forked from mindspore-Ecosystem/mindspore
137 lines
4.6 KiB
Python
137 lines
4.6 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 torch
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from mindspore import Tensor
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from mindspore.nn import Cell
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from mindspore.ops.operations.math_ops import Median
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import mindspore.ops.operations._grad_ops as G
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from mindspore.ops.composite import GradOperation
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class Grad(Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = GradOperation(get_all=False, sens_param=False)
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self.network = network
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def construct(self, input_x):
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gout = self.grad(self.network)(input_x)
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return gout
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class MedianC(Cell):
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def __init__(self, global_median, axis, keep_dims):
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super().__init__()
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self.global_median = global_median
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self.axis = axis
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self.keep_dims = keep_dims
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self.median = Median(self.global_median, self.axis, self.keep_dims)
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def construct(self, x):
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return self.median(x)
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class MedianGrad(Cell):
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def __init__(self, global_median, axis, keep_dims):
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super().__init__()
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self.global_median = global_median
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self.axis = axis
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self.keep_dims = keep_dims
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self.median_grad = G.MedianGrad(self.global_median, self.axis, self.keep_dims)
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def construct(self, dy, x, y, indices):
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return self.median_grad(dy, x, y, indices)
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class MedianFactory():
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def __init__(self, input_shape, global_median, axis=0, keep_dims=False, dtype=np.float32):
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super().__init__()
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self.dtype = dtype
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self.input = np.random.randn(*input_shape).astype(self.dtype)
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self.global_median = global_median
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self.axis = axis
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self.keep_dims = keep_dims
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self.output_grad_np = np.random.randn(*input_shape).astype(dtype=dtype)
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def forward_mindspore_impl(self):
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net = MedianC(self.global_median, self.axis, self.keep_dims)
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y, indices = net(Tensor(self.input))
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return y.asnumpy(), indices.asnumpy()
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def grad_mindspore_impl(self):
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input_x = Tensor(self.input)
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net = MedianC(self.global_median, self.axis, self.keep_dims)
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grad_net = Grad(net)
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res = grad_net(input_x)
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return res.asnumpy()
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def forward_pytorch_impl(self):
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input_pt = torch.from_numpy(self.input)
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indices = None
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if self.global_median is False:
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y, indices = torch.median(input_pt, axis=self.axis, keepdim=self.keep_dims)
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else:
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y = torch.median(input_pt)
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indices_np = None if indices is None else indices.numpy().astype(np.int64)
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return y.numpy().astype(self.dtype), indices_np
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def global_grad_pytorch_impl(self):
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input_pt = torch.from_numpy(self.input)
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input_pt.requires_grad = True
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y = torch.median(input_pt)
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y.backward()
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return input_pt.grad.numpy()
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def grad_pytorch_impl(self):
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input_pt = torch.from_numpy(self.input)
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input_pt.requires_grad = True
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y, _ = torch.median(input_pt, axis=self.axis, keepdim=self.keep_dims)
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y.sum().backward()
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return input_pt.grad.numpy()
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def forward_cmp(self):
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y_pytorch, _ = self.forward_pytorch_impl()
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y_mindspore, _ = self.forward_mindspore_impl()
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assert np.allclose(y_pytorch, y_mindspore)
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def grad_cmp(self):
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grad_ms = self.grad_mindspore_impl()
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if self.global_median is False:
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grad_torch = self.grad_pytorch_impl()
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else:
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grad_torch = self.global_grad_pytorch_impl()
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assert np.allclose(grad_ms, grad_torch)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_median_gpu():
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"""
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Feature: Test median.
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Description: Test median and mediangrad in Gpu with different global_median parameter.
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Expectation: the result match given one.
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"""
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fact = MedianFactory(input_shape=(5, 5), global_median=True, axis=0, keep_dims=False)
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fact.forward_cmp()
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fact.grad_cmp()
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fact2 = MedianFactory(input_shape=(5, 5, 5), global_median=False, axis=1, keep_dims=True)
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fact2.forward_cmp()
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fact2.grad_cmp()
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