def plot_reconstruction_test_batch(sess,
codebook,
decoder,
test_img_crops,
noof_scene_views,
obj_id,
eval_dir=None):
encoder = codebook._encoder
dataset = codebook._dataset
sample_views = np.random.choice(noof_scene_views,
np.min([100, noof_scene_views]),
replace=False)
sample_batch = []
i = 0
j = 0
while i < 16:
if obj_id in test_img_crops[sample_views[j]]:
sample_batch.append(test_img_crops[sample_views[j]][obj_id][0])
i += 1
j += 1
x = np.array(sample_batch).squeeze()
if x.dtype == 'uint8':
x = x / 255.
print('converted uint8 to float type')
reconst = sess.run(decoder.x, feed_dict={encoder.x: x})
nearest_neighbors = []
for xi in x:
Rs_est = codebook.nearest_rotation(sess, xi, top_n=8)
pred_views = []
pred_views.append(xi * 255)
for R_est in Rs_est:
pred_views.append(dataset.render_rot(R_est, downSample=1))
nearest_neighbors.append(
tiles(np.array(pred_views), 1, len(pred_views), 10, 10))
all_nns_img = tiles(np.array(nearest_neighbors), len(nearest_neighbors), 1,
10, 10)
reconstruction_imgs = np.hstack((tiles(x, 4, 4), tiles(reconst, 4, 4)))
cv2.imwrite(os.path.join(eval_dir, 'figures', 'reconstruction_imgs.png'),
reconstruction_imgs * 255)
cv2.imwrite(
os.path.join(eval_dir, 'figures', 'nearest_neighbors_imgs.png'),
all_nns_img)
def show_nearest_rotation(pred_views, test_crop, view):
print np.array(pred_views).shape
nearest_views = tiles(np.array(pred_views),1,len(pred_views),10,10)
cv2.imshow('nearest_views',cv2.resize(nearest_views/255.,(len(pred_views)*256,256)))
cv2.imshow('test_crop',cv2.resize(test_crop,(256,256)))
def main():
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
if workspace_path == None:
print('Please define a workspace path:\n')
print('export AE_WORKSPACE_PATH=/path/to/workspace\n')
exit(-1)
gentle_stop = np.array((1,), dtype=np.bool)
gentle_stop[0] = False
def on_ctrl_c(signal, frame):
gentle_stop[0] = True
signal.signal(signal.SIGINT, on_ctrl_c)
parser = argparse.ArgumentParser()
parser.add_argument("experiment_name")
parser.add_argument("-d", action='store_true', default=False)
parser.add_argument("-gen", action='store_true', default=False)
parser.add_argument("-vis_emb", action='store_true', default=False)
parser.add_argument('--at_step', default=None, type=int, required=False)
arguments = parser.parse_args()
full_name = arguments.experiment_name.split('/')
experiment_name = full_name.pop()
experiment_group = full_name.pop() if len(full_name) > 0 else ''
debug_mode = arguments.d
generate_data = arguments.gen
at_step = arguments.at_step
cfg_file_path = u.get_config_file_path(workspace_path, experiment_name, experiment_group)
log_dir = u.get_log_dir(workspace_path, experiment_name, experiment_group)
checkpoint_file = u.get_checkpoint_basefilename(log_dir)
ckpt_dir = u.get_checkpoint_dir(log_dir)
train_fig_dir = u.get_train_fig_dir(log_dir)
dataset_path = u.get_dataset_path(workspace_path)
if not os.path.exists(cfg_file_path):
print('Could not find config file:\n')
print('{}\n'.format(cfg_file_path))
exit(-1)
args = configparser.ConfigParser()
args.read(cfg_file_path)
num_iter = args.getint('Training', 'NUM_ITER') if not debug_mode else np.iinfo(np.int32).max
save_interval = args.getint('Training', 'SAVE_INTERVAL')
num_gpus = 1
model_type = args.get('Dataset', 'MODEL')
with tf.variable_scope(experiment_name, reuse=tf.AUTO_REUSE):
dataset = factory.build_dataset(dataset_path, args)
multi_queue = factory.build_multi_queue(dataset, args)
dev_splits = np.array_split(np.arange(24), num_gpus)
iterator = multi_queue.create_iterator(dataset_path, args)
all_object_views = tf.concat([inp[0] for inp in multi_queue.next_element],0)
bs = multi_queue._batch_size
encoding_splits = []
for dev in range(num_gpus):
with tf.device('/device:GPU:%s' % dev):
encoder = factory.build_encoder(all_object_views[dev_splits[dev][0]*bs:(dev_splits[dev][-1]+1)*bs], args, is_training=False)
encoding_splits.append(tf.split(encoder.z, len(dev_splits[dev]),0))
with tf.variable_scope(experiment_name):
decoders = []
for dev in range(num_gpus):
with tf.device('/device:GPU:%s' % dev):
for j,i in enumerate(dev_splits[dev]):
decoders.append(factory.build_decoder(multi_queue.next_element[i], encoding_splits[dev][j], args, is_training=False, idx=i))
ae = factory.build_ae(encoder, decoders, args)
codebook = factory.build_codebook(encoder, dataset, args)
train_op = factory.build_train_op(ae, args)
saver = tf.train.Saver(save_relative_paths=True)
dataset.load_bg_images(dataset_path)
multi_queue.create_tfrecord_training_images(dataset_path, args)
widgets = ['Training: ', progressbar.Percentage(),
' ', progressbar.Bar(),
' ', progressbar.Counter(), ' / %s' % num_iter,
' ', progressbar.ETA(), ' ']
bar = progressbar.ProgressBar(maxval=num_iter,widgets=widgets)
gpu_options = tf.GPUOptions(allow_growth=True, per_process_gpu_memory_fraction = 0.9)
config = tf.ConfigProto(gpu_options=gpu_options,log_device_placement=True,allow_soft_placement=True)
with tf.Session(config=config) as sess:
sess.run(multi_queue.bg_img_init.initializer)
sess.run(iterator.initializer)
chkpt = tf.train.get_checkpoint_state(ckpt_dir)
if chkpt and chkpt.model_checkpoint_path:
if at_step is None:
checkpoint_file_basename = u.get_checkpoint_basefilename(log_dir,latest=args.getint('Training', 'NUM_ITER'))
else:
checkpoint_file_basename = u.get_checkpoint_basefilename(log_dir,latest=at_step)
print('loading ', checkpoint_file_basename)
saver.restore(sess, checkpoint_file_basename)
else:
if encoder._pre_trained_model != 'False':
encoder.saver.restore(sess, encoder._pre_trained_model)
all_vars = set([var for var in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)])
var_list = all_vars.symmetric_difference([v[1] for v in list(encoder.fil_var_list.items())])
sess.run(tf.variables_initializer(var_list))
print(sess.run(tf.report_uninitialized_variables()))
else:
sess.run(tf.global_variables_initializer())
if not debug_mode:
print('Training with %s model' % args.get('Dataset','MODEL'), os.path.basename(args.get('Paths','MODEL_PATH')))
bar.start()
while True:
this,_,reconstr_train,enc_z = sess.run([multi_queue.next_element,multi_queue.next_bg_element,[decoder.x for decoder in decoders], encoder.z])
this_x = np.concatenate([el[0] for el in this])
this_y = np.concatenate([el[2] for el in this])
print(this_x.shape)
reconstr_train = np.concatenate(reconstr_train)
print(this_x.shape)
cv2.imshow('sample batch', np.hstack(( u.tiles(this_x, 4, 6), u.tiles(reconstr_train, 4,6),u.tiles(this_y, 4, 6))) )
k = cv2.waitKey(0)
idx = np.random.randint(0,24)
this_y = np.repeat(this_y[idx:idx+1, :, :], 24, axis=0)
reconstr_train = sess.run([decoder.x for decoder in decoders],feed_dict={encoder._input:this_y})
reconstr_train = np.array(reconstr_train)
print(reconstr_train.shape)
reconstr_train = reconstr_train.squeeze()
cv2.imshow('sample batch 2', np.hstack((u.tiles(this_y, 4, 6), u.tiles(reconstr_train, 4, 6))))
k = cv2.waitKey(0)
if k == 27:
break
if gentle_stop[0]:
break
if not debug_mode:
bar.finish()
if not gentle_stop[0] and not debug_mode:
print('To create the embedding run:\n')
print('ae_embed {}\n'.format(full_name))
def main():
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
if workspace_path is None:
print('Please define a workspace path:\n')
print('export AE_WORKSPACE_PATH=/path/to/workspace\n')
exit(-1)
gentle_stop = np.array((1, ), dtype=np.bool)
gentle_stop[0] = False
def on_ctrl_c(signal, frame):
gentle_stop[0] = True
signal.signal(signal.SIGINT, on_ctrl_c)
parser = argparse.ArgumentParser()
parser.add_argument("experiment_name")
parser.add_argument("-d", action='store_true', default=False)
parser.add_argument("-gen", action='store_true', default=False)
parser.add_argument('--at_step', default=None, type=int, required=False)
arguments = parser.parse_args()
full_name = arguments.experiment_name.split('/')
experiment_name = full_name.pop()
experiment_group = full_name.pop() if len(full_name) > 0 else ''
debug_mode = arguments.d
generate_data = arguments.gen
at_step = arguments.at_step
cfg_file_path = u.get_config_file_path(workspace_path, experiment_name,
experiment_group)
log_dir = u.get_log_dir(workspace_path, experiment_name, experiment_group)
checkpoint_file = u.get_checkpoint_basefilename(log_dir)
ckpt_dir = u.get_checkpoint_dir(log_dir)
train_fig_dir = u.get_train_fig_dir(log_dir)
dataset_path = u.get_dataset_path(workspace_path)
if not os.path.exists(cfg_file_path):
print('Could not find config file:\n')
print(('{}\n'.format(cfg_file_path)))
exit(-1)
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
if not os.path.exists(train_fig_dir):
os.makedirs(train_fig_dir)
if not os.path.exists(dataset_path):
os.makedirs(dataset_path)
args = configparser.ConfigParser(inline_comment_prefixes="#")
args.read(cfg_file_path)
shutil.copy2(cfg_file_path, log_dir)
num_iter = args.getint(
'Training', 'NUM_ITER') if not debug_mode else np.iinfo(np.int32).max
save_interval = args.getint('Training', 'SAVE_INTERVAL')
num_gpus = args.getint('Training', 'NUM_GPUS')
with tf.device('/device:CPU:0'):
with tf.variable_scope(experiment_name, reuse=tf.AUTO_REUSE):
dataset = factory.build_dataset(dataset_path, args)
multi_queue = factory.build_multi_queue(dataset, args)
if generate_data:
# dataset.load_bg_images(dataset_path)
multi_queue.create_tfrecord_training_images(dataset_path, args)
print('finished generating training images')
exit()
dev_splits = np.array_split(np.arange(multi_queue._num_objects),
num_gpus)
iterator = multi_queue.create_iterator(dataset_path, args)
all_x, all_y = list(
zip(*[(inp[0], inp[2]) for inp in multi_queue.next_element]))
all_x, all_y = tf.concat(all_x, axis=0), tf.concat(all_y, axis=0)
print(all_x.shape)
encoding_splits = []
for dev in range(num_gpus):
with tf.device('/device:GPU:%s' % dev):
sta = dev_splits[dev][0] * multi_queue._batch_size
end = (dev_splits[dev][-1] + 1) * multi_queue._batch_size
print(sta, end)
encoder = factory.build_encoder(all_x[sta:end],
args,
target=all_y[sta:end],
is_training=True)
encoding_splits.append(
tf.split(encoder.z, len(dev_splits[dev]), 0))
with tf.variable_scope(experiment_name):
decoders = []
for dev in range(num_gpus):
with tf.device('/device:GPU:%s' % dev):
for j, i in enumerate(dev_splits[dev]):
print(len(encoding_splits))
decoders.append(
factory.build_decoder(multi_queue.next_element[i],
encoding_splits[dev][j],
args,
is_training=True,
idx=i))
ae = factory.build_ae(encoder, decoders, args)
codebook = factory.build_codebook(encoder, dataset, args)
train_op = factory.build_train_op(ae, args)
saver = tf.train.Saver(save_relative_paths=True, max_to_keep=1)
# dataset.get_training_images(dataset_path, args)
# dataset.load_bg_images(dataset_path)
multi_queue.create_tfrecord_training_images(dataset_path, args)
if generate_data:
print(('finished generating synthetic training data for ' +
experiment_name))
print('exiting...')
exit()
widgets = [
'Training: ',
progressbar.Percentage(), ' ',
progressbar.Bar(), ' ',
progressbar.Counter(),
' / %s' % num_iter, ' ',
progressbar.ETA(), ' '
]
bar = progressbar.ProgressBar(maxval=num_iter, widgets=widgets)
gpu_options = tf.GPUOptions(allow_growth=True,
per_process_gpu_memory_fraction=0.9)
config = tf.ConfigProto(gpu_options=gpu_options, allow_soft_placement=True)
with tf.Session(config=config) as sess:
sess.run(multi_queue.bg_img_init.initializer)
sess.run(iterator.initializer)
u.create_summaries(multi_queue, decoders, ae)
merged_loss_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(ckpt_dir, sess.graph)
chkpt = tf.train.get_checkpoint_state(ckpt_dir)
if chkpt and chkpt.model_checkpoint_path:
if at_step is None:
# checkpoint_file_basename = u.get_checkpoint_basefilename(log_dir,latest=args.getint('Training', 'NUM_ITER'))
checkpoint_file_basename = chkpt.model_checkpoint_path
else:
checkpoint_file_basename = u.get_checkpoint_basefilename(
log_dir, latest=at_step)
print(('loading ', checkpoint_file_basename))
saver.restore(sess, checkpoint_file_basename)
# except:
# print 'loading ', chkpt.model_checkpoint_path
# saver.restore(sess, chkpt.model_checkpoint_path)
else:
if encoder._pre_trained_model != 'False':
encoder.saver.restore(sess, encoder._pre_trained_model)
all_vars = set([
var
for var in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
])
var_list = all_vars.symmetric_difference(
[v[1] for v in list(encoder.fil_var_list.items())])
sess.run(tf.variables_initializer(var_list))
print(sess.run(tf.report_uninitialized_variables()))
else:
sess.run(tf.global_variables_initializer())
if not debug_mode:
print(('Training with %s model' % args.get('Dataset', 'MODEL'),
os.path.basename(args.get('Paths', 'MODEL_PATH'))))
bar.start()
for i in range(encoder.global_step.eval(), num_iter):
if not debug_mode:
# print 'before optimize'
sess.run([train_op, multi_queue.next_bg_element])
# print 'after optimize'
if (i + 1) % 100 == 0:
merged_summaries = sess.run(merged_loss_summary)
summary_writer.add_summary(merged_summaries, i)
bar.update(i)
if (i + 1) % save_interval == 0:
saver.save(sess,
checkpoint_file,
global_step=encoder.global_step)
# this_x, this_y = sess.run([queue.x, queue.y])
# reconstr_train = sess.run(decoder.x,feed_dict={queue.x:this_x})
this, reconstr_train = sess.run([
multi_queue.next_element,
[decoder.x for decoder in decoders]
])
this_x = np.concatenate([el[0] for el in this])
this_y = np.concatenate([el[2] for el in this])
# reconstr_train = sess.run(,feed_dict={queue.x:this_x})
reconstr_train = np.concatenate(reconstr_train)
for imgs in [this_x, this_y, reconstr_train]:
np.random.seed(0)
np.random.shuffle(imgs)
train_imgs = np.hstack(
(u.tiles(this_x, 4,
4), u.tiles(reconstr_train, 4,
4), u.tiles(this_y, 4, 4)))
cv2.imwrite(
os.path.join(train_fig_dir,
'training_images_%s.png' % i),
train_imgs * 255)
else:
this, _, reconstr_train = sess.run([
multi_queue.next_element, multi_queue.next_bg_element,
[decoder.x for decoder in decoders]
])
this_x = np.concatenate([el[0] for el in this])
this_y = np.concatenate([el[2] for el in this])
print(this_x.shape, reconstr_train[0].shape,
len(reconstr_train))
reconstr_train = np.concatenate(reconstr_train, axis=0)
for imgs in [this_x, this_y, reconstr_train]:
np.random.seed(0)
np.random.shuffle(imgs)
print(this_x.shape)
cv2.imshow(
'sample batch',
np.hstack((u.tiles(this_x, 4,
6), u.tiles(reconstr_train, 4,
6), u.tiles(this_y, 4, 6))))
k = cv2.waitKey(0)
if k == 27:
break
if gentle_stop[0]:
break
if not debug_mode:
bar.finish()
if not gentle_stop[0] and not debug_mode:
print('To create the embedding run:\n')
print(('ae_embed {}\n'.format(full_name)))
def main():
tf.disable_eager_execution()
workspace_path = os.environ.get('AE_WORKSPACE_PATH')
if workspace_path is None:
print('Please define a workspace path:\n')
print('export AE_WORKSPACE_PATH=/path/to/workspace\n')
exit(-1)
gentle_stop = np.array((1,), dtype=np.bool)
gentle_stop[0] = False
def on_ctrl_c(signal, frame):
gentle_stop[0] = True
signal.signal(signal.SIGINT, on_ctrl_c)
parser = argparse.ArgumentParser()
parser.add_argument("experiment_name")
parser.add_argument("-d", action='store_true', default=False)
parser.add_argument("-gen", action='store_true', default=False)
arguments = parser.parse_args()
full_name = arguments.experiment_name.split('/')
experiment_name = full_name.pop()
experiment_group = full_name.pop() if len(full_name) > 0 else ''
debug_mode = arguments.d
generate_data = arguments.gen
cfg_file_path = u.get_config_file_path(workspace_path, experiment_name, experiment_group)
log_dir = u.get_log_dir(workspace_path, experiment_name, experiment_group)
checkpoint_file = u.get_checkpoint_basefilename(log_dir)
ckpt_dir = u.get_checkpoint_dir(log_dir)
train_fig_dir = u.get_train_fig_dir(log_dir)
dataset_path = u.get_dataset_path(workspace_path)
if not os.path.exists(cfg_file_path):
print('Could not find config file:\n')
print('{}\n'.format(cfg_file_path))
exit(-1)
if not os.path.exists(ckpt_dir):
os.makedirs(ckpt_dir)
if not os.path.exists(train_fig_dir):
os.makedirs(train_fig_dir)
if not os.path.exists(dataset_path):
os.makedirs(dataset_path)
args = configparser.ConfigParser()
args.read(cfg_file_path)
shutil.copy2(cfg_file_path, log_dir)
with tf.variable_scope(experiment_name):
dataset = factory.build_dataset(dataset_path, args)
queue = factory.build_queue(dataset, args)
encoder = factory.build_encoder(queue.x, args, is_training=True)
decoder = factory.build_decoder(queue.y, encoder, args, is_training=True)
ae = factory.build_ae(encoder, decoder, args)
codebook = factory.build_codebook(encoder, dataset, args)
train_op = factory.build_train_op(ae, args)
saver = tf.train.Saver(save_relative_paths=True)
num_iter = args.getint('Training', 'NUM_ITER') if not debug_mode else 100000
save_interval = args.getint('Training', 'SAVE_INTERVAL')
model_type = args.get('Dataset', 'MODEL')
if model_type=='dsprites':
dataset.get_sprite_training_images(args)
else:
dataset.get_training_images(dataset_path, args)
dataset.load_bg_images(dataset_path)
if generate_data:
print('finished generating synthetic training data for ' + experiment_name)
print('exiting...')
exit()
widgets = ['Training: ', progressbar.Percentage(),
' ', progressbar.Bar(),
' ', progressbar.Counter(), ' / %s' % num_iter,
' ', progressbar.ETA(), ' ']
bar = progressbar.ProgressBar(maxval=num_iter,widgets=widgets)
gpu_options = tf.GPUOptions(allow_growth=True, per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=config) as sess:
chkpt = tf.train.get_checkpoint_state(ckpt_dir)
if chkpt and chkpt.model_checkpoint_path:
saver.restore(sess, chkpt.model_checkpoint_path)
else:
sess.run(tf.global_variables_initializer())
merged_loss_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(ckpt_dir, sess.graph)
if not debug_mode:
print('Training with %s model' % args.get('Dataset','MODEL'), os.path.basename(args.get('Paths','MODEL_PATH')))
bar.start()
queue.start(sess)
for i in range(ae.global_step.eval(), num_iter):
if not debug_mode:
sess.run(train_op)
if i % 10 == 0:
loss = sess.run(merged_loss_summary)
summary_writer.add_summary(loss, i)
bar.update(i)
if (i+1) % save_interval == 0:
saver.save(sess, checkpoint_file, global_step=ae.global_step)
this_x, this_y = sess.run([queue.x, queue.y])
reconstr_train = sess.run(decoder.x,feed_dict={queue.x:this_x})
train_imgs = np.hstack(( u.tiles(this_x, 4, 4), u.tiles(reconstr_train, 4,4),u.tiles(this_y, 4, 4)))
cv2.imwrite(os.path.join(train_fig_dir,'training_images_%s.png' % i), train_imgs*255)
else:
this_x, this_y = sess.run([queue.x, queue.y])
reconstr_train = sess.run(decoder.x,feed_dict={queue.x:this_x})
cv2.imshow('sample batch', np.hstack(( u.tiles(this_x, 3, 3), u.tiles(reconstr_train, 3,3),u.tiles(this_y, 3, 3))) )
k = cv2.waitKey(0)
if k == 27:
break
if gentle_stop[0]:
break
queue.stop(sess)
if not debug_mode:
bar.finish()
if not gentle_stop[0] and not debug_mode:
print('To create the embedding run:\n')
print('ae_embed {}\n'.format(full_name))
def plot_latent_revolutions(num_obj, codebook):
encoder = codebook._encoder
dataset = codebook._dataset
# generate PCA directions from all objects
Rs, lon_lat, _ = eval_plots.generate_view_points(noof=201, num_cyclo=5)
all_ztrain = []
for i in range(0, num_obj * 2, 4):
syn_crops = []
z_train = np.zeros((len(Rs), encoder.latent_space_size))
for R in Rs:
syn_crops.append(dataset.render_rot(R, obj_id=i) / 255.)
for a, e in u.batch_iteration_indices(len(Rs), 200):
print(e)
z_train[a:e] = sess.run(encoder.z,
feed_dict={encoder._input: syn_crops[a:e]})
all_ztrain.append(z_train)
all_ztrain = np.array(all_ztrain).reshape(-1, 128)
pca_all = eval_plots.compute_pca_plot_embedding('',
all_ztrain,
lon_lat=list(lon_lat) * 5,
save=False)
Rs, lon_lat, _ = eval_plots.generate_azim_elev_points(noof=36 * 8)
fig = plt.figure(figsize=(3 * num_obj, 3 * 4))
fig.subplots_adjust(top=0.95, bottom=0.05)
# plt.title('Embedding Principal Components')
imgs = []
axes = []
for o in range(0, num_obj * 4):
syn_crops = []
for R in Rs:
if o >= 2 * num_obj and o < 3 * num_obj:
R_rot = np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
syn_crops.append(
dataset.render_rot(np.dot(R, R_rot), obj_id=o) / 255.)
else:
syn_crops.append(dataset.render_rot(R, obj_id=o) / 255.)
syn_crops = np.array(syn_crops)
imgs.append(syn_crops[np.linspace(0, len(syn_crops), 8,
endpoint=False).astype(np.int32)])
# im = u.tiles(np.array(syn_crops),12,18*4,scale=0.66)
z_train = np.zeros((len(Rs), encoder.latent_space_size))
# cv2.imshow('',im)
# cv2.waitKey(1)
for a, e in u.batch_iteration_indices(len(Rs), 200):
print(e)
z_train[a:e] = sess.run(encoder.z,
feed_dict={encoder._input: syn_crops[a:e]})
# eval_plots.compute_pca_plot_embedding('',z_train,lon_lat=lon_lat,save=False)
ax = fig.add_subplot(4, num_obj, o + 1, projection='3d')
# if o>=3*num_obj:
# pca_all=None
eval_plots.compute_pca_plot_azelin(36 * 8 + 1,
z_train,
pca=pca_all,
save=False,
inter_factor=1,
normalize=False,
fig=fig,
ax=ax)
axes.append(ax)
axes[-1].legend()
# for j in range(len(Rs)):
# Rs_est = codebook.nearest_rotation(sess, syn_crops[j], top_n=1)
# est_view = dataset.render_rot(Rs_est.squeeze(),obj_id=0)/255.
# cv2.imshow('inserted_view',syn_crops[j])
# cv2.imshow('est_view',est_view)
# cv2.waitKey(0)
def on_move(event):
ax_i = axes.index(event.inaxes)
for ax_ in axes:
# if ax_ is not axes[ax_i]:
ax_.view_init(elev=axes[ax_i].elev, azim=axes[ax_i].azim)
ax_.set_xlim3d(axes[ax_i].get_xlim3d())
ax_.set_ylim3d(axes[ax_i].get_ylim3d())
ax_.set_zlim3d(axes[ax_i].get_zlim3d())
fig.canvas.draw_idle()
c1 = fig.canvas.mpl_connect('motion_notify_event', on_move)
im = u.tiles(np.array(imgs).reshape(-1, 128, 128, 3),
num_obj * 4,
8,
scale=1)
cv2.imshow('', im)
cv2.waitKey(1)
plt.show()