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add align_corners and align_mode for image_resize

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test=develop
revert-15296-async_double_buffered_py_reader
tink2123 6 years ago
parent e07900d317
commit 48cc484643
6 changed files with 529 additions and 100 deletions
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73
paddle/fluid/operators/interpolate_op.cc
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@ -82,6 +82,18 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker {
"bilinear interpolation and \"nearest\" for nearest "
"neighbor interpolation.")
.SetDefault("bilinear");
AddAttr<bool>(
"align_corners",
"an optinal bool. Defaults to True. "
"If True, the centers of 4 corner pixels of the input and output "
"tensors are aligned, preserving the values at the corner pixels, "
"if Flase, are not aligned")
.SetDefault(true);
AddAttr<int>("align_mode",
"(int, default \'0\'), align_corners mode , can be \'0\' "
"for pytorch calculation method, can be \'1\' for "
"tensorflow calculation method.")
.SetDefault(0);
AddComment(R"DOC(
This operator samples input X to given output shape by using specified
interpolation method, the interpolation methods can be \"nearest\"
@ -98,6 +110,67 @@ 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
of interpolation can be selected by them.
Example:
for scale:
if align_corners = True and out_{size}>1 :
scale_{factor} = (in_{size}-1.0)/(out_{size}-1.0)
else:
scale_{factor} = float(in_{size}/out_{size})
Nearest neighbor interpolation:
case 1:
align_corners = False
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = \left \lfloor {H_{in} * scale_{}factor}} \right \rfloor
W_out = \left \lfloor {W_{in} * scale_{}factor}} \right \rfloor
case 2:
align_corners = True
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = round(H_{in} * scale_{factor})
W_out = round(W_{in} * scale_{factor})
Bilinear interpolation:
case 1:
align_corners = False , align_mode = 0
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = (H_{in}+0.5) * scale_{factor} - 0.5
W_out = (W_{in}+0.5) * scale_{factor} - 0.5
case 2:
align_corners = False , align_mode = 1
or
align_corners = True
input : (N,C,H_in,W_in)
output: (N,C,H_out,W_out) where:
H_out = H_{in} * scale_{factor}
W_out = W_{in} * scale_{factor}
For details of nearest neighbor interpolation, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Nearest-neighbor_interpolation

96
paddle/fluid/operators/interpolate_op.cu
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@ -23,7 +23,8 @@ __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 float ratio_h, const float ratio_w) {
const size_t num_channels, const float ratio_h, const float ratio_w,
const bool align_corners) {
int nthreads = output_h * output_w;
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
@ -35,10 +36,14 @@ __global__ void KeNearestNeighborInterpFw(
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 = static_cast<int>(ratio_h * out_img_idy + 0.5);
int in_img_idy = (align_corners)
? static_cast<int>(ratio_h * out_img_idy + 0.5)
: static_cast<int>(ratio_h * out_img_idy);
int out_img_idx = tid % out_img_w;
int in_img_idx = static_cast<int>(ratio_w * out_img_idx + 0.5);
int in_img_idx = (align_corners)
? static_cast<int>(ratio_w * out_img_idx + 0.5)
: static_cast<int>(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];
@ -50,7 +55,8 @@ __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 float ratio_h, const float ratio_w) {
const size_t num_channels, const float ratio_h, const float ratio_w,
const bool align_corners) {
int nthreads = output_h * output_w;
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
@ -62,10 +68,14 @@ __global__ void KeNearestNeighborInterpBw(
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 = static_cast<int>(ratio_h * out_img_idy + 0.5);
int in_img_idy = (align_corners)
? static_cast<int>(ratio_h * out_img_idy + 0.5)
: static_cast<int>(ratio_h * out_img_idy);
int out_img_idx = tid % out_img_w;
int in_img_idx = static_cast<int>(ratio_w * out_img_idx + 0.5);
int in_img_idx = (align_corners)
? static_cast<int>(ratio_w * out_img_idx + 0.5)
: static_cast<int>(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];
@ -79,7 +89,8 @@ __global__ void KeBilinearInterpFw(
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 float ratio_h, const float ratio_w) {
const size_t num_channels, const float ratio_h, const float ratio_w,
const bool align_corners, const int align_mode) {
int nthreads = output_h * output_w;
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
@ -91,15 +102,23 @@ __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 in_img_idy = (align_mode == 0 && !align_corners)
? static_cast<int>(ratio_h * (out_img_idy + 0.5) - 0.5)
: static_cast<int>(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 h1lambda = (align_mode == 0 && !align_corners)
? 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 = ratio_w * out_img_idx;
int in_img_idx = (align_mode == 0 && !align_corners)
? static_cast<int>(ratio_w * (out_img_idx + 0.5) - 0.5)
: static_cast<int>(ratio_w * out_img_idx);
int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
T w1lambda = ratio_w * out_img_idx - in_img_idx;
T w1lambda = (align_mode == 0 && !align_corners)
? 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;
const T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
@ -118,7 +137,8 @@ __global__ void KeBilinearInterpBw(
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 ratio_w) {
const size_t num_channels, const T ratio_h, const T ratio_w,
const bool align_corners, const int align_mode) {
int nthreads = output_h * output_w;
int tid = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
@ -130,15 +150,24 @@ __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 in_img_idy = (align_mode == 0 && !align_corners)
? ratio_h * (out_img_idy + 0.5) - 0.5
: 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 h1lambda = (align_mode == 0 && !align_corners)
? 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 = ratio_w * out_img_idx;
int in_img_idx = (align_mode == 0 && !align_corners)
? ratio_w * (out_img_idx + 0.5) - 0.5
: ratio_w * out_img_idx;
int w_id = (in_img_idx < in_img_w - 1) ? 1 : 0;
T w1lambda = ratio_w * out_img_idx - in_img_idx;
T w1lambda = (align_mode == 0 && !align_corners)
? 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;
T* in_pos = &in[out_id_h * input_w + channel_id * in_img_size +
@ -175,6 +204,9 @@ class InterpolateOpCUDAKernel : public framework::OpKernel<T> {
out_w = size_data[1];
}
bool align_corners = ctx.Attr<bool>("align_corners");
int align_mode = ctx.Attr<int>("align_mode");
int n = input->dims()[0];
int c = input->dims()[1];
int in_h = input->dims()[2];
@ -188,10 +220,12 @@ class InterpolateOpCUDAKernel : public framework::OpKernel<T> {
int in_chw = c * in_hw;
int out_chw = c * out_hw;
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
float ratio_h = (align_corners && out_h > 1)
? static_cast<float>(in_h - 1) / (out_h - 1)
: static_cast<float>(in_h) / out_h;
float ratio_w = (align_corners && out_w > 1)
? static_cast<float>(in_w - 1) / (out_w - 1)
: static_cast<float>(in_w) / out_w;
if (in_h == out_h && in_w == out_w) {
framework::TensorCopy(*input, ctx.GetPlace(), output);
@ -206,12 +240,12 @@ class InterpolateOpCUDAKernel : public framework::OpKernel<T> {
KeNearestNeighborInterpFw<
T><<<grid_dim, 512, 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);
out_chw, c, ratio_h, ratio_w, align_corners);
} else if ("bilinear" == interp_method) {
KeBilinearInterpFw<
T><<<grid_dim, 512, 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);
out_chw, c, ratio_h, ratio_w, align_corners, align_mode);
}
}
};
@ -234,6 +268,10 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel<T> {
int out_h = ctx.Attr<int>("out_h");
int out_w = ctx.Attr<int>("out_w");
auto out_size = ctx.Input<Tensor>("OutSize");
bool align_corners = ctx.Attr<bool>("align_corners");
int align_mode = ctx.Attr<int>("align_mode");
if (out_size != nullptr) {
Tensor sizes;
framework::TensorCopy(*out_size, platform::CPUPlace(), &sizes);
@ -252,10 +290,12 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel<T> {
int in_chw = c * in_hw;
int out_chw = c * out_hw;
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
float ratio_h = (align_corners && out_h > 1)
? static_cast<float>(in_h - 1) / (out_h - 1)
: static_cast<float>(in_h) / out_h;
float ratio_w = (align_corners && out_w > 1)
? static_cast<float>(in_w - 1) / (out_w - 1)
: static_cast<float>(in_w) / out_w;
if (in_h == out_h && in_w == out_w) {
framework::TensorCopy(*output_grad, ctx.GetPlace(), input_grad);
@ -270,12 +310,12 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel<T> {
KeNearestNeighborInterpBw<
T><<<grid_dim, 512, 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);
out_w, n, out_chw, c, ratio_h, ratio_w, align_corners);
} else if ("bilinear" == interp_method) {
KeBilinearInterpBw<
T><<<grid_dim, 512, 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);
out_w, n, out_chw, c, ratio_h, ratio_w, align_corners, align_mode);
}
}
};

102
paddle/fluid/operators/interpolate_op.h
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@ -26,14 +26,17 @@ template <typename T>
static void NearestNeighborInterpolate(const Tensor& input, Tensor* output,
const float ratio_h, const float ratio_w,
const int n, const int c,
const int out_h, const int out_w) {
const int out_h, const int out_w,
const bool align_corners) {
auto input_t = EigenTensor<T, 4>::From(input);
auto output_t = EigenTensor<T, 4>::From(*output);
for (int k = 0; k < out_h; k++) { // loop for images
int in_k = static_cast<int>(ratio_h * k + 0.5);
int in_k = (align_corners) ? static_cast<int>(ratio_h * k + 0.5)
: static_cast<int>(ratio_h * k);
for (int l = 0; l < out_w; l++) {
int in_l = static_cast<int>(ratio_w * l + 0.5);
int in_l = (align_corners) ? static_cast<int>(ratio_w * l + 0.5)
: static_cast<int>(ratio_w * l);
for (int i = 0; i < n; i++) { // loop for batches
for (int j = 0; j < c; j++) { // loop for channels
@ -48,20 +51,29 @@ template <typename T>
static void BilinearInterpolation(const Tensor& input, Tensor* output,
const float ratio_h, const float ratio_w,
const int in_h, const int in_w, const int n,
const int c, const int out_h,
const int out_w) {
const int c, const int out_h, const int out_w,
const bool align_corners,
const bool align_mode) {
auto input_t = EigenTensor<T, 4>::From(input);
auto output_t = EigenTensor<T, 4>::From(*output);
for (int k = 0; k < out_h; k++) { // loop for images
int y_n = static_cast<int>(ratio_h * k);
int y_n = (align_mode == 0 && !align_corners)
? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
: static_cast<int>(ratio_h * k);
int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
float d_n = ratio_h * k - y_n;
float d_n = (align_mode == 0 && !align_corners)
? 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 = static_cast<int>(ratio_w * l);
int x_w = (align_mode == 0 && !align_corners)
? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
: static_cast<int>(ratio_w * l);
int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
float d_w = ratio_w * l - x_w;
float d_w = (align_mode == 0 && !align_corners)
? 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
@ -78,19 +90,20 @@ static void BilinearInterpolation(const Tensor& input, Tensor* output,
}
template <typename T>
static void NearestNeighborInterpolateGrad(const Tensor& output_grad,
Tensor* input_grad,
const float ratio_h,
const float ratio_w, const int n,
const int c, const int out_h,
const int out_w) {
static void NearestNeighborInterpolateGrad(
const Tensor& output_grad, Tensor* input_grad, const float ratio_h,
const float ratio_w, const int n, const int c, const int out_h,
const int out_w, const bool align_corners) {
auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
for (int k = 0; k < out_h; k++) { // loop for images
int in_k = static_cast<int>(ratio_h * k + 0.5);
int in_k = (align_corners) ? static_cast<int>(ratio_h * k + 0.5)
: static_cast<int>(ratio_h * k);
for (int l = 0; l < out_w; l++) {
int in_l = static_cast<int>(ratio_w * l + 0.5);
int in_l = (align_corners) ? static_cast<int>(ratio_w * l + 0.5)
: static_cast<int>(ratio_w * l);
for (int i = 0; i < n; i++) { // loop for batches
for (int j = 0; j < c; j++) { // loop for channels
@ -106,19 +119,29 @@ static void BilinearInterpolationGrad(const Tensor& output_grad,
Tensor* input_grad, const float ratio_h,
const float ratio_w, const int in_h,
const int in_w, const int n, const int c,
const int out_h, const int out_w) {
const int out_h, const int out_w,
const bool align_corners,
const int align_mode) {
auto input_grad_t = EigenTensor<T, 4>::From(*input_grad);
auto output_grad_t = EigenTensor<T, 4>::From(output_grad);
for (int k = 0; k < out_h; k++) { // loop for images
int y_n = static_cast<int>(ratio_h * k);
int y_n = (align_mode == 0 && !align_corners)
? static_cast<int>(ratio_h * (k + 0.5) - 0.5)
: static_cast<int>(ratio_h * k);
int y_s = (y_n + 1) < (in_h - 1) ? (y_n + 1) : (in_h - 1);
float d_n = ratio_h * k - y_n;
float d_n = (align_mode == 0 && !align_corners)
? 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 = static_cast<int>(ratio_w * l);
int x_w = (align_mode == 0 && !align_corners)
? static_cast<int>(ratio_w * (l + 0.5) - 0.5)
: static_cast<int>(ratio_w * l);
int x_e = (x_w + 1) < (in_w - 1) ? (x_w + 1) : (in_w - 1);
float d_w = ratio_w * l - x_w;
float d_w = (align_mode == 0 && !align_corners)
? 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
@ -134,7 +157,6 @@ static void BilinearInterpolationGrad(const Tensor& output_grad,
}
}
}
template <typename T>
class InterpolateKernel : public framework::OpKernel<T> {
public:
@ -151,6 +173,8 @@ class InterpolateKernel : public framework::OpKernel<T> {
out_h = out_size_data[0];
out_w = out_size_data[1];
}
bool align_corners = ctx.Attr<bool>("align_corners");
int align_mode = ctx.Attr<int>("align_mode");
const int n = input->dims()[0];
const int c = input->dims()[1];
@ -168,17 +192,19 @@ class InterpolateKernel : public framework::OpKernel<T> {
return;
}
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
float ratio_h = (align_corners && out_h > 1)
? static_cast<float>(in_h - 1) / (out_h - 1)
: static_cast<float>(in_h) / out_h;
float ratio_w = (align_corners && out_w > 1)
? static_cast<float>(in_w - 1) / (out_w - 1)
: static_cast<float>(in_w) / out_w;
if ("bilinear" == interp_method) {
BilinearInterpolation<T>(*input, output, ratio_h, ratio_w, in_h, in_w, n,
c, out_h, out_w);
c, out_h, out_w, align_corners, align_mode);
} else if ("nearest" == interp_method) {
NearestNeighborInterpolate<T>(*input, output, ratio_h, ratio_w, n, c,
out_h, out_w);
out_h, out_w, align_corners);
}
}
};
@ -200,6 +226,8 @@ class InterpolateGradKernel : public framework::OpKernel<T> {
out_h = out_size_data[0];
out_w = out_size_data[1];
}
bool align_corners = ctx.Attr<bool>("align_corners");
int align_mode = ctx.Attr<int>("align_mode");
const int n = input->dims()[0];
const int c = input->dims()[1];
@ -217,17 +245,21 @@ class InterpolateGradKernel : public framework::OpKernel<T> {
return;
}
float ratio_h =
(out_h > 1) ? static_cast<float>(in_h - 1) / (out_h - 1) : 0.f;
float ratio_w =
(out_w > 1) ? static_cast<float>(in_w - 1) / (out_w - 1) : 0.f;
float ratio_h = (align_corners && out_h > 1)
? static_cast<float>(in_h - 1) / (out_h - 1)
: static_cast<float>(in_h) / out_h;
float ratio_w = (align_corners && out_w > 1)
? static_cast<float>(in_w - 1) / (out_w - 1)
: static_cast<float>(in_w) / out_w;
if ("bilinear" == interp_method) {
BilinearInterpolationGrad<T>(*output_grad, input_grad, ratio_h, ratio_w,
in_h, in_w, n, c, out_h, out_w);
in_h, in_w, n, c, out_h, out_w,
align_corners, align_mode);
} else if ("nearest" == interp_method) {
NearestNeighborInterpolateGrad<T>(*output_grad, input_grad, ratio_h,
ratio_w, n, c, out_h, out_w);
ratio_w, n, c, out_h, out_w,
align_corners);
}
}
};

207
python/paddle/fluid/layers/nn.py
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File diff suppressed because it is too large Load Diff

94
python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py
Unescape View File

@ -20,7 +20,13 @@ from op_test import OpTest
import paddle.fluid.core as core
def bilinear_interp_np(input, out_h, out_w, out_size=None, actual_shape=None):
def bilinear_interp_np(input,
out_h,
out_w,
out_size=None,
actual_shape=None,
align_corners=True,
align_mode=0):
"""bilinear interpolation implement in shape [N, C, H, W]"""
if out_size is not None:
out_h = out_size[0]
@ -29,25 +35,41 @@ def bilinear_interp_np(input, out_h, out_w, out_size=None, actual_shape=None):
out_h = actual_shape[0]
out_w = actual_shape[1]
batch_size, channel, in_h, in_w = input.shape
if out_h > 1:
ratio_h = ratio_w = 0.0
if (align_corners and out_h > 1):
ratio_h = (in_h - 1.0) / (out_h - 1.0)
else:
ratio_h = 0.0
if out_w > 1:
ratio_h = 1.0 * in_h / out_h
if (align_corners and out_w > 1):
ratio_w = (in_w - 1.0) / (out_w - 1.0)
else:
ratio_w = 0.0
ratio_w = 1.0 * in_w / out_w
out = np.zeros((batch_size, channel, out_h, out_w))
for i in range(out_h):
h = int(ratio_h * i)
if (align_mode == 0 and not align_corners):
h = int(ratio_h * (i + 0.5) - 0.5)
else:
h = int(ratio_h * i)
hid = 1 if h < in_h - 1 else 0
h1lambda = ratio_h * i - h
if (align_mode == 0 and not align_corners):
h1lambda = ratio_h * (i + 0.5) - 0.5 - h
else:
h1lambda = ratio_h * i - h
h2lambda = 1.0 - h1lambda
for j in range(out_w):
w = int(ratio_w * j)
if (align_mode == 0 and not align_corners):
w = int(ratio_w * (j + 0.5) - 0.5)
else:
w = int(ratio_w * j)
wid = 1 if w < in_w - 1 else 0
w1lambda = ratio_w * j - w
if (align_mode == 0 and not align_corners):
w1lambda = ratio_w * (j + 0.5) - 0.5 - w
else:
w1lambda = ratio_w * j - w
w2lambda = 1.0 - w1lambda
out[:, :, i, j] = h2lambda*(w2lambda*input[:, :, h, w] +
@ -66,7 +88,8 @@ class TestBilinearInterpOp(OpTest):
input_np = np.random.random(self.input_shape).astype("float32")
output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
self.out_size, self.actual_shape)
self.out_size, self.actual_shape,
self.align_corners, self.align_mode)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
@ -75,7 +98,9 @@ class TestBilinearInterpOp(OpTest):
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'align_mode': self.align_mode
}
self.outputs = {'Out': output_np}
@ -91,6 +116,8 @@ class TestBilinearInterpOp(OpTest):
self.out_h = 2
self.out_w = 2
self.out_size = np.array([3, 3]).astype("int32")
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpCase1(TestBilinearInterpOp):
@ -99,6 +126,8 @@ class TestBilinearInterpCase1(TestBilinearInterpOp):
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpCase2(TestBilinearInterpOp):
@ -107,6 +136,8 @@ class TestBilinearInterpCase2(TestBilinearInterpOp):
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpCase3(TestBilinearInterpOp):
@ -115,6 +146,8 @@ class TestBilinearInterpCase3(TestBilinearInterpOp):
self.input_shape = [1, 1, 128, 64]
self.out_h = 64
self.out_w = 128
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpCase4(TestBilinearInterpOp):
@ -124,6 +157,8 @@ class TestBilinearInterpCase4(TestBilinearInterpOp):
self.out_h = 1
self.out_w = 1
self.out_size = np.array([2, 2]).astype("int32")
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpCase5(TestBilinearInterpOp):
@ -133,6 +168,8 @@ class TestBilinearInterpCase5(TestBilinearInterpOp):
self.out_h = 12
self.out_w = 12
self.out_size = np.array([11, 11]).astype("int32")
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpCase6(TestBilinearInterpOp):
@ -142,6 +179,8 @@ class TestBilinearInterpCase6(TestBilinearInterpOp):
self.out_h = 64
self.out_w = 128
self.out_size = np.array([65, 129]).astype("int32")
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpActualShape(TestBilinearInterpOp):
@ -151,6 +190,8 @@ class TestBilinearInterpActualShape(TestBilinearInterpOp):
self.out_h = 64
self.out_w = 32
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpOpUint8(OpTest):
@ -162,14 +203,17 @@ class TestBilinearInterpOpUint8(OpTest):
input_np = np.random.randint(
low=0, high=256, size=self.input_shape).astype("uint8")
output_np = bilinear_interp_np(input_np, self.out_h, self.out_w,
self.out_size, self.actual_shape)
self.out_size, self.actual_shape,
self.align_corners, self.align_mode)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method
'interp_method': self.interp_method,
'align_corners': self.align_corners,
'align_mode': self.align_mode
}
self.outputs = {'Out': output_np}
@ -181,6 +225,8 @@ class TestBilinearInterpOpUint8(OpTest):
self.input_shape = [1, 3, 9, 6]
self.out_h = 10
self.out_w = 9
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpCase1Uint8(TestBilinearInterpOpUint8):
@ -189,6 +235,8 @@ class TestBilinearInterpCase1Uint8(TestBilinearInterpOpUint8):
self.input_shape = [2, 3, 128, 64]
self.out_h = 120
self.out_w = 50
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpCase2Uint8(TestBilinearInterpOpUint8):
@ -198,6 +246,26 @@ class TestBilinearInterpCase2Uint8(TestBilinearInterpOpUint8):
self.out_h = 5
self.out_w = 13
self.out_size = np.array([6, 15]).astype("int32")
self.align_corners = False
self.align_mode = 0
class TestBilinearInterpOtherMethod1(TestBilinearInterpOp):
def set_align_mode(self):
self.align_mode = 1
self.align_corners = False
class TestBilinearInterpWithMethod2(TestBilinearInterpOp):
def set_align_mode(self):
self.align_corners = True
self.align_mode = 1
class TestBilinearInterpWithMethod3(TestBilinearInterpOp):
def set_align_mode(self):
self.align_corners = True
self.align_mode = 0
if __name__ == "__main__":

57
python/paddle/fluid/tests/unittests/test_nearest_interp_op.py
Unescape View File

@ -24,7 +24,8 @@ def nearest_neighbor_interp_np(X,
out_h,
out_w,
out_size=None,
actual_shape=None):
actual_shape=None,
align_corners=True):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if out_size is not None:
out_h = out_size[0]
@ -35,17 +36,29 @@ def nearest_neighbor_interp_np(X,
n, c, in_h, in_w = X.shape
ratio_h = ratio_w = 0.0
if out_h > 1:
if (align_corners and out_h > 1):
ratio_h = (in_h - 1.0) / (out_h - 1.0)
if out_w > 1:
else:
ratio_h = 1.0 * in_h / out_h
if (align_corners and out_w > 1):
ratio_w = (in_w - 1.0) / (out_w - 1.0)
else:
ratio_w = 1.0 * in_w / out_w
out = np.zeros((n, c, out_h, out_w))
for i in range(out_h):
in_i = int(ratio_h * i + 0.5)
for j in range(out_w):
in_j = int(ratio_w * j + 0.5)
out[:, :, i, j] = X[:, :, in_i, in_j]
if align_corners:
for i in range(out_h):
in_i = int(ratio_h * i + 0.5)
for j in range(out_w):
in_j = int(ratio_w * j + 0.5)
out[:, :, i, j] = X[:, :, in_i, in_j]
else:
for i in range(out_h):
in_i = int(ratio_h * i)
for j in range(out_w):
in_j = int(ratio_w * j)
out[:, :, i, j] = X[:, :, in_i, in_j]
return out.astype(X.dtype)
@ -59,7 +72,8 @@ class TestNearestInterpOp(OpTest):
input_np = np.random.random(self.input_shape).astype("float32")
output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
self.out_size, self.actual_shape)
self.out_size, self.actual_shape,
self.align_corners)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
@ -68,7 +82,8 @@ class TestNearestInterpOp(OpTest):
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method
'interp_method': self.interp_method,
'align_corners': self.align_corners,
}
self.outputs = {'Out': output_np}
@ -84,6 +99,7 @@ class TestNearestInterpOp(OpTest):
self.out_h = 2
self.out_w = 2
self.out_size = np.array([3, 3]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpCase1(TestNearestInterpOp):
@ -92,6 +108,7 @@ class TestNearestNeighborInterpCase1(TestNearestInterpOp):
self.input_shape = [4, 1, 7, 8]
self.out_h = 1
self.out_w = 1
self.align_corners = False
class TestNearestNeighborInterpCase2(TestNearestInterpOp):
@ -100,6 +117,7 @@ class TestNearestNeighborInterpCase2(TestNearestInterpOp):
self.input_shape = [3, 3, 9, 6]
self.out_h = 12
self.out_w = 12
self.align_corners = True
class TestNearestNeighborInterpCase3(TestNearestInterpOp):
@ -108,6 +126,7 @@ class TestNearestNeighborInterpCase3(TestNearestInterpOp):
self.input_shape = [1, 1, 128, 64]
self.out_h = 64
self.out_w = 128
self.align_corners = True
class TestNearestNeighborInterpCase4(TestNearestInterpOp):
@ -117,6 +136,7 @@ class TestNearestNeighborInterpCase4(TestNearestInterpOp):
self.out_h = 1
self.out_w = 1
self.out_size = np.array([2, 2]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpCase5(TestNearestInterpOp):
@ -126,6 +146,7 @@ class TestNearestNeighborInterpCase5(TestNearestInterpOp):
self.out_h = 12
self.out_w = 12
self.out_size = np.array([11, 11]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpCase6(TestNearestInterpOp):
@ -135,6 +156,7 @@ class TestNearestNeighborInterpCase6(TestNearestInterpOp):
self.out_h = 64
self.out_w = 128
self.out_size = np.array([65, 129]).astype("int32")
self.align_corners = True
class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
@ -144,6 +166,7 @@ class TestNearestNeighborInterpActualShape(TestNearestInterpOp):
self.out_h = 64
self.out_w = 32
self.out_size = np.array([66, 40]).astype("int32")
self.align_corners = True
class TestNearestInterpOpUint8(OpTest):
@ -155,14 +178,16 @@ class TestNearestInterpOpUint8(OpTest):
input_np = np.random.randint(
low=0, high=256, size=self.input_shape).astype("uint8")
output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w,
self.out_size, self.actual_shape)
self.out_size, self.actual_shape,
self.align_corners)
self.inputs = {'X': input_np}
if self.out_size is not None:
self.inputs['OutSize'] = self.out_size
self.attrs = {
'out_h': self.out_h,
'out_w': self.out_w,
'interp_method': self.interp_method
'interp_method': self.interp_method,
'align_corners': self.align_corners
}
self.outputs = {'Out': output_np}
@ -174,6 +199,7 @@ class TestNearestInterpOpUint8(OpTest):
self.input_shape = [1, 3, 9, 6]
self.out_h = 10
self.out_w = 9
self.align_corners = True
class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8):
@ -182,6 +208,7 @@ class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8):
self.input_shape = [2, 3, 128, 64]
self.out_h = 120
self.out_w = 50
self.align_corners = False
class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8):
@ -191,6 +218,12 @@ class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8):
self.out_h = 5
self.out_w = 13
self.out_size = np.array([6, 15]).astype("int32")
self.align_corners = True
class TestNearestInterpWithoutCorners(TestNearestInterpOp):
def set_align_corners(self):
self.align_corners = False
if __name__ == "__main__":

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