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[MLU] add floor kernel and grid_sampler kernel (PaddlePaddle#44498)
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
// | ||
// Licensed under the Apache License, Version 2.0 (the "License"); | ||
// you may not use this file except in compliance with the License. | ||
// You may obtain a copy of the License at | ||
// | ||
// http://www.apache.org/licenses/LICENSE-2.0 | ||
// | ||
// Unless required by applicable law or agreed to in writing, software | ||
// distributed under the License is distributed on an "AS IS" BASIS, | ||
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
// See the License for the specific language governing permissions and | ||
// limitations under the License. | ||
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#include "paddle/fluid/framework/op_registry.h" | ||
#include "paddle/fluid/operators/mlu/mlu_baseop.h" | ||
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namespace paddle { | ||
namespace operators { | ||
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using Tensor = framework::Tensor; | ||
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template <typename T> | ||
class GridSamplerMLUKernel : public framework::OpKernel<T> { | ||
public: | ||
void Compute(const framework::ExecutionContext& ctx) const override { | ||
PADDLE_ENFORCE_EQ( | ||
platform::is_mlu_place(ctx.GetPlace()), | ||
true, | ||
platform::errors::Unavailable("This kernel only runs on MLU.")); | ||
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// input and output data | ||
const Tensor* input = ctx.Input<Tensor>("X"); | ||
const Tensor* grid = ctx.Input<Tensor>("Grid"); | ||
Tensor* output = ctx.Output<Tensor>("Output"); | ||
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int n = input->dims()[0]; | ||
int c = input->dims()[1]; | ||
int out_h = grid->dims()[1]; | ||
int out_w = grid->dims()[2]; | ||
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output->mutable_data<T>({n, c, out_h, out_w}, ctx.GetPlace()); | ||
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// attrs | ||
// paddle.nn.functional.grid_sample(x, grid, mode='bilinear', | ||
// padding_mode='zeros', align_corners=True, name=None) | ||
const std::string mode = ctx.Attr<std::string>("mode"); | ||
const std::string padding_mode = ctx.Attr<std::string>("padding_mode"); | ||
bool align_corners = ctx.Attr<bool>("align_corners"); | ||
const std::string data_format = | ||
paddle::framework::DataLayoutToString(input->layout()); | ||
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PADDLE_ENFORCE_EQ( | ||
mode == "bilinear", | ||
true, | ||
platform::errors::Unavailable( | ||
"Only support bilinear mode in mlu grid_sample kernel.")); | ||
PADDLE_ENFORCE_EQ( | ||
padding_mode == "zeros", | ||
true, | ||
platform::errors::Unavailable( | ||
"Only support zeros padding_mode in mlu grid_sample kernel.")); | ||
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Tensor trans_input(input->dtype()); | ||
// transpose input from NCHW to NHWC | ||
const std::vector<int> perm_to_nhwc = {0, 2, 3, 1}; | ||
TransposeFromMLUTensor<T>( | ||
ctx, perm_to_nhwc, input, &trans_input, true /*need_reshape_or_alloc*/); | ||
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Tensor tmp_output(output->dtype()); | ||
tmp_output.mutable_data<T>({n, out_h, out_w, c}, ctx.GetPlace()); | ||
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MLUCnnlGridSampleDesc grid_sample_desc(mode, padding_mode, align_corners); | ||
MLUCnnlTensorDesc input_desc( | ||
trans_input, CNNL_LAYOUT_NHWC, ToCnnlDataType<T>()); | ||
MLUCnnlTensorDesc grid_desc(*grid, CNNL_LAYOUT_NHWC, ToCnnlDataType<T>()); | ||
MLUCnnlTensorDesc tmp_output_desc( | ||
tmp_output, CNNL_LAYOUT_NHWC, ToCnnlDataType<T>()); | ||
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MLUCnnl::GridSample(ctx, | ||
grid_sample_desc.get(), | ||
input_desc.get(), | ||
GetBasePtr(&trans_input), | ||
grid_desc.get(), | ||
GetBasePtr(grid), | ||
tmp_output_desc.get(), | ||
GetBasePtr(&tmp_output)); | ||
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// transpose output from NHWC to NCHW | ||
const std::vector<int> perm_to_nchw = { | ||
0, | ||
3, | ||
1, | ||
2, | ||
}; | ||
TransposeFromMLUTensor<T>(ctx, | ||
perm_to_nchw, | ||
&tmp_output, | ||
output, | ||
false /*need_reshape_or_alloc*/); | ||
} | ||
}; | ||
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} // namespace operators | ||
} // namespace paddle | ||
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namespace ops = paddle::operators; | ||
namespace plat = paddle::platform; | ||
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REGISTER_OP_MLU_KERNEL(grid_sampler, | ||
ops::GridSamplerMLUKernel<float>, | ||
ops::GridSamplerMLUKernel<plat::float16>); |
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59 changes: 59 additions & 0 deletions
59
python/paddle/fluid/tests/unittests/mlu/test_floor_op_mlu.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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from __future__ import print_function | ||
import unittest | ||
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import numpy as np | ||
import sys | ||
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sys.path.append('..') | ||
from op_test import OpTest | ||
import paddle | ||
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paddle.enable_static() | ||
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class TestFloor(OpTest): | ||
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def setUp(self): | ||
self.op_type = "floor" | ||
self.place = paddle.device.MLUPlace(0) | ||
self.__class__.use_mlu = True | ||
self.init_dtype() | ||
self.__class__.no_need_check_grad = True | ||
self.python_api = paddle.floor | ||
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np.random.seed(1024) | ||
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype) | ||
out = np.floor(x) | ||
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self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)} | ||
self.outputs = {'Out': out} | ||
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def test_check_output(self): | ||
self.check_output_with_place(self.place, check_eager=False) | ||
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def init_dtype(self): | ||
self.dtype = np.float32 | ||
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class TestFloorFP16(TestFloor): | ||
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def init_dtype(self): | ||
self.dtype = np.float16 | ||
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if __name__ == '__main__': | ||
unittest.main() |
223 changes: 223 additions & 0 deletions
223
python/paddle/fluid/tests/unittests/mlu/test_grid_sampler_op_mlu.py
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
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import paddle | ||
import unittest | ||
import numpy as np | ||
import paddle.fluid.core as core | ||
import sys | ||
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sys.path.append('..') | ||
from op_test import OpTest | ||
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paddle.enable_static() | ||
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def AffineGrid(theta, grid_shape): | ||
n = grid_shape[0] | ||
h = grid_shape[1] | ||
w = grid_shape[2] | ||
h_idx = np.repeat(np.linspace(-1, 1, h)[np.newaxis, :], w, | ||
axis=0).T[:, :, np.newaxis] | ||
w_idx = np.repeat(np.linspace(-1, 1, w)[np.newaxis, :], h, | ||
axis=0)[:, :, np.newaxis] | ||
grid = np.concatenate([w_idx, h_idx, np.ones([h, w, 1])], | ||
axis=2) # h * w * 3 | ||
grid = np.repeat(grid[np.newaxis, :], n, axis=0) # n * h * w *3 | ||
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ret = np.zeros([n, h * w, 2]) | ||
theta = theta.transpose([0, 2, 1]) | ||
for i in range(len(theta)): | ||
ret[i] = np.dot(grid[i].reshape([h * w, 3]), theta[i]) | ||
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return ret.reshape([n, h, w, 2]).astype("float32") | ||
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def getGridPointValue(data, x, y): | ||
data_shape = data.shape | ||
N = data_shape[0] | ||
C = data_shape[1] | ||
in_H = data_shape[2] | ||
in_W = data_shape[3] | ||
out_H = x.shape[1] | ||
out_W = x.shape[2] | ||
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#out = np.zeros(data_shape, dtype='float32') | ||
out = np.zeros([N, C, out_H, out_W], dtype='float32') | ||
for i in range(N): | ||
for j in range(out_H): | ||
for k in range(out_W): | ||
if y[i, j, k] < 0 or y[i, j, k] > in_H - 1 or x[ | ||
i, j, k] < 0 or x[i, j, k] > in_W - 1: | ||
out[i, :, j, k] = 0 | ||
else: | ||
out[i, :, j, k] = data[i, :, y[i, j, k], x[i, j, k]] | ||
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return out | ||
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def clip(x, min_n, max_n): | ||
return np.maximum(np.minimum(x, max_n), min_n) | ||
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def unnormalizeAndClip(grid_slice, max_val, align_corners, padding_mode): | ||
if align_corners: | ||
grid_slice = 0.5 * ((grid_slice.astype('float32') + 1.0) * max_val) | ||
else: | ||
grid_slice = 0.5 * ((grid_slice.astype('float32') + 1.0) * | ||
(max_val + 1)) - 0.5 | ||
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if padding_mode == "border": | ||
grid_slice = clip(grid_slice, 0, max_val) | ||
elif padding_mode == "reflection": | ||
double_range = 2 * max_val if align_corners else (max_val + 1) * 2 | ||
grid_abs = np.abs(grid_slice) if align_corners else np.abs(grid_slice + | ||
0.5) | ||
extra = grid_abs - np.floor(grid_abs / double_range) * double_range | ||
grid_slice = np.minimum(extra, double_range - extra) | ||
grid_slice = grid_slice if align_corners else clip( | ||
grid_slice - 0.5, 0, max_val) | ||
return grid_slice | ||
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def GridSampler(data, | ||
grid, | ||
align_corners=True, | ||
mode="bilinear", | ||
padding_mode="zeros"): | ||
dims = data.shape | ||
N = dims[0] | ||
in_C = dims[1] | ||
in_H = dims[2] | ||
in_W = dims[3] | ||
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out_H = grid.shape[1] | ||
out_W = grid.shape[2] | ||
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x = grid[:, :, :, 0] | ||
y = grid[:, :, :, 1] | ||
y_max = in_H - 1 | ||
x_max = in_W - 1 | ||
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x = unnormalizeAndClip(x, x_max, align_corners, padding_mode) | ||
y = unnormalizeAndClip(y, y_max, align_corners, padding_mode) | ||
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if mode == "bilinear": | ||
x0 = np.floor(x).astype('int32') | ||
x1 = x0 + 1 | ||
y0 = np.floor(y).astype('int32') | ||
y1 = y0 + 1 | ||
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wa = np.tile(((x1 - x) * (y1 - y)).reshape((N, 1, out_H, out_W)), | ||
(1, in_C, 1, 1)) | ||
wb = np.tile(((x1 - x) * (y - y0)).reshape((N, 1, out_H, out_W)), | ||
(1, in_C, 1, 1)) | ||
wc = np.tile(((x - x0) * (y1 - y)).reshape((N, 1, out_H, out_W)), | ||
(1, in_C, 1, 1)) | ||
wd = np.tile(((x - x0) * (y - y0)).reshape((N, 1, out_H, out_W)), | ||
(1, in_C, 1, 1)) | ||
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va = getGridPointValue(data, x0, y0) | ||
vb = getGridPointValue(data, x0, y1) | ||
vc = getGridPointValue(data, x1, y0) | ||
vd = getGridPointValue(data, x1, y1) | ||
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out = (wa * va + wb * vb + wc * vc + wd * vd).astype('float32') | ||
elif mode == "nearest": | ||
x = np.round(x).astype('int32') | ||
y = np.round(y).astype('int32') | ||
out = getGridPointValue(data, x, y) | ||
return out | ||
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class TestGridSamplerOp(OpTest): | ||
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def setUp(self): | ||
self.place = paddle.device.MLUPlace(0) | ||
self.__class__.use_mlu = True | ||
self.__class__.no_need_check_grad = True | ||
self.op_type = 'grid_sampler' | ||
self.align_corners = True | ||
self.padding_mode = "zeros" | ||
self.mode = "bilinear" | ||
self.initTestCase() | ||
x = np.random.randint(0, 255, self.x_shape).astype('float32') | ||
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theta = np.zeros(self.theta_shape).astype('float32') | ||
for i in range(self.theta_shape[0]): | ||
for j in range(2): | ||
for k in range(3): | ||
theta[i, j, k] = np.random.rand(1)[0] | ||
grid = AffineGrid(theta, self.grid_shape) | ||
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self.inputs = {'X': x, 'Grid': grid} | ||
self.attrs = { | ||
'use_cudnn': False, | ||
"align_corners": self.align_corners, | ||
"padding_mode": self.padding_mode, | ||
"mode": self.mode | ||
} | ||
self.outputs = { | ||
'Output': | ||
GridSampler(x, grid, self.align_corners, self.mode, | ||
self.padding_mode) | ||
} | ||
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def test_check_output(self): | ||
self.check_output_with_place(self.place) | ||
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def initTestCase(self): | ||
self.x_shape = (2, 3, 8, 8) | ||
self.grid_shape = (2, 7, 9, 2) | ||
self.theta_shape = (2, 2, 3) | ||
self.align_corners = False | ||
self.padding_mode = "zeros" | ||
self.mode = "bilinear" | ||
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class Case1(TestGridSamplerOp): | ||
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def initTestCase(self): | ||
self.x_shape = (2, 3, 5, 6) | ||
self.grid_shape = (2, 8, 9, 2) | ||
self.theta_shape = (2, 2, 3) | ||
self.align_corners = True | ||
self.padding_mode = "zeros" | ||
self.mode = "bilinear" | ||
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class LargeInputCase(TestGridSamplerOp): | ||
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def initTestCase(self): | ||
self.x_shape = (2, 3, 128, 128) | ||
self.grid_shape = (2, 130, 130, 2) | ||
self.theta_shape = (2, 2, 3) | ||
self.align_corners = False | ||
self.padding_mode = "zeros" | ||
self.mode = "bilinear" | ||
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class Case2(LargeInputCase): | ||
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def initTestCase(self): | ||
self.x_shape = (2, 3, 128, 128) | ||
self.grid_shape = (2, 130, 130, 2) | ||
self.theta_shape = (2, 2, 3) | ||
self.align_corners = True | ||
self.padding_mode = "zeros" | ||
self.mode = "bilinear" | ||
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if __name__ == "__main__": | ||
unittest.main() |
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