416 lines
12 KiB
Python
416 lines
12 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 ops """
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import numpy as np
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import mindspore.nn as nn
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import mindspore.ops.composite as C
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import mindspore.ops.functional as F
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import mindspore.ops.operations as P
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from mindspore import Tensor
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from mindspore.common.api import _executor
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grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
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class InputBackward(nn.Cell):
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""" InputBackward definition """
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def __init__(self, network, c1=None, c2=None):
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super(InputBackward, self).__init__()
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self.network = network
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self.network.set_train()
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self.grad = grad_all_with_sens
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self.c1 = c1
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self.c2 = c2
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def construct(self, *inputs):
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pass
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def construct1(self, x1, sens):
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return self.grad(self.network)(x1, sens)
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def construct2(self, x1, x2, sens):
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return self.grad(self.network)(x1, x2, sens)
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def construct3(self, x1, x2, x3, sens):
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return self.grad(self.network)(x1, x2, x3, sens)
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def construct4(self, x1, x2, x3, x4, sens):
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return self.grad(self.network)(x1, x2, x3, x4, sens)
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def construct5(self, x1, x2, x3, x4, x5, sens):
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return self.grad(self.network)(x1, x2, x3, x4, x5, sens)
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def construct6(self, x1, x2, x3, x4, x5, x6, sens):
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return self.grad(self.network)(x1, x2, x3, x4, x5, x6, sens)
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def construct7(self, x1, x2, x3, x4, x5, x6, x7, sens):
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return self.grad(self.network)(x1, x2, x3, x4, x5, x6, x7, sens)
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class InputOpNet(nn.Cell):
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""" InputOpNet definition """
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def __init__(self, op, get_first=False,
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c1=None, c2=None, c3=None, c4=None):
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super(InputOpNet, self).__init__()
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self.op = op
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self.get_first = get_first
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self.c1 = c1
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self.c2 = c2
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self.c3 = c3
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self.c4 = c4
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def construct(self, *inputs):
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pass
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def construct0_c0_fack(self, data):
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x = self.op() + data
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if self.get_first:
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x = x[0]
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return x
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def construct0_c1_fack(self, data):
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x = self.op(self.c1) + data
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if self.get_first:
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x = x[0]
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return x
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def construct0_c2_fack(self, data):
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x = self.op(self.c1, self.c2) + data
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if self.get_first:
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x = x[0]
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return x
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def construct0_c0(self):
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x = self.op()
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if self.get_first:
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x = x[0]
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return x
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def construct0_c1(self):
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x = self.op(self.c1)
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if self.get_first:
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x = x[0]
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return x
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def construct0_c2(self):
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x = self.op(self.c1, self.c2)
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if self.get_first:
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x = x[0]
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return x
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def construct1_c0(self, x1):
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x = self.op(x1)
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if self.get_first:
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x = x[0]
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return x
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def construct1_c1(self, x1):
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x = self.op(x1, self.c1)
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if self.get_first:
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x = x[0]
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return x
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def construct1_c2(self, x1):
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x = self.op(x1, self.c1, self.c2)
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if self.get_first:
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x = x[0]
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return x
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def construct1_c3(self, x1):
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x = self.op(x1, self.c1, self.c2, self.c3)
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if self.get_first:
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x = x[0]
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return x
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def construct1_c4(self, x1):
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x = self.op(x1, self.c1, self.c2, self.c3, self.c4)
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if self.get_first:
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x = x[0]
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return x
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def constructc1_1(self, x1):
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x = self.op(self.c1, x1)
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if self.get_first:
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x = x[0]
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return x
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def construct2_c0(self, x1, x2):
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x = self.op(x1, x2)
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if self.get_first:
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x = x[0]
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return x
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def construct2_c1(self, x1, x2):
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x = self.op(x1, x2, self.c1)
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if self.get_first:
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x = x[0]
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return x
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def construct2_c3(self, x1, x2):
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x = self.op(x1, x2, self.c1, self.c2, self.c3)
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if self.get_first:
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x = x[0]
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return x
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def construct3_c0(self, x1, x2, x3):
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x = self.op(x1, x2, x3)
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if self.get_first:
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x = x[0]
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return x
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def construct3_c1(self, x1, x2, x3):
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x = self.op(x1, x2, x3, self.c1)
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if self.get_first:
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x = x[0]
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return x
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def construct4_c0(self, x1, x2, x3, x4):
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x = self.op(x1, x2, x3, x4)
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if self.get_first:
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x = x[0]
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return x
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def construct4_c1(self, x1, x2, x3, x4):
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x = self.op(x1, x2, x3, x4, self.c1)
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if self.get_first:
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x = x[0]
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return x
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def construct5_c0(self, x1, x2, x3, x4, x5):
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x = self.op(x1, x2, x3, x4, x5)
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if self.get_first:
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x = x[0]
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return x
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def construct6_c0(self, x1, x2, x3, x4, x5, x6):
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x = self.op(x1, x2, x3, x4, x5, x6)
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if self.get_first:
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x = x[0]
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return x
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def construct5_c1(self, x1, x2, x3, x4, x5):
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x = self.op(x1, x2, x3, x4, x5, self.c1)
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if self.get_first:
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x = x[0]
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return x
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class NetOutputAsLoss(nn.Cell):
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""" NetOutputAsLoss definition """
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def __init__(self, network, output_index):
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super(NetOutputAsLoss, self).__init__()
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self.network = network
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self.output_index = output_index
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def construct(self, *inputs):
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pass
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def construct1(self, x1):
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predict = self.network(x1)[self.output_index]
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return predict
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def construct2(self, x1, x2):
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predict = self.network(x1, x2)[self.output_index]
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return predict
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def construct3(self, x1, x2, x3):
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predict = self.network(x1, x2, x3)[self.output_index]
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return predict
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def construct4(self, x1, x2, x3, x4):
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predict = self.network(x1, x2, x3, x4)[self.output_index]
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return predict
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def construct5(self, x1, x2, x3, x4, x5):
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predict = self.network(x1, x2, x3, x4, x5)[self.output_index]
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return predict
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def get_loss_fun(construct_net, num_input, output_index):
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net = NetOutputAsLoss(construct_net, output_index)
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f = getattr(net, 'construct%d' % num_input)
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setattr(net, "construct", f)
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return net
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def build_construct_graph(net, *inputs, execute=True):
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net.set_train()
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_executor.compile(net, *inputs)
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if execute:
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_executor(net, inputs)
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def build_backward_graph(net, output_shapes, inputs, execute=True):
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inputs = append_sens_to_inputs(output_shapes, inputs)
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net = gen_backward_net(net, len(inputs) - 1)
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net.set_train()
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_executor.compile(net, inputs)
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if execute:
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_executor(net, inputs)
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def convert(shp, dtype=np.float32, scale=6):
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if isinstance(shp, list):
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if not shp:
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return Tensor((np.random.rand() * scale).astype(dtype))
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return Tensor((np.random.rand(*shp) * scale).astype(dtype))
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return shp
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def gen_inputs(input_shapes, config):
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add_fack_input = config.get('add_fack_input', False)
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if not input_shapes and add_fack_input:
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return [Tensor(np.array([1.0]).astype(config.get('fack_input_type', np.float32)))]
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return [convert(shp) for shp in input_shapes]
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def gen_backward_inputs(input_shapes, output_shapes, config):
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add_fack_input = config.get('add_fack_input', False)
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if not input_shapes and add_fack_input:
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inputs = [Tensor(np.array([1.0]))]
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else:
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inputs = [convert(shp) for shp in input_shapes]
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sens_shape = output_shapes[0]
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sens = convert(sens_shape)
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return inputs + [sens]
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def append_sens_to_inputs(output_shapes, inputs):
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inputs = inputs
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sens = Tensor(np.random.normal(0, 1, output_shapes).astype(np.float32))
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return inputs + [sens]
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def gen_net(shapes, config, get_first=False):
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"""
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gen_net function
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"""
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add_fack_input = config.get('add_fack_input', False)
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op = config['op']
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if 'const' not in config:
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const_input = []
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else:
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const_input = config['const']
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const_first = False
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if 'const_first' in config:
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const_first = config['const_first']
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net = InputOpNet(op, get_first, *const_input)
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if const_first:
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fn_name = 'constructc%d_%d' % (len(const_input), len(shapes))
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else:
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fn_name = 'construct%d_c%d' % (len(shapes), len(const_input))
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if add_fack_input:
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fn_name += '_fack'
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f = getattr(net, fn_name)
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setattr(net, "construct", f)
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return net
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def gen_backward_net(construct_net, input_num):
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net = InputBackward(construct_net)
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f = getattr(net, 'construct%d' % input_num)
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setattr(net, "construct", f)
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return net
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def batch_tuple_tensor(data, batch_size):
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ret = [Tensor(np.tile(d.asnumpy(), (batch_size, 1))) for d in data]
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return tuple(ret)
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class OutPutWrap(nn.Cell):
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"""
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OutPutWrap definition
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"""
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def __init__(self, network, num_output, output_is_tuple):
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super(OutPutWrap, self).__init__()
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self.network = network
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self.num_output = num_output
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self.one = Tensor(np.array([1]))
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self.dtype = P.DType()
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self.cast = P.Cast()
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self.output_is_tuple = output_is_tuple
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def construct(self, *inputs):
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pass
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def construct1(self, x1):
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ret = F.make_tuple()
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predict = self.network(x1)
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if self.num_output == 1 and self.output_is_tuple == 0:
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return predict * self.cast(self.one, self.dtype(predict))
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for i in range(self.num_output):
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ret = ret + F.make_tuple(predict[i] * self.cast(self.one, self.dtype(predict[i])))
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return ret
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def construct2(self, x1, x2):
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ret = F.make_tuple()
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predict = self.network(x1, x2)
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if self.num_output == 1 and self.output_is_tuple == 0:
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return predict * self.cast(self.one, self.dtype(predict))
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for i in range(self.num_output):
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ret = ret + F.make_tuple(predict[i] * self.cast(self.one, self.dtype(predict[i])))
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return ret
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def construct3(self, x1, x2, x3):
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ret = F.make_tuple()
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predict = self.network(x1, x2, x3)
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if self.num_output == 1 and self.output_is_tuple == 0:
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return predict * self.cast(self.one, self.dtype(predict))
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for i in range(self.num_output):
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ret = ret + F.make_tuple(predict[i] * self.cast(self.one, self.dtype(predict[i])))
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return ret
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def construct4(self, x1, x2, x3, x4):
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ret = F.make_tuple()
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predict = self.network(x1, x2, x3, x4)
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if self.num_output == 1 and self.output_is_tuple == 0:
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return predict * self.cast(self.one, self.dtype(predict))
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for i in range(self.num_output):
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ret = ret + F.make_tuple(predict[i] * self.cast(self.one, self.dtype(predict[i])))
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return ret
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def construct5(self, x1, x2, x3, x4, x5):
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ret = F.make_tuple()
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predict = self.network(x1, x2, x3, x4, x5)
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if self.num_output == 1 and self.output_is_tuple == 0:
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return predict * self.cast(self.one, self.dtype(predict))
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for i in range(self.num_output):
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ret = ret + F.make_tuple(predict[i] * self.cast(self.one, self.dtype(predict[i])))
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return ret
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def construct6(self, x1, x2, x3, x4, x5, x6):
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ret = F.make_tuple()
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predict = self.network(x1, x2, x3, x4, x5, x6)
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if self.num_output == 1 and self.output_is_tuple == 0:
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return predict * self.cast(self.one, self.dtype(predict))
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for i in range(self.num_output):
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ret = ret + F.make_tuple(predict[i] * self.cast(self.one, self.dtype(predict[i])))
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return ret
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def get_output_wrap(network, num_input, num_output, output_is_tuple=0):
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net = OutPutWrap(network, num_output, output_is_tuple)
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f = getattr(net, 'construct%d' % num_input)
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setattr(net, "construct", f)
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return net
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