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提交 df4a3544 编写于 作者: D dengkaipeng
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nearest neighbor interp add cuda kernel. test=develop

上级 97556119
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Showing with 111 addition and 93 deletion
paddle/fluid/API.spec
浏览文件 @ df4a3544
......@@ -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
浏览文件 @ df4a3544
......@@ -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
浏览文件 @ df4a3544
......@@ -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
浏览文件 @ df4a3544
......@@ -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
浏览文件 @ df4a3544
......@@ -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):
......
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