從chainer中copy出來的。官方有cpu和gpu的分別實現
尊重原創，請看原始碼chainer_roipooling
import cupy as cp
import numpy as np
bottom_data = cp.random.randn(1,
3,40,
40, dtype=np.float32)
# 特徵feature
batch, channels, height, width = bottom_data.shape
spatial_scale =
1.0# 原始特徵和feature的比例
rois = cp.array([[
0,2,
2,10,
10],[
0,3,
3,20,
20]], dtype=np.float32)
# rois
pooled_weight =
7# 池化之後的寬度
pooled_height =
7# 池化之後的高度
top_data = cp.zeros((2
,3, pooled_height, pooled_weight)
, dtype=np.float32)
# 輸出的feature map
argmax_data = cp.zeros(top_data.shape, np.int32)
# 最大值對應的索引
## 定義核函式
roi_pooling_2d_fwd = cp.elementwisekernel(
''' raw t bottom_data, t spatial_scale, int32 channels,
int32 height, int32 width, int32 pooled_height, int32 pooled_width,
raw t bottom_rois
''','t top_data, int32 argmax_data'
,'''
// pos in output filter
int pw = i % pooled_width;
int ph = (i / pooled_width) % pooled_height;
int c = (i / pooled_width / pooled_height) % channels;
int num = i / pooled_width / pooled_height / channels;
int roi_batch_ind = bottom_rois[num * 5 + 0];
int roi_start_w = round(bottom_rois[num * 5 + 1] * spatial_scale);
int roi_start_h = round(bottom_rois[num * 5 + 2] * spatial_scale);
int roi_end_w = round(bottom_rois[num * 5 + 3] * spatial_scale);
int roi_end_h = round(bottom_rois[num * 5 + 4] * spatial_scale);
// force malformed rois to be 1x1
int roi_width = max(roi_end_w - roi_start_w + 1, 1);
int roi_height = max(roi_end_h - roi_start_h + 1, 1);
float bin_size_h = static_cast(roi_height)
/ static_cast(pooled_height);
float bin_size_w = static_cast(roi_width)
/ static_cast(pooled_width);
int hstart = static_cast(floor(static_cast(ph)
* bin_size_h));
int wstart = static_cast(floor(static_cast(pw)
* bin_size_w));
int hend = static_cast(ceil(static_cast(ph + 1)
* bin_size_h));
int wend = static_cast(ceil(static_cast(pw + 1)
* bin_size_w));
// add roi offsets and clip to input boundaries
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);
// define an empty pooling region to be zero
float maxval = is_empty ? 0 : -1e+37;
// if nothing is pooled, argmax=-1 causes nothing to be backprop'd
int maxidx = -1;
int data_offset = (roi_batch_ind * channels + c) * height * width;
for (int h = hstart; h < hend; ++h) }}
top_data = maxval;
argmax_data = maxidx;
''','roi_pooling_2d_fwd'
)roi_pooling_2d_fwd(bottom_data, spatial_scale, channels, height, width,
pooled_height, pooled_weight, rois, top_data, argmax_data)
top_data.shape
>>
>(2
,3,7
,7)
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