# make data reader chipFiles = os.path.join(patchDir, 'fileList.txt') # use uabCrossValMaker to get fileLists for training and validation idx, file_list = uabCrossValMaker.uabUtilGetFolds( r'/media/ei-edl01/user/bh163/tasks/2018.03.02.res_gan', 'deeplab_inria_cp_0.txt', 'force_tile') # use first 5 tiles for validation file_list_train = uabCrossValMaker.make_file_list_by_key( idx, file_list, [i for i in range(6, 37)]) file_list_valid = uabCrossValMaker.make_file_list_by_key( idx, file_list, [i for i in range(0, 6)]) dataReader_train = uabDataReader.ImageLabelReaderCitySampleControl( [3], [0, 1, 2], patchDir, file_list, (321, 321), 5, city_dict, city_alpha, block_mean=np.append([0], img_mean)) for plt_cnt in range(10000): _, _, city_batch = dataReader_train.readerAction() for city_name in city_batch: city_cnt[city_dict[city_name]] += 1 ind = np.arange(5) plt.bar(ind, city_cnt) plt.xticks(ind, ['austin', 'chicago', 'kitsap', 'tyrol-w', 'vienna']) plt.xlabel('City Name') plt.ylabel('Counts') plt.savefig(os.path.join(img_dir, 'reader_check.png'))
def main(flags): city_dict = {'austin': 0, 'chicago': 1, 'kitsap': 2, 'tyrol-w': 3, 'vienna': 4} city_alpha = [0.2, 0.5, 0.1, 0.1, 0.1] # make network # 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') model = uabMakeNetwork_DeepLabV2.DeeplabV3({'X':X, 'Y':y}, trainable=mode, model_name=flags.model_name, input_size=flags.input_size, batch_size=flags.batch_size, learn_rate=flags.learning_rate, decay_step=flags.decay_step, decay_rate=flags.decay_rate, epochs=flags.epochs, start_filter_num=flags.sfn) model.create_graph('X', class_num=flags.num_classes) # create collection # the original file is in /ei-edl01/data/uab_datasets/inria blCol = uab_collectionFunctions.uabCollection('inria') opDetObj = bPreproc.uabOperTileDivide(255) # inria GT has value 0 and 255, we map it back to 0 and 1 # [3] is the channel id of GT rescObj = uabPreprocClasses.uabPreprocMultChanOp([], 'GT_Divide.tif', 'Map GT to (0, 1)', [3], opDetObj) rescObj.run(blCol) img_mean = blCol.getChannelMeans([0, 1, 2]) # get mean of rgb info # extract patches extrObj = uab_DataHandlerFunctions.uabPatchExtr([0, 1, 2, 4], # extract all 4 channels cSize=flags.input_size, # patch size as 572*572 numPixOverlap=int(model.get_overlap()/2), # overlap as 92 extSave=['jpg', 'jpg', 'jpg', 'png'], # save rgb files as jpg and gt as png isTrain=True, gtInd=3, pad=model.get_overlap()) # pad around the tiles patchDir = extrObj.run(blCol) # make data reader # use uabCrossValMaker to get fileLists for training and validation idx, file_list = uabCrossValMaker.uabUtilGetFolds(patchDir, 'fileList.txt', 'force_tile') # use first 5 tiles for validation file_list_train = uabCrossValMaker.make_file_list_by_key(idx, file_list, [i for i in range(6, 37)]) file_list_valid = uabCrossValMaker.make_file_list_by_key(idx, file_list, [i for i in range(0, 6)]) dataReader_train = uabDataReader.ImageLabelReaderCitySampleControl( [3], [0, 1, 2], patchDir, file_list_train, flags.input_size, flags.batch_size, city_dict, city_alpha, dataAug='flip,rotate', block_mean=np.append([0], img_mean)) # no augmentation needed for validation dataReader_valid = uabDataReader.ImageLabelReaderCitySampleControl( [3], [0, 1, 2], patchDir, file_list_valid, flags.input_size, flags.batch_size, city_dict, city_alpha, dataAug=' ', block_mean=np.append([0], img_mean)) # train start_time = time.time() model.train_config('X', 'Y', flags.n_train, flags.n_valid, flags.input_size, uabRepoPaths.modelPath, loss_type='xent', par_dir='Inria_Domain') model.run(train_reader=dataReader_train, valid_reader=dataReader_valid, pretrained_model_dir=flags.res_dir, isTrain=True, img_mean=img_mean, verb_step=100, # print a message every 100 step(sample) save_epoch=5, # save the model every 5 epochs gpu=GPU, tile_size=flags.tile_size, patch_size=flags.input_size ) duration = time.time() - start_time print('duration {:.2f} hours'.format(duration/60/60))
def main(flags): city_dict = { 'austin': 0, 'chicago': 1, 'kitsap': 2, 'tyrol-w': 3, 'vienna': 4 } llh_all = np.load(flags.llh_file) city_llh = softmax(llh_all[city_dict[flags.train_city], :], flags.t) # make network # 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') model = uabMakeNetwork_UNet.UnetModelCrop({ 'X': X, 'Y': y }, trainable=mode, model_name=flags.model_name, input_size=flags.input_size, batch_size=flags.batch_size, learn_rate=flags.learning_rate, decay_step=flags.decay_step, decay_rate=flags.decay_rate, epochs=flags.epochs, start_filter_num=flags.sfn) model.create_graph('X', class_num=flags.num_classes) # create collection # the original file is in /ei-edl01/data/uab_datasets/inria blCol = uab_collectionFunctions.uabCollection('inria') opDetObj = bPreproc.uabOperTileDivide( 255) # inria GT has value 0 and 255, we map it back to 0 and 1 # [3] is the channel id of GT rescObj = uabPreprocClasses.uabPreprocMultChanOp([], 'GT_Divide.tif', 'Map GT to (0, 1)', [3], opDetObj) rescObj.run(blCol) img_mean = blCol.getChannelMeans([0, 1, 2]) # get mean of rgb info # extract patches extrObj = uab_DataHandlerFunctions.uabPatchExtr( [0, 1, 2, 4], cSize=flags.input_size, numPixOverlap=int(model.get_overlap()), extSave=['jpg', 'jpg', 'jpg', 'png'], isTrain=True, gtInd=3, pad=model.get_overlap() // 2) patchDir = extrObj.run(blCol) # make data reader # use uabCrossValMaker to get fileLists for training and validation idx_city, file_list = uabCrossValMaker.uabUtilGetFolds( patchDir, 'fileList.txt', 'city') idx_tile, _ = uabCrossValMaker.uabUtilGetFolds(patchDir, 'fileList.txt', 'force_tile') idx = [j * 10 + i for i, j in zip(idx_city, idx_tile)] # use first city for validation filter_train = [] filter_valid = [] for i in range(5): for j in range(1, 37): if i == city_dict[flags.train_city] and j <= 5: filter_valid.append(j * 10 + i) elif j > 5: filter_train.append(j * 10 + i) # use first city for validation file_list_train = uabCrossValMaker.make_file_list_by_key( idx, file_list, filter_train) file_list_valid = uabCrossValMaker.make_file_list_by_key( idx, file_list, filter_valid) dataReader_train = uabDataReader.ImageLabelReaderCitySampleControl( [3], [0, 1, 2], patchDir, file_list_train, flags.input_size, flags.batch_size, city_dict, city_llh, dataAug='flip,rotate', block_mean=np.append([0], img_mean)) # no augmentation needed for validation dataReader_valid = uabDataReader.ImageLabelReader([3], [0, 1, 2], patchDir, file_list_valid, flags.input_size, flags.batch_size, dataAug=' ', block_mean=np.append( [0], img_mean), batch_code=0) # train start_time = time.time() model.train_config('X', 'Y', flags.n_train, flags.n_valid, flags.input_size, uabRepoPaths.modelPath, loss_type='xent', par_dir='Inria_Domain_Selection') model.run( train_reader=dataReader_train, valid_reader=dataReader_valid, pretrained_model_dir=flags.pred_model_dir, isTrain=True, img_mean=img_mean, verb_step=100, save_epoch=5, gpu=GPU, tile_size=flags.tile_size, patch_size=flags.input_size, ) duration = time.time() - start_time print('duration {:.2f} hours'.format(duration / 60 / 60))