def call_impl(self, env, shape, dtype, order):
assert order.value == 'C'
dt = utils.onnx_dtype(dtype.value)
return env.calc('ConstantFill',
inputs=[shape.to_tensor(env).name],
input_as_shape=1,
dtype=dt)
def call_impl(self, env, shape, fill_value, dtype, order):
assert order.value == 'C'
res = env.calc(
'Expand',
inputs=[fill_value.to_tensor(env).name,
shape.to_tensor(env).name],
)
if not dtype.is_none():
dt = utils.onnx_dtype(dtype.value)
res = castto(res, dt, env)
return res
def to_tensor(self,
env: 'utils.Env',
dtype: type = None) -> onnx.ValueInfoProto:
if self.is_py:
self.const_value = Value(self.value)
# TODO(hamaji): Rewrite `totensor` to convert a Python
# list to a tensor.
self.value = utils.totensor(self.value, env, dtype=dtype)
self.is_py = False
else:
if self.is_sequence():
self.value = env.calc('ChainerSequenceStack',
inputs=[self.value.name])
self.is_py = False
if dtype is not None:
dt = utils.onnx_dtype(dtype)
self.value = env.calc('Cast', inputs=[self.value.name], to=dt)
self.value.type.tensor_type.elem_type = dt
assert self.is_tensor()
return self.value
