def check_nbla_infer(tmpdir, x, y, batch_size, on_memory):
if not command_exists('nbla'):
pytest.skip('An executable `nbla` is not in path.')
# A. save a created graph to nnp.
contents = {
'networks': [{
'name': 'graph',
'batch_size': 1,
'outputs': {
'y': y
},
'names': {
'x': x
}
}],
'executors': [{
'name': 'runtime',
'network': 'graph',
'data': ['x'],
'output': ['y']
}]
}
from nnabla.utils.save import save
tmpdir.ensure(dir=True)
tmppath = tmpdir.join('tmp.nnp')
nnp_file = tmppath.strpath
save(nnp_file, contents)
# B. Get result with nnp_graph
from nnabla.utils import nnp_graph
nnp = nnp_graph.NnpLoader(nnp_file)
graph = nnp.get_network('graph', batch_size=batch_size)
x2 = graph.inputs['x']
y2 = graph.outputs['y']
x2.d = np.random.randn(*x2.shape).astype(np.float32)
y2.forward()
# C. Get result with nbla
input_bin = tmpdir.join('tmp_in.bin')
input_bin_file = input_bin.strpath
x2.d.tofile(input_bin_file)
output_bin = tmpdir.join('tmp_out')
if on_memory:
check_call([
'nbla', 'infer', '-O', '-e', 'runtime', '-b',
str(batch_size), '-o', output_bin.strpath, nnp_file, input_bin_file
])
else:
check_call([
'nbla', 'infer', '-e', 'runtime', '-b',
str(batch_size), '-o', output_bin.strpath, nnp_file, input_bin_file
])
# D. Compare
y3 = np.fromfile(output_bin.strpath + '_0.bin',
dtype=np.float32).reshape(y2.shape)
assert np.allclose(y2.d, y3)
def save_model(self):
from nnabla.utils.save import save
with nn.parameter_scope(self.name_q):
save(self.save_path.get_filepath('qnet_{:08d}.nnp'.format(self.update_count)),
{'networks': [
{'name': 'qnet',
'batch_size': self.v.s.shape[0],
'outputs': {'q': self.v.q},
'names': {'s': self.v.s}}
]
})