[0-size Tensor Job2 No.105] Add 0-size Tensor support for roi_align

paddle/phi/kernels/cpu/roi_align_grad_kernel.cc

@@ -17,6 +17,7 @@
 #include "paddle/phi/backends/cpu/cpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/empty_kernel.h"
+#include "paddle/phi/kernels/full_kernel.h"
 #include "paddle/phi/kernels/funcs/math_function.h"
 
 namespace phi {
@@ -90,7 +91,11 @@ void RoiAlignGradKernel(const Context& dev_ctx,
  if (!dx) {
  return;
  }
-
+ if (x.numel() == 0 || boxes.numel() == 0) {
+ phi::Full<T, Context>(
+ dev_ctx, phi::IntArray(common::vectorize(dx->dims())), 0, dx);
+ return;
+ }
  DenseTensor roi_batch_id_list = Empty<int>(dev_ctx, {rois_num});
  int* box_batch_id_data = roi_batch_id_list.data<int>();
 

paddle/phi/kernels/cpu/roi_align_kernel.cc

@@ -17,6 +17,7 @@
 #include "paddle/phi/backends/cpu/cpu_context.h"
 #include "paddle/phi/core/kernel_registry.h"
 #include "paddle/phi/kernels/empty_kernel.h"
+#include "paddle/phi/kernels/full_kernel.h"
 
 namespace phi {
 
@@ -193,8 +194,9 @@ void RoiAlignKernel(const Context& dev_ctx,
  int width = static_cast<int>(in_dims[3]);
  int rois_num = static_cast<int>(boxes.dims()[0]);
 
- if (rois_num == 0) {
- dev_ctx.template Alloc<T>(out);
+ if (x.numel() == 0 || boxes.numel() == 0) {
+ phi::Full<T, Context>(
+ dev_ctx, phi::IntArray(common::vectorize(out->dims())), 0, out);
  return;
  }
 

paddle/phi/kernels/gpu/roi_align_kernel.cu

@@ -19,6 +19,7 @@
 #include "paddle/phi/common/memory_utils.h"
 #include "paddle/phi/common/place.h"
 #include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
 
 namespace phi {
 
@@ -149,6 +150,11 @@ void RoiAlignKernel(const Context& dev_ctx,
  dev_ctx.template Alloc<T>(out);
  return;
  }
+ if (x.numel() == 0) {
+ phi::Full<T, Context>(
+ dev_ctx, phi::IntArray(common::vectorize(out->dims())), 0, out);
+ return;
+ }
  auto in_dims = x.dims();
  int batch_size = in_dims[0];
  int channels = in_dims[1];

paddle/phi/kernels/xpu/roi_align_grad_kernel.cc

@@ -18,6 +18,7 @@
 #include "paddle/phi/backends/xpu/xpu_context.h"
 #include "paddle/phi/common/memory_utils.h"
 #include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/full_kernel.h"
 
 namespace phi {
 
@@ -41,6 +42,11 @@ void RoiAlignGradKernel(const Context& dev_ctx,
  if (!dx) {
  return;
  }
+ if (x.numel() == 0 || boxes.numel() == 0) {
+ phi::Full<T, Context>(
+ dev_ctx, phi::IntArray(common::vectorize(dx->dims())), 0, dx);
+ return;
+ }
  DenseTensor roi_batch_id_list;
  roi_batch_id_list.Resize({rois_num});
  auto cplace = phi::CPUPlace();

paddle/phi/kernels/xpu/roi_align_kernel.cc

@@ -18,7 +18,7 @@
 #include "paddle/phi/backends/xpu/xpu_context.h"
 #include "paddle/phi/common/memory_utils.h"
 #include "paddle/phi/core/kernel_registry.h"
-
+#include "paddle/phi/kernels/full_kernel.h"
 namespace phi {
 
 template <typename T, typename Context>
@@ -40,8 +40,9 @@ void RoiAlignKernel(const Context& dev_ctx,
 
  int rois_num = boxes.dims()[0];
 
- if (rois_num == 0) {
- dev_ctx.template Alloc<T>(out);
+ if (x.numel() == 0 || boxes.numel() == 0) {
+ phi::Full<T, Context>(
+ dev_ctx, phi::IntArray(common::vectorize(out->dims())), 0, out);
  return;
  }
 

test/legacy_test/test_roi_align_op.py

@@ -283,5 +283,57 @@ def init_test_case(self):
  self.x = np.random.random(self.x_dim).astype('float64')
 
 
+class TestROIAlignOp_ZeroSize(TestROIAlignOp):
+ def init_test_case(self):
+ self.batch_size = 3
+ self.channels = 3
+ self.height = 0
+ self.width = 6
+
+ # n, c, h, w
+ self.x_dim = (self.batch_size, self.channels, self.height, self.width)
+
+ self.spatial_scale = 1.0 / 2.0
+ self.pooled_height = 2
+ self.pooled_width = 2
+ self.sampling_ratio = -1
+ self.aligned = False
+
+ self.x = np.random.random(self.x_dim).astype('float64')
+
+ 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.random_integers(
+ 0, self.width // self.spatial_scale - self.pooled_width
+ )
+ x2 = np.random.random_integers(
+ x1 + self.pooled_width, self.width // self.spatial_scale
+ )
+ if self.height == 0:
+ y1 = 0
+ y2 = 0
+ else:
+ y1 = np.random.random_integers(
+ 0,
+ self.height // self.spatial_scale - self.pooled_height,
+ )
+ 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_num = len(rois)
+ self.rois = np.array(rois).astype("float64")
+ self.boxes_num = np.array(
+ [bno + 1 for bno in range(self.batch_size)]
+ ).astype('int32')
+
+
 if __name__ == '__main__':
  unittest.main()