def valid_one_epoch(self, sess, data_loader, data_num, epoch, step):
'''
Generate once of image which remove instruments.
Args:
data_loader: training or validation data_loader.
data_num: number of data.
'''
total_loss, total_im_warp_loss, total_flow_loss = 0, 0, 0
total_l1, total_im_l1, total_l2, total_im_l2, total_psnr = 0, 0, 0, 0, 0
count = 0
t0 = time.time()
im_warp_loss, flow_loss = 0, 0
#True number to loss
t_count = 0
for count in range(1, data_num):
step += 1
images, images_da, mask, fn, flag, rect_param = data_loader.get_next_sequence_valid(
)
if flag == False:
#print(fn)
# Can't find prev data.
continue
# Origin optical flow
for frame in range(1, cfgs.seq_frames):
im, last_im = images[frame], images[frame - 1]
flow = sess.run(self.flow_tensor,
feed_dict={
self.flow_img0: im,
self.flow_img1: last_im
})
# Da optical flow
for frame in range(1, cfgs.seq_frames):
im, last_im = images_da[frame], images_da[frame - 1]
flow_da = sess.run(self.flow_tensor,
feed_dict={
self.flow_img0: im,
self.flow_img1: last_im
})
normal_flow, normal_max_v = normal_data(flow)
inpt_input, flag_ = concat_data(normal_flow, mask, cfgs.grid)
#epd_sd_mask, epd_rect_param = expand_sdm(rect_param, cfgs.gamma, cfgs.epd_ratio)
sd_mask = sdm(rect_param, cfgs.gamma)
if flag_ == False:
print(fn)
#After grid, area of mask = 0
continue
t_count += 1
loss, im_warp_loss, flow_loss,\
l_inpt_p, \
l1_e, l2_e, im_l1_e, im_l2_e, psnr,\
l_ori_im, l_warped_im,\
inpt_warped_im, inpt_flow, pred_flow \
=sess.run([self.loss, self.im_warp_loss, self.flow_loss_,\
self.local_patch_inpt_flow, \
self.l1_e, self.l2_e, self.w_l1_e, self.w_l2_e, self.w_psnr,\
self.local_ori_im, self.local_warped_im,\
self.inpt_warped_im, self.inpt_pred_flow, self.pred_complete_flow],\
feed_dict={self.flow_img1: last_im,
self.flow_img0: im,
self.ori_cur_im: images[1],
self.ori_flow: flow,
self.max_v: normal_max_v,
self.inpt_data: inpt_input,
#self.epd_sd_mask: epd_sd_mask,
#self.epd_rect_param: epd_rect_param,
self.sd_mask: sd_mask,
self.rect_param: rect_param})
#if count % 10 == 0:
if False:
self.view(np.expand_dims(fn, 0),
inpt_warped_im,
images_da[cfgs.seq_frames - 2],
images_da[cfgs.seq_frames - 1],
step,
f_path='valid')
self.view_patch_one(l_ori_im[0],
fn,
step,
'l_ori',
f_path='valid')
self.view_patch_one(l_warped_im[0],
fn,
step,
'l_warped',
f_path='valid')
self.view_flow_patch_one(l_inpt_p[0],
fn,
step,
'l_inpt_p',
f_path='valid')
self.view_flow_one(flow[0], fn, step, f_path='valid')
self.view_flow_one(inpt_flow[0], fn, step, '_inpt', 'valid')
self.view_flow_one(flow_da[0], fn, step, '_da', 'valid')
self.view_flow_one(pred_flow[0],
fn,
step,
'complete',
f_path='valid')
#3. calculate loss
total_loss += loss
total_im_warp_loss += im_warp_loss
total_flow_loss += flow_loss
total_l1 += l1_e
total_im_l1 += im_l1_e
total_l2 += l2_e
total_im_l2 += im_l2_e
total_psnr += psnr
#4. time consume
time_consumed = time.time() - t0
time_per_batch = time_consumed / count
#5. print
#line = 'Valid epoch %2d\t lr = %g\t step = %4d\t count = %4d\t loss = %.4f\t m_loss=%.4f\t m_imW_loss = %.4f\t m_f_loss = %.4f\t time = %.2f' % (epoch, cfgs.inpt_lr, step, count, loss, (total_loss/t_count), (total_im_warp_loss/t_count), (total_flow_loss/t_count), time_per_batch)
line = 'Valid epoch %2d\t step = %4d\t count = %4d\t m_imW_loss = %.2f\t m_f_loss = %.2f\t l1_e = %.3f\t im_l1_e = %.3f\t l2_e = %.3f\t im_l2_e = %.3f\t psnr = %4f\t' % (
epoch, step, count, (total_im_warp_loss / t_count),
(total_flow_loss / t_count), (total_l1 / count),
(total_im_l1 / count), (total_l2 / count),
(total_im_l2 / count), (total_psnr / count))
utils.clear_line(len(line))
print('\r' + line, end='')
#End one epoch
#count -= 1
print(
'\nepoch %5d\t learning_rate = %g\t mean_loss = %.4f\t m_imW_loss = %.4f\t m_f_loss = %.4f\t '
% (epoch, cfgs.inpt_lr, (total_loss / t_count),
(total_im_warp_loss / t_count), (total_flow_loss / t_count)))
print('Take time %3.1f' % (time.time() - t0))
return step
def valid_one_epoch(self, sess, data_loader, data_num, epoch, step):
'''
Generate once of image which remove instruments.
Args:
data_loader: training or validation data_loader.
data_num: number of data.
'''
sum_acc, sum_acc_iou, sum_acc_ellip, total_loss = 0, 0, 0, 0
count = 0
t0 = time.time()
mean_acc, mean_acc_iou, mean_acc_label, mean_acc_ellip = 0, 0, 0, 0
self.ellip_acc, self.accu, self.accu_iou, loss = 0, 0, 0, 0
for count in range(1, data_num):
step += 1
images, images_da, mask, fn, flag, mask_sum = data_loader.get_next_sequence(
)
if flag == False:
print(fn)
# Can't find prev data.
continue
# Origin optical flow
for frame in range(1, cfgs.seq_frames):
im, last_im = images[frame], images[frame - 1]
flow = sess.run(self.flow_tensor,
feed_dict={
self.flow_img0: im,
self.flow_img1: last_im
})
# Da optical flow
for frame in range(1, cfgs.seq_frames):
im, last_im = images_da[frame], images_da[frame - 1]
flow_da = sess.run(self.flow_tensor,
feed_dict={
self.flow_img0: im,
self.flow_img1: last_im
})
normal_flow, normal_max_v = normal_data(flow)
inpt_input, flag_ = concat_data(normal_flow, mask, cfgs.grid)
if flag_ == False:
print(fn)
#After grid, area of mask = 0
continue
loss, inpt_warped_im, inpt_flow, pred_flow =sess.run([self.loss, \
self.inpt_warped_im, self.inpt_pred_flow, self.pred_complete_flow],\
feed_dict={self.flow_img1: last_im,
self.flow_img0: im,
self.ori_flow: flow,
self.max_v: normal_max_v,
self.inpt_data: inpt_input})
#if count % 20 == 0:
if True:
self.view(np.expand_dims(fn, 0),
inpt_warped_im,
images_da[cfgs.seq_frames - 2],
images_da[cfgs.seq_frames - 1],
step,
f_path='valid')
self.view_flow_one(flow[0], fn, step, f_path='valid')
self.view_flow_one(inpt_flow[0], fn, step, '_inpt', 'valid')
self.view_flow_one(flow_da[0], fn, step, '_da', 'valid')
self.view_flow_one(pred_flow[0],
fn,
step,
'complete',
f_path='valid')
#2. calculate accurary
self.ellip_acc = 0
sum_acc += self.accu
sum_acc_iou += self.accu_iou
sum_acc_ellip += self.ellip_acc
mean_acc = sum_acc / count
mean_acc_iou = sum_acc_iou / count
mean_acc_ellip = sum_acc_ellip / count
#3. calculate loss
total_loss += loss
#4. time consume
time_consumed = time.time() - t0
time_per_batch = time_consumed / count
#5. print
line = 'Valid epoch %2d\t lr = %g\t step = %4d\t count = %4d\t loss = %.4f\t m_loss=%.4f\t max_v = %.2f\t fn = %s\t time = %.2f' % (
epoch, cfgs.inpt_lr, step, count, loss,
(total_loss / count), normal_max_v, fn, time_per_batch)
utils.clear_line(len(line))
print('\r' + line, end='')
#End one epoch
#count -= 1
print(
'\nepoch %5d\t learning_rate = %g\t mean_loss = %.4f\t train_acc = %.2f%%\t train_iou_acc = %.2f%%\t train_ellip_acc = %.2f'
% (epoch, cfgs.inpt_lr, (total_loss / count), (sum_acc / count),
(sum_acc_iou / count), (sum_acc_ellip / count)))
print('Take time %3.1f' % (time.time() - t0))
return step