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pass unit test for margin_rank_loss_op

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revert-4814-Add_sequence_project_op
Yibing Liu 8 years ago
parent 2f12256186
commit 6b3e9ccb3a
4 changed files with 45 additions and 45 deletions
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49
paddle/operators/margin_rank_loss_op.cc
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@ -19,11 +19,7 @@ namespace operators {
class MarginRankLossOp : public framework::OperatorWithKernel {
public:
MarginRankLossOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
@ -35,13 +31,11 @@ class MarginRankLossOp : public framework::OperatorWithKernel {
auto label_dims = ctx.Input<framework::Tensor>("Label")->dims();
auto x1_dims = ctx.Input<framework::Tensor>("X1")->dims();
auto x2_dims = ctx.Input<framework::Tensor>("X2")->dims();
PADDLE_ENFORCE((label_dims.size() == 1) && (x1_dims.size() == 1) &&
(x2_dims.size() == 1),
"The rank of all inputs must be 1.");
PADDLE_ENFORCE((label_dims == x1_dims) && (x1_dims == x2_dims),
"All inputs must have the same size");
ctx.Output<framework::LoDTensor>("Out")->Resize(label_dims);
PADDLE_ENFORCE((label_dims == x1_dims) && (x1_dims == x2_dims) &&
(label_dims.size() == 2) && (label_dims[1] == 1),
"All inputs must be vector with the same size");
ctx.Output<framework::LoDTensor>("Activated")->Resize(label_dims);
ctx.Output<framework::LoDTensor>("Out")->Resize(label_dims);
}
};
@ -51,18 +45,27 @@ class MarginRankLossOpMaker : public framework::OpProtoAndCheckerMaker {
MarginRankLossOpMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("Label", "The label indicating X1 ranked higher than X2 or not.");
AddInput("X1", "The first input of MarginRankLossOp.");
AddInput("X2", "The second input of MarginRankLossOp");
AddAttr<AttrType>("margin", "Margin for MarginRankLossOp").SetDefault(0);
AddOutput("Out", "The output loss of MarginRankLoss operator");
AddInput("X1", "The first input of MarginRankLossOp, row vector.");
AddInput("X2", "The second input of MarginRankLossOp, row vector.");
AddInput("Label",
"The label indicating X1 ranked higher than X2 "
"or not, row vector.");
AddAttr<AttrType>("margin", "Margin for MarginRankLossOp, scalar.")
.SetDefault(0);
AddOutput("Activated",
"Intermediate tensor to indicate "
"whether Output(Out) is activated")
"Intermediate tensor to indicate whether each element of "
"Output(Out) is activated")
.AsIntermediate();
AddComment(R"DOC(MarginRankLoss operator
AddOutput("Out", "The output loss of MarginRankLoss operator");
AddComment(R"DOC(
MarginRankLoss operator measures the loss given a pair of input {`X1`, `X2`}
and `Label` with attribuute `margin`, where `Label == 1` indicating X1 is
ranked higher than `X2`, otherwise `Label == -1`. The loss turns out
loss(X1, X2, Label) = max(0, -Label * (X1-X2) + margin)
loss(x1, x2, y) = max(0, -label * (x1-x2) + margin)
For batch input, `X1`, `X2` and `Label` all have the same size batch_size x 1.
)DOC");
}
@ -70,11 +73,7 @@ loss(x1, x2, y) = max(0, -label * (x1-x2) + margin)
class MarginRankLossGradOp : public framework::OperatorWithKernel {
public:
MarginRankLossGradOp(const std::string &type,
const framework::VariableNameMap &inputs,
const framework::VariableNameMap &outputs,
const framework::AttributeMap &attrs)
: OperatorWithKernel(type, inputs, outputs, attrs) {}
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {

10
paddle/operators/margin_rank_loss_op.cu
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@ -14,9 +14,11 @@
#include "paddle/operators/margin_rank_loss_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
margin_rank_loss,
paddle::operators::MarginRankLossKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(margin_rank_loss_grad,
paddle::operators::MarginRankLossGradKernel<
paddle::platform::GPUPlace, float>);
ops::MarginRankLossKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
margin_rank_loss_grad,
ops::MarginRankLossGradKernel<paddle::platform::GPUPlace, float>);

10
paddle/operators/margin_rank_loss_op.h
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@ -46,8 +46,8 @@ template <typename Place, typename T, typename AttrType = T>
class MarginRankLossKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& ctx) const {
auto* out_t = ctx.Output<framework::LoDTensor>("Out");
auto* act_t = ctx.Output<framework::LoDTensor>("Activated");
auto* out_t = ctx.Output<framework::Tensor>("Out");
auto* act_t = ctx.Output<framework::Tensor>("Activated");
auto* label_t = ctx.Input<framework::Tensor>("Label");
auto* x1_t = ctx.Input<framework::Tensor>("X1");
@ -65,8 +65,8 @@ class MarginRankLossKernel : public framework::OpKernel {
auto x2 = framework::EigenVector<T>::Flatten(*x2_t);
auto& dev = ctx.GetEigenDevice<Place>();
act.device(dev) = (-label * (x1 - x2) + margin).unaryExpr(Heaviside<T>());
out.device(dev) = (-label * (x1 - x2) + margin).unaryExpr(ReLU<T>());
act.device(dev) = out.unaryExpr(Heaviside<T>());
}
};
@ -78,15 +78,15 @@ class MarginRankLossGradKernel : public framework::OpKernel {
ctx.Output<framework::LoDTensor>(framework::GradVarName("X1"));
auto* d_x2_t =
ctx.Output<framework::LoDTensor>(framework::GradVarName("X2"));
auto* act_t = ctx.Output<framework::LoDTensor>("Activated");
auto* act_t = ctx.Input<framework::Tensor>("Activated");
auto* d_out_t = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* label_t = ctx.Input<framework::Tensor>("Label");
auto& dev = ctx.GetEigenDevice<Place>();
auto d_out = framework::EigenVector<T>::Flatten(*d_out_t);
auto act = framework::EigenVector<T>::Flatten(*act_t);
auto label = framework::EigenVector<T>::Flatten(*label_t);
auto& dev = ctx.GetEigenDevice<Place>();
// compute d_x1
if (d_x1_t) {

21
python/paddle/v2/framework/tests/test_margin_rank_loss_op.py
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@ -8,23 +8,23 @@ class TestMarginRankLossOp(OpTest):
self.op_type = "margin_rank_loss"
batch_size = 5
margin = 0.1
# labels_{i} = {0, 1.0} or {0, 0.5, 1.0}
label = np.random.randint(0, 2, size=(batch_size, )).astype("float32")
x1 = np.random.random((batch_size, )).astype("float32")
x2 = np.random.random((batch_size, )).astype("float32")
# labels_{i} = {-1, 1}
label = 2 * np.random.randint(
0, 2, size=(batch_size, 1)).astype("float32") - 1
x1 = np.random.random((batch_size, 1)).astype("float32")
x2 = np.random.random((batch_size, 1)).astype("float32")
# loss = max(0, -label * (x1 - x2) + margin)
loss = [
max(0, -label[i] * (x1[i] - x2[i]) + margin)
for i in range(batch_size)
]
loss = -label * (x1 - x2) + margin
loss = np.where(loss > 0, loss, 0)
act = np.where(loss > 0, 1., 0.)
self.attrs = {'margin': margin}
self.inputs = {'Label': label, 'X1': x1, 'X2': x2}
self.outputs = {'Out': loss}
self.outputs = {'Activated': act, 'Out': loss}
def test_check_output(self):
self.check_output()
"""
def test_check_grad(self):
self.check_grad(["X1", "X2"], "Out")
@ -33,7 +33,6 @@ class TestMarginRankLossOp(OpTest):
def test_check_grad_ignore_x2(self):
self.check_grad(["X1"], "Out", no_grad_set=set('X2'))
"""
if __name__ == '__main__':

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