def main():
# This will get number of pixels in each image (they must all be the same!)
imgsize = 0
# Read in images from car, convert to grayscale, scale down, and flatten for use as input
images = []
for k in range(SAFESIZE):
image = loadgray(IMAGEDIR + '/image%03d.png' % k)
imgsize = np.prod(image.shape)
images.append(image)
# All but last image is safe (01 = no-crash; 10 = crash)
targets = []
for k in range(SAFESIZE - 1):
targets.append([0, 1])
targets.append([1, 0])
# with tf.Graph().as_default():
x = tf.compat.v1.placeholder('float', [None, imgsize]) # car FPV images
y = tf.compat.v1.placeholder('float',
[None, 2]) # 01 = no-crash; 10 = crash
output = inference(x, imgsize, 2)
cost = loss(output, y)
global_step = tf.Variable(0, name='global_step', trainable=False)
train_op = training(cost, global_step)
sess = tf.compat.v1.Session()
init_op = tf.compat.v1.global_variables_initializer()
sess.run(init_op)
# Training cycle
for epoch in range(training_epochs):
# Fit training using batch data
sess.run(train_op, feed_dict={x: images, y: targets})
# Compute average loss
avg_cost = sess.run(cost, feed_dict={x: images, y: targets})
# Display logs per epoch step
if epoch % display_step == 0:
print('Epoch:', '%04d' % epoch, 'cost =',
'{:.9f}'.format(avg_cost))
print('Optimization Finished; saving weights to ' + PARAMFILE)
params = [
sess.run(param) for param in tf.compat.v1.trainable_variables()
]
pickle.dump(params, open(PARAMFILE, 'wb'))
# go forward
car_controls.throttle = INITIAL_THROTTLE
car_controls.steering = 0
client.setCarControls(car_controls)
# Load saved training params as ordinary NumPy
W, b = pickle.load(open('params.pkl', 'rb'))
with tf.Graph().as_default():
# Placeholder for an image
x = tf.placeholder('float', [None, IMGSIZE])
# Our inference engine, intialized with weights we just loaded
output = inference(x, IMGSIZE, 2, W, b)
# TensorFlow initialization boilerplate
sess = tf.Session()
init_op = tf.global_variables_initializer()
sess.run(init_op)
# Once the brakes come on, we need to keep them on for a while before exiting; otherwise,
# the vehicle will resume moving.
brakingCount = 0
# Loop until we detect a collision
while True:
# Get RGBA camera images from the car
responses = client.simGetImages(
