forked from mindspore-Ecosystem/mindspore
342 lines
10 KiB
Python
342 lines
10 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test nn ops """
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import numpy as np
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from numpy.random import normal
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import pytest
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import mindspore.nn as nn
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import mindspore.context as context
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from mindspore.ops.composite import core
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from mindspore.common.api import jit
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from mindspore import Tensor
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from mindspore.ops import functional as F
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from mindspore.ops import prim_attr_register, PrimitiveWithInfer
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context.set_context(mode=context.GRAPH_MODE)
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class FakeOp(PrimitiveWithInfer):
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@prim_attr_register
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def __init__(self):
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""""""
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def infer_shape(self, x, y):
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self.second_shape = y
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self.add_prim_attr("second_shape", y)
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return x
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def infer_dtype(self, x, y):
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return x
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# test the normal case that should generate independent primitive because of different
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# generated attributes after inference
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def test_conv2d_same_primitive():
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class Conv2DSameNet(nn.Cell):
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def __init__(self):
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super(Conv2DSameNet, self).__init__()
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self.conv1 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
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self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
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def construct(self, x, y):
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r1 = self.conv1(x)
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r2 = self.conv2(y)
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return (r1, r2)
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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net = Conv2DSameNet()
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net(t1, t2)
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# test free variable function list as parameter
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def test_remove_and_fv_2():
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@core(loop_can_uroll=True)
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def inner_loop(x, input_data, fv_func_list):
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ret = ()
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for fv_fn in fv_func_list:
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ele = fv_fn(input_data)
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ret += (ele,)
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return ret
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@jit
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def out_loop(input1, input_data0, input_data1):
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ret = ()
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def fv_func1(y):
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return input1 * y
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def fv_func2(y):
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return input1 - y
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fv_func_list = [fv_func1, fv_func2]
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ele0 = inner_loop(input1, input_data0, fv_func_list)
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ele1 = inner_loop(input1, input_data1, fv_func_list)
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ret = (ele0, ele1)
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return ret
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input_data0 = Tensor(normal(0, 0.1, (3, 3)))
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input_data1 = Tensor(normal(0, 0.1, (3, 1)))
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input1 = Tensor(normal(0, 0.1, (3, 3)))
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out_loop(input1, input_data0, input_data1)
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# test cell as high order argument
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# The graph with free variables used as argument is not supported yet
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# because of the limit of inference specialize system
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def test_conv2d_op_with_argi_1():
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class Conv2dNet(nn.Cell):
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def __init__(self):
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super(Conv2dNet, self).__init__()
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def construct(self, op, x):
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return op(x)
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class OpsNet(nn.Cell):
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def __init__(self, net):
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super(OpsNet, self).__init__()
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self.opnet = net
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self.conv2 = nn.Conv2d(16, 64, (1, 41), (1, 4), "same", 0, 1, has_bias=True)
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def construct(self, x, y):
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conv_op = self.conv2
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a = self.opnet(conv_op, x)
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b = self.opnet(conv_op, y)
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return (a, b)
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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net = OpsNet(Conv2dNet())
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net(t1, t2)
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def test_conv2d_op_with_arg():
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class FackOpNet(nn.Cell):
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def __init__(self):
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super(FackOpNet, self).__init__()
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self.op = FakeOp()
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def construct(self, x, y):
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return self.op(x, y)
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class OpNet(nn.Cell):
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def __init__(self):
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super(OpNet, self).__init__()
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def construct(self, op, x, y):
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return op(x, y)
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class OpsNet(nn.Cell):
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def __init__(self, net):
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super(OpsNet, self).__init__()
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self.opnet = net
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self.op = FackOpNet()
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def construct(self, x, y):
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op = self.op
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a = self.opnet(op, x, y)
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b = self.opnet(op, y, x)
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return (a, b)
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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net = OpsNet(OpNet())
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net(t1, t2)
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def test_conv2d_op_with_arg_same_input():
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class FackOpNet(nn.Cell):
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def __init__(self):
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super(FackOpNet, self).__init__()
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self.op = FakeOp()
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def construct(self, x, y):
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return self.op(x, y)
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class OpNet(nn.Cell):
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def __init__(self):
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super(OpNet, self).__init__()
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def construct(self, op, x, y):
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return op(x, y)
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class OpsNet(nn.Cell):
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def __init__(self, net):
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super(OpsNet, self).__init__()
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self.opnet = net
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self.op = FackOpNet()
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def construct(self, x, y):
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op = self.op
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a = self.opnet(op, x, x)
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b = self.opnet(op, y, x)
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return (a, b)
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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net = OpsNet(OpNet())
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net(t1, t2)
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# test op with partial
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def test_op_as_partial():
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class OpAsPartial(nn.Cell):
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def __init__(self):
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super(OpAsPartial, self).__init__()
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self.op = FakeOp()
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def construct(self, x, y, z):
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partial_op = F.partial(self.op, x)
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a = partial_op(y)
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b = partial_op(z)
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return a, b
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
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net = OpAsPartial()
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net(t1, t2, t3)
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# test op with partial
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def test_op_as_partial_inside():
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class OpAsPartial(nn.Cell):
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def __init__(self):
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super(OpAsPartial, self).__init__()
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self.op = FakeOp()
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def construct(self, x, y, z):
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partial_op = F.partial(self.op, x)
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a = partial_op(y)
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b = partial_op(z)
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return a, b
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class OuterNet(nn.Cell):
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def __init__(self):
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super(OuterNet, self).__init__()
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self.net = OpAsPartial()
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def construct(self, x, y, z):
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a, b = self.net(x, y, z)
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return a, b
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
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net = OuterNet()
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net(t1, t2, t3)
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# test op with partial case 2
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def test_op_as_partial_independent():
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class OpAsPartial(nn.Cell):
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def __init__(self):
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super(OpAsPartial, self).__init__()
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self.op = FakeOp()
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def construct(self, x, y, z):
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partial_op1 = F.partial(self.op, x)
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a = partial_op1(y)
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partial_op2 = F.partial(self.op, x)
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b = partial_op2(z)
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return a, b
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
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net = OpAsPartial()
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net(t1, t2, t3)
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def test_nest_partial():
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class NestPartial(nn.Cell):
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def __init__(self):
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super(NestPartial, self).__init__()
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self.op = FakeOp()
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def construct(self, x, y, z):
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partial_op1 = F.partial(self.op)
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partial_op2 = F.partial(partial_op1, x)
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a = partial_op2(y)
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partial_op3 = F.partial(self.op)
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partial_op4 = F.partial(partial_op3, x)
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b = partial_op4(z)
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return a, b
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
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net = NestPartial()
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net(t1, t2, t3)
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# high order argument
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# op and op args as network arguments
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def test_op_with_arg_as_input():
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class WithOpArgNet(nn.Cell):
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def __init__(self):
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super(WithOpArgNet, self).__init__()
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def construct(self, op, x, y):
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return op(x, y)
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class OpsNet(nn.Cell):
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def __init__(self, net):
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super(OpsNet, self).__init__()
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self.opnet = net
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self.op = FakeOp()
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def construct(self, x, y, z):
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op = self.op
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a = self.opnet(op, x, z)
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b = self.opnet(op, x, y)
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return (a, b)
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
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net = OpsNet(WithOpArgNet())
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net(t1, t2, t3)
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# The partial application used as argument is not supported yet
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# because of the limit of inference specialize system
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@pytest.mark.skip("poly in infer")
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def test_partial_as_arg():
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class PartialArgNet(nn.Cell):
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def __init__(self):
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super(PartialArgNet, self).__init__()
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def construct(self, partial_op, y):
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return partial_op(y)
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class OpsNet(nn.Cell):
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def __init__(self, net):
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super(OpsNet, self).__init__()
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self.partial_net = net
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self.op = FakeOp()
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def construct(self, x, y, z):
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partial_op = F.partial(self.op, x)
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a = self.partial_net(partial_op, z)
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b = self.partial_net(partial_op, y)
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return (a, b)
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t1 = Tensor(np.ones([1, 16, 1, 1918]).astype(np.float32))
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t2 = Tensor(np.ones([1, 16, 1, 3840]).astype(np.float32))
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t3 = Tensor(np.ones([1, 16, 1, 1234]).astype(np.float32))
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net = OpsNet(PartialArgNet())
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net(t1, t2, t3)
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