forked from mindspore-Ecosystem/mindspore
Add assignment operators and math operators tests
This commit is contained in:
Margaret_wangrui
parent
cc7a2b74ac
commit
5744cf7902
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# 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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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor, Parameter
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from mindspore.common.initializer import initializer
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE)
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class Assign(nn.Cell):
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def __init__(self, x, y):
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super(Assign, self).__init__()
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self.x = Parameter(initializer(x, x.shape), name="x")
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self.y = Parameter(initializer(y, y.shape), name="y")
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self.assign = P.Assign()
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def construct(self):
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self.assign(self.y, self.x)
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return self.y
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def test_assign_bool():
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x = Tensor(np.ones([3, 3]).astype(np.bool_))
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y = Tensor(np.zeros([3, 3]).astype(np.bool_))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.bool_)
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print(output)
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assert np.all(output == output_expect)
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def test_assign_int8():
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x = Tensor(np.ones([3, 3]).astype(np.int8))
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y = Tensor(np.zeros([3, 3]).astype(np.int8))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.int8)
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print(output)
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assert np.all(output == output_expect)
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def test_assign_uint8():
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x = Tensor(np.ones([3, 3]).astype(np.uint8))
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y = Tensor(np.zeros([3, 3]).astype(np.uint8))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.uint8)
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print(output)
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assert np.all(output == output_expect)
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def test_assign_int16():
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x = Tensor(np.ones([3, 3]).astype(np.int16))
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y = Tensor(np.zeros([3, 3]).astype(np.int16))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.int16)
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print(output)
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assert np.all(output == output_expect)
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def test_assign_uint16():
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x = Tensor(np.ones([3, 3]).astype(np.uint16))
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y = Tensor(np.zeros([3, 3]).astype(np.uint16))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.uint16)
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print(output)
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assert np.all(output == output_expect)
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def test_assign_int32():
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x = Tensor(np.ones([3, 3]).astype(np.int32))
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y = Tensor(np.zeros([3, 3]).astype(np.int32))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.int32)
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print(output)
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assert np.all(output == output_expect)
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def test_assign_uint32():
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x = Tensor(np.ones([3, 3]).astype(np.uint32))
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y = Tensor(np.zeros([3, 3]).astype(np.uint32))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.uint32)
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print(output)
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assert np.all(output == output_expect)
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def test_assign_int64():
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x = Tensor(np.ones([3, 3]).astype(np.int64))
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y = Tensor(np.zeros([3, 3]).astype(np.int64))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.int64)
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print(output)
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assert np.all(output == output_expect)
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def test_assign_uint64():
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x = Tensor(np.ones([3, 3]).astype(np.uint64))
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y = Tensor(np.zeros([3, 3]).astype(np.uint64))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.ones([3, 3]).astype(np.uint64)
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print(output)
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assert np.all(output == output_expect)
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def test_assign_float16():
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x = Tensor(np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float16))
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y = Tensor(np.array([[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8],
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[0.1, 0.2, 0.3]]).astype(np.float16))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float16)
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print(output)
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assert np.all(output - output_expect < 1e-6)
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def test_assign_float32():
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x = Tensor(np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float32))
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y = Tensor(np.array([[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8],
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[0.1, 0.2, 0.3]]).astype(np.float32))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float32)
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print(output)
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assert np.all(output - output_expect < 1e-6)
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def test_assign_float64():
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x = Tensor(np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float64))
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y = Tensor(np.array([[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8],
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[0.1, 0.2, 0.3]]).astype(np.float64))
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assign = Assign(x, y)
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output = assign()
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output = output.asnumpy()
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output_expect = np.array([[0.1, 0.2, 0.3],
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[0.4, 0.5, 0.5],
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[0.6, 0.7, 0.8]]).astype(np.float64)
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print(output)
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assert np.all(output - output_expect < 1e-6)
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class AssignAdd(nn.Cell):
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def __init__(self, x, y):
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super(AssignAdd, self).__init__()
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self.x = Parameter(initializer(x, x.shape), name="x")
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self.y = Parameter(initializer(y, y.shape), name="y")
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self.assignadd = P.AssignAdd()
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def construct(self):
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self.assignadd(self.y, self.x)
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return self.y
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def test_number_assignadd_number():
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input_x = 2
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result1 = 5
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result2 = 5
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result1 += input_x
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assignadd = AssignAdd(result2, input_x)
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result2 = assignadd()
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expect = 7
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assert np.all(result1 == expect)
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assert np.all(result2 == expect)
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def test_tensor_assignadd_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]]))
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result1 = Tensor(np.array([[4, -2], [2, 17]]))
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result2 = Tensor(np.array([[4, -2], [2, 17]]))
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result1 += input_x
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result2 = AssignAdd(result2, input_x)()
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expect = Tensor(np.array([[6, 0], [5, 20]]))
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assert np.all(result1.asnumpy() == expect)
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assert np.all(result2.asnumpy() == expect)
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def test_tensor_assignadd_number():
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input_x = 3
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result1 = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
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result2 = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
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result1 += input_x
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result2 = AssignAdd(result2, input_x)()
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expect = Tensor(np.array([[7, 1], [5, 20]]))
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assert np.all(result1.asnumpy() == expect)
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assert np.all(result2.asnumpy() == expect)
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def test_number_assignadd_tensor():
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result1 = 3
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result2 = 3
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input_x = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
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result1 += input_x
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result2 = AssignAdd(result2, input_x)()
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expect = Tensor(np.array([[7, 1], [5, 20]]))
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assert np.all(result1.asnumpy() == expect)
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assert np.all(result2.asnumpy() == expect)
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def test_tuple_assignadd_tuple():
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result1 = (1, 2, 3, 4)
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result2 = (1, 2, 3, 4)
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input_x = (2, 3, 4, 5, 6)
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result1 += input_x
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result2 = AssignAdd(result2, input_x)()
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expect = (1, 2, 3, 4, 2, 3, 4, 5, 6)
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assert np.all(result1.asnumpy() == expect)
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assert np.all(result2.asnumpy() == expect)
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def test_string_assignadd_string():
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result1 = "string111"
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result2 = "string111"
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input_x = "string222"
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result1 += input_x
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result2 = AssignAdd(result2, input_x)()
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expect = "string111string222"
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assert result1 == expect
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assert result2 == expect
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class AssignSub(nn.Cell):
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def __init__(self, x, y):
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super(AssignSub, self).__init__()
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self.x = Parameter(initializer(x, x.shape), name="x")
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self.y = Parameter(initializer(y, y.shape), name="y")
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self.assignsub = P.AssignSub()
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def construct(self):
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self.assignsub(self.y, self.x)
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return self.y
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def test_number_assignsub_number():
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input_x = 2
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result1 = 5
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result2 = 5
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result1 -= input_x
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result2 = AssignSub(result2, input_x)()
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expect = 3
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assert np.all(result1 == expect)
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assert np.all(result2 == expect)
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def test_tensor_assignsub_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]]))
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result1 = Tensor(np.array([[4, -2], [2, 17]]))
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result2 = Tensor(np.array([[4, -2], [2, 17]]))
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result1 -= input_x
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result2 = AssignSub(result2, input_x)()
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expect = Tensor(np.array([[2, -4], [-1, 14]]))
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assert np.all(result1.asnumpy() == expect)
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assert np.all(result2.asnumpy() == expect)
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def test_tensor_assignsub_number():
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input_x = 3
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result1 = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
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result2 = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
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result1 -= input_x
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result2 = AssignSub(result2, input_x)()
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expect = Tensor(np.array([[1, -5], [-1, 14]]))
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assert np.all(result1.asnumpy() == expect)
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assert np.all(result2.asnumpy() == expect)
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def test_number_assignsub_tensor():
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result1 = 3
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result2 = 3
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input_x = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
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result1 -= input_x
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result2 = AssignSub(result2, input_x)()
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expect = Tensor(np.array([[-1, 5], [1, -14]]))
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assert np.all(result1.asnumpy() == expect)
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assert np.all(result2.asnumpy() == expect)
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def test_number_assignmul_number():
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input_x = 2
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result = 5
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result *= input_x
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expect = 10
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assert np.all(result == expect)
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def test_tensor_assignmul_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]]))
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result = Tensor(np.array([[4, -2], [2, 17]]))
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result *= input_x
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expect = Tensor(np.array([[8, -4], [6, 51]]))
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assert np.all(result.asnumpy() == expect)
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def test_tensor_assignmul_number():
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input_x = 3
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result = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
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result *= input_x
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expect = Tensor(np.array([[12, -6], [6, 51]]))
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assert np.all(result.asnumpy() == expect)
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def test_number_assignmul_tensor():
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result = 3
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input_x = Tensor(np.array([[4, -2], [2, 17]])).astype(np.float16)
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result *= input_x
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expect = Tensor(np.array([[12, -6], [6, 51]]))
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assert np.all(result.asnumpy() == expect)
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def test_number_assigndiv_number():
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input_x = 2
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result = 5
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result /= input_x
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expect = 2.5
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assert np.all(result == expect)
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def test_tensor_assigndiv_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]]))
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result = Tensor(np.array([[4, -2], [6, 15]]))
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result /= input_x
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expect = Tensor(np.array([[2, -1], [2, 5]]))
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assert np.all(result.asnumpy() == expect)
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def test_tensor_assigndiv_number():
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input_x = 3
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result = Tensor(np.array([[9, -3], [6, 15]])).astype(np.float16)
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result /= input_x
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expect = Tensor(np.array([[3, -1], [2, 5]]))
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assert np.all(result.asnumpy() == expect)
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def test_number_assigndiv_tensor():
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result = 3
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input_x = Tensor(np.array([[2, -2], [2, -2]])).astype(np.float16)
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result /= input_x
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expect = Tensor(np.array([[1.5, -1.5], [1.5, -1.5]]))
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assert np.all(result.asnumpy() == expect)
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def test_number_assignmod_number():
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input_x = 2
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result = 5
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result %= input_x
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expect = 1
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assert np.all(result == expect)
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def test_tensor_assignmod_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]]))
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result = Tensor(np.array([[4, -2], [6, 15]]))
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result %= input_x
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expect = Tensor(np.array([[0, 0], [0, 0]]))
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assert np.all(result.asnumpy() == expect)
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def test_tensor_assignmod_number():
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input_x = 3
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result = Tensor(np.array([[9, -3], [7, 15]])).astype(np.float16)
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result %= input_x
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expect = Tensor(np.array([[0, 0], [1, 0]]))
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assert np.all(result.asnumpy() == expect)
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def test_number_assignmod_tensor():
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result = 3
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input_x = Tensor(np.array([[2, -2], [2, -2]])).astype(np.float16)
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result %= input_x
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expect = Tensor(np.array([[1, -1], [1, -1]]))
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assert np.all(result.asnumpy() == expect)
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def test_number_assignmulmul_number():
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input_x = 2
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result = 5
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result **= input_x
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expect = 25
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assert np.all(result == expect)
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def test_tensor_assignmulmul_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]]))
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result = Tensor(np.array([[4, -2], [6, 5]]))
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result **= input_x
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expect = Tensor(np.array([[16, 4], [216, 125]]))
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assert np.all(result.asnumpy() == expect)
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def test_tensor_assignmulmul_number():
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input_x = 3
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result = Tensor(np.array([[9, -3], [7, 5]])).astype(np.float16)
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result **= input_x
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expect = Tensor(np.array([[729, -27], [343, 125]]))
|
||||
assert np.all(result.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_number_assignmulmul_tensor():
|
||||
result = 3
|
||||
input_x = Tensor(np.array([[2, 2], [2, 2]])).astype(np.float16)
|
||||
result **= input_x
|
||||
expect = Tensor(np.array([[9, 9], [9, 9]]))
|
||||
assert np.all(result.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_number_assigndivdiv_number():
|
||||
input_x = 2
|
||||
result = 5
|
||||
result //= input_x
|
||||
expect = 2
|
||||
assert np.all(result == expect)
|
||||
|
||||
|
||||
def test_tensor_assigndivdiv_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [3, 3]]))
|
||||
result = Tensor(np.array([[4, -2], [6, 6]]))
|
||||
result //= input_x
|
||||
expect = Tensor(np.array([[2, -1], [2, 2]]))
|
||||
assert np.all(result.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_assigndivdiv_number():
|
||||
input_x = 3
|
||||
result = Tensor(np.array([[9, -3], [15, 9]])).astype(np.float16)
|
||||
result //= input_x
|
||||
expect = Tensor(np.array([[3, -1], [5, 3]]))
|
||||
assert np.all(result.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_number_assigndivdiv_tensor():
|
||||
result = 3
|
||||
input_x = Tensor(np.array([[1, 2], [2, 2]])).astype(np.float16)
|
||||
result //= input_x
|
||||
expect = Tensor(np.array([[3, 1], [1, 1]]))
|
||||
assert np.all(result.asnumpy() == expect)
|
||||
|
|
@ -0,0 +1,587 @@
|
|||
# Copyright 2021 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 math ops """
|
||||
import numpy as np
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.ops import operations as P
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE)
|
||||
|
||||
|
||||
class Add(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Add, self).__init__()
|
||||
self.add = P.Add()
|
||||
|
||||
def construct(self, x, y):
|
||||
z = self.add(x, y)
|
||||
return z
|
||||
|
||||
|
||||
def test_number_add_number():
|
||||
input_x = 0.1
|
||||
input_y = -3.2
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = -3.1
|
||||
assert result1 == expect
|
||||
assert result2 == expect
|
||||
|
||||
|
||||
def test_tensor_add_tensor_int8():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.int8)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.int8)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_int16():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.int16)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.int16)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_int32():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.int32)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.int32)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_int64():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.int64)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.int64)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_uint8():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.uint8)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.uint8)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_uint16():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.uint16)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.uint16)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_uint32():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.uint32)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.uint32)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_uint64():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.uint64)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.uint64)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_float16():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.float16)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.float16)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_float32():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.float32)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.float32)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_tensor_float64():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.float64)
|
||||
input_y = Tensor(np.zeros(shape=[3])).astype(np.float64)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3])
|
||||
assert np.all(result1.asnumpy() == expect)
|
||||
assert np.all(result2.asnumpy() == expect)
|
||||
|
||||
|
||||
def test_tensor_add_number():
|
||||
input_x = Tensor(np.ones(shape=[3])).astype(np.float32)
|
||||
input_y = -0.4
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = np.ones(shape=[3]) * 0.6
|
||||
assert np.all(result1.asnumpy() == expect.astype(np.float32))
|
||||
assert np.all(result2.asnumpy() == expect.astype(np.float32))
|
||||
|
||||
|
||||
def test_tuple_add_tuple():
|
||||
input_x = (Tensor(np.ones(shape=[3])).astype(np.float32))
|
||||
input_y = (Tensor(np.ones(shape=[3])).astype(np.float32) * 2)
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = (np.ones(shape=[3]) * 3)
|
||||
assert np.all(result1.asnumpy() == expect.astype(np.float32))
|
||||
assert np.all(result2.asnumpy() == expect.astype(np.float32))
|
||||
|
||||
|
||||
def test_tuple_add_tuple_shape():
|
||||
input_x = (Tensor(np.ones(shape=[3])).astype(np.float32))
|
||||
input_y = (Tensor(np.ones(shape=[4])).astype(np.float32) * 2)
|
||||
|
||||
result1 = input_x + input_y
|
||||
add_net = Add()
|
||||
result2 = add_net(input_x, input_y)
|
||||
expect = (np.ones(shape=[3]) * 3)
|
||||
assert np.all(result1.asnumpy() == expect.astype(np.float32))
|
||||
assert np.all(result2.asnumpy() == expect.astype(np.float32))
|
||||
|
||||
|
||||
def test_string_add_string():
|
||||
input_x = "string111_"
|
||||
input_y = "add_string222"
|
||||
result = input_x + input_y
|
||||
expect = "string111_add_string222"
|
||||
assert result == expect
|
||||
|
||||
|
||||
def test_list_add_list():
|
||||
input_x = [1, 3, 5, 7, 9]
|
||||
input_y = ["0", "6"]
|
||||
result = input_x + input_y
|
||||
expect = [1, 3, 5, 7, 9, "0", "6"]
|
||||
assert result == expect
|
||||
|
||||
|
||||
class Sub(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Sub, self).__init__()
|
||||
self.sub = P.Sub()
|
||||
|
||||
def construct(self, x, y):
|
||||
z = self.sub(x, y)
|
||||
return z
|
||||
|
||||
|
||||
def test_number_sub_number():
|
||||
input_x = 10.11
|
||||
input_y = 902
|
||||
result1 = input_x - input_y
|
||||
sub_net = Sub()
|
||||
result2 = sub_net(input_x, input_y)
|
||||
expect = -891.89
|
||||
assert np.all(result1 == expect)
|
||||
assert np.all(result2 == expect)
|
||||
|
||||
|
||||
def test_tensor_sub_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [3, 3]]))
|
||||
input_y = Tensor(np.array([[1, 2], [-3, 3]]))
|
||||
result1 = input_x - input_y
|
||||
sub_net = Sub()
|
||||
result2 = sub_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[1, 0], [6, 0]]))
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_tensor_sub_number():
|
||||
input_x = Tensor(np.array([[2, 2], [3, 3]]))
|
||||
input_y = -2
|
||||
result1 = input_x - input_y
|
||||
sub_net = Sub()
|
||||
result2 = sub_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[4, 4], [5, 5]]))
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_number_sub_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [3, 3]]))
|
||||
input_y = -2
|
||||
result1 = input_x - input_y
|
||||
sub_net = Sub()
|
||||
result2 = sub_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[-4, -4], [-5, -5]]))
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
class Mul(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Mul, self).__init__()
|
||||
self.mul = P.Mul()
|
||||
|
||||
def construct(self, x, y):
|
||||
z = self.mul(x, y)
|
||||
return z
|
||||
|
||||
|
||||
def test_number_mul_number():
|
||||
input_x = 4.91
|
||||
input_y = 0.16
|
||||
result1 = input_x * input_y
|
||||
mul_net = Mul()
|
||||
result2 = mul_net(input_x, input_y)
|
||||
expect = 0.7856
|
||||
diff1 = result1 - expect
|
||||
diff2 = result2 - expect
|
||||
error = 1.0e-6
|
||||
assert np.all(diff1 < error)
|
||||
assert np.all(-diff1 < error)
|
||||
assert np.all(diff2 < error)
|
||||
assert np.all(-diff2 < error)
|
||||
|
||||
|
||||
def test_tensor_mul_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
|
||||
input_y = Tensor(np.array([[1, 2], [3, 1]])).astype(np.float32)
|
||||
result1 = input_x * input_y
|
||||
mul_net = Mul()
|
||||
result2 = mul_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[2, 4], [9, 3]]))
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_tensor_mul_number():
|
||||
input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
|
||||
input_y = -1
|
||||
result1 = input_x * input_y
|
||||
mul_net = Mul()
|
||||
result2 = mul_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[-2, -2], [-3, -3]]))
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_number_mul_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
|
||||
input_y = -1
|
||||
result1 = input_x * input_y
|
||||
mul_net = Mul()
|
||||
result2 = mul_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[-2, -2], [-3, -3]]))
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
class Div(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Div, self).__init__()
|
||||
self.div = P.Div()
|
||||
|
||||
def construct(self, x, y):
|
||||
z = self.div(x, y)
|
||||
return z
|
||||
|
||||
|
||||
def test_number_div_number():
|
||||
input_x = 4
|
||||
input_y = -1
|
||||
result1 = input_x / input_y
|
||||
div_net = Div()
|
||||
result2 = div_net(input_x, input_y)
|
||||
expect = -4
|
||||
assert np.all(result1 == expect)
|
||||
assert np.all(result2 == expect)
|
||||
|
||||
|
||||
def test_tensor_div_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
|
||||
input_y = Tensor(np.array([[1, 2], [3, 1]])).astype(np.float32)
|
||||
result1 = input_x / input_y
|
||||
div_net = Div()
|
||||
result2 = div_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[2, 1], [1, 3]]))
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_tensor_div_number():
|
||||
input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
|
||||
input_y = 2
|
||||
result1 = input_x / input_y
|
||||
div_net = Div()
|
||||
result2 = div_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[1, 1], [1.5, 1.5]]))
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_number_div_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = 2
|
||||
result1 = input_x / input_y
|
||||
div_net = Div()
|
||||
result2 = div_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[1, 1], [0.5, 0.5]]))
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
class Mod(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Mod, self).__init__()
|
||||
self.mod = P.Mod()
|
||||
|
||||
def construct(self, x, y):
|
||||
z = self.mod(x, y)
|
||||
return z
|
||||
|
||||
|
||||
def test_number_mod_number():
|
||||
input_x = 19
|
||||
input_y = 2
|
||||
result1 = input_x % input_y
|
||||
mod_net = Mod()
|
||||
result2 = mod_net(input_x, input_y)
|
||||
expect = 1
|
||||
assert np.all(result1 == expect)
|
||||
assert np.all(result2 == expect)
|
||||
|
||||
|
||||
def test_tensor_mod_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
result1 = input_x % input_y
|
||||
mod_net = Mod()
|
||||
result2 = mod_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[0, 0], [0, 0]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_tensor_mod_number():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = -1
|
||||
result1 = input_x % input_y
|
||||
mod_net = Mod()
|
||||
result2 = mod_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[0, 0], [0, 0]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_number_mod_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = 5
|
||||
result1 = input_x % input_y
|
||||
mod_net = Mod()
|
||||
result2 = mod_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[1, 1], [1, 1]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
class Pow(nn.Cell):
|
||||
def __init__(self):
|
||||
super(Pow, self).__init__()
|
||||
self.pow = P.Pow()
|
||||
|
||||
def construct(self, x, y):
|
||||
z = self.pow(x, y)
|
||||
return z
|
||||
|
||||
|
||||
def test_number_pow_number():
|
||||
input_x = 2
|
||||
input_y = 5
|
||||
result1 = input_x ** input_y
|
||||
pow_net = Pow()
|
||||
result2 = pow_net(input_x, input_y)
|
||||
expect = 32
|
||||
assert np.all(result1 == expect)
|
||||
assert np.all(result2 == expect)
|
||||
|
||||
|
||||
def test_tensor_pow_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
result1 = input_x ** input_y
|
||||
pow_net = Pow()
|
||||
result2 = pow_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[4, 4], [256, 256]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_tensor_pow_number():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = 3
|
||||
result1 = input_x ** input_y
|
||||
pow_net = Pow()
|
||||
result2 = pow_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[8, 8], [64, 64]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_number_pow_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = 3
|
||||
result1 = input_x ** input_y
|
||||
pow_net = Pow()
|
||||
result2 = pow_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[9, 9], [81, 81]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
class FloorDiv(nn.Cell):
|
||||
def __init__(self):
|
||||
super(FloorDiv, self).__init__()
|
||||
self.floordiv = P.FloorDiv()
|
||||
|
||||
def construct(self, x, y):
|
||||
z = self.floordiv(x, y)
|
||||
return z
|
||||
|
||||
|
||||
def test_number_floordiv_number():
|
||||
input_x = 2
|
||||
input_y = 5
|
||||
result1 = input_x // input_y
|
||||
floordiv_net = FloorDiv()
|
||||
result2 = floordiv_net(input_x, input_y)
|
||||
expect = 0
|
||||
assert np.all(result1 == expect)
|
||||
assert np.all(result2 == expect)
|
||||
|
||||
|
||||
def test_tensor_floordiv_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = Tensor(np.array([[1, 2], [-2, 4]])).astype(np.float32)
|
||||
result1 = input_x // input_y
|
||||
floordiv_net = FloorDiv()
|
||||
result2 = floordiv_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[2, 1], [-2, 1]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_tensor_floordiv_number():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = 3
|
||||
result1 = input_x // input_y
|
||||
floordiv_net = FloorDiv()
|
||||
result2 = floordiv_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[0, 0], [1, 1]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_number_floordiv_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = 3
|
||||
result1 = input_x // input_y
|
||||
floordiv_net = FloorDiv()
|
||||
result2 = floordiv_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[1, 1], [0, 0]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_number_floormod_number():
|
||||
input_x = 2
|
||||
input_y = 5
|
||||
result1 = input_x // input_y
|
||||
floordiv_net = FloorDiv()
|
||||
result2 = floordiv_net(input_x, input_y)
|
||||
expect = 2
|
||||
assert np.all(result1 == expect)
|
||||
assert np.all(result2 == expect)
|
||||
|
||||
|
||||
def test_tensor_floormod_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = Tensor(np.array([[1, 2], [-2, 4]])).astype(np.float32)
|
||||
result1 = input_x // input_y
|
||||
floordiv_net = FloorDiv()
|
||||
result2 = floordiv_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[1, 0], [-2, 0]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_tensor_floormod_number():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = 3
|
||||
result1 = input_x // input_y
|
||||
floordiv_net = FloorDiv()
|
||||
result2 = floordiv_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[2, 2], [1, 1]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
|
||||
|
||||
def test_number_floormod_tensor():
|
||||
input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
|
||||
input_y = 3
|
||||
result1 = input_x // input_y
|
||||
floordiv_net = FloorDiv()
|
||||
result2 = floordiv_net(input_x, input_y)
|
||||
expect = Tensor(np.array([[1, 1], [3, 3]])).astype(np.float32)
|
||||
assert np.all(result1.asnumpy() == expect.asnumpy())
|
||||
assert np.all(result2.asnumpy() == expect.asnumpy())
|
||||
Loading…
Reference in New Issue