forked from mindspore-Ecosystem/mindspore
667 lines
20 KiB
Python
667 lines
20 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.common.dtype as mstype
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from mindspore import Tensor, ops, ParameterTuple
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from mindspore.ops.composite import GradOperation
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from mindspore.nn import Cell
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class _Grad(Cell):
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def __init__(self, grad, network, wrt_params=False, real_inputs_count=None):
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super().__init__()
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self.network = network
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self.grad = grad
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self.sens_param = self.grad.sens_param
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self.wrt_params = wrt_params
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self.real_inputs_count = real_inputs_count
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if self.wrt_params:
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self.params = ParameterTuple(self.network.trainable_params())
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def construct(self, *inputs):
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if self.real_inputs_count is None or self.sens_param is False:
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if self.wrt_params:
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return self.grad(self.network, self.params)(*inputs)
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return self.grad(self.network)(*inputs)
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real_inputs = inputs[:self.real_inputs_count]
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sense_param_inputs = inputs[self.real_inputs_count:]
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if self.wrt_params:
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return self.grad(self.network, self.params)(*real_inputs, sense_param_inputs)
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return self.grad(self.network)(*real_inputs, sense_param_inputs)
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class GradOfFirstInput(_Grad):
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"""
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get grad of first input
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"""
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def __init__(self, network, sens_param=True, real_inputs_count=None):
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super().__init__(grad=GradOperation(sens_param=sens_param),
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network=network, real_inputs_count=real_inputs_count)
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class CommonFunc():
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def __init__(self, ms_net, np_net, input_np, input_dyn):
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super(CommonFunc, self).__init__()
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self.ms_net = ms_net
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self.ms_net.set_inputs(input_dyn)
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self.ms_net.set_grad()
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self.np_net = np_net
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self.input_np = input_np
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self.input_np_t = Tensor(input_np)
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self.out_np = np.array(1).astype(input_np.dtype)
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def forward_cmp(self):
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out_ms = self.ms_net(self.input_np_t)
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self.out_np = self.np_net(self.input_np)
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assert np.all(out_ms.asnumpy() == self.out_np)
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def grad_impl(self):
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grad_net = GradOfFirstInput(self.ms_net)
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grad_net.set_train()
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grad_net(self.input_np_t, Tensor(self.out_np))
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_ellipsis():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is ellipsis.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def construct(self, x):
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x = x[...]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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x = x[...]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None,), dtype=mstype.float32)
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input_np = np.random.randn(4).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_bool():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is bool.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def construct(self, x):
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x = x[True]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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x = x[True]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None, 3), dtype=mstype.float32)
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input_np = np.random.randn(2, 3).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_none():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is None.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def construct(self, x):
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x = x[None]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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x = x[None]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None, 3), dtype=mstype.float32)
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input_np = np.random.randn(2, 3).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_tensor():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is tensor of int.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def __init__(self):
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super().__init__()
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self.index = Tensor([0, 1])
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def construct(self, x):
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index = self.index
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x = x[index]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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x = x[[0, 1]]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None, 4), dtype=mstype.float32)
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input_np = np.random.randn(3, 4).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_tensor_001():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is dynamic shape tensor.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def __init__(self):
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super().__init__()
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self.unique = ops.Unique()
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self.index = Tensor([1, 1, 1, 2])
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def construct(self, x):
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index = self.unique(self.index)[0]
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x = x[index]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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index = np.unique(np.array([1, 1, 1, 2]))
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x = x[index]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None, 3), dtype=mstype.float32)
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input_np = np.random.randn(3, 3).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_slice():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is slice.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def construct(self, x):
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x = x[2:4]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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x = x[2:4]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None, 4), dtype=mstype.float32)
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input_np = np.random.randn(6, 4).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_slice_001():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is slice with negative int.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def construct(self, x):
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x = x[-3:-1]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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x = x[-3:-1]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None, 4), dtype=mstype.float32)
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input_np = np.random.randn(6, 4).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_int():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is int.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def construct(self, x):
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x = x[-3]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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x = x[-3]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None, 4), dtype=mstype.float32)
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input_np = np.random.randn(3, 4).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level1
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_int_001():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is int with control flow.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def __init__(self):
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super().__init__()
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self.extra = 0
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def construct(self, x):
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index = 1 if self.extra > 1 else 2
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x = x[index]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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x = x[2]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None, 2), dtype=mstype.float32)
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input_np = np.random.randn(3, 2).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_int_002():
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"""
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Feature: Test Tensor slice for twice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is int.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def construct(self, x):
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x = x[3][4]
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return x
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class NumpyNet():
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@classmethod
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def __call__(cls, x):
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x = x[3][4]
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return x
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net_ms = Net()
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net_np = NumpyNet()
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dynamic_input = Tensor(shape=(None, None, 3), dtype=mstype.float32)
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input_np = np.random.randn(5, 5, 3).astype(np.float32)
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context.set_context(mode=context.PYNATIVE_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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context.set_context(mode=context.GRAPH_MODE)
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fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
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fact.forward_cmp()
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fact.grad_impl()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_x86_gpu_training
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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.env_onecard
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def test_dynamic_getitem_list():
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"""
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Feature: Test Tensor slice for dynamic shape in feed mode.
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Description: The input shape is dynamic and the tensor index is list of bool and int.
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Expectation: Assert the result is equal the numpy result.
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"""
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class Net(Cell):
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def construct(self, x):
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index = [False, 1]
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x = x[index]
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|
return x
|
|
|
|
class NumpyNet():
|
|
@classmethod
|
|
def __call__(cls, x):
|
|
index = [False, 1]
|
|
x = x[index]
|
|
return x
|
|
|
|
net_ms = Net()
|
|
net_np = NumpyNet()
|
|
dynamic_input = Tensor(shape=(None,), dtype=mstype.float32)
|
|
input_np = np.random.randn(5).astype(np.float32)
|
|
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
|
|
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_dynamic_getitem_tuple():
|
|
"""
|
|
Feature: Test Tensor slice for dynamic shape in feed mode.
|
|
Description: The input shape is dynamic and the tensor index is tuple of tensor and slice.
|
|
Expectation: Assert the result is equal the numpy result.
|
|
"""
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.extra = Tensor(0)
|
|
self.extra2 = Tensor(2)
|
|
|
|
def construct(self, x):
|
|
x = x[self.extra, self.extra:self.extra2, ...]
|
|
return x
|
|
|
|
class NumpyNet():
|
|
@classmethod
|
|
def __call__(cls, x):
|
|
x = x[0, 0:2, ...]
|
|
return x
|
|
|
|
net_ms = Net()
|
|
net_np = NumpyNet()
|
|
dynamic_input = Tensor(shape=(2, None, 3), dtype=mstype.float32)
|
|
input_np = np.random.randn(2, 4, 3).astype(np.float32)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
|
|
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_dynamic_getitem_tuple_001():
|
|
"""
|
|
Feature: Test Tensor slice for dynamic shape in feed mode.
|
|
Description: The input shape is dynamic and the tensor index is tuple of advanced indices.
|
|
Expectation: Assert the result is equal the numpy result.
|
|
"""
|
|
class Net(Cell):
|
|
def construct(self, x):
|
|
index = (..., True, 4, slice(0, 2), None)
|
|
x = x[index]
|
|
return x
|
|
|
|
class NumpyNet():
|
|
@classmethod
|
|
def __call__(cls, x):
|
|
index = (..., True, 4, slice(0, 2), None)
|
|
x = x[index]
|
|
return x
|
|
|
|
net_ms = Net()
|
|
net_np = NumpyNet()
|
|
dynamic_input = Tensor(shape=(3, 4, None, 2), dtype=mstype.float32)
|
|
input_np = np.random.randn(3, 4, 5, 2).astype(np.float32)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
|
|
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_dynamic_getitem_tuple_002():
|
|
"""
|
|
Feature: Test Tensor slice for twice for dynamic shape in feed mode.
|
|
Description: The input shape is dynamic and the tensor index is tuple of advanced indices.
|
|
Expectation: Assert the result is equal the numpy result.
|
|
"""
|
|
class Net(Cell):
|
|
def __init__(self):
|
|
super().__init__()
|
|
self.extra = Tensor([2, 3])
|
|
|
|
def construct(self, x):
|
|
|
|
x = x[True, [1, 2]][..., self.extra]
|
|
return x
|
|
|
|
class NumpyNet():
|
|
@classmethod
|
|
def __call__(cls, x):
|
|
x = x[True, [1, 2]][..., [2, 3]]
|
|
return x
|
|
|
|
net_ms = Net()
|
|
net_np = NumpyNet()
|
|
dynamic_input = Tensor(shape=(None, 4, 5, 2, None),
|
|
dtype=mstype.float32) # (1,2,4,5,2,None)
|
|
input_np = np.random.randn(3, 4, 5, 2, 4).astype(np.float32)
|
|
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
|
|
|
|
@pytest.mark.level0
|
|
@pytest.mark.platform_x86_cpu
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.platform_arm_ascend_training
|
|
@pytest.mark.platform_x86_ascend_training
|
|
@pytest.mark.env_onecard
|
|
def test_dynamic_getitem_tuple_003():
|
|
"""
|
|
Feature: Test Tensor slice for twice for dynamic shape in feed mode.
|
|
Description: The input shape is dynamic and the tensor index is tuple of advanced indices.
|
|
Expectation: Assert the result is equal the numpy result.
|
|
"""
|
|
class Net(Cell):
|
|
def construct(self, x):
|
|
x = x[:, :, :, :1]
|
|
return x
|
|
|
|
class NumpyNet():
|
|
@classmethod
|
|
def __call__(cls, x):
|
|
x = x[:, :, :, :1]
|
|
return x
|
|
|
|
net_ms = Net()
|
|
net_np = NumpyNet()
|
|
dynamic_input = Tensor(shape=(4, None, 5, None, 6, None),
|
|
dtype=mstype.float32) # (1,2,4,5,2,None)
|
|
input_np = np.random.randn(4, 4, 5, 5, 6, 4).astype(np.float32)
|
|
|
|
context.set_context(mode=context.PYNATIVE_MODE)
|
|
fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|
|
context.set_context(mode=context.GRAPH_MODE)
|
|
fact = CommonFunc(net_ms, net_np, input_np, dynamic_input)
|
|
fact.forward_cmp()
|
|
fact.grad_impl()
|