forked from mindspore-Ecosystem/mindspore
588 lines
18 KiB
Python
588 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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""" test math ops """
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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
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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 Add(nn.Cell):
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def __init__(self):
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super(Add, self).__init__()
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self.add = P.Add()
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def construct(self, x, y):
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z = self.add(x, y)
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return z
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def test_number_add_number():
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input_x = 0.1
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input_y = -3.2
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = -3.1
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assert result1 == expect
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assert result2 == expect
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def test_tensor_add_tensor_int8():
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input_x = Tensor(np.ones(shape=[3])).astype(np.int8)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.int8)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_int16():
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input_x = Tensor(np.ones(shape=[3])).astype(np.int16)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.int16)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_int32():
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input_x = Tensor(np.ones(shape=[3])).astype(np.int32)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.int32)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_int64():
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input_x = Tensor(np.ones(shape=[3])).astype(np.int64)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.int64)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_uint8():
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input_x = Tensor(np.ones(shape=[3])).astype(np.uint8)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.uint8)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_uint16():
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input_x = Tensor(np.ones(shape=[3])).astype(np.uint16)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.uint16)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_uint32():
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input_x = Tensor(np.ones(shape=[3])).astype(np.uint32)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.uint32)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_uint64():
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input_x = Tensor(np.ones(shape=[3])).astype(np.uint64)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.uint64)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_float16():
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input_x = Tensor(np.ones(shape=[3])).astype(np.float16)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.float16)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_float32():
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input_x = Tensor(np.ones(shape=[3])).astype(np.float32)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.float32)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_tensor_float64():
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input_x = Tensor(np.ones(shape=[3])).astype(np.float64)
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input_y = Tensor(np.zeros(shape=[3])).astype(np.float64)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3])
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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_add_number():
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input_x = Tensor(np.ones(shape=[3])).astype(np.float32)
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input_y = -0.4
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = np.ones(shape=[3]) * 0.6
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assert np.all(result1.asnumpy() == expect.astype(np.float32))
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assert np.all(result2.asnumpy() == expect.astype(np.float32))
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def test_tuple_add_tuple():
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input_x = (Tensor(np.ones(shape=[3])).astype(np.float32))
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input_y = (Tensor(np.ones(shape=[3])).astype(np.float32) * 2)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = (np.ones(shape=[3]) * 3)
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assert np.all(result1.asnumpy() == expect.astype(np.float32))
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assert np.all(result2.asnumpy() == expect.astype(np.float32))
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def test_tuple_add_tuple_shape():
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input_x = (Tensor(np.ones(shape=[3])).astype(np.float32))
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input_y = (Tensor(np.ones(shape=[4])).astype(np.float32) * 2)
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result1 = input_x + input_y
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add_net = Add()
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result2 = add_net(input_x, input_y)
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expect = (np.ones(shape=[3]) * 3)
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assert np.all(result1.asnumpy() == expect.astype(np.float32))
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assert np.all(result2.asnumpy() == expect.astype(np.float32))
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def test_string_add_string():
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input_x = "string111_"
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input_y = "add_string222"
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result = input_x + input_y
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expect = "string111_add_string222"
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assert result == expect
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def test_list_add_list():
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input_x = [1, 3, 5, 7, 9]
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input_y = ["0", "6"]
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result = input_x + input_y
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expect = [1, 3, 5, 7, 9, "0", "6"]
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assert result == expect
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class Sub(nn.Cell):
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def __init__(self):
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super(Sub, self).__init__()
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self.sub = P.Sub()
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def construct(self, x, y):
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z = self.sub(x, y)
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return z
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def test_number_sub_number():
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input_x = 10.11
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input_y = 902
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result1 = input_x - input_y
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sub_net = Sub()
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result2 = sub_net(input_x, input_y)
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expect = -891.89
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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_sub_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]]))
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input_y = Tensor(np.array([[1, 2], [-3, 3]]))
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result1 = input_x - input_y
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sub_net = Sub()
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result2 = sub_net(input_x, input_y)
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expect = Tensor(np.array([[1, 0], [6, 0]]))
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_tensor_sub_number():
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input_x = Tensor(np.array([[2, 2], [3, 3]]))
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input_y = -2
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result1 = input_x - input_y
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sub_net = Sub()
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result2 = sub_net(input_x, input_y)
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expect = Tensor(np.array([[4, 4], [5, 5]]))
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_number_sub_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]]))
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input_y = -2
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result1 = input_x - input_y
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sub_net = Sub()
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result2 = sub_net(input_x, input_y)
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expect = Tensor(np.array([[-4, -4], [-5, -5]]))
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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class Mul(nn.Cell):
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def __init__(self):
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super(Mul, self).__init__()
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self.mul = P.Mul()
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def construct(self, x, y):
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z = self.mul(x, y)
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return z
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def test_number_mul_number():
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input_x = 4.91
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input_y = 0.16
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result1 = input_x * input_y
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mul_net = Mul()
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result2 = mul_net(input_x, input_y)
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expect = 0.7856
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diff1 = result1 - expect
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diff2 = result2 - expect
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error = 1.0e-6
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assert np.all(diff1 < error)
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assert np.all(-diff1 < error)
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assert np.all(diff2 < error)
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assert np.all(-diff2 < error)
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def test_tensor_mul_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
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input_y = Tensor(np.array([[1, 2], [3, 1]])).astype(np.float32)
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result1 = input_x * input_y
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mul_net = Mul()
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result2 = mul_net(input_x, input_y)
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expect = Tensor(np.array([[2, 4], [9, 3]]))
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_tensor_mul_number():
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input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
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input_y = -1
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result1 = input_x * input_y
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mul_net = Mul()
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result2 = mul_net(input_x, input_y)
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expect = Tensor(np.array([[-2, -2], [-3, -3]]))
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_number_mul_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
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input_y = -1
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result1 = input_x * input_y
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mul_net = Mul()
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result2 = mul_net(input_x, input_y)
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expect = Tensor(np.array([[-2, -2], [-3, -3]]))
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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class Div(nn.Cell):
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def __init__(self):
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super(Div, self).__init__()
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self.div = P.Div()
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def construct(self, x, y):
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z = self.div(x, y)
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return z
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def test_number_div_number():
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input_x = 4
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input_y = -1
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result1 = input_x / input_y
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div_net = Div()
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result2 = div_net(input_x, input_y)
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expect = -4
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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_div_tensor():
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input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
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input_y = Tensor(np.array([[1, 2], [3, 1]])).astype(np.float32)
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result1 = input_x / input_y
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div_net = Div()
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result2 = div_net(input_x, input_y)
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expect = Tensor(np.array([[2, 1], [1, 3]]))
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_tensor_div_number():
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input_x = Tensor(np.array([[2, 2], [3, 3]])).astype(np.float32)
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input_y = 2
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result1 = input_x / input_y
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div_net = Div()
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result2 = div_net(input_x, input_y)
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expect = Tensor(np.array([[1, 1], [1.5, 1.5]]))
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_number_div_tensor():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = 2
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result1 = input_x / input_y
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div_net = Div()
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result2 = div_net(input_x, input_y)
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expect = Tensor(np.array([[1, 1], [0.5, 0.5]]))
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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class Mod(nn.Cell):
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def __init__(self):
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super(Mod, self).__init__()
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self.mod = P.Mod()
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def construct(self, x, y):
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z = self.mod(x, y)
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return z
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def test_number_mod_number():
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input_x = 19
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input_y = 2
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result1 = input_x % input_y
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mod_net = Mod()
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result2 = mod_net(input_x, input_y)
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expect = 1
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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_mod_tensor():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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result1 = input_x % input_y
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mod_net = Mod()
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result2 = mod_net(input_x, input_y)
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expect = Tensor(np.array([[0, 0], [0, 0]])).astype(np.float32)
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_tensor_mod_number():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = -1
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result1 = input_x % input_y
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mod_net = Mod()
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result2 = mod_net(input_x, input_y)
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expect = Tensor(np.array([[0, 0], [0, 0]])).astype(np.float32)
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_number_mod_tensor():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = 5
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result1 = input_x % input_y
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mod_net = Mod()
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result2 = mod_net(input_x, input_y)
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expect = Tensor(np.array([[1, 1], [1, 1]])).astype(np.float32)
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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class Pow(nn.Cell):
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def __init__(self):
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super(Pow, self).__init__()
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self.pow = P.Pow()
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def construct(self, x, y):
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z = self.pow(x, y)
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return z
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def test_number_pow_number():
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input_x = 2
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input_y = 5
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result1 = input_x ** input_y
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pow_net = Pow()
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result2 = pow_net(input_x, input_y)
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expect = 32
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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_pow_tensor():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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result1 = input_x ** input_y
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pow_net = Pow()
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result2 = pow_net(input_x, input_y)
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expect = Tensor(np.array([[4, 4], [256, 256]])).astype(np.float32)
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_tensor_pow_number():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = 3
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result1 = input_x ** input_y
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pow_net = Pow()
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result2 = pow_net(input_x, input_y)
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expect = Tensor(np.array([[8, 8], [64, 64]])).astype(np.float32)
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_number_pow_tensor():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = 3
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result1 = input_x ** input_y
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pow_net = Pow()
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result2 = pow_net(input_x, input_y)
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expect = Tensor(np.array([[9, 9], [81, 81]])).astype(np.float32)
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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class FloorDiv(nn.Cell):
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def __init__(self):
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super(FloorDiv, self).__init__()
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self.floordiv = P.FloorDiv()
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def construct(self, x, y):
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z = self.floordiv(x, y)
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return z
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def test_number_floordiv_number():
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input_x = 2
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input_y = 5
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result1 = input_x // input_y
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floordiv_net = FloorDiv()
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result2 = floordiv_net(input_x, input_y)
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expect = 0
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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_floordiv_tensor():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = Tensor(np.array([[1, 2], [-2, 4]])).astype(np.float32)
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result1 = input_x // input_y
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floordiv_net = FloorDiv()
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result2 = floordiv_net(input_x, input_y)
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expect = Tensor(np.array([[2, 1], [-2, 1]])).astype(np.float32)
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_tensor_floordiv_number():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = 3
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result1 = input_x // input_y
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floordiv_net = FloorDiv()
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result2 = floordiv_net(input_x, input_y)
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expect = Tensor(np.array([[0, 0], [1, 1]])).astype(np.float32)
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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def test_number_floordiv_tensor():
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input_x = Tensor(np.array([[2, 2], [4, 4]])).astype(np.float32)
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input_y = 3
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result1 = input_x // input_y
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floordiv_net = FloorDiv()
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result2 = floordiv_net(input_x, input_y)
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expect = Tensor(np.array([[1, 1], [0, 0]])).astype(np.float32)
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assert np.all(result1.asnumpy() == expect.asnumpy())
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assert np.all(result2.asnumpy() == expect.asnumpy())
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|
|
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def test_number_floormod_number():
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input_x = 2
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|
input_y = 5
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result1 = input_x // input_y
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floordiv_net = FloorDiv()
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result2 = floordiv_net(input_x, input_y)
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expect = 2
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assert np.all(result1 == expect)
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assert np.all(result2 == expect)
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|
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def test_tensor_floormod_tensor():
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|
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()
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|
result2 = floordiv_net(input_x, input_y)
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|
expect = Tensor(np.array([[1, 0], [-2, 0]])).astype(np.float32)
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|
assert np.all(result1.asnumpy() == expect.asnumpy())
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|
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())
|