未验证 提交 a9919903 编写于 作者: Z zhiboniu 提交者: GitHub

phi_multiclass_nms3 (#44613)

上级 e439d735
......@@ -13,8 +13,10 @@ limitations under the License. */
#include <glog/logging.h>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/nms_util.h"
#include "paddle/phi/infermeta/ternary.h"
namespace paddle {
namespace operators {
......@@ -609,12 +611,6 @@ class MultiClassNMS3Op : public MultiClassNMS2Op {
const framework::VariableNameMap& outputs,
const framework::AttributeMap& attrs)
: MultiClassNMS2Op(type, inputs, outputs, attrs) {}
void InferShape(framework::InferShapeContext* ctx) const override {
MultiClassNMS2Op::InferShape(ctx);
ctx->SetOutputDim("NmsRoisNum", {-1});
}
};
class MultiClassNMS3OpMaker : public MultiClassNMS2OpMaker {
......@@ -633,6 +629,10 @@ class MultiClassNMS3OpMaker : public MultiClassNMS2OpMaker {
} // namespace operators
} // namespace paddle
DECLARE_INFER_SHAPE_FUNCTOR(multiclass_nms3,
MultiClassNMSShapeFunctor,
PD_INFER_META(phi::MultiClassNMSInferMeta));
namespace ops = paddle::operators;
REGISTER_OPERATOR(
multiclass_nms,
......@@ -658,7 +658,5 @@ REGISTER_OPERATOR(
ops::MultiClassNMS3Op,
ops::MultiClassNMS3OpMaker,
paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
REGISTER_OP_CPU_KERNEL(multiclass_nms3,
ops::MultiClassNMSKernel<float>,
ops::MultiClassNMSKernel<double>);
paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
MultiClassNMSShapeFunctor);
......@@ -1615,6 +1615,15 @@
func : multi_dot
backward : multi_dot_grad
- api : multiclass_nms3
args : (Tensor bboxes, Tensor scores, Tensor rois_num, float score_threshold, int nms_top_k, int keep_top_k, float nms_threshold=0.3, bool normalized=true, float nms_eta=1.0, int background_label=0)
output : Tensor(out), Tensor(index), Tensor(nms_rois_num)
infer_meta :
func : MultiClassNMSInferMeta
kernel :
func : multiclass_nms3
optional : rois_num
# multinomial
- api : multinomial
args : (Tensor x, int num_samples, bool replacement)
......
......@@ -743,6 +743,99 @@ void LinspaceInferMeta(const MetaTensor& start,
LinspaceRawInferMeta(start, stop, number, out);
}
void MultiClassNMSInferMeta(const MetaTensor& bboxes,
const MetaTensor& scores,
const MetaTensor& rois_num,
float score_threshold,
int nms_top_k,
int keep_top_k,
float nms_threshold,
bool normalized,
float nms_eta,
int background_label,
MetaTensor* out,
MetaTensor* index,
MetaTensor* nms_rois_num,
MetaConfig config) {
auto box_dims = bboxes.dims();
auto score_dims = scores.dims();
auto score_size = score_dims.size();
if (config.is_runtime) {
PADDLE_ENFORCE_EQ(
score_size == 2 || score_size == 3,
true,
errors::InvalidArgument("The rank of Input(Scores) must be 2 or 3"
". But received rank = %d",
score_size));
PADDLE_ENFORCE_EQ(
box_dims.size(),
3,
errors::InvalidArgument("The rank of Input(BBoxes) must be 3"
". But received rank = %d",
box_dims.size()));
if (score_size == 3) {
PADDLE_ENFORCE_EQ(box_dims[2] == 4 || box_dims[2] == 8 ||
box_dims[2] == 16 || box_dims[2] == 24 ||
box_dims[2] == 32,
true,
errors::InvalidArgument(
"The last dimension of Input"
"(BBoxes) must be 4 or 8, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax] or "
"4 points: [x1, y1, x2, y2, x3, y3, x4, y4] or "
"8 points: [xi, yi] i= 1,2,...,8 or "
"12 points: [xi, yi] i= 1,2,...,12 or "
"16 points: [xi, yi] i= 1,2,...,16"));
PADDLE_ENFORCE_EQ(
box_dims[1],
score_dims[2],
errors::InvalidArgument(
"The 2nd dimension of Input(BBoxes) must be equal to "
"last dimension of Input(Scores), which represents the "
"predicted bboxes."
"But received box_dims[1](%s) != socre_dims[2](%s)",
box_dims[1],
score_dims[2]));
} else {
PADDLE_ENFORCE_EQ(box_dims[2],
4,
errors::InvalidArgument(
"The last dimension of Input"
"(BBoxes) must be 4. But received dimension = %d",
box_dims[2]));
PADDLE_ENFORCE_EQ(
box_dims[1],
score_dims[1],
errors::InvalidArgument(
"The 2nd dimension of Input"
"(BBoxes) must be equal to the 2nd dimension of Input(Scores). "
"But received box dimension = %d, score dimension = %d",
box_dims[1],
score_dims[1]));
}
}
PADDLE_ENFORCE_NE(out,
nullptr,
errors::InvalidArgument(
"The out in MultiClassNMSInferMeta can't be nullptr."));
PADDLE_ENFORCE_NE(
index,
nullptr,
errors::InvalidArgument(
"The index in MultiClassNMSInferMeta can't be nullptr."));
// Here the box_dims[0] is not the real dimension of output.
// It will be rewritten in the computing kernel.
out->set_dims(phi::make_ddim({-1, box_dims[2] + 2}));
out->set_dtype(bboxes.dtype());
index->set_dims(phi::make_ddim({-1, box_dims[2] + 2}));
index->set_dtype(DataType::INT32);
nms_rois_num->set_dims(phi::make_ddim({-1}));
nms_rois_num->set_dtype(DataType::INT32);
}
void NllLossRawInferMeta(const MetaTensor& input,
const MetaTensor& label,
const MetaTensor& weight,
......
......@@ -123,6 +123,21 @@ void LinspaceInferMeta(const MetaTensor& start,
DataType dtype,
MetaTensor* out);
void MultiClassNMSInferMeta(const MetaTensor& bboxes,
const MetaTensor& scores,
const MetaTensor& rois_num,
float score_threshold,
int nms_top_k,
int keep_top_k,
float nms_threshold,
bool normalized,
float nms_eta,
int background_label,
MetaTensor* out,
MetaTensor* index,
MetaTensor* nms_rois_num,
MetaConfig config = MetaConfig());
void NllLossRawInferMeta(const MetaTensor& input,
const MetaTensor& label,
const MetaTensor& weight,
......
......@@ -80,7 +80,8 @@ set(COMMON_KERNEL_DEPS
lod_utils
custom_kernel
string_infermeta
utf8proc)
utf8proc
gpc)
copy_if_different(${kernel_declare_file} ${kernel_declare_file_final})
......
此差异已折叠。
// 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.
#pragma once
#include "paddle/phi/core/dense_tensor.h"
namespace phi {
template <typename T, typename Context>
void MultiClassNMSKernel(const Context& ctx,
const DenseTensor& bboxes,
const DenseTensor& scores,
const paddle::optional<DenseTensor>& rois_num,
float score_threshold,
int nms_top_k,
int keep_top_k,
float nms_threshold,
bool normalized,
float nms_eta,
int background_label,
DenseTensor* out,
DenseTensor* index,
DenseTensor* nms_rois_num);
} // namespace phi
// 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/core/compat/op_utils.h"
namespace phi {
KernelSignature MultiClassNMS3OpArgumentMapping(
const ArgumentMappingContext& ctx) {
return KernelSignature("multiclass_nms3",
{"BBoxes", "Scores", "RoisNum"},
{"score_threshold",
"nms_top_k",
"keep_top_k",
"nms_threshold",
"normalized",
"nms_eta",
"background_label"},
{"Out", "Index", "NmsRoisNum"});
}
} // namespace phi
PD_REGISTER_ARG_MAPPING_FN(multiclass_nms3,
phi::MultiClassNMS3OpArgumentMapping);
......@@ -1457,6 +1457,7 @@ class OpTest(unittest.TestCase):
# see details: https://stackoverflow.com/questions/38331703/why-does-numpys-broadcasting-sometimes-allow-comparing-arrays-of-different-leng
if expect_np.size == 0:
self.op_test.assertTrue(actual_np.size == 0) # }}}
# print("actual_np, expect_np", actual_np, expect_np)
self._compare_numpy(name, actual_np, expect_np)
if isinstance(expect, tuple):
self._compare_list(name, actual, expect)
......
......@@ -19,7 +19,81 @@ import copy
from op_test import OpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
from paddle.fluid import Program, program_guard, in_dygraph_mode, _non_static_mode
from paddle.fluid.layer_helper import LayerHelper
from paddle import _C_ops
def multiclass_nms3(bboxes,
scores,
rois_num=None,
score_threshold=0.3,
nms_top_k=1000,
keep_top_k=100,
nms_threshold=0.3,
normalized=True,
nms_eta=1.,
background_label=-1,
return_index=True,
return_rois_num=True,
name=None):
helper = LayerHelper('multiclass_nms3', **locals())
if in_dygraph_mode():
attrs = (score_threshold, nms_top_k, keep_top_k, nms_threshold,
normalized, nms_eta, background_label)
output, index, nms_rois_num = _C_ops.final_state_multiclass_nms3(
bboxes, scores, rois_num, *attrs)
if not return_index:
index = None
return output, index, nms_rois_num
elif _non_static_mode():
attrs = ('background_label', background_label, 'score_threshold',
score_threshold, 'nms_top_k', nms_top_k, 'nms_threshold',
nms_threshold, 'keep_top_k', keep_top_k, 'nms_eta', nms_eta,
'normalized', normalized)
output, index, nms_rois_num = _C_ops.multiclass_nms3(
bboxes, scores, rois_num, *attrs)
if not return_index:
index = None
return output, index, nms_rois_num
else:
output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
index = helper.create_variable_for_type_inference(dtype='int32')
inputs = {'BBoxes': bboxes, 'Scores': scores}
outputs = {'Out': output, 'Index': index}
if rois_num is not None:
inputs['RoisNum'] = rois_num
if return_rois_num:
nms_rois_num = helper.create_variable_for_type_inference(
dtype='int32')
outputs['NmsRoisNum'] = nms_rois_num
helper.append_op(type="multiclass_nms3",
inputs=inputs,
attrs={
'background_label': background_label,
'score_threshold': score_threshold,
'nms_top_k': nms_top_k,
'nms_threshold': nms_threshold,
'keep_top_k': keep_top_k,
'nms_eta': nms_eta,
'normalized': normalized
},
outputs=outputs)
output.stop_gradient = True
index.stop_gradient = True
if not return_index:
index = None
if not return_rois_num:
nms_rois_num = None
return output, nms_rois_num, index
def softmax(x):
......@@ -541,8 +615,9 @@ class TestMulticlassNMS2LoDInput(TestMulticlassNMSLoDInput):
'normalized': normalized,
}
def test_check_output(self):
self.check_output()
def test_check_output(self):
self.check_output()
class TestMulticlassNMS2LoDNoOutput(TestMulticlassNMS2LoDInput):
......@@ -590,6 +665,7 @@ class TestMulticlassNMSError(unittest.TestCase):
class TestMulticlassNMS3Op(TestMulticlassNMS2Op):
def setUp(self):
self.python_api = multiclass_nms3
self.set_argument()
N = 7
M = 1200
......@@ -623,8 +699,8 @@ class TestMulticlassNMS3Op(TestMulticlassNMS2Op):
self.op_type = 'multiclass_nms3'
self.inputs = {'BBoxes': boxes, 'Scores': scores}
self.outputs = {
'Out': (nmsed_outs, [lod]),
'Index': (index_outs, [lod]),
'Out': nmsed_outs,
'Index': index_outs,
'NmsRoisNum': np.array(lod).astype('int32')
}
self.attrs = {
......@@ -638,7 +714,7 @@ class TestMulticlassNMS3Op(TestMulticlassNMS2Op):
}
def test_check_output(self):
self.check_output()
self.check_output(check_eager=True)
class TestMulticlassNMS3OpNoOutput(TestMulticlassNMS3Op):
......@@ -649,71 +725,6 @@ class TestMulticlassNMS3OpNoOutput(TestMulticlassNMS3Op):
self.score_threshold = 2.0
class TestMulticlassNMS3LoDInput(TestMulticlassNMS2LoDInput):
def setUp(self):
self.set_argument()
M = 1200
C = 21
BOX_SIZE = 4
box_lod = [[1200]]
background = 0
nms_threshold = 0.3
nms_top_k = 400
keep_top_k = 200
score_threshold = self.score_threshold
normalized = False
scores = np.random.random((M, C)).astype('float32')
scores = np.apply_along_axis(softmax, 1, scores)
boxes = np.random.random((M, C, BOX_SIZE)).astype('float32')
boxes[:, :, 0] = boxes[:, :, 0] * 10
boxes[:, :, 1] = boxes[:, :, 1] * 10
boxes[:, :, 2] = boxes[:, :, 2] * 10 + 10
boxes[:, :, 3] = boxes[:, :, 3] * 10 + 10
det_outs, lod = lod_multiclass_nms(boxes, scores, background,
score_threshold, nms_threshold,
nms_top_k, keep_top_k, box_lod,
normalized)
det_outs = np.array(det_outs)
nmsed_outs = det_outs[:, :-1].astype('float32') if len(
det_outs) else det_outs
self.op_type = 'multiclass_nms3'
self.inputs = {
'BBoxes': (boxes, box_lod),
'Scores': (scores, box_lod),
'RoisNum': np.array(box_lod).astype('int32')
}
self.outputs = {
'Out': (nmsed_outs, [lod]),
'NmsRoisNum': np.array(lod).astype('int32')
}
self.attrs = {
'background_label': 0,
'nms_threshold': nms_threshold,
'nms_top_k': nms_top_k,
'keep_top_k': keep_top_k,
'score_threshold': score_threshold,
'nms_eta': 1.0,
'normalized': normalized,
}
def test_check_output(self):
self.check_output()
class TestMulticlassNMS3LoDNoOutput(TestMulticlassNMS3LoDInput):
def set_argument(self):
# Here set 2.0 to test the case there is no outputs.
# In practical use, 0.0 < score_threshold < 1.0
self.score_threshold = 2.0
if __name__ == '__main__':
paddle.enable_static()
unittest.main()
Markdown is supported
0% .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑!
想要评论请 注册