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Merge pull request #8193 from qingqing01/ssd_target_assign

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Add target assigner operator for SSD detection.
emailweixu-patch-1
qingqing01 7 years ago committed by GitHub
parent 931375ffeb 53b6ee1954
commit ae0740ce66
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GPG Key ID: 4AEE18F83AFDEB23
6 changed files with 566 additions and 13 deletions
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8
paddle/framework/mixed_vector.h
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@ -60,6 +60,14 @@ class Vector : public std::vector<T> {
T *data() { return std::vector<T>::data(); }
const T *data() const { return std::vector<T>::data(); }
T *data(const platform::Place &place) {
if (platform::is_cpu_place(place)) {
return data();
} else {
return cuda_data();
}
}
/* Synchronize host vector to device vector */
void CopyToCUDA();
/* Synchronize device vector to host vector */

202
paddle/operators/target_assign_op.cc
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@ -0,0 +1,202 @@
/* 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.
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/operators/target_assign_op.h"
namespace paddle {
namespace operators {
class TargetAssignOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
// checkout inputs
PADDLE_ENFORCE(ctx->HasInput("EncodedGTBBox"),
"Input(EncodedGTBBox) of TargetAssignOp should not be null");
PADDLE_ENFORCE(ctx->HasInput("GTScoreLabel"),
"Input(GTScoreLabel) of TargetAssignOp should not be null");
PADDLE_ENFORCE(ctx->HasInput("MatchIndices"),
"Input(MatchIndices) of TargetAssignOp should not be null");
PADDLE_ENFORCE(ctx->HasInput("NegIndices"),
"Input(NegIndices) of TargetAssignOp should not be null");
// checkout outputs
PADDLE_ENFORCE(
ctx->HasOutput("PredBBoxLabel"),
"Output(PredBBoxLabel) of TargetAssignOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("PredBBoxWeight"),
"Output(PredBBoxWeight) of TargetAssignOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("PredScoreLabel"),
"Output(PredScoreLabel) of TargetAssignOp should not be null.");
PADDLE_ENFORCE(
ctx->HasOutput("PredScoreWeight"),
"Output(PredScoreWeight) of TargetAssignOp should not be null.");
auto blabel_dims = ctx->GetInputDim("EncodedGTBBox");
auto slabel_dims = ctx->GetInputDim("GTScoreLabel");
auto mi_dims = ctx->GetInputDim("MatchIndices");
auto neg_dims = ctx->GetInputDim("NegIndices");
PADDLE_ENFORCE_EQ(blabel_dims.size(), 3UL,
"The rank of Input(EncodedGTBBox) must be 3.");
PADDLE_ENFORCE_EQ(slabel_dims.size(), 2UL,
"The rank of Input(GTScoreLabel) must be 2.");
PADDLE_ENFORCE_EQ(mi_dims.size(), 2UL,
"The rank of Input(MatchIndices) must be 2.");
PADDLE_ENFORCE_EQ(neg_dims.size(), 2UL,
"The rank of Input(NegIndices) must be 2.");
PADDLE_ENFORCE_EQ(blabel_dims[0], slabel_dims[0],
"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 (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.");
auto n = mi_dims[0];
auto np = mi_dims[1];
ctx->SetOutputDim("PredBBoxLabel", {n, np, 4});
ctx->SetOutputDim("PredBBoxWeight", {n, np, 1});
ctx->SetOutputDim("PredScoreLabel", {n, np, 1});
ctx->SetOutputDim("PredScoreWeight", {n, np, 1});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(
ctx.Input<framework::LoDTensor>("EncodedGTBBox")->type()),
ctx.device_context());
}
};
class TargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
public:
TargetAssignOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("EncodedGTBBox",
"(LoDTensor), The encoded ground-truth bounding boxes with shape "
"[Ng, Np, 4], where Ng is the total number of ground-truth boxes "
"in this mini-batch, Np the number of predictions, 4 is the "
"number of coordinate in [xmin, ymin, xmax, ymax] layout.");
AddInput("GTScoreLabel",
"(LoDTensor, default LoDTensor<int>), The input ground-truth "
"labels with shape [Ng, 1], where the Ng is the same as it in "
"the input of EncodedGTBBox.");
AddInput("MatchIndices",
"(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 "
"indices with shape [Neg, 1], where is the total number of "
"negative example indices.");
AddAttr<int>("background_label",
"(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 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.");
AddOutput("PredScoreWeight",
"(Tensor), The weight for PredScoreLabel with the shape "
"of [N, Np, 1]");
AddComment(R"DOC(
This operator is, for given the encoded boxes between prior boxes and
ground-truth boxes and ground-truth class labels, to assign classification
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.
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 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 (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);
}
}
}
};
template struct NegTargetAssignFunctor<platform::CPUDeviceContext, float>;
template struct NegTargetAssignFunctor<platform::CPUDeviceContext, double>;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(target_assign, ops::TargetAssignOp,
ops::TargetAssignOpMaker);
REGISTER_OP_CPU_KERNEL(
target_assign,
ops::TargetAssignKernel<paddle::platform::CPUDeviceContext, float>,
ops::TargetAssignKernel<paddle::platform::CPUDeviceContext, double>);

61
paddle/operators/target_assign_op.cu
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@ -0,0 +1,61 @@
/* 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.
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/operators/target_assign_op.h"
namespace paddle {
namespace operators {
template <typename T>
__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 = row_start + neg_indices[i];
out_label[id] = background_label;
out_label_wt[id] = 1.;
}
}
template <typename 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;
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 NegTargetAssignFunctor<platform::CUDADeviceContext, float>;
template struct NegTargetAssignFunctor<platform::CUDADeviceContext, double>;
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
target_assign,
ops::TargetAssignKernel<paddle::platform::CUDADeviceContext, float>,
ops::TargetAssignKernel<paddle::platform::CUDADeviceContext, double>);

160
paddle/operators/target_assign_op.h
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@ -0,0 +1,160 @@
/* 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.
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. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/platform/assert.h"
#include "paddle/platform/for_range.h"
namespace paddle {
namespace operators {
template <typename T>
struct TargetAssignFunctor {
const T* gt_box_;
const int* gt_label_;
const int* match_indices_;
const size_t* lod_;
const int background_label_;
const int64_t num_;
const int64_t num_prior_box_;
T* out_box_;
T* out_box_wt_;
int* out_label_;
T* out_label_wt_;
TargetAssignFunctor(const T* gt_box, const int* gt_label,
const int* match_indices, const size_t* lod,
const int background_label, const int64_t num,
const int64_t np, T* out_box, T* out_box_wt,
int* out_label, T* out_label_wt)
: gt_box_(gt_box),
gt_label_(gt_label),
match_indices_(match_indices),
lod_(lod),
background_label_(background_label),
num_(num),
num_prior_box_(np),
out_box_(out_box),
out_box_wt_(out_box_wt),
out_label_(out_label),
out_label_wt_(out_label_wt) {}
HOSTDEVICE void operator()(size_t i) const {
int row = i / num_prior_box_;
int col = i - row * num_prior_box_;
size_t row_off = lod_[row];
int offset = 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_ + ((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_[row_off + id];
obox_wt[0] = static_cast<T>(1.);
olabel_wt[0] = static_cast<T>(1.);
} else {
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] = static_cast<T>(0.);
olabel_wt[0] = static_cast<T>(0.);
}
}
};
template <typename DeviceContext, typename T>
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,
T* out_label_wt) const;
};
template <typename DeviceContext, typename T>
class TargetAssignKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* enc_gt_box = ctx.Input<framework::LoDTensor>("EncodedGTBBox");
auto* gt_label = ctx.Input<framework::LoDTensor>("GTScoreLabel");
auto* match_indices = ctx.Input<framework::Tensor>("MatchIndices");
auto* neg_indices = ctx.Input<framework::LoDTensor>("NegIndices");
auto* out_box = ctx.Output<framework::Tensor>("PredBBoxLabel");
auto* out_box_wt = ctx.Output<framework::Tensor>("PredBBoxWeight");
auto* out_label = ctx.Output<framework::Tensor>("PredScoreLabel");
auto* out_label_wt = ctx.Output<framework::Tensor>("PredScoreWeight");
PADDLE_ENFORCE_EQ(enc_gt_box->lod().size(), 1UL);
PADDLE_ENFORCE_EQ(gt_label->lod().size(), 1UL);
PADDLE_ENFORCE_EQ(neg_indices->lod().size(), 1UL);
int background_label = ctx.Attr<int>("background_label");
const T* box_data = enc_gt_box->data<T>();
const int* label_data = gt_label->data<int>();
const int* match_idx_data = match_indices->data<int>();
const int* neg_idx_data = neg_indices->data<int>();
T* obox_data = out_box->mutable_data<T>(ctx.GetPlace());
T* obox_wt_data = out_box_wt->mutable_data<T>(ctx.GetPlace());
int* olabel_data = out_label->mutable_data<int>(ctx.GetPlace());
T* olabel_wt_data = out_label_wt->mutable_data<T>(ctx.GetPlace());
int64_t num = match_indices->dims()[0];
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());
TargetAssignFunctor<T> functor(box_data, label_data, match_idx_data,
gt_lod_data, background_label, num,
num_prior_box, obox_data, obox_wt_data,
olabel_data, olabel_wt_data);
auto& device_ctx = ctx.template device_context<DeviceContext>();
platform::ForRange<DeviceContext> for_range(device_ctx,
num * num_prior_box);
for_range(functor);
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);
}
};
} // namespace operators
} // namespace paddle

26
paddle/platform/assert.h
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@ -1,16 +1,16 @@
// 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.
// 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.
/* 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.
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. */
#pragma once

122
python/paddle/v2/fluid/tests/test_target_assign_op.py
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@ -0,0 +1,122 @@
# 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.
# 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.
import unittest
import numpy as np
import random
from op_test import OpTest
def gen_match_and_neg_indices(num_prior, gt_lod, neg_lod):
if len(gt_lod) != len(neg_lod):
raise AssertionError("The input arguments are illegal.")
batch_size = len(gt_lod) - 1
match_indices = -1 * np.ones((batch_size, num_prior)).astype('int32')
neg_indices = np.zeros((neg_lod[-1], 1)).astype('int32')
for n in range(batch_size):
gt_num = gt_lod[n + 1] - gt_lod[n]
ids = random.sample([i for i in range(num_prior)], gt_num)
match_indices[n, ids] = [i for i in range(gt_num)]
ret_ids = set([i for i in range(num_prior)]) - set(ids)
s = neg_lod[n]
e = neg_lod[n + 1]
l = e - s
neg_ids = random.sample(ret_ids, l)
neg_indices[s:e, :] = np.array(neg_ids).astype('int32').reshape(l, 1)
return match_indices, neg_indices
def target_assign(encoded_box, gt_label, match_indices, neg_indices, gt_lod,
neg_lod, background_label):
batch_size, num_prior = match_indices.shape
# init target bbox
trg_box = np.zeros((batch_size, num_prior, 4)).astype('float32')
# init weight for target bbox
trg_box_wt = np.zeros((batch_size, num_prior, 1)).astype('float32')
# init target label
trg_label = np.ones((batch_size, num_prior, 1)).astype('int32')
trg_label = trg_label * background_label
# init weight for target label
trg_label_wt = np.zeros((batch_size, num_prior, 1)).astype('float32')
for i in range(batch_size):
cur_indices = match_indices[i]
col_ids = np.where(cur_indices > -1)
col_val = cur_indices[col_ids]
gt_start = gt_lod[i]
# target bbox
for v, c in zip(col_val + gt_start, col_ids[0].tolist()):
trg_box[i][c][:] = encoded_box[v][c][:]
# weight for target bbox
trg_box_wt[i][col_ids] = 1.0
trg_label[i][col_ids] = gt_label[col_val + gt_start]
trg_label_wt[i][col_ids] = 1.0
# set target label weight to 1.0 for the negative samples
neg_ids = neg_indices[neg_lod[i]:neg_lod[i + 1]]
trg_label_wt[i][neg_ids] = 1.0
return trg_box, trg_box_wt, trg_label, trg_label_wt
class TestTargetAssginOp(OpTest):
def setUp(self):
self.op_type = "target_assign"
num_prior = 120
num_class = 21
gt_lod = [0, 5, 11, 23]
neg_lod = [0, 4, 7, 13]
batch_size = len(gt_lod) - 1
num_gt = gt_lod[-1]
background_label = 0
encoded_box = np.random.random((num_gt, num_prior, 4)).astype('float32')
gt_label = np.random.randint(
num_class, size=(num_gt, 1)).astype('int32')
match_indices, neg_indices = gen_match_and_neg_indices(num_prior,
gt_lod, neg_lod)
trg_box, trg_box_wt, trg_label, trg_label_wt = target_assign(
encoded_box, gt_label, match_indices, neg_indices, gt_lod, neg_lod,
background_label)
self.inputs = {
'EncodedGTBBox': (encoded_box, [gt_lod]),
'GTScoreLabel': (gt_label, [gt_lod]),
'MatchIndices': (match_indices),
'NegIndices': (neg_indices, [neg_lod]),
}
self.attrs = {'background_label': background_label}
self.outputs = {
'PredBBoxLabel': (trg_box),
'PredBBoxWeight': (trg_box_wt),
'PredScoreLabel': (trg_label),
'PredScoreWeight': (trg_label_wt),
}
def test_check_output(self):
self.check_output()
if __name__ == '__main__':
unittest.main()
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