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lrn supports channel_last input, test=develop (#20954)

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custom_op_abi
Zhang Ting 6 years ago committed by Aurelius84
parent 9b666cae67
commit de9bec607e
5 changed files with 204 additions and 76 deletions
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88
paddle/fluid/operators/lrn_op.cc
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@ -14,7 +14,9 @@ limitations under the License. */
#include "paddle/fluid/operators/lrn_op.h"
#include <string>
#include <vector>
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
@ -23,18 +25,41 @@ namespace paddle {
namespace operators {
using framework::Tensor;
using DataLayout = framework::DataLayout;
template <typename T>
struct LRNFunctor<platform::CPUDeviceContext, T> {
void operator()(const framework::ExecutionContext& ctx,
const framework::Tensor& input, framework::Tensor* out,
framework::Tensor* mid, int N, int C, int H, int W, int n,
T k, T alpha, T beta) {
const T* idata = input.data<T>();
T k, T alpha, T beta, const DataLayout data_layout) {
auto place = ctx.GetPlace();
auto blas = math::GetBlas<platform::CPUDeviceContext, T>(ctx);
T* odata = out->mutable_data<T>(place);
T* mdata = mid->mutable_data<T>(place);
math::Transpose<platform::CPUDeviceContext, T, 4> transpose;
auto& dev_ctx = ctx.template device_context<platform::CPUDeviceContext>();
Tensor in_transpose, mid_transpose, out_transpose;
// if channel_last, transpose to channel_first
if (data_layout == DataLayout::kNHWC) {
auto in_dims = input.dims();
std::vector<int64_t> shape(
{in_dims[0], in_dims[3], in_dims[1], in_dims[2]});
in_transpose.mutable_data<T>(framework::make_ddim(shape), place);
mid_transpose.mutable_data<T>(framework::make_ddim(shape), place);
out_transpose.mutable_data<T>(framework::make_ddim(shape), place);
std::vector<int> axis = {0, 3, 1, 2};
transpose(dev_ctx, input, &in_transpose, axis);
} else {
in_transpose = input;
mid_transpose = *mid;
out_transpose = *out;
mid_transpose.mutable_data<T>(mid->dims(), place);
out_transpose.mutable_data<T>(out->dims(), place);
}
const T* idata = in_transpose.data<T>();
T* odata = out_transpose.data<T>();
T* mdata = mid_transpose.data<T>();
Tensor squared;
T* sdata = squared.mutable_data<T>({1, C + n - 1, H, W}, place);
std::memset(sdata, 0, sizeof(T) * squared.numel());
@ -67,6 +92,13 @@ struct LRNFunctor<platform::CPUDeviceContext, T> {
// compute the final output
blas.VPOW(mid->numel(), mdata, -beta, odata);
blas.VMUL(mid->numel(), odata, idata, odata);
// if channel_last, transpose the output(NCHW) to channel_last
if (data_layout == DataLayout::kNHWC) {
std::vector<int> axis = {0, 2, 3, 1};
transpose(dev_ctx, mid_transpose, mid, axis);
transpose(dev_ctx, out_transpose, out, axis);
}
}
};
template struct LRNFunctor<platform::CPUDeviceContext, float>;
@ -78,7 +110,7 @@ struct LRNGradFunctor<platform::CPUDeviceContext, T> {
const framework::Tensor& x, const framework::Tensor& out,
const framework::Tensor& mid, framework::Tensor* x_g,
const framework::Tensor& out_g, int N, int C, int H, int W,
int n, T alpha, T beta) {
int n, T alpha, T beta, const DataLayout data_layout) {
T ratio = -2 * alpha * beta;
auto x_g_e = framework::EigenVector<T>::Flatten(*x_g);
x_g_e = x_g_e.constant(0.0);
@ -93,17 +125,17 @@ struct LRNGradFunctor<platform::CPUDeviceContext, T> {
const int end = start + n;
for (int m = 0; m < N; m++) {
for (int i = 0; i < C; i++) {
auto i_x = e_x.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto i_x_g = e_x_g.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto i_out_g = e_out_g.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto offsets = Eigen::array<int, 4>({{m, i, 0, 0}});
auto extents = Eigen::array<int, 4>({{1, 1, H, W}});
if (data_layout == DataLayout::kNHWC) {
offsets = Eigen::array<int, 4>({{m, 0, 0, i}});
extents = Eigen::array<int, 4>({{1, H, W, 1}});
}
auto i_mid = e_mid.slice(Eigen::array<int, 4>({{m, i, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto i_x = e_x.slice(offsets, extents);
auto i_x_g = e_x_g.slice(offsets, extents);
auto i_out_g = e_out_g.slice(offsets, extents);
auto i_mid = e_mid.slice(offsets, extents);
i_x_g = i_mid.pow(-beta) * i_out_g;
for (int c = start; c < end; c++) {
@ -112,14 +144,14 @@ struct LRNGradFunctor<platform::CPUDeviceContext, T> {
continue;
}
auto c_out = e_out.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto c_mid = e_mid.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
auto c_out_g = e_out_g.slice(Eigen::array<int, 4>({{m, ch, 0, 0}}),
Eigen::array<int, 4>({{1, 1, H, W}}));
if (data_layout != DataLayout::kNHWC) {
offsets = Eigen::array<int, 4>({{m, ch, 0, 0}});
} else {
offsets = Eigen::array<int, 4>({{m, 0, 0, ch}});
}
auto c_out = e_out.slice(offsets, extents);
auto c_mid = e_mid.slice(offsets, extents);
auto c_out_g = e_out_g.slice(offsets, extents);
i_x_g += ratio * c_out_g * c_out * i_x / c_mid;
}
@ -156,9 +188,8 @@ class LRNOp : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {
@ -242,8 +273,8 @@ $$
Function implementation:
Inputs and outpus are in NCHW format, while input.shape.ndims() equals 4.
And dimensions 0 ~ 3 represent batch size, feature maps, rows,
Inputs and outpus are in NCHW or NHWC format, while input.shape.ndims() equals 4.
If NCHW, the dimensions 0 ~ 3 represent batch size, feature maps, rows,
and columns, respectively.
Input and Output in the formula above is for each map(i) of one image, and
@ -275,9 +306,8 @@ class LRNOpGrad : public framework::OperatorWithKernel {
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
#ifdef PADDLE_WITH_MKLDNN
if (library_ == framework::LibraryType::kPlain &&
platform::CanMKLDNNBeUsed(ctx)) {

71
paddle/fluid/operators/lrn_op.cu
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@ -17,15 +17,20 @@ limitations under the License. */
namespace paddle {
namespace operators {
using DataLayout = framework::DataLayout;
template <typename T>
__global__ void KeCMRNormFillScale(int img_size, const T* in, T* mid, int C,
int H, int W, int size, T k, T alpha) {
int H, int W, int size, T k, T alpha,
const DataLayout data_layout) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < img_size) {
const int w = idx % W;
const int h = (idx / W) % H;
const int n = idx / W / H;
const int offset = (n * C * H + h) * W + w;
const int offset =
(data_layout != DataLayout::kNHWC ? (n * C * H + h) * W + w
: ((n * H + h) * W + w) * C);
in += offset;
mid += offset;
@ -37,15 +42,21 @@ __global__ void KeCMRNormFillScale(int img_size, const T* in, T* mid, int C,
int index = 0;
while (index < C + post_pad) {
if (index < C) {
T val = in[index * step];
int in_idx = (data_layout != DataLayout::kNHWC ? index * step : index);
T val = in[in_idx];
accum += val * val;
}
if (index >= size) {
T val = in[(index - size) * step];
int in_idx = (data_layout != DataLayout::kNHWC ? (index - size) * step
: index - size);
T val = in[in_idx];
accum -= val * val;
}
if (index >= post_pad) {
mid[(index - post_pad) * step] = k + accum * alpha;
int mid_idx =
(data_layout != DataLayout::kNHWC ? (index - post_pad) * step
: index - post_pad);
mid[mid_idx] = k + accum * alpha;
}
++index;
}
@ -64,14 +75,14 @@ __global__ void KeCMRNormOutput(int input_size, const T* in, const T* mid,
template <typename T>
void CrossMapNormal(const framework::ExecutionContext& ctx, const T* inputs,
T* outputs, T* mid, int N, int C, int H, int W, int n, T k,
T alpha, T beta) {
T alpha, T beta, const DataLayout data_layout) {
int img_size = N * H * W;
const int block_size = 1024;
int grid_size = (img_size + block_size - 1) / block_size;
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
KeCMRNormFillScale<T><<<grid_size, block_size, 0, dev_ctx.stream()>>>(
img_size, inputs, mid, C, H, W, n, k, alpha);
img_size, inputs, mid, C, H, W, n, k, alpha, data_layout);
int input_size = N * H * W * C;
grid_size = (input_size + block_size - 1) / block_size;
@ -84,10 +95,11 @@ struct LRNFunctor<platform::CUDADeviceContext, T> {
void operator()(const framework::ExecutionContext& ctx,
const framework::Tensor& input, framework::Tensor* out,
framework::Tensor* mid, int N, int C, int H, int W, int n,
T k, T alpha, T beta) {
CrossMapNormal<T>(
ctx, input.data<T>(), out->mutable_data<T>(ctx.GetPlace()),
mid->mutable_data<T>(ctx.GetPlace()), N, C, H, W, n, k, alpha, beta);
T k, T alpha, T beta, const DataLayout data_layout) {
CrossMapNormal<T>(ctx, input.data<T>(),
out->mutable_data<T>(ctx.GetPlace()),
mid->mutable_data<T>(ctx.GetPlace()), N, C, H, W, n, k,
alpha, beta, data_layout);
}
};
@ -97,14 +109,16 @@ template struct LRNFunctor<platform::CUDADeviceContext, double>;
template <typename T>
__global__ void KeCMRNormDiff(int img_size, const T* x, const T* out,
const T* mid, T* x_g, const T* out_g, int C,
int H, int W, int size, T negative_beta,
T ratio) {
int H, int W, int size, T negative_beta, T ratio,
const DataLayout data_layout) {
const int idx = threadIdx.x + blockIdx.x * blockDim.x;
if (idx < img_size) {
const int w = idx % W;
const int h = (idx / W) % H;
const int n = idx / W / H;
const int offset = (n * C * H + h) * W + w;
const int offset =
(data_layout != DataLayout::kNHWC ? (n * C * H + h) * W + w
: ((n * H + h) * W + w) * C);
x += offset;
out += offset;
mid += offset;
@ -120,18 +134,20 @@ __global__ void KeCMRNormDiff(int img_size, const T* x, const T* out,
// TODO(gongwb): optimize this with thread shared array.
while (index < C + post_pad) {
if (index < C) {
x_g[index * step] = 0.0;
accum += out_g[index * step] * out[index * step] / mid[index * step];
int idx = (data_layout != DataLayout::kNHWC ? index * step : index);
x_g[idx] = 0.0;
accum += out_g[idx] * out[idx] / mid[idx];
}
if (index >= size) {
accum -= out_g[(index - size) * step] * out[(index - size) * step] /
mid[(index - size) * step];
int idx = (data_layout != DataLayout::kNHWC ? (index - size) * step
: index - size);
accum -= out_g[idx] * out[idx] / mid[idx];
}
if (index >= post_pad) {
x_g[(index - post_pad) * step] +=
out_g[(index - post_pad) * step] *
pow(mid[(index - post_pad) * step], negative_beta) -
ratio * x[(index - post_pad) * step] * accum;
int idx = (data_layout != DataLayout::kNHWC ? (index - post_pad) * step
: index - post_pad);
x_g[idx] +=
out_g[idx] * pow(mid[idx], negative_beta) - ratio * x[idx] * accum;
}
++index;
}
@ -141,7 +157,8 @@ __global__ void KeCMRNormDiff(int img_size, const T* x, const T* out,
template <typename T>
void CrossMapNormalGrad(const framework::ExecutionContext& ctx, const T* x,
const T* out, const T* mid, T* x_g, const T* out_g,
int N, int C, int H, int W, int n, T alpha, T beta) {
int N, int C, int H, int W, int n, T alpha, T beta,
const DataLayout data_layout) {
int img_size = N * H * W;
const int block_size = 1024;
@ -149,8 +166,8 @@ void CrossMapNormalGrad(const framework::ExecutionContext& ctx, const T* x,
auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
KeCMRNormDiff<T><<<grid_size, block_size, 0, dev_ctx.stream()>>>(
img_size, x, out, mid, x_g, out_g, C, H, W, n, -beta,
2.0f * alpha * beta);
img_size, x, out, mid, x_g, out_g, C, H, W, n, -beta, 2.0f * alpha * beta,
data_layout);
}
template <typename T>
@ -159,10 +176,10 @@ struct LRNGradFunctor<platform::CUDADeviceContext, T> {
const framework::Tensor& x, const framework::Tensor& out,
const framework::Tensor& mid, framework::Tensor* x_g,
const framework::Tensor& out_g, int N, int C, int H, int W,
int n, T alpha, T beta) {
int n, T alpha, T beta, const DataLayout data_layout) {
CrossMapNormalGrad<T>(ctx, x.data<T>(), out.data<T>(), mid.data<T>(),
x_g->mutable_data<T>(ctx.GetPlace()), out_g.data<T>(),
N, C, H, W, n, alpha, beta);
N, C, H, W, n, alpha, beta, data_layout);
}
};

32
paddle/fluid/operators/lrn_op.h
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@ -14,6 +14,8 @@ limitations under the License. */
#pragma once
#include <string>
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
@ -21,12 +23,15 @@ limitations under the License. */
namespace paddle {
namespace operators {
using DataLayout = framework::DataLayout;
template <typename place, typename T>
struct LRNFunctor {
void operator()(const framework::ExecutionContext& ctx,
const framework::Tensor& input, framework::Tensor* out,
framework::Tensor* mid, int N, int C, int H, int W, int n,
T k, T alpha, T beta);
T k, T alpha, T beta,
const DataLayout data_layout = DataLayout::kAnyLayout);
};
template <typename DeviceContext, typename T>
@ -42,11 +47,14 @@ class LRNKernel : public framework::OpKernel<T> {
const Tensor& x = *ctx.Input<Tensor>("X");
auto x_dims = x.dims();
const std::string data_layout_str = ctx.Attr<std::string>("data_format");
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_layout_str);
// NCHW
int N = x_dims[0];
int C = x_dims[1];
int H = x_dims[2];
int W = x_dims[3];
int C = (data_layout != DataLayout::kNHWC ? x_dims[1] : x_dims[3]);
int H = (data_layout != DataLayout::kNHWC ? x_dims[2] : x_dims[1]);
int W = (data_layout != DataLayout::kNHWC ? x_dims[3] : x_dims[2]);
Tensor* out = ctx.Output<Tensor>("Out");
out->mutable_data<T>(ctx.GetPlace());
@ -65,7 +73,7 @@ class LRNKernel : public framework::OpKernel<T> {
PADDLE_ENFORCE(k >= 0.0, "k should >= 0.0");
LRNFunctor<DeviceContext, T> f;
f(ctx, x, out, mid, N, C, H, W, n, k, alpha, beta);
f(ctx, x, out, mid, N, C, H, W, n, k, alpha, beta, data_layout);
}
};
@ -75,7 +83,8 @@ struct LRNGradFunctor {
const framework::Tensor& x, const framework::Tensor& out,
const framework::Tensor& mid, framework::Tensor* x_g,
const framework::Tensor& out_g, int N, int C, int H, int W,
int n, T alpha, T beta);
int n, T alpha, T beta,
const DataLayout data_layout = DataLayout::kAnyLayout);
};
/**
@ -106,15 +115,18 @@ class LRNGradKernel : public framework::OpKernel<T> {
const Tensor& out = *ctx.Input<Tensor>("Out");
const Tensor& out_g = *ctx.Input<Tensor>(framework::GradVarName("Out"));
const Tensor& mid = *ctx.Input<Tensor>("MidOut");
const std::string data_layout_str = ctx.Attr<std::string>("data_format");
const framework::DataLayout data_layout =
framework::StringToDataLayout(data_layout_str);
auto x_g = ctx.Output<Tensor>(framework::GradVarName("X"));
x_g->mutable_data<T>(ctx.GetPlace());
auto x_dims = x.dims();
int N = x_dims[0];
int C = x_dims[1];
int H = x_dims[2];
int W = x_dims[3];
int C = (data_layout != DataLayout::kNHWC ? x_dims[1] : x_dims[3]);
int H = (data_layout != DataLayout::kNHWC ? x_dims[2] : x_dims[1]);
int W = (data_layout != DataLayout::kNHWC ? x_dims[3] : x_dims[2]);
int n = ctx.Attr<int>("n");
T alpha = ctx.Attr<T>("alpha");
@ -125,7 +137,7 @@ class LRNGradKernel : public framework::OpKernel<T> {
"is_test attribute should be set to False in training phase.");
LRNGradFunctor<DeviceContext, T> f;
f(ctx, x, out, mid, x_g, out_g, N, C, H, W, n, alpha, beta);
f(ctx, x, out, mid, x_g, out_g, N, C, H, W, n, alpha, beta, data_layout);
}
};

31
python/paddle/fluid/layers/nn.py
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@ -9277,7 +9277,8 @@ def lod_append(x, level):
return out
def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None,
data_format='NCHW'):
"""
This operator implements the Local Response Normalization Layer.
This layer performs a type of "lateral inhibition" by normalizing over local input regions.
@ -9298,13 +9299,18 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
Args:
input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W], where N is the batch size, C is the input channel, H is Height, W is weight. The data type is float32. The rank of this tensor must be 4, otherwise it will raise ValueError.
input (Variable): Input feature, 4D-Tensor with the shape of [N,C,H,W] or [N, H, W, C],
where N is the batch size, C is the input channel, H is Height, W is weight. The data
type is float32. The rank of this tensor must be 4, otherwise it will raise ValueError.
n (int, optional): The number of channels to sum over. Default: 5
k (float, optional): An offset, positive. Default: 1.0
alpha (float, optional): The scaling parameter, positive. Default:1e-4
beta (float, optional): The exponent, positive. Default:0.75
name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`
name (str, optional): The default value is None. Normally there is no need for user to set
this property. For more information, please refer to :ref:`api_guide_Name`
data_format(str, optional): The data format of the input and output data. An optional string
from: `"NCHW"`, `"NHWC"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`. Default: 'NCHW'.
Returns:
Variable: A tensor variable storing the transformation result with the same shape and data type as input.
@ -9327,8 +9333,12 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
if dims != 4:
raise ValueError(
"dims of input must be 4(not %d), and it's order must be NCHW" %
"Input's dimension size of Op(lrn) must be 4, but received %d." %
(dims))
if data_format not in ['NCHW', 'NHWC']:
raise ValueError(
"Attr(data_format) of Op(lrn) got wrong value: received " +
data_format + " but only NCHW or NHWC supported.")
mid_out = helper.create_variable_for_type_inference(
dtype=dtype, stop_gradient=True)
@ -9340,10 +9350,13 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
"Out": lrn_out,
"MidOut": mid_out,
},
attrs={"n": n,
"k": k,
"alpha": alpha,
"beta": beta})
attrs={
"n": n,
"k": k,
"alpha": alpha,
"beta": beta,
"data_format": data_format
})
return lrn_out

58
python/paddle/fluid/tests/unittests/test_lrn_op.py
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@ -16,6 +16,8 @@ from __future__ import print_function
import unittest
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.core as core
from op_test import OpTest
@ -60,12 +62,15 @@ class TestLRNOp(OpTest):
'n': self.n,
'k': self.k,
'alpha': self.alpha,
'beta': self.beta
'beta': self.beta,
'data_format': self.data_format
}
return attrs
def setUp(self):
self.op_type = "lrn"
self.init_test_case()
self.N = 2
self.C = 3
self.H = 5
@ -77,11 +82,18 @@ class TestLRNOp(OpTest):
self.beta = 0.75
self.x = self.get_input()
self.out, self.mid_out = self.get_out()
if self.data_format == 'NHWC':
self.x = np.transpose(self.x, [0, 2, 3, 1])
self.out = np.transpose(self.out, [0, 2, 3, 1])
self.mid_out = np.transpose(self.mid_out, [0, 2, 3, 1])
self.inputs = {'X': self.x}
self.outputs = {'Out': self.out, 'MidOut': self.mid_out}
self.attrs = self.get_attrs()
def init_test_case(self):
self.data_format = 'NCHW'
def test_check_output(self):
self.check_output()
@ -89,5 +101,49 @@ class TestLRNOp(OpTest):
self.check_grad(['X'], 'Out', max_relative_error=0.01)
class TestLRNOpAttrDataFormat(TestLRNOp):
def init_test_case(self):
self.data_format = 'NHWC'
class TestLRNAPI(OpTest):
def test_case(self):
data1 = fluid.data(name='data1', shape=[2, 4, 5, 5], dtype='float32')
data2 = fluid.data(name='data2', shape=[2, 5, 5, 4], dtype='float32')
out1 = fluid.layers.lrn(data1, data_format='NCHW')
out2 = fluid.layers.lrn(data2, data_format='NHWC')
data1_np = np.random.random((2, 4, 5, 5)).astype("float32")
data2_np = np.transpose(data1_np, [0, 2, 3, 1])
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
results = exe.run(fluid.default_main_program(),
feed={"data1": data1_np,
"data2": data2_np},
fetch_list=[out1, out2],
return_numpy=True)
self.assertTrue(
np.allclose(results[0], np.transpose(results[1], (0, 3, 1, 2))))
def test_exception(self):
input1 = fluid.data(name="input1", shape=[2, 4, 5, 5], dtype="float32")
input2 = fluid.data(
name="input2", shape=[2, 4, 5, 5, 5], dtype="float32")
def _attr_data_fromat():
out = fluid.layers.lrn(input1, data_format='NDHW')
def _input_dim_size():
out = fluid.layers.lrn(input2)
self.assertRaises(ValueError, _attr_data_fromat)
self.assertRaises(ValueError, _input_dim_size)
if __name__ == "__main__":
unittest.main()

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