(train_loader.dataset))
t, epoch, best_eval_acc = 0, 0, 0
while epoch < int(args.max_epochs):
if 'pacman' in args.model_type:
planner_lossFn = MaskedNLLCriterion().cuda()
controller_lossFn = MaskedNLLCriterion().cuda()
done = False
model.train()
all_envs_loaded = train_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, _, planner_img_feats, planner_actions_in, \
planner_actions_out, planner_action_lengths, planner_masks, \
controller_img_feats, controller_actions_in, planner_hidden_idx, \
controller_outs, controller_action_lengths, controller_masks = batch
questions_var = Variable(questions.cuda())
planner_img_feats_var = Variable(planner_img_feats.cuda())
planner_actions_in_var = Variable(
planner_actions_in.cuda())
planner_actions_out_var = Variable(
planner_actions_out.cuda())
planner_action_lengths = planner_action_lengths.cuda()
planner_masks_var = Variable(planner_masks.cuda())
controller_img_feats_var = Variable(
controller_img_feats.cuda())
controller_actions_in_var = Variable(
controller_actions_in.cuda())
planner_hidden_idx_var = Variable(
planner_hidden_idx.cuda())
controller_outs_var = Variable(controller_outs.cuda())
controller_action_lengths = controller_action_lengths.cuda(
)
controller_masks_var = Variable(controller_masks.cuda())
planner_action_lengths, perm_idx = planner_action_lengths.sort(
0, descending=True)
questions_var = questions_var[perm_idx]
planner_img_feats_var = planner_img_feats_var[perm_idx]
planner_actions_in_var = planner_actions_in_var[perm_idx]
planner_actions_out_var = planner_actions_out_var[perm_idx]
planner_masks_var = planner_masks_var[perm_idx]
controller_img_feats_var = controller_img_feats_var[
perm_idx]
controller_actions_in_var = controller_actions_in_var[
perm_idx]
controller_outs_var = controller_outs_var[perm_idx]
planner_hidden_idx_var = planner_hidden_idx_var[perm_idx]
controller_action_lengths = controller_action_lengths[
perm_idx]
controller_masks_var = controller_masks_var[perm_idx]
planner_scores, controller_scores, planner_hidden = model(
questions_var, planner_img_feats_var,
planner_actions_in_var,
planner_action_lengths.cpu().numpy(),
planner_hidden_idx_var, controller_img_feats_var,
controller_actions_in_var, controller_action_lengths)
planner_logprob = F.log_softmax(planner_scores, dim=1)
controller_logprob = F.log_softmax(
controller_scores, dim=1)
planner_loss = planner_lossFn(
planner_logprob,
planner_actions_out_var[:, :planner_action_lengths.max(
)].contiguous().view(-1, 1),
planner_masks_var[:, :planner_action_lengths.max()]
.contiguous().view(-1, 1))
controller_loss = controller_lossFn(
controller_logprob,
controller_outs_var[:, :controller_action_lengths.max(
)].contiguous().view(-1, 1),
controller_masks_var[:, :controller_action_lengths.max(
)].contiguous().view(-1, 1))
# zero grad
optim.zero_grad()
# update metrics
# metrics.update(
# [planner_loss.data[0], controller_loss.data[0]])
# backprop and update
(planner_loss + controller_loss).backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
# if t % args.print_every == 0:
# print(metrics.get_stat_string())
# if args.to_log == 1:
# metrics.dump_log()
print('[CHECK][Cache:%d][Total:%d]' %
(len(train_loader.dataset.img_data_cache),
len(train_loader.dataset.env_list)))
if all_envs_loaded == False:
train_loader.dataset._load_envs(in_order=True)
if len(train_loader.dataset.pruned_env_set) == 0:
done = True
if args.to_cache == False:
train_loader.dataset._load_envs(
start_idx=0, in_order=True)
else:
done = True
invalids = []
done = False
model.eval()
while done == False:
for batch in tqdm(eval_loader):
if batch is None:
continue
model.load_state_dict(shared_model.state_dict())
model.cuda()
idx, question, answer, actions, action_length = batch
metrics_slug = {}
h3d = eval_loader.dataset.episode_house
# evaluate at multiple initializations
for i in [10, 30, 50]:
t += 1
if i > action_length[0]:
invalids.append([idx[0], i])
continue
question_var = Variable(question.cuda())
controller_step = False
planner_hidden = model.planner_nav_rnn.init_hidden(1)
# forward through planner till spawn
planner_actions_in, planner_img_feats, controller_step, controller_action_in, controller_img_feat, init_pos = eval_loader.dataset.get_hierarchical_features_till_spawn(
actions[0, :action_length[0] + 1].numpy(), i)
planner_actions_in_var = Variable(
planner_actions_in.cuda())
planner_img_feats_var = Variable(
planner_img_feats.cuda())
for step in range(planner_actions_in.size(0)):
planner_scores, planner_hidden = model.planner_step(
question_var, planner_img_feats_var[step].view(
1, 1,
3200), planner_actions_in_var[step].view(
1, 1), planner_hidden)
if controller_step == True:
controller_img_feat_var = Variable(
controller_img_feat.cuda())
controller_action_in_var = Variable(
torch.LongTensor(1, 1).fill_(
int(controller_action_in)).cuda())
controller_scores = model.controller_step(
controller_img_feat_var.view(1, 1, 3200),
controller_action_in_var.view(1, 1),
planner_hidden[0])
prob = F.softmax(controller_scores, dim=1)
controller_action = int(
prob.max(1)[1].data.cpu().numpy()[0])
if controller_action == 1:
controller_step = True
else:
controller_step = False
action = int(controller_action_in)
action_in = torch.LongTensor(
1, 1).fill_(action + 1).cuda()
else:
prob = F.softmax(planner_scores, dim=1)
action = int(prob.max(1)[1].data.cpu().numpy()[0])
action_in = torch.LongTensor(
1, 1).fill_(action + 1).cuda()
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[0], i])
continue
episode_length = 0
episode_done = True
controller_action_counter = 0
dists_to_target, pos_queue, pred_actions = [
init_dist_to_target
], [init_pos], []
planner_actions, controller_actions = [], []
if action != 3:
# take the first step
img, _, _ = h3d.step(action)
img = torch.from_numpy(img.transpose(
2, 0, 1)).float() / 255.0
img_feat_var = eval_loader.dataset.cnn(
Variable(img.view(1, 3, 224,
224).cuda())).view(
1, 1, 3200)
for step in range(args.max_episode_length):
episode_length += 1
if controller_step == False:
planner_scores, planner_hidden = model.planner_step(
question_var, img_feat_var,
Variable(action_in), planner_hidden)
prob = F.softmax(planner_scores, dim=1)
action = int(
prob.max(1)[1].data.cpu().numpy()[0])
planner_actions.append(action)
pred_actions.append(action)
img, _, episode_done = h3d.step(action)
episode_done = episode_done or episode_length >= args.max_episode_length
img = torch.from_numpy(img.transpose(
2, 0, 1)).float() / 255.0
img_feat_var = eval_loader.dataset.cnn(
Variable(img.view(1, 3, 224, 224)
.cuda())).view(1, 1, 3200)
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 == True:
break
# query controller to continue or not
controller_action_in = Variable(
torch.LongTensor(1,
1).fill_(action).cuda())
controller_scores = model.controller_step(
img_feat_var, controller_action_in,
planner_hidden[0])
prob = F.softmax(controller_scores, dim=1)
controller_action = int(
prob.max(1)[1].data.cpu().numpy()[0])
if controller_action == 1 and controller_action_counter < 4:
controller_action_counter += 1
controller_step = True
else:
controller_action_counter = 0
controller_step = False
controller_action = 0
controller_actions.append(controller_action)
action_in = torch.LongTensor(
1, 1).fill_(action + 1).cuda()
# compute stats
metrics_slug['d_0_' + str(i)] = dists_to_target[0]
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)] = np.array(
dists_to_target).min()
metrics_slug['ep_len_' + str(i)] = episode_length
if action == 3:
metrics_slug['stop_' + str(i)] = 1
else:
metrics_slug['stop_' + str(i)] = 0
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 metrics.metric_names:
if i not in metrics_slug:
metrics_list.append(metrics.metrics[
metrics.metric_names.index(i)][0])
else:
metrics_list.append(metrics_slug[i])
# update metrics
metrics.update(metrics_list)
try:
print(metrics.get_stat_string(mode=0))
except:
pass
print('epoch', epoch)
print('invalids', len(invalids))
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
# checkpoint if best val loss
print("ecoch {}: if {} > best_eval_acc {}".format(epoch, metrics.metrics[8][0], best_eval_acc))
if metrics.metrics[8][0] > best_eval_acc: # d_D_50
best_eval_acc = metrics.metrics[8][0]
if epoch % args.eval_every == 0 and args.to_log == 1:
metrics.dump_log()
model_state = get_state(model)
aad = dict(args.__dict__)
ad = {}
for i in aad:
if i[0] != '_':
ad[i] = aad[i]
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_d_D_50_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_d_D_50:%.04f]' % best_eval_acc)
eval_loader.dataset._load_envs(start_idx=0, in_order=True)
epoch += 1
def train(rank, args, shared_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.model_type == 'pacman':
model_kwargs = {'question_vocab': load_vocab(args.vocab_json)}
model = NavPlannerControllerModel(**model_kwargs)
else:
exit()
optim = torch.optim.Adamax(filter(lambda p: p.requires_grad,
shared_model.parameters()),
lr=args.learning_rate)
train_loader_kwargs = {
'questions_h5': args.train_h5,
'vocab': args.vocab_json,
'batch_size': args.batch_size,
'input_type': args.model_type,
'split': 'train',
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)]
}
args.output_log_path = os.path.join(args.log_dir,
'train_' + str(rank) + '.json')
if 'pacman' in args.model_type:
metrics = NavMetric(info={
'split': 'train',
'thread': rank
},
metric_names=['planner_loss', 'controller_loss'],
log_json=args.output_log_path)
else:
metrics = NavMetric(info={
'split': 'train',
'thread': rank
},
metric_names=['loss'],
log_json=args.output_log_path)
train_loader = EqaDataLoader(**train_loader_kwargs)
print('train_loader has %d samples' % len(train_loader.dataset))
logging.info('TRAIN: train loader has {} samples'.format(
len(train_loader.dataset)))
t, epoch = 0, 0
while epoch < int(args.max_epochs):
planner_lossFn = MaskedNLLCriterion().cuda()
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, _, planner_img_feats, planner_actions_in, planner_actions_out, planner_positions, planner_masks, planner_action_lengths = batch
# calcualte var of input data(qustion,action,image)
questions_var = Variable(questions.cuda())
planner_img_feats_var = Variable(planner_img_feats.cuda())
planner_actions_in_var = Variable(planner_actions_in.cuda())
planner_actions_out_var = Variable(planner_actions_out.cuda())
planner_positions_var = Variable(planner_positions.cuda())
planner_masks_var = Variable(planner_masks.cuda())
planner_action_lengths = planner_action_lengths.cuda() #
# find the question and image that need most action
planner_action_lengths, perm_idx = planner_action_lengths.sort(
0, descending=True)
questions_var = questions_var[perm_idx]
planner_img_feats_var = planner_img_feats_var[perm_idx]
planner_actions_in_var = planner_actions_in_var[perm_idx]
planner_actions_out_var = planner_actions_out_var[perm_idx]
planner_masks_var = planner_masks_var[perm_idx]
planner_positions_var = planner_positions_var[perm_idx]
'''
print('action')
print(planner_actions_out_var)
print('position')
print(planner_positions_var)
print('image')
print(planner_img_feats_var)
'''
#print(planner_masks_var)
planner_scores, planner_hidden = model(
questions_var, planner_img_feats_var, planner_actions_in_var,
planner_positions_var,
planner_action_lengths.cpu().numpy().astype(np.long))
planner_logprob = F.log_softmax(planner_scores, dim=1)
planner_loss = planner_lossFn(
planner_logprob,
planner_actions_out_var.contiguous().view(-1, 1),
planner_masks_var.contiguous().view(-1, 1))
'''
planner_loss = planner_lossFn(
planner_logprob.view(-1,21,2),
planner_actions_out_var.float())
planner_loss = planner_loss.mean(2)
print('loss')
print(planner_loss)
print('masks')
print(planner_masks_var.float()[:,:-1])
'''
#planner_loss = planner_loss * planner_masks_var.float()[:,:-1]
#planner_loss = planner_loss.mean()
#TODO masked
# zero grad
optim.zero_grad()
# update metrics
print("TRAINING PACMAN planner-loss:{}".format(
planner_loss.item()))
logging.info("TRAINING PACMAN planner-loss:{}".format(
planner_loss.item()))
# backprop and update
(planner_loss).backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
#if t % args.print_every == 0:
# print(metrics.get_stat_string())
# logging.info("TRAIN: metrics: {}".format(metrics.get_stat_string()))
epoch += 1
if epoch % args.save_every == 0:
model_state = get_state(model)
optimizer_state = optim.state_dict()
aad = dict(args.__dict__)
ad = {}
for i in aad:
if i[0] != '_':
ad[i] = aad[i]
checkpoint = {
'args': ad,
'state': model_state,
'epoch': epoch,
'optimizer': optimizer_state
}
checkpoint_path = '%s/epoch_%d_thread_%d.pt' % (
args.checkpoint_dir, epoch, rank)
print('Saving checkpoint to %s' % checkpoint_path)
logging.info(
"TRAIN: Saving checkpoint to {}".format(checkpoint_path))
torch.save(checkpoint, checkpoint_path)
def train(rank, args, shared_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
optim = torch.optim.Adam(filter(lambda p: p.requires_grad,
shared_model.parameters()),
lr=args.learning_rate)
train_loader_kwargs = {
'questions_h5': args.train_h5,
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': args.batch_size,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': 'train',
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.to_cache
}
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': 1,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.to_cache
}
args.output_log_path = os.path.join(args.log_dir,
'train_' + str(rank) + '.json')
metrics = VqaMetric(
info={
'split': 'train',
'thread': rank
},
metric_names=['loss', 'accuracy', 'mean_rank', 'mean_reciprocal_rank'],
log_json=args.output_log_path)
eval_loader = EqaDataLoader(**eval_loader_kwargs)
train_loader = EqaDataLoader(**train_loader_kwargs)
if args.input_type == 'ques,image':
train_loader.dataset._load_envs(start_idx=0, in_order=True)
print('train_loader has %d samples' % len(train_loader.dataset))
t, epoch, best_eval_acc = 0, 0, 0
while epoch < int(args.max_epochs):
if args.input_type == 'ques':
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
if t % args.print_every == 0:
print(metrics.get_stat_string())
if args.to_log == 1:
metrics.dump_log()
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = train_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in train_loader:
t += 1
model.load_state_dict(shared_model.state_dict())
model.train()
model.cuda()
idx, questions, answers, images, _, _, _ = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# zero grad
optim.zero_grad()
# update metrics
# accuracy, ranks = metrics.compute_ranks(scores.data.cpu(), answers)
# metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
# backprop and update
loss.backward()
ensure_shared_grads(model.cpu(), shared_model)
optim.step()
#if t % args.print_every == 0:
# print(metrics.get_stat_string())
# if args.to_log == 1:
# metrics.dump_log()
if all_envs_loaded == False:
train_loader.dataset._load_envs(in_order=True)
if len(train_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
env_done = False
env_all_envs_loaded = eval_loader.dataset._check_if_all_envs_loaded(
)
while env_done == False:
_loss, _accuracy, _ranks = None, None, None
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers, images, _, _, _ = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
if _loss is None:
_loss = loss.data[0]
_accuracy = accuracy
_ranks = ranks
else:
_loss = torch.cat([_loss, loss.data[0]])
_accuracy = torch.cat([_accuracy, accuracy])
_ranks = torch.cat([_ranks, ranks])
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
if env_all_envs_loaded == False:
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
env_done = True
else:
env_done = True
epoch += 1
# checkpoint if best val accuracy
if metrics.metrics[1][0] > best_eval_acc:
best_eval_acc = metrics.metrics[1][0]
if epoch % args.eval_every == 0 and args.to_log == 1:
metrics.dump_log()
model_state = get_state(model)
if args.checkpoint_path != False:
ad = checkpoint['args']
else:
ad = args.__dict__
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_accuracy_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_accuracy:%.04f]' % best_eval_acc)
def eval(rank, args, shared_nav_model, shared_ans_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.model_type == 'pacman':
model_kwargs = {'question_vocab': load_vocab(args.vocab_json)}
nav_model = NavPlannerControllerModel(**model_kwargs)
else:
exit()
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
ans_model = VqaLstmCnnAttentionModel(**model_kwargs)
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'data_json': args.data_json,
'vocab': args.vocab_json,
'target_obj_conn_map_dir': args.target_obj_conn_map_dir,
'map_resolution': args.map_resolution,
'batch_size': 1,
'input_type': args.model_type,
'num_frames': 5,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': False
}
eval_loader = EqaDataLoader(**eval_loader_kwargs)
print('eval_loader has %d samples' % len(eval_loader.dataset))
args.output_nav_log_path = os.path.join(args.log_dir,
'nav_eval_' + str(rank) + '.json')
args.output_ans_log_path = os.path.join(args.log_dir,
'ans_eval_' + str(rank) + '.json')
t, epoch, best_eval_acc = 0, 0, 0.0
while epoch < int(args.max_epochs):
start_time = time.time()
invalids = []
nav_model.load_state_dict(shared_nav_model.state_dict())
nav_model.eval()
ans_model.load_state_dict(shared_ans_model.state_dict())
ans_model.eval()
ans_model.cuda()
# that's a lot of numbers
nav_metrics = NavMetric(
info={'split': args.eval_split,
'thread': rank},
metric_names=[
'd_0_10', 'd_0_30', 'd_0_50', 'd_T_10', 'd_T_30', 'd_T_50',
'd_D_10', 'd_D_30', 'd_D_50', 'd_min_10', 'd_min_30',
'd_min_50', 'r_T_10', 'r_T_30', 'r_T_50', 'r_e_10', 'r_e_30',
'r_e_50', 'stop_10', 'stop_30', 'stop_50', 'ep_len_10',
'ep_len_30', 'ep_len_50'
],
log_json=args.output_nav_log_path)
vqa_metrics = VqaMetric(
info={'split': args.eval_split,
'thread': rank},
metric_names=[
'accuracy_10', 'accuracy_30', 'accuracy_50', 'mean_rank_10',
'mean_rank_30', 'mean_rank_50', 'mean_reciprocal_rank_10',
'mean_reciprocal_rank_30', 'mean_reciprocal_rank_50'
],
log_json=args.output_ans_log_path)
if 'pacman' in args.model_type:
done = False
while done == False:
for batch in tqdm(eval_loader):
nav_model.load_state_dict(shared_nav_model.state_dict())
nav_model.eval()
nav_model.cuda()
idx, question, answer, actions, action_length = batch
metrics_slug = {}
h3d = eval_loader.dataset.episode_house
# evaluate at multiple initializations
for i in [10, 30, 50]:
t += 1
if i > action_length[0]:
invalids.append([idx[0], i])
continue
question_var = Variable(question.cuda())
controller_step = False
planner_hidden = nav_model.planner_nav_rnn.init_hidden(
1)
# forward through planner till spawn
planner_actions_in, planner_img_feats, controller_step, controller_action_in, controller_img_feat, init_pos = eval_loader.dataset.get_hierarchical_features_till_spawn(
actions[0, :action_length[0] + 1].numpy(), i)
planner_actions_in_var = Variable(
planner_actions_in.cuda())
planner_img_feats_var = Variable(
planner_img_feats.cuda())
for step in range(planner_actions_in.size(0)):
planner_scores, planner_hidden = nav_model.planner_step(
question_var, planner_img_feats_var[step].view(
1, 1,
3200), planner_actions_in_var[step].view(
1, 1), planner_hidden)
if controller_step == True:
controller_img_feat_var = Variable(
controller_img_feat.cuda())
controller_action_in_var = Variable(
torch.LongTensor(1, 1).fill_(
int(controller_action_in)).cuda())
controller_scores = nav_model.controller_step(
controller_img_feat_var.view(1, 1, 3200),
controller_action_in_var.view(1, 1),
planner_hidden[0])
prob = F.softmax(controller_scores, dim=1)
controller_action = int(
prob.max(1)[1].data.cpu().numpy()[0])
if controller_action == 1:
controller_step = True
else:
controller_step = False
action = int(controller_action_in)
action_in = torch.LongTensor(
1, 1).fill_(action + 1).cuda()
else:
prob = F.softmax(planner_scores, dim=1)
action = int(prob.max(1)[1].data.cpu().numpy()[0])
action_in = torch.LongTensor(
1, 1).fill_(action + 1).cuda()
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[0], i])
continue
episode_length = 0
episode_done = True
controller_action_counter = 0
dists_to_target, pos_queue, pred_actions = [
init_dist_to_target
], [init_pos], []
planner_actions, controller_actions = [], []
if action != 3:
# take the first step
img, _, _ = h3d.step(action)
img = torch.from_numpy(img.transpose(
2, 0, 1)).float() / 255.0
img_feat_var = eval_loader.dataset.cnn(
Variable(img.view(1, 3, 224,
224).cuda())).view(
1, 1, 3200)
for step in range(args.max_episode_length):
episode_length += 1
if controller_step == False:
planner_scores, planner_hidden = nav_model.planner_step(
question_var, img_feat_var,
Variable(action_in), planner_hidden)
prob = F.softmax(planner_scores, dim=1)
action = int(
prob.max(1)[1].data.cpu().numpy()[0])
planner_actions.append(action)
pred_actions.append(action)
img, _, episode_done = h3d.step(action)
episode_done = episode_done or episode_length >= args.max_episode_length
img = torch.from_numpy(img.transpose(
2, 0, 1)).float() / 255.0
img_feat_var = eval_loader.dataset.cnn(
Variable(img.view(1, 3, 224, 224)
.cuda())).view(1, 1, 3200)
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 == True:
break
# query controller to continue or not
controller_action_in = Variable(
torch.LongTensor(1,
1).fill_(action).cuda())
controller_scores = nav_model.controller_step(
img_feat_var, controller_action_in,
planner_hidden[0])
prob = F.softmax(controller_scores, dim=1)
controller_action = int(
prob.max(1)[1].data.cpu().numpy()[0])
if controller_action == 1 and controller_action_counter < 4:
controller_action_counter += 1
controller_step = True
else:
controller_action_counter = 0
controller_step = False
controller_action = 0
controller_actions.append(controller_action)
action_in = torch.LongTensor(
1, 1).fill_(action + 1).cuda()
# run answerer here
if len(pos_queue) < 5:
pos_queue = eval_loader.dataset.episode_pos_queue[len(
pos_queue) - 5:] + pos_queue
images = eval_loader.dataset.get_frames(
h3d, pos_queue[-5:], preprocess=True)
images_var = Variable(
torch.from_numpy(images).cuda()).view(
1, 5, 3, 224, 224)
scores, att_probs = ans_model(images_var, question_var)
ans_acc, ans_rank = vqa_metrics.compute_ranks(
scores.data.cpu(), answer)
pred_answer = scores.max(1)[1].data[0]
print('[Q_GT]', ' '.join([
eval_loader.dataset.vocab['questionIdxToToken'][x]
for x in question[0] if x != 0
]))
print('[A_GT]', eval_loader.dataset.vocab[
'answerIdxToToken'][answer[0]])
print('[A_PRED]', eval_loader.dataset.vocab[
'answerIdxToToken'][pred_answer])
# compute stats
metrics_slug['accuracy_' + str(i)] = ans_acc[0]
metrics_slug['mean_rank_' + str(i)] = ans_rank[0]
metrics_slug['mean_reciprocal_rank_'
+ str(i)] = 1.0 / ans_rank[0]
metrics_slug['d_0_' + str(i)] = dists_to_target[0]
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)] = np.array(
dists_to_target).min()
metrics_slug['ep_len_' + str(i)] = episode_length
if action == 3:
metrics_slug['stop_' + str(i)] = 1
else:
metrics_slug['stop_' + str(i)] = 0
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
# navigation 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])
nav_metrics.update(metrics_list)
# vqa metrics
metrics_list = []
for i in vqa_metrics.metric_names:
if i not in metrics_slug:
metrics_list.append(vqa_metrics.metrics[
vqa_metrics.metric_names.index(i)][0])
else:
metrics_list.append(metrics_slug[i])
vqa_metrics.update(metrics_list)
try:
print(nav_metrics.get_stat_string(mode=0))
print(vqa_metrics.get_stat_string(mode=0))
except:
pass
print('epoch', epoch)
print('invalids', len(invalids))
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
epoch += 1
# checkpoint if best val accuracy
if vqa_metrics.metrics[2][0] > best_eval_acc: # ans_acc_50
best_eval_acc = vqa_metrics.metrics[2][0]
if epoch % args.eval_every == 0 and args.to_log == 1:
vqa_metrics.dump_log()
nav_metrics.dump_log()
model_state = get_state(nav_model)
aad = dict(args.__dict__)
ad = {}
for i in aad:
if i[0] != '_':
ad[i] = aad[i]
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_ans_50_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_ans_acc_50:%.04f]' % best_eval_acc)
eval_loader.dataset._load_envs(start_idx=0, in_order=True)
def eval(rank, args, shared_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.input_type == 'ques':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmModel(**model_kwargs)
elif args.input_type == 'ques,image':
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
model = VqaLstmCnnAttentionModel(**model_kwargs)
lossFn = torch.nn.CrossEntropyLoss().cuda()
eval_loader_kwargs = {
'questions_h5': getattr(args, args.eval_split + '_h5'),
'data_json': args.data_json,
'vocab': args.vocab_json,
'batch_size': 1,
'input_type': args.input_type,
'num_frames': args.num_frames,
'split': args.eval_split,
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank%len(args.gpus)],
'to_cache': args.to_cache
}
eval_loader = EqaDataLoader(**eval_loader_kwargs)
print('eval_loader has %d samples' % len(eval_loader.dataset))
args.output_log_path = os.path.join(args.log_dir,
'eval_' + str(rank) + '.json')
t, epoch, best_eval_acc = 0, 0, 0
while epoch < int(args.max_epochs):
model.load_state_dict(shared_model.state_dict())
model.eval()
metrics = VqaMetric(
info={'split': args.eval_split},
metric_names=[
'loss', 'accuracy', 'mean_rank', 'mean_reciprocal_rank'
],
log_json=args.output_log_path)
if args.input_type == 'ques':
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
scores = model(questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update([loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
elif args.input_type == 'ques,image':
done = False
all_envs_loaded = eval_loader.dataset._check_if_all_envs_loaded()
while done == False:
for batch in eval_loader:
t += 1
model.cuda()
idx, questions, answers, images, _, _, _ = batch
questions_var = Variable(questions.cuda())
answers_var = Variable(answers.cuda())
images_var = Variable(images.cuda())
scores, att_probs = model(images_var, questions_var)
loss = lossFn(scores, answers_var)
# update metrics
accuracy, ranks = metrics.compute_ranks(
scores.data.cpu(), answers)
metrics.update(
[loss.data[0], accuracy, ranks, 1.0 / ranks])
print(metrics.get_stat_string(mode=0))
if all_envs_loaded == False:
eval_loader.dataset._load_envs()
if len(eval_loader.dataset.pruned_env_set) == 0:
done = True
else:
done = True
epoch += 1
# checkpoint if best val accuracy
if metrics.metrics[1][0] > best_eval_acc:
best_eval_acc = metrics.metrics[1][0]
if epoch % args.eval_every == 0 and args.to_log == 1:
metrics.dump_log()
model_state = get_state(model)
if args.checkpoint_path != False:
ad = checkpoint['args']
else:
ad = args.__dict__
checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
checkpoint_path = '%s/epoch_%d_accuracy_%.04f.pt' % (
args.checkpoint_dir, epoch, best_eval_acc)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
print('[best_eval_accuracy:%.04f]' % best_eval_acc)
def train(rank, args, shared_nav_model, shared_ans_model):
torch.cuda.set_device(args.gpus.index(args.gpus[rank % len(args.gpus)]))
if args.model_type == 'pacman':
model_kwargs = {'question_vocab': load_vocab(args.vocab_json)}
nav_model = NavPlannerControllerModel(**model_kwargs)
else:
exit()
model_kwargs = {'vocab': load_vocab(args.vocab_json)}
ans_model = VqaLstmCnnAttentionModel(**model_kwargs)
optim = torch.optim.SGD(filter(lambda p: p.requires_grad,
shared_nav_model.parameters()),
lr=args.learning_rate)
train_loader_kwargs = {
'questions_h5': args.train_h5,
'data_json': args.data_json,
'vocab': args.vocab_json,
'target_obj_conn_map_dir': args.target_obj_conn_map_dir,
'map_resolution': args.map_resolution,
'batch_size': 1, # FOR REINFORCE!!!
'input_type': args.model_type,
'num_frames': 5,
'split': 'train',
'max_threads_per_gpu': args.max_threads_per_gpu,
'gpu_id': args.gpus[rank % len(args.gpus)],
'to_cache': args.to_cache
}
args.output_nav_log_path = os.path.join(args.log_dir,
'nav_train_' + str(rank) + '.json')
args.output_ans_log_path = os.path.join(args.log_dir,
'ans_train_' + str(rank) + '.json')
nav_model.load_state_dict(shared_nav_model.state_dict())
nav_model.cuda()
ans_model.load_state_dict(shared_ans_model.state_dict())
ans_model.eval()
ans_model.cuda()
# Saty: add coverage metric here (Should be inculcated into the reward structure)
nav_metrics = NavMetric(info={
'split': 'train',
'thread': rank
},
metric_names=[
'planner_loss', 'controller_loss', 'reward',
'episode_length'
],
log_json=args.output_nav_log_path)
vqa_metrics = VqaMetric(
info={
'split': 'train',
'thread': rank
},
metric_names=['accuracy', 'mean_rank', 'mean_reciprocal_rank'],
log_json=args.output_ans_log_path)
train_loader = EqaDataLoader(**train_loader_kwargs)
print('train_loader has %d samples' % len(train_loader.dataset))
t, epoch = 0, 0
p_losses, c_losses, reward_list, episode_length_list = [], [], [], []
nav_metrics.update([10.0, 10.0, 0, 100])
mult = 0.1
best_eval_acc = 0.0
while epoch < int(args.max_epochs):
print('###############################')
print('[train] Epoch is:', epoch)
print('###############################')
if 'pacman' in args.model_type:
planner_lossFn = MaskedNLLCriterion().cuda()
controller_lossFn = MaskedNLLCriterion().cuda()
done = False
all_envs_loaded = train_loader.dataset._check_if_all_envs_loaded()
#himi changes
envs = 1
while done == False:
for batch in train_loader:
nav_model.load_state_dict(shared_nav_model.state_dict())
nav_model.eval()
nav_model.cuda()
idx, question, answer, actions, action_length = batch
metrics_slug = {}
h3d = train_loader.dataset.episode_house
# evaluate at multiple initializations
# for i in [10, 30, 50]:
t += 1
question_var = Variable(question.cuda())
controller_step = False
planner_hidden = nav_model.planner_nav_rnn.init_hidden(1)
# forward through planner till spawn
(planner_actions_in, planner_img_feats, controller_step, controller_action_in, controller_img_feat, init_pos, _ ) = \
train_loader.dataset.get_hierarchical_features_till_spawn(
actions[0, :action_length[0] + 1].numpy(),
max(3, int(mult * action_length[0])))
planner_actions_in_var = Variable(
planner_actions_in.cuda())
planner_img_feats_var = Variable(planner_img_feats.cuda())
# Sati: Run Planner till target_pos_idx -> This is from get_hierarchical_features...
# need T-1 hidden state for planner! -> GT img_feats, Actions and question!
for step in range(planner_actions_in.size(0)):
planner_scores, planner_hidden = \
nav_model.planner_step(
question_var,
planner_img_feats_var[step].view(1, 1, 3200),
planner_actions_in_var[step].view(1, 1),
planner_hidden)
if controller_step == True:
controller_img_feat_var = Variable(
controller_img_feat.cuda())
controller_action_in_var = Variable(
torch.LongTensor(1, 1).fill_(
int(controller_action_in)).cuda())
controller_scores = nav_model.controller_step(
controller_img_feat_var.view(1, 1, 3200),
controller_action_in_var.view(1, 1),
planner_hidden[0])
prob = F.softmax(controller_scores, dim=1)
controller_action = int(
prob.max(1)[1].data.cpu().numpy()[0])
if controller_action == 1:
controller_step = True
else:
controller_step = False
action = int(controller_action_in)
action_in = torch.LongTensor(1, 1).fill_(action +
1).cuda()
else:
prob = F.softmax(planner_scores, dim=1)
action = int(prob.max(1)[1].data.cpu().numpy()[0])
action_in = torch.LongTensor(1, 1).fill_(action +
1).cuda()
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[0], i])
continue
episode_length = 0
episode_done = True
controller_action_counter = 0
dists_to_target, pos_queue = [init_dist_to_target
], [init_pos]
rewards, planner_actions, planner_log_probs, controller_actions, controller_log_probs = [], [], [], [], []
if action != 3:
# take the first step -> Include coverage in reward!
img, rwd, episode_done = h3d.step(action,
step_reward=True)
img = torch.from_numpy(img.transpose(
2, 0, 1)).float() / 255.0
img_feat_var = train_loader.dataset.cnn(
Variable(img.view(1, 3, 224,
224).cuda())).view(1, 1, 3200)
for step in range(args.max_episode_length):
episode_length += 1
if controller_step == False:
planner_scores, planner_hidden = nav_model.planner_step(
question_var, img_feat_var,
Variable(action_in), planner_hidden)
planner_prob = F.softmax(planner_scores, dim=1)
planner_log_prob = F.log_softmax(
planner_scores, dim=1)
action = planner_prob.multinomial(
num_samples=1).data
planner_log_prob = planner_log_prob.gather(
1, Variable(action))
planner_log_probs.append(
planner_log_prob.cpu())
action = int(action.cpu().numpy()[0, 0])
planner_actions.append(action)
img, rwd, episode_done = h3d.step(action,
step_reward=True)
episode_done = episode_done or episode_length >= args.max_episode_length
rewards.append(rwd)
img = torch.from_numpy(img.transpose(
2, 0, 1)).float() / 255.0
img_feat_var = train_loader.dataset.cnn(
Variable(img.view(1, 3, 224,
224).cuda())).view(
1, 1, 3200)
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 == True:
break
# query controller to continue or not
controller_action_in = Variable(
torch.LongTensor(1, 1).fill_(action).cuda())
controller_scores = nav_model.controller_step(
img_feat_var, controller_action_in,
planner_hidden[0])
controller_prob = F.softmax(controller_scores,
dim=1)
controller_log_prob = F.log_softmax(
controller_scores, dim=1)
controller_action = controller_prob.multinomial(
num_samples=1).data
if int(controller_action[0]
) == 1 and controller_action_counter < 4:
controller_action_counter += 1
controller_step = True
else:
controller_action_counter = 0
controller_step = False
controller_action.fill_(0)
controller_log_prob = controller_log_prob.gather(
1, Variable(controller_action))
controller_log_probs.append(
controller_log_prob.cpu())
controller_action = int(
controller_action.cpu().numpy()[0, 0])
controller_actions.append(controller_action)
action_in = torch.LongTensor(1, 1).fill_(action +
1).cuda()
# run answerer here
ans_acc = [0]
if action == 3:
if len(pos_queue) < 5:
pos_queue = train_loader.dataset.episode_pos_queue[
len(pos_queue) - 5:] + pos_queue
images = train_loader.dataset.get_frames(
h3d, pos_queue[-5:], preprocess=True)
images_var = Variable(
torch.from_numpy(images).cuda()).view(
1, 5, 3, 224, 224)
scores, att_probs = ans_model(images_var, question_var)
ans_acc, ans_rank = vqa_metrics.compute_ranks(
scores.data.cpu(), answer)
vqa_metrics.update([ans_acc, ans_rank, 1.0 / ans_rank])
rewards.append(h3d.success_reward * ans_acc[0])
R = torch.zeros(1, 1)
planner_loss = 0
controller_loss = 0
planner_rev_idx = -1
for i in reversed(range(len(rewards))):
R = 0.99 * R + rewards[i]
advantage = R - nav_metrics.metrics[2][1]
if i < len(controller_actions):
controller_loss = controller_loss - controller_log_probs[
i] * Variable(advantage)
if controller_actions[
i] == 0 and planner_rev_idx + len(
planner_log_probs) >= 0:
planner_loss = planner_loss - planner_log_probs[
planner_rev_idx] * Variable(advantage)
planner_rev_idx -= 1
elif planner_rev_idx + len(planner_log_probs) >= 0:
planner_loss = planner_loss - planner_log_probs[
planner_rev_idx] * Variable(advantage)
planner_rev_idx -= 1
controller_loss /= max(1, len(controller_log_probs))
planner_loss /= max(1, len(planner_log_probs))
optim.zero_grad()
if isinstance(planner_loss, float) == False and isinstance(
controller_loss, float) == False:
p_losses.append(planner_loss.data[0, 0])
c_losses.append(controller_loss.data[0, 0])
reward_list.append(np.sum(rewards))
episode_length_list.append(episode_length)
(planner_loss + controller_loss).backward()
ensure_shared_grads(nav_model.cpu(), shared_nav_model)
optim.step()
if len(reward_list) > 50:
nav_metrics.update([
p_losses, c_losses, reward_list,
episode_length_list
])
envs += 1
print('[train train_eqa.py] Envs is: ', envs)
print(nav_metrics.get_stat_string())
if args.to_log == 1 and envs % 10 == 0:
vqa_metrics.dump_log()
nav_metrics.dump_log()
model_state = get_state(nav_model)
aad = dict(args.__dict__)
ad = {}
for i in aad:
if i[0] != '_':
ad[i] = aad[i]
checkpoint = {
'args': ad,
'state': model_state,
'epoch': epoch
}
checkpoint_path = '%s/epoch_%d_ans_10_envs_%d.pt' % (
args.checkpoint_dir, epoch, envs)
print('Saving checkpoint to %s' % checkpoint_path)
torch.save(checkpoint, checkpoint_path)
##################################
if args.to_log == 1:
nav_metrics.dump_log()
if nav_metrics.metrics[2][1] > 0.35:
mult = min(mult + 0.1, 1.0)
p_losses, c_losses, reward_list, episode_length_list = [], [], [], []
#Himi changes, checking every 100
print('Out of that loop')
# if args.to_log == 1 and envs % 2 == 0 :
# vqa_metrics.dump_log()
# nav_metrics.dump_log()
# model_state = get_state(nav_model)
# aad = dict(args.__dict__)
# ad = {}
# for i in aad:
# if i[0] != '_':
# ad[i] = aad[i]
# checkpoint = {'args': ad, 'state': model_state, 'epoch': epoch}
# checkpoint_path = '%s/epoch_%d_ans_10_envs_%d.pt' % (
# args.checkpoint_dir, epoch, envs)
# print('Saving checkpoint to %s' % checkpoint_path)
# torch.save(checkpoint, checkpoint_path)
# ##################################
if all_envs_loaded == False:
train_loader.dataset._load_envs(in_order=True)
if len(train_loader.dataset.pruned_env_set) == 0:
done = True
if args.to_cache == False:
train_loader.dataset._load_envs(start_idx=0,
in_order=True)
else:
done = True
epoch += 1
best_eval_acc = eval(0, args, nav_model, ans_model, best_eval_acc,
epoch)