def test(self, restore_model, save_dir):
dataset = BasicDataset(data_list_file=self.dataset_config['data_list_file'], img_dir=self.dataset_config['img_dir'])
save_name_list = dataset.data_list[:, 2]
iterator = dataset.create_one_shot_iterator(dataset.data_list, num_parallel_calls=self.num_input_threads)
batch_img0, batch_img1, batch_img2 = iterator.get_next()
flow_fw, flow_bw = pyramid_processing(batch_img0, batch_img1, batch_img2, train=False, trainable=False, is_scale=True)
# For KITTI we set max_flow=256, while for Sintel we set max_flow=128
flow_fw_color = flow_to_color(flow_fw['full_res'], mask=None, max_flow=256)
flow_bw_color = flow_to_color(flow_bw['full_res'], mask=None, max_flow=256)
restore_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
saver = tf.train.Saver(var_list=restore_vars)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(iterator.initializer)
saver.restore(sess, restore_model)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for i in range(dataset.data_num):
np_flow_fw, np_flow_bw, np_flow_fw_color, np_flow_fw_color = sess.run([flow_fw['full_res'], flow_bw['full_res'], flow_fw_color, flow_bw_color])
misc.imsave('%s/flow_fw_color_%s.png' % (save_dir, save_name_list[i]), np_flow_fw_color[0])
misc.imsave('%s/flow_bw_color_%s.png' % (save_dir, save_name_list[i]), np_flow_bw_color[0])
write_flo('%s/flow_fw_%s.flo' % (save_dir, save_name_list[i]), np_flow_fw[0])
write_flo('%s/flow_bw_%s.flo' % (save_dir, save_name_list[i]), np_flow_bw[0])
print('Finish %d/%d' % (i, dataset.data_num))
def test(self, restore_model, save_dir, is_normalize_img=True, prefix=''):
dataset = BasicDataset(data_list_file=self.dataset_config['data_list_file'], img_dir=self.dataset_config['img_dir'] + prefix, is_normalize_img=is_normalize_img)
save_name_list = dataset.data_list[:, -1]
iterator = dataset.create_one_shot_iterator(dataset.data_list, num_parallel_calls=self.num_input_threads)
batch_img0, batch_img1, batch_img2 = iterator.get_next()
img_shape = tf.shape(batch_img0)
h = img_shape[1]
w = img_shape[2]
new_h = tf.where(tf.equal(tf.mod(h, 64), 0), h, (tf.to_int32(tf.floor(h / 64) + 1)) * 64)
new_w = tf.where(tf.equal(tf.mod(w, 64), 0), w, (tf.to_int32(tf.floor(w / 64) + 1)) * 64)
batch_img0 = tf.image.resize_images(batch_img0, [new_h, new_w], method=1, align_corners=True)
batch_img1 = tf.image.resize_images(batch_img1, [new_h, new_w], method=1, align_corners=True)
batch_img2 = tf.image.resize_images(batch_img2, [new_h, new_w], method=1, align_corners=True)
flow_fw, flow_bw = pyramid_processing(batch_img0, batch_img1, batch_img2, train=False, trainable=False, is_scale=True)
flow_fw['full_res'] = flow_resize(flow_fw['full_res'], [h, w], method=1)
flow_bw['full_res'] = flow_resize(flow_bw['full_res'], [h, w], method=1)
flow_fw_color = flow_to_color(flow_fw['full_res'], mask=None, max_flow=256)
flow_bw_color = flow_to_color(flow_bw['full_res'], mask=None, max_flow=256)
restore_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
saver = tf.train.Saver(var_list=restore_vars)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(iterator.initializer)
saver.restore(sess, restore_model)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for i in range(dataset.data_num):
np_flow_fw, np_flow_bw, np_flow_fw_color, np_flow_bw_color = sess.run([flow_fw['full_res'], flow_bw['full_res'], flow_fw_color, flow_bw_color])
misc.imsave(('%s/' + '%s.png') % (save_dir, save_name_list[i]), np_flow_fw_color[0])
# misc.imsave(('%s/' + prefix + '_%s.png') % (save_dir, save_name_list[i]), np_flow_fw_color[0])
# misc.imsave(('%s/' + prefix + '_flow_fw_color_%s.png') % (save_dir, save_name_list[i]), np_flow_fw_color[0])
# misc.imsave(('%s/' + prefix + '_flow_bw_color_%s.png') % (save_dir, save_name_list[i]), np_flow_bw_color[0])
# write_flo('%s/flow_fw_%s.flo' % (save_dir, save_name_list[i]), np_flow_fw[0])
# write_flo('%s/flow_bw_%s.flo' % (save_dir, save_name_list[i]), np_flow_bw[0])
print('Finish %d/%d' % (i+1, dataset.data_num))
def eval(self, restore_model, save_dir, is_normalize_img=True):
from test_datasets_eval import BasicDataset
from error_metrics import flow_error_avg, outlier_pct, merge_dictionaries
dataset = BasicDataset(data_list_file=self.dataset_config['data_list_file'], img_dir=self.dataset_config['img_dir'], is_normalize_img=is_normalize_img)
save_name_list = dataset.data_list[:, -1]
iterator = dataset.create_one_shot_iterator(dataset.data_list, num_parallel_calls=self.num_input_threads)
batch_img0, batch_img1, batch_img2, flow_noc, flow_occ, mask_noc, mask_occ = iterator.get_next()
img_shape = tf.shape(batch_img0)
h = img_shape[1]
w = img_shape[2]
new_h = tf.where(tf.equal(tf.mod(h, 64), 0), h, (tf.to_int32(tf.floor(h / 64) + 1)) * 64)
new_w = tf.where(tf.equal(tf.mod(w, 64), 0), w, (tf.to_int32(tf.floor(w / 64) + 1)) * 64)
batch_img0 = tf.image.resize_images(batch_img0, [new_h, new_w], method=1, align_corners=True)
batch_img1 = tf.image.resize_images(batch_img1, [new_h, new_w], method=1, align_corners=True)
batch_img2 = tf.image.resize_images(batch_img2, [new_h, new_w], method=1, align_corners=True)
flow_fw, _ = pyramid_processing(batch_img0, batch_img1, batch_img2, train=False, trainable=False, is_scale=True)
flow_fw['full_res'] = flow_resize(flow_fw['full_res'], [h, w], method=1)
flow_fw_color = flow_to_color(flow_fw['full_res'], mask=None, max_flow=256)
error_fw_color = flow_error_image(flow_fw['full_res'], flow_occ, mask_occ)
errors = {}
errors['EPE_noc'] = flow_error_avg(flow_noc, flow_fw['full_res'], mask_noc)
errors['EPE_all'] = flow_error_avg(flow_occ, flow_fw['full_res'], mask_occ)
errors['outliers_noc'] = outlier_pct(flow_noc, flow_fw['full_res'], mask_noc)
errors['outliers_all'] = outlier_pct(flow_occ, flow_fw['full_res'], mask_occ)
restore_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
saver = tf.train.Saver(var_list=restore_vars)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(iterator.initializer)
saver.restore(sess, restore_model)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
sum_EPE_noc = 0.
sum_EPE_all = 0.
sum_outliers_noc = 0.
sum_outliers_all = 0.
for i in range(dataset.data_num):
np_flow_fw, np_flow_fw_color, np_error_fw_color = sess.run([flow_fw['full_res'], flow_fw_color, error_fw_color])
EPE_noc, EPE_all, outliers_noc, outliers_all = sess.run([errors['EPE_noc'], errors['EPE_all'], errors['outliers_noc'], errors['outliers_all']])
sum_EPE_noc += EPE_noc
sum_EPE_all += EPE_all
sum_outliers_noc += outliers_noc
sum_outliers_all += outliers_all
misc.imsave('%s/%s_10.png' % (save_dir, save_name_list[i]), np_flow_fw_color[0])
misc.imsave('%s/error_%s.png' % (save_dir, save_name_list[i]), np_error_fw_color[0])
#write_flo('%s/flow_fw_%s.flo' % (save_dir, save_name_list[i]), np_flow_fw[0])
print('Finish %d/%d' % (i+1, dataset.data_num))
print("EPE_noc: %f, EPE_all: %f" % (sum_EPE_noc/dataset.data_num, sum_EPE_all/dataset.data_num))
print("F1_noc: %f, F1_all: %f" % (sum_outliers_noc/dataset.data_num, sum_outliers_all/dataset.data_num))