Modify RoI pooling op to use LoDTensor and expose it into Python API (#10208)

* modify roi pool with lod and expose ROI Pooling into Python API

* make lod code brief

* make doc more clearly

* make doc more clearly

doc/fluid/api/layers.rst

@@ -479,6 +479,13 @@ label_smooth
 ..  autofunction:: paddle.fluid.layers.label_smooth
     :noindex:
 
+roi_pool
+---------
+
+..  autofunction:: paddle.fluid.layers.roi_pool
+    :noindex:
+
+    
 ops
 ===
 
@@ -820,3 +827,5 @@ topk
 
 ..  autofunction:: paddle.fluid.layers.topk
     :noindex:
+
+

paddle/fluid/operators/roi_pool_op.cc

@@ -18,8 +18,7 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
-
-static constexpr int kROISize = 5;
+using LoDTensor = framework::LoDTensor;
 
 class ROIPoolOp : public framework::OperatorWithKernel {
  public:
@@ -40,11 +39,11 @@ class ROIPoolOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(input_dims.size() == 4,
                    "The format of input tensor is NCHW.");
     PADDLE_ENFORCE(rois_dims.size() == 2,
-                   "ROIs should be a 2-D tensor of shape (num_rois, 5)"
-                   "given as [[batch_id, x1, y1, x2, y2], …].");
+                   "ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
+                   "given as [[x1, y1, x2, y2], …].");
     PADDLE_ENFORCE(rois_dims[1] == kROISize,
-                   "ROIs should be a 2-D tensor of shape (num_rois, 5)"
-                   "given as [[batch_id, x1, y1, x2, y2], …].");
+                   "ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
+                   "given as [[x1, y1, x2, y2], …].");
 
     int pooled_height = ctx->Attrs().Get<int>("pooled_height");
     int pooled_width = ctx->Attrs().Get<int>("pooled_width");
@@ -109,10 +108,10 @@ class ROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
              "H is the height of the feature, and "
              "W is the width of the feature.");
     AddInput("ROIs",
-             "(Tensor), "
+             "(LoDTensor), "
              "ROIs (Regions of Interest) to pool over. "
-             "should be a 2-D tensor of shape (num_rois, 5)"
-             "given as [[batch_id, x1, y1, x2, y2], …]. "
+             "should be a 2-D LoDTensor of shape (num_rois, 4)"
+             "given as [[x1, y1, x2, y2], …]. "
              "Where batch_id is the id of the data, "
              "(x1, y1) is the top left coordinates, and "
              "(x2, y2) is the bottom right coordinates.");

paddle/fluid/operators/roi_pool_op.cu

@@ -19,10 +19,10 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
 
 static constexpr int kNumCUDAThreads = 512;
 static constexpr int kNumMaxinumNumBlocks = 4096;
-static constexpr int kROISize = 5;
 
 static inline int NumBlocks(const int N) {
   return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads,
@@ -30,13 +30,11 @@ static inline int NumBlocks(const int N) {
 }
 
 template <typename T>
-__global__ void GPUROIPoolForward(const int nthreads, const T* input_data,
-                                  const int64_t* input_rois,
-                                  const float spatial_scale, const int channels,
-                                  const int height, const int width,
-                                  const int pooled_height,
-                                  const int pooled_width, T* output_data,
-                                  int64_t* argmax_data) {
+__global__ void GPUROIPoolForward(
+    const int nthreads, const T* input_data, const int64_t* input_rois,
+    const float spatial_scale, const int channels, const int height,
+    const int width, const int pooled_height, const int pooled_width,
+    int* roi_batch_id_data, T* output_data, int64_t* argmax_data) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int offset = blockDim.x * gridDim.x;
   for (size_t i = index; i < nthreads; i += offset) {
@@ -46,11 +44,11 @@ __global__ void GPUROIPoolForward(const int nthreads, const T* input_data,
     int n = index / pooled_width / pooled_height / channels;
 
     const int64_t* offset_input_rois = input_rois + n * kROISize;
-    int roi_batch_ind = offset_input_rois[0];
-    int roi_start_w = round(offset_input_rois[1] * spatial_scale);
-    int roi_start_h = round(offset_input_rois[2] * spatial_scale);
-    int roi_end_w = round(offset_input_rois[3] * spatial_scale);
-    int roi_end_h = round(offset_input_rois[4] * spatial_scale);
+    int roi_batch_ind = roi_batch_id_data[n];
+    int roi_start_w = round(offset_input_rois[0] * spatial_scale);
+    int roi_start_h = round(offset_input_rois[1] * spatial_scale);
+    int roi_end_w = round(offset_input_rois[2] * spatial_scale);
+    int roi_end_h = round(offset_input_rois[3] * spatial_scale);
 
     int roi_width = max(roi_end_w - roi_start_w + 1, 1);
     int roi_height = max(roi_end_h - roi_start_h + 1, 1);
@@ -93,7 +91,8 @@ __global__ void GPUROIPoolBackward(
     const int nthreads, const int64_t* input_rois, const T* output_grad,
     const int64_t* argmax_data, const int num_rois, const float spatial_scale,
     const int channels, const int height, const int width,
-    const int pooled_height, const int pooled_width, T* input_grad) {
+    const int pooled_height, const int pooled_width, int* roi_batch_id_data,
+    T* input_grad) {
   int index = blockIdx.x * blockDim.x + threadIdx.x;
   int offset = blockDim.x * gridDim.x;
   for (int i = index; i < nthreads; i += offset) {
@@ -102,8 +101,7 @@ __global__ void GPUROIPoolBackward(
     int c = (index / pooled_width / pooled_height) % channels;
     int n = index / pooled_width / pooled_height / channels;
 
-    const int64_t* offset_input_rois = input_rois + n * kROISize;
-    int roi_batch_ind = offset_input_rois[0];
+    int roi_batch_ind = roi_batch_id_data[n];
     int input_offset = (roi_batch_ind * channels + c) * height * width;
     int output_offset = (n * channels + c) * pooled_height * pooled_width;
     const T* offset_output_grad = output_grad + output_offset;
@@ -124,7 +122,7 @@ class GPUROIPoolOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in = ctx.Input<Tensor>("X");
-    auto* rois = ctx.Input<Tensor>("ROIs");
+    auto* rois = ctx.Input<LoDTensor>("ROIs");
     auto* out = ctx.Output<Tensor>("Out");
     auto* argmax = ctx.Output<Tensor>("Argmax");
 
@@ -133,23 +131,46 @@ class GPUROIPoolOpKernel : public framework::OpKernel<T> {
     auto spatial_scale = ctx.Attr<float>("spatial_scale");
 
     auto in_dims = in->dims();
+    int batch_size = in_dims[0];
     auto in_stride = framework::stride(in_dims);
     int channels = in_dims[1];
     int height = in_dims[2];
     int width = in_dims[3];
 
-    size_t rois_num = rois->dims()[0];
+    int rois_num = rois->dims()[0];
     if (rois_num == 0) return;
 
     int output_size = out->numel();
     int blocks = NumBlocks(output_size);
     int threads = kNumCUDAThreads;
 
+    framework::Tensor roi_batch_id_list;
+    roi_batch_id_list.Resize({rois_num});
+    int* roi_batch_id_data =
+        roi_batch_id_list.mutable_data<int>(platform::CPUPlace());
+    auto rois_lod = rois->lod().back();
+    int rois_batch_size = rois_lod.size() - 1;
+    PADDLE_ENFORCE_EQ(
+        rois_batch_size, batch_size,
+        "The rois_batch_size and imgs batch_size must be the same.");
+    int rois_num_with_lod = rois_lod[rois_batch_size];
+    PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
+                      "The rois_num from input and lod must be the same.");
+    for (int n = 0; n < rois_batch_size; ++n) {
+      for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
+        roi_batch_id_data[i] = n;
+      }
+    }
+
+    framework::Tensor roi_batch_id_list_gpu;
+    framework::TensorCopy(roi_batch_id_list, ctx.GetPlace(),
+                          ctx.device_context(), &roi_batch_id_list_gpu);
+
     GPUROIPoolForward<
         T><<<blocks, threads, 0, ctx.cuda_device_context().stream()>>>(
         output_size, in->data<T>(), rois->data<int64_t>(), spatial_scale,
         channels, height, width, pooled_height, pooled_width,
-        out->mutable_data<T>(ctx.GetPlace()),
+        roi_batch_id_list_gpu.data<int>(), out->mutable_data<T>(ctx.GetPlace()),
         argmax->mutable_data<int64_t>(ctx.GetPlace()));
   }
 };
@@ -159,7 +180,7 @@ class GPUROIPoolGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in = ctx.Input<Tensor>("X");
-    auto* rois = ctx.Input<Tensor>("ROIs");
+    auto* rois = ctx.Input<LoDTensor>("ROIs");
     auto* argmax = ctx.Input<Tensor>("Argmax");
 
     auto* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
@@ -169,12 +190,27 @@ class GPUROIPoolGradOpKernel : public framework::OpKernel<T> {
     auto pooled_width = ctx.Attr<int>("pooled_width");
     auto spatial_scale = ctx.Attr<float>("spatial_scale");
 
-    size_t rois_num = rois->dims()[0];
+    int rois_num = rois->dims()[0];
     int channels = in->dims()[1];
     int height = in->dims()[2];
     int width = in->dims()[3];
 
     if (x_grad) {
+      framework::Tensor roi_batch_id_list;
+      roi_batch_id_list.Resize({rois_num});
+      int* roi_batch_id_data =
+          roi_batch_id_list.mutable_data<int>(platform::CPUPlace());
+      auto rois_lod = rois->lod().back();
+      int rois_batch_size = rois_lod.size() - 1;
+      for (int n = 0; n < rois_batch_size; ++n) {
+        for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
+          roi_batch_id_data[i] = n;
+        }
+      }
+      framework::Tensor roi_batch_id_list_gpu;
+      framework::TensorCopy(roi_batch_id_list, ctx.GetPlace(),
+                            ctx.device_context(), &roi_batch_id_list_gpu);
+
       x_grad->mutable_data<T>(ctx.GetPlace());
       math::SetConstant<Place, T> set_zero;
       set_zero(ctx.cuda_device_context(), x_grad, static_cast<T>(0));
@@ -189,6 +225,7 @@ class GPUROIPoolGradOpKernel : public framework::OpKernel<T> {
             output_grad_size, rois->data<int64_t>(), out_grad->data<T>(),
             argmax->data<int64_t>(), rois_num, spatial_scale, channels, height,
             width, pooled_height, pooled_width,
+            roi_batch_id_list_gpu.data<int>(),
             x_grad->mutable_data<T>(ctx.GetPlace()));
       }
     }

paddle/fluid/operators/roi_pool_op.h

@@ -21,12 +21,14 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
+static constexpr int kROISize = 4;
+
 template <typename DeviceContext, typename T>
 class CPUROIPoolOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in = ctx.Input<framework::Tensor>("X");
-    auto* rois = ctx.Input<framework::Tensor>("ROIs");
+    auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
     auto* out = ctx.Output<framework::Tensor>("Out");
     auto* argmax = ctx.Output<framework::Tensor>("Argmax");
 
@@ -47,24 +49,36 @@ class CPUROIPoolOpKernel : public framework::OpKernel<T> {
     auto out_stride = framework::stride(out->dims());
 
     const T* input_data = in->data<T>();
-    const int64_t* rois_data = rois->data<int64_t>();
-    T* output_data = out->mutable_data<T>(ctx.GetPlace());
-    int64_t* argmax_data = argmax->mutable_data<int64_t>(ctx.GetPlace());
 
-    for (int n = 0; n < rois_num; ++n) {
-      int roi_batch_id = rois_data[0];
-      PADDLE_ENFORCE_GE(roi_batch_id, 0);
-      PADDLE_ENFORCE_LT(roi_batch_id, batch_size);
-      rois_data += roi_stride[0];
+    framework::Tensor roi_batch_id_list;
+    roi_batch_id_list.Resize({rois_num});
+    int* roi_batch_id_data =
+        roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
+
+    auto rois_lod = rois->lod().back();
+    int rois_batch_size = rois_lod.size() - 1;
+    PADDLE_ENFORCE_EQ(
+        rois_batch_size, batch_size,
+        "The rois_batch_size and imgs batch_size must be the same.");
+    int rois_num_with_lod = rois_lod[rois_batch_size];
+    PADDLE_ENFORCE_EQ(rois_num, rois_num_with_lod,
+                      "The rois_num from input and lod must be the same.");
+    for (int n = 0; n < rois_batch_size; ++n) {
+      for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
+        roi_batch_id_data[i] = n;
+      }
     }
 
-    rois_data = rois->data<int64_t>();
+    T* output_data = out->mutable_data<T>(ctx.GetPlace());
+    int64_t* argmax_data = argmax->mutable_data<int64_t>(ctx.GetPlace());
+
+    const int64_t* rois_data = rois->data<int64_t>();
     for (int n = 0; n < rois_num; ++n) {
-      int roi_batch_id = rois_data[0];
-      int roi_start_w = round(rois_data[1] * spatial_scale);
-      int roi_start_h = round(rois_data[2] * spatial_scale);
-      int roi_end_w = round(rois_data[3] * spatial_scale);
-      int roi_end_h = round(rois_data[4] * spatial_scale);
+      int roi_batch_id = roi_batch_id_data[n];
+      int roi_start_w = round(rois_data[0] * spatial_scale);
+      int roi_start_h = round(rois_data[1] * spatial_scale);
+      int roi_end_w = round(rois_data[2] * spatial_scale);
+      int roi_end_h = round(rois_data[3] * spatial_scale);
 
       // Force malformed ROIs to be 1x1
       int roi_height = std::max(roi_end_h - roi_start_h + 1, 1);
@@ -133,7 +147,7 @@ class CPUROIPoolGradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in = ctx.Input<framework::Tensor>("X");
-    auto* rois = ctx.Input<framework::Tensor>("ROIs");
+    auto* rois = ctx.Input<framework::LoDTensor>("ROIs");
     auto* argmax = ctx.Input<framework::Tensor>("Argmax");
     auto* out_grad =
         ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
@@ -143,6 +157,20 @@ class CPUROIPoolGradOpKernel : public framework::OpKernel<T> {
     auto pooled_width = ctx.Attr<int>("pooled_width");
 
     if (in_grad) {
+      int rois_num = rois->dims()[0];
+      framework::Tensor roi_batch_id_list;
+      roi_batch_id_list.Resize({rois_num});
+      int* roi_batch_id_data =
+          roi_batch_id_list.mutable_data<int>(ctx.GetPlace());
+
+      auto rois_lod = rois->lod().back();
+      int rois_batch_size = rois_lod.size() - 1;
+      for (int n = 0; n < rois_batch_size; ++n) {
+        for (size_t i = rois_lod[n]; i < rois_lod[n + 1]; ++i) {
+          roi_batch_id_data[i] = n;
+        }
+      }
+
       const int64_t* rois_data = rois->data<int64_t>();
       const T* out_grad_data = out_grad->data<T>();
       const int64_t* argmax_data = argmax->data<int64_t>();
@@ -156,11 +184,10 @@ class CPUROIPoolGradOpKernel : public framework::OpKernel<T> {
       auto roi_stride = framework::stride(rois->dims());
       auto out_stride = framework::stride(out_grad->dims());
 
-      int rois_num = rois->dims()[0];
       int channels = in->dims()[1];
 
       for (int n = 0; n < rois_num; ++n) {
-        int roi_batch_idx = rois_data[0];
+        int roi_batch_idx = roi_batch_id_data[n];
         T* batch_grad_data = in_grad_data + roi_batch_idx * in_stride[0];
         for (int c = 0; c < channels; ++c) {
           for (int ph = 0; ph < pooled_height; ++ph) {

python/paddle/fluid/layers/nn.py

@@ -79,6 +79,7 @@ __all__ = [
     'lrn',
     'pad',
     'label_smooth',
+    'roi_pool',
 ]
 
 
@@ -3759,3 +3760,53 @@ def label_smooth(label,
         outputs={"Out": smooth_label},
         attrs={"epsilon": float(epsilon)})
     return smooth_label
+
+
+def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
+    """
+    Region of interest pooling (also known as RoI pooling) is to perform 
+        is to perform max pooling on inputs of nonuniform sizes to obtain
+        fixed-size feature maps (e.g. 7*7).
+    The operator has three steps: 
+        1. Dividing each region proposal into equal-sized sections with 
+           the pooled_width and pooled_height 
+        2. Finding the largest value in each section 
+        3. Copying these max values to the output buffer
+
+    Args:
+        input (Variable): The input for ROI pooling.
+        rois (Variable): ROIs (Regions of Interest) to pool over. It should
+                         be a 2-D one level LoTensor of shape [num_rois, 4].
+                         The layout is [x1, y1, x2, y2], where (x1, y1)
+                         is the top left coordinates, and (x2, y2) is the 
+                         bottom right coordinates. The num_rois is the 
+                         total number of ROIs in this batch data.
+        pooled_height (integer): The pooled output height. Default: 1
+        pooled_width (integer): The pooled output width. Default: 1
+        spatial_scale (float): Multiplicative spatial scale factor. To
+                               translate ROI coords from their input scale
+                               to the scale used when pooling. Default: 1.0
+
+    Returns:
+        pool_out (Variable): The output is a 4-D tensor of the shape 
+                             (num_rois, channels, pooled_h, pooled_w).
+
+    Examples:
+            pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0) 
+    """
+    helper = LayerHelper('roi_pool', **locals())
+    dtype = helper.input_dtype()
+    pool_out = helper.create_tmp_variable(dtype)
+    argmaxes = helper.create_tmp_variable(dtype='int32')
+    helper.append_op(
+        type="roi_pool",
+        inputs={"X": input,
+                "ROIs": rois},
+        outputs={"Out": pool_out,
+                 "Argmax": argmaxes},
+        attrs={
+            "pooled_height": pooled_height,
+            "pooled_width": pooled_width,
+            "spatial_scale": spatial_scale
+        })
+    return pool_out

python/paddle/fluid/tests/unittests/test_layers.py

@@ -359,6 +359,16 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(indices)
         print(str(program))
 
+    def test_roi_pool(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
+            rois = layers.data(
+                name="rois", shape=[4], dtype="float32", lod_level=1)
+            output = layers.roi_pool(x, rois, 7, 7, 0.6)
+            self.assertIsNotNone(output)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_roi_pool_op.py

@@ -25,7 +25,7 @@ class TestROIPoolOp(OpTest):
         self.make_rois()
         self.calc_roi_pool()
 
-        self.inputs = {'X': self.x, 'ROIs': self.rois}
+        self.inputs = {'X': self.x, 'ROIs': (self.rois[:, 1:5], self.rois_lod)}
 
         self.attrs = {
             'spatial_scale': self.spatial_scale,
@@ -36,7 +36,7 @@ class TestROIPoolOp(OpTest):
         self.outputs = {'Out': self.outs, 'Argmax': self.argmaxes}
 
     def init_test_case(self):
-        self.batch_size = 5
+        self.batch_size = 3
         self.channels = 3
         self.height = 6
         self.width = 4
@@ -47,7 +47,6 @@ class TestROIPoolOp(OpTest):
         self.spatial_scale = 1.0 / 4.0
         self.pooled_height = 2
         self.pooled_width = 2
-        self.rois_num = 2
 
         self.x = np.random.random(self.x_dim).astype('float32')
 
@@ -106,20 +105,24 @@ class TestROIPoolOp(OpTest):
 
     def make_rois(self):
         rois = []
-        batch_ids = np.random.randint(0, self.batch_size, size=self.rois_num)
-        for i in range(self.rois_num):
-            x1 = np.random.random_integers(
-                0, self.width / self.spatial_scale - self.pooled_width)
-            y1 = np.random.random_integers(
-                0, self.height / self.spatial_scale - self.pooled_height)
-
-            x2 = np.random.random_integers(x1 + self.pooled_width,
-                                           self.width / self.spatial_scale)
-            y2 = np.random.random_integers(y1 + self.pooled_height,
-                                           self.height / self.spatial_scale)
-
-            roi = [batch_ids[i], x1, y1, x2, y2]
-            rois.append(roi)
+        self.rois_lod = [[]]
+        for bno in range(self.batch_size):
+            self.rois_lod[0].append(len(rois))
+            for i in range(bno + 1):
+                x1 = np.random.random_integers(
+                    0, self.width / self.spatial_scale - self.pooled_width)
+                y1 = np.random.random_integers(
+                    0, self.height / self.spatial_scale - self.pooled_height)
+
+                x2 = np.random.random_integers(x1 + self.pooled_width,
+                                               self.width / self.spatial_scale)
+                y2 = np.random.random_integers(y1 + self.pooled_height,
+                                               self.height / self.spatial_scale)
+
+                roi = [bno, x1, y1, x2, y2]
+                rois.append(roi)
+        self.rois_lod[0].append(len(rois))
+        self.rois_num = len(rois)
         self.rois = np.array(rois).astype("int64")
 
     def setUp(self):