forked from huawei/mindspore2022
621 lines
18 KiB
Python
621 lines
18 KiB
Python
# Copyright 2021 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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"""Generate the mindir for bprop"""
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import numpy as np
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import mindspore.nn as nn
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from mindspore import context
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from mindspore import Tensor, Parameter
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from mindspore.ops import operations as P
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import mindspore.ops.functional as F
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import mindspore.ops as ops
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from mindspore.ops.operations import _inner_ops as inner
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import mindspore.common.dtype as mstype
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from mindspore.common.initializer import initializer
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from mindspore.ops.bprop_mindir import serializable_bprop_ops
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from mindspore._c_expression import load_mindir
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import mindspore.ops._grad as g
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class Net(nn.Cell):
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def __init__(self, op):
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super(Net, self).__init__()
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self.op = op
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def construct(self, *inputs):
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return self.op(*inputs)
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class TupleInputNet(nn.Cell):
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def __init__(self, op):
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super(TupleInputNet, self).__init__()
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self.op = op
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def construct(self, x):
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return self.op((x,))
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class GradNet(nn.Cell):
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def __init__(self, network):
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super(GradNet, self).__init__()
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self.grad = ops.GradOperation(get_all=True)
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self.network = network
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def construct(self, *inputs):
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gout = self.grad(self.network)(*inputs)
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return gout
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def test_load_mindir_dir():
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"""
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Feature: Bprop pre-compilation.
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Description: Load all the mindir files of serializable bprop.
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Expectation: All are loaded successfully.
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"""
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bprop_path = g.__file__
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bprop_installed_dir = bprop_path[: bprop_path.rindex('/')]
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bprop_mindir_export_dir = bprop_installed_dir + "/../bprop_mindir"
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for op in serializable_bprop_ops:
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if isinstance(op, str):
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op_name = op
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else:
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op_name = op.__name__
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file_name = bprop_mindir_export_dir + "/" + op_name + "_bprop.mindir"
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graph = load_mindir(file_name)
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assert not graph is None
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def test_relu():
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x = Tensor(np.array([[[[-1, 1, 10],
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[1, -1, 1],
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[10, 1, -1]]]]).astype(np.float32))
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relu = Net(P.ReLU())
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grad = GradNet(relu)
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grad.compile(x)
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def test_identity():
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x = Tensor(np.array([1, 2, 3, 4]).astype(np.int64))
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identity = Net(P.Identity())
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grad = GradNet(identity)
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grad.compile(x)
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def test_range():
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x = Tensor(np.array([1, 2, 3, 2]).astype(np.int64))
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range_net = Net(inner.Range(1.0, 8.0, 2.0))
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grad = GradNet(range_net)
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grad.compile(x)
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def test_ones_like():
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x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))
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ones_like = Net(P.OnesLike())
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grad = GradNet(ones_like)
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grad.compile(x)
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def test_zeros_like():
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x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.int32))
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zeros_like = Net(P.ZerosLike())
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grad = GradNet(zeros_like)
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grad.compile(x)
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def test_argmax():
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x = Tensor(np.array([[1, 20, 5], [67, 8, 9], [130, 24, 15]]).astype(np.float32))
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argmax = Net(P.Argmax())
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grad = GradNet(argmax)
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grad.compile(x)
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def test_argmin():
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x = Tensor(np.array([[1, 20, 5], [67, 8, 9], [130, 24, 15]]).astype(np.float32))
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argmin = Net(P.Argmin())
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grad = GradNet(argmin)
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grad.compile(x)
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def test_broadcast():
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x = Tensor(np.array([1, 2, 5, 2]).astype(np.float32))
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broadcast = TupleInputNet(P.Broadcast(1))
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grad = GradNet(broadcast)
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grad.compile(x)
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def test_is_finite():
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x = Tensor(np.ones([2, 4]).astype(np.int32))
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is_finite = Net(P.IsFinite())
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grad = GradNet(is_finite)
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grad.compile(x)
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def test_approximate_equal():
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x = Tensor(np.array([1, 2, 3]).astype(np.float32))
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y = Tensor(np.array([2, 4, 6]).astype(np.float32))
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approximate_equal = Net(P.ApproximateEqual(2.))
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grad = GradNet(approximate_equal)
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grad.compile(x, y)
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def test_logical_not():
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x = Tensor(np.array([True, False, True]).astype(np.bool))
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logical_not = Net(P.LogicalNot())
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grad = GradNet(logical_not)
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grad.compile(x)
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def test_sign():
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x = Tensor(np.array([[2.0, 0.0, -1.0]]).astype(np.float32))
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sign = Net(P.Sign())
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grad = GradNet(sign)
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grad.compile(x)
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def test_round():
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x = Tensor(np.array([0.8, 1.5, 2.3, 2.5, -4.5]).astype(np.float32))
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round_net = Net(P.Round())
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grad = GradNet(round_net)
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grad.compile(x)
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def test_lin_space():
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start = Tensor(1, mstype.float32)
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stop = Tensor(10, mstype.float32)
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num = 5
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lin_space = Net(P.LinSpace())
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grad = GradNet(lin_space)
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grad.compile(start, stop, num)
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def test_dropout_gen_mask():
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x = (2, 4, 2, 2)
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keep_prob = Tensor(1.0, mstype.float32)
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dropout_gen_mask = Net(P.DropoutGenMask(10, 28))
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grad = GradNet(dropout_gen_mask)
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grad.compile(x, keep_prob)
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def test_onehot():
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indices = Tensor(np.array([0, 1, 2]).astype(np.int32))
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depth, on_value, off_value = 3, Tensor(1.0, mstype.float32), Tensor(0.0, mstype.float32)
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one_hot = Net(P.OneHot())
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grad = GradNet(one_hot)
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grad.compile(indices, depth, on_value, off_value)
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def test_assign():
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class AssignNet(nn.Cell):
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def __init__(self):
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super(AssignNet, self).__init__()
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self.assign = P.Assign()
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self.variable = Parameter(Tensor([1.0], mstype.float32), name="variable")
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def construct(self, x):
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return self.assign(self.variable, x)
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value = Tensor([2.0], mstype.float32)
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assign = AssignNet()
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grad = GradNet(assign)
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grad.compile(value)
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def test_assign_add():
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class AssignAddNet(nn.Cell):
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def __init__(self):
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super(AssignAddNet, self).__init__()
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self.assign_add = P.AssignAdd()
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self.variable = Parameter(initializer(1, [1], mstype.int64), name="global_step")
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def construct(self, x):
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return self.assign_add(self.variable, x)
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value = Tensor(np.ones([1]).astype(np.int64) * 100)
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assign_add = AssignAddNet()
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grad = GradNet(assign_add)
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grad.compile(value)
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def test_assign_sub():
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class AssignSubNet(nn.Cell):
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def __init__(self):
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super(AssignSubNet, self).__init__()
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self.assign = P.AssignSub()
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self.variable = Parameter(initializer(1, [1], mstype.int32), name="global_step")
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def construct(self, x):
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return self.assign(self.variable, x)
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value = Tensor(np.ones([1]).astype(np.int32) * 100)
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assign_sub = AssignSubNet()
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grad = GradNet(assign_sub)
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grad.compile(value)
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def test_iou():
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anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]).astype(np.float16))
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gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]).astype(np.float16))
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iou = Net(P.IOU())
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grad = GradNet(iou)
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grad.compile(anchor_boxes, gt_boxes)
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def test_bn_training_reduce():
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x = Tensor(np.ones([128, 3, 32, 3]).astype(np.float32))
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bn_training_reduce = Net(P.BNTrainingReduce())
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grad = GradNet(bn_training_reduce)
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grad.compile(x)
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def test_equal():
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x = Tensor([2.0], mstype.float32)
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y = Tensor([2.0], mstype.float32)
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equal = Net(P.Equal())
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grad = GradNet(equal)
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grad.compile(x, y)
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def test_not_equal():
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x = Tensor([2.0], mstype.float32)
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y = Tensor([2.0], mstype.float32)
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not_equal = Net(P.NotEqual())
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grad = GradNet(not_equal)
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grad.compile(x, y)
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def test_greater():
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x = Tensor(np.array([1, 2, 3]), mstype.int32)
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y = Tensor(np.array([1, 1, 4]), mstype.int32)
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greater = Net(P.Greater())
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grad = GradNet(greater)
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grad.compile(x, y)
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def test_greater_equal():
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x = Tensor(np.array([1, 2, 3]), mstype.int32)
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y = Tensor(np.array([1, 1, 4]), mstype.int32)
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greater_equal = Net(P.GreaterEqual())
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grad = GradNet(greater_equal)
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grad.compile(x, y)
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def test_less():
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x = Tensor(np.array([1, 2, 3]), mstype.int32)
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y = Tensor(np.array([1, 1, 4]), mstype.int32)
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less = Net(P.Less())
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grad = GradNet(less)
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grad.compile(x, y)
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def test_less_equal():
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x = Tensor(np.array([1, 2, 3]), mstype.int32)
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y = Tensor(np.array([1, 1, 4]), mstype.int32)
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less_equal = Net(P.LessEqual())
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grad = GradNet(less_equal)
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grad.compile(x, y)
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def test_logical_and():
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x = Tensor(np.array([True, False, True]), mstype.bool_)
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y = Tensor(np.array([True, True, False]), mstype.bool_)
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logical_and = Net(P.LogicalAnd())
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grad = GradNet(logical_and)
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grad.compile(x, y)
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def test_logical_or():
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x = Tensor(np.array([True, False, True]), mstype.bool_)
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y = Tensor(np.array([True, True, False]), mstype.bool_)
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logical_or = Net(P.LogicalOr())
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grad = GradNet(logical_or)
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grad.compile(x, y)
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def test_reduce_all():
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x = Tensor(np.array([[True, False], [True, True]]))
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reduce_all = Net(P.ReduceAll(keep_dims=True))
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grad = GradNet(reduce_all)
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grad.compile(x)
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def test_reduce_any():
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x = Tensor(np.array([[True, False], [True, True]]))
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reduce_all = Net(P.ReduceAny(keep_dims=True))
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grad = GradNet(reduce_all)
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grad.compile(x)
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def test_dropout_do_mask():
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input_x = Tensor(np.ones([2, 2, 3]), mstype.float32)
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keep_prob = Tensor(0.5, mstype.float32)
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mask = Tensor(np.ones([2]), mstype.uint8)
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dropout_do_mask = Net(P.DropoutDoMask())
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grad = GradNet(dropout_do_mask)
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grad.compile(input_x, mask, keep_prob)
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def test_select():
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"""
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Feature: Bprop pre-compilation.
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Description: Compile the backward graph for the select op.
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Expectation: Load the bprop mindir successfully.
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"""
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input_cond = Tensor([True, False])
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x = Tensor(np.array([1, 2]), mstype.int32)
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y = Tensor(np.array([1, 1]), mstype.int32)
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select = Net(P.Select())
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grad = GradNet(select)
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grad.compile(input_cond, x, y)
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def test_scatter_max():
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"""
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Feature: Bprop pre-compilation.
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Description: Compile the backward graph for the scatter_max op.
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Expectation: Load the bprop mindir successfully.
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"""
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class ScatterMaxNet(nn.Cell):
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def __init__(self):
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super(ScatterMaxNet, self).__init__()
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self.scatter_max = P.ScatterMax()
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self.input_x = Parameter(Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mstype.float32),
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name="input_x")
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def construct(self, indices, updates):
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return self.scatter_max(self.input_x, indices, updates)
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indices = Tensor(np.array([[0, 0], [1, 1]]), mstype.int32)
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updates = Tensor(np.ones([2, 2, 3]) * 88, mstype.float32)
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scatter_max = ScatterMaxNet()
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grad = GradNet(scatter_max)
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grad.compile(indices, updates)
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def test_relu_grad():
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"""
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Feature: Bprop pre-compilation.
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Description: Compile the backward graph for the relu_grad op.
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Expectation: Load the bprop mindir successfully.
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"""
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x = Tensor(np.array([[[[-1, 1, 10],
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[1, -1, 1],
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[10, 1, -1]]]]).astype(np.float32))
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relu = Net(P.ReLU())
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grad1 = GradNet(relu)
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grad2 = GradNet(grad1)
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grad2.compile(x)
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def test_tuple_getitem():
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"""
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Feature: Bprop pre-compilation.
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Description: Compile the backward graph for the tuple_getitem op.
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Expectation: Load the bprop mindir successfully.
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"""
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class TupleGetitemNet(nn.Cell):
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def __init__(self):
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super(TupleGetitemNet, self).__init__()
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self.maxpool_arg = P.MaxPoolWithArgmax(pad_mode="VALID", kernel_size=2, strides=1)
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def construct(self, x):
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output = self.maxpool_arg(x)
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return output[0]
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x = Tensor(np.arange(1 * 3 * 3 * 4).reshape((1, 3, 3, 4)), mstype.float32)
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tuple_getitem = TupleGetitemNet()
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grad = GradNet(tuple_getitem)
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grad.compile(x)
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def test_depend():
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"""
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Feature: Bprop pre-compilation.
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Description: Compile the backward graph for the depend op.
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Expectation: Load the bprop mindir successfully.
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"""
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class DependNet(nn.Cell):
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def __init__(self):
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super(DependNet, self).__init__()
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self.softmax = P.Softmax()
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self.depend = ops.Depend()
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def construct(self, x, y):
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mul = x * y
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y = self.depend(y, mul)
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output = self.softmax(y)
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return output
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x = Tensor(np.ones([4, 5]), mstype.float32)
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y = Tensor(np.ones([4, 5]), mstype.float32)
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depend = DependNet()
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grad = GradNet(depend)
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grad.compile(x, y)
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def test_stop_gradient():
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"""
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Feature: Bprop pre-compilation.
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Description: Compile the backward graph for the stop_gradient op.
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Expectation: Load the bprop mindir successfully.
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"""
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class StopGradientNet(nn.Cell):
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def construct(self, x, y):
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c = x * y
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c_s = F.stop_gradient(c)
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return c_s
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x = Tensor(np.ones([4, 5]), mstype.float32)
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y = Tensor(np.ones([4, 5]), mstype.float32)
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stop_gradient = StopGradientNet()
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grad = GradNet(stop_gradient)
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grad.compile(x, y)
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def test_switch():
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"""
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Feature: Bprop pre-compilation.
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Description: Compile the backward graph for the switch op.
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Expectation: Load the bprop mindir successfully.
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"""
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context.set_context(mode=context.PYNATIVE_MODE)
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class SwitchNet(nn.Cell):
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def construct(self, x, y):
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if x > y:
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return x
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return y
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x = Tensor(np.array([3]), mstype.float32)
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y = Tensor(np.array([2]), mstype.float32)
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switch_net = SwitchNet()
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grad = GradNet(switch_net)
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grad.compile(x, y)
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def test_update_state():
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"""
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Feature: Bprop pre-compilation.
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Description: Compile the backward graph for the update_state op.
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Expectation: Load the bprop mindir successfully.
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"""
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class UpdateStateNet(nn.Cell):
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def __init__(self):
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super(UpdateStateNet, self).__init__()
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self.assign_add = P.AssignAdd()
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self.variable = Parameter(initializer(1, [1], mstype.int64), name="global_step")
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def construct(self, x):
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return self.assign_add(self.variable, x)
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value = Tensor(np.ones([1]).astype(np.int64) * 100)
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update_state = UpdateStateNet()
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grad = GradNet(update_state)
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grad.compile(value)
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def test_load():
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"""
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Feature: Bprop pre-compilation.
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Description: Compile the backward graph for the load op.
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Expectation: Load the bprop mindir successfully.
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"""
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class LoadNet(nn.Cell):
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def __init__(self):
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super(LoadNet, self).__init__()
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self.add = P.Add()
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self.variable = Parameter(initializer(1, [1], mstype.int64), name="global_step")
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def construct(self, x):
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return self.add(self.variable, x)
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value = Tensor(np.ones([1]).astype(np.int64) * 100)
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load = LoadNet()
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grad = GradNet(load)
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grad.compile(value)
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|
|
|
|
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def test_floor_div():
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"""
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|
Feature: Bprop pre-compilation.
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|
Description: Compile the backward graph for the floor_div op.
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|
Expectation: Load the bprop mindir successfully.
|
|
"""
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x = Tensor(np.array([2, 4, -1]), mstype.int32)
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y = Tensor(np.array([3, 3, 3]), mstype.int32)
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floor_div = Net(P.FloorDiv())
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grad = GradNet(floor_div)
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grad.compile(x, y)
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|
|
|
|
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def test_truncate_div():
|
|
"""
|
|
Feature: Bprop pre-compilation.
|
|
Description: Compile the backward graph for the truncate_div op.
|
|
Expectation: Load the bprop mindir successfully.
|
|
"""
|
|
x = Tensor(np.array([2, 4, -1]), mstype.int32)
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y = Tensor(np.array([3, 3, 3]), mstype.int32)
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truncate_div = Net(P.TruncateDiv())
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|
grad = GradNet(truncate_div)
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|
grad.compile(x, y)
|
|
|
|
|
|
def test_minimum():
|
|
"""
|
|
Feature: Bprop pre-compilation.
|
|
Description: Compile the backward graph for the minimum op.
|
|
Expectation: Load the bprop mindir successfully.
|
|
"""
|
|
x = Tensor(np.array([1.0, 5.0, 3.0]), mstype.float32)
|
|
y = Tensor(np.array([4.0, 2.0, 6.0]), mstype.float32)
|
|
minimum = Net(P.Minimum())
|
|
grad = GradNet(minimum)
|
|
grad.compile(x, y)
|
|
|
|
|
|
def test_maximum():
|
|
"""
|
|
Feature: Bprop pre-compilation.
|
|
Description: Compile the backward graph for the maximum op.
|
|
Expectation: Load the bprop mindir successfully.
|
|
"""
|
|
x = Tensor(np.array([1.0, 5.0, 3.0]), mstype.float32)
|
|
y = Tensor(np.array([4.0, 2.0, 6.0]), mstype.float32)
|
|
maximum = Net(P.Maximum())
|
|
grad = GradNet(maximum)
|
|
grad.compile(x, y)
|
|
|
|
|
|
def test_is_nan():
|
|
"""
|
|
Feature: Bprop pre-compilation.
|
|
Description: Compile the backward graph for the is_nan op.
|
|
Expectation: Load the bprop mindir successfully.
|
|
"""
|
|
x = Tensor(np.array([np.log(-1), 1, np.log(0)]), mstype.float32)
|
|
is_nan = Net(P.IsNan())
|
|
grad = GradNet(is_nan)
|
|
grad.compile(x)
|
|
|
|
|
|
def test_is_inf():
|
|
"""
|
|
Feature: Bprop pre-compilation.
|
|
Description: Compile the backward graph for the is_inf op.
|
|
Expectation: Load the bprop mindir successfully.
|
|
"""
|
|
x = Tensor(np.array([np.log(-1), 1, np.log(0)]), mstype.float32)
|
|
is_inf = Net(P.IsInf())
|
|
grad = GradNet(is_inf)
|
|
grad.compile(x)
|
|
|
|
|
|
def test_relu_v2():
|
|
"""
|
|
Feature: Bprop pre-compilation.
|
|
Description: Compile the backward graph for the relu_v2 op.
|
|
Expectation: Load the bprop mindir successfully.
|
|
"""
|
|
x = Tensor(np.array([[[[1, -2], [-3, 4]], [[-5, 6], [7, -8]]]]), mstype.float32)
|
|
relu_v2 = Net(P.ReLUV2())
|
|
grad = GradNet(relu_v2)
|
|
grad.compile(x)
|