forked from mindspore-Ecosystem/mindspore
1250 lines
44 KiB
Python
1250 lines
44 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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""" test_tensor_slice """
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import numpy as np
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import pytest
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from mindspore import Tensor
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from mindspore import Parameter
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from mindspore import context
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from mindspore import dtype as mstype
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from mindspore.nn import Cell
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from mindspore.common.parameter import ParameterTuple
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from mindspore.ops import composite as C
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grad_by_list_with_sens = C.GradOperation(get_by_list=True, sens_param=True)
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def setup_module():
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context.set_context(mode=context.PYNATIVE_MODE)
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class NetWorkSlicePositive(Cell):
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def __init__(self):
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super(NetWorkSlicePositive, self).__init__()
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self.tensor_ret0 = Tensor(np.ones([1, 2, 3], np.int32))
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self.tensor_ret1 = Tensor(np.ones([4, 8, 10], np.int32))
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self.tensor_ret2 = Tensor(np.ones([6, 8, 10], np.int32))
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self.tensor_ret3 = Tensor(np.ones([3, 8, 10], np.int32))
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def construct(self, tensor):
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ret0 = tensor[3:4:1, 1:5:2, 3:6:1] + self.tensor_ret0
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ret1 = tensor[-6:4:1, 0:8:1, ::1] + self.tensor_ret1
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ret2 = tensor[::, ::, ::] + self.tensor_ret2
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ret3 = tensor[::2] + self.tensor_ret3
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return ret0, ret1, ret2, ret3
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_slice_positive():
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net = NetWorkSlicePositive()
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input_np = np.arange(6*8*10).reshape(6, 8, 10).astype(np.int32)
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input_0 = Tensor(input_np)
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output0, output1, output2, output3 = net(input_0)
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assert np.all(output0.asnumpy() == input_np[3:4:1, 1:5:2, 3:6:1] + np.ones([1, 2, 3]))
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assert np.all(output1.asnumpy() == input_np[-6:4:1, 0:8:1, ::1] + np.ones([4, 8, 10]))
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assert np.all(output2.asnumpy() == input_np[::, ::, ::] + np.ones([6, 8, 10]))
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assert np.all(output3.asnumpy() == input_np[::2] + np.ones([3, 8, 10]))
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class NetWorkSliceEllipsis(Cell):
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def __init__(self):
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super(NetWorkSliceEllipsis, self).__init__()
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self.tensor_ret0 = Tensor(np.ones([2, 7, 8], np.int32))
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self.tensor_ret1 = Tensor(np.ones([6, 7, 8, 9], np.int32))
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self.tensor_ret2 = Tensor(np.ones([1, 6, 7, 8, 9], np.int32))
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def construct(self, tensor):
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ret0 = tensor[0:4:2, ..., 1] + self.tensor_ret0
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ret1 = tensor[...] + self.tensor_ret1
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ret2 = tensor[None] + self.tensor_ret2
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ret3 = tensor[True] + self.tensor_ret2
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return ret0, ret1, ret2, ret3
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_slice_ellipsis():
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net = NetWorkSliceEllipsis()
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input_np = np.arange(6*7*8*9).reshape(6, 7, 8, 9).astype(np.int32)
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input_0 = Tensor(input_np)
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output0, output1, output2, output3 = net(input_0)
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assert np.all(output0.asnumpy() == input_np[0:4:2, ..., 1] + np.ones([2, 7, 8]))
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assert np.all(output1.asnumpy() == input_np[...] + np.ones([6, 7, 8, 9]))
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assert np.all(output2.asnumpy() == input_np[None] + np.ones([6, 7, 8, 9]))
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assert np.all(output3.asnumpy() == input_np[True] + np.ones([1, 6, 7, 8, 9]))
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class NetWorkReduceDimension(Cell):
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def __init__(self):
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super(NetWorkReduceDimension, self).__init__()
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self.tensor_ret1 = Tensor(np.ones([3, 10], np.int32))
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self.tensor_ret2 = Tensor(np.ones([6, 8], np.int32))
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self.tensor_ret3 = Tensor(np.array(8, np.int32))
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self.tensor_ret4 = Tensor(np.ones([8, 10], np.int32))
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def construct(self, tensor):
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ret1 = tensor[::2, 1, ::1] + self.tensor_ret1
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ret2 = tensor[::, ::, 0] + self.tensor_ret2
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ret3 = tensor[3, 2, 5] + self.tensor_ret3
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ret4 = tensor[1] + self.tensor_ret4
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return ret1, ret2, ret3, ret4
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_reduce_dimension():
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net = NetWorkReduceDimension()
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input_np = np.arange(6*8*10).reshape(6, 8, 10).astype(np.int32)
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input_0 = Tensor(input_np)
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output1, output2, output3, output4 = net(input_0)
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assert np.all(output1.asnumpy() == input_np[::2, 1, ::1] + np.ones([3, 10]))
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assert np.all(output2.asnumpy() == input_np[::, ::, 0] + np.ones([6, 8]))
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assert np.all(output3.asnumpy() == input_np[3, 2, 5] + np.array(8, np.int32))
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assert np.all(output4.asnumpy() == input_np[1] + np.ones([8, 10]))
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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class NetWorkSliceStep(Cell):
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def __init__(self):
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super(NetWorkSliceStep, self).__init__()
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self.tensor_ret1 = Tensor(np.ones([6, 5, 10], np.int32))
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self.tensor_ret2 = Tensor(np.ones([3, 5, 5], np.int32))
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def construct(self, tensor):
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ret1 = tensor[::1, -5::, ::-1] + self.tensor_ret1
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ret2 = tensor[::2, -5::, ::2] + self.tensor_ret2
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return ret1, ret2
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@pytest.mark.level1
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# ascend op stridedslice has bug, and has not been fixed.
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_step_negative():
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net = NetWorkSliceStep()
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input_np = np.arange(6*8*10).reshape(6, 8, 10).astype(np.int32)
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input_0 = Tensor(input_np)
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output1, output2 = net(input_0)
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assert np.all(output1.asnumpy() == input_np[::1, -5::, ::-1] + np.ones([6, 5, 10]))
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assert np.all(output2.asnumpy() == input_np[::2, -5::, ::2] + np.ones([3, 5, 5]))
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class TensorGetItemByThreeTensors(Cell):
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def __init__(self):
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super(TensorGetItemByThreeTensors, self).__init__()
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self.const0 = Tensor(np.ones((4, 5, 8, 10)), mstype.int32)
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self.const1 = Tensor(np.ones((3, 4, 5, 10)), mstype.int32)
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self.const2 = Tensor(np.ones((5, 3, 4, 5)), mstype.int32)
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def construct(self, x, index_0, index_1, index_2):
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ret0 = x[index_0] + self.const0
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ret1 = x[index_0, index_1] + self.const1
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ret2 = x[index_0, index_1, index_2] + self.const2
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return ret0, ret1, ret2
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_getitem_by_tensors():
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"""This testcase may encounter a sync stream error occasionally"""
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net = TensorGetItemByThreeTensors()
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input_x = np.arange(6*8*10).reshape(6, 8, 10).astype(np.int32)
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index_0 = np.random.randint(6, size=(3, 4, 5)).astype(np.int32)
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index_1 = np.random.randint(6, size=(4, 5)).astype(np.int32)
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index_2 = np.random.randint(6, size=(5, 3, 4, 5)).astype(np.int32)
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input_x_ms = Tensor(input_x)
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index_0_ms = Tensor(index_0)
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index_1_ms = Tensor(index_1)
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input_2_ms = Tensor(index_2)
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output0, output1, output2 = net(input_x_ms, index_0_ms, index_1_ms, input_2_ms)
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assert np.all(output0.asnumpy() == input_x[index_0] + np.ones([4, 5, 8, 10]))
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assert np.all(output1.asnumpy() == input_x[index_0, index_1] + np.ones([3, 4, 5, 10]))
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assert np.all(output2.asnumpy() == input_x[index_0, index_1, index_2] + np.ones([5, 3, 4, 5]))
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class TensorGetItemByMixedTensorsBasicCase(Cell):
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def __init__(self, c0, c1, c2, c3, c4, c5):
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super(TensorGetItemByMixedTensorsBasicCase, self).__init__()
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self.const0 = Tensor(c0)
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self.const1 = Tensor(c1)
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self.const2 = Tensor(c2)
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self.const3 = Tensor(c3)
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self.const4 = Tensor(c4)
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self.const5 = Tensor(c5)
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def construct(self, tensor, index_0, index_1):
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ret0 = tensor[index_0, index_1, 0:3] + self.const0
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ret1 = tensor[0:3, index_0, ...] + self.const1
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ret2 = tensor[0, index_0, index_1] + self.const2
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ret3 = tensor[..., index_0, 0:3] + self.const3
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ret4 = tensor[0:2, index_0, index_1] + self.const4
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ret5 = tensor[..., index_0, index_1] + self.const5
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return ret0, ret1, ret2, ret3, ret4, ret5
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_getitem_by_mixed_tensors():
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const0 = np.ones((3, 4, 5, 3), np.float32)
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const1 = np.ones((3, 3, 4, 5, 5), np.float32)
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const2 = np.ones((3, 4, 5), np.float32)
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const3 = np.ones((3, 3, 4, 5, 3), np.float32)
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const4 = np.ones((2, 3, 4, 5), np.float32)
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const5 = np.ones((3, 3, 4, 5), np.float32)
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net = TensorGetItemByMixedTensorsBasicCase(const0, const1, const2, const3, const4, const5)
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input_np = np.arange(3 * 4 * 5).reshape((3, 4, 5)).astype(np.float32)
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input_ms = Tensor(input_np, mstype.float32)
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index_np_0 = np.random.randint(3, size=(3, 4, 5)).astype(np.int32)
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index_np_1 = np.random.randint(4, size=(4, 5)).astype(np.int32)
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index_0 = Tensor(index_np_0, mstype.int32)
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index_1 = Tensor(index_np_1, mstype.int32)
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out0, out1, out2, out3, out4, out5 = net(input_ms, index_0, index_1)
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assert np.all(out0.asnumpy() == (input_np[index_np_0, index_np_1, 0:3] + const0))
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assert np.all(out1.asnumpy() == (input_np[0:3, index_np_0, ...] + const1))
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assert np.all(out2.asnumpy() == (input_np[0, index_np_0, index_np_1] + const2))
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assert np.all(out3.asnumpy() == (input_np[..., index_np_0, 0:3] + const3))
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assert np.all(out4.asnumpy() == (input_np[0:2, index_np_0, index_np_1] + const4))
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assert np.all(out5.asnumpy() == (input_np[..., index_np_0, index_np_1] + const5))
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class TensorItemByNone(Cell):
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def construct(self, tensor):
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ret = tensor.item()
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return ret
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_item_by_none():
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net = TensorItemByNone()
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input_1d_np = np.array([1]).astype(np.float32)
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input_1d_ms = Tensor(input_1d_np, mstype.float32)
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input_3d_np = np.random.randint(3, size=(3, 4, 5)).astype(np.int32)
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input_3d_ms = Tensor(input_3d_np, mstype.float32)
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output_ms = net(input_1d_ms)
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assert np.all(output_ms.asnumpy() == input_1d_np.item())
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with pytest.raises(ValueError):
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net(input_3d_ms)
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class TensorItemByItem(Cell):
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def construct(self, tensor, index):
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ret = tensor.item(index)
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return ret
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_item_by_int():
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net = TensorItemByItem()
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input_1d_np = np.array([1]).astype(np.float32)
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input_1d_ms = Tensor(input_1d_np, mstype.float32)
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input_3d_np = np.random.randint(3, size=(3, 4, 5)).astype(np.int32)
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input_3d_ms = Tensor(input_3d_np, mstype.float32)
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index_np_1, index_np_2, index_np_3, index_np_4 = 0, 1.0, 30, 60
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output_1d_ms = net(input_1d_ms, index_np_1)
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output_3d_ms_1 = net(input_3d_ms, index_np_1)
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output_3d_ms_2 = net(input_3d_ms, index_np_3)
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assert np.all(output_1d_ms.asnumpy() == input_1d_np.item(index_np_1))
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assert np.all(output_3d_ms_1.asnumpy() == input_3d_np.item(index_np_1))
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assert np.all(output_3d_ms_2.asnumpy() == input_3d_np.item(index_np_3))
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with pytest.raises(TypeError):
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net(input_1d_ms, index_np_2)
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with pytest.raises(IndexError):
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net(input_1d_ms, index_np_3)
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with pytest.raises(TypeError):
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net(input_3d_ms, index_np_2)
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with pytest.raises(IndexError):
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net(input_3d_ms, index_np_4)
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_item_by_tuple():
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net = TensorItemByItem()
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input_1d_np = np.array([1]).astype(np.float32)
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input_1d_ms = Tensor(input_1d_np, mstype.float32)
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input_3d_np = np.random.randint(3, size=(3, 4, 5)).astype(np.int32)
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input_3d_ms = Tensor(input_3d_np, mstype.float32)
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index_np_1 = (0,)
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index_np_2 = (1, 2)
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index_np_3 = (1, 2, 3)
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index_np_4 = (3, 4, 4)
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index_np_5 = (1, 2, 3, 4)
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output_1d_ms = net(input_1d_ms, index_np_1)
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output_3d_ms = net(input_3d_ms, index_np_3)
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assert np.all(output_1d_ms.asnumpy() == input_1d_np.item(index_np_1))
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assert np.all(output_3d_ms.asnumpy() == input_3d_np.item(index_np_3))
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with pytest.raises(ValueError):
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net(input_1d_ms, index_np_2)
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with pytest.raises(ValueError):
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net(input_3d_ms, index_np_2)
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with pytest.raises(IndexError):
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net(input_3d_ms, index_np_4)
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with pytest.raises(ValueError):
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net(input_3d_ms, index_np_5)
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class TensorSetItemByMixedTensors_0(Cell):
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def __init__(self, value):
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super(TensorSetItemByMixedTensors_0, self).__init__()
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self.const = Tensor(np.ones((3, 4, 5), np.float32))
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self.param = Parameter(Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)),
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mstype.float32),
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name="x")
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self.value = value
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def construct(self, index_0, index_1, index_2):
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self.param[0:2, index_0, index_1] = self.value
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ret = self.param + self.const
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return ret
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@pytest.mark.level1
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_setitem_by_mixed_tensors_0():
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value = 88.0
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net = TensorSetItemByMixedTensors_0(value)
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index_0 = np.random.randint(3, size=(3, 4, 5))
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index_1 = np.random.randint(4, size=(4, 5))
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index_2 = np.random.randint(3, size=(2, 1, 4, 5))
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index_0_ms = Tensor(index_0, mstype.int32)
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index_1_ms = Tensor(index_1, mstype.int32)
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index_2_ms = Tensor(index_2, mstype.int32)
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input_np = np.arange(3 * 4 * 5).reshape((3, 4, 5)).astype(np.float32)
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const = np.ones((3, 4, 5), np.float32)
|
|
out = net(index_0_ms, index_1_ms, index_2_ms)
|
|
input_np[0:2, index_0, index_1] = value
|
|
assert np.all(out.asnumpy() == (input_np + const))
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
class TensorSetItemByMixedTensors_1(Cell):
|
|
def __init__(self, value):
|
|
super(TensorSetItemByMixedTensors_1, self).__init__()
|
|
self.const = Tensor(np.ones((3, 4, 5), np.float32))
|
|
self.param = Parameter(Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)), mstype.float32),
|
|
name="x")
|
|
self.value = value
|
|
|
|
def construct(self, index_0, index_1, index_2):
|
|
self.param[0:2, index_0, ...] = self.value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_mixed_tensors_1():
|
|
value = 88.0
|
|
net = TensorSetItemByMixedTensors_1(value)
|
|
index_0 = np.random.randint(3, size=(3, 4, 5))
|
|
index_1 = np.random.randint(4, size=(4, 5))
|
|
index_2 = np.random.randint(3, size=(2, 1, 4, 5))
|
|
index_0_ms = Tensor(index_0, mstype.int32)
|
|
index_1_ms = Tensor(index_1, mstype.int32)
|
|
index_2_ms = Tensor(index_2, mstype.int32)
|
|
input_np = np.arange(3 * 4 * 5).reshape((3, 4, 5)).astype(np.float32)
|
|
const = np.ones((3, 4, 5), np.float32)
|
|
out = net(index_0_ms, index_1_ms, index_2_ms)
|
|
input_np[0:2, index_0, ...] = value
|
|
assert np.all(out.asnumpy() == (input_np + const))
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
class TensorSetItemByMixedTensors_2(Cell):
|
|
def __init__(self, value):
|
|
super(TensorSetItemByMixedTensors_2, self).__init__()
|
|
self.const = Tensor(np.ones((3, 4, 5), np.float16))
|
|
self.param = Parameter(Tensor(np.arange(3 * 4 * 5).reshape((3, 4, 5)), mstype.float16),
|
|
name="x")
|
|
self.value = value
|
|
|
|
def construct(self, index_0, index_1, index_2):
|
|
self.param[..., index_0, 1] = self.value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_mixed_tensors_2():
|
|
value = 88.0
|
|
net = TensorSetItemByMixedTensors_2(value)
|
|
index_0 = np.random.randint(3, size=(3, 4, 5))
|
|
index_1 = np.random.randint(4, size=(4, 5))
|
|
index_2 = np.random.randint(3, size=(2, 1, 4, 5))
|
|
index_0_ms = Tensor(index_0, mstype.int32)
|
|
index_1_ms = Tensor(index_1, mstype.int32)
|
|
index_2_ms = Tensor(index_2, mstype.int32)
|
|
input_np = np.arange(3 * 4 * 5).reshape((3, 4, 5)).astype(np.float32)
|
|
const = np.ones((3, 4, 5), np.float32)
|
|
out = net(index_0_ms, index_1_ms, index_2_ms)
|
|
input_np[..., index_0, 1] = value
|
|
assert np.all(out.asnumpy() == (input_np + const))
|
|
|
|
|
|
class TensorGetItemByMixedTensorsIndexError(Cell):
|
|
def construct(self, x, index_0, index_1):
|
|
ret = x[index_0, index_1, 0:3, ..., 0:5, [1, 2, 3, 4]]
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_getitem_by_mixed_tensor_exception():
|
|
input_ms = Tensor(np.arange(3 * 4 * 5 * 6 * 7 * 8 * 9).reshape((3, 4, 5, 6, 7, 8, 9)), mstype.int32)
|
|
index_0 = Tensor(np.random.randint(3, size=(3, 4, 5)), mstype.int32)
|
|
index_1 = Tensor(np.random.randint(4, size=(3, 4, 5)), mstype.int32)
|
|
net1 = TensorGetItemByMixedTensorsIndexError()
|
|
with pytest.raises(ValueError):
|
|
net1(input_ms, index_0, index_1)
|
|
|
|
|
|
class TensorSetItemByOneTensorWithNumber(Cell):
|
|
def __init__(self, value):
|
|
super(TensorSetItemByOneTensorWithNumber, self).__init__()
|
|
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
|
|
self.value = value
|
|
|
|
def construct(self, index):
|
|
self.param[index] = self.value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_one_tensor_with_number():
|
|
value = 0.0
|
|
net = TensorSetItemByOneTensorWithNumber(value)
|
|
index_np = np.random.randint(4, size=(5, 4))
|
|
index = Tensor(index_np, mstype.int32)
|
|
input_data = np.arange(6 * 7 * 8).reshape((6, 7, 8))
|
|
const = np.ones((6, 7, 8)).astype(np.float32)
|
|
out = net(index)
|
|
input_data[index_np] = value
|
|
assert np.all(out.asnumpy() == (input_data + const))
|
|
|
|
|
|
class TensorSetItemByOneTensorWithTensor(Cell):
|
|
def __init__(self):
|
|
super(TensorSetItemByOneTensorWithTensor, self).__init__()
|
|
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
|
|
|
|
def construct(self, index, value):
|
|
self.param[index] = value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_one_tensor_with_tensor():
|
|
net = TensorSetItemByOneTensorWithTensor()
|
|
index_np = np.random.randint(4, size=(5, 4))
|
|
index = Tensor(index_np, mstype.int32)
|
|
input_data = np.arange(6 * 7 * 8).reshape((6, 7, 8))
|
|
const = np.ones((6, 7, 8)).astype(np.float32)
|
|
value = np.zeros((4, 7, 8)).astype(np.float32)
|
|
value_ms = Tensor(value, mstype.float32)
|
|
out = net(index, value_ms)
|
|
input_data[index_np] = value
|
|
assert np.all(out.asnumpy() == (input_data + const))
|
|
|
|
|
|
class TensorSetItemByOneTensorWithTupleOfNumber(Cell):
|
|
def __init__(self, value):
|
|
super(TensorSetItemByOneTensorWithTupleOfNumber, self).__init__()
|
|
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
|
|
self.value = value
|
|
|
|
def construct(self, index):
|
|
self.param[index] = self.value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_one_tensor_with_tuple_number():
|
|
value = (0.0, 1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7)
|
|
net = TensorSetItemByOneTensorWithTupleOfNumber(value)
|
|
input_np = np.random.randint(5, size=(5, 4))
|
|
input_ms = Tensor(input_np, mstype.int32)
|
|
input_data = np.arange(6 * 7 * 8).reshape((6, 7, 8)).astype(np.float32)
|
|
const = np.ones((6, 7, 8)).astype(np.float32)
|
|
out = net(input_ms)
|
|
input_data[input_np] = value
|
|
assert np.all(out.asnumpy() == (input_data + const))
|
|
|
|
|
|
class TensorSetItemByOneTensorWithTupleOfTensor(Cell):
|
|
def __init__(self):
|
|
super(TensorSetItemByOneTensorWithTupleOfTensor, self).__init__()
|
|
self.const = Tensor(np.ones((6, 3, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 3 * 8).reshape((6, 3, 8)), mstype.float32), name="x")
|
|
|
|
def construct(self, index, value_0, value_1, value_2):
|
|
self.param[index] = (value_0, value_1, value_2)
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_one_tensor_with_tuple_tensors():
|
|
net = TensorSetItemByOneTensorWithTupleOfTensor()
|
|
input_np = np.random.randint(6, size=(5, 4)).astype(np.int32)
|
|
input_ms = Tensor(input_np, mstype.int32)
|
|
input_data = np.arange(6 * 3 * 8).reshape((6, 3, 8)).astype(np.float32)
|
|
value_0_np = np.zeros((8,), np.float32)
|
|
value_1_np = np.ones((8,), np.float32)
|
|
value_2_np = np.ones((8,), np.float32)*2
|
|
value_0 = Tensor(value_0_np)
|
|
value_1 = Tensor(value_1_np)
|
|
value_2 = Tensor(value_2_np)
|
|
const = np.ones((6, 3, 8)).astype(np.float32)
|
|
out = net(input_ms, value_0, value_1, value_2)
|
|
input_data[input_np] = (value_0_np, value_1_np, value_2_np)
|
|
assert np.all(out.asnumpy() == (input_data + const))
|
|
|
|
|
|
class TensorSetItemByTensorsWithNumber(Cell):
|
|
def __init__(self, value):
|
|
super(TensorSetItemByTensorsWithNumber, self).__init__()
|
|
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
|
|
self.value = value
|
|
|
|
def construct(self, index_0, index_1, index_2):
|
|
self.param[index_0, index_1, index_2] = self.value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
@pytest.mark.level0
|
|
def test_setitem_by_tensors_with_number():
|
|
value = 0.0
|
|
net = TensorSetItemByTensorsWithNumber(value)
|
|
index_0 = np.random.randint(6, size=(3, 4, 5))
|
|
index_1 = np.random.randint(7, size=(4, 5))
|
|
index_2 = np.random.randint(8, size=(5, 3, 4, 5))
|
|
index_0_ms = Tensor(index_0, mstype.int32)
|
|
index_1_ms = Tensor(index_1, mstype.int32)
|
|
index_2_ms = Tensor(index_2, mstype.int32)
|
|
out = net(index_0_ms, index_1_ms, index_2_ms)
|
|
const = np.ones((6, 7, 8)).astype(np.float32)
|
|
input_data = np.arange(6 * 7 * 8).reshape((6, 7, 8)).astype(np.float32)
|
|
input_data[index_0, index_1, index_2] = value
|
|
assert np.all(out.asnumpy() == (input_data + const))
|
|
|
|
|
|
class TensorSetItemByTensorsWithTensor(Cell):
|
|
def __init__(self):
|
|
super(TensorSetItemByTensorsWithTensor, self).__init__()
|
|
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
|
|
|
|
def construct(self, index_0, index_1, index_2, value):
|
|
self.param[index_0, index_1, index_2] = value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_tensors_with_tensor():
|
|
net = TensorSetItemByTensorsWithTensor()
|
|
index_0 = np.random.randint(6, size=(3, 4, 5))
|
|
index_1 = np.random.randint(7, size=(4, 5))
|
|
index_2 = np.random.randint(8, size=(5, 3, 4, 5))
|
|
value = np.zeros((4, 5)).astype(np.float32)
|
|
index_0_ms = Tensor(index_0, mstype.int32)
|
|
index_1_ms = Tensor(index_1, mstype.int32)
|
|
index_2_ms = Tensor(index_2, mstype.int32)
|
|
value_ms = Tensor(value, mstype.float32)
|
|
out = net(index_0_ms, index_1_ms, index_2_ms, value_ms)
|
|
const = np.ones((6, 7, 8)).astype(np.float32)
|
|
input_data = np.arange(6 * 7 * 8).reshape((6, 7, 8)).astype(np.float32)
|
|
input_data[index_0, index_1, index_2] = value
|
|
assert np.all(out.asnumpy() == (input_data + const))
|
|
|
|
|
|
class TensorSetItemByTensorsWithTensorNumberError(Cell):
|
|
def __init__(self):
|
|
super(TensorSetItemByTensorsWithTensorNumberError, self).__init__()
|
|
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
|
|
|
|
def construct(self, index_0, index_1, index_2, index_3, value):
|
|
self.param[index_0, index_1, index_2, index_3] = value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_tensors_with_tensor_error():
|
|
index_0 = Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32)
|
|
index_1 = Tensor(np.random.randint(7, size=(4, 5)), mstype.int32)
|
|
index_2 = Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)
|
|
index_3 = Tensor(np.random.randint(8, size=(1, 3, 4, 5)), mstype.int32)
|
|
value = Tensor(np.zeros((2, 5)), mstype.float32)
|
|
net = TensorSetItemByTensorsWithTensorNumberError()
|
|
with pytest.raises(IndexError):
|
|
net(index_0, index_1, index_2, index_3, value)
|
|
|
|
|
|
class TensorSetItemByTensorsWithTupleOfNumber(Cell):
|
|
def __init__(self, value):
|
|
super(TensorSetItemByTensorsWithTupleOfNumber, self).__init__()
|
|
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
|
|
self.value = value
|
|
|
|
def construct(self, index_0, index_1, index_2):
|
|
self.param[index_0, index_1, index_2] = self.value
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
# GPU op has bug, and has not been fixed.
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_tensors_with_tuple_of_number():
|
|
value = (0.0, 1.1, 2.2, 3.3, 4.4)
|
|
net = TensorSetItemByTensorsWithTupleOfNumber(value)
|
|
index_0 = np.random.randint(6, size=(3, 4, 5))
|
|
index_1 = np.random.randint(7, size=(4, 5))
|
|
index_2 = np.random.randint(8, size=(5, 3, 4, 5))
|
|
index_0_ms = Tensor(index_0, mstype.int32)
|
|
index_1_ms = Tensor(index_1, mstype.int32)
|
|
index_2_ms = Tensor(index_2, mstype.int32)
|
|
input_data = np.arange(6 * 7 * 8).reshape((6, 7, 8)).astype(np.float32)
|
|
input_data[index_0, index_1, index_2] = value
|
|
const = np.ones((6, 7, 8)).astype(np.float32)
|
|
out = net(index_0_ms, index_1_ms, index_2_ms)
|
|
assert np.all(out.asnumpy() == (input_data + const))
|
|
|
|
|
|
class TensorSetItemByTensorsWithTupleOfTensor(Cell):
|
|
def __init__(self):
|
|
super(TensorSetItemByTensorsWithTupleOfTensor, self).__init__()
|
|
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
|
|
|
|
def construct(self, index_0, index_1, index_2, value_0, value_1, value_2):
|
|
self.param[index_0, index_1, index_2] = (value_0, value_1, value_2)
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
# GPU op has bug, and has not been fixed.
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_tensors_with_tuple_of_tensor():
|
|
value_0 = np.zeros((4, 5))
|
|
value_1 = np.ones((4, 5))
|
|
value_2 = np.ones((4, 5)) * 2
|
|
value_0_ms = Tensor(value_0, mstype.float32)
|
|
value_1_ms = Tensor(value_1, mstype.float32)
|
|
value_2_ms = Tensor(value_2, mstype.float32)
|
|
net = TensorSetItemByTensorsWithTupleOfTensor()
|
|
index_0 = np.random.randint(6, size=(3, 4, 5))
|
|
index_1 = np.random.randint(7, size=(4, 5))
|
|
index_2 = np.random.randint(8, size=(5, 3, 4, 5))
|
|
index_0_ms = Tensor(index_0, mstype.int32)
|
|
index_1_ms = Tensor(index_1, mstype.int32)
|
|
index_2_ms = Tensor(index_2, mstype.int32)
|
|
input_data = np.arange(6 * 7 * 8).reshape((6, 7, 8)).astype(np.float32)
|
|
input_data[index_0, index_1, index_2] = (value_0, value_1, value_2)
|
|
const = np.ones((6, 7, 8)).astype(np.float32)
|
|
out = net(index_0_ms, index_1_ms, index_2_ms, value_0_ms, value_1_ms, value_2_ms)
|
|
assert np.all(out.asnumpy() == (input_data + const))
|
|
|
|
|
|
class TensorSetItemByTensorsWithTupleOfTensorNumberError(Cell):
|
|
def __init__(self):
|
|
super(TensorSetItemByTensorsWithTupleOfTensorNumberError, self).__init__()
|
|
self.const = Tensor(np.ones((6, 7, 8)), mstype.float32)
|
|
self.param = Parameter(Tensor(np.arange(6 * 7 * 8).reshape((6, 7, 8)), mstype.float32), name="x")
|
|
|
|
def construct(self, index_0, index_1, index_2, value_0, value_1):
|
|
self.param[index_0, index_1, index_2] = (value_0, value_1)
|
|
ret = self.param + self.const
|
|
return ret
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_by_tensor_with_tuple_of_tensor_error():
|
|
net = TensorSetItemByTensorsWithTupleOfTensorNumberError()
|
|
index_0_ms = Tensor(np.random.randint(6, size=(3, 4, 5)), mstype.int32)
|
|
index_1_ms = Tensor(np.random.randint(7, size=(4, 5)), mstype.int32)
|
|
index_2_ms = Tensor(np.random.randint(8, size=(5, 3, 4, 5)), mstype.int32)
|
|
value_0 = np.zeros((4, 5))
|
|
value_1 = np.ones((4, 5))
|
|
value_0_ms = Tensor(value_0, mstype.float32)
|
|
value_1_ms = Tensor(value_1, mstype.float32)
|
|
with pytest.raises(ValueError):
|
|
net(index_0_ms, index_1_ms, index_2_ms, value_0_ms, value_1_ms)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_setitem_grad():
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super(Net, self).__init__()
|
|
self.weight = Parameter(
|
|
Tensor(np.ones([4, 4, 5]), dtype=mstype.float32), "b1", requires_grad=True)
|
|
|
|
def construct(self, a, b):
|
|
a[1:3:1, ::] = b
|
|
c = a + self.weight
|
|
return c
|
|
|
|
class GradNet(Cell):
|
|
def __init__(self, net):
|
|
super(GradNet, self).__init__()
|
|
self.net = net
|
|
self.weights = ParameterTuple(net.trainable_params())
|
|
|
|
def construct(self, x, y, sens):
|
|
return grad_by_list_with_sens(self.net, self.weights)(x, y, sens)
|
|
net = GradNet(Net())
|
|
x = Tensor(np.ones([4, 4, 5]).astype(np.float32), mstype.float32)
|
|
y = Tensor(np.array([3]).astype(np.float32), mstype.float32)
|
|
sens = Tensor(np.ones([4, 4, 5]).astype(np.float32), mstype.float32)
|
|
net(x, y, sens)
|
|
|
|
|
|
class TensorAssignWithSliceError1(Cell):
|
|
def construct(self, a, b):
|
|
a[1:3:-1, ::] = b
|
|
return a
|
|
|
|
|
|
class TensorAssignWithSliceError2(Cell):
|
|
def construct(self, a, b):
|
|
a[1:3:-1] = b
|
|
return a
|
|
|
|
|
|
class TensorAssignWithSlice2(Cell):
|
|
def construct(self, a, b, ck):
|
|
a[1:5] = b
|
|
a[3:4] = 5
|
|
a[-1:1:-1] = b
|
|
a[-1:3:-1] = 5
|
|
a[::] = b
|
|
a[::] = 9
|
|
z = a + ck
|
|
return z
|
|
|
|
|
|
class TensorAssignWithSlice(Cell):
|
|
def __init__(self):
|
|
super(TensorAssignWithSlice, self).__init__()
|
|
self.c = 2.0
|
|
|
|
def construct(self, a, b, ck):
|
|
a[1:3, ::] = b
|
|
a[2:3:, 3:] = b
|
|
a[::] = b
|
|
a[::] = self.c
|
|
a[::, ::] = b
|
|
a[::, ::] = self.c
|
|
a[2:3:, 0:, 4:1:-1] = b
|
|
a[2:3:, 0:, 4:1:-1] = self.c
|
|
z = a + ck
|
|
return z
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_assign_slice_value_1():
|
|
net = TensorAssignWithSlice()
|
|
a = np.arange(60).reshape(3, 4, 5)
|
|
b = np.array([1]).astype(np.float32) # Tensor([1], dtype=mstype.float32)
|
|
ck = np.arange(60).reshape(3, 4, 5)
|
|
ta = Tensor(a, dtype=mstype.float32)
|
|
tb = Tensor(b, dtype=mstype.float32)
|
|
tck = Tensor(ck, dtype=mstype.float32)
|
|
out = net(ta, tb, tck)
|
|
a[1:3, ::] = b
|
|
a[2:3:, 3:] = b
|
|
a[::] = b
|
|
a[::] = 2.0
|
|
a[::, ::] = b
|
|
a[::, ::] = 2.0
|
|
a[2:3:, 0:, 4:1:-1] = b
|
|
a[2:3:, 0:, 4:1:-1] = 2.0
|
|
z = a + ck
|
|
assert np.all(z == out.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_assign_slice_value_2():
|
|
net2 = TensorAssignWithSlice2()
|
|
a = np.array([1, 2, 3, 4, 5, 6, 7, 8])
|
|
ck = np.array([1, 2, 3, 4, 5, 6, 7, 8])
|
|
b = np.array([1]).astype(np.float32) # Tensor([1], dtype=mstype.float32)
|
|
tb = Tensor(b, dtype=mstype.float32)
|
|
ta = Tensor(a, dtype=mstype.float32)
|
|
tck = Tensor(ck, dtype=mstype.float32)
|
|
out = net2(ta, tb, tck)
|
|
a[1:5] = b
|
|
a[3:4] = 5
|
|
a[-1:1:-1] = b
|
|
a[-1:3:-1] = 5
|
|
a[::] = b
|
|
a[::] = 9
|
|
z = a + ck
|
|
assert np.all(z == out.asnumpy())
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_assign_exception():
|
|
net = TensorAssignWithSlice()
|
|
net2 = TensorAssignWithSlice2()
|
|
# The test case is no longer appropriate since x[1:3:-1] = np.array(2) does
|
|
# not incur an error in numpy, which leaves the original array unchanged after
|
|
# the assign operation.
|
|
# net_e1 = TensorAssignWithSliceError1()
|
|
# net_e2 = TensorAssignWithSliceError2()
|
|
a = np.arange(60).reshape(3, 4, 5)
|
|
ck = np.arange(60).reshape(3, 4, 5)
|
|
b = Tensor([1], dtype=mstype.float32)
|
|
Ta = Tensor(a, dtype=mstype.float32)
|
|
Tck = Tensor(ck, dtype=mstype.float32)
|
|
Ta4d = Tensor(a.reshape(1, 3, 4, 5), dtype=mstype.float32)
|
|
Ta4d_ck = Tensor(ck.reshape(1, 3, 4, 5), dtype=mstype.float32)
|
|
Tb = Tensor([1, 3], dtype=mstype.float32)
|
|
Tc = Tensor([], dtype=mstype.float32)
|
|
t = Tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=mstype.float32)
|
|
tck = Tensor([1, 2, 3, 4, 5, 6, 7, 8], dtype=mstype.float32)
|
|
# Error for A[Slice] = Number
|
|
# 1. A[Slice] = Number, Slice error
|
|
# with pytest.raises(ValueError):
|
|
# net_e2(t, 2)
|
|
|
|
# Error for A[Slice] = U, U is a Tensor
|
|
# 1. A[Slice] = U, u.size is error
|
|
with pytest.raises(ValueError):
|
|
net2(t, Tb, tck)
|
|
# 2. A[Slice] = U, U is empty
|
|
with pytest.raises(ValueError):
|
|
net2(t, Tc, tck)
|
|
# 3. A[Slice] = U, U.size error
|
|
with pytest.raises(ValueError):
|
|
net2(t, Tb, tck)
|
|
|
|
# Error for A[Tuple(Slice...)] = Tensor
|
|
# 1. A[Tuple(Slice...)] = U, U is empty
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tc, Tck)
|
|
# 2. A[Tuple(Slice...)] = U, U.size error
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb, Tck)
|
|
# 3. A[Tuple(Slice...)] = U, Slice error
|
|
# with pytest.raises(IndexError):
|
|
# net_e1(Ta, b)
|
|
|
|
# Error for A[Tuple(Slice...)] = Number
|
|
# 1. A[Tuple(Slice...)] = Number, Slice error
|
|
# with pytest.raises(IndexError):
|
|
# net_e1(Ta, 2)
|
|
|
|
net = TensorAssignWithInteger()
|
|
# Error for A[Number] = scalar/Tensor
|
|
# 1. A[Number] = U, U is a Tensor, u.size not match
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb, Tck)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tc, Tck)
|
|
# 2. A[Number] = U, the number index error
|
|
with pytest.raises(IndexError):
|
|
net(Ta4d, b, Ta4d_ck)
|
|
|
|
# Error for A[(n,m)] = scalar/Tensor
|
|
# 1. A[(n,m)] = U, U is a tensor. u.size not match
|
|
net = TensorAssignWithTupleInteger()
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tc, Tck)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb, Tck)
|
|
# 2. A[(n,m)] = U, the number index error
|
|
with pytest.raises(IndexError):
|
|
net(Ta4d, b, Ta4d_ck)
|
|
|
|
# Error for A[...] = U or A[1:, ...] = u
|
|
# 1. A[...] = scalar/tensor
|
|
net = TensorAssignWithEllipsis()
|
|
net(Ta, Ta4d)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tc)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb)
|
|
# 2. A[::, 1:, ...] = scalar/tensor
|
|
net = TensorAssignWithTupleEllipsis()
|
|
net(Ta, b)
|
|
with pytest.raises(ValueError):
|
|
net(Ta, Tb)
|
|
|
|
|
|
class TensorAssignWithTupleEllipsis2(Cell):
|
|
def construct(self, a, b):
|
|
a[1:, ..., ::] = b
|
|
return a
|
|
|
|
|
|
class TensorAssignWithTupleEllipsis(Cell):
|
|
def construct(self, a, b):
|
|
a[:2, ...] = 1.0
|
|
a[1:, ...] = b
|
|
return a
|
|
|
|
|
|
class TensorAssignWithEllipsis(Cell):
|
|
def construct(self, a, b):
|
|
a[...] = 1
|
|
a[...] = b
|
|
return a
|
|
|
|
|
|
class TensorAssignWithInteger(Cell):
|
|
def construct(self, a, b, ck):
|
|
a[1] = 1
|
|
a[0] = b
|
|
z = a + ck
|
|
return z
|
|
|
|
|
|
class TensorAssignWithTupleInteger(Cell):
|
|
def construct(self, a, b, ck):
|
|
a[(1)] = 1
|
|
a[(1)] = b
|
|
a[(1, 1)] = b
|
|
a[(1, 1)] = 1
|
|
z = a + ck
|
|
return z
|
|
|
|
|
|
class TensorAssignWithBoolTensorIndex(Cell):
|
|
def __init__(self):
|
|
super(TensorAssignWithBoolTensorIndex, self).__init__()
|
|
self.t = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
|
|
self.u_scalar = 5
|
|
|
|
def construct(self, a, b, c, u_tensor):
|
|
a[c] = self.u_scalar
|
|
a[b] = u_tensor
|
|
z = a + self.t
|
|
return z
|
|
|
|
|
|
class TensorAssignWithBoolTensorIndexError(Cell):
|
|
def construct(self, a, b, c, u_tensor):
|
|
a[b][c] = u_tensor
|
|
return a
|
|
|
|
|
|
class TensorAssignWithBoolTensorIndex2(Cell):
|
|
def __init__(self):
|
|
super(TensorAssignWithBoolTensorIndex2, self).__init__()
|
|
self.t = Tensor(np.ones([3, 4, 5]), dtype=mstype.float32)
|
|
self.u_scalar = 5
|
|
|
|
def construct(self, a, u_tensor):
|
|
a[a > 8] = u_tensor
|
|
a[a >= 6] = self.u_scalar
|
|
a[a < 3] = self.u_scalar
|
|
a[a <= 5] = u_tensor
|
|
a[a == 5] = self.u_scalar
|
|
z = a + self.t
|
|
return z
|
|
|
|
|
|
class TensorAssignWithBoolTensorIndex2Error(Cell):
|
|
def construct(self, a, u_tensor):
|
|
a[a > 8][a > 5] = u_tensor
|
|
return a
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_assign_bool_index_0():
|
|
a = np.arange(60).reshape(3, 4, 5)
|
|
b = a > 5
|
|
c = a < 3
|
|
Ta = Tensor(a, dtype=mstype.float32)
|
|
Tb = Tensor(b)
|
|
Tc = Tensor(c)
|
|
u_tensor = Tensor([1], dtype=mstype.float32)
|
|
net1 = TensorAssignWithBoolTensorIndex()
|
|
out = net1(Ta, Tb, Tc, u_tensor)
|
|
res = np.arange(60).reshape(3, 4, 5)
|
|
res[c] = 5
|
|
res[b] = 1
|
|
res = res + np.ones([3, 4, 5])
|
|
assert np.all(out.asnumpy() == res)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_assign_bool_index_1():
|
|
a = np.arange(60).reshape(3, 4, 5)
|
|
Ta = Tensor(a, dtype=mstype.float32)
|
|
u_tensor = Tensor([1], dtype=mstype.float32)
|
|
net2 = TensorAssignWithBoolTensorIndex2()
|
|
out = net2(Ta, u_tensor)
|
|
res = np.arange(60).reshape(3, 4, 5)
|
|
res[res > 8] = 1
|
|
res[res >= 6] = 5
|
|
res[res < 3] = 5
|
|
res[res <= 5] = 1
|
|
res[res == 5] = 5
|
|
res = res + np.ones([3, 4, 5])
|
|
assert np.all(out.asnumpy() == res)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_assign_bool_index_exception():
|
|
a = np.arange(60).reshape(3, 4, 5)
|
|
b = a > 5
|
|
c = a < 3
|
|
Ta = Tensor(a, dtype=mstype.float32)
|
|
Tb = Tensor(b)
|
|
Tc = Tensor(c)
|
|
Td = Tensor([True, True])
|
|
u_tensor = Tensor([1], dtype=mstype.float32)
|
|
u_tensor_error = Tensor([1, 2], dtype=mstype.float32)
|
|
u_scalar = 5
|
|
net1 = TensorAssignWithBoolTensorIndex()
|
|
net2 = TensorAssignWithBoolTensorIndex2()
|
|
with pytest.raises(ValueError):
|
|
net1(Ta, Td, Tc, u_tensor)
|
|
with pytest.raises(IndexError):
|
|
net1(Ta, u_tensor, Tc, u_tensor)
|
|
with pytest.raises(ValueError):
|
|
net1(Ta, Tb, Td, u_tensor)
|
|
with pytest.raises(IndexError):
|
|
net1(Ta, Tb, Ta, u_tensor)
|
|
with pytest.raises(ValueError):
|
|
net1(Ta, Tb, Tc, u_tensor_error)
|
|
# net1(Ta, u_tensor, Tc, u_tensor_error, u_scalar)
|
|
with pytest.raises(ValueError):
|
|
net2(Ta, u_tensor_error)
|
|
net3 = TensorAssignWithBoolTensorIndexError()
|
|
with pytest.raises(IndexError):
|
|
net3(Ta, Tb, Tc, u_tensor)
|
|
with pytest.raises(IndexError):
|
|
net3(Ta, Tb, Tc, u_scalar)
|
|
net4 = TensorAssignWithBoolTensorIndex2Error()
|
|
with pytest.raises(IndexError):
|
|
net4(Ta, u_tensor)
|
|
with pytest.raises(IndexError):
|
|
net4(Ta, u_scalar)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_slice_reduce_out_of_bounds_neg():
|
|
class NetWork(Cell):
|
|
def __init__(self):
|
|
super(NetWork, self).__init__()
|
|
self.tensor_ret = Tensor(np.array(9, np.int32))
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def construct(self, tensor):
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ret = tensor[-7, 3, 4]
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return ret
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|
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input_tensor = Tensor(np.ones([6, 8, 10], np.int32))
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net = NetWork()
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with pytest.raises(IndexError) as ex:
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net(input_tensor)
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assert "'begin[0]' must be in [-6, 6) when 'shrink_axis_mask' is greater than 0, " \
|
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"but got 'shrink_axis_mask': 7, 'strides[0]': 1, 'begin[0]': -7." in str(ex.value)
|
|
|
|
|
|
@pytest.mark.level1
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|
@pytest.mark.platform_arm_ascend_training
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|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
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|
@pytest.mark.env_onecard
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|
def test_tensor_slice_reduce_out_of_bounds_positive():
|
|
class NetWork(Cell):
|
|
def __init__(self):
|
|
super(NetWork, self).__init__()
|
|
self.tensor_ret = Tensor(np.array(9, np.int32))
|
|
|
|
def construct(self, tensor):
|
|
ret = tensor[6, 3, 4]
|
|
return ret
|
|
|
|
input_tensor = Tensor(np.ones([6, 8, 10], np.int32))
|
|
net = NetWork()
|
|
with pytest.raises(IndexError) as ex:
|
|
net(input_tensor)
|
|
assert "'begin[0]' must be in [-6, 6) when 'shrink_axis_mask' is greater than 0, " \
|
|
"but got 'shrink_axis_mask': 7, 'strides[0]': 1, 'begin[0]': 6." in str(ex.value)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_tensor_range():
|
|
a = np.arange(4*5*6).reshape(4, 5, 6).astype(np.float32)
|
|
ta = Tensor(a, mstype.float32)
|
|
ms_out = []
|
|
for item in ta:
|
|
ms_out.append(item)
|
|
np_out = []
|
|
for item in a:
|
|
np_out.append(item)
|
|
for i, elem in enumerate(ms_out):
|
|
assert np.all(elem.asnumpy() == np_out[i])
|