def single_function_model(function, inputs, outputs, **kwargs):
inputs = [nnoir.Value(x[0], shape=x[1], dtype='<f4') for x in inputs]
outputs = [nnoir.Value(x[0], shape=x[1], dtype='<f4') for x in outputs]
nodes = inputs + outputs
input_names = [x.name for x in inputs]
output_names = [x.name for x in outputs]
functions = [
getattr(nnoir.functions, function)(input_names, output_names, **kwargs)
]
actual = nnoir.NNOIR(function.encode(), b'nnoir2chainer_test', "0.1",
input_names, output_names, nodes, functions).pack()
expected = nnoir.load(
os.path.join(os.path.abspath(os.path.dirname(__file__)),
function + '.nnoir')).pack()
print(actual)
print(expected)
assert expected == actual
def single_function_model(function, _inputs, _outputs, **kwargs):
inputs = [nnoir.Value(x[0], shape=x[1], dtype='<f4') for x in _inputs]
outputs = [nnoir.Value(x[0], shape=x[1], dtype='<f4') for x in _outputs]
nodes = inputs + outputs
input_names = [x.name for x in inputs]
output_names = [x.name for x in outputs]
functions = [
getattr(nnoir.functions,
function.partition('_')[0])(input_names, output_names,
**kwargs)
]
actual = nnoir.NNOIR(function.encode(), b'nnoir2chainer_test', "0.1",
input_names, output_names, nodes, functions)
expected = nnoir.load(
os.path.join(os.path.abspath(os.path.dirname(__file__)),
function + '.nnoir'))
xs = [np.random.randn(*x[1]).astype('<f4') for x in _inputs]
actuals = actual.run(*xs)
expecteds = expected.run(*xs)
assert len(expecteds) == len(actuals)
for a, e in zip(actuals, expecteds):
print(a - e)
assert (a == e).all()
def save_and_run(self, model: NNOIR):
with tempfile.NamedTemporaryFile(delete=TMP_REMOVE) as f:
model.dump(f.name)
reload_nnoir: NNOIR = nnoir.load(f.name)
return self.execute_nnoir(reload_nnoir)