add align_flag

test=develop

paddle/fluid/operators/interpolate_op.cc

@@ -110,7 +110,7 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
           to perform linear interpolation first in one direction, and then 
           again in the other direction.
 
-          Align_corners and align_mode are optinal parameters,The calculation method 
+          Align_corners and align_mode are optinal parameters,the calculation method 
           of interpolation can be selected by them.
           
           Example:

paddle/fluid/operators/interpolate_op.cu

@@ -94,6 +94,7 @@ __global__ void KeBilinearInterpFw(
   int nthreads = output_h * output_w;
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   int stride = blockDim.x * gridDim.x;
+  bool align_flag = (align_mode == 0 && !align_corners);
   for (; tid < nthreads; tid += stride) {
     int out_id_h = tid / output_w;
     int out_id_w = tid % output_w;
@@ -102,24 +103,22 @@ __global__ void KeBilinearInterpFw(
     int channel_id = out_id_w / out_img_size;
 
     int out_img_idy = (out_id_w % out_img_size) / out_img_w;
-    int in_img_idy = (align_mode == 0 && !align_corners)
+    int in_img_idy = align_flag
                          ? static_cast<int>(ratio_h * (out_img_idy + 0.5) - 0.5)
                          : static_cast<int>(ratio_h * out_img_idy);
     in_img_idy = (in_img_idy > 0) ? in_img_idy : 0;
     int h_id = (in_img_idy < in_img_h - 1) ? 1 : 0;
-    T h1lambda = (align_mode == 0 && !align_corners)
-                     ? ratio_h * (out_img_idy + 0.5) - 0.5 - in_img_idy
+    T h1lambda = align_flag ? ratio_h * (out_img_idy + 0.5) - 0.5 - in_img_idy
                             : ratio_h * out_img_idy - in_img_idy;
     T h2lambda = 1.f - h1lambda;
 
     int out_img_idx = tid % out_img_w;
-    int in_img_idx = (align_mode == 0 && !align_corners)
+    int in_img_idx = align_flag
                          ? static_cast<int>(ratio_w * (out_img_idx + 0.5) - 0.5)
                          : static_cast<int>(ratio_w * out_img_idx);
     in_img_idx = (in_img_idx > 0) ? in_img_idx : 0;
     int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
-    T w1lambda = (align_mode == 0 && !align_corners)
-                     ? ratio_w * (out_img_idx + 0.5) - 0.5 - in_img_idx
+    T w1lambda = align_flag ? ratio_w * (out_img_idx + 0.5) - 0.5 - in_img_idx
                             : ratio_w * out_img_idx - in_img_idx;
     T w2lambda = 1.f - w1lambda;
 
@@ -144,6 +143,7 @@ __global__ void KeBilinearInterpBw(
   int nthreads = output_h * output_w;
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   int stride = blockDim.x * gridDim.x;
+  bool align_flag = (align_mode == 0 && !align_corners);
   for (; tid < nthreads; tid += stride) {
     int out_id_h = tid / output_w;
     int out_id_w = tid % output_w;
@@ -152,25 +152,21 @@ __global__ void KeBilinearInterpBw(
     int channel_id = out_id_w / out_img_size;
 
     int out_img_idy = (out_id_w % out_img_size) / out_img_w;
-    int in_img_idy = (align_mode == 0 && !align_corners)
-                         ? ratio_h * (out_img_idy + 0.5) - 0.5
+    int in_img_idy = align_flag ? ratio_h * (out_img_idy + 0.5) - 0.5
                                 : ratio_h * out_img_idy;
     in_img_idy = (in_img_idy > 0) ? in_img_idy : 0;
     int h_id = (in_img_idy < in_img_h - 1) ? 1 : 0;
-    T h1lambda = (align_mode == 0 && !align_corners)
-                     ? ratio_h * (out_img_idy + 0.5) - 0.5 - in_img_idy
+    T h1lambda = align_flag ? ratio_h * (out_img_idy + 0.5) - 0.5 - in_img_idy
                             : ratio_h * out_img_idy - in_img_idy;
 
     T h2lambda = 1.f - h1lambda;
 
     int out_img_idx = tid % out_img_w;
-    int in_img_idx = (align_mode == 0 && !align_corners)
-                         ? ratio_w * (out_img_idx + 0.5) - 0.5
+    int in_img_idx = align_flag ? ratio_w * (out_img_idx + 0.5) - 0.5
                                 : ratio_w * out_img_idx;
     in_img_idx = (in_img_idx > 0) ? in_img_idx : 0;
     int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
-    T w1lambda = (align_mode == 0 && !align_corners)
-                     ? ratio_w * (out_img_idx + 0.5) - 0.5 - in_img_idx
+    T w1lambda = align_flag ? ratio_w * (out_img_idx + 0.5) - 0.5 - in_img_idx
                             : ratio_w * out_img_idx - in_img_idx;
     T w2lambda = 1.f - w1lambda;
 

paddle/fluid/operators/interpolate_op.h

@@ -56,15 +56,14 @@ static void BilinearInterpolation(const Tensor& input, Tensor* output,
                                   const bool align_mode) {
   auto input_t = EigenTensor<T, 4>::From(input);
   auto output_t = EigenTensor<T, 4>::From(*output);
+  bool align_flag = (align_mode == 0 && !align_corners);
   for (int k = 0; k < out_h; k++) {  // loop for images
-    int y_n = (align_mode == 0 && !align_corners)
-                  ? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
+    int y_n = align_flag ? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
                          : static_cast<int>(ratio_h * k);
     y_n = (y_n > 0) ? y_n : 0;
     int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
-    float d_n = (align_mode == 0 && !align_corners)
-                    ? ratio_h * (k + 0.5) - 0.5 - y_n
-                    : ratio_h * k - y_n;
+    float d_n =
+        align_flag ? ratio_h * (k + 0.5) - 0.5 - y_n : ratio_h * k - y_n;
     float d_s = 1.f - d_n;
 
     for (int l = 0; l < out_w; l++) {
@@ -73,9 +72,8 @@ static void BilinearInterpolation(const Tensor& input, Tensor* output,
                     : static_cast<int>(ratio_w * l);
       x_w = (x_w > 0) ? x_w : 0;
       int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
-      float d_w = (align_mode == 0 && !align_corners)
-                      ? ratio_w * (l + 0.5) - 0.5 - x_w
-                      : ratio_w * l - x_w;
+      float d_w =
+          align_flag ? ratio_w * (l + 0.5) - 0.5 - x_w : ratio_w * l - x_w;
       float d_e = 1.f - d_w;
 
       for (int i = 0; i < n; i++) {    // loop for batches
@@ -126,26 +124,23 @@ static void BilinearInterpolationGrad(const Tensor& output_grad,
                                       const int align_mode) {
   auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
   auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
+  bool align_flag = (align_mode == 0 && !align_corners);
   for (int k = 0; k < out_h; k++) {  // loop for images
-    int y_n = (align_mode == 0 && !align_corners)
-                  ? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
+    int y_n = align_flag ? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
                          : static_cast<int>(ratio_h * k);
     y_n = (y_n > 0) ? y_n : 0;
     int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
-    float d_n = (align_mode == 0 && !align_corners)
-                    ? ratio_h * (k + 0.5) - 0.5 - y_n
-                    : ratio_h * k - y_n;
+    float d_n =
+        align_flag ? ratio_h * (k + 0.5) - 0.5 - y_n : ratio_h * k - y_n;
     float d_s = 1.f - d_n;
 
     for (int l = 0; l < out_w; l++) {
-      int x_w = (align_mode == 0 && !align_corners)
-                    ? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
+      int x_w = align_flag ? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
                            : static_cast<int>(ratio_w * l);
       x_w = (x_w > 0) ? x_w : 0;
       int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
-      float d_w = (align_mode == 0 && !align_corners)
-                      ? ratio_w * (l + 0.5) - 0.5 - x_w
-                      : ratio_w * l - x_w;
+      float d_w =
+          align_flag ? ratio_w * (l + 0.5) - 0.5 - x_w : ratio_w * l - x_w;
       float d_e = 1.f - d_w;
 
       for (int i = 0; i < n; i++) {    // loop for batches

python/paddle/fluid/layers/nn.py

@@ -6552,7 +6552,7 @@ def image_resize(input,
     to perform linear interpolation first in one direction, and then 
     again in the other direction.
 
-    Align_corners and align_mode are optinal parameters,The calculation method 
+    Align_corners and align_mode are optinal parameters,the calculation method 
     of interpolation can be selected by them.
 
     Example:
@@ -6758,11 +6758,11 @@ def resize_bilinear(input,
     For details of bilinear interpolation, please refer to Wikipedia:
     https://en.wikipedia.org/wiki/Bilinear_interpolation
 
-    Align_corners and align_mode are optinal parameters,The calculation 
+    Align_corners and align_mode are optinal parameters,the calculation 
     method of interpolation can be selected by them.
 
 
-    Align_corners and align_mode are optinal parameters,The calculation method 
+    Align_corners and align_mode are optinal parameters,the calculation method 
     of interpolation can be selected by them.
 
     Example: