You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
307 lines
9.1 KiB
307 lines
9.1 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
""" test nn ops """
|
|
import numpy as np
|
|
|
|
import mindspore.nn as nn
|
|
import mindspore.context as context
|
|
|
|
from mindspore import Tensor
|
|
from mindspore.ops import functional as F
|
|
from mindspore.ops import prim_attr_register, PrimitiveWithInfer
|
|
|
|
context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
|
|
|
|
|
|
class FakeOp(PrimitiveWithInfer):
|
|
@prim_attr_register
|
|
def __init__(self):
|
|
""""""
|
|
|
|
def infer_shape(self, x, y):
|
|
self.second_shape = y
|
|
self.add_prim_attr("second_shape", y)
|
|
return x
|
|
|
|
def infer_dtype(self, x, y):
|
|
return x
|
|
|
|
|
|
# test the normal case that should generate independent primitive because of different
|
|
# generated attributes after inference
|
|
def test_conv2d_same_primitive():
|
|
class Conv2DSameNet(nn.Cell):
|
|
def __init__(self):
|
|
super(Conv2DSameNet, self).__init__()
|
|
self.conv1 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
|
|
self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
|
|
|
|
def construct(self, x, y):
|
|
r1 = self.conv1(x)
|
|
r2 = self.conv2(y)
|
|
return (r1, r2)
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
net = Conv2DSameNet()
|
|
net(t1, t2)
|
|
|
|
|
|
# test cell as high order argument
|
|
# The graph with free variables used as argument is not supported yet
|
|
# because of the limit of inference specialize system
|
|
def Xtest_conv2d_op_with_arg():
|
|
class Conv2dNet(nn.Cell):
|
|
def __init__(self):
|
|
super(Conv2dNet, self).__init__()
|
|
|
|
def construct(self, op, x):
|
|
return op(x)
|
|
|
|
class OpsNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(OpsNet, self).__init__()
|
|
self.opnet = net
|
|
self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
|
|
|
|
def construct(self, x, y):
|
|
conv_op = self.conv2
|
|
a = self.opnet(conv_op, x)
|
|
b = self.opnet(conv_op, y)
|
|
return (a, b)
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
net = OpsNet(Conv2dNet())
|
|
net(t1, t2)
|
|
|
|
|
|
def test_conv2d_op_with_arg():
|
|
class FackOpNet(nn.Cell):
|
|
def __init__(self):
|
|
super(FackOpNet, self).__init__()
|
|
self.op = FakeOp()
|
|
|
|
def construct(self, x, y):
|
|
return self.op(x, y)
|
|
|
|
class OpNet(nn.Cell):
|
|
def __init__(self):
|
|
super(OpNet, self).__init__()
|
|
|
|
def construct(self, op, x, y):
|
|
return op(x, y)
|
|
|
|
class OpsNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(OpsNet, self).__init__()
|
|
self.opnet = net
|
|
self.op = FackOpNet()
|
|
|
|
def construct(self, x, y):
|
|
op = self.op
|
|
a = self.opnet(op, x, y)
|
|
b = self.opnet(op, y, x)
|
|
return (a, b)
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
net = OpsNet(OpNet())
|
|
net(t1, t2)
|
|
|
|
|
|
def test_conv2d_op_with_arg_same_input():
|
|
class FackOpNet(nn.Cell):
|
|
def __init__(self):
|
|
super(FackOpNet, self).__init__()
|
|
self.op = FakeOp()
|
|
|
|
def construct(self, x, y):
|
|
return self.op(x, y)
|
|
|
|
class OpNet(nn.Cell):
|
|
def __init__(self):
|
|
super(OpNet, self).__init__()
|
|
|
|
def construct(self, op, x, y):
|
|
return op(x, y)
|
|
|
|
class OpsNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(OpsNet, self).__init__()
|
|
self.opnet = net
|
|
self.op = FackOpNet()
|
|
|
|
def construct(self, x, y):
|
|
op = self.op
|
|
a = self.opnet(op, x, x)
|
|
b = self.opnet(op, y, x)
|
|
return (a, b)
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
net = OpsNet(OpNet())
|
|
net(t1, t2)
|
|
|
|
|
|
# test op with partial
|
|
def test_op_as_partial():
|
|
class OpAsPartial(nn.Cell):
|
|
def __init__(self):
|
|
super(OpAsPartial, self).__init__()
|
|
self.op = FakeOp()
|
|
|
|
def construct(self, x, y, z):
|
|
partial_op = F.partial(self.op, x)
|
|
a = partial_op(y)
|
|
b = partial_op(z)
|
|
return a, b
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
|
|
net = OpAsPartial()
|
|
net(t1, t2, t3)
|
|
|
|
|
|
# test op with partial
|
|
def test_op_as_partial_inside():
|
|
class OpAsPartial(nn.Cell):
|
|
def __init__(self):
|
|
super(OpAsPartial, self).__init__()
|
|
self.op = FakeOp()
|
|
|
|
def construct(self, x, y, z):
|
|
partial_op = F.partial(self.op, x)
|
|
a = partial_op(y)
|
|
b = partial_op(z)
|
|
return a, b
|
|
|
|
class OuterNet(nn.Cell):
|
|
def __init__(self):
|
|
super(OuterNet, self).__init__()
|
|
self.net = OpAsPartial()
|
|
|
|
def construct(self, x, y, z):
|
|
a, b = self.net(x, y, z)
|
|
return a, b
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
|
|
net = OuterNet()
|
|
net(t1, t2, t3)
|
|
|
|
|
|
# test op with partial case 2
|
|
def test_op_as_partial_independent():
|
|
class OpAsPartial(nn.Cell):
|
|
def __init__(self):
|
|
super(OpAsPartial, self).__init__()
|
|
self.op = FakeOp()
|
|
|
|
def construct(self, x, y, z):
|
|
partial_op1 = F.partial(self.op, x)
|
|
a = partial_op1(y)
|
|
partial_op2 = F.partial(self.op, x)
|
|
b = partial_op2(z)
|
|
return a, b
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
|
|
net = OpAsPartial()
|
|
net(t1, t2, t3)
|
|
|
|
|
|
def test_nest_partial():
|
|
class NestPartial(nn.Cell):
|
|
def __init__(self):
|
|
super(NestPartial, self).__init__()
|
|
self.op = FakeOp()
|
|
|
|
def construct(self, x, y, z):
|
|
partial_op1 = F.partial(self.op)
|
|
partial_op2 = F.partial(partial_op1, x)
|
|
a = partial_op2(y)
|
|
partial_op3 = F.partial(self.op)
|
|
partial_op4 = F.partial(partial_op3, x)
|
|
b = partial_op4(z)
|
|
return a, b
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
|
|
net = NestPartial()
|
|
net(t1, t2, t3)
|
|
|
|
|
|
# high order argument
|
|
# op and op args as network arguments
|
|
def test_op_with_arg_as_input():
|
|
class WithOpArgNet(nn.Cell):
|
|
def __init__(self):
|
|
super(WithOpArgNet, self).__init__()
|
|
|
|
def construct(self, op, x, y):
|
|
return op(x, y)
|
|
|
|
class OpsNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(OpsNet, self).__init__()
|
|
self.opnet = net
|
|
self.op = FakeOp()
|
|
|
|
def construct(self, x, y, z):
|
|
op = self.op
|
|
a = self.opnet(op, x, z)
|
|
b = self.opnet(op, x, y)
|
|
return (a, b)
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
|
|
net = OpsNet(WithOpArgNet())
|
|
net(t1, t2, t3)
|
|
|
|
|
|
# The partial application used as argument is not supported yet
|
|
# because of the limit of inference specialize system
|
|
def Xtest_partial_as_arg():
|
|
class PartialArgNet(nn.Cell):
|
|
def __init__(self):
|
|
super(PartialArgNet, self).__init__()
|
|
|
|
def construct(self, partial_op, y):
|
|
return partial_op(y)
|
|
|
|
class OpsNet(nn.Cell):
|
|
def __init__(self, net):
|
|
super(OpsNet, self).__init__()
|
|
self.partial_net = net
|
|
self.op = FakeOp()
|
|
|
|
def construct(self, x, y, z):
|
|
partial_op = F.partial(self.op, x)
|
|
a = self.partial_net(partial_op, z)
|
|
b = self.partial_net(partial_op, y)
|
|
return (a, b)
|
|
|
|
t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
|
|
t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
|
|
t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
|
|
net = OpsNet(PartialArgNet())
|
|
net(t1, t2, t3)
|