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Revert the new non_max_suppression_v2 op for the 1.2 release so we can do API review (tensorflow#10603)
* Revert "Allow tensor as iou_threshold parameter to tf.image.non_max_suppression. (tensorflow#9887)" This reverts commit 513b1e4. It needs API review before going into an official release. * Update goldens
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-303
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6 files changed

+57
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‎tensorflow/core/kernels/non_max_suppression_op.cc‎

Lines changed: 49 additions & 93 deletions
Original file line numberDiff line numberDiff line change
@@ -33,7 +33,6 @@ limitations under the License.
3333
#include "tensorflow/core/platform/logging.h"
3434

3535
namespace tensorflow {
36-
namespace {
3736

3837
typedef Eigen::ThreadPoolDevice CPUDevice;
3938

@@ -90,63 +89,6 @@ static inline float ComputeIOU(typename TTypes<float, 2>::ConstTensor boxes,
9089
return intersection_area / (area_i + area_j - intersection_area);
9190
}
9291

93-
void DoNonMaxSuppressionOp(OpKernelContext* context,
94-
const Tensor& boxes,
95-
const Tensor& scores,
96-
const Tensor& max_output_size,
97-
const float iou_threshold) {
98-
OP_REQUIRES(context, iou_threshold >= 0 && iou_threshold <= 1,
99-
errors::InvalidArgument("iou_threshold must be in [0, 1]"));
100-
101-
int num_boxes = 0;
102-
ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
103-
if (!context->status().ok()) {
104-
return;
105-
}
106-
107-
const int output_size =
108-
std::min(max_output_size.scalar<int>()(), num_boxes);
109-
typename TTypes<float, 2>::ConstTensor boxes_data =
110-
boxes.tensor<float, 2>();
111-
112-
std::vector<float> scores_data(num_boxes);
113-
std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
114-
std::vector<int> sorted_indices;
115-
DecreasingArgSort(scores_data, &sorted_indices);
116-
117-
std::vector<bool> active(num_boxes, true);
118-
std::vector<int> selected;
119-
int num_active = active.size();
120-
for (int i = 0; i < num_boxes; ++i) {
121-
if (num_active == 0 || selected.size() >= output_size) break;
122-
if (active[i]) {
123-
selected.push_back(sorted_indices[i]);
124-
} else {
125-
continue;
126-
}
127-
for (int j = i + 1; j < num_boxes; ++j) {
128-
if (active[j]) {
129-
float iou =
130-
ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
131-
if (iou > iou_threshold) {
132-
active[j] = false;
133-
num_active--;
134-
}
135-
}
136-
}
137-
}
138-
139-
// Allocate output tensor
140-
Tensor* output = nullptr;
141-
TensorShape output_shape({static_cast<int>(selected.size())});
142-
OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
143-
typename TTypes<int, 1>::Tensor selected_indices_data =
144-
output->tensor<int, 1>();
145-
std::copy_n(selected.begin(), selected.size(), selected_indices_data.data());
146-
}
147-
148-
} // namespace
149-
15092
template <typename Device>
15193
class NonMaxSuppressionOp : public OpKernel {
15294
public:
@@ -156,6 +98,9 @@ class NonMaxSuppressionOp : public OpKernel {
15698
}
15799

158100
void Compute(OpKernelContext* context) override {
101+
OP_REQUIRES(context, iou_threshold_ >= 0 && iou_threshold_ <= 1,
102+
errors::InvalidArgument("iou_threshold must be in [0, 1]"));
103+
159104
// boxes: [num_boxes, 4]
160105
const Tensor& boxes = context->input(0);
161106
// scores: [num_boxes]
@@ -167,48 +112,59 @@ class NonMaxSuppressionOp : public OpKernel {
167112
errors::InvalidArgument("max_output_size must be 0-D, got shape ",
168113
max_output_size.shape().DebugString()));
169114

170-
DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_);
171-
}
115+
int num_boxes = 0;
116+
ParseAndCheckBoxSizes(context, boxes, scores, &num_boxes);
117+
if (!context->status().ok()) {
118+
return;
119+
}
172120

173-
private:
174-
float iou_threshold_;
175-
};
121+
const int output_size =
122+
std::min(max_output_size.scalar<int>()(), num_boxes);
123+
typename TTypes<float, 2>::ConstTensor boxes_data =
124+
boxes.tensor<float, 2>();
125+
126+
std::vector<float> scores_data(num_boxes);
127+
std::copy_n(scores.flat<float>().data(), num_boxes, scores_data.begin());
128+
std::vector<int> sorted_indices;
129+
DecreasingArgSort(scores_data, &sorted_indices);
130+
131+
std::vector<bool> active(num_boxes, true);
132+
std::vector<int> selected;
133+
int num_active = active.size();
134+
for (int i = 0; i < num_boxes; ++i) {
135+
if (num_active == 0 || selected.size() >= output_size) break;
136+
if (active[i]) {
137+
selected.push_back(sorted_indices[i]);
138+
} else {
139+
continue;
140+
}
141+
for (int j = i + 1; j < num_boxes; ++j) {
142+
if (active[j]) {
143+
float iou =
144+
ComputeIOU(boxes_data, sorted_indices[i], sorted_indices[j]);
145+
if (iou > iou_threshold_) {
146+
active[j] = false;
147+
num_active--;
148+
}
149+
}
150+
}
151+
}
176152

177-
template <typename Device>
178-
class NonMaxSuppressionV2Op : public OpKernel {
179-
public:
180-
explicit NonMaxSuppressionV2Op(OpKernelConstruction* context)
181-
: OpKernel(context) {
153+
// Allocate output tensor
154+
Tensor* output = nullptr;
155+
TensorShape output_shape({static_cast<int>(selected.size())});
156+
OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output));
157+
typename TTypes<int, 1>::Tensor selected_indices_data =
158+
output->tensor<int, 1>();
159+
std::copy_n(selected.begin(), selected.size(),
160+
selected_indices_data.data());
182161
}
183162

184-
void Compute(OpKernelContext* context) override {
185-
// boxes: [num_boxes, 4]
186-
const Tensor& boxes = context->input(0);
187-
// scores: [num_boxes]
188-
const Tensor& scores = context->input(1);
189-
// max_output_size: scalar
190-
const Tensor& max_output_size = context->input(2);
191-
OP_REQUIRES(
192-
context, TensorShapeUtils::IsScalar(max_output_size.shape()),
193-
errors::InvalidArgument("max_output_size must be 0-D, got shape ",
194-
max_output_size.shape().DebugString()));
195-
// iou_threshold: scalar
196-
const Tensor& iou_threshold = context->input(3);
197-
OP_REQUIRES(
198-
context, TensorShapeUtils::IsScalar(iou_threshold.shape()),
199-
errors::InvalidArgument("iou_threshold must be 0-D, got shape ",
200-
iou_threshold.shape().DebugString()));
201-
202-
const float iou_threshold_val = iou_threshold.scalar<float>()();
203-
204-
DoNonMaxSuppressionOp(context, boxes, scores, max_output_size, iou_threshold_val);
205-
}
163+
private:
164+
float iou_threshold_;
206165
};
207166

208167
REGISTER_KERNEL_BUILDER(Name("NonMaxSuppression").Device(DEVICE_CPU),
209168
NonMaxSuppressionOp<CPUDevice>);
210169

211-
REGISTER_KERNEL_BUILDER(Name("NonMaxSuppressionV2").Device(DEVICE_CPU),
212-
NonMaxSuppressionV2Op<CPUDevice>);
213-
214170
} // namespace tensorflow

‎tensorflow/core/kernels/non_max_suppression_op_test.cc‎

Lines changed: 4 additions & 161 deletions
Original file line numberDiff line numberDiff line change
@@ -141,174 +141,18 @@ TEST_F(NonMaxSuppressionOpTest, TestInconsistentBoxAndScoreShapes) {
141141
AddInputFromArray<float>(TensorShape({5}), {.9f, .75f, .6f, .95f, .5f});
142142
AddInputFromArray<int>(TensorShape({}), {30});
143143
Status s = RunOpKernel();
144-
145-
ASSERT_FALSE(s.ok());
146-
EXPECT_TRUE(
147-
StringPiece(s.ToString()).contains("scores has incompatible shape"))
148-
<< s;
149-
}
150-
151-
TEST_F(NonMaxSuppressionOpTest, TestInvalidIOUThreshold) {
152-
MakeOp(1.2);
153-
AddInputFromArray<float>(TensorShape({1, 4}), {0, 0, 1, 1});
154-
AddInputFromArray<float>(TensorShape({1}), {.9f});
155-
AddInputFromArray<int>(TensorShape({}), {3});
156-
Status s = RunOpKernel();
157-
158-
ASSERT_FALSE(s.ok());
159-
EXPECT_TRUE(
160-
StringPiece(s.ToString()).contains("iou_threshold must be in [0, 1]"))
161-
<< s;
162-
}
163-
164-
TEST_F(NonMaxSuppressionOpTest, TestEmptyInput) {
165-
MakeOp(.5);
166-
AddInputFromArray<float>(TensorShape({0, 4}), {});
167-
AddInputFromArray<float>(TensorShape({0}), {});
168-
AddInputFromArray<int>(TensorShape({}), {30});
169-
TF_ASSERT_OK(RunOpKernel());
170-
171-
Tensor expected(allocator(), DT_INT32, TensorShape({0}));
172-
test::FillValues<int>(&expected, {});
173-
test::ExpectTensorEqual<int>(expected, *GetOutput(0));
174-
}
175-
176-
//
177-
// NonMaxSuppressionV2Op Tests
178-
//
179-
180-
class NonMaxSuppressionV2OpTest : public OpsTestBase {
181-
protected:
182-
void MakeOp() {
183-
TF_EXPECT_OK(NodeDefBuilder("non_max_suppression_op", "NonMaxSuppressionV2")
184-
.Input(FakeInput(DT_FLOAT))
185-
.Input(FakeInput(DT_FLOAT))
186-
.Input(FakeInput(DT_INT32))
187-
.Input(FakeInput(DT_FLOAT))
188-
.Finalize(node_def()));
189-
TF_EXPECT_OK(InitOp());
190-
}
191-
};
192-
193-
TEST_F(NonMaxSuppressionV2OpTest, TestSelectFromThreeClusters) {
194-
MakeOp();
195-
AddInputFromArray<float>(TensorShape({6, 4}),
196-
{0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f,
197-
0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101});
198-
AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
199-
AddInputFromArray<int>(TensorShape({}), {3});
200-
AddInputFromArray<float>(TensorShape({}), {.5f});
201-
TF_ASSERT_OK(RunOpKernel());
202-
203-
Tensor expected(allocator(), DT_INT32, TensorShape({3}));
204-
test::FillValues<int>(&expected, {3, 0, 5});
205-
test::ExpectTensorEqual<int>(expected, *GetOutput(0));
206-
}
207-
208-
TEST_F(NonMaxSuppressionV2OpTest, TestSelectFromThreeClustersFlippedCoordinates) {
209-
MakeOp();
210-
AddInputFromArray<float>(TensorShape({6, 4}),
211-
{1, 1, 0, 0, 0, 0.1f, 1, 1.1f, 0, .9f, 1, -0.1f,
212-
0, 10, 1, 11, 1, 10.1f, 0, 11.1f, 1, 101, 0, 100});
213-
AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
214-
AddInputFromArray<int>(TensorShape({}), {3});
215-
AddInputFromArray<float>(TensorShape({}), {.5f});
216-
TF_ASSERT_OK(RunOpKernel());
217-
218-
Tensor expected(allocator(), DT_INT32, TensorShape({3}));
219-
test::FillValues<int>(&expected, {3, 0, 5});
220-
test::ExpectTensorEqual<int>(expected, *GetOutput(0));
221-
}
222-
223-
TEST_F(NonMaxSuppressionV2OpTest, TestSelectAtMostTwoBoxesFromThreeClusters) {
224-
MakeOp();
225-
AddInputFromArray<float>(TensorShape({6, 4}),
226-
{0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f,
227-
0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101});
228-
AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
229-
AddInputFromArray<int>(TensorShape({}), {2});
230-
AddInputFromArray<float>(TensorShape({}), {.5f});
231-
TF_ASSERT_OK(RunOpKernel());
232-
233-
Tensor expected(allocator(), DT_INT32, TensorShape({2}));
234-
test::FillValues<int>(&expected, {3, 0});
235-
test::ExpectTensorEqual<int>(expected, *GetOutput(0));
236-
}
237-
238-
TEST_F(NonMaxSuppressionV2OpTest, TestSelectAtMostThirtyBoxesFromThreeClusters) {
239-
MakeOp();
240-
AddInputFromArray<float>(TensorShape({6, 4}),
241-
{0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f,
242-
0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101});
243-
AddInputFromArray<float>(TensorShape({6}), {.9f, .75f, .6f, .95f, .5f, .3f});
244-
AddInputFromArray<int>(TensorShape({}), {30});
245-
AddInputFromArray<float>(TensorShape({}), {.5f});
246-
TF_ASSERT_OK(RunOpKernel());
247-
248-
Tensor expected(allocator(), DT_INT32, TensorShape({3}));
249-
test::FillValues<int>(&expected, {3, 0, 5});
250-
test::ExpectTensorEqual<int>(expected, *GetOutput(0));
251-
}
252-
253-
TEST_F(NonMaxSuppressionV2OpTest, TestSelectSingleBox) {
254-
MakeOp();
255-
AddInputFromArray<float>(TensorShape({1, 4}), {0, 0, 1, 1});
256-
AddInputFromArray<float>(TensorShape({1}), {.9f});
257-
AddInputFromArray<int>(TensorShape({}), {3});
258-
AddInputFromArray<float>(TensorShape({}), {.5f});
259-
TF_ASSERT_OK(RunOpKernel());
260-
261-
Tensor expected(allocator(), DT_INT32, TensorShape({1}));
262-
test::FillValues<int>(&expected, {0});
263-
test::ExpectTensorEqual<int>(expected, *GetOutput(0));
264-
}
265-
266-
TEST_F(NonMaxSuppressionV2OpTest, TestSelectFromTenIdenticalBoxes) {
267-
MakeOp();
268-
269-
int num_boxes = 10;
270-
std::vector<float> corners(num_boxes * 4);
271-
std::vector<float> scores(num_boxes);
272-
for (int i = 0; i < num_boxes; ++i) {
273-
corners[i * 4 + 0] = 0;
274-
corners[i * 4 + 1] = 0;
275-
corners[i * 4 + 2] = 1;
276-
corners[i * 4 + 3] = 1;
277-
scores[i] = .9;
278-
}
279-
AddInputFromArray<float>(TensorShape({num_boxes, 4}), corners);
280-
AddInputFromArray<float>(TensorShape({num_boxes}), scores);
281-
AddInputFromArray<int>(TensorShape({}), {3});
282-
AddInputFromArray<float>(TensorShape({}), {.5f});
283-
TF_ASSERT_OK(RunOpKernel());
284-
285-
Tensor expected(allocator(), DT_INT32, TensorShape({1}));
286-
test::FillValues<int>(&expected, {0});
287-
test::ExpectTensorEqual<int>(expected, *GetOutput(0));
288-
}
289-
290-
TEST_F(NonMaxSuppressionV2OpTest, TestInconsistentBoxAndScoreShapes) {
291-
MakeOp();
292-
AddInputFromArray<float>(TensorShape({6, 4}),
293-
{0, 0, 1, 1, 0, 0.1f, 1, 1.1f, 0, -0.1f, 1, 0.9f,
294-
0, 10, 1, 11, 0, 10.1f, 1, 11.1f, 0, 100, 1, 101});
295-
AddInputFromArray<float>(TensorShape({5}), {.9f, .75f, .6f, .95f, .5f});
296-
AddInputFromArray<int>(TensorShape({}), {30});
297-
AddInputFromArray<float>(TensorShape({}), {.5f});
298-
Status s = RunOpKernel();
299144

300145
ASSERT_FALSE(s.ok());
301146
EXPECT_TRUE(
302147
StringPiece(s.ToString()).contains("scores has incompatible shape"))
303148
<< s;
304149
}
305150

306-
TEST_F(NonMaxSuppressionV2OpTest, TestInvalidIOUThreshold) {
307-
MakeOp();
151+
TEST_F(NonMaxSuppressionOpTest, TestInvalidIOUThreshold) {
152+
MakeOp(1.2);
308153
AddInputFromArray<float>(TensorShape({1, 4}), {0, 0, 1, 1});
309154
AddInputFromArray<float>(TensorShape({1}), {.9f});
310155
AddInputFromArray<int>(TensorShape({}), {3});
311-
AddInputFromArray<float>(TensorShape({}), {1.2f});
312156
Status s = RunOpKernel();
313157

314158
ASSERT_FALSE(s.ok());
@@ -317,12 +161,11 @@ TEST_F(NonMaxSuppressionV2OpTest, TestInvalidIOUThreshold) {
317161
<< s;
318162
}
319163

320-
TEST_F(NonMaxSuppressionV2OpTest, TestEmptyInput) {
321-
MakeOp();
164+
TEST_F(NonMaxSuppressionOpTest, TestEmptyInput) {
165+
MakeOp(.5);
322166
AddInputFromArray<float>(TensorShape({0, 4}), {});
323167
AddInputFromArray<float>(TensorShape({0}), {});
324168
AddInputFromArray<int>(TensorShape({}), {30});
325-
AddInputFromArray<float>(TensorShape({}), {.5f});
326169
TF_ASSERT_OK(RunOpKernel());
327170

328171
Tensor expected(allocator(), DT_INT32, TensorShape({0}));

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