nearest neighbor interp add cuda kernel. test=develop

paddle/fluid/API.spec

@@ -121,6 +121,7 @@ paddle.fluid.layers.dice_loss ArgSpec(args=['input', 'label', 'epsilon'], vararg
 paddle.fluid.layers.image_resize ArgSpec(args=['input', 'out_shape', 'scale', 'name', 'resample'], varargs=None, keywords=None, defaults=(None, None, None, 'BILINEAR'))
 paddle.fluid.layers.image_resize_short ArgSpec(args=['input', 'out_short_len', 'resample'], varargs=None, keywords=None, defaults=('BILINEAR',))
 paddle.fluid.layers.resize_bilinear ArgSpec(args=['input', 'out_shape', 'scale', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
+paddle.fluid.layers.resize_nearest ArgSpec(args=['input', 'out_shape', 'scale', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.layers.gather ArgSpec(args=['input', 'index'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_scatter ArgSpec(args=['input', 'index', 'updates', 'name'], varargs=None, keywords=None, defaults=(None,))

paddle/fluid/operators/nearest_neighbor_interp_op.cc

@@ -25,9 +25,9 @@ class NearestNeighborInterpOp : public framework::OperatorWithKernel {
  protected:
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of BilinearInterOp should not be null.");
+                   "Input(X) of NearestNeighborInterOp should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of BilinearInterOp should not be null.");
+                   "Output(Out) of NearestNeighborInterOp should not be null.");
 
     auto dim_x = ctx->GetInputDim("X");  // NCHW format
     int out_h = ctx->Attrs().Get<int>("out_h");
@@ -64,8 +64,9 @@ class NearestNeighborInterpOpMaker : public framework::OpProtoAndCheckerMaker {
         .AsDispensable();
     AddOutput("Out", "The dimension of output is (N x C x out_h x out_w)");
 
-    AddAttr<int>("out_h", "output height of bilinear interpolation op.");
-    AddAttr<int>("out_w", "output width of bilinear interpolation op.");
+    AddAttr<int>("out_h",
+                 "output height of nearest neighbor interpolation op.");
+    AddAttr<int>("out_w", "output width of nearest neighbor interpolation op.");
     AddComment(R"DOC(
           Nearest neighbor interpolation is to perform nearest neighbor interpolation
           in bot the 3rd dimention(in height direction) and the 4th dimention(in width 

paddle/fluid/operators/nearest_neighbor_interp_op.cu

@@ -15,17 +15,14 @@
 namespace paddle {
 namespace operators {
 
-template <typename T, size_t D, int MajorType = Eigen::RowMajor,
-          typename IndexType = Eigen::DenseIndex>
-using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
 using framework::Tensor;
 
 template <typename T>
-__global__ void KeBilinearInterpFw(
+__global__ void KeNearestNeighborInterpFw(
     const T* in, const size_t in_img_h, const size_t in_img_w,
     const size_t input_h, const size_t input_w, T* out, const size_t out_img_h,
     const size_t out_img_w, const size_t output_h, const size_t output_w,
-    const size_t num_channels, const T ratio_h, const T ratioW) {
+    const size_t num_channels, const T ratio_h, const T ratio_w) {
   int nthreads = output_h * output_w;
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   if (tid < nthreads) {
@@ -36,34 +33,22 @@ __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 = ratio_h * out_img_idy;
-    int h_id = (in_img_idy < in_img_h - 1) ? 1 : 0;
-    T h1lambda = ratio_h * out_img_idy - in_img_idy;
-    T h2lambda = 1.f - h1lambda;
+    int in_img_idy = static_cast<int>(round(ratio_h * out_img_idy));
 
     int out_img_idx = tid % out_img_w;
-    int in_img_idx = ratioW * out_img_idx;
-    int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
-    T w1lambda = ratioW * out_img_idx - in_img_idx;
-    T w2lambda = 1.f - w1lambda;
-
-    const T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
-                          in_img_idy * in_img_w + in_img_idx];
-
-    // bilinear interpolation
-    out[out_id_h * output_w + out_id_w] =
-        h2lambda * (w2lambda * in_pos[0] + w1lambda * in_pos[w_id]) +
-        h1lambda * (w2lambda * in_pos[h_id * in_img_w] +
-                    w1lambda * in_pos[h_id * in_img_w + w_id]);
+    int in_img_idx = static_cast<int>(round(ratio_w * out_img_idx));
+
+    out[tid] = in[out_id_h * input_w + channel_id * in_img_size +
+                  in_img_idy * in_img_w + in_img_idx];
   }
 }
 
 template <typename T>
-__global__ void KeBilinearInterpBw(
+__global__ void KeNearestNeighborInterpBw(
     T* in, const size_t in_img_h, const size_t in_img_w, const size_t input_h,
     const size_t input_w, const T* out, const size_t out_img_h,
     const size_t out_img_w, const size_t output_h, const size_t output_w,
-    const size_t num_channels, const T ratio_h, const T ratioW) {
+    const size_t num_channels, const T ratio_h, const T ratio_w) {
   int nthreads = output_h * output_w;
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   if (tid < nthreads) {
@@ -74,25 +59,15 @@ __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 = ratio_h * out_img_idy;
-    int h_id = (in_img_idy < in_img_h - 1) ? 1 : 0;
-    T h1lambda = ratio_h * out_img_idy - in_img_idy;
-    T h2lambda = 1.f - h1lambda;
+    int in_img_idy = static_cast<int>(round(ratio_h * out_img_idy));
 
     int out_img_idx = tid % out_img_w;
-    int in_img_idx = ratioW * out_img_idx;
-    int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
-    T w1lambda = ratioW * out_img_idx - in_img_idx;
-    T w2lambda = 1.f - w1lambda;
+    int in_img_idx = static_cast<int>(round(ratio_w * out_img_idx));
 
     T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
                     in_img_idy * in_img_w + in_img_idx];
-    const T* out_pos = &out[out_id_h * output_w + out_id_w];
-    atomicAdd(&in_pos[0], h2lambda * w2lambda * out_pos[0]);
-    atomicAdd(&in_pos[w_id], h2lambda * w1lambda * out_pos[0]);
-    atomicAdd(&in_pos[h_id * in_img_w], h1lambda * w2lambda * out_pos[0]);
-    atomicAdd(&in_pos[h_id * in_img_w + w_id],
-              h1lambda * w1lambda * out_pos[0]);
+    const T out_pos = out[out_id_h * output_w + out_id_w];
+    atomicAdd(in_pos, out_pos);
   }
 }
 
@@ -102,48 +77,49 @@ class NearestNeighborInterpOpCUDAKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
                    "This kernel only runs on GPU device.");
-    auto* input_t = ctx.Input<Tensor>("X");      // float tensor
-    auto* output_t = ctx.Output<Tensor>("Out");  // float tensor
-    auto* input = input_t->data<T>();
+    auto* input = ctx.Input<Tensor>("X");      // float tensor
+    auto* output = ctx.Output<Tensor>("Out");  // float tensor
+    auto* input_data = input->data<T>();
 
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
-    auto out_dims = output_t->dims();
-    auto out_size_t = ctx.Input<Tensor>("OutSize");
-    if (out_size_t != nullptr) {
+    auto out_size = ctx.Input<Tensor>("OutSize");
+    if (out_size != nullptr) {
       Tensor sizes;
-      framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
+      framework::TensorCopy(*out_size, platform::CPUPlace(), &sizes);
       auto size_data = sizes.data<int>();
       out_h = size_data[0];
       out_w = size_data[1];
     }
-    auto* output = output_t->mutable_data<T>(
-        {out_dims[0], out_dims[1], out_h, out_w}, ctx.GetPlace());
 
-    int batch_size = input_t->dims()[0];
-    int channels = input_t->dims()[1];
-    int in_h = input_t->dims()[2];
-    int in_w = input_t->dims()[3];
+    int n = input->dims()[0];
+    int c = input->dims()[1];
+    int in_h = input->dims()[2];
+    int in_w = input->dims()[3];
+
+    auto* output_data =
+        output->mutable_data<T>({n, c, out_h, out_w}, ctx.GetPlace());
 
     int in_hw = in_h * in_w;
     int out_hw = out_h * out_w;
-    int in_chw = channels * in_hw;
-    int out_chw = channels * out_hw;
+    int in_chw = c * in_hw;
+    int out_chw = c * out_hw;
 
     T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
     T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
 
     if (in_h == out_h && in_w == out_w) {
-      memcpy(output, input, input_t->numel() * sizeof(T));
-    } else {
-      int threadNum = batch_size * out_chw;
-      int blocks = (threadNum + 1024 - 1) / 1024;
-
-      KeBilinearInterpFw<
-          T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
-          input, in_h, in_w, batch_size, in_chw, output, out_h, out_w,
-          batch_size, out_chw, channels, ratio_h, ratio_w);
+      memcpy(output_data, input_data, input->numel() * sizeof(T));
+      return;
     }
+
+    int threadNum = n * out_chw;
+    int blocks = (threadNum + 1024 - 1) / 1024;
+
+    KeNearestNeighborInterpFw<
+        T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
+        input_data, in_h, in_w, n, in_chw, output_data, out_h, out_w, n,
+        out_chw, c, ratio_h, ratio_w);
   }
 };
 
@@ -151,52 +127,53 @@ template <typename T>
 class NearestNeighborInterpGradOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* d_input_t = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* d_output_t = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* d_output = d_output_t->data<T>();
-    auto* d_input = d_input_t->mutable_data<T>(ctx.GetPlace());
+    auto* input_grad = ctx.Output<Tensor>(framework::GradVarName("X"));
+    auto* output_grad = ctx.Input<Tensor>(framework::GradVarName("Out"));
+    auto* output_grad_data = output_grad->data<T>();
+    auto* input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace());
 
     auto& device_ctx =
         ctx.template device_context<platform::CUDADeviceContext>();
     math::SetConstant<platform::CUDADeviceContext, T> zero;
-    zero(device_ctx, d_input_t, static_cast<T>(0.0));
+    zero(device_ctx, input_grad, static_cast<T>(0.0));
 
     int out_h = ctx.Attr<int>("out_h");
     int out_w = ctx.Attr<int>("out_w");
 
-    auto out_size_t = ctx.Input<Tensor>("OutSize");
-    if (out_size_t != nullptr) {
+    auto out_size = ctx.Input<Tensor>("OutSize");
+    if (out_size != nullptr) {
       Tensor sizes;
-      framework::TensorCopy(*out_size_t, platform::CPUPlace(), &sizes);
+      framework::TensorCopy(*out_size, platform::CPUPlace(), &sizes);
       auto size_data = sizes.data<int>();
       out_h = size_data[0];
       out_w = size_data[1];
     }
 
-    int batch_size = d_input_t->dims()[0];
-    int channels = d_input_t->dims()[1];
-    int in_h = d_input_t->dims()[2];
-    int in_w = d_input_t->dims()[3];
+    int n = input_grad->dims()[0];
+    int c = input_grad->dims()[1];
+    int in_h = input_grad->dims()[2];
+    int in_w = input_grad->dims()[3];
 
     int in_hw = in_h * in_w;
     int out_hw = out_h * out_w;
-    int in_chw = channels * in_hw;
-    int out_chw = channels * out_hw;
+    int in_chw = c * in_hw;
+    int out_chw = c * out_hw;
 
     T ratio_h = (out_h > 1) ? static_cast<T>(in_h - 1) / (out_h - 1) : 0.f;
     T ratio_w = (out_w > 1) ? static_cast<T>(in_w - 1) / (out_w - 1) : 0.f;
 
     if (in_h == out_h && in_w == out_w) {
-      memcpy(d_input, d_output, d_input_t->numel() * sizeof(T));
-    } else {
-      int threadNum = batch_size * out_chw;
-      int blocks = (threadNum + 1024 - 1) / 1024;
-
-      KeBilinearInterpBw<
-          T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
-          d_input, in_h, in_w, batch_size, in_chw, d_output, out_h, out_w,
-          batch_size, out_chw, channels, ratio_h, ratio_w);
+      memcpy(input_grad, output_grad, input_grad->numel() * sizeof(T));
+      return;
     }
+
+    int threadNum = n * out_chw;
+    int blocks = (threadNum + 1024 - 1) / 1024;
+
+    KeNearestNeighborInterpBw<
+        T><<<blocks, 1024, 0, ctx.cuda_device_context().stream()>>>(
+        input_grad_data, in_h, in_w, n, in_chw, output_grad_data, out_h, out_w,
+        n, out_chw, c, ratio_h, ratio_w);
   }
 };
 
@@ -206,5 +183,5 @@ class NearestNeighborInterpGradOpCUDAKernel : public framework::OpKernel<T> {
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(nearest_neighbor_interp,
                         ops::NearestNeighborInterpOpCUDAKernel<float>);
-REGISTER_OP_CUDA_KERNEL(nearest_neighborinterp_grad,
+REGISTER_OP_CUDA_KERNEL(nearest_neighbor_interp_grad,
                         ops::NearestNeighborInterpGradOpCUDAKernel<float>);

python/paddle/fluid/layers/nn.py

@@ -101,6 +101,7 @@ __all__ = [
     'image_resize',
     'image_resize_short',
     'resize_bilinear',
+    'resize_nearest',
     'gather',
     'scatter',
     'sequence_scatter',
@@ -5584,6 +5585,7 @@ def image_resize(input,
     Supporting resample methods:
 
         'BILINEAR' : Bilinear interpolation
+        'NEAREST' : Nearest neighbor interpolation
 
     Args:
         input (Variable): The input tensor of image resize layer,
@@ -5610,13 +5612,17 @@ def image_resize(input,
 
             out = fluid.layers.image_resize(input, out_shape=[12, 12])
     """
-    resample_methods = {'BILINEAR': 'bilinear_interp'}
+    resample_methods = {
+        'BILINEAR': 'bilinear_interp',
+        'NEAREST': 'nearest_neighbor_interp'
+    }
     if resample not in resample_methods:
         raise ValueError(
-            "The 'resample' of image_resize can only be 'BILINEAR' currently.")
+            "The 'resample' of image_resize can only be 'BILINEAR' and 'NEAREST' currently."
+        )
     if out_shape is None and scale is None:
         raise ValueError("One of out_shape and scale must not be None")
-    helper = LayerHelper('bilinear_interp', **locals())
+    helper = LayerHelper(resample_methods[resample], **locals())
     dtype = helper.input_dtype()
 
     def _is_list_or_turple_(data):
@@ -5672,6 +5678,29 @@ def resize_bilinear(input, out_shape=None, scale=None, name=None):
     return image_resize(input, out_shape, scale, name, 'BILINEAR')
 
 
+@templatedoc(op_type="bilinear_interp")
+def resize_nearest(input, out_shape=None, scale=None, name=None):
+    """
+    ${comment}
+
+    Args:
+        input(${x_type}): ${x_comment}.
+
+        out_shape(${out_size_type}): ${out_size_comment}.
+
+        scale(float|None): The multiplier for the input height or width. At
+             least one of out_shape or scale must be set. And out_shape has
+             a higher priority than scale. Default: None.
+
+        name(str|None): The output variable name.
+
+    Returns:
+        ${out_comment}.
+    """
+
+    return image_resize(input, out_shape, scale, name, 'NEAREST')
+
+
 def image_resize_short(input, out_short_len, resample='BILINEAR'):
     """
     Resize a batch of images. The short edge of input images will be

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

@@ -485,6 +485,16 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(output)
         print(str(program))
 
+    def test_resize_bilinear(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[3, 9, 6], dtype="float32")
+            output = layers.resize_nearest(x, out_shape=[12, 12])
+            self.assertIsNotNone(output)
+            output = layers.resize_nearest(x, scale=3)
+            self.assertIsNotNone(output)
+        print(str(program))
+
     def test_polygon_box_transform(self):
         program = Program()
         with program_guard(program):