提交 c9ba51ea 编写于 作者: T tensor-tang

Merge remote-tracking branch 'ups/develop' into feature/libxsmm

......@@ -86,8 +86,9 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
std::minstd_rand engine,
std::vector<int>* inds) const {
std::uniform_real_distribution<float> uniform(0, 1);
if (inds->size() > num) {
for (int i = num; i < inds->size(); ++i) {
const int64_t size = static_cast<int64_t>(inds->size());
if (size > num) {
for (int64_t i = num; i < size; ++i) {
int rng_ind = std::floor(uniform(engine) * i);
if (rng_ind < num)
std::iter_swap(inds->begin() + rng_ind + offset,
......
......@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/im2sequence_op.h"
#include <string>
#include <vector>
namespace paddle {
......@@ -28,20 +29,19 @@ class Im2SequenceOp : public framework::OperatorWithKernel {
"Input(X) of Im2SequenceOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of Im2SequenceOp op should not be null.");
auto in_dim = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(in_dim.size(), 4,
"Input(X) format must be 4D tensor, eg., NCHW.");
auto kernels = ctx->Attrs().Get<std::vector<int>>("kernels");
auto strides = ctx->Attrs().Get<std::vector<int>>("strides");
auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int batch_size = in_dim[0];
int img_channels = in_dim[1];
int img_height = in_dim[2];
int img_width = in_dim[3];
auto kernels = ctx->Attrs().Get<std::vector<int>>("kernels");
auto strides = ctx->Attrs().Get<std::vector<int>>("strides");
auto paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
int output_height = Im2SeqOutputSize(img_height, kernels[0], paddings[0],
paddings[2], strides[0]);
int output_width = Im2SeqOutputSize(img_width, kernels[1], paddings[1],
......@@ -61,6 +61,10 @@ class Im2SequenceOpMaker : public framework::OpProtoAndCheckerMaker {
"C: channels"
"H: height"
"W: width");
AddInput("Y",
"(Tensor) The input tensor of image real size(H, W)."
"2-D with shape [batchsize, 2]")
.AsDispensable();
AddOutput("Out", "(LodTensor) The output data of im2sequence op,");
AddAttr<std::vector<int>>("kernels",
"(vector<int>), the "
......@@ -73,6 +77,13 @@ class Im2SequenceOpMaker : public framework::OpProtoAndCheckerMaker {
"(vector<int> default:{0, 0, 0, 0}), the "
"paddings(up_pad, left_pad, down_pad, right_pad)")
.SetDefault({0, 0, 0, 0});
AddAttr<std::vector<int>>("out_stride",
"the attribute is valid only when input(Y)"
"is not NULL.this attribute represents the"
"scaling of the pic through the CNN"
"(vector<int> dedault:{1,1}),the out_stride"
" (out_stride_height, out_stride_width)")
.SetDefault({1, 1});
AddComment(R"DOC(
This op uses kernels to scan images and converts these images to sequences.
After expanding, The number of time steps are output_height * output_width
......@@ -123,7 +134,7 @@ output.data = [[ 6. 2. 8. 3. 2. 4. 6. 3.]
[ 7. 1. 7. 9. 2. 1. 3. 5.]
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8, 9}
output.dims = {8, 8}
output.lod = [[0, 4, 8]]
)DOC");
......
......@@ -13,6 +13,7 @@
limitations under the License. */
#pragma once
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/eigen.h"
......@@ -39,51 +40,107 @@ class Im2SequenceKernel : public framework::OpKernel<T> {
void Compute(const framework::ExecutionContext& ctx) const override {
const Tensor* in = ctx.Input<Tensor>("X");
LoDTensor* out = ctx.Output<LoDTensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
// TODO(wanghaoshuang): Add layout checker after 'set_layout'
// being available for python API
// PADDLE_ENFORCE_EQ(in->layout(), framework::DataLayout::kNCHW,
// "Input(X) layout must be NCHW");
auto in_dim = in->dims();
int batch_size = in_dim[0];
int img_channels = in_dim[1];
int img_height = in_dim[2];
int img_width = in_dim[3];
auto kernels = ctx.Attr<std::vector<int>>("kernels");
auto strides = ctx.Attr<std::vector<int>>("strides");
auto paddings = ctx.Attr<std::vector<int>>("paddings");
if (ctx.HasInput("Y") && batch_size > 1) {
const Tensor* imgrealsize = ctx.Input<Tensor>("Y");
auto out_stride = ctx.Attr<std::vector<int>>("out_stride");
Tensor cpu_shape_tensor;
TensorCopySync(*imgrealsize, platform::CPUPlace(), &cpu_shape_tensor);
std::vector<int> imgreal_h;
std::vector<int> imgreal_w;
std::vector<int> output_height;
std::vector<int> output_width;
int result = 0;
for (int i = 0; i < batch_size; i++) {
int tmp_real_h = static_cast<int>((cpu_shape_tensor.data<T>())[2 * i]);
int tmp_real_w =
static_cast<int>((cpu_shape_tensor.data<T>())[2 * i + 1]);
if (tmp_real_h % out_stride[0] == 0) {
tmp_real_h = tmp_real_h / out_stride[0];
} else {
tmp_real_h = tmp_real_h / out_stride[0] + 1;
}
if (tmp_real_w % out_stride[1] == 0) {
tmp_real_w = tmp_real_w / out_stride[1];
} else {
tmp_real_w = tmp_real_w / out_stride[1] + 1;
}
imgreal_h.push_back(tmp_real_h);
imgreal_w.push_back(tmp_real_w);
output_height.push_back(Im2SeqOutputSize(
imgreal_h[i], kernels[0], paddings[0], paddings[2], strides[0]));
output_width.push_back(Im2SeqOutputSize(
imgreal_w[i], kernels[1], paddings[1], paddings[3], strides[1]));
result += output_height[i] * output_width[i];
}
out->mutable_data<T>({result, img_channels * kernels[0] * kernels[1]},
ctx.GetPlace());
const std::vector<int> dilations({1, 1});
int offset_out = 0;
for (int i = 0; i < batch_size; i++) {
const Tensor src =
in->Slice(i, i + 1).Resize({img_channels, img_height, img_width});
Tensor dst = out->Slice(offset_out,
offset_out + output_height[i] * output_width[i])
.Resize({output_height[i], output_width[i],
img_channels, kernels[0], kernels[1]});
offset_out += output_height[i] * output_width[i];
math::Im2ColFunctor<math::ColFormat::kOCF, DeviceContext, T> f;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
f(dev_ctx, src, dilations, strides, paddings, &dst);
}
framework::LoD lod(1);
lod[0].reserve(batch_size + 1);
int offset = 0;
lod[0].push_back(offset);
for (int i = 0; i < batch_size; ++i) {
offset += output_height[i] * output_width[i];
lod[0].push_back(offset);
}
out->set_lod(lod);
} else {
out->mutable_data<T>(ctx.GetPlace());
int output_height = Im2SeqOutputSize(img_height, kernels[0], paddings[0],
paddings[2], strides[0]);
int output_width = Im2SeqOutputSize(img_width, kernels[1], paddings[1],
paddings[3], strides[1]);
const std::vector<int> dilations({1, 1});
auto out_dims = out->dims();
out->Resize({batch_size, out->numel() / batch_size});
for (int i = 0; i < batch_size; i++) {
const Tensor src =
in->Slice(i, i + 1).Resize({img_channels, img_height, img_width});
Tensor dst = out->Slice(i, i + 1).Resize(
{output_height, output_width, img_channels, kernels[0], kernels[1]});
Tensor dst =
out->Slice(i, i + 1).Resize({output_height, output_width,
img_channels, kernels[0], kernels[1]});
math::Im2ColFunctor<math::ColFormat::kOCF, DeviceContext, T> f;
auto& dev_ctx = ctx.template device_context<DeviceContext>();
f(dev_ctx, src, dilations, strides, paddings, &dst);
}
out->Resize(out_dims);
// set lod information
// TODO(wanghaoshuang): Move this to InferShape
framework::LoD lod(1);
lod[0].reserve(batch_size + 1);
for (int i = 0, offset = 0; i < batch_size + 1; ++i) {
int offset = 0;
lod[0].push_back(offset);
for (int i = 0; i < batch_size; ++i) {
offset += output_height * output_width;
lod[0].push_back(offset);
}
out->set_lod(lod);
}
}
};
template <typename DeviceContext, typename T>
......
......@@ -43,21 +43,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int col_height = col->dims()[3];
int col_width = col->dims()[4];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
((dilation[0] * (filter_height - 1) + 1))) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
((dilation[1] * (filter_width - 1) + 1))) /
stride[1] +
1,
col_width,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
int channels_col = im_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
......@@ -178,17 +163,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
int col_height = col->dims()[0];
int col_width = col->dims()[1];
PADDLE_ENFORCE_EQ(
(im_height + padding[0] + padding[2] - filter_height) / stride[0] + 1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(im_width + padding[1] + padding[3] - filter_width) / stride[1] + 1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
const T* im_data = im.data<T>();
T* col_data = col->data<T>();
......
......@@ -77,21 +77,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int col_height = col->dims()[3];
int col_width = col->dims()[4];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
(dilation[0] * (filter_height - 1) + 1)) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
(dilation[1] * (filter_width - 1) + 1)) /
stride[1] +
1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
int num_outputs = im_channels * col_height * col_width;
int blocks = (num_outputs + 1024 - 1) / 1024;
int block_x = 512;
......@@ -274,21 +259,6 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
int col_height = col->dims()[0];
int col_width = col->dims()[1];
PADDLE_ENFORCE_EQ((im_height + padding[0] + padding[2] -
(dilation[0] * (filter_height - 1) + 1)) /
stride[0] +
1,
col_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ((im_width + padding[1] + padding[3] -
(dilation[1] * (filter_width - 1) + 1)) /
stride[1] +
1,
col_width,
"col_width and padding(padding_left, padding_right) are "
"inconsistent.");
int block_dim_x = 0;
int block_dim_y = 0;
if (filter_height <= 4 && filter_width <= 4) {
......
......@@ -123,7 +123,8 @@ def _append_grad_suffix_(name):
def _addup_repetitive_outputs_(op_descs):
"""
In backward part, an variable may be the output of more than one ops.
In this case, the variable should be the accumulation of all the outputs.
And one op may yield its multiple outputs to the same variable.
In these cases, the variable should be the accumulation of all the outputs.
`sum_op`s are added to implement the accumulate.
"""
pending_sum_ops = []
......@@ -136,7 +137,9 @@ def _addup_repetitive_outputs_(op_descs):
"sum", {"X": renamed_vars[var_name]}, {"Out": [var_name]},
{"use_mkldnn": False}), idx))
renamed_vars[var_name] = [var_name]
for var_name in op_desc.output_arg_names():
for param_idx, param_name in enumerate(op_desc.output_names()):
arg_names = op_desc.output(param_name)
for arg_idx, var_name in enumerate(arg_names):
if var_name == core.empty_var_name(
) or var_name in op_desc.input_arg_names():
# empty variable or inplace op
......@@ -154,11 +157,26 @@ def _addup_repetitive_outputs_(op_descs):
_rename_arg_(op_descs, var_name, new_name, 0, idx)
_rename_arg_(pending_sum_ops, var_name, new_name)
for p in op_desc.output_names()[:param_idx]:
p_arg_names = op_desc.output(p)
if var_name in p_arg_names:
op_desc.set_output(p, [
new_name if x == var_name else x
for x in p_arg_names
])
arg_names = [
new_name if x == var_name else x
for x in arg_names[:arg_idx]
] + arg_names[arg_idx:]
new_name = var_name + "@RENAME@" + \
str(var_rename_count[var_name])
var_rename_count[var_name] += 1
op_desc.rename_output(var_name, new_name)
arg_names[arg_idx] = new_name
op_desc.set_output(param_name, arg_names)
renamed_vars[var_name].append(new_name)
for var_name, inputs in renamed_vars.iteritems():
if len(inputs) > 1:
pending_sum_ops.append(
......
......@@ -11,6 +11,20 @@
# 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.
# Copyright (c ) 2018 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.
"""
All layers just related to the neural network.
"""
......@@ -3900,7 +3914,13 @@ def transpose(x, perm, name=None):
return out
def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
def im2sequence(input,
filter_size=1,
stride=1,
padding=0,
input_image_size=None,
out_stride=1,
name=None):
"""
Extracts image patches from the input tensor to form a tensor of shape
{input.batch_size * output_height * output_width, filter_size_H *
......@@ -3937,6 +3957,15 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
padding_up = padding_down = padding_left = padding_right = padding
Default: padding = 0.
input_image_size(Variable): the input contains image real size.It's dim
is [batchsize, 2]. It is dispensable.It is just for batch inference.
out_stride(int|tuple): The scaling of image through CNN. It is
dispensable. It is valid only when input_image_size is not null.
If out_stride is tuple, it must contain two intergers,
(out_stride_H, out_stride_W). Otherwise,
the out_stride_H = out_stride_W = out_stride.
name (int): The name of this layer. It is optional.
Returns:
......@@ -3987,7 +4016,7 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
[ 5. 7. 2. 4. 1. 3. 9. 0.]
[ 7. 9. 4. 8. 3. 5. 0. 8.]]
output.dims = {8, 9}
output.dims = {8, 8}
output.lod = [[4, 4]]
......@@ -4009,18 +4038,17 @@ def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
if len(padding) == 2:
padding.append(padding[0])
padding.append(padding[1])
inputs = {"X": input}
attrs = {"kernels": filter_size, "strides": stride, "padding": padding}
if input_image_size:
if isinstance(out_stride, int):
out_stride = [out_stride, out_stride]
inputs["Y"] = input_image_size
attrs["out_stride"] = out_stride
helper = LayerHelper('im2sequence', **locals())
out = helper.create_tmp_variable(dtype=helper.input_dtype())
helper.append_op(
type='im2sequence',
inputs={'X': input},
outputs={'Out': out},
attrs={
'kernels': filter_size,
'strides': stride,
'paddings': padding,
})
type='im2sequence', inputs=inputs, outputs={'Out': out}, attrs=attrs)
return out
......
......@@ -16,20 +16,45 @@ import numpy as np
from op_test import OpTest
def get_output_shape(attrs, in_shape):
def get_output_shape(attrs, in_shape, img_real_size):
batchsize = in_shape[0]
img_height = in_shape[2]
img_width = in_shape[3]
paddings = np.array(attrs['paddings']).astype("int32")
kernels = np.array(attrs['kernels']).astype("int32")
strides = np.array(attrs['strides']).astype("int32")
output_height = np.zeros((1, batchsize)).astype("int32")
output_width = np.zeros((1, batchsize)).astype("int32")
if len(img_real_size):
out_stride = np.array(attrs['out_stride']).astype("int32")
imgreal_h = 0
imgreal_w = 0
for index in range(batchsize):
if img_real_size[index, 0] % out_stride[0] == 0:
imgreal_h = img_real_size[index, 0] / out_stride[0]
else:
imgreal_h = img_real_size[index, 0] / out_stride[0] + 1
if img_real_size[index, 0] % out_stride[1] == 0:
imgreal_w = img_real_size[index, 1] / out_stride[1]
else:
imgreal_w = img_real_size[index, 0] / out_stride[1] + 1
output_height[0,index] = \
1 + \
(imgreal_h + paddings[0] + paddings[2] - kernels[0] + strides[0] - 1) / \
strides[0]
paddings = attrs['paddings']
kernels = attrs['kernels']
strides = attrs['strides']
output_height = \
output_width[0,index] = \
1 + \
(imgreal_w + paddings[1] + paddings[3] - kernels[1] + strides[1] - 1) / \
strides[1]
else:
for index in range(batchsize):
output_height[0,index] = \
1 + \
(img_height + paddings[0] + paddings[2] - kernels[0] + strides[0] - 1) / \
strides[0]
output_width = \
output_width[0,index] = \
1 + \
(img_width + paddings[1] + paddings[3] - kernels[1] + strides[1] - 1) / \
strides[1]
......@@ -75,22 +100,25 @@ def im2col(attrs, im, col):
im_row_offset][im_col_offset]
def Im2Sequence(inputs, attrs):
output_height, output_width = get_output_shape(attrs, inputs.shape)
def Im2Sequence(inputs, img_real_size, attrs):
output_height, output_width = get_output_shape(attrs, inputs.shape,
img_real_size)
img_channels = inputs.shape[1]
batch_size = inputs.shape[0]
out = np.zeros([
batch_size, output_height, output_width, img_channels,
out = []
for index in range(batch_size):
tmp = np.zeros([
output_height[0, index], output_width[0, index], img_channels,
attrs['kernels'][0], attrs['kernels'][1]
]).astype("float32")
for i in range(len(inputs)):
im2col(attrs, inputs[i], out[i])
out = out.reshape([
batch_size * output_height * output_width,
out.append(tmp)
for index in range(len(inputs)):
im2col(attrs, inputs[index], out[index])
out[index] = out[index].reshape([
output_height[0, index] * output_width[0, index],
img_channels * attrs['kernels'][0] * attrs['kernels'][1]
])
out = np.concatenate(out, axis=0)
return out
......@@ -103,7 +131,7 @@ class TestBlockExpandOp(OpTest):
self.attrs = {
'kernels': [2, 2],
'strides': [1, 1],
'paddings': [1, 1, 1, 1]
'paddings': [1, 1, 1, 1],
}
def setUp(self):
......@@ -113,7 +141,8 @@ class TestBlockExpandOp(OpTest):
self.batch_size, self.img_channels, self.img_height, self.img_width
]).astype("float32")
out = Im2Sequence(x, self.attrs)
real_size = np.array([]).astype("float32")
out = Im2Sequence(x, real_size, self.attrs)
self.inputs = {'X': x}
self.outputs = {'Out': out}
......@@ -133,20 +162,20 @@ class TestBlockExpandOpCase2(TestBlockExpandOp):
self.attrs = {
'kernels': [2, 1],
'strides': [2, 1],
'paddings': [2, 1, 2, 1]
'paddings': [2, 1, 2, 1],
}
class TestBlockExpandOpCase3(TestBlockExpandOp):
def config(self):
self.batch_size = 3
self.batch_size = 2
self.img_channels = 1
self.img_height = 4
self.img_width = 5
self.attrs = {
'kernels': [2, 1],
'strides': [2, 1],
'paddings': [2, 0, 2, 0]
'paddings': [2, 0, 2, 0],
}
......@@ -159,9 +188,94 @@ class TestBlockExpandOpCase4(TestBlockExpandOp):
self.attrs = {
'kernels': [2, 2],
'strides': [1, 1],
'paddings': [0, 0, 0, 0]
'paddings': [0, 0, 0, 0],
}
class TestBlockExpandOpCase5(OpTest):
def config(self):
self.batch_size = 1
self.img_channels = 3
self.img_height = 4
self.img_width = 5
self.attrs = {
'kernels': [2, 1],
'strides': [2, 1],
'paddings': [2, 1, 2, 1],
'out_stride': [2, 2],
}
def setUp(self):
self.config()
self.op_type = "im2sequence"
x = np.random.uniform(0.1, 1, [
self.batch_size, self.img_channels, self.img_height, self.img_width
]).astype("float32")
real_size = np.array([[8, 10], [5, 8]]).astype("float32")
out = np.array(Im2Sequence(x, real_size, self.attrs))
self.inputs = {'X': x, 'Y': real_size} #l ??
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
class TestBlockExpandOpCase6(OpTest):
def config(self):
self.batch_size = 3
self.img_channels = 1
self.img_height = 4
self.img_width = 5
self.attrs = {
'kernels': [2, 1],
'strides': [1, 1],
'paddings': [0, 0, 0, 0],
'out_stride': [1, 1],
}
def setUp(self):
self.config()
self.op_type = "im2sequence"
x = np.random.uniform(0.1, 1, [
self.batch_size, self.img_channels, self.img_height, self.img_width
]).astype("float32")
real_size = np.array([[8, 10], [5, 8], [5, 8]]).astype("float32")
out = np.array(Im2Sequence(x, real_size, self.attrs))
self.inputs = {'X': x, 'Y': real_size} #l ??
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
class TestBlockExpandOpCase7(OpTest):
def config(self):
self.batch_size = 2
self.img_channels = 2
self.img_height = 3
self.img_width = 3
self.attrs = {
'kernels': [2, 2],
'strides': [1, 1],
'paddings': [1, 0, 1, 0],
'out_stride': [2, 2],
}
def setUp(self):
self.config()
self.op_type = "im2sequence"
x = np.random.uniform(0.1, 1, [
self.batch_size, self.img_channels, self.img_height, self.img_width
]).astype("float32")
real_size = np.array([[6, 6], [4, 4]]).astype("float32")
out = np.array(Im2Sequence(x, real_size, self.attrs))
self.inputs = {'X': x, 'Y': real_size}
self.outputs = {'Out': out}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()
#set shiftwidth=4 set expandtab set tabstop=4
......@@ -251,12 +251,16 @@ class TestBook(unittest.TestCase):
print(str(program))
def test_im2sequence(self):
print("test_im2sequence")
program = Program()
with program_guard(program):
x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
y = layers.data(name='y', shape=[], dtype='float32')
output = layers.im2sequence(
input=x, stride=[1, 1], filter_size=[2, 2])
input=x,
input_image_size=y,
stride=[1, 1],
filter_size=[2, 2],
out_stride=[1, 1])
self.assertIsNotNone(output)
print(str(program))
......
......@@ -181,6 +181,14 @@ else:
command = "patchelf --set-rpath '$ORIGIN/../libs/' ${PADDLE_BINARY_DIR}/python/paddle/fluid/core.so"
if os.system(command) != 0:
raise Exception("patch core.so failed, command: %s" % command)
if '${WITH_FLUID_ONLY}'== 'OFF':
# change rpath of _swig_paddle.so.
if "@APPLE@" == "1":
command = "install_name_tool -id \"@loader_path/../paddle/libs/\" ${PADDLE_BINARY_DIR}/python/py_paddle/_swig_paddle.so"
else:
command = "patchelf --set-rpath '$ORIGIN/../paddle/libs/' ${PADDLE_BINARY_DIR}/python/py_paddle/_swig_paddle.so"
if os.system(command) != 0:
raise Exception("patch _swig_paddle.so failed, command: %s" % command)
setup(name='${PACKAGE_NAME}',
version='${PADDLE_VERSION}',
......
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