forked from mindspore-Ecosystem/mindspore
!49791 remove the deformable_offsets
Merge pull request !49791 from huangxinjing/code_docs_remove_deformable
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huangxinjing
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fc7346ffad
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# 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.nn as nn
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from mindspore import Tensor
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from mindspore.ops.composite import GradOperation
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from mindspore.ops import operations as P
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from mindspore.ops.operations import nn_ops as NN
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class Net(nn.Cell):
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def __init__(self, out_channel, kernel_size, pad, stride, dilation):
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super(Net, self).__init__()
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self.net = NN.DeformableOffsets(ksize=(kernel_size, kernel_size),
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pads=(pad, pad, pad, pad),
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strides=(stride, stride, stride, stride),
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dilations=(dilation, dilation, dilation, dilation),
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deformable_groups=1,
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modulated=True,
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data_format="NCHW")
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self.conv = P.Conv2D(out_channel,
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kernel_size,
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mode=1,
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pad_mode="pad",
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pad=pad,
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stride=kernel_size,
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dilation=1,
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group=1,
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data_format="NCHW")
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def construct(self, x, w, offset):
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x = self.net(x, offset)
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return self.conv(x, w)
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class Grad(nn.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=True, sens_param=True)
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self.network = network
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def construct(self, x, w, offset, output_grad):
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return self.grad(self.network)(x, w, offset, output_grad)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_deformable_conv2d_grad():
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""""
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Feature: deformable_conv2d_grad function
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Description: Test case for simplest deformable_conv2d_grad
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Expectation: The results are as expected
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=True)
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kernel_size = 2
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stride = 1
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pad = 0
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dilation = 1
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# x shape [1, 64, 2, 2]
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x = Tensor(np.ones([1, 64, 2, 2]).astype(np.float32) * 0.1)
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# weight shape [1, 64, 2, 2]
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weight = Tensor(np.ones([1, 64, 2, 2]).astype(np.float32) * 0.1)
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# offsets shape [1, 12, 1, 1]
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offsets = Tensor(np.ones([1, 12, 1, 1]).astype(np.float32) * 0.1)
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# out_channel, kernel_size, pad, stride, dilation
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dfm_conv2d_net = Net(1, kernel_size, pad, stride, dilation)
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out = dfm_conv2d_net(x, weight, offsets)
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grad_net = Grad(dfm_conv2d_net)
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grad_output = grad_net(x, weight, offsets, out)
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expect_out = np.array([[[[0.2310471]]]]).astype(np.float32)
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expect_grad_x = np.array([[[[0.00187125, 0.00207916], [0.00207916, 0.00231018]]] * 64]).astype(np.float32)
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expect_grad_weight = np.array([[[[0.00231128, 0.00208033], [0.00208033, 0.0018723]]] * 64]).astype((np.float32))
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expect_grad_offset = np.array([[[0]], [[-0.01478]], [[0]], [[-0.01331]],
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[[0]], [[0]], [[-0.01478]], [[-0.01331]],
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[[0.14785]], [[0.13307]], [[0.13307]], [[0.11976]]]).astype((np.float32))
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assert np.allclose(out.asnumpy(), expect_out, 0.0001, 0.0001)
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assert np.allclose(grad_output[0].asnumpy(), expect_grad_x, 0.0001, 0.0001)
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assert np.allclose(grad_output[1].asnumpy(), expect_grad_weight, 0.0001, 0.0001)
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assert np.allclose(grad_output[2].asnumpy(), expect_grad_offset, 0.0001, 0.0001)
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@ -1,217 +0,0 @@
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# 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 pytest
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import numpy as np
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from mindspore.ops.operations import _grad_ops as G
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from mindspore.ops import composite as C
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from mindspore import nn
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from mindspore import Tensor
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from mindspore import dtype
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from mindspore.ops.operations import nn_ops
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from mindspore.ops import functional as F
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grad_all = C.GradOperation(get_all=True)
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class TestNetwork(nn.Cell):
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def __init__(self):
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super(TestNetwork, self).__init__()
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stride = (1, 1, 1, 1)
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pad = (0, 0, 0, 0)
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ksize = (2, 2)
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self.deformable_offsets_grad_op = G.DeformableOffsetsGrad(stride, pad, ksize)
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def construct(self, dout, x, offsets):
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output = self.deformable_offsets_grad_op(dout, x, offsets)
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return output
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def test_grad_infer():
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"""
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Feature: CPU operation.
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Description: Test of CPU operation: DeformableOffsetsGrad
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Expectation: No exception raised.
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"""
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dout = Tensor(np.ones([1, 1, 2, 2]), dtype.float32)
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x = Tensor(np.ones([1, 1, 2, 2]), dtype.float32)
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offsets = Tensor(np.array([0.1] * 12).astype(np.float32).reshape([1, 12, 1, 1]))
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net = TestNetwork()
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grad = net(dout, x, offsets)
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print("grad_x:", grad[0])
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print("grad_offset:", grad[1])
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return grad
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class ForwardNet(nn.Cell):
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def __init__(self):
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super(ForwardNet, self).__init__()
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stride = (1, 1, 1, 1)
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pad = (0, 0, 0, 0)
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ksize = (2, 2)
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self.deformable_offsets_grad_op = nn_ops.DeformableOffsets(stride, pad, ksize)
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def construct(self, x, offsets):
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output = self.deformable_offsets_grad_op(x, offsets)
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return output
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class BackwardNet(nn.Cell):
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def __init__(self, net):
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super(BackwardNet, self).__init__()
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self.net = net
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def construct(self, *inputs):
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out = self.net(*inputs)
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return out, grad_all(self.net)(*inputs)
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def test_auto_diff():
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"""
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Feature: CPU operation.
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Description: Test of CPU operation: DeformableOffsetsGrad by auto diff.
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Expectation: No exception raised.
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"""
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x = Tensor(np.ones([1, 1, 2, 2]), dtype.float32)
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offsets = Tensor(np.array([0.1] * 12).astype(np.float32).reshape([1, 12, 1, 1]))
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forward_net = ForwardNet()
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net = BackwardNet(forward_net)
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grad = net(x, offsets)
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print("grad_x:", grad[0])
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print("grad_offset:", grad[1])
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return grad
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class NetDeformableOffsetsGrad(nn.Cell):
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def __init__(self, data_format):
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super(NetDeformableOffsetsGrad, self).__init__()
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strides = (1, 1, 1, 1)
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pads = (0, 0, 0, 0)
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ksize = (3, 3)
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self.grad_op = G.DeformableOffsetsGrad(strides, pads, ksize, data_format=data_format)
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def construct(self, grad, input_x, offsets):
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return self.grad_op(grad, input_x, offsets)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('data_type', [np.float16, np.float32])
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def test_deformable_offsets_grad_nchw(data_type):
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"""
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Feature: DeformableOffsetsGrad cpu kernel
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Description: test the rightness of DeformableOffsetsGrad gpu kernel
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Expectation: the output is same as expected result
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"""
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net = NetDeformableOffsetsGrad(data_format="NCHW")
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dout = Tensor(np.ones([1, 2, 3, 3]).astype(data_type))
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x = Tensor(np.ones([1, 2, 4, 4]).astype(data_type))
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offsets = Tensor(np.ones([1, 27, 1, 1]).astype(data_type) * 0.1)
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output = net(dout, x, offsets)
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expect_grad_x = np.array([[[0.081, 0.09, 0.09, 0.009],
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[0.09, 0.1, 0.1, 0.01],
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[0.09, 0.1, 0.1, 0.01],
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[0.009, 0.01, 0.01, 0.001]],
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[[0.081, 0.09, 0.09, 0.009],
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[0.09, 0.1, 0.1, 0.01],
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[0.09, 0.1, 0.1, 0.01],
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[0.009, 0.01, 0.01, 0.001]]]
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).astype(data_type)
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expect_grad_offset = np.array([0] * 18 + [2.0] * 9).astype(data_type).reshape([1, 27, 1, 1])
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rtol = 1e-5
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if data_type == np.float16:
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rtol = 1e-3
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assert np.allclose(output[0].asnumpy(), expect_grad_x, rtol)
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assert np.allclose(output[1].asnumpy(), expect_grad_offset, rtol)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('data_type', [np.float16, np.float32])
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def test_deformable_offsets_grad_nhwc(data_type):
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"""
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Feature: DeformableOffsetsGrad cpu kernel
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Description: test the rightness of DeformableOffsetsGrad gpu kernel
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Expectation: the output is same as expected result
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"""
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net = NetDeformableOffsetsGrad(data_format="NHWC")
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dout = Tensor(np.ones([1, 3, 3, 2]).astype(data_type))
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x = Tensor(np.ones([1, 4, 4, 2]).astype(data_type))
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offsets = Tensor(np.ones([1, 1, 1, 27]).astype(data_type) * 0.1)
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output = net(dout, x, offsets)
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expect_grad_x = np.array([[[0.081, 0.081],
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[0.09, 0.09],
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[0.09, 0.09],
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[0.009, 0.009]],
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[[0.09, 0.09],
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[0.1, 0.1],
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[0.1, 0.1],
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[0.01, 0.01]],
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[[0.09, 0.09],
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[0.1, 0.1],
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[0.1, 0.1],
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[0.01, 0.01]],
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[[0.009, 0.009],
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[0.01, 0.01],
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[0.01, 0.01],
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[0.001, 0.001]]
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]
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).astype(data_type)
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expect_grad_offset = np.array([0] * 18 + [2.0] * 9).astype(data_type).reshape([1, 1, 1, 27])
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rtol = 1e-5
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if data_type == np.float16:
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rtol = 1e-3
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assert np.allclose(output[0].asnumpy(), expect_grad_x, rtol)
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assert np.allclose(output[1].asnumpy(), expect_grad_offset, rtol)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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def test_vmap():
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""""
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Feature: Feature: DeformableOffsetsGrad cpu kernel
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Description: Test case with vmap.
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Expectation: The results are as expected.
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"""
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def cal_deformable_offsets_grad(dout, x, offsets):
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net = NetDeformableOffsetsGrad(data_format="NCHW")
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return net(dout, x, offsets)
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dout = Tensor(np.arange(2 * 1 * 2 * 3 * 3).reshape(2, 1, 2, 3, 3), dtype.float32)
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x = Tensor(np.arange(2 * 1 * 2 * 4 * 4).reshape(2, 1, 2, 4, 4), dtype.float32)
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offsets = Tensor(np.arange(2 * 1 * 27 * 1 * 1).reshape(2, 1, 27, 1, 1) * 0.1, dtype.float32)
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vmap_deformable_offset_grad = F.vmap(cal_deformable_offsets_grad, in_axes=(0, 0, 0), out_axes=0)
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out1 = vmap_deformable_offset_grad(dout, x, offsets)
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def manually_batched(dout, x, offsets):
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output_dx = []
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output_d_offsets = []
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for i in range(x.shape[0]):
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dx, d_offsets = cal_deformable_offsets_grad(dout[i], x[i], offsets[i])
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output_dx.append(dx)
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output_d_offsets.append(d_offsets)
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return F.stack(output_dx), F.stack(output_d_offsets)
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out2 = manually_batched(dout, x, offsets)
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assert np.allclose(out1[0].asnumpy(), out2[0].asnumpy())
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assert np.allclose(out1[1].asnumpy(), out2[1].asnumpy())
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