def _test_masks():
learner = AdversarialLearner()
learner.setup_inference(FLAGS, aug_test=False)
saver = tf.train.Saver([var for var in tf.trainable_variables()])
CategoryIou = {}
CategoryMae = {}
# manages multi-threading
sv = tf.train.Supervisor(logdir=FLAGS.test_save_dir,
save_summaries_secs=0,
saver=None)
with sv.managed_session() as sess:
checkpoint = FLAGS.ckpt_file
if checkpoint:
saver.restore(sess, checkpoint)
print("Resume model from checkpoint {}".format(checkpoint))
else:
raise IOError("Checkpoint file not found")
sess.run(learner.test_iterator.initializer)
n_steps = int(np.ceil(learner.test_samples / float(FLAGS.batch_size)))
progbar = Progbar(target=n_steps)
i = 0
for step in range(n_steps):
if sv.should_stop():
break
try:
inference = learner.inference(sess)
except tf.errors.OutOfRangeError:
print("End of testing dataset") # ==> "End of dataset"
break
# Now write images in the test folder
for batch_num in range(inference['input_image'].shape[0]):
# select mask
generated_mask = inference['gen_masks'][batch_num]
gt_mask = inference['gt_masks'][batch_num]
category = inference['img_fname'][batch_num].decode("utf-8").split('/')[-2]
iou, out_mask = compute_IoU(gt_mask=gt_mask, pred_mask_f=generated_mask)
mae = compute_mae(gt_mask=gt_mask, pred_mask_f=out_mask)
try:
CategoryIou[category].append(iou)
CategoryMae[category].append(mae)
except:
CategoryIou[category] = [iou]
CategoryMae[category] = [mae]
if FLAGS.generate_visualization:
# Verbose image generation
save_dir = os.path.join(FLAGS.test_save_dir, category)
if not os.path.isdir(save_dir):
os.mkdir(save_dir)
filename = os.path.join(save_dir,
"frame_{:08d}.png".format(len(CategoryIou[category])))
preprocessed_bgr = postprocess_image(inference['input_image'][batch_num])
preprocessed_mask = postprocess_mask(out_mask)
# Overlap images
results = cv2.addWeighted(preprocessed_bgr, 0.5,
preprocessed_mask, 0.4, 0)
results = cv2.resize(results, (des_width, des_height))
cv2.imwrite(filename, results)
matlab_fname = os.path.join(save_dir,
'result_{}.mat'.format(len(CategoryIou[category])))
sio.savemat(matlab_fname,
{'flow':inference['gt_flow'][batch_num],
'img1':cv2.cvtColor(preprocessed_bgr, cv2.COLOR_BGR2RGB),
'pred_mask': out_mask, #inference['gen_masks'][batch_num],
'gt_mask': inference['gt_masks'][batch_num]} )
i+=1
progbar.update(step)
tot_ious = 0
tot_maes = 0
per_cat_iou = []
for cat, list_iou in CategoryIou.items():
print("Category {}: IoU is {} and MAE is {}".format(cat, np.mean(list_iou), np.mean(CategoryMae[cat])))
tot_ious += np.sum(list_iou)
tot_maes += np.sum(CategoryMae[cat])
per_cat_iou.append(np.mean(list_iou))
print("The Average over the dataset: IoU is {} and MAE is {}".format(tot_ious/float(i), tot_maes/float(i)))
print("The Average over sequences IoU is {}".format(np.mean(per_cat_iou)))
print("Success: Processed {} frames".format(i))
def _test_masks():
learner = AdversarialLearner()
learner.setup_inference(FLAGS, aug_test=True)
saver = tf.train.Saver([var for var in tf.trainable_variables()])
CategoryIou = {}
CategoryMae = {}
# manages multi-threading
sv = tf.train.Supervisor(logdir=FLAGS.test_save_dir,
save_summaries_secs=0,
saver=None)
test_crops = learner.test_crops
with sv.managed_session() as sess:
checkpoint = FLAGS.ckpt_file
if checkpoint:
saver.restore(sess, checkpoint)
print("Resume model from checkpoint {}".format(checkpoint))
else:
raise IOError("Checkpoint file not found")
sess.run(learner.test_iterator.initializer)
n_steps = int(learner.test_samples)
progbar = Progbar(target=n_steps)
i = 0
for step in range(n_steps):
if sv.should_stop():
break
try:
inference = learner.inference(sess)
except tf.errors.OutOfRangeError:
print("End of testing dataset") # ==> "End of dataset"
break
# Now write images in the test folder
outputs = inference['outs']
fname = inference['img_fname']
cropped_iou = []
cropped_mae = []
for crop in test_crops:
# select mask
generated_mask = outputs['pred_masks'][crop]
gt_mask = outputs['gt_masks'][crop]
iou, out_mask = compute_IoU(gt_mask=gt_mask,
pred_mask_f=generated_mask)
# Take the best one
outputs['pred_masks'][crop] = out_mask
mae = compute_mae(gt_mask=gt_mask, pred_mask_f=out_mask)
cropped_iou.append(iou)
cropped_mae.append(mae)
cropped_iou = np.mean(cropped_iou)
cropped_mae = np.mean(cropped_mae)
category = fname.decode("utf-8").split('/')[-2]
try:
CategoryIou[category].append(cropped_iou)
CategoryMae[category].append(cropped_mae)
except:
CategoryIou[category] = [iou]
CategoryMae[category] = [mae]
if FLAGS.generate_visualization:
# Verbose image generation
save_dir = os.path.join(FLAGS.test_save_dir, category)
if not os.path.isdir(save_dir):
os.mkdir(save_dir)
filename = os.path.join(
save_dir,
"frame_{:08d}.png".format(len(CategoryIou[category])))
# Take last one (for debugging only)
preprocessed_bgr = postprocess_image(
outputs['img_1s'][test_crops[-1]])
preprocessed_mask = postprocess_mask(
outputs['pred_masks'][test_crops[-1]])
# Overlap images
results = cv2.addWeighted(preprocessed_bgr, 0.5,
preprocessed_mask, 0.4, 0)
results = cv2.resize(results, (des_width, des_height))
cv2.imwrite(filename, results)
# Now write everything in the matlab file for post-processing
matlab_fname = os.path.join(
save_dir,
'result_{}.mat'.format(len(CategoryIou[category])))
matlab_out = {}
for crop in test_crops:
matlab_out['img_1_{:03d}'.format(int(
crop * 100))] = outputs['img_1s'][crop]
matlab_out['pred_mask_{:03d}'.format(int(
crop * 100))] = outputs['pred_masks'][crop]
matlab_out['gt_mask_{:03d}'.format(int(
crop * 100))] = outputs['gt_masks'][crop]
sio.savemat(matlab_fname, matlab_out)
progbar.update(step)
i += 1
tot_ious = 0
tot_maes = 0
for cat, list_iou in CategoryIou.items():
print("Category {}: IoU is {} and MAE is {}".format(
cat, np.mean(list_iou), np.mean(CategoryMae[cat])))
tot_ious += np.sum(list_iou)
tot_maes += np.sum(CategoryMae[cat])
print("The Average over the dataset: IoU is {} and MAE is {}".format(
tot_ious / float(i), tot_maes / float(i)))
print("Success: Processed {} frames".format(i))