def test_authentic_unet(rsr_data_dir, test_data_dir, input_size, model_name, num_classes, ckdir, city, batch_size, ds_name='inria', GPU='0', random_seed=1234): import re import scipy.misc import tensorflow as tf from network import unet from dataReader import image_reader from rsrClassData import rsrClassData def name_has_city(city_list, name): for city in city_list: if city in name: return True return False city = city.split(',') # set gpu os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ['CUDA_VISIBLE_DEVICES'] = GPU tf.reset_default_graph() # environment settings np.random.seed(random_seed) tf.set_random_seed(random_seed) # data prepare step Data = rsrClassData(rsr_data_dir) (collect_files_test, meta_test) = Data.getCollectionByName(test_data_dir) # image reader coord = tf.train.Coordinator() # define place holder X = tf.placeholder(tf.float32, shape=[None, input_size[0], input_size[1], 3], name='X') y = tf.placeholder(tf.int32, shape=[None, input_size[0], input_size[1], 1], name='y') mode = tf.placeholder(tf.bool, name='mode') # initialize model model = unet.UnetModel_Origin({ 'X': X, 'Y': y }, trainable=mode, model_name=model_name, input_size=input_size) model.create_graph('X', num_classes) model.make_update_ops('X', 'Y') # set ckdir model.make_ckdir(ckdir) # set up graph and initialize config = tf.ConfigProto() result_dict = {} # run training with tf.Session(config=config) as sess: init = tf.global_variables_initializer() sess.run(init) saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1) if os.path.exists(model.ckdir) and tf.train.get_checkpoint_state( model.ckdir): latest_check_point = tf.train.latest_checkpoint(model.ckdir) saver.restore(sess, latest_check_point) print('loaded model from {}'.format(latest_check_point)) threads = tf.train.start_queue_runners(coord=coord, sess=sess) for (image_name, label_name) in collect_files_test: #if city in image_name: if name_has_city(city, image_name): if ds_name == 'inria': city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0] tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0] else: city_name = os.path.basename(image_name)[:3] tile_id = os.path.basename(image_name)[9:12] # load reader iterator_test = image_reader.image_label_iterator( os.path.join(rsr_data_dir, image_name), batch_size=batch_size, tile_dim=meta_test['dim_image'][:2], patch_size=input_size, overlap=184, padding=92) # run result = model.test('X', sess, iterator_test) pred_label_img = get_output_label( result, (meta_test['dim_image'][0] + 184, meta_test['dim_image'][1] + 184), input_size, meta_test['colormap'], overlap=184, output_image_dim=meta_test['dim_image'], output_patch_size=(input_size[0] - 184, input_size[1] - 184)) # evaluate truth_label_img = scipy.misc.imread( os.path.join(rsr_data_dir, label_name)) iou = iou_metric(truth_label_img, pred_label_img) result_dict['{}{}'.format(city_name, tile_id)] = iou coord.request_stop() coord.join(threads) return result_dict
def main(flags): # set gpu os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ['CUDA_VISIBLE_DEVICES'] = flags.GPU # environment settings np.random.seed(flags.random_seed) tf.set_random_seed(flags.random_seed) # data prepare step Data = rsrClassData(flags.rsr_data_dir) (collect_files_test, meta_test) = Data.getCollectionByName(flags.test_data_dir) # image reader coord = tf.train.Coordinator() # define place holder X = tf.placeholder(tf.float32, shape=[None, flags.input_size[0], flags.input_size[1], 3], name='X') y = tf.placeholder(tf.int32, shape=[None, flags.input_size[0], flags.input_size[1], 1], name='y') mode = tf.placeholder(tf.bool, name='mode') # initialize model model = unet.UnetModel({'X':X, 'Y':y}, trainable=mode, model_name=flags.model_name, input_size=flags.input_size) model.create_graph('X', flags.num_classes) model.make_update_ops('X', 'Y') # set ckdir model.make_ckdir(flags.ckdir) # set up graph and initialize config = tf.ConfigProto() # run training start_time = time.time() with tf.Session(config=config) as sess: init = tf.global_variables_initializer() sess.run(init) saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1) if os.path.exists(model.ckdir) and tf.train.get_checkpoint_state(model.ckdir): latest_check_point = tf.train.latest_checkpoint(model.ckdir) saver.restore(sess, latest_check_point) threads = tf.train.start_queue_runners(coord=coord, sess=sess) try: for (image_name, label_name) in collect_files_test: if flags.city_name in image_name: city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0] tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0] # load reader iterator_test = image_reader.image_label_iterator( os.path.join(flags.rsr_data_dir, image_name), batch_size=flags.batch_size, tile_dim=meta_test['dim_image'][:2], patch_size=flags.input_size, overlap=0) # run result = model.test('X', sess, iterator_test) pred_label_img = sis_utils.get_output_label(result, meta_test['dim_image'], flags.input_size, meta_test['colormap']) # evaluate truth_label_img = scipy.misc.imread(os.path.join(flags.rsr_data_dir, label_name)) iou = sis_utils.iou_metric(truth_label_img, pred_label_img) print('{}_{}: iou={:.2f}'.format(city_name, tile_id, iou*100)) finally: coord.request_stop() coord.join(threads) duration = time.time() - start_time print('duration {:.2f} minutes'.format(duration/60))
def main(flags): # set gpu os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ['CUDA_VISIBLE_DEVICES'] = flags.GPU # environment settings np.random.seed(flags.random_seed) tf.set_random_seed(flags.random_seed) # data prepare step Data = rsrClassData(flags.rsr_data_dir) (collect_files_test, meta_test) = Data.getCollectionByName(flags.test_data_dir) # image reader coord = tf.train.Coordinator() # define place holder X = tf.placeholder( tf.float32, shape=[None, flags.input_size[0], flags.input_size[1], 3], name='X') y = tf.placeholder( tf.int32, shape=[None, flags.input_size[0], flags.input_size[1], 1], name='y') mode = tf.placeholder(tf.bool, name='mode') # initialize model model = unet.UnetModel({ 'X': X, 'Y': y }, trainable=mode, model_name=flags.model_name, input_size=flags.input_size) model.create_graph('X', flags.num_classes) model.make_update_ops('X', 'Y') # set ckdir model.make_ckdir(flags.ckdir) # set up graph and initialize config = tf.ConfigProto() # run training start_time = time.time() with tf.Session(config=config) as sess: init = tf.global_variables_initializer() sess.run(init) saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1) if os.path.exists(model.ckdir) and tf.train.get_checkpoint_state( model.ckdir): print('loading model from {}'.format(model.ckdir)) latest_check_point = tf.train.latest_checkpoint(model.ckdir) saver.restore(sess, latest_check_point) threads = tf.train.start_queue_runners(coord=coord, sess=sess) tile_cnt = 0 error_map = np.zeros(flags.input_size) fig = plt.figure(figsize=(20.0, 12.0)) try: for (image_name, label_name) in collect_files_test: if flags.city_name in image_name: tile_cnt += 1 city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0] tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0] print('Evaluating {}_{}.tif'.format(city_name, tile_id)) # load reader iterator_test = image_reader.image_label_iterator( os.path.join(flags.rsr_data_dir, image_name), batch_size=flags.batch_size, tile_dim=meta_test['dim_image'][:2], patch_size=flags.input_size, overlap=0) # run result = model.test('X', sess, iterator_test) # evaluate label_img = scipy.misc.imread( os.path.join(flags.rsr_data_dir, label_name)) error_map += evaluate_on_patch(result, label_img, meta_test['dim_image'][:2], flags.input_size) plt.subplot(230 + tile_cnt) plt.imshow(error_map / np.max(error_map), cmap='hot') plt.colorbar() plt.title('Error Map {}_{}'.format(city_name, tile_id)) plt.suptitle('Patch Size = {}'.format(flags.input_size)) dist, error = error_vs_dist(error_map) plt.subplot(236) plt.plot(dist, error, 'r.') plt.title('Error VS Dist ({})'.format(city_name)) plt.xlabel('Dist to center of patch') plt.ylabel('#Errors') save_dir = sis_utils.make_task_img_folder(IMG_SAVE_DIR) plt.savefig(os.path.join( save_dir, 'M-{}_PS-{}'.format(flags.model_name, flags.input_size[0])), bbox_inches='tight') plt.show() finally: coord.request_stop() coord.join(threads) duration = time.time() - start_time print('duration {:.2f} minutes'.format(duration / 60))
def test(flags, ep, ds, lr, save_dir): # set gpu os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ['CUDA_VISIBLE_DEVICES'] = flags.GPU # environment settings np.random.seed(flags.random_seed) tf.set_random_seed(flags.random_seed) # data prepare step Data = rsrClassData(flags.rsr_data_dir) (collect_files_test, meta_test) = Data.getCollectionByName(flags.test_data_dir) # image reader coord = tf.train.Coordinator() # define place holder X = tf.placeholder(tf.float32, shape=[None, flags.input_size[0], flags.input_size[1], 3], name='X') y = tf.placeholder(tf.int32, shape=[None, flags.input_size[0], flags.input_size[1], 1], name='y') mode = tf.placeholder(tf.bool, name='mode') # initialize model flags.model_name = 'ResUnetShrinkInria_fr_resample_mean_reduced_EP-{}_DS-{}.0_LR-{}'.format(ep, ds, lr) model = unet.ResUnetModel_shrink({'X':X, 'Y':y}, trainable=mode, model_name=flags.model_name, input_size=flags.input_size) model.create_graph('X', flags.num_classes) model.make_update_ops('X', 'Y') # set ckdir model.make_ckdir(flags.ckdir) # set up graph and initialize config = tf.ConfigProto() # run training start_time = time.time() with tf.Session(config=config) as sess: init = tf.global_variables_initializer() sess.run(init) saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1) if os.path.exists(model.ckdir) and tf.train.get_checkpoint_state(model.ckdir): latest_check_point = tf.train.latest_checkpoint(model.ckdir) saver.restore(sess, latest_check_point) print('loaded {}'.format(latest_check_point)) threads = tf.train.start_queue_runners(coord=coord, sess=sess) try: iou_record = {} for (image_name, label_name) in collect_files_test: c_names = flags.city_name.split(',') for c_name in c_names: if c_name in image_name: city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0] tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0] # load reader iterator_test = image_reader.image_label_iterator( os.path.join(flags.rsr_data_dir, image_name), batch_size=flags.batch_size, tile_dim=meta_test['dim_image'][:2], patch_size=flags.input_size, overlap=0, padding=0, image_mean=IMG_MEAN) # run result = model.test('X', sess, iterator_test) pred_label_img = sis_utils.get_output_label(result, meta_test['dim_image'], flags.input_size, meta_test['colormap'], overlap=0, output_image_dim=meta_test['dim_image'], output_patch_size=flags.input_size, make_map=False) # evaluate truth_label_img = scipy.misc.imread(os.path.join(flags.rsr_data_dir, label_name)) iou = sis_utils.iou_metric(truth_label_img, pred_label_img * 255) '''plt.subplot(121) plt.imshow(truth_label_img) plt.subplot(122) plt.imshow(pred_label_img) plt.show()''' iou_record[image_name] = iou print('{}_{}: iou={:.2f}'.format(city_name, tile_id, iou*100)) finally: coord.request_stop() coord.join(threads) duration = time.time() - start_time print('duration {:.2f} minutes'.format(duration/60)) np.save(os.path.join(save_dir, '{}.npy').format(model.model_name), iou_record) iou_mean = [] for _, val in iou_record.items(): iou_mean.append(val) print('{}: {}'.format(flags.model_name, np.mean(iou_mean))) return np.mean(iou_mean)
def test_and_save(flags, model_name, save_dir): # set gpu os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ['CUDA_VISIBLE_DEVICES'] = flags.GPU # environment settings np.random.seed(flags.random_seed) tf.set_random_seed(flags.random_seed) # data prepare step Data = rsrClassData(flags.rsr_data_dir) (collect_files_test, meta_test) = Data.getCollectionByName(flags.test_data_dir) # image reader coord = tf.train.Coordinator() # define place holder X = tf.placeholder(tf.float32, shape=[None, flags.input_size[0], flags.input_size[1], 3], name='X') y = tf.placeholder(tf.int32, shape=[None, flags.input_size[0], flags.input_size[1], 1], name='y') mode = tf.placeholder(tf.bool, name='mode') # initialize model if 'appendix' in model_name: model = unet.UnetModel_Height_Appendix({'X':X, 'Y':y}, trainable=mode, model_name=model_name, input_size=flags.input_size) elif 'Res' in model_name: model = unet.ResUnetModel_Crop({'X': X, 'Y': y}, trainable=mode, model_name=model_name, input_size=flags.input_size) else: model = unet.UnetModel_Origin({'X':X, 'Y':y}, trainable=mode, model_name=model_name, input_size=flags.input_size) if 'large' in model_name: model.create_graph('X', flags.num_classes, start_filter_num=40) else: model.create_graph('X', flags.num_classes) model.make_update_ops('X', 'Y') # set ckdir model.make_ckdir(flags.ckdir) # set up graph and initialize config = tf.ConfigProto() # make fold if not exists save_path = os.path.join(save_dir, 'temp_save', model_name) if not os.path.exists(save_path): os.makedirs(save_path) else: return save_path # run training start_time = time.time() with tf.Session(config=config) as sess: init = tf.global_variables_initializer() sess.run(init) saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1) if os.path.exists(model.ckdir) and tf.train.get_checkpoint_state(model.ckdir): latest_check_point = tf.train.latest_checkpoint(model.ckdir) saver.restore(sess, latest_check_point) print('loaded {}'.format(latest_check_point)) threads = tf.train.start_queue_runners(coord=coord, sess=sess) try: for (image_name, label_name) in collect_files_test: c_names = flags.city_name.split(',') for c_name in c_names: if c_name in image_name: city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0] tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0] print('Scoring {}_{} using {}...'.format(city_name, tile_id, model_name)) # load reader iterator_test = image_reader.image_label_iterator( os.path.join(flags.rsr_data_dir, image_name), batch_size=flags.batch_size, tile_dim=meta_test['dim_image'][:2], patch_size=flags.input_size, overlap=184, padding=92, image_mean=IMG_MEAN) # run result = model.test('X', sess, iterator_test, soft_pred=True) pred_label_img = sis_utils.get_output_label(result, (meta_test['dim_image'][0]+184, meta_test['dim_image'][1]+184), flags.input_size, meta_test['colormap'], overlap=184, output_image_dim=meta_test['dim_image'], output_patch_size=(flags.input_size[0]-184, flags.input_size[1]-184), make_map=False, soft_pred=True) file_name = os.path.join(save_path, '{}_{}.npy'.format(city_name, tile_id)) np.save(file_name, pred_label_img) finally: coord.request_stop() coord.join(threads) duration = time.time() - start_time print('duration {:.2f} minutes'.format(duration/60)) return save_path
def main(flags): # set gpu os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ['CUDA_VISIBLE_DEVICES'] = flags.GPU # environment settings np.random.seed(flags.random_seed) tf.set_random_seed(flags.random_seed) for city in ['bellingham', 'bloomington', 'innsbruck', 'sfo', 'tyrol-e']: if not os.path.exists('./temp'): os.makedirs('./temp') flags.city_name = city for m_name in MODEL_NAME: tf.reset_default_graph() # data prepare step Data = rsrClassData(flags.rsr_data_dir) (collect_files_test, meta_test) = Data.getCollectionByName(flags.test_data_dir) # image reader coord = tf.train.Coordinator() # define place holder X = tf.placeholder( tf.float32, shape=[None, flags.input_size[0], flags.input_size[1], 3], name='X') y = tf.placeholder( tf.int32, shape=[None, flags.input_size[0], flags.input_size[1], 1], name='y') mode = tf.placeholder(tf.bool, name='mode') # initialize model model = unet.UnetModel({ 'X': X, 'Y': y }, trainable=mode, model_name=m_name, input_size=flags.input_size) model.create_graph('X', flags.num_classes) model.make_update_ops('X', 'Y') # set ckdir model.make_ckdir(flags.ckdir) # set up graph and initialize config = tf.ConfigProto() # run training start_time = time.time() with tf.Session(config=config) as sess: init = tf.global_variables_initializer() sess.run(init) saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1) if os.path.exists( model.ckdir) and tf.train.get_checkpoint_state( model.ckdir): print('loading model from {}'.format(model.ckdir)) latest_check_point = tf.train.latest_checkpoint( model.ckdir) saver.restore(sess, latest_check_point) threads = tf.train.start_queue_runners(coord=coord, sess=sess) for (image_name, ) in collect_files_test: if flags.city_name in image_name: city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0] tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0] print('Evaluating {}_{} at patch size: {}'.format( city_name, tile_id, flags.input_size)) # load reader iterator_test = image_reader.image_label_iterator( os.path.join(flags.rsr_data_dir, image_name), batch_size=flags.batch_size, tile_dim=meta_test['dim_image'][:2], patch_size=flags.input_size, overlap=0) # run result = model.test('X', sess, iterator_test) raw_pred = patch_extractor.un_patchify( result, meta_test['dim_image'], flags.input_size) file_name = '{}_{}_{}.npy'.format( m_name, city_name, tile_id) np.save(os.path.join('./temp', file_name), raw_pred) coord.request_stop() coord.join(threads) duration = time.time() - start_time print('duration {:.2f} minutes'.format(duration / 60)) for tile_num in range(36): output = np.zeros((5000, 5000, 2)) for m_name in MODEL_NAME: raw_pred = np.load( os.path.join( './temp', '{}_{}_{}.npy'.format(m_name, flags.city_name, tile_num + 1))) output += raw_pred # combine results labels_pred = sis_utils.get_pred_labels(output) output_pred = sis_utils.make_output_file(labels_pred, meta_test['colormap']) scipy.misc.imsave( os.path.join(FIG_SAVE_DIR, '{}{}.tif'.format(flags.city_name, tile_num + 1)), output_pred) shutil.rmtree('./temp')
def test_real_across_city(flags, MODEL_NAME, patch_size): # set gpu os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ['CUDA_VISIBLE_DEVICES'] = flags.GPU tf.reset_default_graph() # environment settings np.random.seed(flags.random_seed) tf.set_random_seed(flags.random_seed) result_dict = {} for city in ['austin', 'chicago', 'kitsap', 'tyrol-w', 'vienna']: if not os.path.exists('./temp'): os.makedirs('./temp') flags.city_name = city for m_name in MODEL_NAME: tf.reset_default_graph() # data prepare step Data = rsrClassData(flags.rsr_data_dir) (collect_files_test, meta_test) = Data.getCollectionByName(flags.test_data_dir) # image reader coord = tf.train.Coordinator() # define place holder X = tf.placeholder(tf.float32, shape=[None, patch_size[0], patch_size[1], 3], name='X') y = tf.placeholder(tf.int32, shape=[None, patch_size[0], patch_size[1], 1], name='y') mode = tf.placeholder(tf.bool, name='mode') # initialize model model = unet.UnetModel_Origin({ 'X': X, 'Y': y }, trainable=mode, model_name=m_name, input_size=patch_size) model.create_graph('X', flags.num_classes) model.make_update_ops('X', 'Y') # set ckdir model.make_ckdir(flags.ckdir) # set up graph and initialize config = tf.ConfigProto() # run training start_time = time.time() with tf.Session(config=config) as sess: init = tf.global_variables_initializer() sess.run(init) saver = tf.train.Saver(var_list=tf.global_variables(), max_to_keep=1) if os.path.exists( model.ckdir) and tf.train.get_checkpoint_state( model.ckdir): print('loading model from {}'.format(model.ckdir)) latest_check_point = tf.train.latest_checkpoint( model.ckdir) saver.restore(sess, latest_check_point) threads = tf.train.start_queue_runners(coord=coord, sess=sess) for (image_name, label_name) in collect_files_test: if flags.city_name in image_name: city_name = re.findall('[a-z\-]*(?=[0-9]+\.)', image_name)[0] tile_id = re.findall('[0-9]+(?=\.tif)', image_name)[0] print('Evaluating {}_{} at patch size: {}'.format( city_name, tile_id, patch_size)) # load reader iterator_test = image_reader.image_label_iterator( os.path.join(flags.rsr_data_dir, image_name), batch_size=flags.batch_size, tile_dim=meta_test['dim_image'][:2], patch_size=patch_size, overlap=184, padding=92) # run result = model.test('X', sess, iterator_test) raw_pred = patch_extractor.un_patchify_shrink( result, (meta_test['dim_image'][0] + 184, meta_test['dim_image'][1] + 184), tile_dim_output=meta_test['dim_image'], patch_size=patch_size, patch_size_output=(patch_size[0] - 184, patch_size[1] - 184), overlap=184) file_name = '{}_{}_{}.npy'.format( m_name.split('/')[-1], city_name, tile_id) np.save(os.path.join('./temp', file_name), raw_pred) coord.request_stop() coord.join(threads) duration = time.time() - start_time print('duration {:.2f} minutes'.format(duration / 60)) for tile_num in range(5): output = np.zeros((5000, 5000, 2)) for m_name in MODEL_NAME: raw_pred = np.load( os.path.join( './temp', '{}_{}_{}.npy'.format( m_name.split('/')[-1], city, tile_num + 1))) output += raw_pred # combine results pred_label_img = sis_utils.get_pred_labels(output) output_pred = sis_utils.make_output_file(pred_label_img, meta_test['colormap']) # evaluate truth_label_img = scipy.misc.imread( os.path.join(flags.rsr_data_dir, 'inria', 'truth', '{}{}.tif'.format(city, tile_num + 1))) iou = sis_utils.iou_metric(truth_label_img, output_pred) print('{}_{}: iou={:.2f}'.format(city, tile_id, iou * 100)) result_dict['{}{}'.format(city, tile_id)] = iou shutil.rmtree('./temp') return result_dict