def run():
global global_t
global dataset
global data_ind
if constants.OBJECT_DETECTION:
from darknet_object_detection import detector
detector.setup_detectors(constants.PARALLEL_SIZE)
os.environ["CUDA_VISIBLE_DEVICES"] = str(constants.GPU_ID)
try:
with tf.variable_scope('global_network'):
if constants.END_TO_END_BASELINE:
global_network = EndToEndBaselineNetwork()
else:
global_network = QAPlannerNetwork(constants.RL_GRU_SIZE, 1, 1)
global_network.create_net()
if constants.USE_NAVIGATION_AGENT:
with tf.variable_scope('nav_global_network') as net_scope:
free_space_network = FreeSpaceNetwork(constants.GRU_SIZE, 1, 1)
free_space_network.create_net()
else:
net_scope = None
conv_var_list = [v for v in tf.trainable_variables() if 'conv' in v.name and 'weight' in v.name and
(v.get_shape().as_list()[0] != 1 or v.get_shape().as_list()[1] != 1)]
for var in conv_var_list:
tf_util.conv_variable_summaries(var, scope=var.name.replace('/', '_')[:-2])
conv_image_summary = tf.summary.merge_all()
# prepare session
sess = tf_util.Session()
# Instantiate singletons without scope.
if constants.PREDICT_DEPTH:
from depth_estimation_network import depth_estimator
with tf.variable_scope('') as depth_scope:
depth_estimator = depth_estimator.get_depth_estimator(sess)
else:
depth_scope = None
training_threads = []
learning_rate_input = tf.placeholder(tf.float32, name='learning_rate')
grad_applier = RMSPropApplier(learning_rate=learning_rate_input,
decay=constants.RMSP_ALPHA,
momentum=0.0,
epsilon=constants.RMSP_EPSILON,
clip_norm=constants.GRAD_NORM_CLIP)
for i in range(constants.PARALLEL_SIZE):
training_thread = A3CTrainingThread(
i, sess,
learning_rate_input,
grad_applier,
constants.MAX_TIME_STEP,
net_scope,
depth_scope)
training_threads.append(training_thread)
if constants.RUN_TEST:
testing_thread = A3CTestingThread(constants.PARALLEL_SIZE + 1, sess, net_scope, depth_scope)
sess.run(tf.global_variables_initializer())
# Initialize pretrained weights after init.
if constants.PREDICT_DEPTH:
depth_estimator.load_weights()
episode_reward_input = tf.placeholder(tf.float32, name='ep_reward')
episode_length_input = tf.placeholder(tf.float32, name='ep_length')
exist_answer_correct_input = tf.placeholder(tf.float32, name='exist_ans')
count_answer_correct_input = tf.placeholder(tf.float32, name='count_ans')
contains_answer_correct_input = tf.placeholder(tf.float32, name='contains_ans')
percent_invalid_actions_input = tf.placeholder(tf.float32, name='inv')
scalar_summaries = [
tf.summary.scalar("Episode Reward", episode_reward_input),
tf.summary.scalar("Episode Length", episode_length_input),
tf.summary.scalar("Percent Invalid Actions", percent_invalid_actions_input),
]
exist_summary = tf.summary.scalar("Answer Correct Existence", exist_answer_correct_input),
count_summary = tf.summary.scalar("Answer Correct Counting", count_answer_correct_input),
contains_summary = tf.summary.scalar("Answer Correct Containing", contains_answer_correct_input),
exist_summary_op = tf.summary.merge(scalar_summaries + [exist_summary])
count_summary_op = tf.summary.merge(scalar_summaries + [count_summary])
contains_summary_op = tf.summary.merge(scalar_summaries + [contains_summary])
summary_ops = [exist_summary_op, count_summary_op, contains_summary_op]
summary_placeholders = {
"episode_reward_input": episode_reward_input,
"episode_length_input": episode_length_input,
"exist_answer_correct_input": exist_answer_correct_input,
"count_answer_correct_input": count_answer_correct_input,
"contains_answer_correct_input": contains_answer_correct_input,
"percent_invalid_actions_input" : percent_invalid_actions_input,
}
if constants.RUN_TEST:
test_episode_reward_input = tf.placeholder(tf.float32, name='test_ep_reward')
test_episode_length_input = tf.placeholder(tf.float32, name='test_ep_length')
test_exist_answer_correct_input = tf.placeholder(tf.float32, name='test_exist_ans')
test_count_answer_correct_input = tf.placeholder(tf.float32, name='test_count_ans')
test_contains_answer_correct_input = tf.placeholder(tf.float32, name='test_contains_ans')
test_percent_invalid_actions_input = tf.placeholder(tf.float32, name='test_inv')
test_scalar_summaries = [
tf.summary.scalar("Test Episode Reward", test_episode_reward_input),
tf.summary.scalar("Test Episode Length", test_episode_length_input),
tf.summary.scalar("Test Percent Invalid Actions", test_percent_invalid_actions_input),
]
exist_summary = tf.summary.scalar("Test Answer Correct Existence", test_exist_answer_correct_input),
count_summary = tf.summary.scalar("Test Answer Correct Counting", test_count_answer_correct_input),
contains_summary = tf.summary.scalar("Test Answer Correct Containing", test_contains_answer_correct_input),
test_exist_summary_op = tf.summary.merge(test_scalar_summaries + [exist_summary])
test_count_summary_op = tf.summary.merge(test_scalar_summaries + [count_summary])
test_contains_summary_op = tf.summary.merge(test_scalar_summaries + [contains_summary])
test_summary_ops = [test_exist_summary_op, test_count_summary_op, test_contains_summary_op]
if not constants.DEBUG:
time_str = py_util.get_time_str()
summary_writer = tf.summary.FileWriter(os.path.join(constants.LOG_FILE, time_str), sess.graph)
else:
summary_writer = None
# init or load checkpoint with saver
vars_to_save = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='global_network')
saver = tf.train.Saver(vars_to_save, max_to_keep=3)
print('-------------- Looking for checkpoints in ', constants.CHECKPOINT_DIR)
global_t = tf_util.restore_from_dir(sess, constants.CHECKPOINT_DIR)
if constants.USE_NAVIGATION_AGENT:
print('now trying to restore nav model')
tf_util.restore_from_dir(sess, 'logs/checkpoints/navigation', True)
sess.graph.finalize()
for i in range(constants.PARALLEL_SIZE):
sess.run(training_threads[i].sync)
training_threads[i].agent.reset()
times = []
if not constants.DEBUG and constants.RECORD_FEED_DICT:
import h5py
NUM_RECORD = 10000 * len(constants.USED_QUESTION_TYPES)
time_str = py_util.get_time_str()
if not os.path.exists('question_data_dump'):
os.mkdir('question_data_dump')
dataset = h5py.File('question_data_dump/maps_' + time_str + '.h5', 'w')
dataset.create_dataset('question_data/existence_answer_placeholder', (NUM_RECORD, 1), dtype=np.int32)
dataset.create_dataset('question_data/counting_answer_placeholder', (NUM_RECORD, 1), dtype=np.int32)
dataset.create_dataset('question_data/question_type_placeholder', (NUM_RECORD, 1), dtype=np.int32)
dataset.create_dataset('question_data/question_object_placeholder', (NUM_RECORD, 1), dtype=np.int32)
dataset.create_dataset('question_data/question_container_placeholder', (NUM_RECORD, 21), dtype=np.int32)
dataset.create_dataset('question_data/pose_placeholder', (NUM_RECORD, 3), dtype=np.int32)
dataset.create_dataset('question_data/image_placeholder', (NUM_RECORD, 300, 300, 3), dtype=np.uint8)
dataset.create_dataset('question_data/map_mask_placeholder',
(NUM_RECORD, constants.SPATIAL_MAP_HEIGHT, constants.SPATIAL_MAP_WIDTH, 27), dtype=np.uint8)
dataset.create_dataset('question_data/meta_action_placeholder', (NUM_RECORD, 7), dtype=np.int32)
dataset.create_dataset('question_data/possible_move_placeholder', (NUM_RECORD, 31), dtype=np.int32)
dataset.create_dataset('question_data/answer_weight', (NUM_RECORD, 1), dtype=np.int32)
dataset.create_dataset('question_data/taken_action', (NUM_RECORD, 32), dtype=np.int32)
dataset.create_dataset('question_data/new_episode', (NUM_RECORD, 1), dtype=np.int32)
time_lock = threading.Lock()
data_ind = 0
def train_function(parallel_index):
global global_t
global dataset
global data_ind
print('----------------------------------------thread', parallel_index, 'global_t', global_t)
training_thread = training_threads[parallel_index]
last_global_t = global_t
last_global_t_image = global_t
while global_t < constants.MAX_TIME_STEP:
diff_global_t, ep_length, ep_reward, num_unrolls, feed_dict = training_thread.process(global_t, summary_writer,
summary_ops, summary_placeholders)
time_lock.acquire()
if not constants.DEBUG and constants.RECORD_FEED_DICT:
print(' NEW ENTRY: %d %s' % (data_ind, training_thread.agent.game_state.scene_name))
dataset['question_data/new_episode'][data_ind] = 1
for k, v in feed_dict.items():
key = 'question_data/' + k.name.split('/')[-1].split(':')[0]
if any([s in key for s in {
'gru_placeholder', 'num_unrolls',
'reward_placeholder', 'td_placeholder',
'learning_rate', 'phi_hat_prev_placeholder',
'next_map_mask_placeholder', 'next_pose_placeholder',
'episode_length_placeholder', 'question_count_placeholder',
'supervised_action_labels', 'supervised_action_weights_sigmoid',
'supervised_action_weights', 'question_direction_placeholder',
}]):
continue
v = np.ascontiguousarray(v)
if v.shape[0] == 1:
v = v[0, ...]
if len(v.shape) == 1 and num_unrolls > 1:
v = v[:, np.newaxis]
if 'map_mask_placeholder' in key:
v[:, :, :, :2] *= constants.STEPS_AHEAD
v[:, :, :, :2] += 2
data_loc = dataset[key][data_ind:data_ind + num_unrolls, ...]
data_len = data_loc.shape[0]
dataset[key][data_ind:data_ind + num_unrolls, ...] = v[:data_len, ...]
dataset.flush()
data_ind += num_unrolls
if data_ind >= NUM_RECORD:
# Everything is done
dataset.close()
raise Exception('Fake exception to exit the process. Don\'t worry, everything is fine.')
if ep_length > 0:
times.append((ep_length, ep_reward))
print('Num episodes', len(times), 'Episode means', np.mean(times, axis=0))
time_lock.release()
global_t += diff_global_t
# periodically save checkpoints to disk
if not (constants.DEBUG or constants.RECORD_FEED_DICT) and parallel_index == 0:
if global_t - last_global_t_image > 10000:
print('Ran conv image summary')
summary_im_str = sess.run(conv_image_summary)
summary_writer.add_summary(summary_im_str, global_t)
last_global_t_image = global_t
if global_t - last_global_t > 10000:
print('Save checkpoint at timestamp %d' % global_t)
tf_util.save(saver, sess, constants.CHECKPOINT_DIR, global_t)
last_global_t = global_t
def test_function():
global global_t
last_global_t_test = 0
while global_t < constants.MAX_TIME_STEP:
time.sleep(1)
if global_t - last_global_t_test > 10000:
# RUN TEST
sess.run(testing_thread.sync)
from game_state import QuestionGameState
if testing_thread.agent.game_state is None:
testing_thread.agent.game_state = QuestionGameState(sess=sess)
for q_type in constants.USED_QUESTION_TYPES:
answers_correct = 0.0
ep_lengths = 0.0
ep_rewards = 0.0
invalid_percents = 0.0
rows = list(range(len(testing_thread.agent.game_state.test_datasets[q_type])))
random.shuffle(rows)
rows = rows[:16]
print('()()()()()()()rows', rows)
for rr,row in enumerate(rows):
answer_correct, answer, gt_answer, ep_length, ep_reward, invalid_percent, scene_num, seed, required_interaction = testing_thread.process((row, q_type))
answers_correct += int(answer_correct)
ep_lengths += ep_length
ep_rewards += ep_reward
invalid_percents += invalid_percent
print('############################### TEST ITERATION ##################################')
print('ep ', (rr + 1))
print('average correct', answers_correct / (rr + 1))
print('#################################################################################')
answers_correct /= len(rows)
ep_lengths /= len(rows)
ep_rewards /= len(rows)
invalid_percents /= len(rows)
# Write the summary
test_summary_str = sess.run(test_summary_ops[q_type], feed_dict={
test_episode_reward_input : ep_rewards,
test_episode_length_input : ep_lengths,
test_exist_answer_correct_input : answers_correct,
test_count_answer_correct_input : answers_correct,
test_contains_answer_correct_input : answers_correct,
test_percent_invalid_actions_input : invalid_percents,
})
summary_writer.add_summary(test_summary_str, global_t)
summary_writer.flush()
last_global_t_test = global_t
testing_thread.agent.game_state.env.stop()
testing_thread.agent.game_state = None
train_threads = []
for i in range(constants.PARALLEL_SIZE):
train_threads.append(threading.Thread(target=train_function, args=(i,)))
train_threads[i].daemon = True
for t in train_threads:
t.start()
if constants.RUN_TEST:
test_thread = threading.Thread(target=test_function)
test_thread.daemon = True
test_thread.start()
for t in train_threads:
t.join()
if constants.RUN_TEST:
test_thread.join()
if not constants.DEBUG:
if not os.path.exists(constants.CHECKPOINT_DIR):
os.makedirs(constants.CHECKPOINT_DIR)
saver.save(sess, constants.CHECKPOINT_DIR + '/' + 'checkpoint', global_step = global_t)
summary_writer.close()
print('Saved.')
except KeyboardInterrupt:
print('Press Ctrl+C to stop')
except:
import traceback
traceback.print_exc()
finally:
if not constants.DEBUG:
print('Now saving data. Please wait')
tf_util.save(saver, sess, constants.CHECKPOINT_DIR, global_t)
summary_writer.close()
print('Saved.')
def run():
try:
with tf.variable_scope('nav_global_network'):
network = FreeSpaceNetwork(constants.GRU_SIZE,
constants.BATCH_SIZE,
constants.NUM_UNROLLS)
network.create_net()
training_step = network.training_op
with tf.variable_scope('loss'):
loss_summary_op = tf.summary.merge([
tf.summary.scalar('loss', network.loss),
])
summary_full = tf.summary.merge_all()
conv_var_list = [
v for v in tf.trainable_variables()
if 'conv' in v.name and 'weight' in v.name and
(v.get_shape().as_list()[0] != 1 or v.get_shape().as_list()[1] != 1
)
]
for var in conv_var_list:
tf_util.conv_variable_summaries(var,
scope=var.name.replace('/',
'_')[:-2])
summary_with_images = tf.summary.merge_all()
# prepare session
sess = tf_util.Session()
seq_inds = np.zeros((constants.BATCH_SIZE, 2), dtype=np.int32)
sequence_generators = []
for thread_index in range(constants.PARALLEL_SIZE):
gpus = str(constants.GPU_ID).split(',')
sequence_generator = SequenceGenerator(sess)
sequence_generators.append(sequence_generator)
sess.run(tf.global_variables_initializer())
if not (constants.DEBUG or constants.DRAWING):
from utils import py_util
time_str = py_util.get_time_str()
summary_writer = tf.summary.FileWriter(
os.path.join(constants.LOG_FILE, time_str), sess.graph)
else:
summary_writer = None
saver = tf.train.Saver(max_to_keep=3)
# init or load checkpoint
start_it = tf_util.restore_from_dir(sess, constants.CHECKPOINT_DIR)
sess.graph.finalize()
data_lock = threading.Lock()
def load_new_data(thread_index):
global data_buffer
global data_counts
sequence_generator = sequence_generators[thread_index]
counter = 0
while True:
while not (len(data_buffer) < constants.REPLAY_BUFFER_SIZE
or np.max(data_counts) > 0):
time.sleep(1)
counter += 1
if constants.DEBUG:
print('\nThread %d' % thread_index)
new_data, bounds, goal_pose = sequence_generator.generate_episode(
)
new_data = {
key: ([new_data[ii][key] for ii in range(len(new_data))])
for key in new_data[0]
}
new_data['goal_pose'] = goal_pose
new_data['memory'] = np.zeros(
(constants.SPATIAL_MAP_HEIGHT, constants.SPATIAL_MAP_WIDTH,
constants.MEMORY_SIZE))
new_data['gru_state'] = np.zeros(constants.GRU_SIZE)
if constants.DRAWING:
new_data['debug_images'] = sequence_generator.debug_images
data_lock.acquire()
if len(data_buffer) < constants.REPLAY_BUFFER_SIZE:
data_counts[len(data_buffer)] = 0
data_buffer.append(new_data)
counts = data_counts[:len(data_buffer)]
if counter % 10 == 0:
print(
'Buffer size %d Num used %d Max used amount %d' %
(len(data_buffer), len(
counts[counts > 0]), np.max(counts)))
else:
max_count_ind = np.argmax(data_counts)
data_buffer[max_count_ind] = new_data
data_counts[max_count_ind] = 0
if counter % 10 == 0:
print('Num used %d Max used amount %d' %
(len(data_counts[data_counts > 0]),
np.max(data_counts)))
data_lock.release()
threads = []
for i in range(constants.PARALLEL_SIZE):
load_data_thread = threading.Thread(target=load_new_data,
args=(i, ))
load_data_thread.daemon = True
load_data_thread.start()
threads.append(load_data_thread)
time.sleep(1)
sequences = [None] * constants.BATCH_SIZE
curr_it = 0
dataTimeTotal = 0.00001
solverTimeTotal = 0.00001
summaryTimeTotal = 0.00001
totalTimeTotal = 0.00001
chosen_inds = set()
loc_to_chosen_ind = {}
for iteration in range(start_it, constants.MAX_TIME_STEP):
if iteration == start_it or iteration % 10 == 1:
currentTimeStart = time.time()
tStart = time.time()
batch_data = []
batch_action = []
batch_memory = []
batch_gru_state = []
batch_labels = []
batch_pose = []
batch_mask = []
batch_goal_pose = []
batch_pose_indicator = []
batch_possible_label = []
batch_debug_images = []
for bb in range(constants.BATCH_SIZE):
if seq_inds[bb, 0] == seq_inds[bb, 1]:
# Pick a new random sequence
pickable_inds = set(
np.where(data_counts < 100)[0]) - chosen_inds
count_size = len(pickable_inds)
while count_size == 0:
pickable_inds = set(
np.where(data_counts < 100)[0]) - chosen_inds
count_size = len(pickable_inds)
time.sleep(1)
random_ind = random.sample(pickable_inds, 1)[0]
data_lock.acquire()
sequences[bb] = data_buffer[random_ind]
goal_pose = sequences[bb]['goal_pose']
sequences[bb]['memory'] = np.zeros(
(constants.SPATIAL_MAP_HEIGHT,
constants.SPATIAL_MAP_WIDTH, constants.MEMORY_SIZE))
sequences[bb]['gru_state'] = np.zeros(constants.GRU_SIZE)
data_counts[random_ind] += 1
if bb in loc_to_chosen_ind:
chosen_inds.remove(loc_to_chosen_ind[bb])
loc_to_chosen_ind[bb] = random_ind
chosen_inds.add(random_ind)
data_lock.release()
seq_inds[bb, 0] = 0
seq_inds[bb, 1] = len(sequences[bb]['color'])
data_len = min(constants.NUM_UNROLLS,
seq_inds[bb, 1] - seq_inds[bb, 0])
ind0 = seq_inds[bb, 0]
ind1 = seq_inds[bb, 0] + data_len
data = sequences[bb]['color'][ind0:ind1]
action = sequences[bb]['action'][ind0:ind1]
labels = sequences[bb]['label'][ind0:ind1]
memory = sequences[bb]['memory'].copy()
gru_state = sequences[bb]['gru_state'].copy()
pose = sequences[bb]['pose'][ind0:ind1]
goal_pose = sequences[bb]['goal_pose']
mask = sequences[bb]['weight'][ind0:ind1]
pose_indicator = sequences[bb]['pose_indicator'][ind0:ind1]
possible_label = sequences[bb]['possible_label'][ind0:ind1]
if constants.DRAWING:
batch_debug_images.append(
sequences[bb]['debug_images'][ind0:ind1])
if data_len < (constants.NUM_UNROLLS):
seq_inds[bb, :] = 0
data.extend([
np.zeros_like(data[0])
for _ in range(constants.NUM_UNROLLS - data_len)
])
action.extend([
np.zeros_like(action[0])
for _ in range(constants.NUM_UNROLLS - data_len)
])
labels.extend([
np.zeros_like(labels[0])
for _ in range(constants.NUM_UNROLLS - data_len)
])
pose.extend([
pose[-1]
for _ in range(constants.NUM_UNROLLS - data_len)
])
mask.extend([
np.zeros_like(mask[0])
for _ in range(constants.NUM_UNROLLS - data_len)
])
pose_indicator.extend([
np.zeros_like(pose_indicator[0])
for _ in range(constants.NUM_UNROLLS - data_len)
])
possible_label.extend([
np.zeros_like(possible_label[0])
for _ in range(constants.NUM_UNROLLS - data_len)
])
else:
seq_inds[bb, 0] += constants.NUM_UNROLLS
batch_data.append(data)
batch_action.append(action)
batch_memory.append(memory)
batch_gru_state.append(gru_state)
batch_pose.append(pose)
batch_goal_pose.append(goal_pose)
batch_labels.append(labels)
batch_mask.append(mask)
batch_pose_indicator.append(pose_indicator)
batch_possible_label.append(possible_label)
feed_dict = {
network.image_placeholder:
np.ascontiguousarray(batch_data),
network.action_placeholder:
np.ascontiguousarray(batch_action),
network.gru_placeholder:
np.ascontiguousarray(batch_gru_state),
network.pose_placeholder:
np.ascontiguousarray(batch_pose),
network.goal_pose_placeholder:
np.ascontiguousarray(batch_goal_pose),
network.labels_placeholder:
np.ascontiguousarray(batch_labels)[..., np.newaxis],
network.mask_placeholder:
np.ascontiguousarray(batch_mask),
network.pose_indicator_placeholder:
np.ascontiguousarray(batch_pose_indicator),
network.possible_label_placeholder:
np.ascontiguousarray(batch_possible_label),
network.memory_placeholders:
np.ascontiguousarray(batch_memory),
}
dataTEnd = time.time()
summaryTime = 0
if constants.DEBUG or constants.DRAWING:
outputs = sess.run([
training_step, network.loss, network.gru_state,
network.patch_weights_sigm, network.gru_outputs_full,
network.is_possible_sigm,
network.pose_indicator_placeholder,
network.terminal_patches, network.gru_outputs
],
feed_dict=feed_dict)
else:
if iteration == start_it + 10:
run_options = tf.RunOptions(
trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
outputs = sess.run([
training_step, network.loss, network.gru_state,
summary_with_images, network.gru_outputs
],
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)
loss_summary = outputs[3]
summary_writer.add_run_metadata(run_metadata,
'step_%07d' % iteration)
summary_writer.add_summary(loss_summary, iteration)
summary_writer.flush()
elif iteration % 10 == 0:
if iteration % 100 == 0:
outputs = sess.run([
training_step, network.loss, network.gru_state,
summary_with_images, network.gru_outputs
],
feed_dict=feed_dict)
elif iteration % 10 == 0:
outputs = sess.run([
training_step, network.loss, network.gru_state,
loss_summary_op, network.gru_outputs
],
feed_dict=feed_dict)
loss_summary = outputs[3]
summaryTStart = time.time()
summary_writer.add_summary(loss_summary, iteration)
summary_writer.flush()
summaryTime = time.time() - summaryTStart
else:
outputs = sess.run([
training_step, network.loss, network.gru_state,
network.gru_outputs
],
feed_dict=feed_dict)
gru_state_out = outputs[2]
memory_out = outputs[-1]
for mm in range(constants.BATCH_SIZE):
sequences[mm]['memory'] = memory_out[mm, ...]
sequences[mm]['gru_state'] = gru_state_out[mm, ...]
loss = outputs[1]
solverTEnd = time.time()
if constants.DEBUG or constants.DRAWING:
# Look at outputs
patch_weights = outputs[3]
is_possible = outputs[5]
pose_indicator = outputs[6]
terminal_patches = outputs[7]
data_lock.acquire()
for bb in range(constants.BATCH_SIZE):
for tt in range(constants.NUM_UNROLLS):
if batch_mask[bb][tt] == 0:
break
if constants.DRAWING:
import cv2
import scipy.misc
from utils import drawing
curr_image = batch_data[bb][tt]
label = np.flipud(batch_labels[bb][tt])
debug_images = batch_debug_images[bb][tt]
color_image = debug_images['color']
state_image = debug_images['state_image']
label_memory_image = debug_images[
'label_memory'][:, :, 0]
label_memory_image_class = np.argmax(
debug_images['label_memory'][:, :, 1:], axis=2)
label_memory_image_class[0,
0] = constants.NUM_CLASSES
label_patch = debug_images['label']
print('Possible pred %.3f' % is_possible[bb, tt])
print('Possible label %.3f' %
batch_possible_label[bb][tt])
patch = np.flipud(patch_weights[bb, tt, ...])
patch_occupancy = patch[:, :, 0]
print('occ', patch_occupancy)
print('label', label)
terminal_patch = np.flipud(
np.sum(terminal_patches[bb, tt, ...], axis=2))
image_list = [
debug_images['color'],
state_image,
debug_images['label_memory'][:, :, 0],
debug_images['memory_map'][:, :, 0],
label[:, :],
patch_occupancy,
np.flipud(pose_indicator[bb, tt]),
terminal_patch,
]
image = drawing.subplot(image_list, 4, 2,
constants.SCREEN_WIDTH,
constants.SCREEN_HEIGHT)
cv2.imshow('image', image[:, :, ::-1])
cv2.waitKey(0)
else:
pdb.set_trace()
data_lock.release()
if not (constants.DEBUG or constants.DRAWING) and (
iteration % 500 == 0
or iteration == constants.MAX_TIME_STEP - 1):
saverTStart = time.time()
tf_util.save(saver, sess, constants.CHECKPOINT_DIR, iteration)
saverTEnd = time.time()
print('Saver: %.3f' % (saverTEnd - saverTStart))
curr_it += 1
dataTimeTotal += dataTEnd - tStart
summaryTimeTotal += summaryTime
solverTimeTotal += solverTEnd - dataTEnd - summaryTime
totalTimeTotal += time.time() - tStart
if iteration == start_it or (iteration) % 10 == 0:
print('Iteration: %d' % (iteration))
print('Loss: %.3f' % loss)
print('Data: %.3f' % (dataTimeTotal / curr_it))
print('Solver: %.3f' % (solverTimeTotal / curr_it))
print('Summary: %.3f' % (summaryTimeTotal / curr_it))
print('Total: %.3f' % (totalTimeTotal / curr_it))
print('Current: %.3f\n' %
((time.time() - currentTimeStart) / min(10, curr_it)))
except:
import traceback
traceback.print_exc()
finally:
# Save final model
if not (constants.DEBUG or constants.DRAWING):
tf_util.save(saver, sess, constants.CHECKPOINT_DIR, iteration)
def main():
if constants.OBJECT_DETECTION:
from darknet_object_detection import detector
detector.setup_detectors(constants.PARALLEL_SIZE)
with tf.device('/gpu:' + str(constants.GPU_ID)):
with tf.variable_scope('global_network'):
if constants.END_TO_END_BASELINE:
global_network = EndToEndBaselineNetwork()
else:
global_network = QAPlannerNetwork(constants.RL_GRU_SIZE, 1, 1)
global_network.create_net()
if constants.USE_NAVIGATION_AGENT:
with tf.variable_scope('nav_global_network') as net_scope:
free_space_network = FreeSpaceNetwork(constants.GRU_SIZE, 1, 1)
free_space_network.create_net()
else:
net_scope = None
# prepare session
sess = tf_util.Session()
if constants.PREDICT_DEPTH:
from depth_estimation_network import depth_estimator
with tf.variable_scope('') as depth_scope:
depth_estimator = depth_estimator.get_depth_estimator(sess)
else:
depth_scope = None
sess.run(tf.global_variables_initializer())
# Initialize pretrained weights after init.
if constants.PREDICT_DEPTH:
depth_estimator.load_weights()
testing_threads = []
for i in range(constants.PARALLEL_SIZE):
testing_thread = A3CTestingThread(i, sess, net_scope, depth_scope)
testing_threads.append(testing_thread)
tf_util.restore_from_dir(sess, constants.CHECKPOINT_DIR, True)
if constants.USE_NAVIGATION_AGENT:
print('now trying to restore nav model')
tf_util.restore_from_dir(
sess, os.path.join(constants.CHECKPOINT_PREFIX, 'navigation'),
True)
sess.graph.finalize()
question_types = constants.USED_QUESTION_TYPES
rows = []
for q_type in question_types:
curr_rows = list(
range(len(testing_thread.agent.game_state.test_datasets[q_type])))
#curr_rows = list(range(8))
rows.extend(list(zip(curr_rows, [q_type] * len(curr_rows))))
random.shuffle(rows)
answers_correct = []
ep_lengths = []
ep_rewards = []
invalid_percents = []
time_lock = threading.Lock()
if not os.path.exists(constants.LOG_FILE):
os.makedirs(constants.LOG_FILE)
out_file = open(
constants.LOG_FILE + '/results_' + constants.TEST_SET + '_' +
py_util.get_time_str() + '.csv', 'w')
out_file.write(constants.LOG_FILE + '\n')
out_file.write(
'question_type, answer_correct, answer, gt_answer, episode_length, invalid_action_percent, scene number, seed, required_interaction\n'
)
def test_function(thread_ind):
testing_thread = testing_threads[thread_ind]
sess.run(testing_thread.sync)
#from game_state import QuestionGameState
#if testing_thread.agent.game_state is None:
#testing_thread.agent.game_state = QuestionGameState(sess=sess)
while len(rows) > 0:
time_lock.acquire()
if len(rows) == 0:
break
row = rows.pop()
time_lock.release()
answer_correct, answer, gt_answer, ep_length, ep_reward, invalid_percent, scene_num, seed, required_interaction = testing_thread.process(
row)
question_type = row[1] + 1
time_lock.acquire()
output_str = (
'%d, %d, %d, %d, %d, %f, %d, %d, %d\n' %
(question_type, answer_correct, answer, gt_answer, ep_length,
invalid_percent, scene_num, seed, required_interaction))
out_file.write(output_str)
out_file.flush()
answers_correct.append(int(answer_correct))
ep_lengths.append(ep_length)
ep_rewards.append(ep_reward)
invalid_percents.append(invalid_percent)
print('###############################')
print('ep ', row)
print('num episodes', len(answers_correct))
print('average correct', np.mean(answers_correct))
print('invalid percents', np.mean(invalid_percents),
np.median(invalid_percents))
print('###############################')
time_lock.release()
test_threads = []
for i in range(constants.PARALLEL_SIZE):
test_threads.append(threading.Thread(target=test_function, args=(i, )))
for t in test_threads:
t.start()
for t in test_threads:
t.join()
out_file.close()
],
dtype=np.int32)[:2]
return (self.states, self.bounds, goal_pose)
if __name__ == '__main__':
from networks.free_space_network import FreeSpaceNetwork
from utils import tf_util
import tensorflow as tf
sess = tf_util.Session()
with tf.variable_scope('nav_global_network'):
network = FreeSpaceNetwork(constants.GRU_SIZE, 1, 1)
network.create_net()
sess.run(tf.global_variables_initializer())
start_it = tf_util.restore_from_dir(sess, constants.CHECKPOINT_DIR)
import cv2
sequence_generator = SequenceGenerator(sess)
sequence_generator.planner_prob = 1
counter = 0
while True:
states, bounds, goal_pose = sequence_generator.generate_episode()
images = sequence_generator.debug_images
for im_dict in images:
counter += 1
gt_map = (2 - im_dict['label_memory'][:, :, 0])
image_list = [
def run():
try:
os.environ["CUDA_VISIBLE_DEVICES"] = str(constants.GPU_ID)
with tf.variable_scope('global_network'):
network = QAPlannerNetwork(constants.RL_GRU_SIZE, int(constants.BATCH_SIZE), 1)
network.create_net()
training_step = network.training(network.rl_total_loss)
conv_var_list = [v for v in tf.trainable_variables() if 'conv' in v.name and 'weight' in v.name and
(v.get_shape().as_list()[0] != 1 or v.get_shape().as_list()[1] != 1)]
for var in conv_var_list:
tf_util.conv_variable_summaries(var, scope=var.name.replace('/', '_')[:-2])
summary_with_images = tf.summary.merge_all()
with tf.variable_scope('supervised_loss'):
loss_ph = tf.placeholder(tf.float32)
accs_ph = [tf.placeholder(tf.float32) for _ in range(4)]
loss_summary_op = tf.summary.merge([
tf.summary.scalar('supervised_loss', loss_ph),
tf.summary.scalar('acc_1_exist', accs_ph[0]),
tf.summary.scalar('acc_2_count', accs_ph[1]),
tf.summary.scalar('acc_3_contains', accs_ph[2]),
])
# prepare session
sess = tf_util.Session()
sess.run(tf.global_variables_initializer())
if not (constants.DEBUG or constants.DRAWING):
from utils import py_util
time_str = py_util.get_time_str()
summary_writer = tf.summary.FileWriter(os.path.join(constants.LOG_FILE, time_str), sess.graph)
else:
summary_writer = None
# init or load checkpoint with saver
saver = tf.train.Saver(max_to_keep=3)
start_it = tf_util.restore_from_dir(sess, constants.CHECKPOINT_DIR)
sess.graph.finalize()
import h5py
h5_file = sorted(glob.glob('question_data_dump/*.h5'), key=os.path.getmtime)[-1]
dataset = h5py.File(h5_file)
num_entries = np.sum(np.sum(dataset['question_data/pose_placeholder'][...], axis=1) > 0)
print('num_entries', num_entries)
start_inds = dataset['question_data/new_episode'][:num_entries]
start_inds = np.where(start_inds[1:] != 1)[0]
curr_it = 0
data_time_total = 0
solver_time_total = 0
total_time_total = 0
for iteration in range(start_it, constants.MAX_TIME_STEP):
if iteration == start_it or iteration % 10 == 1:
current_time_start = time.time()
t_start = time.time()
rand_inds = np.sort(np.random.choice(start_inds, int(constants.BATCH_SIZE), replace=False))
rand_inds = rand_inds.tolist()
existence_answer_placeholder = dataset['question_data/existence_answer_placeholder'][rand_inds]
counting_answer_placeholder = dataset['question_data/counting_answer_placeholder'][rand_inds]
question_type_placeholder = dataset['question_data/question_type_placeholder'][rand_inds]
question_object_placeholder = dataset['question_data/question_object_placeholder'][rand_inds]
question_container_placeholder = dataset['question_data/question_container_placeholder'][rand_inds]
pose_placeholder = dataset['question_data/pose_placeholder'][rand_inds]
image_placeholder = dataset['question_data/image_placeholder'][rand_inds]
map_mask_placeholder = np.ascontiguousarray(dataset['question_data/map_mask_placeholder'][rand_inds])
meta_action_placeholder = dataset['question_data/meta_action_placeholder'][rand_inds]
possible_move_placeholder = dataset['question_data/possible_move_placeholder'][rand_inds]
taken_action = dataset['question_data/taken_action'][rand_inds]
answer_weight = np.ones((constants.BATCH_SIZE, 1))
map_mask_placeholder = np.ascontiguousarray(map_mask_placeholder, dtype=np.float32)
map_mask_placeholder[:, :, :, :2] -= 2
map_mask_placeholder[:, :, :, :2] /= constants.STEPS_AHEAD
map_mask_starts = map_mask_placeholder.copy()
for bb in range(0, constants.BATCH_SIZE):
object_ind = int(question_object_placeholder[bb])
question_type_ind = int(question_type_placeholder[bb])
if question_type_ind in {2, 3}:
container_ind = np.argmax(question_container_placeholder[bb])
max_map_inds = np.argmax(map_mask_placeholder[bb, ...], axis=2)
map_range = np.where(max_map_inds > 0)
map_range_x = (np.min(map_range[1]), np.max(map_range[1]))
map_range_y = (np.min(map_range[0]), np.max(map_range[0]))
for jj in range(random.randint(0, 100)):
tmp_patch_start = (random.randint(map_range_x[0], map_range_x[1]),
random.randint(map_range_y[0], map_range_y[1]))
tmp_patch_end = (random.randint(map_range_x[0], map_range_x[1]),
random.randint(map_range_y[0], map_range_y[1]))
patch_start = (min(tmp_patch_start[0], tmp_patch_end[0]),
min(tmp_patch_start[1], tmp_patch_end[1]))
patch_end = (max(tmp_patch_start[0], tmp_patch_end[0]),
max(tmp_patch_start[1], tmp_patch_end[1]))
patch = map_mask_placeholder[bb, patch_start[1]:patch_end[1], patch_start[0]:patch_end[0], :]
if question_type_ind in {2, 3}:
obj_mem = patch[:, :, 6 + container_ind] + patch[:, :, 6 + object_ind]
else:
obj_mem = patch[:, :, 6 + object_ind].copy()
obj_mem += patch[:, :, 2] # make sure seen locations stay marked.
if patch.size > 0 and np.max(obj_mem) == 0:
map_mask_placeholder[bb, patch_start[1]:patch_end[1], patch_start[0]:patch_end[0], 6:] = 0
feed_dict = {
network.existence_answer_placeholder: np.ascontiguousarray(existence_answer_placeholder),
network.counting_answer_placeholder: np.ascontiguousarray(counting_answer_placeholder),
network.question_type_placeholder: np.ascontiguousarray(question_type_placeholder),
network.question_object_placeholder: np.ascontiguousarray(question_object_placeholder),
network.question_container_placeholder: np.ascontiguousarray(question_container_placeholder),
network.question_direction_placeholder: np.zeros((constants.BATCH_SIZE, 4), dtype=np.float32),
network.pose_placeholder: np.ascontiguousarray(pose_placeholder),
network.image_placeholder: np.ascontiguousarray(image_placeholder),
network.map_mask_placeholder: map_mask_placeholder,
network.meta_action_placeholder: np.ascontiguousarray(meta_action_placeholder),
network.possible_move_placeholder: np.ascontiguousarray(possible_move_placeholder),
network.taken_action: np.ascontiguousarray(taken_action),
network.answer_weight: np.ascontiguousarray(answer_weight),
network.episode_length_placeholder: np.ones((constants.BATCH_SIZE)),
network.question_count_placeholder: np.zeros((constants.BATCH_SIZE)),
}
new_feed_dict = {}
for key,value in feed_dict.items():
if len(value.squeeze().shape) > 1:
new_feed_dict[key] = np.reshape(value, [int(constants.BATCH_SIZE), 1] + list(value.squeeze().shape[1:]))
else:
new_feed_dict[key] = np.reshape(value, [int(constants.BATCH_SIZE), 1])
feed_dict = new_feed_dict
feed_dict[network.taken_action] = np.reshape(feed_dict[network.taken_action], (constants.BATCH_SIZE, -1))
feed_dict[network.gru_placeholder] = np.zeros((int(constants.BATCH_SIZE), constants.RL_GRU_SIZE))
data_t_end = time.time()
if constants.DEBUG or constants.DRAWING:
outputs = sess.run(
[training_step, network.rl_total_loss, network.existence_answer, network.counting_answer,
network.possible_moves, network.memory_crops_rot, network.taken_action],
feed_dict=feed_dict)
else:
if iteration == start_it + 10:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
outputs = sess.run([training_step, network.rl_total_loss, summary_with_images],
feed_dict=feed_dict,
options=run_options,
run_metadata=run_metadata)
loss_summary = outputs[2]
summary_writer.add_run_metadata(run_metadata, 'step_%07d' % iteration)
summary_writer.add_summary(loss_summary, iteration)
summary_writer.flush()
elif iteration % 10 == 0:
if iteration % 100 == 0:
outputs = sess.run(
[training_step, network.rl_total_loss, summary_with_images],
feed_dict=feed_dict)
loss_summary = outputs[2]
elif iteration % 10 == 0:
outputs = sess.run(
[training_step, network.rl_total_loss,
network.existence_answer,
network.counting_answer,
],
feed_dict=feed_dict)
outputs[2] = outputs[2].reshape(-1, 1)
acc_q0 = np.sum((existence_answer_placeholder == (outputs[2] > 0.5)) *
(question_type_placeholder == 0)) / np.maximum(1, np.sum(question_type_placeholder == 0))
acc_q1 = np.sum((counting_answer_placeholder == np.argmax(outputs[3], axis=1)[..., np.newaxis]) *
(question_type_placeholder == 1)) / np.maximum(1, np.sum(question_type_placeholder == 1))
acc_q2 = np.sum((existence_answer_placeholder == (outputs[2] > 0.5)) *
(question_type_placeholder == 2)) / np.maximum(1, np.sum(question_type_placeholder == 2))
acc_q3 = np.sum((existence_answer_placeholder == (outputs[2] > 0.5)) *
(question_type_placeholder == 3)) / np.maximum(1, np.sum(question_type_placeholder == 3))
curr_loss = outputs[1]
outputs = sess.run([loss_summary_op],
feed_dict={
accs_ph[0]: acc_q0,
accs_ph[1]: acc_q1,
accs_ph[2]: acc_q2,
accs_ph[3]: acc_q3,
loss_ph: curr_loss,
})
loss_summary = outputs[0]
outputs.append(curr_loss)
summary_writer.add_summary(loss_summary, iteration)
summary_writer.flush()
else:
outputs = sess.run([training_step, network.rl_total_loss],
feed_dict=feed_dict)
loss = outputs[1]
solver_t_end = time.time()
if constants.DEBUG or constants.DRAWING:
# Look at outputs
guess_bool = outputs[2].flatten()
guess_count = outputs[3]
possible_moves_pred = outputs[4]
memory_crop = outputs[5]
print('loss', loss)
for bb in range(constants.BATCH_SIZE):
if constants.DRAWING:
import cv2
import scipy.misc
from utils import drawing
object_ind = int(question_object_placeholder[bb])
question_type_ind = question_type_placeholder[bb]
if question_type_ind == 1:
answer = counting_answer_placeholder[bb]
guess = guess_count[bb]
else:
answer = existence_answer_placeholder[bb]
guess = np.concatenate(([1 - guess_bool[bb]], [guess_bool[bb]]))
if question_type_ind[0] in {2, 3}:
container_ind = np.argmax(question_container_placeholder[bb])
obj_mem = np.flipud(map_mask_placeholder[bb, :, :, 6 + container_ind]).copy()
obj_mem += 2 * np.flipud(map_mask_placeholder[bb, :, :, 6 + object_ind])
else:
obj_mem = np.flipud(map_mask_placeholder[bb, :, :, 6 + object_ind])
possible = possible_move_placeholder[bb,...].flatten()
possible = np.concatenate((possible, np.zeros(4)))
possible = possible.reshape(constants.STEPS_AHEAD + 2, constants.STEPS_AHEAD)
possible_pred = possible_moves_pred[bb,...].flatten()
possible_pred = np.concatenate((possible_pred, np.zeros(4)))
possible_pred = possible_pred.reshape(constants.STEPS_AHEAD + 2, constants.STEPS_AHEAD)
mem2 = np.flipud(np.argmax(memory_crop[bb,...], axis=2))
mem2[0, 0] = memory_crop.shape[-1] - 2
# Answer histogram
ans = guess
if len(ans) == 1:
ans = [ans[0], 1 - ans[0]]
ans_size = max(len(ans), 100)
ans_hist = np.zeros((ans_size, ans_size))
for ii,ans_i in enumerate(ans):
ans_hist[:max(int(np.round(ans_i * ans_size)), 1),
int(ii * ans_size / len(ans)):int((ii+1) * ans_size / len(ans))] = (ii + 1)
ans_hist = np.flipud(ans_hist)
image_list = [
image_placeholder[bb,...],
ans_hist,
np.flipud(possible),
np.flipud(possible_pred),
mem2,
np.flipud(np.argmax(map_mask_starts[bb, :, :, 2:], axis=2)),
obj_mem,
]
if question_type_ind == 0:
question_str = 'Ex Q: %s A: %s' % (constants.OBJECTS[object_ind], bool(answer))
elif question_type_ind == 1:
question_str = '# Q: %s A: %d' % (constants.OBJECTS[object_ind], answer)
elif question_type_ind == 2:
question_str = 'Q: %s in %s A: %s' % (
constants.OBJECTS[object_ind],
constants.OBJECTS[container_ind],
bool(answer))
image = drawing.subplot(image_list, 4, 2, constants.SCREEN_WIDTH, constants.SCREEN_HEIGHT,
titles=[question_str, 'A: %s' % str(np.argmax(guess))], border=2)
cv2.imshow('image', image[:, :, ::-1])
cv2.waitKey(0)
else:
pdb.set_trace()
if not (constants.DEBUG or constants.DRAWING) and (iteration % 1000 == 0 or iteration == constants.MAX_TIME_STEP - 1):
saver_t_start = time.time()
tf_util.save(saver, sess, constants.CHECKPOINT_DIR, iteration)
saver_t_end = time.time()
print('Saver: %.3f' % (saver_t_end - saver_t_start))
curr_it += 1
data_time_total += data_t_end - t_start
solver_time_total += solver_t_end - data_t_end
total_time_total += time.time() - t_start
if iteration == start_it or (iteration) % 10 == 0:
print('Iteration: %d' % (iteration))
print('Loss: %.3f' % loss)
print('Data: %.3f' % (data_time_total / curr_it))
print('Solver: %.3f' % (solver_time_total / curr_it))
print('Total: %.3f' % (total_time_total / curr_it))
print('Current: %.3f\n' % ((time.time() - current_time_start) / min(10, curr_it)))
except:
import traceback
traceback.print_exc()
finally:
# Save final model
if not (constants.DEBUG or constants.DRAWING):
tf_util.save(saver, sess, constants.CHECKPOINT_DIR, iteration)
def restore(self):
path = os.path.join(constants.CHECKPOINT_DIR)
print('loading ', path)
iteration = tf_util.restore_from_dir(self.sess, path)
return iteration
tf.Graph().as_default()
sess = tf.Session(config=config)
keras.backend.set_session(sess)
# load tracker
tracker = Tracker(sess,
MEM_SIZE,
IMG_SIZE,
FEATURE_SIZE,
ori_height=height,
ori_width=width,
iou_threshold=0.3,
kl_threshold=0.6)
sess.run(tf.global_variables_initializer())
log_dir = '/home/msis_dasol/master_thesis/RAN/for_paper/VGG16_skip_connection/memsize_5'
tf_util.restore_from_dir(sess, os.path.join(log_dir, 'checkpoints'))
# load detector
yolov3 = YOLOv3(sess)
total_tracking_obejct = 0
var_sizes = [
np.product(list(map(int, v.shape))) * v.dtype.size
for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
]
print('total parameter size :', sum(var_sizes) / (1024**2), 'MB')
print('Start tracking..')
for image_name in image_list:
# load image
image_path = DATA_PATH + image_name
