Accuracy op

paddle/operators/accuracy_op.cc

@@ -0,0 +1,66 @@
+/* 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/accuracy_op.h"
+
+namespace paddle {
+namespace operators {
+
+class AccuracyOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Inference"),
+                            "Input of Inference must be initialized.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("Label"),
+                            "Input of Inference must be initialized.");
+    auto *inference = ctx.Input<framework::Tensor>("Inference");
+    auto *label = ctx.Input<framework::Tensor>("Label");
+
+    PADDLE_ENFORCE_EQ(label->dims().size(), 1, "label must be a vector");
+    PADDLE_ENFORCE_EQ(inference->dims()[0], label->dims()[0],
+                      "inference size must be the same as label size");
+
+    ctx.Output<Tensor>("Accuracy")->Resize({1});
+  }
+};
+
+class AccuracyOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  AccuracyOpMaker(framework::OpProto *proto,
+                  framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    // TODO(typhoonzero): support both inference value and indices.
+    AddInput("Inference", "topk(indices) the network output");
+    AddInput("Label", "Label of the training data");
+    // TODO(typhoonzero): AddInput("Weight", ...
+    AddOutput("Accuracy", "The accuracy of current batch");
+
+    AddComment(
+        R"DOC(Accuracy. It will print accuracy rate for classification.
+The accuracy is:
+..  math::
+accuracy = \\frac{NumOfCorrectPredicts}{NumOfAllSamples})DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_WITHOUT_GRADIENT(accuracy, ops::AccuracyOp, ops::AccuracyOpMaker);
+REGISTER_OP_CPU_KERNEL(accuracy,
+                       ops::AccuracyKernel<paddle::platform::CPUPlace, float>);

paddle/operators/accuracy_op.cu

@@ -0,0 +1,69 @@
+/* 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/accuracy_op.h"
+
+namespace paddle {
+namespace operators {
+
+__global__ void AccuracySingleKernel(const int N, const int D, const int top_k,
+                                     const int* Xdata, const int* labelData,
+                                     float* accuracy) {
+  int correct = 0;
+  for (int row = 0; row < N; row++) {
+    const int label = labelData[row];
+    for (int col = 0; col < D; col++) {
+      const int pred = Xdata[row * D + col];
+      if (pred == label) {
+        ++correct;
+        break;
+      }
+    }
+  }
+  *accuracy = static_cast<float>(correct) / static_cast<float>(N);
+}
+
+template <typename T>
+class AccuracyOpCUDAKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
+                   "It must use GPUPlace.");
+    auto* inference = ctx.Input<Tensor>("Inference");
+    auto* label = ctx.Input<Tensor>("Label");
+    auto* accuracy = ctx.Output<Tensor>("Accuracy");
+    // FIXME(typhoonzero): only support indices currently
+    // if add support for output values, how to detect the data type?
+    const int* inference_data = inference->data<int>();
+    const int* label_data = label->data<int>();
+    float* accuracy_data = accuracy->mutable_data<float>(ctx.GetPlace());
+
+    size_t num_samples = inference->dims()[0];
+    size_t infer_width = inference->dims()[1];
+    cudaMemset((void**)&accuracy_data, 0, sizeof(float));
+
+    if (num_samples == 0) {
+      return;
+    }
+
+    AccuracySingleKernel<<<1, 1>>>(num_samples, infer_width, 1, inference_data,
+                                   label_data, accuracy_data);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+REGISTER_OP_GPU_KERNEL(accuracy,
+                       paddle::operators::AccuracyOpCUDAKernel<float>);

paddle/operators/accuracy_op.h

@@ -0,0 +1,77 @@
+/* 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 <algorithm>
+#include "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>;
+
+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 EigenScalar = framework::EigenScalar<T, MajorType, IndexType>;
+
+template <typename Place, typename T>
+class AccuracyKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* inference = ctx.Input<Tensor>("Inference");
+    auto* label = ctx.Input<Tensor>("Label");
+    auto* accuracy = ctx.Output<Tensor>("Accuracy");
+
+    float* accuracy_data = accuracy->mutable_data<float>(ctx.GetPlace());
+
+    const T* inference_data = inference->data<T>();
+    const T* label_data = label->data<T>();
+
+    size_t num_samples = inference->dims()[0];
+    size_t class_dim = inference->dims()[1];
+    *accuracy_data = 0.0f;
+
+    if (num_samples == 0) {
+      return;
+    }
+
+    int num_correct = 0;
+    // assume inference is already the topk of the output
+    for (size_t i = 0; i < num_samples; ++i) {
+      PADDLE_ENFORCE_GE(label_data[i], 0, "label must >= 0");
+      for (size_t j = 0; j < class_dim; ++j) {
+        if (inference_data[i * class_dim + j] == label_data[i]) {
+          ++num_correct;
+          break;
+        }
+      }
+    }
+
+    // FIXME(typhoonzero): we don't accumulate the accuracy for now.
+    *accuracy_data =
+        static_cast<float>(num_correct) / static_cast<float>(num_samples);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

paddle/pybind/pybind.cc

@@ -51,6 +51,7 @@ USE_OP(minus);
 USE_OP(cos_sim);
 USE_CPU_ONLY_OP(gather);
 USE_CPU_ONLY_OP(scatter);
+USE_OP(accuracy);
 USE_CPU_ONLY_OP(concat);
 USE_OP(top_k);
 USE_OP(squared_l2_distance);

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

@@ -0,0 +1,25 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+class TestAccuracyOp(OpTest):
+    def setUp(self):
+        self.op_type = "accuracy"
+        infer = np.random.randint(0, 2, (32, 1)).astype("int")
+        label = np.random.randint(0, 2, (32, )).astype("int")
+        self.inputs = {'Inference': infer, "Label": label}
+        num_correct = 0
+        for rowid in xrange(32):
+            for ele in infer[rowid]:
+                if ele == label[rowid]:
+                    num_correct += 1
+                    break
+        self.outputs = {'Accuracy': [num_correct / 32.0]}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+if __name__ == '__main__':
+    unittest.main()