def main(_):
if FLAGS.exp_name is None:
FLAGS.exp_name = "reconstrued_results_unitLength" + str(
int(FLAGS.latent_dim / FLAGS.latent_num))
image_shape = [
int(i) for i in FLAGS.image_shape.strip('()[]{}').split(',')
]
dirs = init_directories(FLAGS.exp_name, FLAGS.output_dir)
dirs[
'data'] = '../../npz_datas' if FLAGS.data_dir is None else FLAGS.data_dir
dirs['codes'] = os.path.join(dirs['data'], 'codes/')
create_directories(dirs, FLAGS.train, FLAGS.save_codes)
output_dim = reduce(mul, image_shape, 1)
run_config = tf.ConfigProto(allow_soft_placement=True)
run_config.gpu_options.allow_growth = True
run_config.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.Session(config=run_config)
ae = AE(
session=sess,
arch=FLAGS.arch,
lr=FLAGS.lr,
alpha=FLAGS.alpha,
beta=FLAGS.beta,
latent_dim=FLAGS.latent_dim,
latent_num=FLAGS.latent_num,
class_net_unit_num=FLAGS.class_net_unit_num,
output_dim=output_dim,
batch_size=FLAGS.batch_size,
image_shape=image_shape,
exp_name=FLAGS.exp_name,
dirs=dirs,
vis_reconst=FLAGS.visualize_reconstruct,
)
if FLAGS.save_codes:
sampleNum = 1000 # 50x64 large batch, forward prop only
dataVisualName = 'SVHN10_img_N1000x32x32x3_testForModularity'
data_manager = ShapesDataManager(dirs['data'],
dataVisualName,
FLAGS.batch_size,
image_shape,
shuffle=False,
file_ext=FLAGS.file_ext,
train_fract=1.0,
inf=True)
#data_manager.set_divisor_batch_size()
ae.train_iter, ae.dev_iter, ae.test_iter = data_manager.get_iterators()
ae.session.run(tf.global_variables_initializer())
#saved_step = ae.load()
saved_step = ae.load_fixedNum(4000)
assert saved_step > 1, "A trained model is needed to encode the data!"
pathForSave = 'ValidateEncodedImgs'
if not os.path.exists(pathForSave):
os.mkdir(pathForSave)
codes = []
images = []
for batch_num in range(int(sampleNum / FLAGS.batch_size)):
img_batch, _mask1, _ = next(ae.train_iter)
#code = ae.encode(img_batch) #[batch_size, reg_latent_dim]
code, image = ae.getCodesAndImgs(pathForSave, img_batch, batch_num)
codes.append(code)
images.append(image)
if batch_num < 5 or batch_num % 10 == 0:
print(("Batch number {0}".format(batch_num)))
codes = np.vstack(codes)
images = np.vstack(images)
codes_name = 'CIFAR3_codesModularityMetricsCal'
filename = os.path.join(pathForSave, "codes_" + codes_name)
#np.save(filename, codes)
np.savez(filename + '.npz', imagesNorm0_1=images, codes=codes)
print(("Images and Codes saved to: {0}".format(filename)))
def main(_):
if FLAGS.exp_name is None:
FLAGS.exp_name = "reconstrued_results"
image_shape = [
int(i) for i in FLAGS.image_shape.strip('()[]{}').split(',')
]
dirs = init_directories(FLAGS.exp_name, FLAGS.output_dir)
dirs[
'data'] = '../../../npz_datas_single_test' if FLAGS.data_dir is None else FLAGS.data_dir
dirs['codes'] = os.path.join(dirs['data'], 'codes/')
create_directories(dirs, FLAGS.train, FLAGS.save_codes)
output_dim = reduce(mul, image_shape, 1)
run_config = tf.ConfigProto(allow_soft_placement=True)
run_config.gpu_options.allow_growth = True
run_config.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.Session(config=run_config)
ae = AE(
session=sess,
arch=FLAGS.arch,
lr=FLAGS.lr,
alpha=FLAGS.alpha,
beta=FLAGS.beta,
latent_dim=FLAGS.latent_dim,
latent_num=FLAGS.latent_num,
class_net_unit_num=FLAGS.class_net_unit_num,
output_dim=output_dim,
batch_size=FLAGS.batch_size,
image_shape=image_shape,
exp_name=FLAGS.exp_name,
dirs=dirs,
vis_reconst=FLAGS.visualize_reconstruct,
)
# save codes and images
data1Name = "mnistOffset1_Imgs_GTimages_mask_GTlabel_(32x64)x32x32x1_unitLength1_CodeImageDataset"
data_manager = ShapesDataManager(dirs['data'],
data1Name,
FLAGS.batch_size,
image_shape,
shuffle=False,
file_ext=FLAGS.file_ext,
train_fract=1.0,
inf=True)
ae.train_iter1, ae.dev_iter1, ae.test_iter1 = data_manager.get_iterators()
ae.session.run(tf.global_variables_initializer())
saved_step = ae.load_fixedNum(1875)
assert saved_step > 1, "A trained model is needed to encode the data!"
pathForSave = 'ValidateEncodedImgs_Offset{}'.format(1)
save_name = "reconstrued_results_offset{}".format(1)
try:
os.makedirs(pathForSave)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(pathForSave):
pass
else:
raise
codes = []
images = []
sampleNum = 2048
for batch_num in range(int(sampleNum / FLAGS.batch_size)):
img_batch, _mask1, _ = next(ae.train_iter1)
# code = ae.encode(img_batch) #[batch_size, reg_latent_dim]
code, image = ae.getCodesAndImgs(pathForSave, img_batch, _mask1,
batch_num)
codes.append(code)
images.append(image)
if batch_num < 5 or batch_num % 10 == 0:
print(("Batch number {0}".format(batch_num)))
codes = np.vstack(codes)
images = np.vstack(images)
filename = os.path.join(dirs['codes'], "codes_" + save_name)
# np.save(filename, codes)
np.savez(filename + '.npz', imagesNorm0_1=images, codes=codes)
print(("Images and Codes saved to: {0}".format(filename)))
def main(_):
if FLAGS.exp_name is None:
FLAGS.exp_name = "separateRep_{}_{}_{}_lr_{}".format( FLAGS.arch, FLAGS.latent_dim, FLAGS.image_wh, FLAGS.lr)
image_shape = [int(i) for i in FLAGS.image_shape.strip('()[]{}').split(',')]
dirs = init_directories(FLAGS.exp_name, FLAGS.output_dir)
dirs['data'] = '../../../npz_datas' if FLAGS.data_dir is None else FLAGS.data_dir
dirs['codes'] = os.path.join(dirs['data'], 'codes/')
create_directories(dirs, FLAGS.train, FLAGS.save_codes)
output_dim = reduce(mul, image_shape, 1)
run_config = tf.ConfigProto(allow_soft_placement=True)
run_config.gpu_options.allow_growth = True
run_config.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.Session(config=run_config)
ae = AE(
session=sess,
arch=FLAGS.arch,
lr=FLAGS.lr,
alpha=FLAGS.alpha,
beta=FLAGS.beta,
latent_dim=FLAGS.latent_dim,
latent_num=FLAGS.latent_num,
class_net_unit_num=FLAGS.class_net_unit_num,
output_dim=output_dim,
batch_size=FLAGS.batch_size,
image_shape=image_shape,
exp_name=FLAGS.exp_name,
dirs=dirs,
vis_reconst=FLAGS.visualize_reconstruct,
)
if FLAGS.visualize_reconstruct:
sampleNum =1280 # 20x64 large batch, forward prop only
dataVisualName1='pattern_(20x64)x64x64x3_unitLength9_test_visualdata1'
dataVisualName2='pattern_(20x64)x64x64x3_unitLength9_test_visualdata2'
data_manager = TeapotsDataManager(dirs['data'],
dataVisualName1,dataVisualName2, FLAGS.batch_size,
image_shape, shuffle=False,file_ext=FLAGS.file_ext, train_fract=0.8,
inf=True,supervised=False)
#data_manager.set_divisor_batch_size()
#ae.train_iter, ae.dev_iter, ae.test_iter= data_manager.get_iterators()
ae.train_iter1, ae.dev_iter1, ae.test_iter1,ae.train_iter2, ae.dev_iter2, ae.test_iter2= data_manager.get_iterators()
ae.session.run(tf.global_variables_initializer())
# change the which iteration ckpt you want to use
saved_step = ae.load_fixedNum(inter_num=5750)
assert saved_step > 1, "A trained model is needed to encode the data!"
pathForSave='VisualImgsResults'
try:
os.makedirs(pathForSave)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(pathForSave):
pass
else:
raise
for batch_num in range(int(sampleNum/FLAGS.batch_size)):
img_batch1, _mask1, _ = next(ae.train_iter1)
img_batch2, _mask2, _ = next(ae.train_iter2)
#code = ae.encode(img_batch) #[batch_size, reg_latent_dim]
ae.getVisualImgs(pathForSave,img_batch1, _mask1,img_batch2, _mask2,batch_num)
if batch_num < 5 or batch_num % 10 == 0:
print(("Batch number {0}".format(batch_num)))
print("Images and Codes saved to Folder: VisualImgsResults")
def main(_):
if FLAGS.exp_name is None:
FLAGS.exp_name = "reconstrued_results"
image_shape = [
int(i) for i in FLAGS.image_shape.strip('()[]{}').split(',')
]
dirs = init_directories(FLAGS.exp_name, FLAGS.output_dir)
dirs[
'data'] = '../../../npz_datas_single_test' if FLAGS.data_dir is None else FLAGS.data_dir
dirs['codes'] = os.path.join(dirs['data'], 'codes/')
create_directories(dirs, FLAGS.train, FLAGS.save_codes)
output_dim = reduce(mul, image_shape, 1)
run_config = tf.ConfigProto(allow_soft_placement=True)
run_config.gpu_options.allow_growth = True
run_config.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.Session(config=run_config)
ae = AE(
session=sess,
arch=FLAGS.arch,
lr=FLAGS.lr,
alpha=FLAGS.alpha,
beta=FLAGS.beta,
latent_dim=FLAGS.latent_dim,
latent_num=FLAGS.latent_num,
class_net_unit_num=FLAGS.class_net_unit_num,
output_dim=output_dim,
batch_size=FLAGS.batch_size,
image_shape=image_shape,
exp_name=FLAGS.exp_name,
dirs=dirs,
vis_reconst=FLAGS.visualize_reconstruct,
)
# test get changed images
data1Name = "mnistOffset1_Imgs_GTimages_mask_GTlabel_(32x64)x32x32x1_unitLength1_CodeImageDataset"
data_manager = ShapesDataManager(dirs['data'],
data1Name,
FLAGS.batch_size,
image_shape,
shuffle=False,
file_ext=FLAGS.file_ext,
train_fract=1.0,
inf=True)
ae.train_iter1, ae.dev_iter1, ae.test_iter1 = data_manager.get_iterators()
ae.session.run(tf.global_variables_initializer())
saved_step = ae.load_fixedNum(1875)
assert saved_step > 1, "A trained model is needed to encode the data!"
pathForSave = 'VisualImgsResults'
try:
os.makedirs(pathForSave)
except OSError as exc: # Python >2.5
if exc.errno == errno.EEXIST and os.path.isdir(pathForSave):
pass
else:
raise
assert saved_step > 1, "A trained model is needed to encode the data!"
sampleNum = 2048
for k in range(int(sampleNum / FLAGS.batch_size)):
fixed_x1, fixed_mk1, _ = next(ae.train_iter1)
#print(fixed_x1.shape)
#print(fixed_mk1 )
ae.visualise_reconstruction_path(pathForSave, fixed_x1, fixed_mk1, k)
#print(k)
print('finish encode!')
