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Update doc and follow comments.

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emailweixu-patch-1
dangqingqing 8 years ago
parent 09b78c722c
commit ee7d842190
4 changed files with 83 additions and 52 deletions
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58
paddle/operators/target_assign_op.cc
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@ -1,4 +1,4 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@ -61,10 +61,12 @@ class TargetAssignOp : public framework::OperatorWithKernel {
"The rank of Input(NegIndices) must be 2.");
PADDLE_ENFORCE_EQ(blabel_dims[0], slabel_dims[0],
"The 1st dimension of Input(EncodedGTBBox) and "
"Input(GTScoreLabel) must be the same.");
"The 1st dimension (means the total number of "
"ground-truth bounding boxes) of Input(EncodedGTBBox) "
"and Input(GTScoreLabel) must be the same.");
PADDLE_ENFORCE_EQ(blabel_dims[1], mi_dims[1],
"The 2nd dimension of Input(EncodedGTBBox) and "
"The 2nd dimension (means the number of priod boxes) "
"of Input(EncodedGTBBox) and "
"Input(MatchIndices) must be the same.");
PADDLE_ENFORCE_EQ(blabel_dims[2], 4,
"The 3rd dimension of Input(EncodedGTBBox) must be 4.");
@ -101,31 +103,31 @@ class TargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
"labels with shape [Ng, 1], where the Ng is the same as it in "
"the input of EncodedGTBBox.");
AddInput("MatchIndices",
"(Tensor, default LoDTensor<int>), The input matched indices "
"(Tensor, default Tensor<int>), The input matched indices "
"with shape [N, Np], where N is the batch size, Np is the same "
"as it in the input of EncodedGTBBox. If MatchIndices[i][j] "
"is -1, the j-th prior box is not matched to any ground-truh "
"box in i-th instance.");
AddInput("NegIndices",
"(LoDTensor, default LoDTensor<int>), The input negative example "
"indics with shape [Neg, 1], where is the total number of "
"indices with shape [Neg, 1], where is the total number of "
"negative example indices.");
AddAttr<int>("background_label",
"(int, default 0), Label id for background class.")
"(int, default 0), Label index of background class.")
.SetDefault(0);
AddOutput("PredBBoxLabel",
"(Tensor), The output encoded ground-truth labels "
"with shape [N, Np, 4], N is the batch size and Np, 4 is the "
"same as they in input of EncodedGTBBox. If MatchIndices[i][j] "
"is -1, the PredBBoxLabel[i][j][:] is the encoded ground-truth "
"box for background_label_id in i-th instance.");
"box for background_label in i-th instance.");
AddOutput("PredBBoxWeight",
"(Tensor), The weight for PredBBoxLabel with the shape "
"of [N, Np, 1]");
AddOutput("PredScoreLabel",
"(Tensor, default Tensor<int>), The output score labels for "
"each predictions with shape [N, Np, 1]. If MatchIndices[i][j] "
"is -1, PredScoreLabel[i][j] = background_label_id.");
"is -1, PredScoreLabel[i][j] = background_label.");
AddOutput("PredScoreWeight",
"(Tensor), The weight for PredScoreLabel with the shape "
"of [N, Np, 1]");
@ -136,19 +138,47 @@ and regression targets to each prior box as well as weights to each
prior box. The weights is used to specify which prior box would not contribute
to training loss.
TODO(dang qingqing) add an example.
For each instance, the output `PredBBoxLabel`, `PredBBoxWeight`,
`PredScoreLabel` and `PredScoreWeight` are assigned based on `MatchIndices`.
Assumed that the row offset for each instance in `EncodedGTBBox` is called lod,
this operato assigns classification/regression targets by performing the
following steps:
1. Assigning all outpts based on `MatchIndices`:
If id = MatchIndices[i][j] > 0,
PredBBoxLabel[i][j] = EncodedGTBBox[lod[i] + id][j]
PredBBoxWeight[i][j] = 1.
PredScoreLabel[i][j] = GTScoreLabel[lod[i] + id]
PredScoreWeight[i][j] = 1.
Otherwise,
PredBBoxLabel[j][j] = [0., 0., 0., 0.]
PredBBoxWeight[i][j] = 0.
PredScoreLabel[i][j] = background_label
PredScoreWeight[i][j] = 0.
2. Assigning PredScoreWeight based on `NegIndices`:
Assumed that the row offset for each instance in `NegIndices` is caleed neg_lod,
for i-th instance and all ids of NegIndices in this instance:
PredScoreLabel[i][id] = background_label
PredScoreWeight[i][id] = 1.0
)DOC");
}
};
template <typename T>
struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, T> {
struct NegTargetAssignFunctor<platform::CPUDeviceContext, T> {
void operator()(const platform::CPUDeviceContext& ctx, const int* neg_indices,
const size_t* lod, const int num, const int num_prior_box,
const int background_label, int* out_label, T* out_label_wt) {
for (int i = 0; i < num; ++i) {
for (int j = lod[i]; j < lod[i + 1]; ++j) {
for (size_t j = lod[i]; j < lod[i + 1]; ++j) {
int id = neg_indices[j];
out_label[i * num_prior_box + id] = background_label;
out_label_wt[i * num_prior_box + id] = static_cast<T>(1.0);
@ -157,8 +187,8 @@ struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, T> {
}
};
template struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, float>;
template struct UpdateTargetLabelFunctor<platform::CPUDeviceContext, double>;
template struct NegTargetAssignFunctor<platform::CPUDeviceContext, float>;
template struct NegTargetAssignFunctor<platform::CPUDeviceContext, double>;
} // namespace operators
} // namespace paddle

26
paddle/operators/target_assign_op.cu
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@ -1,4 +1,4 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@ -18,38 +18,38 @@ namespace paddle {
namespace operators {
template <typename T>
__global__ void UpdateTargetLabelKernel(const int* neg_indices,
const size_t* lod, const int num,
const int num_prior_box,
const int background_label,
int* out_label, T* out_label_wt) {
__global__ void NegTargetAssignKernel(const int* neg_indices, const size_t* lod,
const int num, const int num_prior_box,
const int background_label,
int* out_label, T* out_label_wt) {
int bidx = blockIdx.x;
int st = lod[bidx];
int ed = lod[bidx + 1];
int row_start = bidx * num_prior_box;
for (int i = st + threadIdx.x; i < ed; i += blockDim.x) {
int id = neg_indices[i];
out_label[bidx * num_prior_box + id] = background_label;
out_label_wt[bidx * num_prior_box + id] = 1.;
int id = row_start + neg_indices[i];
out_label[id] = background_label;
out_label_wt[id] = 1.;
}
}
template <typename T>
struct UpdateTargetLabelFunctor<platform::CUDADeviceContext, T> {
struct NegTargetAssignFunctor<platform::CUDADeviceContext, T> {
void operator()(const platform::CUDADeviceContext& ctx,
const int* neg_indices, const size_t* lod, const int num,
const int num_prior_box, const int background_label,
int* out_label, T* out_label_wt) {
const int block_size = 256;
const int grid_size = num;
UpdateTargetLabelKernel<T><<<grid_size, block_size, 0, ctx.stream()>>>(
NegTargetAssignKernel<T><<<grid_size, block_size, 0, ctx.stream()>>>(
neg_indices, lod, num, num_prior_box, background_label, out_label,
out_label_wt);
}
};
template struct UpdateTargetLabelFunctor<platform::CUDADeviceContext, float>;
template struct UpdateTargetLabelFunctor<platform::CUDADeviceContext, double>;
template struct NegTargetAssignFunctor<platform::CUDADeviceContext, float>;
template struct NegTargetAssignFunctor<platform::CUDADeviceContext, double>;
} // namespace operators
} // namespace paddle

47
paddle/operators/target_assign_op.h
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@ -1,4 +1,4 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
@ -56,40 +56,41 @@ struct TargetAssignFunctor {
int row = i / num_prior_box_;
int col = i - row * num_prior_box_;
size_t off = lod_[row];
size_t row_off = lod_[row];
int offset = row * num_prior_box_ + col;
int id = match_indices_[row * num_prior_box_ + col];
T* obox = out_box_ + (row * num_prior_box_ + col) * 4;
int* olabel = out_label_ + row * num_prior_box_ + col;
T* obox_wt = out_box_wt_ + row * num_prior_box_ + col;
T* olabel_wt = out_label_wt_ + row * num_prior_box_ + col;
int id = match_indices_[offset];
T* obox = out_box_ + offset * 4;
int* olabel = out_label_ + offset;
T* obox_wt = out_box_wt_ + offset;
T* olabel_wt = out_label_wt_ + offset;
if (id > -1) {
const T* gtbox = gt_box_ + ((off + id) * num_prior_box_ + col) * 4;
const T* gtbox = gt_box_ + ((row_off + id) * num_prior_box_ + col) * 4;
obox[0] = gtbox[0];
obox[1] = gtbox[1];
obox[2] = gtbox[2];
obox[3] = gtbox[3];
olabel[0] = gt_label_[off + id];
obox_wt[0] = 1.;
olabel_wt[0] = 1.;
olabel[0] = gt_label_[row_off + id];
obox_wt[0] = static_cast<T>(1.);
olabel_wt[0] = static_cast<T>(1.);
} else {
obox[0] = 0.;
obox[1] = 0.;
obox[2] = 0.;
obox[3] = 0.;
obox[0] = static_cast<T>(0.);
obox[1] = static_cast<T>(0.);
obox[2] = static_cast<T>(0.);
obox[3] = static_cast<T>(0.);
olabel[0] = background_label_;
obox_wt[0] = 0.;
olabel_wt[0] = 0.;
obox_wt[0] = static_cast<T>(0.);
olabel_wt[0] = static_cast<T>(0.);
}
}
};
template <typename DeviceContext, typename T>
struct UpdateTargetLabelFunctor {
struct NegTargetAssignFunctor {
void operator()(const platform::DeviceContext& ctx, const int* neg_indices,
const size_t* lod, const int num, const int num_prior_box,
const int background_label, int* out_label,
@ -130,7 +131,11 @@ class TargetAssignKernel : public framework::OpKernel<T> {
int64_t num_prior_box = match_indices->dims()[1];
auto gt_lod = enc_gt_box->lod().back();
auto gt_label_lod = gt_label->lod().back();
auto neg_lod = neg_indices->lod().back();
for (size_t i = 0; i < gt_lod.size(); ++i) {
PADDLE_ENFORCE_EQ(gt_lod.data()[i], gt_label_lod.data()[i]);
}
size_t* gt_lod_data = gt_lod.data(ctx.GetPlace());
size_t* neg_lod_data = neg_lod.data(ctx.GetPlace());
@ -145,9 +150,9 @@ class TargetAssignKernel : public framework::OpKernel<T> {
num * num_prior_box);
for_range(functor);
UpdateTargetLabelFunctor<DeviceContext, T> update_functor;
update_functor(device_ctx, neg_idx_data, neg_lod_data, num, num_prior_box,
background_label, olabel_data, olabel_wt_data);
NegTargetAssignFunctor<DeviceContext, T> neg_trg_functor;
neg_trg_functor(device_ctx, neg_idx_data, neg_lod_data, num, num_prior_box,
background_label, olabel_data, olabel_wt_data);
}
};

4
python/paddle/v2/fluid/tests/test_target_assign_op.py
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@ -14,8 +14,6 @@
import unittest
import numpy as np
import math
import sys
import random
from op_test import OpTest
@ -89,8 +87,6 @@ class TestTargetAssginOp(OpTest):
num_class = 21
gt_lod = [0, 5, 11, 23]
neg_lod = [0, 4, 7, 13]
#gt_lod = [0, 2, 5]
#neg_lod = [0, 2, 4]
batch_size = len(gt_lod) - 1
num_gt = gt_lod[-1]
background_label = 0

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