def test(flags, ep, ds, lr, save_dir):
# set gpu
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = flags.GPU
# environment settings
np.random.seed(flags.random_seed)
tf.set_random_seed(flags.random_seed)
# data prepare step
Data = rsrClassData(flags.rsr_data_dir)
(collect_files_test, meta_test) = Data.getCollectionByName(flags.test_data_dir)
# image reader
coord = tf.train.Coordinator()
# define place holder
X = tf.placeholder(tf.float32, shape=[None, flags.input_size[0], flags.input_size[1], 3], name='X')
y = tf.placeholder(tf.int32, shape=[None, flags.input_size[0], flags.input_size[1], 1], name='y')
mode = tf.placeholder(tf.bool, name='mode')
# initialize model
flags.model_name = 'ResUnetShrinkInria_fr_resample_mean_reduced_EP-{}_DS-{}.0_LR-{}'.format(ep, ds, lr)
model = unet.ResUnetModel_shrink({'X':X, 'Y':y}, trainable=mode, model_name=flags.model_name, input_size=flags.input_size)
model.create_graph('X', flags.num_classes)
model.make_update_ops('X', 'Y')
# set ckdir
model.make_ckdir(flags.ckdir)
# set up graph and initialize
config = tf.ConfigProto()
# run training
start_time = time.time()
with tf.Session(config=config) as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1)
if os.path.exists(model.ckdir) and tf.train.get_checkpoint_state(model.ckdir):
latest_check_point = tf.train.latest_checkpoint(model.ckdir)
saver.restore(sess, latest_check_point)
print('loaded {}'.format(latest_check_point))
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
try:
iou_record = {}
for (image_name, label_name) in collect_files_test:
c_names = flags.city_name.split(',')
for c_name in c_names:
if c_name in image_name:
city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0]
tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0]
# load reader
iterator_test = image_reader.image_label_iterator(
os.path.join(flags.rsr_data_dir, image_name),
batch_size=flags.batch_size,
tile_dim=meta_test['dim_image'][:2],
patch_size=flags.input_size,
overlap=0, padding=0,
image_mean=IMG_MEAN)
# run
result = model.test('X', sess, iterator_test)
pred_label_img = sis_utils.get_output_label(result,
meta_test['dim_image'],
flags.input_size,
meta_test['colormap'], overlap=0,
output_image_dim=meta_test['dim_image'],
output_patch_size=flags.input_size,
make_map=False)
# evaluate
truth_label_img = scipy.misc.imread(os.path.join(flags.rsr_data_dir, label_name))
iou = sis_utils.iou_metric(truth_label_img, pred_label_img * 255)
'''plt.subplot(121)
plt.imshow(truth_label_img)
plt.subplot(122)
plt.imshow(pred_label_img)
plt.show()'''
iou_record[image_name] = iou
print('{}_{}: iou={:.2f}'.format(city_name, tile_id, iou*100))
finally:
coord.request_stop()
coord.join(threads)
duration = time.time() - start_time
print('duration {:.2f} minutes'.format(duration/60))
np.save(os.path.join(save_dir, '{}.npy').format(model.model_name), iou_record)
iou_mean = []
for _, val in iou_record.items():
iou_mean.append(val)
print('{}: {}'.format(flags.model_name, np.mean(iou_mean)))
return np.mean(iou_mean)
def main(flags):
# set gpu
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = flags.GPU
# environment settings
np.random.seed(flags.random_seed)
tf.set_random_seed(flags.random_seed)
# data prepare step
Data = rsrClassData(flags.rsr_data_dir)
(collect_files_test, meta_test) = Data.getCollectionByName(flags.test_data_dir)
# image reader
coord = tf.train.Coordinator()
# define place holder
X = tf.placeholder(tf.float32, shape=[None, flags.input_size[0], flags.input_size[1], 3], name='X')
y = tf.placeholder(tf.int32, shape=[None, flags.input_size[0], flags.input_size[1], 1], name='y')
mode = tf.placeholder(tf.bool, name='mode')
# initialize model
model = unet.UnetModel({'X':X, 'Y':y}, trainable=mode, model_name=flags.model_name, input_size=flags.input_size)
model.create_graph('X', flags.num_classes)
model.make_update_ops('X', 'Y')
# set ckdir
model.make_ckdir(flags.ckdir)
# set up graph and initialize
config = tf.ConfigProto()
# run training
start_time = time.time()
with tf.Session(config=config) as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1)
if os.path.exists(model.ckdir) and tf.train.get_checkpoint_state(model.ckdir):
latest_check_point = tf.train.latest_checkpoint(model.ckdir)
saver.restore(sess, latest_check_point)
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
try:
for (image_name, label_name) in collect_files_test:
if flags.city_name in image_name:
city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0]
tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0]
# load reader
iterator_test = image_reader.image_label_iterator(
os.path.join(flags.rsr_data_dir, image_name),
batch_size=flags.batch_size,
tile_dim=meta_test['dim_image'][:2],
patch_size=flags.input_size,
overlap=0)
# run
result = model.test('X', sess, iterator_test)
pred_label_img = sis_utils.get_output_label(result, meta_test['dim_image'],
flags.input_size, meta_test['colormap'])
# evaluate
truth_label_img = scipy.misc.imread(os.path.join(flags.rsr_data_dir, label_name))
iou = sis_utils.iou_metric(truth_label_img, pred_label_img)
print('{}_{}: iou={:.2f}'.format(city_name, tile_id, iou*100))
finally:
coord.request_stop()
coord.join(threads)
duration = time.time() - start_time
print('duration {:.2f} minutes'.format(duration/60))
def test_and_save(flags, model_name, save_dir):
# set gpu
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = flags.GPU
# environment settings
np.random.seed(flags.random_seed)
tf.set_random_seed(flags.random_seed)
# data prepare step
Data = rsrClassData(flags.rsr_data_dir)
(collect_files_test, meta_test) = Data.getCollectionByName(flags.test_data_dir)
# image reader
coord = tf.train.Coordinator()
# define place holder
X = tf.placeholder(tf.float32, shape=[None, flags.input_size[0], flags.input_size[1], 3], name='X')
y = tf.placeholder(tf.int32, shape=[None, flags.input_size[0], flags.input_size[1], 1], name='y')
mode = tf.placeholder(tf.bool, name='mode')
# initialize model
if 'appendix' in model_name:
model = unet.UnetModel_Height_Appendix({'X':X, 'Y':y}, trainable=mode, model_name=model_name, input_size=flags.input_size)
elif 'Res' in model_name:
model = unet.ResUnetModel_Crop({'X': X, 'Y': y}, trainable=mode, model_name=model_name,
input_size=flags.input_size)
else:
model = unet.UnetModel_Origin({'X':X, 'Y':y}, trainable=mode, model_name=model_name, input_size=flags.input_size)
if 'large' in model_name:
model.create_graph('X', flags.num_classes, start_filter_num=40)
else:
model.create_graph('X', flags.num_classes)
model.make_update_ops('X', 'Y')
# set ckdir
model.make_ckdir(flags.ckdir)
# set up graph and initialize
config = tf.ConfigProto()
# make fold if not exists
save_path = os.path.join(save_dir, 'temp_save', model_name)
if not os.path.exists(save_path):
os.makedirs(save_path)
else:
return save_path
# run training
start_time = time.time()
with tf.Session(config=config) as sess:
init = tf.global_variables_initializer()
sess.run(init)
saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1)
if os.path.exists(model.ckdir) and tf.train.get_checkpoint_state(model.ckdir):
latest_check_point = tf.train.latest_checkpoint(model.ckdir)
saver.restore(sess, latest_check_point)
print('loaded {}'.format(latest_check_point))
threads = tf.train.start_queue_runners(coord=coord, sess=sess)
try:
for (image_name, label_name) in collect_files_test:
c_names = flags.city_name.split(',')
for c_name in c_names:
if c_name in image_name:
city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0]
tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0]
print('Scoring {}_{} using {}...'.format(city_name, tile_id, model_name))
# load reader
iterator_test = image_reader.image_label_iterator(
os.path.join(flags.rsr_data_dir, image_name),
batch_size=flags.batch_size,
tile_dim=meta_test['dim_image'][:2],
patch_size=flags.input_size,
overlap=184, padding=92,
image_mean=IMG_MEAN)
# run
result = model.test('X', sess, iterator_test, soft_pred=True)
pred_label_img = sis_utils.get_output_label(result,
(meta_test['dim_image'][0]+184, meta_test['dim_image'][1]+184),
flags.input_size,
meta_test['colormap'], overlap=184,
output_image_dim=meta_test['dim_image'],
output_patch_size=(flags.input_size[0]-184, flags.input_size[1]-184),
make_map=False, soft_pred=True)
file_name = os.path.join(save_path, '{}_{}.npy'.format(city_name, tile_id))
np.save(file_name, pred_label_img)
finally:
coord.request_stop()
coord.join(threads)
duration = time.time() - start_time
print('duration {:.2f} minutes'.format(duration/60))
return save_path