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dropblock-gpu.cc
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#include "./dropblock-gpu-inl.h"
#include "../operator_common.h"
namespace mxnet {
namespace op {
template<typename T>
void DropblockForward(
const int N,
const int nbatches,
const int nchannel,
const int nheight,
const int nwidth,
const T* input_data,
T* mask_out,
T* dropblock_out,
real_t pkeep,
const int block_size
)
{
DCHECK(nbatches!=0);
DCHECK(N>0);
static int iteration=0;
static float p_current=1.0;
if (p_current>pkeep)
{
++iteration;
p_current-=((p_current-pkeep)/20000.0)*iteration;
}
index_t feat_size=nheight;
double gamma=((1 - p_current) / (block_size * block_size)) * ((feat_size * feat_size) /
((feat_size - block_size + 1) *
(feat_size - block_size + 1)));
index_t mask_reduction = block_size / 2;
index_t mask_height, mask_width;
if ((block_size % 2) != 0) {
mask_height = nheight - mask_reduction * 2;
mask_width = nwidth - mask_reduction * 2;
} else {
mask_height = nheight - mask_reduction * 2 + 1;
mask_width = nwidth - mask_reduction * 2 + 1;
}
index_t mask_area = mask_height * mask_width;
index_t n = static_cast<int>(mask_area * gamma);
//实现np.arange()操作
std::vector<int> a;
for (int j = 0; j < mask_area; ++j) {
a.push_back(j);
}
//声明动态三维数组
std::vector<std::vector<std::vector<int>>> mask(nbatches, std::vector<std::vector<int >>(1,std::vector<int>(mask_area,
0)));
//实现random.sample(a,n)的操作
int l = 0;
while (l < n) {
index_t randnum = rand() % mask_area;
if (a[randnum] != -100) {
a[randnum] = -100;
++l;
}
}
for (int j = 0; j < nbatches; ++j) {
for (int k = 0; k < mask_area; ++k) {
if (a[k] == -100) {
mask[j][0][k] = 1;
}
}
}
std::vector<std::vector<std::vector<std::vector<int>>>> mask_new(nbatches,
std::vector<std::vector<std::vector<int>>>(
1,
std::vector<std::vector<int>>(
mask_height,
std::vector<int>(
mask_width))));
index_t mask_i = 0;
index_t mask_j = 0;
for (int m = 0; m < nbatches; ++m) {
for (int n = 0; n < 1; ++n) {
for (int l = 0; l < mask_area; ++l) {
mask_i = l / mask_width;
mask_j = l % mask_width;
mask_new[m][n][mask_i][mask_j] = mask[m][n][l];
}
}
}
//生成卷积所使用的weight_mat
std::vector<std::vector<std::vector<std::vector<int>>>> weight_mat(nbatches,
std::vector<std::vector<std::vector<int>>>(
1,
std::vector<std::vector<int>>(
block_size,
std::vector<int>(
block_size,
1))));
index_t padding=0;
if(block_size==3)
{
padding=block_size/2 +1;
}
else if (block_size==5)
{
padding=ceil(block_size/2.0)+1;
}
else if(block_size>5)
{
padding=ceil(block_size/2.0)+2;
}
index_t padding_height = mask_height + 2 * padding;
index_t padding_width = mask_width + 2 * padding;
std::vector<std::vector<std::vector<std::vector<int>>>> mask_padding(nbatches,
std::vector<std::vector<std::vector<int>>>(
1,
std::vector<std::vector<int>>(
padding_height,
std::vector<int>(
padding_width))));
for (int n = 0; n < nbatches; ++n) {
for (int j = 0; j < 1; ++j) {
for (int k = 0; k < padding_height; ++k) {
for (int m = 0; m < padding_width; ++m) {
if (k < padding || m < padding) {
mask_padding[n][j][k][m] = 0;
} else if (k > (mask_height + 1) || m > (mask_width + 1)) {
mask_padding[n][j][k][m] = 0;
} else {
mask_padding[n][j][k][m] = mask_new[n][j][k - padding][m - padding];
}
}
}
}
}
std::vector<std::vector<std::vector<int >>> mask_1d(nbatches,std::vector<std::vector<int>>(1,std::vector<int>(padding_height*padding_width)));
for (int n = 0; n < nbatches; ++n) {
for (int j = 0; j < 1; ++j) {
for (int k = 0; k < padding_height; ++k) {
for (int m = 0; m < padding_width; ++m) {
mask_1d[n][j][m + k * padding_width] = mask_padding[n][j][k][m];
}
}
}
}
std::vector<std::vector<std::vector<int>>> kernel_1d(nbatches,std::vector<std::vector<int>>(1,std::vector<int>(block_size*block_size)));
for (int n = 0; n < nbatches; ++n) {
for (int j = 0; j < 1; ++j) {
for (int k = 0; k < block_size; ++k) {
for (int m = 0; m < block_size; ++m) {
kernel_1d[n][j][m + k * block_size] = weight_mat[n][j][k][m];
}
}
}
}
//计算卷积输出矩阵的维数
index_t outm = padding_height - block_size + 1;
//计算卷积过程中的被卷积矩阵的宽和高
index_t convAw = block_size * block_size;
index_t convAh = padding_height * padding_width;
//定义一个卷积过程中的矩阵
std::vector<std::vector<std::vector<int>>> A_convert(nbatches, std::vector<std::vector<int>>(1,
std::vector<int>(
convAh *
convAw)));
for (int n = 0; n < nbatches; ++n) {
for (int j = 0; j < 1; ++j) {
for (int k = 0; k < outm; ++k) {
for (int m = 0; m < outm; ++m) {
index_t wh = k * outm * convAw + m * convAw;//k*9*9+m*121
index_t col1 = k * padding_height + m;//k*11+m 0
index_t col2 = (k + 1) * padding_height + m;//(k+1)*11+m 11
index_t col3 = (k + 2) * padding_height + m;//(k+2)*11+m 22
index_t col4 = (k + 3) * padding_height + m;//(k+3)*11+m
index_t col5 = (k + 4) * padding_height + m;//(k+4)*11+m
index_t col6 = (k + 5) * padding_height + m;
index_t col7 = (k + 6) * padding_height + m;
if (block_size == 3) {
A_convert[n][j][wh] = mask_1d[n][j][col1];
A_convert[n][j][wh + 1] = mask_1d[n][j][col1 + 1];
A_convert[n][j][wh + 2] = mask_1d[n][j][col1 + 2];
A_convert[n][j][wh + 3] = mask_1d[n][j][col2];
A_convert[n][j][wh + 4] = mask_1d[n][j][col2 + 1];
A_convert[n][j][wh + 5] = mask_1d[n][j][col2 + 2];
A_convert[n][j][wh + 6] = mask_1d[n][j][col3];
A_convert[n][j][wh + 7] = mask_1d[n][j][col3 + 1];
A_convert[n][j][wh + 8] = mask_1d[n][j][col3 + 2];
} else if (block_size == 5) {
A_convert[n][j][wh] = mask_1d[n][j][col1];
A_convert[n][j][wh + 1] = mask_1d[n][j][col1 + 1];
A_convert[n][j][wh + 2] = mask_1d[n][j][col1 + 2];
A_convert[n][j][wh + 3] = mask_1d[n][j][col1 + 3];
A_convert[n][j][wh + 4] = mask_1d[n][j][col1 + 4];
A_convert[n][j][wh + 5] = mask_1d[n][j][col2];
A_convert[n][j][wh + 6] = mask_1d[n][j][col2 + 1];
A_convert[n][j][wh + 7] = mask_1d[n][j][col2 + 2];
A_convert[n][j][wh + 8] = mask_1d[n][j][col2 + 3];
A_convert[n][j][wh + 9] = mask_1d[n][j][col2 + 4];
A_convert[n][j][wh + 10] = mask_1d[n][j][col3];
A_convert[n][j][wh + 11] = mask_1d[n][j][col3 + 1];
A_convert[n][j][wh + 12] = mask_1d[n][j][col3 + 2];
A_convert[n][j][wh + 13] = mask_1d[n][j][col3 + 3];
A_convert[n][j][wh + 14] = mask_1d[n][j][col3 + 4];
A_convert[n][j][wh + 15] = mask_1d[n][j][col4];
A_convert[n][j][wh + 16] = mask_1d[n][j][col4 + 1];
A_convert[n][j][wh + 17] = mask_1d[n][j][col4 + 2];
A_convert[n][j][wh + 18] = mask_1d[n][j][col4 + 3];
A_convert[n][j][wh + 19] = mask_1d[n][j][col4 + 4];
A_convert[n][j][wh + 20] = mask_1d[n][j][col5];
A_convert[n][j][wh + 21] = mask_1d[n][j][col5 + 1];
A_convert[n][j][wh + 22] = mask_1d[n][j][col5 + 2];
A_convert[n][j][wh + 23] = mask_1d[n][j][col5 + 3];
A_convert[n][j][wh + 24] = mask_1d[n][j][col5 + 4];
}else if (block_size == 7) {
A_convert[n][j][wh] = mask_1d[n][j][col1];
A_convert[n][j][wh + 1] = mask_1d[n][j][col1 + 1];
A_convert[n][j][wh + 2] = mask_1d[n][j][col1 + 2];
A_convert[n][j][wh + 3] = mask_1d[n][j][col1 + 3];
A_convert[n][j][wh + 4] = mask_1d[n][j][col1 + 4];
A_convert[n][j][wh + 5] = mask_1d[n][j][col1 + 5];
A_convert[n][j][wh + 6] = mask_1d[n][j][col1 + 6];
A_convert[n][j][wh + 7] = mask_1d[n][j][col2];
A_convert[n][j][wh + 8] = mask_1d[n][j][col2 + 1];
A_convert[n][j][wh + 9] = mask_1d[n][j][col2 + 2];
A_convert[n][j][wh + 10] = mask_1d[n][j][col2 + 3];
A_convert[n][j][wh + 11] = mask_1d[n][j][col2 + 4];
A_convert[n][j][wh + 12] = mask_1d[n][j][col2 + 5];
A_convert[n][j][wh + 13] = mask_1d[n][j][col2 + 6];
A_convert[n][j][wh + 14] = mask_1d[n][j][col3];
A_convert[n][j][wh + 15] = mask_1d[n][j][col3 + 1];
A_convert[n][j][wh + 16] = mask_1d[n][j][col3 + 2];
A_convert[n][j][wh + 17] = mask_1d[n][j][col3 + 3];
A_convert[n][j][wh + 18] = mask_1d[n][j][col3 + 4];
A_convert[n][j][wh + 19] = mask_1d[n][j][col3 + 5];
A_convert[n][j][wh + 20] = mask_1d[n][j][col3 + 6];
A_convert[n][j][wh + 21] = mask_1d[n][j][col4];
A_convert[n][j][wh + 22] = mask_1d[n][j][col4 + 1];
A_convert[n][j][wh + 23] = mask_1d[n][j][col4 + 2];
A_convert[n][j][wh + 24] = mask_1d[n][j][col4 + 3];
A_convert[n][j][wh + 25] = mask_1d[n][j][col4 + 4];
A_convert[n][j][wh + 26] = mask_1d[n][j][col4 + 5];
A_convert[n][j][wh + 27] = mask_1d[n][j][col4 + 6];
A_convert[n][j][wh + 28] = mask_1d[n][j][col5];
A_convert[n][j][wh + 29] = mask_1d[n][j][col5 + 1];
A_convert[n][j][wh + 30] = mask_1d[n][j][col5 + 2];
A_convert[n][j][wh + 31] = mask_1d[n][j][col5 + 3];
A_convert[n][j][wh + 32] = mask_1d[n][j][col5 + 4];
A_convert[n][j][wh + 33] = mask_1d[n][j][col5 + 5];
A_convert[n][j][wh + 34] = mask_1d[n][j][col5 + 6];
A_convert[n][j][wh + 35] = mask_1d[n][j][col6];
A_convert[n][j][wh + 36] = mask_1d[n][j][col6 + 1];
A_convert[n][j][wh + 37] = mask_1d[n][j][col6 + 2];
A_convert[n][j][wh + 38] = mask_1d[n][j][col6 + 3];
A_convert[n][j][wh + 39] = mask_1d[n][j][col6 + 4];
A_convert[n][j][wh + 40] = mask_1d[n][j][col6 + 5];
A_convert[n][j][wh + 41] = mask_1d[n][j][col6 + 6];
A_convert[n][j][wh + 42] = mask_1d[n][j][col7];
A_convert[n][j][wh + 43] = mask_1d[n][j][col7 + 1];
A_convert[n][j][wh + 44] = mask_1d[n][j][col7 + 2];
A_convert[n][j][wh + 45] = mask_1d[n][j][col7 + 3];
A_convert[n][j][wh + 46] = mask_1d[n][j][col7 + 4];
A_convert[n][j][wh + 47] = mask_1d[n][j][col7 + 5];
A_convert[n][j][wh + 48] = mask_1d[n][j][col7 + 6];
}
}
}
}
}
std::vector<int> C;//存储卷积完了的数字
for (int k = 0; k < nbatches; ++k) {
for (int l = 0; l < 1; ++l) {
for (int m = 0; m < outm; ++m) {
for (int n = 0; n < outm; ++n) {
int result_one_position = 0;
index_t wh = m * outm * convAw + n * convAw;
for (int j = 0; j < convAw; ++j) {
result_one_position += A_convert[k][l][wh + j] * kernel_1d[k][l][j];
}
C.push_back(result_one_position);
}
}
}
}
//重组为4维数组
std::vector<std::vector<std::vector<std::vector<int>>>> mask_conved(nbatches,
std::vector<std::vector<std::vector<int>>>(
1,
std::vector<std::vector<int>>(
outm,
std::vector<int>(
outm))));
index_t delta = block_size / 2;
index_t input_height = mask_height + delta * 2;
index_t input_width = mask_width + delta * 2;
index_t height_to_crop = outm - input_height;
index_t width_to_crop = outm - input_width;
if (height_to_crop != 0) {
for (int k = 0; k < nbatches; ++k) {
for (int l = 0; l < 1; ++l) {
for (int m = 0; m < outm - height_to_crop + 1; ++m) {
for (int n = 0; n < outm; ++n) {
mask_conved[k][l][m][n] = (C[k * outm * (outm - height_to_crop)
+ l * outm * (outm - height_to_crop) +
m * outm + n]==0)? 1:0;
}
}
}
}
}
if (width_to_crop != 0) {
for (int k = 0; k < nbatches; ++k) {
for (int l = 0; l < 1; ++l) {
for (int m = 0; m < outm; ++m) {
for (int n = 0; n < outm - width_to_crop + 1; ++n) {
mask_conved[k][l][m][n] =( C[k * outm * (outm - width_to_crop) +
l * outm * (outm - width_to_crop) +
m * (outm - width_to_crop) + n]==0)? 1:0;
}
}
}
}
}
if ((width_to_crop != 0)&&(height_to_crop!=0)) {
for (int k = 0; k < nbatches; ++k) {
for (int l = 0; l < 1; ++l) {
for (int m = 0; m < outm-height_to_crop+1; ++m) {
for (int n = 0; n < outm - width_to_crop + 1; ++n) {
mask_conved[k][l][m][n] =( C[k * (outm-height_to_crop) * (outm - width_to_crop) +
l * (outm-height_to_crop) * (outm - width_to_crop) +
m * (outm - width_to_crop) + n]==0)? 1:0;
}
}
}
}
}
for (int k = 0; k < nbatches; ++k) {
for (int l = 0; l < 1; ++l) {
for (int m = 0; m < outm; ++m) {
for (int n = 0; n < outm; ++n) {
mask_conved[k][l][m][n] =(C[k * outm * outm + l * outm * outm + m * outm + n]==0)? 1:0;
}
}
}
}
//把mask_conved变为一个1D的数组来与indata进行计算
std::vector<int> mask_conved_1d(nbatches * nchannel * nheight * nwidth);
for (int k = 0; k < nbatches; ++k) {
for (int l = 0; l < nchannel; ++l) {
for (int m = 0; m < nheight; ++m) {
for (int n = 0; n < nwidth; ++n) {
mask_conved_1d[k * nchannel * nheight * nwidth
+ l * nheight * nwidth +
m * nwidth + n] = mask_conved[k][0][m][n];
}
}
}
}
for (index_t i = 0; i < N; ++i) {
mask_out[i] = mask_conved_1d[i] * (1.0f / p_current);
dropblock_out[i] = mask_out[i] * input_data[i];
}
}
template <typename T>
void DropblockBackward(const index_t N, T* pgdata ,const T* pgrad, const T* pmask){
for (size_t i=0;i<N;++i)
{
pgdata[i]=pgrad[i]*pmask[i];
}
}
template<typename xpu>
void DropblockForwardCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob>& inputs,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &outputs)
{
using namespace mshadow;
const GPUDropblockParam& param=nnvm::get<GPUDropblockParam>(attrs.parsed);
if (req[gpudropblock::kOut]!=kNullOp)
{
CHECK_EQ(inputs.size(),1U);
if(ctx.is_train) {
CHECK_EQ(outputs.size(),2U);
}
Stream<cpu> *s=ctx.get_stream<cpu>();
const int count=outputs[gpudropblock::kData].Size();
const int nbatches=inputs[gpudropblock::kData].shape_[0];
const int nchannel=inputs[gpudropblock::kData].shape_[1];
const int nheight=inputs[gpudropblock::kData].shape_[2];
const int nwidth=inputs[gpudropblock::kData].shape_[3];
int iteration=0;
const TBlob &out=outputs[gpudropblock::kOut];
if(ctx.is_train||(param.mode==gpudropblock::kAlways))
{
MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_,DType,{
const DType *input_data=inputs[gpudropblock::kData].dptr<DType>();
DType *mask_out=outputs[gpudropblock::kMask].dptr<DType>();
DType *dropblock_out=outputs[gpudropblock::kOut].dptr<DType>();
DropblockForward<DType>(count,nbatches,nchannel,nheight,nwidth,
input_data,mask_out,dropblock_out,param.p,param.block_size);
})
}
else {
const TBlob& data = inputs[gpudropblock::kData];
if (req[gpudropblock::kOut] == kWriteTo) {
mxnet_op::copy(s, out, data);
} else {
MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_,DType,{MXNET_ASSIGN_REQ_SWITCH(req[gpudropblock::kOut], Req, {
mxnet_op::Kernel<mxnet_op::op_with_req<mshadow_op::identity, Req>, xpu>::Launch(
s, out.Size(), out.dptr<DType>(), data.dptr<DType>());//identity:input==output
});
})
}
}
}
}
template<typename xpu>
void DropblockBackwardCompute(const nnvm::NodeAttrs& attrs,
const OpContext& ctx,
const std::vector<TBlob> &inputs,
const std::vector<OpReqType> &req,
const std::vector<TBlob> &outputs
)
{
const GPUDropblockParam& param =nnvm::get<GPUDropblockParam>(attrs.parsed);
CHECK_EQ(inputs.size(),2U);
CHECK_EQ(outputs.size(),1);
CHECK_EQ(req.size(),1);
std::vector<TBlob> out_grads(2);
std::vector<TBlob> out_data(2);
out_grads[gpudropblock::kOut]=inputs[0];
out_data[gpudropblock::kMask]=inputs[1];
using namespace mshadow;
using namespace mshadow::expr;
Stream<cpu> *s =ctx.get_stream<cpu>();
if(ctx.is_train || param.mode==gpudropblock::kAlways){
const TBlob &gdata=outputs[gpudropblock::kData];
const TBlob &grad=out_grads[gpudropblock::kOut];
const TBlob &mask=out_data[gpudropblock::kMask];
const index_t count =gdata.Size();
MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_,DType,{
DType *pgdata=gdata.dptr<DType>();
const DType *pgrad=grad.dptr<DType>();
const DType *pmask=mask.dptr<DType>();
DropblockBackward <DType>(count,pgdata,pgrad,pmask);
})
} else{
const TBlob& gdata = outputs[gpudropblock::kData];
const TBlob& grad = out_grads[gpudropblock::kOut];
if (req[gpudropblock::kData] == kWriteTo) {
mxnet_op::copy(s, gdata, grad);
} else {
MSHADOW_REAL_TYPE_SWITCH(inputs[0].type_flag_,DType,{MXNET_ASSIGN_REQ_SWITCH(req[gpudropblock::kData], Req, {
mxnet_op::Kernel<mxnet_op::op_with_req<mshadow_op::identity, Req>, xpu>::Launch(
s, gdata.Size(), gdata.dptr<DType>(), grad.dptr<DType>());
});})
}
}
}
DMLC_REGISTER_PARAMETER(GPUDropblockParam);
NNVM_REGISTER_OP(GPUDropblock)
.describe(R"(Applies dropblock operation to input array.
- During training, dropout a neighboring area in the feature map,in other word,some certain area of the input is set to zero with probability p.
The whole array is rescaled by :math:`1/(1-p)` to keep the expected
sum of the input unchanged.
- During testing, this operator does not change the input if mode is 'training'.
If mode is 'always', the same computaion as during training will be applied.
- The input data must be a 4-D tensor with same height and width.
Example::
random.seed(998)
input_array = array([[[[ 0. 1. 2. 3. 4. 5. 6. 7. 8.]
[ 9. 10. 11. 12. 13. 14. 15. 16. 17.]
[ 18. 19. 20. 21. 22. 23. 24. 25. 26.]
[ 27. 28. 29. 30. 31. 32. 33. 34. 35.]
[ 36. 37. 38. 39. 40. 41. 42. 43. 44.]
[ 45. 46. 47. 48. 49. 50. 51. 52. 53.]
[ 54. 55. 56. 57. 58. 59. 60. 61. 62.]
[ 63. 64. 65. 66. 67. 68. 69. 70. 71.]
[ 72. 73. 74. 75. 76. 77. 78. 79. 80.]]
[[ 81. 82. 83. 84. 85. 86. 87. 88. 89.]
[ 90. 91. 92. 93. 94. 95. 96. 97. 98.]
[ 99. 100. 101. 102. 103. 104. 105. 106. 107.]
[108. 109. 110. 111. 112. 113. 114. 115. 116.]
[117. 118. 119. 120. 121. 122. 123. 124. 125.]
[126. 127. 128. 129. 130. 131. 132. 133. 134.]
[135. 136. 137. 138. 139. 140. 141. 142. 143.]
[144. 145. 146. 147. 148. 149. 150. 151. 152.]
[153. 154. 155. 156. 157. 158. 159. 160. 161.]]
[[162. 163. 164. 165. 166. 167. 168. 169. 170.]
[171. 172. 173. 174. 175. 176. 177. 178. 179.]
[180. 181. 182. 183. 184. 185. 186. 187. 188.]
[189. 190. 191. 192. 193. 194. 195. 196. 197.]
[198. 199. 200. 201. 202. 203. 204. 205. 206.]
[207. 208. 209. 210. 211. 212. 213. 214. 215.]
[216. 217. 218. 219. 220. 221. 222. 223. 224.]
[225. 226. 227. 228. 229. 230. 231. 232. 233.]
[234. 235. 236. 237. 238. 239. 240. 241. 242.]]]])
a = symbol.Variable('a')
dropblock = symbol.GPUDropblock(a, p = 0.2,block_size=3)
executor = dropblock.simple_bind(a = input_array.shape)
## If training
dropblock = symbol.GPUDropblock(a,p=0.2,block_size=3,mode='always')
executor.forward(is_train = True, a = input_array)
executor.outputs
[[[[ 0. 2. 4. 6. 8. 10. 12. 14. 16.]
[ 18. 20. 22. 24. 26. 28. 30. 32. 34.]
[ 36. 0. 0. 0. 44. 46. 48. 50. 52.]
[ 54. 0. 0. 0. 62. 64. 66. 68. 70.]
[ 72. 0. 0. 0. 80. 82. 84. 86. 88.]
[ 90. 92. 94. 96. 0. 0. 0. 104. 106.]
[108. 110. 112. 114. 0. 0. 0. 122. 124.]
[126. 128. 130. 132. 0. 0. 0. 140. 142.]
[144. 146. 148. 150. 152. 154. 156. 158. 160.]]
[[162. 164. 166. 168. 170. 172. 174. 176. 178.]
[180. 182. 184. 186. 188. 190. 192. 194. 196.]
[198. 0. 0. 0. 206. 208. 210. 212. 214.]
[216. 0. 0. 0. 224. 226. 228. 230. 232.]
[234. 0. 0. 0. 242. 244. 246. 248. 250.]
[252. 254. 256. 258. 0. 0. 0. 266. 268.]
[270. 272. 274. 276. 0. 0. 0. 284. 286.]
[288. 290. 292. 294. 0. 0. 0. 302. 304.]
[306. 308. 310. 312. 314. 316. 318. 320. 322.]]
[[324. 326. 328. 330. 332. 334. 336. 338. 340.]
[342. 344. 346. 348. 350. 352. 354. 356. 358.]
[360. 0. 0. 0. 368. 370. 372. 374. 376.]
[378. 0. 0. 0. 386. 388. 390. 392. 394.]
[396. 0. 0. 0. 404. 406. 408. 410. 412.]
[414. 416. 418. 420. 0. 0. 0. 428. 430.]
[432. 434. 436. 438. 0. 0. 0. 446. 448.]
[450. 452. 454. 456. 0. 0. 0. 464. 466.]
[468. 470. 472. 474. 476. 478. 480. 482. 484.]]]]
## If testing
dropblock = symbol.GPUDropblock(a,p=0.2,block_size=3,mode='training')
executor.forward(is_train = False, a = input_array)
executor.outputs
[[[[ 0. 1. 2. 3. 4. 5. 6. 7. 8.]
[ 9. 10. 11. 12. 13. 14. 15. 16. 17.]
[ 18. 19. 20. 21. 22. 23. 24. 25. 26.]
[ 27. 28. 29. 30. 31. 32. 33. 34. 35.]
[ 36. 37. 38. 39. 40. 41. 42. 43. 44.]
[ 45. 46. 47. 48. 49. 50. 51. 52. 53.]
[ 54. 55. 56. 57. 58. 59. 60. 61. 62.]
[ 63. 64. 65. 66. 67. 68. 69. 70. 71.]
[ 72. 73. 74. 75. 76. 77. 78. 79. 80.]]
[[ 81. 82. 83. 84. 85. 86. 87. 88. 89.]
[ 90. 91. 92. 93. 94. 95. 96. 97. 98.]
[ 99. 100. 101. 102. 103. 104. 105. 106. 107.]
[108. 109. 110. 111. 112. 113. 114. 115. 116.]
[117. 118. 119. 120. 121. 122. 123. 124. 125.]
[126. 127. 128. 129. 130. 131. 132. 133. 134.]
[135. 136. 137. 138. 139. 140. 141. 142. 143.]
[144. 145. 146. 147. 148. 149. 150. 151. 152.]
[153. 154. 155. 156. 157. 158. 159. 160. 161.]]
[[162. 163. 164. 165. 166. 167. 168. 169. 170.]
[171. 172. 173. 174. 175. 176. 177. 178. 179.]
[180. 181. 182. 183. 184. 185. 186. 187. 188.]
[189. 190. 191. 192. 193. 194. 195. 196. 197.]
[198. 199. 200. 201. 202. 203. 204. 205. 206.]
[207. 208. 209. 210. 211. 212. 213. 214. 215.]
[216. 217. 218. 219. 220. 221. 222. 223. 224.]
[225. 226. 227. 228. 229. 230. 231. 232. 233.]
[234. 235. 236. 237. 238. 239. 240. 241. 242.]]]]
)" ADD_FILELINE)
.set_num_inputs(1)
.set_num_outputs(2)
.set_attr_parser(ParamParser<GPUDropblockParam>)
.set_attr<nnvm::FListInputNames>("FListInputNames",
[](const NodeAttrs& attrs) {
return std::vector<std::string>{"data"};
})
.set_attr<nnvm::FListOutputNames>("FListOutputNames",
[](const NodeAttrs& attrs) {
return std::vector<std::string>{"output", "mask"};
})
.set_attr<nnvm::FNumVisibleOutputs>("FNumVisibleOutputs",
[](const NodeAttrs& attrs) {
return 1;
})
.set_attr<mxnet::FInferShape>("FInferShape", [](const nnvm::NodeAttrs& attrs,
std::vector<TShape> *in_shape, std::vector<TShape> *out_shape){
using namespace mshadow;
CHECK_EQ(in_shape->size(), 1U);
const GPUDropblockParam& param = nnvm::get<GPUDropblockParam>(attrs.parsed);
TShape dshape(in_shape->at(0));
if (dshape.ndim() == 0) return false;
TShape datashape=in_shape->at(gpudropblock::kData);
CHECK_EQ(datashape.ndim(),4)<<"data should be a 4D tensor";
out_shape->clear();
out_shape->push_back(dshape);
for (index_t i = 0; i < param.axes.ndim(); ++i) {
dshape[param.axes[i]] = 1;
}
out_shape->push_back(dshape);
return true;
})
.set_attr<nnvm::FInferType>("FInferType", [](const nnvm::NodeAttrs& attrs,
std::vector<int> *in_type, std::vector<int> *out_type) {
CHECK_EQ(in_type->size(), 1U);
int dtype = in_type->at(0);
if (dtype == -1) {
LOG(FATAL) << "input type to dropblock is not specified.";
return false;
}
size_t nout = 2;
out_type->clear();
for (size_t i = 0; i < nout; ++i) out_type->push_back(dtype);
return true;
})
//.set_attr<FCreateOpState>("FCreateOpState", CreateDropoutState)
.set_attr<FCompute>("FCompute<cpu>",DropblockForwardCompute<cpu>)
.set_attr<nnvm::FGradient>("FGradient",
[](const nnvm::NodePtr& n, const std::vector<nnvm::NodeEntry>& ograds) {
std::vector<nnvm::NodeEntry> heads;
heads.push_back(ograds[0]);
heads.emplace_back(nnvm::NodeEntry{n, gpudropblock::kMask, 0});
return MakeGradNode("_backward_GPUDropblock", n, heads, n->attrs.dict);
})
.add_argument("data", "NDArray-or-Symbol", "Input array to which dropout will be applied.")
.add_arguments(GPUDropblockParam::__FIELDS__());
NNVM_REGISTER_OP(_backward_GPUDropblock)
.set_attr_parser(ParamParser<GPUDropblockParam>)
.set_num_inputs(2)
.set_num_outputs(1)
.set_attr<nnvm::TIsBackward>("TIsBackward", true)
.set_attr<FCompute>("FCompute<cpu>", DropblockBackwardCompute<cpu>);
} // namespace op
} // namespace mxnet