def _to_var(self, v):
if _is_array(v):
if isinstance(v, chainer.Variable):
v = v.array
v = chainer.backend.to_chx(v)
if self.chainerx_device_name is None:
self.chainerx_device_name = v.device
else:
assert self.chainerx_device_name == v.device
return chainer_compiler_core.value(v)
return chainer_compiler_core.value([self._to_var(a) for a in v])
def test_backprop():
graph = chainer_compiler_core.load(
'out/ch2o_node_Linear_backprop/model.onnx')
params = graph.params()
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 1
assert len(output_names) == 1
fwd_graph, bwd_graph = graph.backward()
assert len(fwd_graph.input_names()) == 1
assert len(fwd_graph.output_names()) == 3
assert len(bwd_graph.input_names()) == 3
assert len(bwd_graph.output_names()) == 2
fwd = fwd_graph.compile()
bwd = bwd_graph.compile()
fwd_inputs = dict(params)
t1 = aranges(5, 7)
fwd_inputs[input_names[0]] = chainer_compiler_core.value(t1)
loss = (chainerx.dot(t1, params['/l1/W'].array().T) +
params['/l1/b'].array())
fwd_outputs = fwd.run(fwd_inputs)
assert len(fwd_outputs) == 3
chainerx.testing.assert_allclose(loss,
fwd_outputs[output_names[0]].array())
grad_loss = aranges(*loss.shape) + 4.2
bwd_inputs = {}
for name in fwd_graph.output_names():
iname = name
value = fwd_outputs[name]
if name in output_names:
iname = 'grad_in@' + name
value = chainer_compiler_core.value(grad_loss)
bwd_inputs[iname] = value
bwd_outputs = bwd.run(bwd_inputs)
grad_w = chainerx.dot(grad_loss.T, t1)
chainerx.testing.assert_allclose(grad_w,
bwd_outputs['grad_out@/l1/W'].array())
grad_b = chainerx.sum(grad_loss, axis=0)
chainerx.testing.assert_allclose(grad_b,
bwd_outputs['grad_out@/l1/b'].array())
def test_dropout_inference():
graph = chainer_compiler_core.load(
os.path.join(ONNX_TEST_DATA, 'node/test_dropout_random/model.onnx'))
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 1
assert len(output_names) == 1
xcvm = graph.compile()
input = chainerx.array(np.random.normal(size=(3, 4, 5)))
inputs = {input_names[0]: chainer_compiler_core.value(input)}
outputs = xcvm.run(inputs)
output = outputs[output_names[0]].array()
assert bool(chainerx.sum(input != output) == 0)
def test_custom_op():
gb = onnx_script.GraphBuilder('pytest_custom_op')
a = np.array(13)
b = np.array(4)
c = np.array(10)
a_v = gb.input('a', a)
b_v = gb.input('b', b)
c_v = gb.input('c', c)
def custom_func(a, b, c):
return a - b, a * b - c
y, z = custom_func(a, b, c)
y_v = 'y'
z_v = 'z'
gb.ChainerDoSomething([a_v, b_v, c_v],
outputs=[y_v, z_v],
function_name='CustomFunction')
gb.output(y_v, y)
gb.output(z_v, z)
gb.gen_test()
graph = chainer_compiler_core.load('out/pytest_custom_op/model.onnx')
params = graph.params()
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 3
assert len(output_names) == 2
xcvm = graph.compile()
inputs = {}
for n, v in [('a', a), ('b', b), ('c', c)]:
inputs[n] = chainer_compiler_core.value(chainerx.array(v))
outputs = xcvm.run(inputs, custom_funcs={'CustomFunction': custom_func})
assert len(outputs) == 2
chainerx.testing.assert_allclose(9, outputs['y'].array())
chainerx.testing.assert_allclose(42, outputs['z'].array())
def test_dropout_training():
graph = chainer_compiler_core.load(
os.path.join(ONNX_TEST_DATA, 'node/test_dropout_random/model.onnx'))
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 1
assert len(output_names) == 1
xcvm = graph.compile()
input = chainerx.array(np.random.normal(size=(3, 4, 5)))
inputs = {input_names[0]: chainer_compiler_core.value(input)}
num_retries = 3
for i in range(num_retries):
outputs = xcvm.run(inputs, training=True)
output = outputs[output_names[0]].array()
ok = bool(chainerx.sum(input != output) > 0)
if ok: break
else:
assert False, 'No dropout was observed in %d attempts' % num_retries
def test_inference():
graph = chainer_compiler_core.load('out/ch2o_node_Linear/model.onnx')
params = graph.params()
input_names = graph.input_names()
output_names = graph.output_names()
assert len(input_names) == 1
assert len(output_names) == 2
xcvm = graph.compile()
inputs = dict(params)
t1 = aranges(5, 7)
inputs[input_names[0]] = chainer_compiler_core.value(t1)
y1 = chainerx.dot(t1, params['/l1/W'].array().T) + params['/l1/b'].array()
y2 = chainerx.dot(t1, params['/l2/W'].array().T)
outputs = xcvm.run(inputs)
assert len(outputs) == 2
chainerx.testing.assert_allclose(y1, outputs[output_names[0]].array())
chainerx.testing.assert_allclose(y2, outputs[output_names[1]].array())
assert 'op_type: "ChainerLinear"' in graph.dump()