def test_video(self):
def reshape_batches(inputs_batches):
return np.reshape(
inputs_batches,
newshape=[
inputs_batches.shape[0] * inputs_batches.shape[1],
inputs_batches.shape[2], inputs_batches.shape[3],
inputs_batches.shape[4]
])
my_multi_test_datasets = multi_test_datasets(batch_size=4,
video_num=4,
frame_interval=2,
is_frame=True,
is_Optical=False,
crop_size=4,
img_size=self.img_size_h)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
self.restore_model_weghts(sess)
video_label = my_multi_test_datasets.init_test_single_videos(
seletced_dataset_idx=2, selected_video_idx=2)
video_lenth = 0
psnr1_list = []
optical_loss_list = []
gray_loss_list = []
optical_frame_list = []
gray_frame_list = []
optical_frame_label_list = []
gray_frame_label_list = []
while 1:
batches = my_multi_test_datasets.get_single_videos_batches()
if not (batches == []):
print(batches.shape)
video_lenth += (batches.shape[0] * batches.shape[1])
batch_data_gray = batches[:, :, :, :, 0:1]
try:
gray_loss, gray_frames, gray_psnr1 = sess.run(
[
self.gray_loss_sequences_frame_mean,
self.gray_train_out_ph,
self.gray_loss_sequences_frame_psnr1
],
feed_dict={
self.gray_train_in_ph: batch_data_gray,
self.phase: False
})
print('gray loss shape', gray_loss.shape)
print('psnr shape', gray_psnr1.shape)
# print('gray loss shape',gray_loss.shape)
gray_loss = gray_loss.flatten()
gray_psnr1 = gray_psnr1.flatten()
gray_frame_list.append(reshape_batches(gray_frames))
gray_frame_label_list.append(
reshape_batches(batch_data_gray))
gray_loss_list.append(gray_loss)
psnr1_list.append(gray_psnr1)
except Exception:
print('get img failed')
else:
break
# print('optical-loss')
# optical_loss_list = max_min_np(np.concatenate(optical_loss_list,axis=0))
# save_roc_auc_plot_img('',optical_loss_list, video_label)
print('gray-loss')
gray_loss_list = max_min_np(np.concatenate(gray_loss_list, axis=0))
print(gray_loss_list)
print(video_label)
save_roc_auc_plot_img('', gray_loss_list, video_label)
print('psnr1-auc')
gray_psnr1 = max_min_np(np.concatenate(psnr1_list, axis=0))
print(gray_psnr1)
save_roc_auc_plot_img('', gray_psnr1, video_label)
gray_frame_list = np.concatenate(gray_frame_list, axis=0)
# optical_frame_list = np.concatenate(optical_frame_list, axis=0)
gray_frame_label_list = np.concatenate(gray_frame_label_list,
axis=0)
# optical_frame_label_list = np.concatenate(optical_frame_label_list, axis=0)
gray_frame_list = np.concatenate(
[gray_frame_list, gray_frame_label_list], axis=1)
# optical_frame_list = np.concatenate([optical_frame_list, optical_frame_label_list], axis=2)
save_batch_images(gray_frame_list, self.gray_img_save_path,
'test_gray.jpg')
return
def test_single_dataset_type2(self):
my_multi_test_datasets = multi_test_datasets(
batch_size=self.batch_size,
video_num=self.video_imgs_num,
frame_interval=2,
is_frame=True,
is_Optical=True,
crop_size=4,
img_size=self.img_size_h)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
self.restore_model_weghts(sess)
seletced_dataset_idx = 3
datasets_op_list = []
datasets_gr_list = []
datasets_to_list = []
datasets_tr_list = []
datasets_la_list = []
for video_idx in range(
my_multi_test_datasets.
multi_datasets[seletced_dataset_idx].video_clips_num):
video_label = my_multi_test_datasets.init_test_single_videos(
seletced_dataset_idx, video_idx)
video_lenth = 0
optical_loss_list = []
gray_loss_list = []
trible_loss_list = []
while 1:
batches = my_multi_test_datasets.get_single_videos_batches(
)
if not (batches == []):
# print(batches.shape)
video_lenth += (batches.shape[0] * batches.shape[1])
optical_loss, gray_loss, mid_stage_loss = sess.run(
[
self.optical_loss_sequences_frame_mean,
self.gray_loss_sequences_frame_mean,
self.midstage_loss_sequences_frame_mean
],
feed_dict={
self.train_in_ph: batches,
self.phase: False
})
print('optical loss shape', optical_loss.shape)
print('gray loss shape', gray_loss.shape)
print('mid stage loss', mid_stage_loss.shape)
trib_loss = np.ones_like(optical_loss, dtype=np.float)
for b_idx in range(optical_loss.shape[0]):
trib_loss[b_idx, :] = 0 * optical_loss[
b_idx, :] + 0 * gray_loss[
b_idx, :] + mid_stage_loss[b_idx]
print(optical_loss[b_idx, :])
print(gray_loss[b_idx, :])
print(mid_stage_loss[b_idx])
print(trib_loss[b_idx, :])
optical_loss = optical_loss.flatten()
gray_loss = gray_loss.flatten()
trib_loss = trib_loss.flatten()
optical_loss_list.append(optical_loss)
gray_loss_list.append(gray_loss)
trible_loss_list.append(trib_loss)
else:
print('optical-loss')
tog_loss = max_min_np(
np.concatenate(optical_loss_list, axis=0) +
np.concatenate(gray_loss_list, axis=0))
datasets_to_list.append(tog_loss)
optical_loss_list = max_min_np(
np.concatenate(optical_loss_list, axis=0))
datasets_op_list.append(optical_loss_list)
gray_loss_list = max_min_np(
np.concatenate(gray_loss_list, axis=0))
datasets_gr_list.append(gray_loss_list)
trible_loss_list = max_min_np(
np.concatenate(trible_loss_list, axis=0))
print('trible - loss - normalized', trible_loss_list)
datasets_tr_list.append(trible_loss_list)
datasets_la_list.append(video_label)
break
datasets_op_list = np.concatenate(datasets_op_list, axis=0)
datasets_gr_list = np.concatenate(datasets_gr_list, axis=0)
datasets_to_list = np.concatenate(datasets_to_list, axis=0)
datasets_tr_list = np.concatenate(datasets_tr_list, axis=0)
datasets_la_list = np.concatenate(datasets_la_list, axis=0)
print('optical-loss')
frame_auc, frame_eer = save_roc_auc_plot_img(
'', datasets_op_list, datasets_la_list)
print('gray-loss')
frame_auc, frame_eer = save_roc_auc_plot_img(
'', datasets_gr_list, datasets_la_list)
print('together-loss')
frame_auc, frame_eer = save_roc_auc_plot_img(
'', datasets_to_list, datasets_la_list)
print('trible-loss')
# print(datasets_tr_list)
print('label')
# print(datasets_la_list)
frame_auc, frame_eer = save_roc_auc_plot_img(
'', datasets_tr_list, datasets_la_list)
frame_auc, frame_eer = save_roc_auc_plot_img(
'', 1 - datasets_tr_list, datasets_la_list)
print('test')
return
def test_single_dataset_type2(self):
my_multi_test_datasets = multi_test_datasets(
batch_size=self.batch_size,
video_num=self.video_imgs_num,
frame_interval=2,
is_frame=True,
is_Optical=True,
crop_size=4,
img_size=self.img_size_h)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options)) as sess:
sess.run(tf.global_variables_initializer())
self.restore_model_weghts(sess)
seletced_dataset_idx = 0
datasets_op_list = []
datasets_gr_list = []
datasets_to_list = []
datasets_tr_list = []
datasets_la_list = []
datasest_psnr_list = []
for video_idx in range(
my_multi_test_datasets.
multi_datasets[seletced_dataset_idx].video_clips_num):
video_label = my_multi_test_datasets.init_test_single_videos(
seletced_dataset_idx, video_idx)
video_lenth = 0
optical_loss_list = []
gray_loss_list = []
trible_loss_list = []
psnr_list = []
while 1:
batches = my_multi_test_datasets.get_single_videos_batches(
)
if not (batches == []):
# print(batches.shape)
video_lenth += (batches.shape[0] * batches.shape[1])
test_loss = self.fetch_net_test_loss(sess, batches)
optical_loss_list.append(
test_loss['optical_loss_sequence'])
gray_loss_list.append(test_loss['gray_loss_sequence'])
trible_loss_list.append(test_loss['mid_loss_sequence'])
psnr_list.append(test_loss['psnr_sequence'])
else:
print('together-loss')
tog_loss = max_min_np(
np.concatenate(optical_loss_list, axis=0) +
np.concatenate(gray_loss_list, axis=0))
datasets_to_list.append(tog_loss)
print('optical-loss')
optical_loss_list = max_min_np(
np.concatenate(optical_loss_list, axis=0))
datasets_op_list.append(optical_loss_list)
print('gray-loss')
gray_loss_list = max_min_np(
np.concatenate(gray_loss_list, axis=0))
datasets_gr_list.append(gray_loss_list)
print('trible-loss')
trible_loss_list = max_min_np(
np.concatenate(trible_loss_list, axis=0))
print('trible - loss - normalized', trible_loss_list)
datasets_tr_list.append(trible_loss_list)
print('psnr list')
psnr_list = max_min_np(
np.concatenate(psnr_list, axis=0))
print(psnr_list.shape)
datasest_psnr_list.append(psnr_list)
datasets_la_list.append(video_label)
break
datasets_op_list = np.concatenate(datasets_op_list, axis=0)
datasets_gr_list = np.concatenate(datasets_gr_list, axis=0)
datasets_to_list = np.concatenate(datasets_to_list, axis=0)
datasets_tr_list = np.concatenate(datasets_tr_list, axis=0)
datasets_la_list = np.concatenate(datasets_la_list, axis=0)
datasest_psnr_list = np.concatenate(datasest_psnr_list, axis=0)
print('optical-loss')
frame_auc, frame_eer = save_roc_auc_plot_img(
'', datasets_op_list, datasets_la_list)
print('gray-loss')
frame_auc, frame_eer = save_roc_auc_plot_img(
'', datasets_gr_list, datasets_la_list)
print('together-loss')
frame_auc, frame_eer = save_roc_auc_plot_img(
'', datasets_to_list, datasets_la_list)
print('trible-loss')
# print(datasets_tr_list)
frame_auc, frame_eer = save_roc_auc_plot_img(
'', datasets_tr_list, datasets_la_list)
frame_auc, frame_eer = save_roc_auc_plot_img(
'', 1 - datasets_tr_list, datasets_la_list)
print('psnr-loss')
frame_auc, frame_eer = save_roc_auc_plot_img(
'', datasest_psnr_list, datasets_la_list)
print('test')
return