Add mean IOU op.

paddle/fluid/operators/mean_iou_op.cc

@@ -0,0 +1,103 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+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/fluid/operators/mean_iou_op.h"
+
+namespace paddle {
+namespace operators {
+
+class MeanIoUOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("predictions"),
+                   "Input (predictions) of MeanIoU op should not be null.");
+    PADDLE_ENFORCE(ctx->HasInput("labels"),
+                   "Input (labels) of MeanIoU op should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("out_mean_iou"),
+                   "Output (out_mean_iou) of MeanIoU op should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("out_wrong"),
+                   "Output (out_wrong) of MeanIoU op should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("out_correct"),
+                   "Output (out_wrong) of MeanIoU op should not be null.");
+
+    int64_t num_classes =
+        static_cast<int64_t>(ctx->Attrs().Get<int>("num_classes"));
+
+    ctx->SetOutputDim("out_mean_iou", {1});
+    ctx->SetOutputDim("out_wrong", {num_classes});
+    ctx->SetOutputDim("out_correct", {num_classes});
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("predictions")->type()),
+        ctx.GetPlace());
+  }
+};
+
+class MeanIoUOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  MeanIoUOpMaker(OpProto* proto, OpAttrChecker* op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("predictions",
+             "A Tensor of prediction results for semantic labels"
+             " with type int32 or int64.");
+    AddInput("labels",
+             "A Tensor of ground truth labels with type int32 or int64."
+             "Its shape should be the same as Input(predictions).");
+    AddInput("in_wrongs",
+             "A list of Tensor with shape "
+             "[num_classes]. They are used to collect wrong number among "
+             "batches. Empty list is also valid here.")
+        .AsDuplicable()
+        .AsDispensable();
+    AddInput(
+        "in_corrects",
+        "A list of Tensor with shape "
+        "[num_classes]. They are used to collect correct number among batches. "
+        "Empty list is also valid here.")
+        .AsDuplicable()
+        .AsDispensable();
+    AddInput("in_mean_iou",
+             "A list of Tensor that Output(mean_iou) should "
+             "be added to. Empty list is also valid here.")
+        .AsDuplicable()
+        .AsDispensable();
+    AddOutput("out_mean_iou",
+              "A Tensor representing the"
+              " mean intersection-over-union.");
+    AddOutput("out_wrong", "A Tensor with shape [num_classes]. ");
+    AddOutput("out_correct", "A Tensor with shape [num_classes]. ");
+    AddAttr<int>("num_classes", "The possible number of labels.");
+
+    AddComment(R"DOC(
+mean-IOU Operator.
+Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows: IOU = true_positive / (true_positive + false_positive + false_negative). The predictions are accumulated in a confusion matrix and mean-IOU is then calculated from it.
+
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(mean_iou, ops::MeanIoUOp, ops::MeanIoUOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+REGISTER_OP_CPU_KERNEL(mean_iou, ops::MeanIoUKernel<int>,
+                       ops::MeanIoUKernel<int64_t>);

paddle/fluid/operators/mean_iou_op.cu

@@ -0,0 +1,164 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+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/fluid/operators/math/math_function.h"
+#include "paddle/fluid/operators/mean_iou_op.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+#include "paddle/fluid/platform/gpu_info.h"
+
+namespace paddle {
+namespace operators {
+
+using platform::PADDLE_CUDA_NUM_THREADS;
+
+#define CUDA_1D_KERNEL_LOOP(i, n)                              \
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
+       i += blockDim.x * gridDim.x)
+
+template <typename T>
+__global__ void CountCUDAKernel(const int num_classes, const int count,
+                                const T* predictions, const T* labels,
+                                int* wrong, int* correct) {
+  extern __shared__ int blcok_cache[];
+  int* wrong_c = blcok_cache;
+  int* correct_c = blcok_cache + num_classes;
+  // init cache
+  for (int i = threadIdx.x; i < num_classes * 2; i += blockDim.x) {
+    blcok_cache[i] = 0;
+  }
+  __syncthreads();
+
+  T pred;
+  T label;
+  CUDA_1D_KERNEL_LOOP(i, count) {
+    pred = predictions[i];
+    label = labels[i];
+    if (pred == label) {
+      atomicAdd(correct_c + pred, 1);
+    } else {
+      atomicAdd(wrong_c + pred, 1);
+      atomicAdd(wrong_c + label, 1);
+    }
+  }
+  __syncthreads();
+
+  for (int i = threadIdx.x; i < num_classes; i += blockDim.x) {
+    atomicAdd(wrong + i, wrong_c[i]);
+    atomicAdd(correct + i, correct_c[i]);
+  }
+}
+
+__global__ void ComputeIoUCUDAKernel(const int num_classes, int* wrong,
+                                     int* correct, float* ious,
+                                     int* valid_count) {
+  __shared__ int valid_count_c;
+  if (threadIdx.x == 0) {
+    valid_count_c = 0;
+  }
+  __syncthreads();
+  CUDA_1D_KERNEL_LOOP(i, num_classes) {
+    int wrong_n = wrong[i];
+    int correct_n = correct[i];
+    int denominator = wrong_n + correct_n;
+    if (denominator > 0) {
+      atomicAdd(&valid_count_c, 1);
+      ious[i] = static_cast<float>(correct_n) / denominator;
+    }
+  }
+  __syncthreads();
+  if (threadIdx.x == 0) {
+    atomicAdd(valid_count, valid_count_c);
+  }
+}
+
+template <typename T>
+class MeanIoUCUDAOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& place = *ctx.template device_context<platform::CUDADeviceContext>()
+                       .eigen_device();
+    // get input and output tensor
+    auto* predictions = ctx.Input<Tensor>("predictions");
+    auto* labels = ctx.Input<Tensor>("labels");
+    auto* out_mean_iou = ctx.Output<Tensor>("out_mean_iou");
+    auto* out_wrong = ctx.Output<Tensor>("out_wrong");
+    auto* out_correct = ctx.Output<Tensor>("out_correct");
+    int num_classes = static_cast<int>(ctx.Attr<int>("num_classes"));
+
+    // get data ptr
+    const T* predictions_data = predictions->data<T>();
+    const T* labels_data = labels->data<T>();
+    int* out_wrong_data = out_wrong->mutable_data<int>(ctx.GetPlace());
+    int* out_correct_data = out_correct->mutable_data<int>(ctx.GetPlace());
+    float* out_mean_iou_data =
+        out_mean_iou->mutable_data<float>(ctx.GetPlace());
+
+    // get eigen tensor
+    auto out_mean_iou_t = EigenTensor<float, 1>::From(*out_mean_iou);
+    auto out_wrong_t = EigenTensor<int, 1>::From(*out_wrong);
+    auto out_correct_t = EigenTensor<int, 1>::From(*out_correct);
+
+    // Tmp tensor
+    Tensor valid_count;
+    int* valid_count_data = valid_count.mutable_data<int>({1}, ctx.GetPlace());
+    auto valid_count_t = EigenTensor<int, 1>::From(valid_count);
+    Tensor ious;
+    float* ious_data = ious.mutable_data<float>(
+        {static_cast<int64_t>(num_classes)}, ctx.GetPlace());
+    auto ious_t = EigenTensor<float, 1>::From(ious);
+
+    // init out_wrong, out_correct and out_mean_iou
+    out_wrong_t.device(place) = out_wrong_t.constant(0);
+    out_correct_t.device(place) = out_correct_t.constant(0);
+    valid_count_t.device(place) = valid_count_t.constant(0);
+    out_mean_iou_t.device(place) = out_mean_iou_t.constant(0.0f);
+
+    // collect pre wrong, correct and mean_iou
+    auto in_mean_ious = ctx.MultiInput<Tensor>("in_mean_iou");
+    for (int i = 0; i < in_mean_ious.size(); ++i) {
+      out_mean_iou_t.device(place) +=
+          EigenTensor<float, 1>::From(*in_mean_ious[i]);
+    }
+    auto in_wrongs = ctx.MultiInput<Tensor>("in_wrongs");
+    for (int i = 0; i < in_wrongs.size(); ++i) {
+      out_wrong_t.device(place) += EigenTensor<int, 1>::From(*in_wrongs[i]);
+    }
+    auto in_corrects = ctx.MultiInput<Tensor>("in_corrects");
+    for (int i = 0; i < in_corrects.size(); ++i) {
+      out_correct_t.device(place) += EigenTensor<int, 1>::From(*in_corrects[i]);
+    }
+    // compute
+    auto stream = ctx.cuda_device_context().stream();
+    int block = PADDLE_CUDA_NUM_THREADS;
+    int grid = (predictions->numel() + block - 1) / block;
+    int cache_size = (num_classes * 2 + 1) * sizeof(int);
+    CountCUDAKernel<T><<<grid, block, cache_size, stream>>>(
+        num_classes, predictions->numel(), predictions_data, labels_data,
+        out_wrong_data, out_correct_data);
+    ctx.device_context().Wait();
+    grid = (num_classes + block - 1) / block;
+    ComputeIoUCUDAKernel<<<grid, block, 0, stream>>>(
+        num_classes, out_wrong_data, out_correct_data, ious_data,
+        valid_count_data);
+    ctx.device_context().Wait();
+    out_mean_iou_t.device(place) += ious_t.sum() / valid_count_t.cast<float>();
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(mean_iou, ops::MeanIoUCUDAOpKernel<int>,
+                        ops::MeanIoUKernel<int64_t>);

paddle/fluid/operators/mean_iou_op.h

@@ -0,0 +1,117 @@
+/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+
+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/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+using Tensor = framework::Tensor;
+
+template <typename T, int D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+
+template <typename T>
+class MeanIoUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& place = *ctx.template device_context<platform::CPUDeviceContext>()
+                       .eigen_device();
+    // get input and output tensor
+    auto* predictions = ctx.Input<Tensor>("predictions");
+    auto* labels = ctx.Input<Tensor>("labels");
+    auto* out_mean_iou = ctx.Output<Tensor>("out_mean_iou");
+    auto* out_wrong = ctx.Output<Tensor>("out_wrong");
+    auto* out_correct = ctx.Output<Tensor>("out_correct");
+    int num_classes = static_cast<int>(ctx.Attr<int>("num_classes"));
+
+    // get data ptr
+    const T* predictions_data = predictions->data<T>();
+    const T* labels_data = labels->data<T>();
+    float* out_mean_iou_data =
+        out_mean_iou->mutable_data<float>(ctx.GetPlace());
+    int* out_wrong_data = out_wrong->mutable_data<int>(ctx.GetPlace());
+    int* out_correct_data = out_correct->mutable_data<int>(ctx.GetPlace());
+
+    // get eigen tensor
+    auto out_mean_iou_t = EigenTensor<float, 1>::From(*out_mean_iou);
+    auto out_wrong_t = EigenTensor<int, 1>::From(*out_wrong);
+    auto out_correct_t = EigenTensor<int, 1>::From(*out_correct);
+
+    // Tmp tensor
+    Tensor denominator;
+    Tensor valid_count;
+    Tensor iou_sum;
+
+    // get data ptr of tmp tensor
+    int* denominator_data = denominator.mutable_data<int>(
+        {static_cast<int64_t>(num_classes)}, ctx.GetPlace());
+    int* valid_count_data = valid_count.mutable_data<int>({1}, ctx.GetPlace());
+    float* iou_sum_data = iou_sum.mutable_data<float>({1}, ctx.GetPlace());
+
+    // get eigen tensor of tmp tensor
+    auto denominator_t = EigenTensor<int, 1>::From(denominator);
+    auto valid_count_t = EigenTensor<int, 1>::From(valid_count);
+    auto iou_sum_t = EigenTensor<float, 1>::From(iou_sum);
+
+    // init out_wrong, out_correct and out_mean_iou
+    out_wrong_t = out_wrong_t.constant(0);
+    out_correct_t = out_correct_t.constant(0);
+    out_mean_iou_t = out_mean_iou_t.constant(0);
+
+    // collect pre wrong, correct and mean_iou
+    auto in_mean_ious = ctx.MultiInput<Tensor>("in_mean_iou");
+    for (int i = 0; i < in_mean_ious.size(); ++i) {
+      out_mean_iou_t.device(place) +=
+          EigenTensor<float, 1>::From(*in_mean_ious[i]);
+    }
+    auto in_wrongs = ctx.MultiInput<Tensor>("in_wrongs");
+    for (int i = 0; i < in_wrongs.size(); ++i) {
+      out_wrong_t.device(place) += EigenTensor<int, 1>::From(*in_wrongs[i]);
+    }
+    auto in_corrects = ctx.MultiInput<Tensor>("in_corrects");
+    for (int i = 0; i < in_corrects.size(); ++i) {
+      out_correct_t.device(place) += EigenTensor<int, 1>::From(*in_corrects[i]);
+    }
+
+    // compute
+    for (int i = 0; i < predictions->numel(); ++i) {
+      if (predictions_data[i] == labels_data[i]) {
+        out_correct_data[predictions_data[i]] += 1;
+      } else {
+        out_wrong_data[labels_data[i]] += 1;
+        out_wrong_data[predictions_data[i]] += 1;
+      }
+    }
+
+    denominator_t = out_wrong_t + out_correct_t;
+    valid_count_t =
+        (denominator_t > denominator_t.constant(0.0f)).cast<int>().sum();
+
+    for (int i = 0; i < num_classes; ++i) {
+      if (denominator_data[i] == 0) {
+        denominator_data[i] = 1;
+      }
+    }
+
+    iou_sum_t =
+        (out_correct_t.cast<float>() / denominator_t.cast<float>()).sum();
+    out_mean_iou_data[0] += (iou_sum_data[0] / valid_count_data[0]);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle