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Fix bug in unit test.

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emailweixu-patch-1
dangqingqing 7 years ago
parent 2731fd9660
commit 35dec3d722
2 changed files with 82 additions and 63 deletions
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84
paddle/operators/multiclass_nms_op.cc
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@ -41,13 +41,22 @@ class MulticlassNMSOp : public framework::OperatorWithKernel {
"The rank of Input(Bboxes) must be 3.");
PADDLE_ENFORCE_EQ(score_dims.size(), 3,
"The rank of Input(Scores) must be 3.");
PADDLE_ENFORCE_EQ(box_dims[2], 4);
PADDLE_ENFORCE_EQ(box_dims[1], 4);
PADDLE_ENFORCE_EQ(box_dims[0], score_dims[2]);
// Here the box_dims[0] is not the real dimension of output.
// It will be rewritten in the computing kernel.
ctx->SetOutputDim("Out", {box_dims[0], 6});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(
ctx.Input<framework::LoDTensor>("Scores")->type()),
ctx.device_context());
}
};
template <class T>
@ -158,12 +167,12 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
const Tensor& scores, const Tensor& bboxes,
std::map<int, std::vector<int>>* indices,
int* num_nmsed_out) const {
int64_t background_label = ctx.Attr<int64_t>("background_label");
int64_t nms_top_k = ctx.Attr<int64_t>("nms_top_k");
int64_t keep_top_k = ctx.Attr<int64_t>("keep_top_k");
int64_t background_label = ctx.Attr<int>("background_label");
int64_t nms_top_k = ctx.Attr<int>("nms_top_k");
int64_t keep_top_k = ctx.Attr<int>("keep_top_k");
T nms_threshold = static_cast<T>(ctx.Attr<float>("nms_threshold"));
T nms_eta = static_cast<T>(ctx.Attr<float>("nms_eta"));
T score_threshold = static_cast<T>(ctx.Attr<float>("confidence_threshold"));
T score_threshold = static_cast<T>(ctx.Attr<float>("score_threshold"));
int64_t class_num = scores.dims()[0];
int64_t predict_dim = scores.dims()[1];
@ -173,7 +182,7 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
Tensor score = scores.Slice(c, c + 1);
NMSFast(bboxes, score, score_threshold, nms_threshold, nms_eta, nms_top_k,
&((*indices)[c]));
num_det += indices[c].size();
num_det += (*indices)[c].size();
}
*num_nmsed_out = num_det;
@ -230,8 +239,8 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
odata[count * kOutputDim + 3] = bdata[1]; // ymin
odata[count * kOutputDim + 4] = bdata[2]; // xmax
odata[count * kOutputDim + 5] = bdata[3]; // ymax
count++;
}
count++;
}
}
@ -240,10 +249,9 @@ class MulticlassNMSKernel : public framework::OpKernel<T> {
auto* scores = ctx.Input<Tensor>("Scores");
auto* outs = ctx.Output<LoDTensor>("Out");
auto box_dims = boxes->dims();
auto score_dims = scores->dims();
int64_t batch_size = box_dims[0];
int64_t batch_size = score_dims[0];
int64_t class_num = score_dims[1];
int64_t predict_dim = score_dims[2];
@ -291,35 +299,37 @@ class MulticlassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
"(Tensor) A 2-D Tensor with shape [M, 4] represents the location "
"predictions with M bboxes. 4 is the number of "
"each location coordinates.");
AddOutput("Scores",
"(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
"confidence predictions. N is the batch size, C is the class "
"number, M is number of predictions for each class, which is "
"the same with Bboxes.");
AddAttr<int64_t>(
AddInput("Scores",
"(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
"confidence predictions. N is the batch size, C is the class "
"number, M is number of predictions for each class, which is "
"the same with Bboxes.");
AddAttr<int>(
"background_label",
"(int64_t, defalut: 0) "
"The index of background label, the background label will be ignored.")
.SetDefault(0);
AddAttr<float>("score_threshold",
"(float) "
"Only consider detections whose confidences are larger than "
"a threshold. If not provided, consider all boxes.");
AddAttr<int>("nms_top_k",
"(int64_t) "
"Maximum number of detections to be kept according to the "
"confidences aftern the filtering detections based on "
"score_threshold");
AddAttr<float>("nms_threshold",
"(float, defalut: 0.3) "
"The threshold to be used in nms.")
"The threshold to be used in NMS.")
.SetDefault(0.3);
AddAttr<int64_t>("nms_top_k",
"(int64_t) "
"Maximum number of results to be kept.");
AddAttr<float>("nms_eta",
"(float) "
"The parameter for adaptive nms.")
"The parameter for adaptive NMS.")
.SetDefault(1.0);
AddAttr<int64_t>("keep_top_k",
"(int64_t) "
"Number of total bboxes to be kept per image after nms "
"step. -1 means keeping all bboxes after nms step.");
AddAttr<float>("confidence_threshold",
"(float) "
"Only consider detections whose confidences are larger than "
"a threshold. If not provided, consider all boxes.");
AddAttr<int>("keep_top_k",
"(int64_t) "
"Number of total bboxes to be kept per image after NMS "
"step. -1 means keeping all bboxes after NMS step.");
AddOutput("Out",
"(LoDTensor) A 2-D LoDTensor with shape [No, 6] represents the "
"detections. Each row has 6 values: "
@ -329,15 +339,21 @@ class MulticlassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
"offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
"no detected bbox.");
AddComment(R"DOC(
This operators is to do multi-class non maximum suppression (NMS) on a batched
This operator is to do multi-class non maximum suppression (NMS) on a batched
of boxes and scores.
This op greedily selects a subset of detection bounding boxes, pruning
away boxes that have high IOU (intersection over union) overlap (> thresh)
with already selected boxes. It operates independently for each class for
which scores are provided, pruning boxes with score less than a provided
threshold prior to applying NMS.
In the NMS step, this operator greedily selects a subset of detection bounding
boxes that have high scores larger than score_threshold, if providing this
threshold, then selects the largest nms_top_k confidences scores if nms_top_k
is larger than -1. Then this operator pruns away boxes that have high IOU
(intersection over union) overlap with already selected boxes by adaptive
threshold NMS based on parameters of nms_threshold and nms_eta.
Aftern NMS step, only at most keep_top_k number of total bboxes are to be kept
per image if keep_top_k is larger than -1.
This operator support multi-class and batched inputs. It applying NMS
independently for each class.
)DOC");
}
};

61
python/paddle/v2/fluid/tests/test_multiclass_nms_op.py
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@ -69,7 +69,7 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
sorted_indices = np.argsort(-all_scores, axis=0)
sorted_scores = all_scores[sorted_indices]
if top_k < -1 and top_k < sorted_indices.shape[0]:
if top_k > -1 and top_k < sorted_indices.shape[0]:
sorted_indices = sorted_indices[:top_k]
sorted_scores = sorted_scores[:top_k]
@ -82,7 +82,7 @@ def nms(boxes, scores, score_threshold, nms_threshold, top_k=200, eta=1.0):
if keep:
kept_idx = selected_indices[k]
overlap = iou(boxes[idx], boxes[kept_idx])
keep = overlap <= adaptive_threshold
keep = True if overlap <= adaptive_threshold else False
else:
break
if keep:
@ -103,14 +103,14 @@ def multiclass_nms(boxes, scores, background, score_threshold, nms_threshold,
if c == background: continue
indices = nms(boxes, scores[c], score_threshold, nms_threshold,
nms_top_k)
selected_indices.append((c, indices))
for idx in indices:
selected_indices.append((c, idx))
num_det += len(indices)
if keep_top_k > -1 and num_det > keep_top_k:
score_index = []
for c, indices in selected_indices:
for idx in indices:
score_index.append((scores[c][idx], c, idx))
for c, idx in selected_indices:
score_index.append((scores[c][idx], c, idx))
sorted_score_index = sorted(
score_index, key=lambda tup: tup[0], reverse=True)
@ -134,19 +134,16 @@ def batched_multiclass_nms(boxes, scores, background, score_threshold,
keep_top_k)
lod.append(lod[-1] + len(nmsed_outs))
if len(nmsed_outs) == 0: continue
for c, indices in nmsed_outs:
for idx in indices:
xmin, ymin, xmax, ymax = boxes[idx][:]
det_outs.append(
(c, scores[n][c][idx], c, xmin, ymin, xmax, ymax))
for c, idx in nmsed_outs:
xmin, ymin, xmax, ymax = boxes[idx][:]
det_outs.append([c, scores[n][c][idx], xmin, ymin, xmax, ymax])
return det_outs, lod
class TestMulticlassNMSOp(OpTest):
def setUp(self):
self.op_type = 'multiclass_nms'
N = 7
M = 1230
M = 1240
C = 21
BOX_SIZE = 4
background = 0
@ -155,7 +152,17 @@ class TestMulticlassNMSOp(OpTest):
keep_top_k = 200
score_threshold = 0.01
scores = np.random.random((N, C, M)).astype('float32')
scores = np.random.random((N * M, C)).astype('float32')
def softmax(x):
shiftx = x - np.max(x).clip(-64.)
exps = np.exp(shiftx)
return exps / np.sum(exps)
scores = np.apply_along_axis(softmax, 1, scores)
scores = np.reshape(scores, (N, M, C))
scores = np.transpose(scores, (0, 2, 1))
boxes = np.random.random((M, BOX_SIZE)).astype('float32')
boxes[:, 0:2] = boxes[:, 0:2] * 0.5
boxes[:, 2:4] = boxes[:, 0:2] * 0.5 + 0.5
@ -163,8 +170,19 @@ class TestMulticlassNMSOp(OpTest):
nmsed_outs, lod = batched_multiclass_nms(boxes, scores, background,
score_threshold, nms_threshold,
nms_top_k, keep_top_k)
nmsed_outs = np.array(nmsed_outs).astype('float32')
self.op_type = 'multiclass_nms'
self.inputs = {'Bboxes': boxes, 'Scores': scores}
self.outputs = {'Out': (nmsed_outs, [lod])}
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,
}
def test_check_output(self):
self.check_output()
@ -182,18 +200,3 @@ class TestIOU(unittest.TestCase):
if __name__ == '__main__':
unittest.main()
# N = 7
# M = 8
# C = 5
# BOX_SIZE = 4
# background = 0
# nms_threshold = 0.3
# nms_top_k = 400
# keep_top_k = 200
# score_threshold = 0.5
# scores = np.random.random((N, C, M)).astype('float32')
# boxes = np.random.random((M, BOX_SIZE)).astype('float32')
# boxes[:, 0 : 2] = boxes[:, 0 : 2] * 0.5
# boxes[:, 2 : 4] = boxes[:, 0 : 2] * 0.5 + 0.5
# print nmsed_outs, lod

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