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(