File size: 4,929 Bytes
1403119 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | import keras
from tqdm import tqdm
import numpy as np
import argparse
from keras import backend as K
import os
import glob
import math
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
parser = argparse.ArgumentParser()
parser.add_argument('--folder', help='name of the folder', required=True)
parser.add_argument('--test', help='file of the test dataset', required=False)
args = parser.parse_args()
folder = args.folder
test_set = args.test
# Read in the parameter information from the current train folder
f = open(folder + "parameters.txt", "r")
lines = f.read()
testdataset = test_set
modelname = lines.split("\n")[0].split(" ")[-1]
BETA = int(lines.split("\n")[4].split(" ")[-1])
weights = glob.glob(folder + "weights/*")
startweight = weights[-1]
errthresh = 3
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
set_session(sess)
# Load and create the model
import cnn_mTD_2048 as cnn
model = cnn.create_cnn_lstm(startweight)
import utilities_pos as utilities
# Create the folder to save the results
if not os.path.exists(folder + "/error/"):
os.makedirs(folder + "/error/")
# Create and open the files to save the results
tests_error = open(folder + "/error/" + folder.split("/")[-2] + "_result_" + testdataset.split("/")[-1], "w")
tests_error_pred= open(folder + "/error/" + folder.split("/")[-2] + "_prediction_" + testdataset.split("/")[-1], "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
# Loss function (not relevant)
def pose_loss3(y_true, y_pred):
return K.sum(K.square(y_pred - y_true))
def rotation_loss3(y_true, y_pred):
return BETA * K.sum(K.square(y_true-y_pred))
# compile the model
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)
model.compile(optimizer=sgd, loss=[pose_loss3, rotation_loss3], options=run_opts)
# Load the test data and create the data generator
testdatasource, timestamps = utilities.get_training_data(testdataset)
datagen = utilities.limited_gen_data(testdatasource)
results = []
results2d = []
i = 0
for ims, xs, qs in tqdm(datagen): # predict over all test samples and calculate the error
inputs = np.zeros([1, 3, 224, 224, 3])
inputs[0, :] = ims
out = model.predict(inputs)
pred_x = out[0][0][1]
pred_q = out[1][0][1]
label_x = xs[1]
label_q = qs[1]
_, theta = getError(pred_x, pred_q, label_x, label_q)
label_x = np.squeeze(label_x)
label_q = np.squeeze(label_q)
pred_x = np.squeeze(pred_x)
pred_q = np.squeeze(pred_q)
if not math.isnan(theta):
# Save the results in the above defined files
error_x = np.linalg.norm(label_x - pred_x)
error_x_2d = np.linalg.norm(label_x - [pred_x[0], pred_x[1], label_x[2]])
results.append([error_x, theta])
results2d.append([error_x_2d])
tests_error.write("\t Image %d: Error xyz: %fm, Error wpqr: %f degrees \n" % (int(i), error_x, theta))
tests_error.flush()
tests_error_pred.write("Timestamp: " + timestamps[i] + ", Label: x: " + str(label_x[0]) + ", y: " + str(label_x[1]) + ", z: " + str(label_x[2]) + ", w0: " + str(label_q[0]) + ", w1: " + str(label_q[1]) + ", w2: " + str(label_q[2]) + ", w3: " + str(label_q[3]) + " Prediction: x: " + str(pred_x[0]) + ", y: " + str(pred_x[1]) + ", z: " + str(pred_x[2]) + ", w0: " + str(pred_q[0]) + ", w1: " + str(pred_q[1]) + ", w2: " + str(pred_q[2]) + ", w3: " + str(pred_q[3]) + "\n")
tests_error_pred.flush()
i = i + 1
# Calculate different values for an overview of the results
mean_result = np.mean(results, axis=0)
median_result = np.median(results, axis=0)
percentile50_result_2d = np.percentile(results2d, axis=0, q=50)
percentile95_result_2d = np.percentile(results2d, axis=0, q=95)
percentile50_result = np.percentile(results, axis=0, q=50)
percentile95_result = np.percentile(results, axis=0, q=95)
# Print and save the overview
print("\n Results: \n \t MAE 2D: %f m \n\t Median 2D: %f m \n\t CEP: %f m \n\t CE95: %f m \n\t MAE 2D: %f deg \n\t Median 2D: %f deg \n\t CE95: %f deg \n" % (mean_result[0], median_result[0], percentile50_result_2d[0], percentile95_result_2d[0], mean_result[1], median_result[1], percentile95_result[1]))
tests_error.write("\n Results: \n \t MAE 2D: %f m \n\t Median 2D: %f m \n\t CEP: %f m \n\t CE95: %f m \n\t MAE 2D: %f deg \n\t Median 2D: %f deg \n\t CE95: %f deg \n" % (mean_result[0], median_result[0], percentile50_result_2d[0], percentile95_result_2d[0], mean_result[1], median_result[1], percentile95_result[1]))
|