| import time |
| import keras |
| import argparse |
| from keras import backend as K |
| from tqdm import tqdm |
| import numpy as np |
| import tensorflow as tf |
| from keras.backend.tensorflow_backend import set_session |
| import os |
|
|
| parser = argparse.ArgumentParser() |
| parser.add_argument('--model', help='name of the model to load', required=True) |
| parser.add_argument('--train', help='name of the train set to load', required=True) |
| parser.add_argument('--test', help='name of the test set to load', required=True) |
| parser.add_argument('--timestamps', help='evaluate test data every x iterations', required=True) |
| parser.add_argument('--beta', help='multiplication fact for rotation loss', required=True) |
| parser.add_argument('--iterations', help='iterations to train for', required=True) |
| parser.add_argument('--batchsize', help='batchsize', required=True) |
| parser.add_argument('--loss', help='loss', required=True) |
|
|
| args = parser.parse_args() |
| modelname = args.model |
| DATASET = args.train |
| DATASET_TEST = args.test |
| TIMESTAMP = int(args.timestamps) |
| BETA = int(args.beta) |
| ITERATIONS = int(args.iterations) |
| batchSize = int(args.batchsize) |
| loss_name = args.loss |
|
|
| config = tf.ConfigProto() |
| config.gpu_options.allow_growth = True |
| sess = tf.Session(config=config) |
| set_session(sess) |
|
|
| |
| startweight = None |
| import cnn_mTD_2048 as cnn |
| model = cnn.create_cnn_lstm(startweight) |
| import utilities_pos as utilities |
|
|
| |
| traindataset = DATASET |
| testdataset = DATASET_TEST |
| currentDate = time.strftime('%Y-%m-%d_%H-%M-%S', time.localtime()) |
| os.mkdir("../results/" + str(currentDate)) |
| os.mkdir("../results/" + str(currentDate) + "/weights/") |
| parameters = open("../results/" + str(currentDate) + "/parameters.txt", "w") |
| parameters.write("Modelname: " + str(modelname) + "\n" + |
| "Dataset: " + str(DATASET) + "\n" + |
| "Dataset test: " + str(DATASET_TEST) + "\n" + |
| "Timestamp: " + str(TIMESTAMP) + "\n" + |
| "Beta: " + str(BETA) + "\n" + |
| "Iterations: " + str(ITERATIONS) + "\n" + |
| "Batch size: " + str(batchSize) + "\n" + |
| "Loss: " + str(loss_name)) |
| parameters.close() |
| outputWeightspath = "../results/" + str(currentDate) + "/weights/" + DATASET.split("/")[-1].split(".txt")[0] + '_b_' + str(BETA) + '_BS_' + str(batchSize) + '_' |
| log = open("../results/" + str(currentDate) + "/log_" + DATASET.split("/")[-1].split(".txt")[0] + '_b_' + str(BETA) + '_BS_' + str(batchSize) + '.txt', "w") |
|
|
|
|
| def getError(posx, posq, actualx, actualq): |
| """ Calculates the position and rotation error """ |
| q1 = actualq / np.linalg.norm(actualq) |
| q2 = posq / np.linalg.norm(posq) |
| d = abs(np.sum(np.multiply(q1, q2))) |
| theta = 2 * np.arccos(d) * 180 / np.pi |
| errx = np.linalg.norm(actualx - posx) |
| return errx, theta |
|
|
|
|
| |
| def rmse_p(y_true, y_pred): |
| return K.sqrt(K.sum(K.square(y_pred-y_true), axis=-1)) |
|
|
|
|
| def rmse_q(y_true, y_pred): |
| return BETA * K.sqrt(K.sum(K.square(y_true-y_pred), axis=-1)) |
|
|
|
|
| def mse_p(y_true, y_pred): |
| return K.mean(K.square(y_pred-y_true), axis=-1) |
|
|
|
|
| def mse_q(y_true, y_pred): |
| return BETA * K.mean(K.square(y_pred-y_true), axis=-1) |
|
|
|
|
| |
| sgd = keras.optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0) |
| run_opts = tf.RunOptions(report_tensor_allocations_upon_oom = True) |
| if loss_name == "rmse": |
| model.compile(optimizer=sgd, loss=[rmse_p, rmse_q], options=run_opts) |
| elif loss_name == "mse": |
| model.compile(optimizer=sgd, loss=[mse_p, mse_q], options=run_opts) |
|
|
| |
| datasource, _ = utilities.get_training_data(traindataset) |
| testdatasource, _ = utilities.get_training_data(testdataset) |
| data_gen = utilities.gen_data_batch(datasource, batchSize) |
| |
| for i in range(ITERATIONS): |
| print(i) |
| X_batch, Y_batch = next(data_gen) |
| history = model.fit(X_batch, Y_batch, nb_epoch=1, batch_size=batchSize) |
| if i % int(TIMESTAMP) == 0: |
| datagen = utilities.limited_gen_data(testdatasource) |
| posxs = [] |
| posqs = [] |
| for ims,xs,qs in tqdm(datagen): |
| inputs = np.zeros([1, 3, 224, 224, 3]) |
| inputs[0,:] = ims |
| out = model.predict(inputs) |
| posx = out[0][0][1] |
| posq = out[1][0][1] |
| actualx = xs[1] |
| actualq = qs[1] |
| errx, theta = getError(posx, posq, actualx, actualq) |
| posxs.append(errx) |
| posqs.append(theta) |
| |
| print('Median error ' + str(np.median(posxs)) + ' m and ' + str(np.median(posqs)) + ' degrees') |
| print("Iteration: " + str(i) + "\n\t" + "Loss is: " + str(history.history['loss'][0])) |
| log.write("Iteration: " + str(i) + "\n\t" + "Loss is: " + str(history.history['loss'][0]) + "\n") |
| log.write("Median error %f m and %f degrees \n" % (np.median(posxs), np.median(posqs))) |
| log.flush() |
| model.save_weights(outputWeightspath + str(i) + '.h5') |
|
|
