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Merge pull request #5831 from wanghaox/roi_pool

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Roi pool operator
release/0.11.0
wanghaox 8 years ago committed by GitHub
parent 65c859db7a cf5b598642
commit 0690cca758
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165
paddle/operators/roi_pool_op.cc
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/roi_pool_op.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
static constexpr int kROISize = 5;
class ROIPoolOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of ROIPoolOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("ROIs"),
"Input(ROIs) of ROIPoolOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of ROIPoolOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Argmax"),
"Output(Argmax) of ROIPoolOp should not be null.");
auto input_dims = ctx->GetInputDim("X");
auto rois_dims = ctx->GetInputDim("ROIs");
PADDLE_ENFORCE(input_dims.size() == 4,
"The format of input tensor is NCHW.");
PADDLE_ENFORCE(rois_dims.size() == 2,
"ROIs should be a 2-D tensor of shape (num_rois, 5)"
"given as [[batch_id, x1, y1, x2, y2], …].");
PADDLE_ENFORCE(rois_dims[1] == kROISize,
"ROIs should be a 2-D tensor of shape (num_rois, 5)"
"given as [[batch_id, x1, y1, x2, y2], …].");
int pooled_height = ctx->Attrs().Get<int>("pooled_height");
int pooled_width = ctx->Attrs().Get<int>("pooled_width");
float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
PADDLE_ENFORCE_GT(pooled_height, 0,
"The pooled output height must greater than 0");
PADDLE_ENFORCE_GT(pooled_width, 0,
"The pooled output width must greater than 0");
PADDLE_ENFORCE_GT(spatial_scale, 0.0f,
"The spatial scale must greater than 0");
auto out_dims = input_dims;
out_dims[0] = rois_dims[0];
out_dims[1] = input_dims[1];
out_dims[2] = pooled_height;
out_dims[3] = pooled_width;
ctx->SetOutputDim("Out", out_dims);
ctx->SetOutputDim("Argmax", out_dims);
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
};
class ROIPoolGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
"The gradient of Out should not be null.");
PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
"The gradient of X should not be null.");
ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
}
protected:
framework::OpKernelType GetKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
ctx.device_context());
}
};
class ROIPoolOpMaker : public framework::OpProtoAndCheckerMaker {
public:
ROIPoolOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X",
"(Tensor), "
"the input of ROIPoolOp. "
"The format of input tensor is NCHW. Where N is batch size, "
"C is the number of input channels, "
"H is the height of the feature, and "
"W is the width of the feature.");
AddInput("ROIs",
"(Tensor), "
"ROIs (Regions of Interest) to pool over. "
"should be a 2-D tensor of shape (num_rois, 5)"
"given as [[batch_id, x1, y1, x2, y2], …]. "
"Where batch_id is the id of the data, "
"(x1, y1) is the top left coordinates, and "
"(x2, y2) is the bottom right coordinates.");
AddOutput("Out",
"(Tensor), "
"The output of ROIPoolOp is a 4-D tensor with shape "
"(num_rois, channels, pooled_h, pooled_w).");
AddOutput("Argmax",
"(Tensor), "
"Argmaxes corresponding to indices in X used "
"for gradient computation. Only output "
"if arg “is_test” is false.").AsIntermediate();
AddAttr<float>("spatial_scale",
"(float, default 1.0), "
"Multiplicative spatial scale factor "
"to translate ROI coords from their input scale "
"to the scale used when pooling.")
.SetDefault(1.0);
AddAttr<int>("pooled_height",
"(int, default 1), "
"The pooled output height.")
.SetDefault(1);
AddAttr<int>("pooled_width",
"(int, default 1), "
"The pooled output width.")
.SetDefault(1);
AddComment(R"DOC(
ROIPool operator
ROI Pooling for Faster-RCNN. The link below is a further introduction:
https://stackoverflow.com/questions/43430056/what-is-roi-layer-in-fast-rcnn
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(roi_pool, ops::ROIPoolOp, ops::ROIPoolOpMaker,
roi_pool_grad, ops::ROIPoolGradOp);
REGISTER_OP_CPU_KERNEL(
roi_pool,
ops::CPUROIPoolOpKernel<paddle::platform::CPUPlace, float>,
ops::CPUROIPoolOpKernel<paddle::platform::CPUPlace, double>);
REGISTER_OP_CPU_KERNEL(
roi_pool_grad,
ops::CPUROIPoolGradOpKernel<paddle::platform::CPUPlace, float>,
ops::CPUROIPoolOpKernel<paddle::platform::CPUPlace, double>);

232
paddle/operators/roi_pool_op.cu
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/roi_pool_op.h"
#include "paddle/platform/cuda_helper.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
static constexpr int kNumCUDAThreads = 512;
static constexpr int kNumMaxinumNumBlocks = 4096;
static constexpr int kROISize = 5;
static inline int NumBlocks(const int N) {
return std::min((N + kNumCUDAThreads - 1) / kNumCUDAThreads,
kNumMaxinumNumBlocks);
}
template <typename T>
__global__ void GPUROIPoolForward(
const int nthreads, const T* input_data, const int64_t* input_rois,
const float spatial_scale, const int channels, const int height,
const int width, const int pooled_height, const int pooled_width,
T* output_data, int64_t* argmax_data) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int offset = blockDim.x * gridDim.x;
for (size_t i = index; i < nthreads; i += offset) {
int pw = index % pooled_width;
int ph = (index / pooled_width) % pooled_height;
int c = (index / pooled_width / pooled_height) % channels;
int n = index / pooled_width / pooled_height / channels;
const int64_t* offset_input_rois = input_rois + n * kROISize;
int roi_batch_ind = offset_input_rois[0];
int roi_start_w = round(offset_input_rois[1] * spatial_scale);
int roi_start_h = round(offset_input_rois[2] * spatial_scale);
int roi_end_w = round(offset_input_rois[3] * spatial_scale);
int roi_end_h = round(offset_input_rois[4] * spatial_scale);
int roi_width = max(roi_end_w - roi_start_w + 1, 1);
int roi_height = max(roi_end_h - roi_start_h + 1, 1);
T bin_size_h = static_cast<T>(roi_height) / static_cast<T>(pooled_height);
T bin_size_w = static_cast<T>(roi_width) / static_cast<T>(pooled_width);
int hstart = static_cast<int>(floor(static_cast<T>(ph) * bin_size_h));
int wstart = static_cast<int>(floor(static_cast<T>(pw) * bin_size_w));
int hend = static_cast<int>(ceil(static_cast<T>(ph + 1) * bin_size_h));
int wend = static_cast<int>(ceil(static_cast<T>(pw + 1) * bin_size_w));
hstart = min(max(hstart + roi_start_h, 0), height);
hend = min(max(hend + roi_start_h, 0), height);
wstart = min(max(wstart + roi_start_w, 0), width);
wend = min(max(wend + roi_start_w, 0), width);
bool is_empty = (hend <= hstart) || (wend <= wstart);
T maxval = is_empty ? 0 : -std::numeric_limits<T>::max();
int maxidx = -1;
const T* offset_input_data =
input_data + (roi_batch_ind * channels + c) * height * width;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
int input_data_index = h * width + w;
if (offset_input_data[input_data_index] > maxval) {
maxval = offset_input_data[input_data_index];
maxidx = input_data_index;
}
}
}
output_data[index] = maxval;
if (argmax_data) {
argmax_data[index] = maxidx;
}
}
}
template <typename T>
__global__ void GPUROIPoolBackward(
const int nthreads,
const int64_t* input_rois,
const T* output_grad,
const int64_t* argmax_data,
const int num_rois,
const float spatial_scale,
const int channels,
const int height,
const int width,
const int pooled_height,
const int pooled_width,
T* input_grad) {
int index = blockIdx.x * blockDim.x + threadIdx.x;
int offset = blockDim.x * gridDim.x;
for (int i = index; i < nthreads; i += offset) {
int pw = index % pooled_width;
int ph = (index / pooled_width) % pooled_height;
int c = (index / pooled_width / pooled_height) % channels;
int n = index / pooled_width / pooled_height / channels;
const int64_t* offset_input_rois = input_rois + n * kROISize;
int roi_batch_ind = offset_input_rois[0];
int input_offset = (roi_batch_ind * channels + c) * height * width;
int output_offset = (n * channels + c) * pooled_height * pooled_width;
const T* offset_output_grad = output_grad + output_offset;
T* offset_input_grad = input_grad + input_offset;
const int64_t* offset_argmax_data = argmax_data + output_offset;
int argmax = offset_argmax_data[ph * pooled_width + pw];
if (argmax != -1) {
platform::CudaAtomicAdd(offset_input_grad + argmax,
static_cast<T>(offset_output_grad[ph * pooled_width + pw]));
}
}
}
template <typename Place, typename T>
class GPUROIPoolOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<Tensor>("X");
auto* rois = ctx.Input<Tensor>("ROIs");
auto* out = ctx.Output<Tensor>("Out");
auto* argmax = ctx.Output<Tensor>("Argmax");
auto pooled_height = ctx.Attr<int>("pooled_height");
auto pooled_width = ctx.Attr<int>("pooled_width");
auto spatial_scale = ctx.Attr<float>("spatial_scale");
auto in_dims = in->dims();
auto in_stride = framework::stride(in_dims);
int channels = in_dims[1];
int height = in_dims[2];
int width = in_dims[3];
size_t rois_num = rois->dims()[0];
if (rois_num== 0) return;
int output_size = out->numel();
int blocks = NumBlocks(output_size);
int threads = kNumCUDAThreads;
GPUROIPoolForward<T>
<<<blocks, threads, 0, ctx.cuda_device_context().stream()>>>(
output_size,
in->data<T>(),
rois->data<int64_t>(),
spatial_scale,
channels,
height,
width,
pooled_height,
pooled_width,
out->mutable_data<T>(ctx.GetPlace()),
argmax->mutable_data<int64_t>(ctx.GetPlace()));
}
};
template <typename Place, typename T>
class GPUROIPoolGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<Tensor>("X");
auto* rois = ctx.Input<Tensor>("ROIs");
auto* argmax = ctx.Input<Tensor>("Argmax");
auto* out_grad =
ctx.Input<Tensor>(framework::GradVarName("Out"));
auto* x_grad =
ctx.Output<Tensor>(framework::GradVarName("X"));
auto pooled_height = ctx.Attr<int>("pooled_height");
auto pooled_width = ctx.Attr<int>("pooled_width");
auto spatial_scale = ctx.Attr<float>("spatial_scale");
size_t rois_num = rois->dims()[0];
int channels = in->dims()[1];
int height = in->dims()[2];
int width = in->dims()[3];
if (x_grad) {
x_grad->mutable_data<T>(ctx.GetPlace());
math::SetConstant<Place, T> set_zero;
set_zero(ctx.device_context(), x_grad, static_cast<T>(0));
int output_grad_size = out_grad->numel();
int blocks = NumBlocks(output_grad_size);
int threads = kNumCUDAThreads;
if (output_grad_size > 0) {
GPUROIPoolBackward<T>
<<<blocks, threads, 0, ctx.cuda_device_context().stream()>>>(
output_grad_size,
rois->data<int64_t>(),
out_grad->data<T>(),
argmax->data<int64_t>(),
rois_num,
spatial_scale,
channels,
height,
width,
pooled_height,
pooled_width,
x_grad->mutable_data<T>(ctx.GetPlace()));
}
}
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
roi_pool,
ops::GPUROIPoolOpKernel<paddle::platform::GPUPlace, float>,
ops::GPUROIPoolOpKernel<paddle::platform::GPUPlace, double>);
REGISTER_OP_GPU_KERNEL(
roi_pool_grad,
ops::GPUROIPoolGradOpKernel<paddle::platform::GPUPlace, float>,
ops::GPUROIPoolOpKernel<paddle::platform::GPUPlace, double>);

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paddle/operators/roi_pool_op.h
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
template <typename Place, typename T>
class CPUROIPoolOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* rois = ctx.Input<framework::Tensor>("ROIs");
auto* out = ctx.Output<framework::Tensor>("Out");
auto* argmax = ctx.Output<framework::Tensor>("Argmax");
auto pooled_height = ctx.Attr<int>("pooled_height");
auto pooled_width = ctx.Attr<int>("pooled_width");
auto spatial_scale = ctx.Attr<float>("spatial_scale");
auto in_dims = in->dims();
int batch_size = in_dims[0];
int channels = in_dims[1];
int height = in_dims[2];
int width = in_dims[3];
int rois_num = rois->dims()[0];
auto in_stride = framework::stride(in_dims);
auto argmax_stride = framework::stride(argmax->dims());
auto roi_stride = framework::stride(rois->dims());
auto out_stride = framework::stride(out->dims());
const T* input_data = in->data<T>();
const int64_t* rois_data = rois->data<int64_t>();
T* output_data = out->mutable_data<T>(ctx.GetPlace());
int64_t* argmax_data = argmax->mutable_data<int64_t>(ctx.GetPlace());
for (int n = 0; n < rois_num; ++n) {
int roi_batch_id = rois_data[0];
PADDLE_ENFORCE_GE(roi_batch_id, 0);
PADDLE_ENFORCE_LT(roi_batch_id, batch_size);
rois_data += roi_stride[0];
}
rois_data = rois->data<int64_t>();
for (int n = 0; n < rois_num; ++n) {
int roi_batch_id = rois_data[0];
int roi_start_w = round(rois_data[1] * spatial_scale);
int roi_start_h = round(rois_data[2] * spatial_scale);
int roi_end_w = round(rois_data[3] * spatial_scale);
int roi_end_h = round(rois_data[4] * spatial_scale);
// Force malformed ROIs to be 1x1
int roi_height = std::max(roi_end_h - roi_start_h + 1, 1);
int roi_width = std::max(roi_end_w - roi_start_w + 1, 1);
const float bin_size_h =
static_cast<float>(roi_height) / static_cast<float>(pooled_height);
const float bin_size_w =
static_cast<float>(roi_width) / static_cast<float>(pooled_width);
const T* batch_data = input_data + roi_batch_id * in_stride[0];
for (int c = 0; c < channels; ++c) {
for (int ph = 0; ph < pooled_height; ++ph) {
for (int pw = 0; pw < pooled_width; ++pw) {
// Compute pooling region for this output unit:
// start (included) = floor(ph * roi_height / pooled_height_)
// end (excluded) = ceil((ph + 1) * roi_height / pooled_height_)
int hstart =
static_cast<int>(floor(static_cast<float>(ph) * bin_size_h));
int wstart =
static_cast<int>(floor(static_cast<float>(pw) * bin_size_w));
int hend =
static_cast<int>(ceil(static_cast<float>(ph + 1) * bin_size_h));
int wend =
static_cast<int>(ceil(static_cast<float>(pw + 1) * bin_size_w));
hstart = std::min(std::max(hstart + roi_start_h, 0), height);
hend = std::min(std::max(hend + roi_start_h, 0), height);
wstart = std::min(std::max(wstart + roi_start_w, 0), width);
wend = std::min(std::max(wend + roi_start_w, 0), width);
const int pool_index = ph * pooled_width + pw;
// Define an empty pooling region to be zero
bool is_empty = (hend <= hstart) || (wend <= wstart);
output_data[pool_index] =
is_empty ? 0 : -std::numeric_limits<T>::max();
argmax_data[pool_index] = -1;
for (int h = hstart; h < hend; ++h) {
for (int w = wstart; w < wend; ++w) {
const int index = h * width + w;
if (batch_data[index] > output_data[pool_index]) {
output_data[pool_index] = batch_data[index];
argmax_data[pool_index] = index;
}
}
}
}
}
batch_data += in_stride[1];
output_data += out_stride[1];
argmax_data += argmax_stride[1];
}
// Increment ROI data pointer
rois_data += roi_stride[0];
}
return;
}
};
template <typename Place, typename T>
class CPUROIPoolGradOpKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& ctx) const override {
auto* in = ctx.Input<framework::Tensor>("X");
auto* rois = ctx.Input<framework::Tensor>("ROIs");
auto* argmax = ctx.Input<framework::Tensor>("Argmax");
auto* out_grad =
ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* x_grad =
ctx.Output<framework::Tensor>(framework::GradVarName("X"));
auto pooled_height = ctx.Attr<int>("pooled_height");
auto pooled_width = ctx.Attr<int>("pooled_width");
if (x_grad) {
int channels = in->dims()[1];
auto in_stride = framework::stride(in->dims());
auto roi_stride = framework::stride(rois->dims());
const int64_t* rois_data = rois->data<int64_t>();
int rois_num = rois->dims()[0];
T* x_grad_data = x_grad->mutable_data<T>(ctx.GetPlace());
math::SetConstant<Place, T> set_zero;
set_zero(ctx.device_context(), x_grad, static_cast<T>(0));
size_t roi_offset = roi_stride[0];
size_t batch_offset = in_stride[0];
size_t channel_offset = in_stride[1];
const T* out_grad_data = out_grad->data<T>();
size_t pool_channel_offset = pooled_height * pooled_width;
const int64_t* argmax_data = argmax->data<int64_t>();
for (size_t n = 0; n < rois_num; ++n) {
size_t roi_batch_idx = rois_data[0];
T* batch_grad_data = x_grad_data + batch_offset * roi_batch_idx;
for (int c = 0; c < channels; ++c) {
for (int ph = 0; ph < pooled_height; ++ph) {
for (int pw = 0; pw < pooled_width; ++pw) {
size_t pool_index = ph * pooled_width + pw;
if (argmax_data[pool_index] >= 0) {
size_t index = static_cast<size_t>(argmax_data[pool_index]);
batch_grad_data[index] += out_grad_data[pool_index];
}
}
}
batch_grad_data += channel_offset;
out_grad_data += pool_channel_offset;
argmax_data += pool_channel_offset;
}
rois_data += roi_offset;
}
}
}
};
} // namespace operators
} // namespace paddle

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python/paddle/v2/fluid/tests/test_roi_pool_op.py
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import unittest
import numpy as np
import math
import sys
from op_test import OpTest
class TestROIPoolOp(OpTest):
def set_data(self):
self.init_test_case()
self.make_rois()
self.calc_roi_pool()
self.inputs = {
'X': self.x,
'ROIs': self.rois}
self.attrs = {
'spatial_scale': self.spatial_scale,
'pooled_height': self.pooled_height,
'pooled_width': self.pooled_width}
self.outputs = {
'Out': self.outs,
'Argmax': self.argmaxes}
def init_test_case(self):
self.batch_size = 5
self.channels = 3
self.height = 6
self.width = 4
# n, c, h, w
self.x_dim = (self.batch_size, self.channels,
self.height, self.width)
self.spatial_scale = 1.0/4.0
self.pooled_height = 2
self.pooled_width = 2
self.rois_num = 2
self.x = np.random.random(self.x_dim).astype('float32')
def calc_roi_pool(self):
out_data = np.zeros(
(self.rois_num, self.channels,
self.pooled_height, self.pooled_width))
argmax_data = np.zeros(
(self.rois_num, self.channels,
self.pooled_height, self.pooled_width))
for i in range(self.rois_num):
roi = self.rois[i]
roi_batch_id = roi[0]
roi_start_w = int(round(roi[1] * self.spatial_scale))
roi_start_h = int(round(roi[2] * self.spatial_scale))
roi_end_w = int(round(roi[3] * self.spatial_scale))
roi_end_h = int(round(roi[4] * self.spatial_scale))
roi_height = int(max(roi_end_h - roi_start_h + 1, 1));
roi_width = int(max(roi_end_w - roi_start_w + 1, 1));
x_i = self.x[roi_batch_id]
bin_size_h = float(roi_height) / float(self.pooled_height)
bin_size_w = float(roi_width) / float(self.pooled_width)
for c in range(self.channels):
for ph in range(self.pooled_height):
for pw in range(self.pooled_width):
hstart = int(math.floor(ph * bin_size_h))
wstart = int(math.floor(pw * bin_size_w))
hend = int(math.ceil((ph + 1) * bin_size_h))
wend = int(math.ceil((pw + 1) * bin_size_w))
hstart = min(max(hstart + roi_start_h, 0), self.height)
hend = min(max(hend + roi_start_h, 0), self.height)
wstart = min(max(wstart + roi_start_w, 0), self.width)
wend = min(max(wend + roi_start_w, 0), self.width)
is_empty = (hend <= hstart) or (wend <= wstart)
if is_empty:
out_data[i, c, ph, pw] = 0
else:
out_data[i, c, ph, pw] = -sys.float_info.max
argmax_data[i, c, ph, pw] = -1
for h in range(hstart, hend):
for w in range(wstart, wend):
if x_i[c, h, w] > out_data[i, c, ph, pw]:
out_data[i, c, ph, pw] = x_i[c, h, w]
argmax_data[i, c, ph, pw] = h * \
self.width + w
self.outs = out_data.astype('float32')
self.argmaxes = argmax_data.astype('int64')
def make_rois(self):
rois = []
batch_ids = np.random.randint(0, self.batch_size, size=self.rois_num)
for i in range(self.rois_num):
x1 = np.random.random_integers(
0, self.width / self.spatial_scale - self.pooled_width)
y1 = np.random.random_integers(
0, self.height / self.spatial_scale - self.pooled_height)
x2 = np.random.random_integers(
x1 + self.pooled_width, self.width / self.spatial_scale)
y2 = np.random.random_integers(
y1 + self.pooled_height, self.height / self.spatial_scale)
roi = [batch_ids[i], x1, y1, x2, y2]
rois.append(roi)
self.rois = np.array(rois).astype("int64")
def setUp(self):
self.op_type = "roi_pool"
self.set_data()
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(['X'], 'Out')
if __name__ == '__main__':
unittest.main()
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