def _run_app(self):
''' Internal version of run app '''
# suppress output streams
sys.stdout = open(os.devnull, 'w')
sys.stderr = open(os.devnull, 'w')
# Run flask without auto reload
run_app(False)
moves.append(Move(0.747, 1.051, -0.005, 4.0)) moves.append(Move(0.748, 1.052, -0.005, 4.0)) moves.append(Move(0.75, 1.054, -0.005, 4.0)) moves.append(Move(0.751, 1.055, -0.005, 4.0)) moves.append(Move(None, None, 0.05, 8)) moves.append(Move(1.089, 1.102, 0.05, 8)) moves.append(Move(None, None, -0.005, 4.0)) moves.append(Move(1.175, 1.102, -0.005, 4.0)) moves.append(Move(1.176, 1.101, -0.005, 4.0)) moves.append(Move(1.178, 1.099, -0.005, 4.0)) moves.append(Move(1.179, 1.095, -0.005, 4.0)) moves.append(Move(1.179, 1.066, -0.005, 4.0)) moves.append(Move(1.178, 1.065, -0.005, 4.0)) moves.append(Move(1.176, 1.063, -0.005, 4.0)) moves.append(Move(1.172, 1.062, -0.005, 4.0)) moves.append(Move(1.089, 1.062, -0.005, 4.0)) moves.append(Move(1.088, 1.063, -0.005, 4.0)) moves.append(Move(1.086, 1.065, -0.005, 4.0)) moves.append(Move(1.085, 1.068, -0.005, 4.0)) moves.append(Move(0.965, 1.068, -0.005, 4.0)) moves.append(Move(0.965, 1.051, -0.005, 4.0)) moves.append(Move(0.968, 1.051, -0.005, 4.0)) moves.append(Move(0.968, 1.053, -0.005, 4.0)) moves.append(Move(0.971, 1.054, -0.005, 4.0)) moves.append(Move(0.972, 1.055, -0.005, 4.0)) moves.append(Move(0.993, 1.055, -0.005, 4.0)) moves.append(Move(0.994, 1.054, -0.005, 4.0)) moves.append(Move(0.996, 1.052, -0.005, 4.0)) moves.append(Move(0.997, 1.048, -0.005, 4.0)) run_app(moves)
"unrolled_gan": False, #Whether or not to use unrolled_gan
"unrolling_steps": None,
"z_uniform": False,
"z_dim": 256,
"ali": False,
"visualize":
True, # "True for visualizing, False for nothing [False]"
"eval_infvo_lbfgsb_maxiter":
-1, # "UnrolledGAN's Inferene via Optimization evaluation.
# maxiter for the l-bfgs-b scipy implementation."
"eval_mnist_stacked_examples":
50000 # "number of examples to generate for UGAN's MNIST stacked evalutation"
}
FLAGS = AttributeDict(flags)
FLAGS.main_output_dir = str(
"DCGAN/" + flags["dataset"]) + "/ali_dcgan_run" + str(i)
FLAGS.sample_dir = FLAGS.main_output_dir + "/samples"
FLAGS.checkpoint_dir = FLAGS.main_output_dir + "/checkpoint"
# now that settings are configured, run the GAN
output_str = run_app(FLAGS)
text_file = open(FLAGS.main_output_dir + "/output.txt", "w")
text_file.writelines(pp.pformat(FLAGS))
text_file.write("\n")
text_file.write("Result: %s" % output_str)
text_file.close()
tf.reset_default_graph()
from tensorpack.utils.serialize import loads
from main import Sphere, build_cylinder_from_3dpts, Frame, run_app
if __name__ == '__main__':
B = 600.0
drawer = GLDrawer('winname', [(-B, B)] * 3)
drawer.start()
cnt = 0
ctx = zmq.Context()
sok = ctx.socket(zmq.PULL)
print 'Starting server at 0.0.0.0:8888 ...'
sok.bind('tcp://0.0.0.0:8888')
def get_frame():
global cnt
cnt += 1
data = loads(sok.recv(copy=False).bytes)
data = data * 100
print data
spheres = [Sphere(3, pos) for pos in data]
spheres[0].radius = 10
cyls = build_cylinder_from_3dpts(data)
f = Frame(spheres, cyls)
return f
run_app(drawer.draw_callback, get_frame)
sleep(100)