Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into quantize_transpiler_update

paddle/fluid/API.spec

@@ -73,7 +73,6 @@ paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program',
 paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
 paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.io.get_inference_program ArgSpec(args=['target_vars', 'main_program'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False))
 paddle.fluid.initializer.UniformInitializer.__init__ ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0))
 paddle.fluid.initializer.NormalInitializer.__init__ ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0))
@@ -296,6 +295,7 @@ paddle.fluid.layers.ssd_loss ArgSpec(args=['location', 'confidence', 'gt_box', '
 paddle.fluid.layers.detection_map ArgSpec(args=['detect_res', 'label', 'class_num', 'background_label', 'overlap_threshold', 'evaluate_difficult', 'has_state', 'input_states', 'out_states', 'ap_version'], varargs=None, keywords=None, defaults=(0, 0.3, True, None, None, None, 'integral'))
 paddle.fluid.layers.rpn_target_assign ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True))
 paddle.fluid.layers.anchor_generator ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None))
+paddle.fluid.layers.roi_perspective_transform ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,))
 paddle.fluid.layers.generate_proposal_labels ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True))
 paddle.fluid.layers.generate_proposals ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None))
 paddle.fluid.layers.iou_similarity ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)

paddle/fluid/operators/detection/CMakeLists.txt

@@ -31,5 +31,6 @@ polygon_box_transform_op.cu)
 detection_library(rpn_target_assign_op SRCS rpn_target_assign_op.cc)
 detection_library(generate_proposal_labels_op SRCS generate_proposal_labels_op.cc)
 detection_library(generate_proposals_op SRCS generate_proposals_op.cc)
+detection_library(roi_perspective_transform_op SRCS roi_perspective_transform_op.cc roi_perspective_transform_op.cu)
 #Export local libraries to parent
 set(DETECTION_LIBRARY ${LOCAL_DETECTION_LIBS} PARENT_SCOPE)

paddle/fluid/operators/detection/roi_perspective_transform_op.cc

+/* 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 <algorithm>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/math_function.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using LoDTensor = framework::LoDTensor;
+
+static constexpr int kROISize = 4;
+
+template <typename T>
+bool GT_E(T a, T b) {
+  return (a > b) || fabs(a - b) < 1e-4;
+}
+
+template <typename T>
+bool LT_E(T a, T b) {
+  return (a < b) || fabs(a - b) < 1e-4;
+}
+
+template <typename T>
+bool GT(T a, T b) {
+  return (a - b) > 1e-4;
+}
+
+/*
+*check if (x, y) is in the boundary of roi
+*/
+template <typename T>
+bool in_quad(T x, T y, T roi_x[], T roi_y[]) {
+  for (int i = 0; i < 4; i++) {
+    T xs = roi_x[i];
+    T ys = roi_y[i];
+    T xe = roi_x[(i + 1) % 4];
+    T ye = roi_y[(i + 1) % 4];
+    if (fabs(ys - ye) < 1e-4) {
+      if (fabs(y - ys) < 1e-4 && fabs(y - ye) < 1e-4 &&
+          GT_E<T>(x, std::min(xs, xe)) && LT_E<T>(x, std::max(xs, xe))) {
+        return true;
+      }
+    } else {
+      T intersec_x = (y - ys) * (xe - xs) / (ye - ys) + xs;
+      if (fabs(intersec_x - x) < 1e-4 && GT_E<T>(y, std::min(ys, ye)) &&
+          LT_E<T>(y, std::max(ys, ye))) {
+        return true;
+      }
+    }
+  }
+
+  int n_cross = 0;
+  for (int i = 0; i < 4; i++) {
+    T xs = roi_x[i];
+    T ys = roi_y[i];
+    T xe = roi_x[(i + 1) % 4];
+    T ye = roi_y[(i + 1) % 4];
+    if (fabs(ys - ye) < 1e-4) {
+      continue;
+    }
+    if (LT_E<T>(y, std::min(ys, ye)) || GT<T>(y, std::max(ys, ye))) {
+      continue;
+    }
+    T intersec_x = (y - ys) * (xe - xs) / (ye - ys) + xs;
+    if (fabs(intersec_x - x) < 1e-4) {
+      return true;
+    }
+    if (GT<T>(intersec_x, x)) {
+      n_cross++;
+    }
+  }
+  return (n_cross % 2 == 1);
+}
+
+/**
+ * Get the matrix of perspective transform.
+ *
+ * dx1 = x1 - x2
+ * dx2 = x3 - x2
+ * dx3 = x0 - x1 + x2 - x3
+ * dy1 = y1 - y2
+ * dy2 = y3 - y2
+ * dy3 = y0 - y1 + y2 - y3
+ *
+ * a11 = (x1 - x0 + a31 * (w - 1) * x1) / (w - 1)
+ * a12 = (x3 - x0 + a32 * (h - 1) * x3) / (h - 1)
+ * a13 = x0
+ * a21 = (y1 - y0 + a31 * (w - 1) * y1) / (w - 1)
+ * a22 = (y3 - y0 + a32 * (h - 1) * y3) / (h - 1)
+ * a23 = y0
+ * a31 = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) / (w - 1)
+ * a32 = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) / (h - 1)
+ * a33 = 1
+ *
+ */
+template <typename T>
+void get_transform_matrix(const int transformed_width,
+                          const int transformed_height, T roi_x[], T roi_y[],
+                          T matrix[]) {
+  T x0 = roi_x[0];
+  T x1 = roi_x[1];
+  T x2 = roi_x[2];
+  T x3 = roi_x[3];
+  T y0 = roi_y[0];
+  T y1 = roi_y[1];
+  T y2 = roi_y[2];
+  T y3 = roi_y[3];
+
+  // Estimate the height and width of RoI
+  T len1 = sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1));
+  T len2 = sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2));
+  T len3 = sqrt((x2 - x3) * (x2 - x3) + (y2 - y3) * (y2 - y3));
+  T len4 = sqrt((x3 - x0) * (x3 - x0) + (y3 - y0) * (y3 - y0));
+  T estimated_height = (len2 + len4) / 2.0;
+  T estimated_width = (len1 + len3) / 2.0;
+
+  // Get the normalized height and normalized width
+  int normalized_height = transformed_height;
+  int normalized_width =
+      std::round(estimated_width * (normalized_height - 1) / estimated_height) +
+      1;
+  normalized_width = std::min(normalized_width, transformed_width);
+
+  T dx1 = x1 - x2;
+  T dx2 = x3 - x2;
+  T dx3 = x0 - x1 + x2 - x3;
+  T dy1 = y1 - y2;
+  T dy2 = y3 - y2;
+  T dy3 = y0 - y1 + y2 - y3;
+
+  matrix[6] = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) /
+              (normalized_width - 1);
+  matrix[7] = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) /
+              (normalized_height - 1);
+  matrix[8] = 1;
+
+  matrix[3] = (y1 - y0 + matrix[6] * (normalized_width - 1) * y1) /
+              (normalized_width - 1);
+  matrix[4] = (y3 - y0 + matrix[7] * (normalized_height - 1) * y3) /
+              (normalized_height - 1);
+  matrix[5] = y0;
+
+  matrix[0] = (x1 - x0 + matrix[6] * (normalized_width - 1) * x1) /
+              (normalized_width - 1);
+  matrix[1] = (x3 - x0 + matrix[7] * (normalized_height - 1) * x3) /
+              (normalized_height - 1);
+  matrix[2] = x0;
+}
+
+/**
+ * Get the source coordinates in the input feature map.
+ *
+ * (u, v, w)^matrix = matrix * (out_w, out_h, 1)^matrix
+ *
+ * in_w = u / w
+ * in_h = v / w
+ *
+ */
+template <typename T>
+void get_source_coords(T matrix[], int out_w, int out_h, T* in_w, T* in_h) {
+  T u = matrix[0] * out_w + matrix[1] * out_h + matrix[2];
+  T v = matrix[3] * out_w + matrix[4] * out_h + matrix[5];
+  T w = matrix[6] * out_w + matrix[7] * out_h + matrix[8];
+
+  in_w[0] = u / w;
+  in_h[0] = v / w;
+}
+
+/**
+ * Perform bilinear interpolation in the input feature map.
+ */
+template <typename T>
+void bilinear_interpolate(const T* in_data, const int channels, const int width,
+                          const int height, int in_n, int in_c, T in_w, T in_h,
+                          T* val) {
+  // Deal with cases that source coords are out of feature map boundary
+  if (GT<T>(-0.5, in_w) || GT<T>(in_w, width - 0.5) || GT<T>(-0.5, in_h) ||
+      GT<T>(in_h, height - 0.5)) {
+    // empty
+    val[0] = 0.0;
+    return;
+  }
+
+  if (GT<T>(0, in_w)) {
+    in_w = 0;
+  }
+  if (GT<T>(0, in_h)) {
+    in_h = 0;
+  }
+
+  int in_w_floor = floor(in_w);
+  int in_h_floor = floor(in_h);
+  int in_w_ceil;
+  int in_h_ceil;
+
+  if (GT_E<T>(in_w_floor, width - 1)) {
+    in_w_ceil = in_w_floor = width - 1;
+    in_w = static_cast<T>(in_w_floor);
+  } else {
+    in_w_ceil = in_w_floor + 1;
+  }
+
+  if (GT_E<T>(in_h_floor, height - 1)) {
+    in_h_ceil = in_h_floor = height - 1;
+    in_h = static_cast<T>(in_h_floor);
+  } else {
+    in_h_ceil = in_h_floor + 1;
+  }
+  T w_floor = in_w - in_w_floor;
+  T h_floor = in_h - in_h_floor;
+  T w_ceil = 1 - w_floor;
+  T h_ceil = 1 - h_floor;
+  const T* data = in_data + (in_n * channels + in_c) * height * width;
+  // Do bilinear interpolation
+  T v1 = data[in_h_floor * width + in_w_floor];
+  T v2 = data[in_h_ceil * width + in_w_floor];
+  T v3 = data[in_h_ceil * width + in_w_ceil];
+  T v4 = data[in_h_floor * width + in_w_ceil];
+  T w1 = w_ceil * h_ceil;
+  T w2 = w_ceil * h_floor;
+  T w3 = w_floor * h_floor;
+  T w4 = w_floor * h_ceil;
+  val[0] = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4;
+}
+
+template <typename T>
+class CPUROIPerspectiveTransformOpKernel : 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::LoDTensor>("ROIs");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+
+    auto transformed_height = ctx.Attr<int>("transformed_height");
+    auto transformed_width = ctx.Attr<int>("transformed_width");
+    auto spatial_scale = ctx.Attr<float>("spatial_scale");
+
+    auto in_dims = in->dims();
+    int channels = in_dims[1];
+    int in_height = in_dims[2];
+    int in_width = in_dims[3];
+    int rois_num = rois->dims()[0];
+
+    const T* input_data = in->data<T>();
+
+    framework::Tensor roi2image;
+    roi2image.Resize({rois_num});
+    int* roi2image_data = roi2image.mutable_data<int>(ctx.GetPlace());
+    auto lod = rois->lod().back();
+    for (int i = 0; i < lod.size() - 1; ++i) {
+      for (int j = lod[i]; j < lod[i + 1]; ++j) {
+        roi2image_data[j] = i;
+      }
+    }
+
+    T* output_data = out->mutable_data<T>(ctx.GetPlace());
+    const T* rois_data = rois->data<T>();
+
+    for (int n = 0; n < rois_num; ++n) {
+      const T* n_rois = rois_data + n * 8;
+      T roi_x[4];
+      T roi_y[4];
+      for (int k = 0; k < 4; ++k) {
+        roi_x[k] = n_rois[2 * k] * spatial_scale;
+        roi_y[k] = n_rois[2 * k + 1] * spatial_scale;
+      }
+      int image_id = roi2image_data[n];
+      // Get transform matrix
+      T transform_matrix[9];
+      get_transform_matrix<T>(transformed_width, transformed_height, roi_x,
+                              roi_y, transform_matrix);
+
+      for (int c = 0; c < channels; ++c) {
+        for (int out_h = 0; out_h < transformed_height; ++out_h) {
+          for (int out_w = 0; out_w < transformed_width; ++out_w) {
+            int out_index =
+                n * channels * transformed_height * transformed_width +
+                c * transformed_height * transformed_width +
+                out_h * transformed_width + out_w;
+            T in_w, in_h;
+            get_source_coords<T>(transform_matrix, out_w, out_h, &in_w, &in_h);
+            if (in_quad<T>(in_w, in_h, roi_x, roi_y)) {
+              if (GT<T>(-0.5, in_w) ||
+                  GT<T>(in_w, static_cast<T>(in_width - 0.5)) ||
+                  GT<T>(-0.5, in_h) ||
+                  GT<T>(in_h, static_cast<T>(in_height - 0.5))) {
+                output_data[out_index] = 0.0;
+              } else {
+                bilinear_interpolate(input_data, channels, in_width, in_height,
+                                     image_id, c, in_w, in_h,
+                                     output_data + out_index);
+              }
+            } else {
+              output_data[out_index] = 0.0;
+            }
+          }
+        }
+      }
+    }
+  }
+};
+
+template <typename T>
+T get_feature_gradient(T xs, T ys, int w, int h, const int width,
+                       const int height) {
+  if (GT<T>(-0.5, xs) || GT<T>(xs, width - 0.5) || GT<T>(-0.5, ys) ||
+      GT<T>(ys, height - 0.5)) {
+    return 0;
+  }
+
+  if (GT<T>(0, xs)) {
+    xs = 0;
+  }
+  if (GT<T>(0, ys)) {
+    ys = 0;
+  }
+
+  int xs_floor = floor(xs);
+  int ys_floor = floor(ys);
+  int xs_ceil;
+  int ys_ceil;
+
+  if (GT_E(xs_floor, width - 1)) {
+    xs_ceil = xs_floor = width - 1;
+    xs = static_cast<T>(xs_floor);
+  } else {
+    xs_ceil = xs_floor + 1;
+  }
+
+  if (GT_E(ys_floor, height - 1)) {
+    ys_ceil = ys_floor = height - 1;
+    ys = static_cast<T>(ys_floor);
+  } else {
+    ys_ceil = ys_floor + 1;
+  }
+
+  T weight = 0;
+  if (w == xs_floor) {
+    if (h == ys_floor) {
+      weight = (w + 1 - xs) * (h + 1 - ys);
+    } else if (h == ys_ceil) {
+      weight = (w + 1 - xs) * (ys + 1 - h);
+    }
+  } else if (w == xs_ceil) {
+    if (h == ys_floor) {
+      weight = (xs + 1 - w) * (h + 1 - ys);
+    } else if (h == ys_ceil) {
+      weight = (xs + 1 - w) * (ys + 1 - h);
+    }
+  }
+  return weight;
+}
+
+template <typename T>
+class CPUROIPerspectiveTransformGradOpKernel : 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::LoDTensor>("ROIs");
+    auto* out_grad =
+        ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+
+    auto transformed_height = ctx.Attr<int>("transformed_height");
+    auto transformed_width = ctx.Attr<int>("transformed_width");
+    auto spatial_scale = ctx.Attr<float>("spatial_scale");
+
+    auto in_dims = in->dims();
+    int batch_size = in_dims[0];
+    int channels = in_dims[1];
+    int in_height = in_dims[2];
+    int in_width = in_dims[3];
+    int rois_num = rois->dims()[0];
+
+    T* in_grad_data = in_grad->mutable_data<T>(ctx.GetPlace());
+    const T* out_grad_data = out_grad->data<T>();
+    const T* rois_data = rois->data<T>();
+
+    framework::Tensor roi2image;
+    roi2image.Resize({rois_num});
+    int* roi2image_data = roi2image.mutable_data<int>(ctx.GetPlace());
+    auto lod = rois->lod().back();
+    for (int i = 0; i < lod.size() - 1; ++i) {
+      for (int j = lod[i]; j < lod[i + 1]; ++j) {
+        roi2image_data[j] = i;
+      }
+    }
+
+    for (int n = 0; n < batch_size; ++n) {
+      for (int c = 0; c < channels; ++c) {
+        for (int in_h = 0; in_h < in_height; ++in_h) {
+          for (int in_w = 0; in_w < in_width; ++in_w) {
+            T gradient = 0.0;
+            for (int roi_idx = lod[n]; roi_idx < lod[n + 1]; ++roi_idx) {
+              const T* rois = rois_data + roi_idx * 8;
+              T roi_x[4];
+              T roi_y[4];
+              for (int k = 0; k < 4; ++k) {
+                roi_x[k] = rois[2 * k] * spatial_scale;
+                roi_y[k] = rois[2 * k + 1] * spatial_scale;
+              }
+
+              // Get transform matrix
+              T matrix[9];
+              get_transform_matrix<T>(transformed_width, transformed_height,
+                                      roi_x, roi_y, matrix);
+              const T* out_grad_ptr = out_grad_data +
+                                      (roi_idx * channels + c) *
+                                          transformed_height *
+                                          transformed_width;
+              for (int out_h = 0; out_h < transformed_height; ++out_h) {
+                for (int out_w = 0; out_w < transformed_width; ++out_w) {
+                  T src_w;
+                  T src_h;
+                  get_source_coords<T>(matrix, out_w, out_h, &src_w, &src_h);
+                  if (in_quad<T>(src_w, src_h, roi_x, roi_y)) {
+                    if (GT<T>(-0.5, src_w) ||
+                        GT<T>(src_w, static_cast<T>(in_width - 0.5)) ||
+                        GT<T>(-0.5, src_h) ||
+                        GT<T>(src_h, static_cast<T>(in_height - 0.5))) {
+                      continue;
+                    }
+                    T weight = get_feature_gradient<T>(src_w, src_h, in_w, in_h,
+                                                       in_width, in_height);
+                    gradient +=
+                        out_grad_ptr[out_h * transformed_width + out_w] *
+                        weight;
+                  }
+                }
+              }
+            }
+            int out_idx = (n * channels + c) * in_height * in_width +
+                          in_h * in_width + in_w;
+            in_grad_data[out_idx] = gradient;
+          }
+        }
+      }
+    }
+  }
+};
+
+class ROIPerspectiveTransformOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("X"),
+                   "Input(X) of ROIPerspectiveTransformOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasInput("ROIs"),
+        "Input(ROIs) of ROIPerspectiveTransformOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("Out"),
+        "Output(Out) of ROIPerspectiveTransformOp should not be null.");
+    auto input_dims = ctx->GetInputDim("X");
+    auto rois_dims = ctx->GetInputDim("ROIs");
+
+    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 LoDTensor of shape (num_rois, 8)"
+                   "given as [[x0, y0, x1, y1, x2, y2, x3, y3], ...]");
+    PADDLE_ENFORCE(rois_dims[1] == 8,
+                   "ROIs should be a 2-D LoDTensor of shape (num_rois, 8)"
+                   "given as [[x0, y0, x1, y1, x2, y2, x3, y3], ...].");
+
+    int transformed_height = ctx->Attrs().Get<int>("transformed_height");
+    int transformed_width = ctx->Attrs().Get<int>("transformed_width");
+    float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
+
+    PADDLE_ENFORCE_GT(transformed_height, 0,
+                      "The transformed output height must greater than 0");
+    PADDLE_ENFORCE_GT(transformed_width, 0,
+                      "The transformed output width must greater than 0");
+    PADDLE_ENFORCE_GT(spatial_scale, 0.0f,
+                      "The spatial scale must greater than 0");
+    std::vector<int64_t> out_dims_v({rois_dims[0],   // num_rois
+                                     input_dims[1],  // channels
+                                     static_cast<int64_t>(transformed_height),
+                                     static_cast<int64_t>(transformed_width)});
+    auto out_dims = framework::make_ddim(out_dims_v);
+
+    ctx->SetOutputDim("Out", out_dims);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
+        ctx.device_context());
+  }
+};
+
+class ROIPerspectiveTransformGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
+                   "The gradient of Out should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
+                   "The gradient of X should not be null.");
+    ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
+        ctx.device_context());
+  }
+};
+
+class ROIPerspectiveTransformOpMaker
+    : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor), "
+             "the input of ROIPerspectiveTransformOp. "
+             "The format of input tensor is NCHW. Where N is batch size, "
+             "C is the number of input channels, "
+             "H is the height of the feature, and "
+             "W is the width of the feature.");
+    AddInput("ROIs",
+             "(LoDTensor), "
+             "ROIs (Regions of Interest) to be transformed. "
+             "should be a 2-D LoDTensor of shape (num_rois, 8)"
+             "given as [[x1, y1, x2, y2, x3, y3, x4, y4], ...]."
+             "(x1, y1) is the top left coordinates, and "
+             "(x2, y2) is the top right coordinates, and"
+             "(x3, y3) is the bottom right coordinates, and"
+             "(x4, y4) is the bottom left coordinates.");
+    AddOutput(
+        "Out",
+        "(Tensor), "
+        "The output of ROIPerspectiveTransformOp is a 4-D tensor with shape "
+        "(num_rois, channels, transformed_h, transformed_w).");
+    AddAttr<float>("spatial_scale",
+                   "(float, default 1.0), "
+                   "Spatial scale factor to scale ROI coords.")
+        .SetDefault(1.0);
+    AddAttr<int>("transformed_height",
+                 "(int, default 1), "
+                 "The height of transformed output.")
+        .SetDefault(1);
+    AddAttr<int>("transformed_width",
+                 "(int, default 1), "
+                 "The width of transformed output.")
+        .SetDefault(1);
+    AddComment(R"DOC(
+**ROIPerspectiveTransform Operator**
+
+    )DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(roi_perspective_transform, ops::ROIPerspectiveTransformOp,
+                  ops::ROIPerspectiveTransformOpMaker,
+                  paddle::framework::DefaultGradOpDescMaker<true>);
+REGISTER_OPERATOR(roi_perspective_transform_grad,
+                  ops::ROIPerspectiveTransformGradOp);
+REGISTER_OP_CPU_KERNEL(roi_perspective_transform,
+                       ops::CPUROIPerspectiveTransformOpKernel<float>);
+REGISTER_OP_CPU_KERNEL(roi_perspective_transform_grad,
+                       ops::CPUROIPerspectiveTransformGradOpKernel<float>);

paddle/fluid/operators/detection/roi_perspective_transform_op.cu

+/* 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 <algorithm>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/platform/cuda_primitives.h"
+
+namespace paddle {
+namespace operators {
+
+// CUDA: index helpers
+#define idx4_4(index, d1, d2, d3, d4) (index % d4)
+#define idx4_3(index, d1, d2, d3, d4) ((index / d4) % d3)
+#define idx4_2(index, d1, d2, d3, d4) ((index / d4 / d3) % d2)
+#define idx4_1(index, d1, d2, d3, d4) ((index / d4 / d3 / d2) % d1)
+
+#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>
+__device__ bool GT_E(T a, T b) {
+  return (a > b) || fabs(a - b) < 1e-4;
+}
+
+template <typename T>
+__device__ bool LT_E(T a, T b) {
+  return (a < b) || fabs(a - b) < 1e-4;
+}
+
+template <typename T>
+__device__ bool GT(T a, T b) {
+  return (a - b) > 1e-4;
+}
+
+template <typename T>
+__device__ T max(T a, T b) {
+  return a > b ? a : b;
+}
+
+template <typename T>
+__device__ T min(T a, T b) {
+  return a < b ? a : b;
+}
+
+/*
+* check if (x, y) is in the boundary of roi
+*/
+template <typename T>
+__device__ bool in_quad(T x, T y, T roi_x[], T roi_y[]) {
+  for (int i = 0; i < 4; i++) {
+    T start_w = roi_x[i];
+    T start_h = roi_y[i];
+    T end_w = roi_x[(i + 1) % 4];
+    T end_h = roi_y[(i + 1) % 4];
+    if (fabs(start_h - end_h) < 1e-4) {
+      if (fabs(y - start_h) < 1e-4 && fabs(y - end_h) < 1e-4 &&
+          GT_E<T>(x, min<T>(start_w, end_w)) &&
+          LT_E<T>(x, max<T>(start_w, end_w))) {
+        return true;
+      }
+    } else {
+      T intersec_x =
+          (y - start_h) * (end_w - start_w) / (end_h - start_h) + start_w;
+      if (fabs(intersec_x - x) < 1e-4 && GT_E(y, min<T>(start_h, end_h)) &&
+          LT_E<T>(y, max<T>(start_h, end_h))) {
+        return true;
+      }
+    }
+  }
+
+  int n_cross = 0;
+  for (int i = 0; i < 4; i++) {
+    T start_w = roi_x[i];
+    T start_h = roi_y[i];
+    T end_w = roi_x[(i + 1) % 4];
+    T end_h = roi_y[(i + 1) % 4];
+    if (fabs(start_h - end_h) < 1e-4) {
+      continue;
+    }
+    if (LT_E<T>(y, min<T>(start_h, end_h)) ||
+        GT<T>(y, max<T>(start_h, end_h))) {
+      continue;
+    }
+    T intersec_x =
+        (y - start_h) * (end_w - start_w) / (end_h - start_h) + start_w;
+    if (fabs(intersec_x - x) < 1e-4) {
+      return true;
+    }
+    if (GT<T>(intersec_x, x)) {
+      n_cross++;
+    }
+  }
+  return (n_cross % 2 == 1);
+}
+
+/**
+ * Perform bilinear interpolation in the input feature map.
+ */
+template <typename T>
+__device__ void bilinear_interpolate(const T* in_data, const int channels,
+                                     const int width, const int height,
+                                     int in_n, int in_c, T in_w, T in_h,
+                                     T* val) {
+  // Deal with cases that source coords are out of feature map boundary
+  if (GT<T>(-0.5, in_w) || GT<T>(in_w, width - 0.5) || GT<T>(-0.5, in_h) ||
+      GT<T>(in_h, height - 0.5)) {
+    val[0] = 0.0;
+    return;
+  }
+
+  if (GT<T>(0, in_w)) {
+    in_w = 0;
+  }
+  if (GT<T>(0, in_h)) {
+    in_h = 0;
+  }
+
+  int in_w_floor = floor(in_w);
+  int in_h_floor = floor(in_h);
+  int in_w_ceil;
+  int in_h_ceil;
+
+  if (GT_E<T>(in_w_floor, width - 1)) {
+    in_w_ceil = in_w_floor = width - 1;
+    in_w = static_cast<T>(in_w_floor);
+  } else {
+    in_w_ceil = in_w_floor + 1;
+  }
+
+  if (GT_E<T>(in_h_floor, height - 1)) {
+    in_h_ceil = in_h_floor = height - 1;
+    in_h = static_cast<T>(in_h_floor);
+  } else {
+    in_h_ceil = in_h_floor + 1;
+  }
+
+  T w_floor = in_w - in_w_floor;
+  T h_floor = in_h - in_h_floor;
+  T w_ceil = 1 - w_floor;
+  T h_ceil = 1 - h_floor;
+  const T* data = in_data + (in_n * channels + in_c) * height * width;
+  // Do bilinear interpolation
+  T v1 = data[in_h_floor * width + in_w_floor];
+  T v2 = data[in_h_ceil * width + in_w_floor];
+  T v3 = data[in_h_ceil * width + in_w_ceil];
+  T v4 = data[in_h_floor * width + in_w_ceil];
+  T w1 = w_ceil * h_ceil;
+  T w2 = w_ceil * h_floor;
+  T w3 = w_floor * h_floor;
+  T w4 = w_floor * h_ceil;
+  val[0] = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4;
+}
+
+/**
+ * Get the source coordinates in the input feature map.
+ *
+ * (u, v, w)^matrix = T * (out_w, out_h, 1)^matrix
+ *
+ * in_w = u / w
+ * in_h = v / w
+ *
+ */
+template <typename T>
+__device__ void get_source_coords(T matrix[], int out_w, int out_h, T* in_w,
+                                  T* in_h) {
+  T u = matrix[0] * out_w + matrix[1] * out_h + matrix[2];
+  T v = matrix[3] * out_w + matrix[4] * out_h + matrix[5];
+  T w = matrix[6] * out_w + matrix[7] * out_h + matrix[8];
+
+  in_w[0] = u / w;
+  in_h[0] = v / w;
+}
+
+/**
+ * Get the matrix of perspective transform.
+ *
+ * dx1 = x1 - x2
+ * dx2 = x3 - x2
+ * dx3 = x0 - x1 + x2 - x3
+ * dy1 = y1 - y2
+ * dy2 = y3 - y2
+ * dy3 = y0 - y1 + y2 - y3
+ *
+ * a11 = (x1 - x0 + a31 * (w - 1) * x1) / (w - 1)
+ * a12 = (x3 - x0 + a32 * (h - 1) * x3) / (h - 1)
+ * a13 = x0
+ * a21 = (y1 - y0 + a31 * (w - 1) * y1) / (w - 1)
+ * a22 = (y3 - y0 + a32 * (h - 1) * y3) / (h - 1)
+ * a23 = y0
+ * a31 = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) / (w - 1)
+ * a32 = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) / (h - 1)
+ * a33 = 1
+ *
+ */
+template <typename T>
+__device__ void get_transform_matrix(const int transformed_width,
+                                     const int transformed_height, T roi_x[],
+                                     T roi_y[], T matrix[]) {
+  T x0 = roi_x[0];
+  T x1 = roi_x[1];
+  T x2 = roi_x[2];
+  T x3 = roi_x[3];
+  T y0 = roi_y[0];
+  T y1 = roi_y[1];
+  T y2 = roi_y[2];
+  T y3 = roi_y[3];
+
+  // Estimate the height and width of RoI
+  T len1 = sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1));
+  T len2 = sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2));
+  T len3 = sqrt((x2 - x3) * (x2 - x3) + (y2 - y3) * (y2 - y3));
+  T len4 = sqrt((x3 - x0) * (x3 - x0) + (y3 - y0) * (y3 - y0));
+  T estimated_height = (len2 + len4) / 2.0;
+  T estimated_width = (len1 + len3) / 2.0;
+
+  // Get the normalized height and normalized width
+  int normalized_height = transformed_height;
+  int normalized_width =
+      round(estimated_width * (normalized_height - 1) / estimated_height) + 1;
+  normalized_width = min(normalized_width, transformed_width);
+
+  T dx1 = x1 - x2;
+  T dx2 = x3 - x2;
+  T dx3 = x0 - x1 + x2 - x3;
+  T dy1 = y1 - y2;
+  T dy2 = y3 - y2;
+  T dy3 = y0 - y1 + y2 - y3;
+
+  matrix[6] = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) /
+              (normalized_width - 1);
+  matrix[7] = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) /
+              (normalized_height - 1);
+  matrix[8] = 1;
+
+  matrix[3] = (y1 - y0 + matrix[6] * (normalized_width - 1) * y1) /
+              (normalized_width - 1);
+  matrix[4] = (y3 - y0 + matrix[7] * (normalized_height - 1) * y3) /
+              (normalized_height - 1);
+  matrix[5] = y0;
+
+  matrix[0] = (x1 - x0 + matrix[6] * (normalized_width - 1) * x1) /
+              (normalized_width - 1);
+  matrix[1] = (x3 - x0 + matrix[7] * (normalized_height - 1) * x3) /
+              (normalized_height - 1);
+  matrix[2] = x0;
+}
+
+template <typename T>
+__global__ void RoiTransformKernel(const float* input_data,
+                                   const float* rois_data,
+                                   const int* roi2image_data, int num_rois,
+                                   int in_height, int in_width, int channels,
+                                   int transformed_height,
+                                   int transformed_width, float spatial_scale,
+                                   T* output_data) {
+  int output_size =
+      num_rois * transformed_height * transformed_width * channels;
+
+  CUDA_1D_KERNEL_LOOP(index, output_size) {
+    // (n, c, out_h, out_w) is an element in the transformed output
+    int out_w = idx4_4(index, num_rois, channels, transformed_height,
+                       transformed_width);
+    int out_h = idx4_3(index, num_rois, channels, transformed_height,
+                       transformed_width);
+    int c = idx4_2(index, num_rois, channels, transformed_height,
+                   transformed_width);
+    int n = idx4_1(index, num_rois, channels, transformed_height,
+                   transformed_width);
+
+    auto bottom_rois = rois_data + n * 8;
+    int roi_batch_ind = bottom_rois[0];
+    T roi_x[4];
+    T roi_y[4];
+    for (int k = 0; k < 4; ++k) {
+      roi_x[k] = bottom_rois[2 * k] * spatial_scale;
+      roi_y[k] = bottom_rois[2 * k + 1] * spatial_scale;
+    }
+
+    // Get transform matrix
+    T matrix[9];
+    get_transform_matrix<T>(transformed_width, transformed_height, roi_x, roi_y,
+                            matrix);
+
+    // Get source coords
+    T in_w;
+    T in_h;
+    get_source_coords<T>(matrix, out_w, out_h, &in_w, &in_h);
+
+    if (in_quad<T>(in_w, in_h, roi_x, roi_y)) {
+      if (GT<T>(-0.5, in_w) || GT<T>(in_w, static_cast<T>(in_width - 0.5)) ||
+          GT<T>(-0.5, in_h) || GT<T>(in_h, static_cast<T>(in_height - 0.5))) {
+        // Skip if source coords is not in input image
+        output_data[index] = 0.0;
+      } else {
+        // Perform bilinear interpolation
+        int in_n = roi2image_data[n];
+        bilinear_interpolate<T>(input_data, channels, in_width, in_height, in_n,
+                                c, in_w, in_h, output_data + index);
+      }
+
+    } else {
+      // Skip if source coords is not in quad
+      output_data[index] = 0.0;
+    }
+  }
+}
+
+template <typename T>
+class CUDAROIPerspectiveTransformOpKernel : 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::LoDTensor>("ROIs");
+    auto* out = ctx.Output<framework::Tensor>("Out");
+
+    auto transformed_height = ctx.Attr<int>("transformed_height");
+    auto transformed_width = ctx.Attr<int>("transformed_width");
+    auto spatial_scale = ctx.Attr<float>("spatial_scale");
+
+    auto in_dims = in->dims();
+    int batch_size = in_dims[0];
+    int channels = in_dims[1];
+    int in_height = in_dims[2];
+    int in_width = in_dims[3];
+    int rois_num = rois->dims()[0];
+
+    const T* input_data = in->data<T>();
+    T* output_data = out->mutable_data<T>(ctx.GetPlace());
+    const T* rois_data = rois->data<T>();
+
+    framework::Tensor roi2image;
+    framework::Tensor roi2image_dev;
+    roi2image.Resize({rois_num});
+    int* roi2image_data = roi2image.mutable_data<int>(platform::CPUPlace());
+    auto lod = rois->lod().back();
+    for (int i = 0; i < lod.size() - 1; ++i) {
+      for (int j = lod[i]; j < lod[i + 1]; ++j) {
+        roi2image_data[j] = i;
+      }
+    }
+    TensorCopySync(roi2image, ctx.GetPlace(), &roi2image_dev);
+
+    int out_size = rois_num * transformed_height * transformed_width * channels;
+    auto stream = ctx.cuda_device_context().stream();
+    int block = 512;
+    int grid = (out_size + block - 1) / block;
+
+    RoiTransformKernel<T><<<grid, block, 0, stream>>>(
+        input_data, rois_data, roi2image_dev.data<int>(), rois_num, in_height,
+        in_width, channels, transformed_height, transformed_width,
+        spatial_scale, output_data);
+  }
+};
+
+template <typename T>
+__device__ T get_feature_gradient(T xs, T ys, int w, int h, const int width,
+                                  const int height) {
+  if (GT<T>(-0.5, xs) || GT<T>(xs, width - 0.5) || GT<T>(-0.5, ys) ||
+      GT<T>(ys, height - 0.5)) {
+    return 0;
+  }
+
+  if (GT<T>(0, xs)) {
+    xs = 0;
+  }
+  if (GT<T>(0, ys)) {
+    ys = 0;
+  }
+
+  int xs_floor = floor(xs);
+  int ys_floor = floor(ys);
+  int xs_ceil;
+  int ys_ceil;
+
+  if (GT_E<T>(xs_floor, width - 1)) {
+    xs_ceil = xs_floor = width - 1;
+    xs = static_cast<T>(xs_floor);
+  } else {
+    xs_ceil = xs_floor + 1;
+  }
+
+  if (GT_E(ys_floor, height - 1)) {
+    ys_ceil = ys_floor = height - 1;
+    ys = static_cast<T>(ys_floor);
+  } else {
+    ys_ceil = ys_floor + 1;
+  }
+
+  T weight = 0;
+  if (w == xs_floor) {
+    if (h == ys_floor) {
+      weight = (w + 1 - xs) * (h + 1 - ys);
+    } else if (h == ys_ceil) {
+      weight = (w + 1 - xs) * (ys + 1 - h);
+    }
+  } else if (w == xs_ceil) {
+    if (h == ys_floor) {
+      weight = (xs + 1 - w) * (h + 1 - ys);
+    } else if (h == ys_ceil) {
+      weight = (xs + 1 - w) * (ys + 1 - h);
+    }
+  }
+  return weight;
+}
+
+template <typename T>
+__global__ void RoiTransformGradKernel(
+    const size_t* lod, const T* rois_data, int batch_size, int num_rois,
+    int in_height, int in_width, int channels, int transformed_height,
+    int transformed_width, float spatial_scale, const T* out_grad_data,
+    T* in_grad_data) {
+  int input_size = batch_size * in_height * in_width * channels;
+
+  CUDA_1D_KERNEL_LOOP(index, input_size) {
+    // (n, c, h, w) coords in input
+    int in_w = idx4_4(index, batch_size, channels, in_height, in_width);
+    int in_h = idx4_3(index, batch_size, channels, in_height, in_width);
+    int c = idx4_2(index, batch_size, channels, in_height, in_width);
+    int n = idx4_1(index, batch_size, channels, in_height, in_width);
+
+    T gradient = 0.0;
+    // Accumulate gradient over all RoIs that interpolated this element
+    for (int roi_idx = lod[n]; roi_idx < lod[n + 1]; ++roi_idx) {
+      const T* rois = rois_data + roi_idx * 8;
+      T roi_x[4];
+      T roi_y[4];
+      for (int k = 0; k < 4; ++k) {
+        roi_x[k] = rois[2 * k] * spatial_scale;
+        roi_y[k] = rois[2 * k + 1] * spatial_scale;
+      }
+
+      // Get transform matrix
+      T matrix[9];
+      get_transform_matrix<T>(transformed_width, transformed_height, roi_x,
+                              roi_y, matrix);
+
+      const T* out_grad_ptr =
+          out_grad_data +
+          (roi_idx * channels + c) * transformed_height * transformed_width;
+      for (int out_h = 0; out_h < transformed_height; ++out_h) {
+        for (int out_w = 0; out_w < transformed_width; ++out_w) {
+          T src_w;
+          T src_h;
+          get_source_coords<T>(matrix, out_w, out_h, &src_w, &src_h);
+          if (in_quad<T>(src_w, src_h, roi_x, roi_y)) {
+            if (GT<T>(-0.5, src_w) ||
+                GT<T>(src_w, static_cast<T>(in_width - 0.5)) ||
+                GT<T>(-0.5, src_h) ||
+                GT<T>(src_h, static_cast<T>(in_height - 0.5))) {
+              continue;
+            }
+            T weight = get_feature_gradient<T>(src_w, src_h, in_w, in_h,
+                                               in_width, in_height);
+            gradient +=
+                out_grad_ptr[out_h * transformed_width + out_w] * weight;
+          }
+        }
+      }
+    }
+    in_grad_data[index] = gradient;
+  }
+}
+
+template <typename T>
+class CUDAROIPerspectiveTransformGradOpKernel : 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::LoDTensor>("ROIs");
+    auto* out_grad =
+        ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+    auto* in_grad = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+
+    auto transformed_height = ctx.Attr<int>("transformed_height");
+    auto transformed_width = ctx.Attr<int>("transformed_width");
+    auto spatial_scale = ctx.Attr<float>("spatial_scale");
+
+    auto in_dims = in->dims();
+    int batch_size = in_dims[0];
+    int channels = in_dims[1];
+    int in_height = in_dims[2];
+    int in_width = in_dims[3];
+    int rois_num = rois->dims()[0];
+
+    T* in_grad_data = in_grad->mutable_data<T>(ctx.GetPlace());
+    const T* out_grad_data = out_grad->data<T>();
+    const T* rois_data = rois->data<T>();
+
+    auto lod = rois->lod().back();
+    auto lod_data = lod.CUDAData(ctx.GetPlace());
+
+    int in_size = in->numel();
+    auto stream = ctx.cuda_device_context().stream();
+    int block = 512;
+    int grid = (in_size + block - 1) / block;
+
+    RoiTransformGradKernel<T><<<grid, block, 0, stream>>>(
+        lod_data, rois_data, batch_size, rois_num, in_height, in_width,
+        channels, transformed_height, transformed_width, spatial_scale,
+        out_grad_data, in_grad_data);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(roi_perspective_transform,
+                        ops::CUDAROIPerspectiveTransformOpKernel<float>);
+REGISTER_OP_CUDA_KERNEL(roi_perspective_transform_grad,
+                        ops::CUDAROIPerspectiveTransformGradOpKernel<float>);

python/paddle/fluid/__init__.py

@@ -46,7 +46,7 @@ from . import transpiler
 from .param_attr import ParamAttr, WeightNormParamAttr
 from .data_feeder import DataFeeder
 from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
-from .transpiler import DistributeTranspiler, InferenceTranspiler, \
+from .transpiler import DistributeTranspiler, \
     memory_optimize, release_memory, DistributeTranspilerConfig
 from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
 from . import clip

python/paddle/fluid/io.py

@@ -27,8 +27,7 @@ from . import core
 
 __all__ = [
     'save_vars', 'save_params', 'save_persistables', 'load_vars', 'load_params',
-    'load_persistables', 'save_inference_model', 'load_inference_model',
-    'get_inference_program'
+    'load_persistables', 'save_inference_model', 'load_inference_model'
 ]
 
 
@@ -504,23 +503,6 @@ def load_persistables(executor, dirname, main_program=None, filename=None):
         filename=filename)
 
 
-def get_inference_program(target_vars, main_program=None):
-    if main_program is None:
-        main_program = default_main_program()
-    if not isinstance(target_vars, list):
-        target_vars = [target_vars]
-    vars = []
-    for var in target_vars:
-        if isinstance(var, Evaluator):
-            vars.extend(var.states)
-            vars.extend(var.metrics)
-        else:
-            vars.append(var)
-    pruned_program = main_program._prune(targets=vars)
-    inference_program = pruned_program._inference_optimize()
-    return inference_program
-
-
 def prepend_feed_ops(inference_program,
                      feed_target_names,
                      feed_holder_name='feed'):

python/paddle/fluid/layers/detection.py

@@ -39,6 +39,7 @@ __all__ = [
     'detection_map',
     'rpn_target_assign',
     'anchor_generator',
+    'roi_perspective_transform',
     'generate_proposal_labels',
     'generate_proposals',
 ]
@@ -1262,6 +1263,54 @@ def anchor_generator(input,
     return anchor, var
 
 
+def roi_perspective_transform(input,
+                              rois,
+                              transformed_height,
+                              transformed_width,
+                              spatial_scale=1.0):
+    """
+    ROI perspective transform op.
+
+    Args:
+        input (Variable): The input of ROIPerspectiveTransformOp. The format of 
+                          input tensor is NCHW. Where N is batch size, C is the
+                          number of input channels, H is the height of the feature,
+                          and W is the width of the feature.
+        rois (Variable):  ROIs (Regions of Interest) to be transformed. It should be
+                          a 2-D LoDTensor of shape (num_rois, 8). Given as 
+                          [[x1, y1, x2, y2, x3, y3, x4, y4], ...], (x1, y1) is the 
+                          top left coordinates, and (x2, y2) is the top right 
+                          coordinates, and (x3, y3) is the bottom right coordinates, 
+                          and (x4, y4) is the bottom left coordinates.
+        transformed_height (integer): The height of transformed output.
+        transformed_height (integer): The width of transformed output.
+        spatial_scale (float): Spatial scale factor to scale ROI coords. Default: 1.0
+
+    Returns:
+        Variable: The output of ROIPerspectiveTransformOp which is a 4-D tensor with shape 
+                  (num_rois, channels, transformed_h, transformed_w).
+
+    Examples:
+        .. code-block:: python
+
+            out = fluid.layers.roi_perspective_transform(input, rois, 7, 7, 1.0)
+    """
+    helper = LayerHelper('roi_perspective_transform', **locals())
+    dtype = helper.input_dtype()
+    out = helper.create_tmp_variable(dtype)
+    helper.append_op(
+        type="roi_perspective_transform",
+        inputs={"X": input,
+                "ROIs": rois},
+        outputs={"Out": out},
+        attrs={
+            "transformed_height": transformed_height,
+            "transformed_width": transformed_width,
+            "spatial_scale": spatial_scale
+        })
+    return out
+
+
 def generate_proposal_labels(rpn_rois,
                              gt_classes,
                              is_crowd,

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

@@ -437,13 +437,8 @@ def split_data(data, num_part):
     ]
 
 
-def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
+def test_context(test_program, avg_cost, train_exe, dev_count, data_input_names,
                  sum_cost, token_num):
-    # Context to do validation.
-    test_program = train_progm.clone()
-    with fluid.program_guard(test_program):
-        test_program = fluid.io.get_inference_program([avg_cost])
-
     val_data = DataReader(
         src_vocab_fpath=TrainTaskConfig.src_vocab_fpath,
         trg_vocab_fpath=TrainTaskConfig.trg_vocab_fpath,
@@ -505,7 +500,7 @@ def test_context(train_progm, avg_cost, train_exe, dev_count, data_input_names,
 
 
 def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
-               token_num, predict):
+               token_num, predict, test_program):
     # Initialize the parameters.
     if TrainTaskConfig.ckpt_path:
         lr_scheduler.current_steps = TrainTaskConfig.start_step
@@ -554,7 +549,7 @@ def train_loop(exe, train_progm, dev_count, sum_cost, avg_cost, lr_scheduler,
                                                                              -1] + label_data_input_fields
 
     if TrainTaskConfig.val_file_pattern is not None:
-        test = test_context(train_progm, avg_cost, train_exe, dev_count,
+        test = test_context(test_program, avg_cost, train_exe, dev_count,
                             data_input_names, sum_cost, token_num)
 
     # the best cross-entropy value with label smoothing
@@ -1647,6 +1642,8 @@ def get_model(is_dist, is_async):
     local_lr_scheduler = LearningRateScheduler(ModelHyperParams.d_model,
                                                TrainTaskConfig.warmup_steps,
                                                TrainTaskConfig.learning_rate)
+    # Context to do validation.
+    test_program = fluid.default_main_program().clone(for_test=True)
 
     if not is_dist:
         optimizer = fluid.optimizer.Adam(
@@ -1671,7 +1668,7 @@ def get_model(is_dist, is_async):
             epsilon=TrainTaskConfig.eps)
         optimizer.minimize(sum_cost)
 
-    return sum_cost, avg_cost, predict, token_num, local_lr_scheduler
+    return sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program
 
 
 def update_args():
@@ -1705,7 +1702,7 @@ class DistTransformer2x2(TestDistRunnerBase):
     def run_trainer(self, use_cuda, args):
         place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
         TrainTaskConfig.use_gpu = use_cuda
-        sum_cost, avg_cost, predict, token_num, local_lr_scheduler = get_model(
+        sum_cost, avg_cost, predict, token_num, local_lr_scheduler, test_program = get_model(
             args.is_dist, not args.sync_mode)
 
         if args.is_dist:
@@ -1726,7 +1723,7 @@ class DistTransformer2x2(TestDistRunnerBase):
         TrainTaskConfig.local = not args.is_dist
 
         train_loop(startup_exe, trainer_prog, 1, sum_cost, avg_cost,
-                   local_lr_scheduler, token_num, predict)
+                   local_lr_scheduler, token_num, predict, test_program)
 
 
 if __name__ == "__main__":

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

@@ -573,6 +573,16 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(out)
         print(str(program))
 
+    def test_roi_perspective_transform(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name="x", shape=[256, 30, 30], dtype="float32")
+            rois = layers.data(
+                name="rois", shape=[8], dtype="float32", lod_level=1)
+            output = layers.roi_perspective_transform(x, rois, 7, 7, 0.6)
+            self.assertIsNotNone(output)
+        print(str(program))
+
     def test_sequence_enumerate(self):
         program = Program()
         with program_guard(program):

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

+#   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,
+# WITHOUWARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import math
+import sys
+import paddle.compat as cpt
+from op_test import OpTest
+from math import sqrt
+from math import floor
+
+
+def gt_e(a, b):
+    return a > b or abs(a - b) < 1e-4
+
+
+def gt(a, b):
+    return (a - b) > 1e-4
+
+
+def lt_e(a, b):
+    return a < b or abs(a - b) < 1e-4
+
+
+def in_quad(x, y, roi_x, roi_y):
+    # check if (x, y) is in the boundary of roi
+    for i in range(4):
+        xs = roi_x[i]
+        ys = roi_y[i]
+        xe = roi_x[(i + 1) % 4]
+        ye = roi_y[(i + 1) % 4]
+        if abs(ys - ye) < 1e-4:
+            if abs(y - ys) < 1e-4 and abs(y - ye) < 1e-4 and gt_e(
+                    x, min(xs, xe)) and lt_e(x, max(xs, xe)):
+                return True
+        else:
+            intersec_x = (y - ys) * (xe - xs) / (ye - ys) + xs
+            if abs(intersec_x - x) < 1e-4 and gt_e(y, min(ys, ye)) and lt_e(
+                    y, max(ys, ye)):
+                return True
+    n_cross = 0
+    for i in range(4):
+        xs = roi_x[i]
+        ys = roi_y[i]
+        xe = roi_x[(i + 1) % 4]
+        ye = roi_y[(i + 1) % 4]
+        if abs(ys - ye) < 1e-4:
+            continue
+        if lt_e(y, min(ys, ye)) or gt(y, max(ys, ye)):
+            continue
+        intersec_x = (y - ys) * (xe - xs) / (ye - ys) + xs
+        if abs(intersec_x - x) < 1e-4:
+            return True
+        if gt(intersec_x, x):
+            n_cross += 1
+    return (n_cross % 2 == 1)
+
+
+def get_transform_matrix(transformed_width, transformed_height, roi_x, roi_y):
+    x0 = roi_x[0]
+    x1 = roi_x[1]
+    x2 = roi_x[2]
+    x3 = roi_x[3]
+    y0 = roi_y[0]
+    y1 = roi_y[1]
+    y2 = roi_y[2]
+    y3 = roi_y[3]
+
+    len1 = sqrt((x0 - x1) * (x0 - x1) + (y0 - y1) * (y0 - y1))
+    len2 = sqrt((x1 - x2) * (x1 - x2) + (y1 - y2) * (y1 - y2))
+    len3 = sqrt((x2 - x3) * (x2 - x3) + (y2 - y3) * (y2 - y3))
+    len4 = sqrt((x3 - x0) * (x3 - x0) + (y3 - y0) * (y3 - y0))
+    estimated_height = (len2 + len4) / 2.0
+    estimated_width = (len1 + len3) / 2.0
+
+    normalized_height = transformed_height
+    normalized_width = round(estimated_width *
+                             (normalized_height - 1) / estimated_height) + 1
+    normalized_width = min(normalized_width, transformed_width)
+
+    dx1 = x1 - x2
+    dx2 = x3 - x2
+    dx3 = x0 - x1 + x2 - x3
+    dy1 = y1 - y2
+    dy2 = y3 - y2
+    dy3 = y0 - y1 + y2 - y3
+    matrix = np.zeros([9])
+    matrix[6] = (dx3 * dy2 - dx2 * dy3) / (dx1 * dy2 - dx2 * dy1) / (
+        normalized_width - 1)
+    matrix[7] = (dx1 * dy3 - dx3 * dy1) / (dx1 * dy2 - dx2 * dy1) / (
+        normalized_height - 1)
+    matrix[8] = 1
+
+    matrix[3] = (y1 - y0 + matrix[6] *
+                 (normalized_width - 1) * y1) / (normalized_width - 1)
+    matrix[4] = (y3 - y0 + matrix[7] *
+                 (normalized_height - 1) * y3) / (normalized_height - 1)
+    matrix[5] = y0
+
+    matrix[0] = (x1 - x0 + matrix[6] *
+                 (normalized_width - 1) * x1) / (normalized_width - 1)
+    matrix[1] = (x3 - x0 + matrix[7] *
+                 (normalized_height - 1) * x3) / (normalized_height - 1)
+    matrix[2] = x0
+    return matrix
+
+
+def get_source_coords(matrix, out_w, out_h):
+    u = matrix[0] * out_w + matrix[1] * out_h + matrix[2]
+    v = matrix[3] * out_w + matrix[4] * out_h + matrix[5]
+    w = matrix[6] * out_w + matrix[7] * out_h + matrix[8]
+    in_w = u / w
+    in_h = v / w
+    return in_w, in_h
+
+
+def bilinear_interpolate(in_data, in_n, in_c, in_w, in_h):
+
+    batch_size = in_data.shape[0]
+    channels = in_data.shape[1]
+    height = in_data.shape[2]
+    width = in_data.shape[3]
+
+    if gt(-0.5, in_w) or gt(in_w, width - 0.5) or gt(-0.5, in_h) or gt(
+            in_h, height - 0.5):
+        return 0.0
+
+    if gt(0, in_w):
+        in_w = 0
+    if gt(0, in_h):
+        in_h = 0
+
+    in_w_floor = floor(in_w)
+    in_h_floor = floor(in_h)
+
+    if gt_e(in_w_floor, width - 1):
+        in_w_ceil = width - 1
+        in_w_floor = width - 1
+        in_w = in_w_floor
+    else:
+        in_w_ceil = in_w_floor + 1
+
+    if gt_e(in_h_floor, height - 1):
+        in_h_ceil = height - 1
+        in_h_floor = height - 1
+        in_h = in_h_floor
+    else:
+        in_h_ceil = in_h_floor + 1
+
+    w_floor = in_w - in_w_floor
+    h_floor = in_h - in_h_floor
+    w_ceil = 1 - w_floor
+    h_ceil = 1 - h_floor
+    v1 = in_data[in_n][in_c][int(in_h_floor)][int(in_w_floor)]
+    v2 = in_data[in_n][in_c][int(in_h_ceil)][int(in_w_floor)]
+    v3 = in_data[in_n][in_c][int(in_h_ceil)][int(in_w_ceil)]
+    v4 = in_data[in_n][in_c][int(in_h_floor)][int(in_w_ceil)]
+    w1 = w_ceil * h_ceil
+    w2 = w_ceil * h_floor
+    w3 = w_floor * h_floor
+    w4 = w_floor * h_ceil
+    val = w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4
+    return val
+
+
+def lod_convert(lod):
+    ret = [0]
+    for count in lod:
+        ret.append(ret[-1] + count)
+    return ret
+
+
+def roi_transform(in_data, rois, rois_lod, transformed_height,
+                  transformed_width, spatial_scale):
+    channels = in_data.shape[1]
+    in_height = in_data.shape[2]
+    in_width = in_data.shape[3]
+    rois_num = rois.shape[0]
+
+    roi2image = [0] * rois_num
+    rois_lod = lod_convert(rois_lod[0])
+    for i in range(len(rois_lod) - 1):
+        for j in range(rois_lod[i], rois_lod[i + 1]):
+            roi2image[j] = i
+
+    out = np.zeros([rois_num, channels, transformed_height, transformed_width])
+
+    for n in range(rois_num):
+        roi_x = []
+        roi_y = []
+        for k in range(4):
+            roi_x.append(rois[n][2 * k] * spatial_scale)
+            roi_y.append(rois[n][2 * k + 1] * spatial_scale)
+        image_id = roi2image[n]
+        transform_matrix = get_transform_matrix(
+            transformed_width, transformed_height, roi_x, roi_y)
+
+        for c in range(channels):
+            for out_h in range(transformed_height):
+                for out_w in range(transformed_width):
+                    in_w, in_h = get_source_coords(transform_matrix, out_w,
+                                                   out_h)
+                    if in_quad(in_w, in_h, roi_x, roi_y) and gt_e(
+                            in_w, -0.5) and lt_e(in_w, in_width - 0.5) and gt_e(
+                                in_h, -0.5) and lt_e(in_h, in_height - 0.5):
+                        out[n][c][out_h][out_w] = bilinear_interpolate(
+                            in_data, image_id, c, in_w, in_h)
+                    else:
+                        out[n][c][out_h][out_w] = 0.0
+    return out.astype("float32")
+
+
+class TestROIPoolOp(OpTest):
+    def set_data(self):
+        self.init_test_case()
+        self.make_rois()
+
+        self.inputs = {'X': self.x, 'ROIs': (self.rois, self.rois_lod)}
+
+        self.attrs = {
+            'spatial_scale': self.spatial_scale,
+            'transformed_height': self.transformed_height,
+            'transformed_width': self.transformed_width
+        }
+        out = roi_transform(self.x, self.rois, self.rois_lod,
+                            self.transformed_height, self.transformed_width,
+                            self.spatial_scale)
+        self.outputs = {'Out': out}
+
+    def init_test_case(self):
+        self.batch_size = 2
+        self.channels = 2
+        self.height = 8
+        self.width = 8
+
+        # n, c, h, w
+        self.x_dim = (self.batch_size, self.channels, self.height, self.width)
+
+        self.spatial_scale = 1.0 / 2.0
+        self.transformed_height = 2
+        self.transformed_width = 3
+
+        self.x = np.random.random(self.x_dim).astype('float32')
+
+    def make_rois(self):
+        rois = []
+        self.rois_lod = [[]]
+        for bno in range(self.batch_size):
+            self.rois_lod[0].append(bno + 1)
+            for i in range(bno + 1):
+                x1 = np.random.randint(
+                    0,
+                    self.width // self.spatial_scale - self.transformed_width)
+                y1 = np.random.randint(
+                    0,
+                    self.height // self.spatial_scale - self.transformed_height)
+
+                x2 = np.random.randint(x1 + self.transformed_width,
+                                       self.width // self.spatial_scale)
+                y2 = np.random.randint(
+                    0,
+                    self.height // self.spatial_scale - self.transformed_height)
+
+                x3 = np.random.randint(x1 + self.transformed_width,
+                                       self.width // self.spatial_scale)
+                y3 = np.random.randint(y1 + self.transformed_height,
+                                       self.height // self.spatial_scale)
+
+                x4 = np.random.randint(
+                    0,
+                    self.width // self.spatial_scale - self.transformed_width)
+                y4 = np.random.randint(y1 + self.transformed_height,
+                                       self.height // self.spatial_scale)
+
+                roi = [x1, y1, x2, y2, x3, y3, x4, y4]
+                rois.append(roi)
+        self.rois_num = len(rois)
+        self.rois = np.array(rois).astype("float32")
+
+    def setUp(self):
+        self.op_type = "roi_perspective_transform"
+        self.set_data()
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X'], 'Out')
+
+
+if __name__ == '__main__':
+    unittest.main()