Merge pull request #3913 from pkuyym/fix-3789

Complete smooth_l1_loss_op.

paddle/operators/smooth_l1_loss_op.cc

+/* Copyright (c) 2016 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/smooth_l1_loss_op.h"
+
+namespace paddle {
+namespace operators {
+
+class SmoothL1LossOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext& ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "X must be initialized.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Y"), "Y must be initialized.");
+
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* y = ctx.Input<framework::Tensor>("Y");
+    PADDLE_ENFORCE_EQ(x->dims(), y->dims(),
+                      "The shape of X and Y must be the same.");
+    PADDLE_ENFORCE_GE(x->dims().size(), 2,
+                      "The tensor rank of X must be at least 2.");
+    auto* inside_weight = ctx.Input<framework::Tensor>("InsideWeight");
+    if (inside_weight) {
+      auto* outside_weight = ctx.Input<framework::Tensor>("OutsideWeight");
+      PADDLE_ENFORCE_NOT_NULL(outside_weight,
+                              "If weights are provided, must specify both "
+                              "inside and outside weights.");
+      PADDLE_ENFORCE_EQ(inside_weight->dims(), x->dims(),
+                        "The shape of InsideWeight must be same as X.");
+      PADDLE_ENFORCE_EQ(outside_weight->dims(), x->dims(),
+                        "The shape of OutsideWeight must be same as X.");
+    }
+
+    auto* diff = ctx.Output<framework::LoDTensor>("Diff");
+    auto* out = ctx.Output<framework::LoDTensor>("Out");
+    diff->Resize(x->dims());
+    // loss is a two-rank tensor
+    out->Resize({x->dims()[0], 1});
+  }
+};
+
+template <typename AttrType>
+class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  SmoothL1LossOpMaker(framework::OpProto* proto,
+                      framework::OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "The input tensor of smooth l1 loss op."
+             "The rank should be greater or equal to 2 with shape "
+             "[batch_size, value_dim1, value_dim2, ..., value_dimN]");
+    AddInput("Y",
+             "The target tensor of smooth l1 loss op "
+             "with the same shape as X.");
+    AddInput("InsideWeight",
+             "Optional input tensor of smooth l1 loss op with the same shape "
+             "as X. If provided, the result of (X - Y) will be multiplied "
+             "by this tensor element by element.");
+    AddInput("OutsideWeight",
+             "Optinal input of smooth l1 loss op with the same shape as X."
+             "If provided, the output smooth l1 loss will be multiplied by "
+             "this tensor element by element.");
+    AddOutput("Diff", "Intermediate variable to cache InsideWeight*(X-Y).")
+        .AsIntermediate();
+    AddOutput("Out", "Smooth l1 loss.");
+    AddAttr<AttrType>("sigma",
+                      "Hyper parameter of smooth l1 loss op."
+                      "A float scalar with default value 3.0.")
+        .SetDefault(3.0);
+    AddComment(R"DOC(
+Compute smooth l1 loss for input and target. The operator take the 1st
+dimension of input as batch size. For each instance, it will compute
+smooth l1 loss element by element first and sum all losses to one value.
+So the output shape is [batch_size, 1].
+
+The equation is:
+loss = 0.5 * (sigma * (x-y))^2    if abs(x - y) < 1 / sigma^2
+       abs(x - y) - 0.5 / sigma^2 otherwise
+
+)DOC");
+  }
+};
+
+class SmoothL1LossGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext& ctx) const override {
+    auto in_dims = ctx.Input<framework::Tensor>("X")->dims();
+    auto out_dims =
+        ctx.Input<framework::Tensor>(framework::GradVarName("Out"))->dims();
+    auto* x_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
+    auto* y_grad =
+        ctx.Output<framework::LoDTensor>(framework::GradVarName("Y"));
+
+    PADDLE_ENFORCE_GE(out_dims.size(), 2,
+                      "The tensor rank of Input(Out@Grad) should be 2.");
+    PADDLE_ENFORCE_EQ(out_dims[0], in_dims[0],
+                      "The 1st dimension of Input(Out@Grad) must be "
+                      "same as input.");
+    PADDLE_ENFORCE_EQ(out_dims[1], 1,
+                      "The 2nd dimension of Input(Out@Grad) must be 1.");
+
+    if (x_grad) x_grad->Resize(in_dims);
+    if (y_grad) y_grad->Resize(in_dims);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(smooth_l1_loss, ops::SmoothL1LossOp,
+            ops::SmoothL1LossOpMaker<float>, smooth_l1_loss_grad,
+            ops::SmoothL1LossGradOp);
+REGISTER_OP_CPU_KERNEL(
+    smooth_l1_loss, ops::SmoothL1LossKernel<paddle::platform::CPUPlace, float>);
+REGISTER_OP_CPU_KERNEL(
+    smooth_l1_loss_grad,
+    ops::SmoothL1LossGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/smooth_l1_loss_op.cu

+/* Copyright (c) 2016 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. */
+
+#define EIGEN_USE_GPU
+
+#include "paddle/operators/smooth_l1_loss_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(
+    smooth_l1_loss, ops::SmoothL1LossKernel<paddle::platform::GPUPlace, float>);
+REGISTER_OP_GPU_KERNEL(
+    smooth_l1_loss_grad,
+    ops::SmoothL1LossGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/smooth_l1_loss_op.h

+/* Copyright (c) 2016 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/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/platform/hostdevice.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+template <typename T>
+struct SmoothL1LossForward {
+  HOSTDEVICE SmoothL1LossForward(const T& sigma2) : sigma2(sigma2) {}
+
+  HOSTDEVICE T operator()(const T& val) const {
+    T abs_val = std::abs(val);
+    if (abs_val < 1.0 / sigma2) {
+      return 0.5 * val * val * sigma2;
+    } else {
+      return abs_val - 0.5 / sigma2;
+    }
+  }
+
+  T sigma2;
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class SmoothL1LossKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in0 = context.Input<Tensor>("X");
+    auto* in1 = context.Input<Tensor>("Y");
+    auto* in2 = context.Input<Tensor>("InsideWeight");
+    auto* in3 = context.Input<Tensor>("OutsideWeight");
+    auto* out0 = context.Output<Tensor>("Diff");
+    auto* out1 = context.Output<Tensor>("Out");
+
+    out0->mutable_data<T>(context.GetPlace());
+    out1->mutable_data<T>(context.GetPlace());
+    auto place = context.GetEigenDevice<Place>();
+
+    auto sigma = static_cast<T>(context.Attr<AttrType>("sigma"));
+    T sigma2 = sigma * sigma;
+    bool has_weight = (in2 != nullptr) && (in3 != nullptr);
+
+    auto x = EigenVector<T>::Flatten(*in0);
+    auto y = EigenVector<T>::Flatten(*in1);
+    auto diff = EigenVector<T>::Flatten(*out0);
+
+    diff.device(place) = x - y;
+    // multiply inside weight
+    if (has_weight) {
+      auto inside_weight = EigenVector<T>::Flatten(*in2);
+      // cache diff, reused in bp
+      diff.device(place) = diff * inside_weight;
+    }
+
+    auto in_counts = in0->numel();
+    Tensor ptensor_errors;
+    ptensor_errors.mutable_data<T>({static_cast<int>(in_counts)},
+                                   context.GetPlace());
+    auto errors = EigenVector<T>::Flatten(ptensor_errors);
+    // apply smooth l1 forward
+    errors.device(place) = diff.unaryExpr(SmoothL1LossForward<T>(sigma2));
+
+    // multiply outside weight
+    if (has_weight) {
+      auto outside_weight = EigenVector<T>::Flatten(*in3);
+      errors.device(place) = errors * outside_weight;
+    }
+    auto loss = EigenVector<T>::Flatten(*out1);
+    // first dimension of 'X' is the number of samples
+    auto mat_dims =
+        framework::make_ddim({static_cast<int>(in0->dims()[0]),
+                              static_cast<int>(in_counts / in0->dims()[0])});
+    auto errors_mat_view = EigenMatrix<T>::From(ptensor_errors, mat_dims);
+    loss.device(place) = errors_mat_view.sum(Eigen::array<int, 1>({{1}}));
+  }
+};
+
+template <typename T>
+struct SmoothL1LossBackward {
+  HOSTDEVICE SmoothL1LossBackward(const T& sigma2) : sigma2(sigma2) {}
+
+  HOSTDEVICE T operator()(const T& val) const {
+    T abs_val = std::abs(val);
+    if (abs_val < 1.0 / sigma2) {
+      return sigma2 * val;
+    } else {
+      return (0 < val) - (val < 0);
+    }
+  }
+
+  T sigma2;
+};
+
+template <typename Place, typename T, typename AttrType = T>
+class SmoothL1LossGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in0 = context.Input<Tensor>("InsideWeight");
+    auto* in1 = context.Input<Tensor>("OutsideWeight");
+    auto* in2 = context.Input<Tensor>("Diff");
+    auto* og = context.Input<Tensor>(framework::GradVarName("Out"));
+    auto sigma = static_cast<T>(context.Attr<AttrType>("sigma"));
+    T sigma2 = sigma * sigma;
+    bool has_weight = (in0 != nullptr) && (in1 != nullptr);
+
+    auto place = context.GetEigenDevice<Place>();
+
+    auto in_dims = in2->dims();
+    auto counts = in2->numel();
+    auto cols = counts / in_dims[0];
+    auto mat_dims = framework::make_ddim(
+        {static_cast<int>(in_dims[0]), static_cast<int>(cols)});
+
+    Tensor ptensor_diff;
+    ptensor_diff.mutable_data<T>({static_cast<int>(counts)},
+                                 context.GetPlace());
+    auto diff = EigenVector<T>::Flatten(ptensor_diff);
+    // apply smooth l1 backwoard
+    diff.device(place) = EigenVector<T>::Flatten(*in2).unaryExpr(
+        SmoothL1LossBackward<T>(sigma2));
+
+    // compute weights
+    Tensor ptensor_weights;
+    ptensor_weights.mutable_data<T>(mat_dims, context.GetPlace());
+    auto weights = EigenMatrix<T>::From(ptensor_weights);
+    // initialize to 1.0
+    weights.device(place) = weights.constant(static_cast<T>(1.0));
+    if (has_weight) {
+      auto inside_weight = EigenMatrix<T>::From(*in0, mat_dims);
+      auto outside_weight = EigenMatrix<T>::From(*in1, mat_dims);
+      weights.device(place) = inside_weight * outside_weight;
+    }
+
+    // compute gradients
+    auto out_grad = EigenMatrix<T>::From(*og);
+    auto diff_mat_view = EigenMatrix<T>::From(ptensor_diff, mat_dims);
+    auto gradients = out_grad.broadcast(
+                         Eigen::array<int, 2>({{1, static_cast<int>(cols)}})) *
+                     weights * diff_mat_view;
+
+    auto* out0 = context.Output<Tensor>(framework::GradVarName("X"));
+    auto* out1 = context.Output<Tensor>(framework::GradVarName("Y"));
+
+    if (out0) {
+      out0->mutable_data<T>(context.GetPlace());
+      auto x_grad = EigenMatrix<T>::From(*out0, mat_dims);
+      x_grad.device(place) = gradients;
+    }
+
+    if (out1) {
+      out1->mutable_data<T>(context.GetPlace());
+      auto y_grad = EigenMatrix<T>::From(*out1, mat_dims);
+      y_grad.device(place) = -1 * gradients;
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/framework/tests/test_smooth_l1_loss_op.py

+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def smooth_l1_loss_forward(val, sigma2):
+    abs_val = abs(val)
+    if abs_val < 1.0 / sigma2:
+        return 0.5 * val * val * sigma2
+    else:
+        return abs_val - 0.5 / sigma2
+
+
+class TestSmoothL1LossOp1(OpTest):
+    def setUp(self):
+        self.op_type = "smooth_l1_loss"
+        dims = (5, 10)
+        self.inputs = {
+            'X': np.random.random(dims).astype("float32"),
+            'Y': np.random.random(dims).astype("float32")
+        }
+        sigma = 3.0
+        self.attrs = {'sigma': sigma}
+        sigma2 = sigma * sigma
+        diff = self.inputs['X'] - self.inputs['Y']
+        loss = np.vectorize(smooth_l1_loss_forward)(diff, sigma2).sum(1)
+        loss = loss.reshape((dims[0], 1))
+        self.outputs = {'Diff': diff, 'Out': loss}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.02)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'], 'Out', max_relative_error=0.03, no_grad_set=set("X"))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'], 'Out', max_relative_error=0.03, no_grad_set=set('Y'))
+
+
+class TestSmoothL1LossOp2(OpTest):
+    def setUp(self):
+        self.op_type = "smooth_l1_loss"
+        dims = (5, 10)
+        self.inputs = {
+            'X': np.random.random(dims).astype("float32"),
+            'Y': np.random.random(dims).astype("float32"),
+            'InsideWeight': np.random.random(dims).astype("float32"),
+            'OutsideWeight': np.random.random(dims).astype("float32")
+        }
+        sigma = 3.0
+        self.attrs = {'sigma': sigma}
+        sigma2 = sigma * sigma
+        diff = self.inputs['X'] - self.inputs['Y']
+        diff = diff * self.inputs['InsideWeight']
+        loss = np.vectorize(smooth_l1_loss_forward)(diff, sigma2)
+        loss = loss * self.inputs['OutsideWeight']
+        loss = loss.sum(1).reshape((dims[0], 1))
+        self.outputs = {'Diff': diff, 'Out': loss}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X', 'Y'], 'Out', max_relative_error=0.03)
+
+    def test_check_grad_ingore_x(self):
+        self.check_grad(
+            ['Y'],
+            'Out',
+            max_relative_error=0.03,
+            no_grad_set=set(['X', 'InsideWeight', 'OutsideWeight']))
+
+    def test_check_grad_ingore_y(self):
+        self.check_grad(
+            ['X'],
+            'Out',
+            max_relative_error=0.03,
+            no_grad_set=set(['Y', 'InsideWeight', 'OutsideWeight']))
+
+
+if __name__ == '__main__':
+    unittest.main()