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提交 4d42f4fa 编写于 作者: P phlrain
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上级 263b4773
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Showing with 874 addition and 913 deletion
paddle/fluid/operators/lu_op.h
浏览文件 @ 4d42f4fa
...@@ -41,9 +41,12 @@ void SetValueCompute(const framework::ExecutionContext& ctx, ...@@ -41,9 +41,12 @@ void SetValueCompute(const framework::ExecutionContext& ctx,
auto dtype = framework::TransToProtoVarType(in->dtype()); auto dtype = framework::TransToProtoVarType(in->dtype());
auto in_dims = in->dims(); auto in_dims = in->dims();
CheckAndUpdateSliceAttrs<int64_t>(in_dims, axes, starts, ends, &steps); phi::funcs::CheckAndUpdateSliceAttrs<int64_t>(in_dims, axes, starts, ends,
auto slice_dims = GetSliceDims(in_dims, axes, *starts, *ends, &steps); &steps);
auto decrease_slice_dims = GetDecreasedDims(slice_dims, decrease_axes); auto slice_dims =
phi::funcs::GetSliceDims(in_dims, axes, *starts, *ends, &steps);
auto decrease_slice_dims =
phi::funcs::GetDecreasedDims(slice_dims, decrease_axes);
auto slice_dims_for_assign = decrease_slice_dims; auto slice_dims_for_assign = decrease_slice_dims;
if (!none_axes.empty()) { if (!none_axes.empty()) {
...@@ -281,10 +284,10 @@ void SliceCompute(const framework::ExecutionContext& ctx, ...@@ -281,10 +284,10 @@ void SliceCompute(const framework::ExecutionContext& ctx,
} }
} }
CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends); phi::funcs::CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends);
slice_dims = slice_dims = phi::funcs::GetSliceDims<int64_t>(in_dims, axes, starts, ends,
GetSliceDims<int64_t>(in_dims, axes, starts, ends, nullptr, nullptr); nullptr, nullptr);
out_dims = GetDecreasedDims(slice_dims, decrease_axis); out_dims = phi::funcs::GetDecreasedDims(slice_dims, decrease_axis);
// 2.2 Get output // 2.2 Get output
auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>(); auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>();
......
paddle/fluid/operators/set_value_op.h
浏览文件 @ 4d42f4fa
...@@ -25,10 +25,10 @@ ...@@ -25,10 +25,10 @@
#include "paddle/fluid/operators/assign_value_op.h" #include "paddle/fluid/operators/assign_value_op.h"
#include "paddle/fluid/operators/eigen/eigen_function.h" #include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/elementwise/elementwise_op_function.h" #include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
#include "paddle/fluid/operators/slice_utils.h"
#include "paddle/fluid/operators/strided_slice_op.h" #include "paddle/fluid/operators/strided_slice_op.h"
#include "paddle/fluid/operators/utils.h" #include "paddle/fluid/operators/utils.h"
#include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/enforce.h"
#include "paddle/phi/kernels/funcs/slice_utils.h"
namespace paddle { namespace paddle {
namespace operators { namespace operators {
...@@ -188,9 +188,11 @@ class SetValueKernel : public framework::OpKernel<T> { ...@@ -188,9 +188,11 @@ class SetValueKernel : public framework::OpKernel<T> {
} }
auto in_dims = in->dims(); auto in_dims = in->dims();
CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends, &steps); phi::funcs::CheckAndUpdateSliceAttrs(in_dims, axes, &starts, &ends, &steps);
auto slice_dims = GetSliceDims(in_dims, axes, starts, ends, &steps); auto slice_dims =
auto decrease_slice_dims = GetDecreasedDims(slice_dims, decrease_axes); phi::funcs::GetSliceDims(in_dims, axes, starts, ends, &steps);
auto decrease_slice_dims =
phi::funcs::GetDecreasedDims(slice_dims, decrease_axes);
auto slice_dims_for_assign = decrease_slice_dims; auto slice_dims_for_assign = decrease_slice_dims;
if (!none_axes.empty()) { if (!none_axes.empty()) {
......
paddle/fluid/operators/slice_op.cc
浏览文件 @ 4d42f4fa
...@@ -17,6 +17,7 @@ limitations under the License. */ ...@@ -17,6 +17,7 @@ limitations under the License. */
#include <memory> #include <memory>
#include <string> #include <string>
#include <vector> #include <vector>
#include "paddle/phi/kernels/funcs/slice_utils.h"
namespace paddle { namespace paddle {
namespace operators { namespace operators {
...@@ -101,15 +102,17 @@ class SliceOp : public framework::OperatorWithKernel { ...@@ -101,15 +102,17 @@ class SliceOp : public framework::OperatorWithKernel {
"The size of ends must be equal to the size of axes.")); "The size of ends must be equal to the size of axes."));
} }
CheckAndUpdateSliceAttrs<int>(in_dims, axes, &starts, &ends, nullptr, phi::funcs::CheckAndUpdateSliceAttrs<int>(in_dims, axes, &starts, &ends,
&infer_flags); nullptr, &infer_flags);
auto slice_dims = auto slice_dims = phi::funcs::GetSliceDims<int>(in_dims, axes, starts, ends,
GetSliceDims<int>(in_dims, axes, starts, ends, nullptr, &infer_flags); nullptr, &infer_flags);
if (ctx->IsRuntime()) { if (ctx->IsRuntime()) {
out_dims = GetDecreasedDims<int>(slice_dims, decrease_axis, &infer_flags); out_dims = phi::funcs::GetDecreasedDims<int>(slice_dims, decrease_axis,
&infer_flags);
} else { } else {
out_dims = GetDecreasedDims<int>(slice_dims, decrease_axis, nullptr); out_dims =
phi::funcs::GetDecreasedDims<int>(slice_dims, decrease_axis, nullptr);
} }
ctx->SetOutputDim("Out", out_dims); ctx->SetOutputDim("Out", out_dims);
......
paddle/fluid/operators/slice_op.h
浏览文件 @ 4d42f4fa
...@@ -18,7 +18,6 @@ limitations under the License. */ ...@@ -18,7 +18,6 @@ limitations under the License. */
#include <vector> #include <vector>
#include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/eigen/eigen_function.h" #include "paddle/fluid/operators/eigen/eigen_function.h"
#include "paddle/fluid/operators/slice_utils.h"
#include "paddle/fluid/operators/utils.h" #include "paddle/fluid/operators/utils.h"
#include "paddle/phi/kernels/funcs/math_function.h" #include "paddle/phi/kernels/funcs/math_function.h"
......
paddle/phi/kernels/cpu/slice_grad_kernel.cc
浏览文件 @ 4d42f4fa
...@@ -29,5 +29,4 @@ PD_REGISTER_KERNEL(slice_grad, ...@@ -29,5 +29,4 @@ PD_REGISTER_KERNEL(slice_grad,
double, double,
phi::dtype::complex<float>, phi::dtype::complex<float>,
phi::dtype::complex<double>, phi::dtype::complex<double>,
phi::dtype::bfloat16, phi::dtype::bfloat16) {}
phi::dtype::float16) {}
paddle/phi/kernels/funcs/slice_utils.h
浏览文件 @ 4d42f4fa
...@@ -19,6 +19,8 @@ limitations under the License. */ ...@@ -19,6 +19,8 @@ limitations under the License. */
namespace phi { namespace phi {
namespace funcs {
template <typename T = int64_t> template <typename T = int64_t>
inline void CheckAndUpdateSliceAttrs(const DDim in_dims, inline void CheckAndUpdateSliceAttrs(const DDim in_dims,
const std::vector<T>& axes, const std::vector<T>& axes,
...@@ -161,4 +163,5 @@ inline DDim GetDecreasedDims(const DDim slice_dims, ...@@ -161,4 +163,5 @@ inline DDim GetDecreasedDims(const DDim slice_dims,
return decreased_dims; return decreased_dims;
} }
} // namespace funcs
} // namespace phi } // namespace phi
paddle/phi/kernels/gpu/slice_grad_kernel.cu 已删除 100644 → 0
浏览文件 @ 263b4773
// 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.
#include "paddle/phi/kernels/impl/slice_grad_kernel_impl.h"
#include "paddle/phi/kernels/slice_grad_kernel.h"
#include "paddle/phi/backends/gpu/gpu_context.h"
#include "paddle/phi/core/kernel_registry.h"
PD_REGISTER_KERNEL(slice_grad,
GPU,
ALL_LAYOUT,
phi::SliceGradRawKernel,
bool,
int,
int64_t,
float,
double,
phi::dtype::complex<float>,
phi::dtype::complex<double>,
phi::dtype::bfloat16,
phi::dtype::float16) {}
paddle/phi/kernels/gpu/slice_kernel.cu.cc
浏览文件 @ 4d42f4fa
...@@ -29,4 +29,5 @@ PD_REGISTER_KERNEL(slice, ...@@ -29,4 +29,5 @@ PD_REGISTER_KERNEL(slice,
double, double,
phi::dtype::complex<float>, phi::dtype::complex<float>,
phi::dtype::complex<double>, phi::dtype::complex<double>,
phi::dtype::bfloat16) {} phi::dtype::bfloat16,
phi::dtype::float16) {}
paddle/phi/kernels/impl/slice_grad_kernel_impl.h
浏览文件 @ 4d42f4fa
...@@ -66,136 +66,143 @@ void EigenPaddingCompute( ...@@ -66,136 +66,143 @@ void EigenPaddingCompute(
// if dimension less than 3, cannot reduce dimension // if dimension less than 3, cannot reduce dimension
LaunchEigenPadding<T, Context, D>( LaunchEigenPadding<T, Context, D>(
context, d_input, in_dims, d_out, out_dims, paddings); context, d_input, in_dims, d_out, out_dims, paddings);
} } else { // else we can reduce dimension
// } else { // else we can reduce dimension // count not-zero padding number, and record the dimension
// // count not-zero padding number, and record the dimension int need_pad_num = 0, pad_dim = -1;
// int need_pad_num = 0, pad_dim = -1; for (size_t i = 0; i < D; i++) {
// for (size_t i = 0; i < D; i++) { if (paddings[i].first != 0 || paddings[i].second != 0) {
// if (paddings[i].first != 0 || paddings[i].second != 0) { need_pad_num++;
// need_pad_num++; pad_dim = i;
// pad_dim = i; }
// } }
// }
// if (need_pad_num == 1) { if (need_pad_num == 1) {
// // only need padding one dimension, we can reduce dimension. // only need padding one dimension, we can reduce dimension.
// // only the padding dimension is available for us. // only the padding dimension is available for us.
// // How to reduce dimension(5 to 3 for example): // How to reduce dimension(5 to 3 for example):
// // before(D=5): // before(D=5):
// // in_dims: [x1, x2, x3, x4, x5] // in_dims: [x1, x2, x3, x4, x5]
// // padding.first: [0, 0, a, 0, 0] // padding.first: [0, 0, a, 0, 0]
// // padding.second: [0, 0, b, 0, 0] // padding.second: [0, 0, b, 0, 0]
// // | | // | |
// // V V // V V
// // after(D=3): // after(D=3):
// // reshaped_in_dims: [x1*x2, x3, x4*x5] // reshaped_in_dims: [x1*x2, x3, x4*x5]
// // reshaped_padding.first: [0, a, 0] // reshaped_padding.first: [0, a, 0]
// // reshaped_padding.second: [0, b, 0] // reshaped_padding.second: [0, b, 0]
// if (pad_dim == D - 1) { if (pad_dim == D - 1) {
// // only last dimension need padding, // only last dimension need padding,
// // reshape the dimension of tensor in 2: [preceding, padding] // reshape the dimension of tensor in 2: [preceding, padding]
// std::vector<int64_t> in_tore_shape(2, 1), out_tore_shape(2, 1); std::vector<int64_t> in_tore_shape(2, 1), out_tore_shape(2, 1);
// Eigen::array<std::pair<int64_t, int64_t>, 2> reshaped_padding; Eigen::array<std::pair<int64_t, int64_t>, 2> reshaped_padding;
// // first dimension is the accumulate of preceding dimension // first dimension is the accumulate of preceding dimension
// for (int i = 0; i < pad_dim; i++) { for (int i = 0; i < pad_dim; i++) {
// in_tore_shape[0] *= in_dims[i]; in_tore_shape[0] *= in_dims[i];
// out_tore_shape[0] *= out_dims[i]; out_tore_shape[0] *= out_dims[i];
// } }
// // second dimension is the padding dimension // second dimension is the padding dimension
// in_tore_shape[1] = in_dims[pad_dim]; in_tore_shape[1] = in_dims[pad_dim];
// out_tore_shape[1] = out_dims[pad_dim]; out_tore_shape[1] = out_dims[pad_dim];
// // convert array from std::vector to DDim // convert array from std::vector to DDim
// DDim reshaped_in_dims = make_ddim(in_tore_shape); DDim reshaped_in_dims = make_ddim(in_tore_shape);
// DDim reshaped_out_dims = make_ddim(out_tore_shape); DDim reshaped_out_dims = make_ddim(out_tore_shape);
// // after reshape: the first dimension do not need padding, // after reshape: the first dimension do not need padding,
// // set padding[0] zero // set padding[0] zero
// reshaped_padding[0].first = reshaped_padding[0].second = 0; reshaped_padding[0].first = reshaped_padding[0].second = 0;
// // the second dimension is the previous padding dimension // the second dimension is the previous padding dimension
// reshaped_padding[1].first = paddings[pad_dim].first; reshaped_padding[1].first = paddings[pad_dim].first;
// reshaped_padding[1].second = paddings[pad_dim].second; reshaped_padding[1].second = paddings[pad_dim].second;
// LaunchEigenPadding<T, Context, D>(context, d_input, reshaped_in_dims, LaunchEigenPadding<T, Context>(context,
// d_out, d_input,
// reshaped_out_dims, reshaped_padding); reshaped_in_dims,
// } else if (pad_dim == 0) { d_out,
// // only first dimension need padding, reshaped_out_dims,
// // reshape the dimension of tensor in 2: [padding, succeeding] reshaped_padding);
// // similar to (D - 1) } else if (pad_dim == 0) {
// std::vector<int64_t> in_tore_shape(2, 1), out_tore_shape(2, 1); // only first dimension need padding,
// Eigen::array<std::pair<int64_t, int64_t>, 2> reshaped_padding; // reshape the dimension of tensor in 2: [padding, succeeding]
// similar to (D - 1)
std::vector<int64_t> in_tore_shape(2, 1), out_tore_shape(2, 1);
Eigen::array<std::pair<int64_t, int64_t>, 2> reshaped_padding;
// // first dimension is the padding dimension // first dimension is the padding dimension
// in_tore_shape[0] = in_dims[pad_dim]; in_tore_shape[0] = in_dims[pad_dim];
// out_tore_shape[0] = out_dims[pad_dim]; out_tore_shape[0] = out_dims[pad_dim];
// // sencond dimension is the accumulate of succeeding dimension // sencond dimension is the accumulate of succeeding dimension
// for (size_t i = pad_dim + 1; i < D; i++) { for (size_t i = pad_dim + 1; i < D; i++) {
// in_tore_shape[1] *= in_dims[i]; in_tore_shape[1] *= in_dims[i];
// out_tore_shape[1] *= out_dims[i]; out_tore_shape[1] *= out_dims[i];
// } }
// // convert array from std::vector to DDim // convert array from std::vector to DDim
// DDim reshaped_in_dims = make_ddim(in_tore_shape); DDim reshaped_in_dims = make_ddim(in_tore_shape);
// DDim reshaped_out_dims = make_ddim(out_tore_shape); DDim reshaped_out_dims = make_ddim(out_tore_shape);
// // after reshape: // after reshape:
// // the first dimension is the previous padding dimension // the first dimension is the previous padding dimension
// reshaped_padding[0].first = paddings[pad_dim].first; reshaped_padding[0].first = paddings[pad_dim].first;
// reshaped_padding[0].second = paddings[pad_dim].second; reshaped_padding[0].second = paddings[pad_dim].second;
// // the second dimension do not need padding, set padding[1] zero // the second dimension do not need padding, set padding[1] zero
// reshaped_padding[1].first = reshaped_padding[1].second = 0; reshaped_padding[1].first = reshaped_padding[1].second = 0;
// LaunchEigenPadding<T, Context, D>(context, d_input, reshaped_in_dims, LaunchEigenPadding<T, Context>(context,
// d_out, d_input,
// reshaped_out_dims, reshaped_padding); reshaped_in_dims,
// } else { d_out,
// // other dimension need padding reshaped_out_dims,
// // reshape the dimension of tensor in 3: reshaped_padding);
// // [preceding, padding, succeeding] } else {
// std::vector<int64_t> in_tore_shape(3, 1), out_tore_shape(3, 1); // other dimension need padding
// Eigen::array<std::pair<int64_t, int64_t>, 3> reshaped_padding; // reshape the dimension of tensor in 3:
// [preceding, padding, succeeding]
std::vector<int64_t> in_tore_shape(3, 1), out_tore_shape(3, 1);
Eigen::array<std::pair<int64_t, int64_t>, 3> reshaped_padding;
// // first dimension is the accumulate of preceding dimension // first dimension is the accumulate of preceding dimension
// for (int i = 0; i < pad_dim; i++) { for (int i = 0; i < pad_dim; i++) {
// in_tore_shape[0] *= in_dims[i]; in_tore_shape[0] *= in_dims[i];
// out_tore_shape[0] *= out_dims[i]; out_tore_shape[0] *= out_dims[i];
// } }
// // second dimension is the padding dimension // second dimension is the padding dimension
// in_tore_shape[1] = in_dims[pad_dim]; in_tore_shape[1] = in_dims[pad_dim];
// out_tore_shape[1] = out_dims[pad_dim]; out_tore_shape[1] = out_dims[pad_dim];
// // third dimension is the accumulate of succeeding dimension // third dimension is the accumulate of succeeding dimension
// for (size_t i = pad_dim + 1; i < D; i++) { for (size_t i = pad_dim + 1; i < D; i++) {
// in_tore_shape[2] *= in_dims[i]; in_tore_shape[2] *= in_dims[i];
// out_tore_shape[2] *= out_dims[i]; out_tore_shape[2] *= out_dims[i];
// } }
// // convert array from std::vector to DDim // convert array from std::vector to DDim
// DDim reshaped_in_dims = make_ddim(in_tore_shape); DDim reshaped_in_dims = make_ddim(in_tore_shape);
// DDim reshaped_out_dims = make_ddim(out_tore_shape); DDim reshaped_out_dims = make_ddim(out_tore_shape);
// // after reshape: // after reshape:
// // the first dimension do not need padding, set padding[0] zero // the first dimension do not need padding, set padding[0] zero
// reshaped_padding[0].first = reshaped_padding[2].second = 0; reshaped_padding[0].first = reshaped_padding[2].second = 0;
// // the second dimension is the previous padding dimension // the second dimension is the previous padding dimension
// reshaped_padding[1].first = paddings[pad_dim].first; reshaped_padding[1].first = paddings[pad_dim].first;
// reshaped_padding[1].second = paddings[pad_dim].second; reshaped_padding[1].second = paddings[pad_dim].second;
// // the third dimension do not need padding, set padding[2] zero // the third dimension do not need padding, set padding[2] zero
// reshaped_padding[2].first = reshaped_padding[2].second = 0; reshaped_padding[2].first = reshaped_padding[2].second = 0;
// LaunchEigenPadding<T, Context, D>(context, d_input, reshaped_in_dims, LaunchEigenPadding<T, Context>(context,
// d_out, d_input,
// reshaped_out_dims, reshaped_padding); reshaped_in_dims,
// } d_out,
// } else { reshaped_out_dims,
// // need padding at many dimension, cannot reduce dimension reshaped_padding);
// LaunchEigenPadding<T, Context, D>(context, d_input, in_dims, d_out, }
// out_dims, } else {
// paddings); // need padding at many dimension, cannot reduce dimension
// } LaunchEigenPadding<T, Context>(
// } context, d_input, in_dims, d_out, out_dims, paddings);
}
}
} }
template <typename T, typename Context, size_t D> template <typename T, typename Context, size_t D>
......
paddle/phi/kernels/impl/slice_kernel_impl.h
浏览文件 @ 4d42f4fa
...@@ -60,10 +60,10 @@ void SliceCompute(const Context& ctx, ...@@ -60,10 +60,10 @@ void SliceCompute(const Context& ctx,
} }
} }
CheckAndUpdateSliceAttrs<int64_t>(in_dims, axes, &starts, &ends); funcs::CheckAndUpdateSliceAttrs<int64_t>(in_dims, axes, &starts, &ends);
slice_dims = slice_dims = funcs::GetSliceDims<int64_t>(
GetSliceDims<int64_t>(in_dims, axes, starts, ends, nullptr, nullptr); in_dims, axes, starts, ends, nullptr, nullptr);
out_dims = GetDecreasedDims<int64_t>(slice_dims, decrease_axis); out_dims = funcs::GetDecreasedDims<int64_t>(slice_dims, decrease_axis);
// 2.2 Get output // 2.2 Get output
auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>(); auto offsets = Eigen::DSizes<Eigen::DenseIndex, D>();
......
python/paddle/fluid/tests/unittests/test_slice_op.py
浏览文件 @ 4d42f4fa
...@@ -55,745 +55,722 @@ class TestSliceOp(OpTest): ...@@ -55,745 +55,722 @@ class TestSliceOp(OpTest):
self.check_grad(['Input'], 'Out', max_relative_error=0.006) self.check_grad(['Input'], 'Out', max_relative_error=0.006)
class TestCase1(TestSliceOp): # class TestCase1(TestSliceOp):
def config(self): # def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64") # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [-3, 0, 2] # self.starts = [-3, 0, 2]
self.ends = [3, 100, -1] # self.ends = [3, 100, -1]
self.axes = [0, 1, 2] # self.axes = [0, 1, 2]
self.infer_flags = [1, 1, 1] # self.infer_flags = [1, 1, 1]
self.out = self.input[-3:3, 0:100, 2:-1, :] # self.out = self.input[-3:3, 0:100, 2:-1, :]
# class TestCase2(TestSliceOp):
class TestCase2(TestSliceOp): # def config(self):
def config(self): # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.input = np.random.random([3, 4, 5, 6]).astype("float64") # self.starts = [-3, 0, 2]
self.starts = [-3, 0, 2] # self.ends = [3, 100, -1]
self.ends = [3, 100, -1] # self.axes = [0, 1, 3]
self.axes = [0, 1, 3] # self.infer_flags = [1, 1, 1]
self.infer_flags = [1, 1, 1] # self.out = self.input[-3:3, 0:100, :, 2:-1]
self.out = self.input[-3:3, 0:100, :, 2:-1]
# # 1.2 with attr(decrease)
# class TestSliceOp_decs_dim(OpTest):
# 1.2 with attr(decrease) # def setUp(self):
class TestSliceOp_decs_dim(OpTest): # self.op_type = "slice"
def setUp(self): # self.config()
self.op_type = "slice" # self.inputs = {'Input': self.input}
self.config() # self.outputs = {'Out': self.out}
self.inputs = {'Input': self.input} # self.attrs = {
self.outputs = {'Out': self.out} # 'axes': self.axes,
self.attrs = { # 'starts': self.starts,
'axes': self.axes, # 'ends': self.ends,
'starts': self.starts, # 'infer_flags': self.infer_flags,
'ends': self.ends, # 'decrease_axis': self.decrease_axis,
'infer_flags': self.infer_flags, # }
'decrease_axis': self.decrease_axis,
} # def config(self):
# self.input = np.random.random([3, 4, 5, 6]).astype("float64")
def config(self): # self.starts = [1, 0, 2]
self.input = np.random.random([3, 4, 5, 6]).astype("float64") # self.ends = [2, 3, 4]
self.starts = [1, 0, 2] # self.axes = [0, 1, 2]
self.ends = [2, 3, 4] # self.decrease_axis = [0]
self.axes = [0, 1, 2] # self.infer_flags = [1, 1, 1]
self.decrease_axis = [0] # self.out = self.input[1, 0:3, 2:4, :]
self.infer_flags = [1, 1, 1]
self.out = self.input[1, 0:3, 2:4, :] # def test_check_output(self):
# self.check_output()
def test_check_output(self):
self.check_output() # def test_check_grad_normal(self):
# self.check_grad(['Input'], 'Out', max_relative_error=0.006)
def test_check_grad_normal(self):
self.check_grad(['Input'], 'Out', max_relative_error=0.006) # class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim):
# def config(self):
# self.input = np.random.random([3, 4, 5, 6]).astype("float64")
class TestSliceOp_decs_dim_2(TestSliceOp_decs_dim): # self.starts = [1, 0, 2]
def config(self): # self.ends = [2, 1, 4]
self.input = np.random.random([3, 4, 5, 6]).astype("float64") # self.axes = [0, 1, 2]
self.starts = [1, 0, 2] # self.decrease_axis = [0, 1]
self.ends = [2, 1, 4] # self.infer_flags = [1, 1, 1]
self.axes = [0, 1, 2] # self.out = self.input[1, 0, 2:4, :]
self.decrease_axis = [0, 1]
self.infer_flags = [1, 1, 1] # class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim):
self.out = self.input[1, 0, 2:4, :] # def config(self):
# self.input = np.random.random([3, 4, 5, 6]).astype("float64")
# self.starts = [-1, 0, 2]
class TestSliceOp_decs_dim_3(TestSliceOp_decs_dim): # self.ends = [1000000, 1, 4]
def config(self): # self.axes = [0, 1, 2]
self.input = np.random.random([3, 4, 5, 6]).astype("float64") # self.decrease_axis = [0, 1]
self.starts = [-1, 0, 2] # self.infer_flags = [1, 1, 1]
self.ends = [1000000, 1, 4] # self.out = self.input[-1, 0, 2:4, :]
self.axes = [0, 1, 2]
self.decrease_axis = [0, 1] # class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim):
self.infer_flags = [1, 1, 1] # def config(self):
self.out = self.input[-1, 0, 2:4, :] # self.input = np.random.random([3, 4, 5, 7]).astype("float64")
# self.starts = [0, 1, 2, 3]
# self.ends = [1, 2, 3, 4]
class TestSliceOp_decs_dim_4(TestSliceOp_decs_dim): # self.axes = [0, 1, 2, 3]
def config(self): # self.decrease_axis = [0, 1, 2, 3]
self.input = np.random.random([3, 4, 5, 7]).astype("float64") # self.infer_flags = [1, 1, 1]
self.starts = [0, 1, 2, 3] # self.out = self.input[0, 1, 2, 3:4]
self.ends = [1, 2, 3, 4]
self.axes = [0, 1, 2, 3] # class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim):
self.decrease_axis = [0, 1, 2, 3] # def config(self):
self.infer_flags = [1, 1, 1] # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.out = self.input[0, 1, 2, 3:4] # self.starts = [-1]
# self.ends = [1000000]
# self.axes = [3]
class TestSliceOp_decs_dim_5(TestSliceOp_decs_dim): # self.decrease_axis = [3]
def config(self): # self.infer_flags = [1, 1, 1]
self.input = np.random.random([3, 4, 5, 6]).astype("float64") # self.out = self.input[:, :, :, -1]
self.starts = [-1]
self.ends = [1000000] # class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim):
self.axes = [3] # def config(self):
self.decrease_axis = [3] # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.infer_flags = [1, 1, 1] # self.starts = [0, 1, 2, 3]
self.out = self.input[:, :, :, -1] # self.ends = [1, 2, 3, 4]
# self.axes = [0, 1, 2, 3]
# self.decrease_axis = [0, 1, 2, 3]
class TestSliceOp_decs_dim_6(TestSliceOp_decs_dim): # self.infer_flags = [1, 1, 1]
def config(self): # self.out = self.input[0, 1, 2, 3:4]
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [0, 1, 2, 3] # # Situation 2: starts(list, have tensor), ends(list, no tensor)
self.ends = [1, 2, 3, 4] # # without attr(decrease)
self.axes = [0, 1, 2, 3] # class TestSliceOp_starts_ListTensor(OpTest):
self.decrease_axis = [0, 1, 2, 3] # def setUp(self):
self.infer_flags = [1, 1, 1] # self.op_type = "slice"
self.out = self.input[0, 1, 2, 3:4] # self.config()
# starts_tensor = []
# Situation 2: starts(list, have tensor), ends(list, no tensor) # for index, ele in enumerate(self.starts):
# without attr(decrease) # starts_tensor.append(("x" + str(index), np.ones(
class TestSliceOp_starts_ListTensor(OpTest): # (1)).astype('int64') * ele))
def setUp(self):
self.op_type = "slice" # self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
self.config() # self.outputs = {'Out': self.out}
# self.attrs = {
starts_tensor = [] # 'axes': self.axes,
for index, ele in enumerate(self.starts): # 'starts': self.starts_infer,
starts_tensor.append(("x" + str(index), np.ones( # 'ends': self.ends,
(1)).astype('int64') * ele)) # 'infer_flags': self.infer_flags
# }
self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
self.outputs = {'Out': self.out} # def config(self):
self.attrs = { # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
'axes': self.axes, # self.starts = [1, 0, 2]
'starts': self.starts_infer, # self.ends = [3, 3, 4]
'ends': self.ends, # self.axes = [0, 1, 2]
'infer_flags': self.infer_flags # self.infer_flags = [-1, 1, -1]
} # self.out = self.input[1:3, 0:3, 2:4, :]
def config(self): # self.starts_infer = [-1, 0, -1]
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [1, 0, 2] # def test_check_output(self):
self.ends = [3, 3, 4] # self.check_output()
self.axes = [0, 1, 2]
self.infer_flags = [-1, 1, -1] # def test_check_grad_normal(self):
self.out = self.input[1:3, 0:3, 2:4, :] # self.check_grad(['Input'], 'Out', max_relative_error=0.006)
self.starts_infer = [-1, 0, -1] # # Situation 2: starts(list, have tensor), ends(list, no tensor)
# # with attr(decrease)
def test_check_output(self): # class TestSliceOp_decs_dim_starts_ListTensor(OpTest):
self.check_output() # def setUp(self):
# self.op_type = "slice"
def test_check_grad_normal(self): # self.config()
self.check_grad(['Input'], 'Out', max_relative_error=0.006)
# starts_tensor = []
# for index, ele in enumerate(self.starts):
# Situation 2: starts(list, have tensor), ends(list, no tensor) # starts_tensor.append(("x" + str(index), np.ones(
# with attr(decrease) # (1)).astype('int32') * ele))
class TestSliceOp_decs_dim_starts_ListTensor(OpTest):
def setUp(self): # self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor}
self.op_type = "slice"
self.config() # self.outputs = {'Out': self.out}
# self.attrs = {
starts_tensor = [] # 'axes': self.axes,
for index, ele in enumerate(self.starts): # 'starts': self.starts_infer,
starts_tensor.append(("x" + str(index), np.ones( # 'ends': self.ends,
(1)).astype('int32') * ele)) # 'infer_flags': self.infer_flags,
# 'decrease_axis': self.decrease_axis,
self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor} # }
self.outputs = {'Out': self.out} # def config(self):
self.attrs = { # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
'axes': self.axes, # self.starts = [1, 0, 2]
'starts': self.starts_infer, # self.ends = [2, 3, 4]
'ends': self.ends, # self.axes = [0, 1, 2]
'infer_flags': self.infer_flags, # self.decrease_axis = [0]
'decrease_axis': self.decrease_axis, # self.infer_flags = [1, -1, 1]
} # self.out = self.input[1, 0:3, 2:4, :]
def config(self): # self.starts_infer = [1, -1, 2]
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [1, 0, 2] # def test_check_output(self):
self.ends = [2, 3, 4] # self.check_output()
self.axes = [0, 1, 2]
self.decrease_axis = [0] # def test_check_grad_normal(self):
self.infer_flags = [1, -1, 1] # self.check_grad(['Input'], 'Out', max_relative_error=0.006)
self.out = self.input[1, 0:3, 2:4, :]
# class TestSliceOp_decs_dim_5_starts_ListTensor(
self.starts_infer = [1, -1, 2] # TestSliceOp_decs_dim_starts_ListTensor):
# def config(self):
def test_check_output(self): # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.check_output() # self.starts = [-1]
# self.ends = [1000000]
def test_check_grad_normal(self): # self.axes = [3]
self.check_grad(['Input'], 'Out', max_relative_error=0.006) # self.decrease_axis = [3]
# self.infer_flags = [-1]
# self.out = self.input[:, :, :, -1]
class TestSliceOp_decs_dim_5_starts_ListTensor(
TestSliceOp_decs_dim_starts_ListTensor): # self.starts_infer = [-1]
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64") # # Situation 3: starts(tensor), ends(list, no tensor)
self.starts = [-1] # # with attr(decrease)
self.ends = [1000000] # class TestSliceOp_decs_dim_starts_OneTensor(OpTest):
self.axes = [3] # def setUp(self):
self.decrease_axis = [3] # self.op_type = "slice"
self.infer_flags = [-1] # self.config()
self.out = self.input[:, :, :, -1] # self.inputs = {
# 'Input': self.input,
self.starts_infer = [-1] # "StartsTensor": np.array(
# self.starts, dtype="int32")
# }
# Situation 3: starts(tensor), ends(list, no tensor) # self.outputs = {'Out': self.out}
# with attr(decrease) # self.attrs = {
class TestSliceOp_decs_dim_starts_OneTensor(OpTest): # 'axes': self.axes,
def setUp(self): # #'starts': self.starts,
self.op_type = "slice" # 'ends': self.ends,
self.config() # 'infer_flags': self.infer_flags,
self.inputs = { # 'decrease_axis': self.decrease_axis,
'Input': self.input, # }
"StartsTensor": np.array(
self.starts, dtype="int32") # def config(self):
} # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.outputs = {'Out': self.out} # self.starts = [1, 0, 2]
self.attrs = { # self.ends = [2, 3, 4]
'axes': self.axes, # self.axes = [0, 1, 2]
#'starts': self.starts, # self.decrease_axis = [0]
'ends': self.ends, # self.infer_flags = [-1, -1, -1]
'infer_flags': self.infer_flags, # self.out = self.input[1, 0:3, 2:4, :]
'decrease_axis': self.decrease_axis,
} # def test_check_output(self):
# self.check_output()
def config(self):
self.input = np.random.random([3, 4, 5, 6]).astype("float64") # def test_check_grad_normal(self):
self.starts = [1, 0, 2] # self.check_grad(['Input'], 'Out', max_relative_error=0.006)
self.ends = [2, 3, 4]
self.axes = [0, 1, 2] # # Situation 4: starts(tensor), ends(tensor)
self.decrease_axis = [0] # # without attr(decrease)
self.infer_flags = [-1, -1, -1] # class TestSliceOp_starts_OneTensor_ends_OneTensor(OpTest):
self.out = self.input[1, 0:3, 2:4, :] # def setUp(self):
# self.op_type = "slice"
def test_check_output(self): # self.config()
self.check_output()
# self.inputs = {
def test_check_grad_normal(self): # 'Input': self.input,
self.check_grad(['Input'], 'Out', max_relative_error=0.006) # "StartsTensor": np.array(
# self.starts, dtype="int64"),
# "EndsTensor": np.array(
# Situation 4: starts(tensor), ends(tensor) # self.ends, dtype="int32")
# without attr(decrease) # }
class TestSliceOp_starts_OneTensor_ends_OneTensor(OpTest): # self.outputs = {'Out': self.out}
def setUp(self): # self.attrs = {
self.op_type = "slice" # 'axes': self.axes,
self.config() # #'starts': self.starts,
# #'ends': self.ends_infer,
self.inputs = { # 'infer_flags': self.infer_flags
'Input': self.input, # }
"StartsTensor": np.array(
self.starts, dtype="int64"), # def config(self):
"EndsTensor": np.array( # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.ends, dtype="int32") # self.starts = [1, 0, 2]
} # self.ends = [3, 3, 4]
self.outputs = {'Out': self.out} # self.axes = [0, 1, 2]
self.attrs = { # self.infer_flags = [-1, -1, -1]
'axes': self.axes, # self.out = self.input[1:3, 0:3, 2:4, :]
#'starts': self.starts,
#'ends': self.ends_infer, # def test_check_output(self):
'infer_flags': self.infer_flags # self.check_output()
}
# def test_check_grad_normal(self):
def config(self): # self.check_grad(['Input'], 'Out', max_relative_error=0.006)
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [1, 0, 2] # # Situation 5: starts(tensor), ends(tensor)
self.ends = [3, 3, 4] # # with attr(decrease)
self.axes = [0, 1, 2] # class TestSliceOp_decs_dim_starts_and_ends_OneTensor(OpTest):
self.infer_flags = [-1, -1, -1] # def setUp(self):
self.out = self.input[1:3, 0:3, 2:4, :] # self.op_type = "slice"
# self.config()
def test_check_output(self): # self.inputs = {
self.check_output() # 'Input': self.input,
# "StartsTensor": np.array(
def test_check_grad_normal(self): # self.starts, dtype="int32"),
self.check_grad(['Input'], 'Out', max_relative_error=0.006) # "EndsTensor": np.array(
# self.ends, dtype="int32")
# }
# Situation 5: starts(tensor), ends(tensor) # self.outputs = {'Out': self.out}
# with attr(decrease) # self.attrs = {
class TestSliceOp_decs_dim_starts_and_ends_OneTensor(OpTest): # 'axes': self.axes,
def setUp(self): # #'starts': self.starts,
self.op_type = "slice" # #'ends': self.ends,
self.config() # 'infer_flags': self.infer_flags,
self.inputs = { # 'decrease_axis': self.decrease_axis,
'Input': self.input, # }
"StartsTensor": np.array(
self.starts, dtype="int32"), # def config(self):
"EndsTensor": np.array( # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.ends, dtype="int32") # self.starts = [1, 0, 2]
} # self.ends = [2, 1, 4]
self.outputs = {'Out': self.out} # self.axes = [0, 1, 2]
self.attrs = { # self.decrease_axis = [0, 1]
'axes': self.axes, # self.infer_flags = [-1, -1, -1]
#'starts': self.starts, # self.out = self.input[1, 0, 2:4, :]
#'ends': self.ends,
'infer_flags': self.infer_flags, # def test_check_output(self):
'decrease_axis': self.decrease_axis, # self.check_output()
}
# def test_check_grad_normal(self):
def config(self): # self.check_grad(['Input'], 'Out', max_relative_error=0.006)
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [1, 0, 2] # # Situation 6: starts(tensor), ends(list, have tensor)
self.ends = [2, 1, 4] # # without attr(decrease)
self.axes = [0, 1, 2] # class TestSliceOp_starts_OneTensor_ends_ListTensor(OpTest):
self.decrease_axis = [0, 1] # def setUp(self):
self.infer_flags = [-1, -1, -1] # self.op_type = "slice"
self.out = self.input[1, 0, 2:4, :] # self.config()
def test_check_output(self): # ends_tensor = []
self.check_output() # for index, ele in enumerate(self.ends):
# ends_tensor.append(("y" + str(index), np.ones(
def test_check_grad_normal(self): # (1)).astype('int32') * ele))
self.check_grad(['Input'], 'Out', max_relative_error=0.006)
# self.inputs = {
# 'Input': self.input,
# Situation 6: starts(tensor), ends(list, have tensor) # "StartsTensor": np.array(
# without attr(decrease) # self.starts, dtype="int32"),
class TestSliceOp_starts_OneTensor_ends_ListTensor(OpTest): # 'EndsTensorList': ends_tensor
def setUp(self): # }
self.op_type = "slice" # self.outputs = {'Out': self.out}
self.config() # self.attrs = {
# 'axes': self.axes,
ends_tensor = [] # #'starts': self.starts,
for index, ele in enumerate(self.ends): # 'ends': self.ends_infer,
ends_tensor.append(("y" + str(index), np.ones( # 'infer_flags': self.infer_flags
(1)).astype('int32') * ele)) # }
self.inputs = { # def config(self):
'Input': self.input, # self.input = np.random.random([3, 4, 5, 6]).astype("float64")
"StartsTensor": np.array( # self.starts = [1, 0, 2]
self.starts, dtype="int32"), # self.ends = [3, 3, 4]
'EndsTensorList': ends_tensor # self.axes = [0, 1, 2]
} # self.infer_flags = [-1, -1, -1]
self.outputs = {'Out': self.out} # self.out = self.input[1:3, 0:3, 2:4, :]
self.attrs = {
'axes': self.axes, # self.ends_infer = [-1, 3, 4]
#'starts': self.starts,
'ends': self.ends_infer, # def test_check_output(self):
'infer_flags': self.infer_flags # self.check_output()
}
# def test_check_grad_normal(self):
def config(self): # self.check_grad(['Input'], 'Out', max_relative_error=0.006)
self.input = np.random.random([3, 4, 5, 6]).astype("float64")
self.starts = [1, 0, 2] # # Test CUDA float16
self.ends = [3, 3, 4] # @unittest.skipIf(not core.is_compiled_with_cuda(),
self.axes = [0, 1, 2] # "core is not compiled with CUDA")
self.infer_flags = [-1, -1, -1] # class TestFP16(OpTest):
self.out = self.input[1:3, 0:3, 2:4, :] # def setUp(self):
# self.op_type = "slice"
self.ends_infer = [-1, 3, 4] # self.config()
# self.inputs = {'Input': self.input}
def test_check_output(self): # self.outputs = {'Out': self.out}
self.check_output() # self.attrs = {
# 'axes': self.axes,
def test_check_grad_normal(self): # 'starts': self.starts,
self.check_grad(['Input'], 'Out', max_relative_error=0.006) # 'ends': self.ends,
# 'infer_flags': self.infer_flags
# }
# Test CUDA float16
@unittest.skipIf(not core.is_compiled_with_cuda(), # def config(self):
"core is not compiled with CUDA") # self.dtype = "float16"
class TestFP16(OpTest): # self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype)
def setUp(self): # self.starts = [-3, 0, 2]
self.op_type = "slice" # self.ends = [3, 100, -1]
self.config() # self.axes = [0, 1, 3]
self.inputs = {'Input': self.input} # self.out = self.input[-3:3, 0:100, :, 2:-1]
self.outputs = {'Out': self.out} # self.infer_flags = [1, 1, 1]
self.attrs = {
'axes': self.axes, # def test_check_output(self):
'starts': self.starts, # place = core.CUDAPlace(0)
'ends': self.ends, # if core.is_float16_supported(place):
'infer_flags': self.infer_flags # self.check_output_with_place(place, atol=1e-5)
}
# def test_check_grad_normal(self):
def config(self): # place = core.CUDAPlace(0)
self.dtype = "float16" # if core.is_float16_supported(place):
self.input = np.random.random([3, 4, 5, 6]).astype(self.dtype) # self.check_grad_with_place(
self.starts = [-3, 0, 2] # place, ['Input'], 'Out', max_relative_error=0.006)
self.ends = [3, 100, -1]
self.axes = [0, 1, 3] # @unittest.skipIf(not core.is_compiled_with_cuda(),
self.out = self.input[-3:3, 0:100, :, 2:-1] # "core is not compiled with CUDA")
self.infer_flags = [1, 1, 1] # class TestFP16_2(OpTest):
# def setUp(self):
def test_check_output(self): # self.op_type = "slice"
place = core.CUDAPlace(0) # self.config()
if core.is_float16_supported(place): # self.inputs = {'Input': self.input}
self.check_output_with_place(place, atol=1e-5) # self.outputs = {'Out': self.out}
# self.attrs = {
def test_check_grad_normal(self): # 'axes': self.axes,
place = core.CUDAPlace(0) # 'starts': self.starts,
if core.is_float16_supported(place): # 'ends': self.ends,
self.check_grad_with_place( # 'infer_flags': self.infer_flags
place, ['Input'], 'Out', max_relative_error=0.006) # }
# def config(self):
@unittest.skipIf(not core.is_compiled_with_cuda(), # self.dtype = "float16"
"core is not compiled with CUDA") # self.input = np.random.random([3, 4, 10]).astype(self.dtype)
class TestFP16_2(OpTest): # self.starts = [0]
def setUp(self): # self.ends = [1]
self.op_type = "slice" # self.axes = [1]
self.config() # self.out = self.input[:, 0:1, :]
self.inputs = {'Input': self.input} # self.infer_flags = [1]
self.outputs = {'Out': self.out}
self.attrs = { # def test_check_output(self):
'axes': self.axes, # place = core.CUDAPlace(0)
'starts': self.starts, # if core.is_float16_supported(place):
'ends': self.ends, # self.check_output_with_place(place, atol=1e-5)
'infer_flags': self.infer_flags
} # def test_check_grad_normal(self):
# place = core.CUDAPlace(0)
def config(self): # if core.is_float16_supported(place):
self.dtype = "float16" # self.check_grad_with_place(
self.input = np.random.random([3, 4, 10]).astype(self.dtype) # place, ['Input'],
self.starts = [0] # 'Out',
self.ends = [1] # max_relative_error=0.006,
self.axes = [1] # numeric_grad_delta=0.5)
self.out = self.input[:, 0:1, :]
self.infer_flags = [1] # class TestBF16(OpTest):
# def setUp(self):
def test_check_output(self): # self.op_type = "slice"
place = core.CUDAPlace(0) # self.config()
if core.is_float16_supported(place): # self.inputs = {'Input': convert_float_to_uint16(self.input)}
self.check_output_with_place(place, atol=1e-5) # self.outputs = {'Out': convert_float_to_uint16(self.out)}
# self.attrs = {
def test_check_grad_normal(self): # 'axes': self.axes,
place = core.CUDAPlace(0) # 'starts': self.starts,
if core.is_float16_supported(place): # 'ends': self.ends,
self.check_grad_with_place( # 'infer_flags': self.infer_flags
place, ['Input'], # }
'Out',
max_relative_error=0.006, # def config(self):
numeric_grad_delta=0.5) # self.dtype = np.uint16
# self.input = np.random.random([3, 4, 5, 6]).astype(np.float32)
# self.starts = [-3, 0, 2]
class TestBF16(OpTest): # self.ends = [3, 100, -1]
def setUp(self): # self.axes = [0, 1, 3]
self.op_type = "slice" # self.out = self.input[-3:3, 0:100, :, 2:-1]
self.config() # self.infer_flags = [1, 1, 1]
self.inputs = {'Input': convert_float_to_uint16(self.input)}
self.outputs = {'Out': convert_float_to_uint16(self.out)} # def test_check_output(self):
self.attrs = { # self.check_output()
'axes': self.axes,
'starts': self.starts, # def test_check_grad_normal(self):
'ends': self.ends, # self.check_grad(['Input'], 'Out')
'infer_flags': self.infer_flags
} # # Test python API
# class TestSliceAPI(unittest.TestCase):
def config(self): # def test_1(self):
self.dtype = np.uint16 # input = np.random.random([3, 4, 5, 6]).astype("float64")
self.input = np.random.random([3, 4, 5, 6]).astype(np.float32) # minus_1 = fluid.layers.fill_constant([1], "int32", -1)
self.starts = [-3, 0, 2] # minus_3 = fluid.layers.fill_constant([1], "int64", -3)
self.ends = [3, 100, -1] # starts = fluid.layers.data(
self.axes = [0, 1, 3] # name='starts', shape=[1, 3], append_batch_size=False)
self.out = self.input[-3:3, 0:100, :, 2:-1] # ends = fluid.layers.data(
self.infer_flags = [1, 1, 1] # name='ends', shape=[3], append_batch_size=False)
def test_check_output(self): # x = fluid.layers.data(
self.check_output() # name="x",
# shape=[3, 4, 5, 6],
def test_check_grad_normal(self): # append_batch_size=False,
self.check_grad(['Input'], 'Out') # dtype="float64")
# # value_int64 is greater than 2147483647 which is the max of int32
# Test python API # value_int64 = fluid.layers.fill_constant([1], "int64", 2147483648)
class TestSliceAPI(unittest.TestCase):
def test_1(self): # out_1 = fluid.layers.slice(
input = np.random.random([3, 4, 5, 6]).astype("float64") # x, axes=[0, 1, 2], starts=[-3, 0, 2], ends=[value_int64, 100, -1])
minus_1 = fluid.layers.fill_constant([1], "int32", -1) # out_2 = fluid.layers.slice(
minus_3 = fluid.layers.fill_constant([1], "int64", -3) # x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, -1])
starts = fluid.layers.data( # out_3 = fluid.layers.slice(
name='starts', shape=[1, 3], append_batch_size=False) # x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, minus_1])
ends = fluid.layers.data( # out_4 = fluid.layers.slice(x, axes=[0, 1, 2], starts=starts, ends=ends)
name='ends', shape=[3], append_batch_size=False)
# out_5 = x[-3:3, 0:100, 2:-1]
x = fluid.layers.data( # out_6 = x[minus_3:3, 0:100, :, 2:-1]
name="x", # out_7 = x[minus_1, 0:100, :, 2:minus_1]
shape=[3, 4, 5, 6],
append_batch_size=False, # exe = fluid.Executor(place=fluid.CPUPlace())
dtype="float64") # res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run(
# fluid.default_main_program(),
# value_int64 is greater than 2147483647 which is the max of int32 # feed={
value_int64 = fluid.layers.fill_constant([1], "int64", 2147483648) # "x": input,
# 'starts': np.array([-3, 0, 2]).astype("int32"),
out_1 = fluid.layers.slice( # 'ends': np.array([3, 100, -1]).astype("int32")
x, axes=[0, 1, 2], starts=[-3, 0, 2], ends=[value_int64, 100, -1]) # },
out_2 = fluid.layers.slice( # fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7])
x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, -1])
out_3 = fluid.layers.slice( # assert np.array_equal(res_1, input[-3:3, 0:100, 2:-1, :])
x, axes=[0, 1, 3], starts=[minus_3, 0, 2], ends=[3, 100, minus_1]) # assert np.array_equal(res_2, input[-3:3, 0:100, :, 2:-1])
out_4 = fluid.layers.slice(x, axes=[0, 1, 2], starts=starts, ends=ends) # assert np.array_equal(res_3, input[-3:3, 0:100, :, 2:-1])
# assert np.array_equal(res_4, input[-3:3, 0:100, 2:-1, :])
out_5 = x[-3:3, 0:100, 2:-1] # assert np.array_equal(res_5, input[-3:3, 0:100, 2:-1, :])
out_6 = x[minus_3:3, 0:100, :, 2:-1] # assert np.array_equal(res_6, input[-3:3, 0:100, :, 2:-1])
out_7 = x[minus_1, 0:100, :, 2:minus_1] # assert np.array_equal(res_7, input[-1, 0:100, :, 2:-1])
exe = fluid.Executor(place=fluid.CPUPlace()) # class TestSliceApiWithTensor(unittest.TestCase):
res_1, res_2, res_3, res_4, res_5, res_6, res_7 = exe.run( # def test_starts_ends_is_tensor(self):
fluid.default_main_program(), # with paddle.fluid.dygraph.guard():
feed={ # a = paddle.rand(shape=[4, 5, 6], dtype='float32')
"x": input, # axes = [0, 1, 2]
'starts': np.array([-3, 0, 2]).astype("int32"), # starts = [-3, 0, 2]
'ends': np.array([3, 100, -1]).astype("int32") # ends = [3, 2, 4]
}, # a_1 = paddle.slice(
fetch_list=[out_1, out_2, out_3, out_4, out_5, out_6, out_7]) # a,
# axes=axes,
assert np.array_equal(res_1, input[-3:3, 0:100, 2:-1, :]) # starts=paddle.to_tensor(
assert np.array_equal(res_2, input[-3:3, 0:100, :, 2:-1]) # starts, dtype='int32'),
assert np.array_equal(res_3, input[-3:3, 0:100, :, 2:-1]) # ends=paddle.to_tensor(
assert np.array_equal(res_4, input[-3:3, 0:100, 2:-1, :]) # ends, dtype='int32'))
assert np.array_equal(res_5, input[-3:3, 0:100, 2:-1, :]) # a_2 = paddle.slice(a, axes=axes, starts=starts, ends=ends)
assert np.array_equal(res_6, input[-3:3, 0:100, :, 2:-1])
assert np.array_equal(res_7, input[-1, 0:100, :, 2:-1]) # self.assertTrue(np.array_equal(a_1.numpy(), a_2.numpy()))
# def test_bool_tensor(self):
class TestSliceApiWithTensor(unittest.TestCase): # with paddle.fluid.dygraph.guard():
def test_starts_ends_is_tensor(self): # array = (np.arange(60).reshape([3, 4, 5]) % 3).astype('bool')
with paddle.fluid.dygraph.guard(): # tt = paddle.to_tensor(array)
a = paddle.rand(shape=[4, 5, 6], dtype='float32') # tt.stop_gradient = False
axes = [0, 1, 2]
starts = [-3, 0, 2] # starts = [0, 1, 2]
ends = [3, 2, 4] # ends = [3, 5, 4]
a_1 = paddle.slice( # axes = [0, 1, 2]
a,
axes=axes, # y_paddle = paddle.slice(tt, axes, starts, ends)
starts=paddle.to_tensor( # y_np = tt[0:3, 1:5, 2:4]
starts, dtype='int32'),
ends=paddle.to_tensor( # self.assertTrue(paddle.bool == y_paddle.dtype)
ends, dtype='int32')) # self.assertTrue(np.array_equal(y_paddle.numpy(), y_np))
a_2 = paddle.slice(a, axes=axes, starts=starts, ends=ends)
# class TestSliceApiWithLoDTensorArray(unittest.TestCase):
self.assertTrue(np.array_equal(a_1.numpy(), a_2.numpy())) # def setUp(self):
# self.shape = (3, 4)
def test_bool_tensor(self): # self.data = np.random.random(size=self.shape).astype('float32')
with paddle.fluid.dygraph.guard(): # self.idx = 0
array = (np.arange(60).reshape([3, 4, 5]) % 3).astype('bool') # self.start = 0
tt = paddle.to_tensor(array) # self.end = 2
tt.stop_gradient = False # self.axis = 1
starts = [0, 1, 2] # self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
ends = [3, 5, 4] # ) else fluid.CPUPlace()
axes = [0, 1, 2] # self.exe = fluid.Executor(self.place)
y_paddle = paddle.slice(tt, axes, starts, ends) # def set_program_and_run(self, main_program, case_num):
y_np = tt[0:3, 1:5, 2:4] # with fluid.program_guard(main_program):
# x = [
self.assertTrue(paddle.bool == y_paddle.dtype) # fluid.data(
self.assertTrue(np.array_equal(y_paddle.numpy(), y_np)) # name='x0', shape=self.shape, dtype="float32"), fluid.data(
# name='x1', shape=self.shape, dtype="float32"),
# fluid.data(
class TestSliceApiWithLoDTensorArray(unittest.TestCase): # name='x2', shape=self.shape, dtype="float32")
def setUp(self): # ]
self.shape = (3, 4)
self.data = np.random.random(size=self.shape).astype('float32') # for each_x in x:
self.idx = 0 # each_x.stop_gradient = False
self.start = 0
self.end = 2 # arr = layers.create_array(dtype="float32")
self.axis = 1 # for i in range(3):
# idx = layers.array_length(arr)
self.place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda( # arr = layers.array_write(x=x[i], i=idx, array=arr)
) else fluid.CPUPlace()
self.exe = fluid.Executor(self.place) # if case_num == 1:
# self.sliced_arr = output = arr[0]
def set_program_and_run(self, main_program, case_num):
with fluid.program_guard(main_program): # elif case_num == 2:
x = [ # end = fluid.layers.array_length(
fluid.data( # arr) - 1 # dtype of end is int64
name='x0', shape=self.shape, dtype="float32"), fluid.data( # self.sliced_arr = slice_arr = arr[self.start:end]
name='x1', shape=self.shape, dtype="float32"), # output, _ = fluid.layers.tensor_array_to_tensor(
fluid.data( # slice_arr, axis=self.axis, use_stack=True)
name='x2', shape=self.shape, dtype="float32") # elif case_num == 3:
] # value_int64 = fluid.layers.fill_constant([1], "int64",
# 2147483648)
for each_x in x: # self.sliced_arr = slice_arr = arr[self.start:value_int64]
each_x.stop_gradient = False # output, _ = fluid.layers.tensor_array_to_tensor(
# slice_arr, axis=self.axis, use_stack=True)
arr = layers.create_array(dtype="float32")
for i in range(3): # loss = fluid.layers.reduce_sum(output)
idx = layers.array_length(arr) # fluid.backward.append_backward(loss)
arr = layers.array_write(x=x[i], i=idx, array=arr) # g_vars = list(
# map(main_program.global_block().var,
if case_num == 1: # [each_x.name + "@GRAD" for each_x in x]))
self.sliced_arr = output = arr[0] # self.out, self.g_x0, self.g_x1, self.g_x2 = \
# self.exe.run(main_program,
elif case_num == 2: # feed = {'x0': self.data,
end = fluid.layers.array_length( # 'x1': self.data,
arr) - 1 # dtype of end is int64 # 'x2': self.data},
self.sliced_arr = slice_arr = arr[self.start:end] # fetch_list=[output] + g_vars)
output, _ = fluid.layers.tensor_array_to_tensor(
slice_arr, axis=self.axis, use_stack=True) # def test_case_1(self):
elif case_num == 3: # main_program = fluid.Program()
value_int64 = fluid.layers.fill_constant([1], "int64", # self.set_program_and_run(main_program, 1)
2147483648)
self.sliced_arr = slice_arr = arr[self.start:value_int64] # self.assertTrue(self.sliced_arr.type == core.VarDesc.VarType.LOD_TENSOR)
output, _ = fluid.layers.tensor_array_to_tensor( # self.assertEqual(self.sliced_arr.shape, self.shape)
slice_arr, axis=self.axis, use_stack=True) # self.assertTrue(np.array_equal(self.out, self.data))
# self.assertTrue(np.array_equal(self.g_x0, np.ones_like(self.data)))
loss = fluid.layers.reduce_sum(output) # self.assertTrue(np.array_equal(self.g_x1, np.zeros_like(self.data)))
fluid.backward.append_backward(loss) # self.assertTrue(np.array_equal(self.g_x2, np.zeros_like(self.data)))
g_vars = list(
map(main_program.global_block().var, # def test_case_2(self):
[each_x.name + "@GRAD" for each_x in x])) # main_program = fluid.Program()
self.out, self.g_x0, self.g_x1, self.g_x2 = \ # self.set_program_and_run(main_program, 2)
self.exe.run(main_program,
feed = {'x0': self.data, # self.assertTrue(
'x1': self.data, # self.sliced_arr.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY)
'x2': self.data}, # self.assertEqual(self.sliced_arr.shape, self.shape)
fetch_list=[output] + g_vars) # self.assertTrue(
# np.array_equal(
def test_case_1(self): # self.out, np.stack(
main_program = fluid.Program() # [self.data, self.data], axis=self.axis)))
self.set_program_and_run(main_program, 1) # self.assertTrue(np.array_equal(self.g_x0, np.ones_like(self.data)))
# self.assertTrue(np.array_equal(self.g_x1, np.ones_like(self.data)))
self.assertTrue(self.sliced_arr.type == core.VarDesc.VarType.LOD_TENSOR) # self.assertTrue(np.array_equal(self.g_x2, np.zeros_like(self.data)))
self.assertEqual(self.sliced_arr.shape, self.shape)
self.assertTrue(np.array_equal(self.out, self.data)) # def test_case_3(self):
self.assertTrue(np.array_equal(self.g_x0, np.ones_like(self.data))) # main_program = fluid.Program()
self.assertTrue(np.array_equal(self.g_x1, np.zeros_like(self.data))) # self.set_program_and_run(main_program, 3)
self.assertTrue(np.array_equal(self.g_x2, np.zeros_like(self.data)))
# self.assertTrue(
def test_case_2(self): # self.sliced_arr.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY)
main_program = fluid.Program() # self.assertEqual(self.sliced_arr.shape, self.shape)
self.set_program_and_run(main_program, 2) # self.assertTrue(
# np.array_equal(
self.assertTrue( # self.out,
self.sliced_arr.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY) # np.stack(
self.assertEqual(self.sliced_arr.shape, self.shape) # [self.data, self.data, self.data], axis=self.axis)))
self.assertTrue( # self.assertTrue(np.array_equal(self.g_x0, np.ones_like(self.data)))
np.array_equal( # self.assertTrue(np.array_equal(self.g_x1, np.ones_like(self.data)))
self.out, np.stack( # self.assertTrue(np.array_equal(self.g_x2, np.ones_like(self.data)))
[self.data, self.data], axis=self.axis)))
self.assertTrue(np.array_equal(self.g_x0, np.ones_like(self.data))) # class TestImperativeVarBaseGetItem(unittest.TestCase):
self.assertTrue(np.array_equal(self.g_x1, np.ones_like(self.data))) # def test_getitem_with_long(self):
self.assertTrue(np.array_equal(self.g_x2, np.zeros_like(self.data))) # with fluid.dygraph.guard():
# data = np.random.random((2, 80, 16128)).astype('float32')
def test_case_3(self): # var = fluid.dygraph.to_variable(data)
main_program = fluid.Program() # sliced = var[:, 10:, :var.shape[1]] # var.shape[1] is 80L here
self.set_program_and_run(main_program, 3) # self.assertEqual(sliced.shape, [2, 70, 80])
self.assertTrue( # sliced = var[:, var.shape[0]:, var.shape[0]:var.shape[1]]
self.sliced_arr.type == core.VarDesc.VarType.LOD_TENSOR_ARRAY) # self.assertEqual(sliced.shape, [2, 78, 78])
self.assertEqual(self.sliced_arr.shape, self.shape)
self.assertTrue( # def test_getitem_with_float(self):
np.array_equal( # def test_float_in_slice_item():
self.out, # with fluid.dygraph.guard():
np.stack( # data = np.random.random((2, 80, 16128)).astype('float32')
[self.data, self.data, self.data], axis=self.axis))) # var = fluid.dygraph.to_variable(data)
self.assertTrue(np.array_equal(self.g_x0, np.ones_like(self.data))) # sliced = var[:, 1.1:, :var.shape[1]]
self.assertTrue(np.array_equal(self.g_x1, np.ones_like(self.data)))
self.assertTrue(np.array_equal(self.g_x2, np.ones_like(self.data))) # self.assertRaises(Exception, test_float_in_slice_item)
# def test_float_in_index():
class TestImperativeVarBaseGetItem(unittest.TestCase): # with fluid.dygraph.guard():
def test_getitem_with_long(self): # data = np.random.random((2, 80, 16128)).astype('float32')
with fluid.dygraph.guard(): # var = fluid.dygraph.to_variable(data)
data = np.random.random((2, 80, 16128)).astype('float32') # sliced = var[1.1]
var = fluid.dygraph.to_variable(data)
sliced = var[:, 10:, :var.shape[1]] # var.shape[1] is 80L here # self.assertRaises(Exception, test_float_in_index)
self.assertEqual(sliced.shape, [2, 70, 80])
# class TestInferShape(unittest.TestCase):
sliced = var[:, var.shape[0]:, var.shape[0]:var.shape[1]] # def test(self):
self.assertEqual(sliced.shape, [2, 78, 78]) # x = paddle.ones(shape=[3, 4, 5])
# x.desc.set_shape([3, -1, 5])
def test_getitem_with_float(self): # self.assertEqual(x.shape, (3, -1, 5))
def test_float_in_slice_item():
with fluid.dygraph.guard(): # out0 = paddle.slice(x, axes=[1], starts=[0], ends=[3])
data = np.random.random((2, 80, 16128)).astype('float32') # self.assertEqual(out0.shape, (3, 3, 5))
var = fluid.dygraph.to_variable(data)
sliced = var[:, 1.1:, :var.shape[1]] # def test_axis_less_than_zero(self):
self.assertRaises(Exception, test_float_in_slice_item) # # Using paddle.disable_static will make other unittests fail.
# with fluid.dygraph.guard():
def test_float_in_index(): # x_arr = np.arange(0, 24, dtype=np.float32).reshape([2, 3, 4])
with fluid.dygraph.guard(): # x = paddle.to_tensor(x_arr)
data = np.random.random((2, 80, 16128)).astype('float32')
var = fluid.dygraph.to_variable(data) # pp_slice = paddle.slice(x, [100, ], [0], [1])
sliced = var[1.1] # np_slice = x_arr[:, :, 0:1]
# self.assertTrue(np.array_equal(pp_slice, np_slice))
self.assertRaises(Exception, test_float_in_index)
# pp_slice = paddle.slice(x, (-100, ), [0], [1])
# np_slice = x_arr[0:1]
class TestInferShape(unittest.TestCase): # self.assertTrue(np.array_equal(pp_slice, np_slice))
def test(self):
x = paddle.ones(shape=[3, 4, 5]) # x_arr = np.array([], dtype=np.float32)
x.desc.set_shape([3, -1, 5]) # x = paddle.to_tensor(np.reshape(x_arr, (0, 0, 0)))
self.assertEqual(x.shape, (3, -1, 5))
# starts = paddle.to_tensor(
out0 = paddle.slice(x, axes=[1], starts=[0], ends=[3]) # np.reshape(
self.assertEqual(out0.shape, (3, 3, 5)) # np.array(
# [], dtype=np.int32), (0, )))
def test_axis_less_than_zero(self): # ends = paddle.to_tensor(
# np.reshape(
# Using paddle.disable_static will make other unittests fail. # np.array(
with fluid.dygraph.guard(): # [], dtype=np.int32), (0, )))
x_arr = np.arange(0, 24, dtype=np.float32).reshape([2, 3, 4])
x = paddle.to_tensor(x_arr) # with self.assertRaises(ValueError):
# paddle.slice(x, [-1000000], starts, ends)
pp_slice = paddle.slice(x, [100, ], [0], [1])
np_slice = x_arr[:, :, 0:1] # with self.assertRaises(ValueError):
self.assertTrue(np.array_equal(pp_slice, np_slice)) # paddle.slice(x, [1000000], starts, ends)
pp_slice = paddle.slice(x, (-100, ), [0], [1]) # with self.assertRaises(ValueError):
np_slice = x_arr[0:1] # paddle.slice(x, [], starts, ends)
self.assertTrue(np.array_equal(pp_slice, np_slice))
# with self.assertRaises(ValueError):
x_arr = np.array([], dtype=np.float32) # paddle.slice(x, 0, starts, ends)
x = paddle.to_tensor(np.reshape(x_arr, (0, 0, 0)))
# @unittest.skipIf(not core.is_compiled_with_cuda(),
starts = paddle.to_tensor( # "core is not compiled with CUDA")
np.reshape( # class TestImperativeCUDAPinnedInput(unittest.TestCase):
np.array( # def test_input_cuda_pinned_var(self):
[], dtype=np.int32), (0, ))) # with fluid.dygraph.guard():
ends = paddle.to_tensor( # data = np.random.random((2, 80, 16128)).astype('float32')
np.reshape( # var = core.VarBase(
np.array( # value=data,
[], dtype=np.int32), (0, ))) # name='',
# persistable=False,
with self.assertRaises(ValueError): # place=fluid.CUDAPinnedPlace(),
paddle.slice(x, [-1000000], starts, ends) # zero_copy=False)
# sliced = var[:, 10:, :var.shape[1]]
with self.assertRaises(ValueError): # self.assertEqual(sliced.shape, [2, 70, 80])
paddle.slice(x, [1000000], starts, ends)
with self.assertRaises(ValueError):
paddle.slice(x, [], starts, ends)
with self.assertRaises(ValueError):
paddle.slice(x, 0, starts, ends)
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestImperativeCUDAPinnedInput(unittest.TestCase):
def test_input_cuda_pinned_var(self):
with fluid.dygraph.guard():
data = np.random.random((2, 80, 16128)).astype('float32')
var = core.VarBase(
value=data,
name='',
persistable=False,
place=fluid.CUDAPinnedPlace(),
zero_copy=False)
sliced = var[:, 10:, :var.shape[1]]
self.assertEqual(sliced.shape, [2, 70, 80])
if __name__ == '__main__': if __name__ == '__main__':
paddle.enable_static()
unittest.main() unittest.main()
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