def call_impl(self, env, a, axis, dtype, out):
assert axis.is_none() # TODO(hamaji): Not supported yet.
assert dtype.is_none() # TODO(hamaji): Not supported yet.
assert out.is_none() # TODO(hamaji): Not supported yet.
# さらにさらに、入力は1次元のTensorである、と仮定してしまいます
# 戻り値は入力に依らずテンソルらしい
# TODO(satos) さすがに仮定がきつい
v = a.to_tensor(env)
# これ戻り値がテンソルでなくSequenceなら、SplitAxisみたいにかっこよく書けるはず
"""
a = new_tensor()
env.addnode(
'Flatten',
inputs=[v.name],outputs[a.name],
axis=0
)
v = a
a = new_tensor()
env.addnode(
'Squeeze',
inputs=[v.name],outputs[a.name],
axes=[0]
)
"""
ls = env.calc(
'ChainerGenericLen',
inputs=[v.name],
)
def dummy():
return "dummy_" + new_tensor().name
localenv = Env(env.module)
cnt = new_tensor()
cond = new_tensor()
s = new_tensor()
gtx = new_tensor()
tx = localenv.calc(
"ChainerGenericGetItem",
inputs=[gtx.name, cnt.name],
)
ts = localenv.calc(
"Add",
inputs=[tx.name, s.name],
)
ts2 = localenv.calc("Identity", inputs=[ts.name])
zero = totensor(0, env)
res = new_tensor()
env.addnode('Loop',
inputs=[ls.name, "", v.name, zero.name],
outputs=[dummy(), dummy(), res.name],
body=utils.make_graph(localenv.nodes, "Cumsum_subgraph",
[cnt, cond, gtx, s],
[cond, gtx, ts, ts2]))
return res
def compile_model(model, inputs):
# return helper.make_graph([],'dummy',[],[])
init_id2name(model)
# code.InteractiveConsole({'mo': model}).interact()
env = Env(sys.modules[model.__module__])
molk = User_Defined_Link(model, env)
input_tensors = []
for i in inputs:
# TODO(hamaji): Set valid type info.
if isinstance(i, (list, tuple)):
x = new_sequence()
elif i is None:
x = new_tensor()
else:
if isinstance(i, int):
i = np.array(i)
else:
# TODO(durswd): This code requires chainer6.x
i = chainer.cuda.to_cpu(i)
x = new_tensor(dims=i.shape, dtype=i.dtype)
input_tensors.append(x)
input_values = [Value(i) for i in input_tensors]
v = molk.call(input_values, [], env)
dprint('output_tensors', v)
if isinstance(v.value, tuple):
output_tensors = list(v.value) # ばらしてみる
else:
output_tensors = [v] # とりあえず1tensor
# print('env.init_tensors ',env.init_tensors)
input_tensors += list(env.init_tensors.values())
for f in env.restore_funcs:
f()
# for no in env.nodes:
# print(no.op_type)
# print(env.nodes)
# print(input_tensors)
# print(output_tensors)
# for ch in model.namedparams():
# print(ch)
outputs_vi = [o.to_value_info(env) for o in output_tensors]
graph = make_graph(env.nodes, 'name_is_unknown_now', input_tensors,
outputs_vi)
# inputのうち、重みであるものにはinitializerをつける
# batch_sizeやinput_sizeなどの可変なものはできる限りのそのままで
# Chainer compiler 独自のノードを使うとcheckできなくなる...
# checker.check_graph(graph)
mo = helper.make_model(graph)
# print(mo)
return mo