pytorch的torchvision.ops.roi_align这个算子真的是坑我好多天啊！害我连续加班半个月！二阶段目标检测后面用roi_align来提取特征。
接口官方说明：https://pytorch.org/vision/stable/generated/torchvision.ops.roi_align.html?highlight=roi_align#torchvision.ops.roi_align

boxes (Tensor[K, 5] or List[Tensor[L, 4]]) – the box coordinates in (x1, y1, x2, y2) format where the regions will be taken from. The coordinate must satisfy 0 <= x1 < x2 and 0 <= y1 < y2. If a single Tensor is passed, then the first column should contain the index of the corresponding element in the batch, i.e. a number in [0, N - 1]. If a list of Tensors is passed, then each Tensor will correspond to the boxes for an element i in the batch.

我做的是caffe转pytorch，caffe网络用到了roi_align这个算子。caffe的网络prototxt是这么的：

layer {name: "persion_roi_pooling"type: "ROIAlignment"bottom: "p3_conv"  #[b 128 24 72]bottom: "persion_detection_out" #[1 1 18 7]top: "persion_roi_pooling" #[18 128 7 7]propagate_down: truepropagate_down: falseroi_alignment_param {pooled_height: 7pooled_width: 7}
}

这里解释一下，p3_conv是卷积网络的featuremap，其shape是[b 128 24 72]
persion_detection_out是第一阶段输出检测出18个目标，7是[b,label,score,xmin,ymin,xmax,ymax]. 其实roialign里面只需要5个就可以了[b,xmin,ymin,xmax,ymax]，label和score用不到。
然后roialign的输出persion_roi_pooling是[18 128 7 7]。
这里就是把之前的batch丢了，现在是18, 18是目标个数。然后后续还是需要卷积做一些回归任务，比如回归深度，然后接入卷积最后的输出的是[18 1 1 1], 然后和gt那边一堆逻辑操作下来也是的shape是[18， 1]. 所以这个就可以接入smoothl1做回归任务了。
这是caffe的，我们只需要搞懂输入输出就可以了。这里注意一下，输入的bottom: "persion_detection_out" #[1 1 18 7]，这里xmin，ymin是0.2,0.1之类的相对值。

然后到pytorch这边，网上的示例确实也是这样的：https://blog.csdn.net/Bit_Coders/article/details/121203584
box = torch.tensor([[0.0,0.375,0.875,0.625]])
然后自然而然的我在我的实现中也这样。

最后整个pytorch弄完毕，不收敛啊，loss巨大！不知道哪里出问题，然后一层层排查，固定caffe输出和pytorch验证。
验证下来就是经过roialign这个算子之后两边不一样！
当初觉得是两个框架实现这么复杂逻辑哪里不一样正常，我就直接用pytorch的训练就可以了。但是loss依旧大不收敛！！！

自闭了，这个任务搞了好久，任务延期再延，加班再加！

这里略过很多。。
最后发现pytorch的roialign不是和网上说的一样啊，他输入的bbox框坐标是需要相对于input的坐标的啊！比如inpu的featuremap的width是24，那么就要求框坐标是[0-23]之间的数！
在pytorch源码里面/pytorch-master/caffe2/operators/roi_align_op.cu

#include "caffe2/operators/roi_align_op.h"#include <stdio.h>
#include <cfloat>
#include "caffe2/core/context_gpu.h"
#include "caffe2/utils/math.h"namespace caffe2 {namespace {template <typename T>
__device__ T bilinear_interpolate(const T* bottom_data,const int height,const int width,T y,T x) {// deal with cases that inverse elements are out of feature map boundaryif (y < -1.0 || y > height || x < -1.0 || x > width) {// emptyreturn 0;}if (y <= 0) {y = 0;}if (x <= 0) {x = 0;}int y_low = (int)y;int x_low = (int)x;int y_high;int x_high;if (y_low >= height - 1) {y_high = y_low = height - 1;y = (T)y_low;} else {y_high = y_low + 1;}if (x_low >= width - 1) {x_high = x_low = width - 1;x = (T)x_low;} else {x_high = x_low + 1;}T ly = y - y_low;T lx = x - x_low;T hy = 1. - ly, hx = 1. - lx;// do bilinear interpolationT v1 = bottom_data[y_low * width + x_low];T v2 = bottom_data[y_low * width + x_high];T v3 = bottom_data[y_high * width + x_low];T v4 = bottom_data[y_high * width + x_high];T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx;T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);return val;
}template <typename T>
__global__ void RoIAlignForward(const int nthreads,const T* bottom_data,const T spatial_scale,const int channels,const int height,const int width,const int pooled_height,const int pooled_width,const int sampling_ratio,const T* bottom_rois,int roi_cols,T* top_data,bool continuous_coordinate) {CUDA_1D_KERNEL_LOOP(index, nthreads) {// (n, c, ph, pw) is an element in the pooled outputint 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;// RoI could have 4 or 5 columnsconst T* offset_bottom_rois = bottom_rois + n * roi_cols;int roi_batch_ind = 0;if (roi_cols == 5) {roi_batch_ind = offset_bottom_rois[0];offset_bottom_rois++;}// Do not using rounding; this implementation detail is criticalT roi_offset = continuous_coordinate ? T(0.5) : 0;T roi_start_w = offset_bottom_rois[0] * spatial_scale - roi_offset;T roi_start_h = offset_bottom_rois[1] * spatial_scale - roi_offset;T roi_end_w = offset_bottom_rois[2] * spatial_scale - roi_offset;T roi_end_h = offset_bottom_rois[3] * spatial_scale - roi_offset;T roi_width = roi_end_w - roi_start_w;T roi_height = roi_end_h - roi_start_h;if (!continuous_coordinate) { // backward compatibility// Force malformed ROIs to be 1x1roi_width = c10::cuda::compat::max(roi_width, (T)1.);roi_height = c10::cuda::compat::max(roi_height, (T)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);const T* offset_bottom_data =bottom_data + (roi_batch_ind * channels + c) * height * width;// We use roi_bin_grid to sample the grid and mimic integralint roi_bin_grid_h = (sampling_ratio > 0)? sampling_ratio: ceil(roi_height / pooled_height); // e.g., = 2int roi_bin_grid_w =(sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width);// We do average (integral) pooling inside a binconst T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4T output_val = 0.;for (int iy = 0; iy < roi_bin_grid_h; iy++) // e.g., iy = 0, 1{const T y = roi_start_h + ph * bin_size_h +static_cast<T>(iy + .5f) * bin_size_h /static_cast<T>(roi_bin_grid_h); // e.g., 0.5, 1.5for (int ix = 0; ix < roi_bin_grid_w; ix++) {const T x = roi_start_w + pw * bin_size_w +static_cast<T>(ix + .5f) * bin_size_w /static_cast<T>(roi_bin_grid_w);T val = bilinear_interpolate(offset_bottom_data, height, width, y, x);output_val += val;}}output_val /= count;top_data[index] = output_val;}
}} // namespacetemplate <>
C10_EXPORT bool RoIAlignOp<float, CUDAContext>::RunOnDevice() {auto& X = Input(0); // Input data to poolauto& R = Input(1); // RoIs// RoI pooled dataif (R.numel() == 0) {// Handle empty roisOutput(0, {0, X.dim32(1), pooled_h_, pooled_w_}, at::dtype<float>());return true;}assert(sampling_ratio_ >= 0);auto* Y = Output(0, {R.dim32(0), X.dim32(1), pooled_h_, pooled_w_}, at::dtype<float>());int output_size = Y->numel();RoIAlignForward<float><<<CAFFE_GET_BLOCKS(output_size),CAFFE_CUDA_NUM_THREADS,0,context_.cuda_stream()>>>(output_size,X.data<float>(),spatial_scale_,X.dim32(1),X.dim32(2),X.dim32(3),pooled_h_,pooled_w_,sampling_ratio_,R.data<float>(),R.dim32(1),Y->mutable_data<float>(),aligned_);C10_CUDA_KERNEL_LAUNCH_CHECK();return true;
}REGISTER_CUDA_OPERATOR(RoIAlign, RoIAlignOp<float, CUDAContext>);
} // namespace caffe2using RoIAlignOpFloatCUDA = caffe2::RoIAlignOp<float, caffe2::CUDAContext>;C10_EXPORT_CAFFE2_OP_TO_C10_CUDA(RoIAlign, RoIAlignOpFloatCUDA);

通过这段代码：

// Do not using rounding; this implementation detail is criticalT roi_offset = continuous_coordinate ? T(0.5) : 0;T roi_start_w = offset_bottom_rois[0] * spatial_scale - roi_offset;T roi_start_h = offset_bottom_rois[1] * spatial_scale - roi_offset;T roi_end_w = offset_bottom_rois[2] * spatial_scale - roi_offset;T roi_end_h = offset_bottom_rois[3] * spatial_scale - roi_offset;T roi_width = roi_end_w - roi_start_w;T roi_height = roi_end_h - roi_start_h;if (!continuous_coordinate) { // backward compatibility// Force malformed ROIs to be 1x1roi_width = c10::cuda::compat::max(roi_width, (T)1.);roi_height = c10::cuda::compat::max(roi_height, (T)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);

特别是通过这里，roi_width = c10::cuda::compat::max(roi_width, (T)1.);， 这里还要求roi_width最小值为1.

所以我明白了，这里是认为输入框坐标是原图上面的，然后通过spatial_scale来映射到input大小。比如一般是下采样8倍做roialign，所以框坐标是原图上面坐标，然后spatial_scale放1/8就可以了。

所以网上那些介绍roialign示例的时候输入框还是0.1之类的小数这些都是错误的！当然也有对的，比如下面链接里面提到：

假设候选框坐标为左上角(0,105)，右下角：(230，250)，原图和featureMap的spaceRatio为32，那么映射到featureMap上的候选框为：左上角：(0，105/32)，即为(0，3.28125)；右下角：(230/32，250/32),即为(7.1875,7.8125)，那么候选框在特征图上的区域即为下图中红色区域。注意，这里并没有对坐标进行取整，因此是精确的坐标，这就解决了问题二

https://zhuanlan.zhihu.com/p/565986126?utm_id=0

ps：当然我改完pytorch这边，和caffe对比，经过roialign输出还是不一样。但是loss正常了。
(⊙o⊙)…
暂时就不深究了吧！