if gs % 100 == 0:
save_path = saver.save(sess, os.path.join(log_folder, "model.ckpt"), global_step=gs)
logging.debug("Model saved in file: %s" % save_path)
if gs % 200 == 0:
eval_folder = os.path.join(FLAGS.output_dir, 'eval')
if not os.path.exists(eval_folder):
os.makedirs(eval_folder)
logging.debug("validation generated at step [{0}]".format(gs))
feed_dict_to_use[is_training_placeholder] = False
val_pred, val_orig_image, val_annot, val_poss = sess.run([pred, orig_img_tensor, annotation_tensor, probabilities],
feed_dict=feed_dict_to_use)
cv2.imwrite(os.path.join(eval_folder, 'val_{0}_img.jpg'.format(gs)), cv2.cvtColor(np.squeeze(val_orig_image), cv2.COLOR_RGB2BGR))
cv2.imwrite(os.path.join(eval_folder, 'val_{0}_annotation.jpg'.format(gs)), cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(val_annot)), cv2.COLOR_RGB2BGR))
cv2.imwrite(os.path.join(eval_folder, 'val_{0}_prediction.jpg'.format(gs)), cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(val_pred)), cv2.COLOR_RGB2BGR))
crf_ed = perform_crf(val_orig_image, val_poss)
cv2.imwrite(os.path.join(FLAGS.output_dir, 'eval', 'val_{0}_prediction_crfed.jpg'.format(gs)), cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(crf_ed)), cv2.COLOR_RGB2BGR))
overlay = cv2.addWeighted(cv2.cvtColor(np.squeeze(val_orig_image), cv2.COLOR_RGB2BGR), 1, cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(crf_ed)), cv2.COLOR_RGB2BGR), 0.8, 0)
cv2.imwrite(os.path.join(FLAGS.output_dir, 'eval', 'val_{0}_overlay.jpg'.format(gs)), overlay)
coord.request_stop()
coord.join(threads)
save_path = saver.save(sess, os.path.join(log_folder, "model.ckpt"), global_step=gs)
logging.debug("Model saved in file: %s" % save_path)
summary_string_writer.close()
os.makedirs(eval_folder)
logging.debug("validation generated at step [{0}]".format(gs))
feed_dict_to_use[is_training_placeholder] = False
val_pred, val_orig_image, val_annot, val_poss = sess.run(
[pred, orig_img_tensor, annotation_tensor, probabilities],
feed_dict=feed_dict_to_use)
cv2.imwrite(
os.path.join(eval_folder, 'val_{0}_img.jpg'.format(gs)),
cv2.cvtColor(np.squeeze(val_orig_image),
cv2.COLOR_RGB2BGR))
cv2.imwrite(
os.path.join(eval_folder,
'val_{0}_annotation.jpg'.format(gs)),
cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(val_annot)),
cv2.COLOR_RGB2BGR))
cv2.imwrite(
os.path.join(eval_folder,
'val_{0}_prediction.jpg'.format(gs)),
cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(val_pred)),
cv2.COLOR_RGB2BGR))
crf_ed = perform_crf(val_orig_image, val_poss)
cv2.imwrite(
os.path.join(FLAGS.output_dir, 'eval',
'val_{0}_prediction_crfed.jpg'.format(gs)),
cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(crf_ed)),
cv2.COLOR_RGB2BGR))
overlay = cv2.addWeighted(
if gs % 100 == 0:
save_path = saver.save(sess, os.path.join(log_folder, "model.ckpt"), global_step=gs)
logging.debug("Model saved in file: %s" % save_path)
if gs % 200 == 0:
eval_folder = os.path.join(FLAGS.output_dir, 'eval')
if not os.path.exists(eval_folder):
os.makedirs(eval_folder)
logging.debug("validation generated at step [{0}]".format(gs))
feed_dict_to_use[is_training_placeholder] = False
val_pred, val_orig_image, val_annot, val_poss = sess.run([pred, orig_img_tensor, annotation_tensor, probabilities],
feed_dict=feed_dict_to_use)
cv2.imwrite(os.path.join(eval_folder, 'val_{0}_img.jpg'.format(gs)), cv2.cvtColor(np.squeeze(val_orig_image), cv2.COLOR_RGB2BGR))
cv2.imwrite(os.path.join(eval_folder, 'val_{0}_annotation.jpg'.format(gs)), cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(val_annot)), cv2.COLOR_RGB2BGR))
cv2.imwrite(os.path.join(eval_folder, 'val_{0}_prediction.jpg'.format(gs)), cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(val_pred)), cv2.COLOR_RGB2BGR))
crf_ed = perform_crf(val_orig_image, val_poss)
cv2.imwrite(os.path.join(FLAGS.output_dir, 'eval', 'val_{0}_prediction_crfed.jpg'.format(gs)), cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(crf_ed)), cv2.COLOR_RGB2BGR))
overlay = cv2.addWeighted(cv2.cvtColor(np.squeeze(val_orig_image), cv2.COLOR_RGB2BGR), 1, cv2.cvtColor(grayscale_to_voc_impl(np.squeeze(crf_ed)), cv2.COLOR_RGB2BGR), 0.8, 0)
cv2.imwrite(os.path.join(FLAGS.output_dir, 'eval', 'val_{0}_overlay.jpg'.format(gs)), overlay)
coord.request_stop()
coord.join(threads)
save_path = saver.save(sess, os.path.join(log_folder, "model.ckpt"), global_step=gs)
logging.debug("Model saved in file: %s" % save_path)
summary_string_writer.close()
