作者丨欧阳慧宇@知乎（已授权）
来源丨https://zhuanlan.zhihu.com/p/361900997
编辑丨极市平台
极市导读
由于目前solo系列在GitHub中没有转ncnn的项目，作者详细记录了自己solov2的ncnn实现以及后处理后处理优化的一些策略，给需要的人一点借鉴。附有代码地址。
SOLOV2简介solo大家都知道，核心思想是：将分割问题转化为位置分类问题，从而做到不需要anchor，不需要normalization，不需要bounding box detections的实例分割。
https://github.com/Epiphqny/SOLOv2
缘由目前solo系列在github中没有转ncnn的项目
solov2的思想比较简单明了，但是转ncnn还是需要很多比较复杂操作，想在用ncnn实现更加了解solov2内部的算法流程，也熟悉ncnn的一些其他op操作
文字有很多干货：param手动修改、动态op、加速技巧等等
1 、torch导出onnx在导出onnx之前有几部操作：
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修改split_feats: onnx不支持F.interpolate() 中 scale_factor < 1 ；因此对split_feats做了修改为nn.Maxpool 卷积，后面转ncnn手动修改；
def split_feats(self, feats): return [ self.maxpool(feats[0]), # F.interpolate(feats[0],scale_factor=0.5,mode='bilinear'), feats[1], feats[2], feats[3], F.interpolate(feats[4], size=feats[3].shape[-2:], mode='bilinear')]
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添加输入参数:
添加onnx参数，金生coord改为手动输入
def forward(self, feats, eval=False,onnx=False): if onnx: feats,p3_input,p4_input,p5_input = feats new_feats = self.split_feats(feats)
featmap_sizes = [featmap.size()[-2:] for featmap in new_feats] if onnx: new_feats[0] = [new_feats[0],p3_input] new_feats[1] = [new_feats[1],p3_input] new_feats[2] = [new_feats[2],p4_input] new_feats[3] = [new_feats[3],p5_input] new_feats[4] = [new_feats[4],p5_input]
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实现转换代码
实现转换代码，并onnxsim优化，这里需要注意的是输入尽量小一点，因为solo的四个输出头的size比较小（40，36，24，16，12），输入较大时会使F.interpolate() 中 scale_factor < 1 ，input_size = 256时fpn最后一层上采样的scale_factor刚好可以等于2（256%64=0）
from torch import nnimport torchfrom mmcv import Configfrom mmdet.models.builder import build_backbone,build_head,build_neck
class SOLOV2(nn.Module): def __init__(self,confg): super(SOLOV2,self).__init__() self.cfg = Config.fromfile(confg) self.cfg = self.cfg["model"] self.backbone = build_backbone(self.cfg["backbone"]) self.neck = build_neck(self.cfg["neck"]) self.bbox_head = build_head(self.cfg["bbox_head"]) def forward(self,x,p3_input,p4_input,p5_input): x = self.backbone(x) x = self.neck(x) feature_pred,kernel_pred,cate_pred = self.bbox_head([x,p3_input,p4_input,p5_input],eval=True,onnx=True) return feature_pred,kernel_pred,cate_pred
cfg = "./configs/solov2/solov2_r101_3x.py"pretrained = "./weights/solov2.pth"#net = SOLOV2(cfg)state_dict = torch.load(pretrained, map_location=lambda storage, loc: storage)["state_dict"]res = net.load_state_dict(state_dict,strict=False)net.eval()
input_size = 256dummy_input1 = torch.randn(1, 3, input_size, input_size)dummy_input2 = torch.randn(1, 2, input_size//8, input_size//8)dummy_input3 = torch.randn(1, 2, input_size//16, input_size//16)dummy_input4 = torch.randn(1, 2, input_size//32, input_size//32)input_names = ["input","p3_input","p4_input","p5_input"]output_names = ["feature_pred","kernel_pred1","kernel_pred2","kernel_pred3","kernel_pred4","kernel_pred5", "cate_pred1","cate_pred2","cate_pred3","cate_pred4","cate_pred5" ]save_name = "./onnx/solov2.onnx"# torch.onnx.export(model, (dummy_input1, dummy_input2, dummy_input3), "C3AE.onnx", verbose=True, input_names=input_names, output_names=output_names)torch.onnx.export(net, (dummy_input1,dummy_input2,dummy_input3,dummy_input4),save_name , verbose=False, input_names=input_names, output_names=output_names)
import osos.system("python -m onnxsim {0} {0}".format(save_name))
2、onnx转ncnn
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onnx2ncnn
./onnx2ncnn solov2.onnx solov2.param solov2.bin
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ncnn 参数手工优化
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将之前转onnx时的maxpool改为上采样操作：
改为
2、将所有的kernel_pred和cate_pred之前的Interp参数改为对用的w和h：
改为
对应40尺寸的改好了，后面32、24、26、12也是类似的；
3、ncnn推理实现-
coord输入实现
对比torch半的coord实现：
x_range = torch.linspace(-1, 1, kernel_feat.shape[-1], device=kernel_feat.device) y_range = torch.linspace(-1, 1, kernel_feat.shape[-2], device=kernel_feat.device) y, x = torch.meshgrid(y_range, x_range) y = y.expand([kernel_feat.shape[0], 1, -1, -1]) x = x.expand([kernel_feat.shape[0], 1, -1, -1]) coord_feat = torch.cat([x, y], 1)
因为对应stride只有8、16、32，因此ncnn需要生成3个对应尺寸的Mat：
size_t elemsize = sizeof(float);
ncnn::Mat x_p3; ncnn::Mat x_p4; ncnn::Mat x_p5; // coord conv int pw = int(target_w / 8); int ph = int(target_h / 8); x_p3.create(pw, ph, 2, elemsize); float step_h = 2.f / (ph - 1); float step_w = 2.f / (pw - 1); for (int h = 0; h < ph; h++) { for (int w = 0; w < pw; w++) { x_p3.channel(0)[h * pw + w] = -1.f + step_w * (float) w; x_p3.channel(1)[h * pw + w] = -1.f + step_h * (float) h; } }
pw = int(target_w / 16); ph = int(target_h / 16); x_p4.create(pw, ph, 2, elemsize); step_h = 2.f / (ph - 1); step_w = 2.f / (pw - 1); for (int h = 0; h < ph; h++) { for (int w = 0; w < pw; w++) { x_p4.channel(0)[h * pw + w] = -1.f + step_w * (float) w; x_p4.channel(1)[h * pw + w] = -1.f + step_h * (float) h; } }
pw = int(target_w / 32); ph = int(target_h / 32); x_p5.create(pw, ph, 2, elemsize); step_h = 2.f / (ph - 1); step_w = 2.f / (pw - 1); for (int h = 0; h < ph; h++) { for (int w = 0; w < pw; w++) { x_p5.channel(0)[h * pw + w] = -1.f + step_w * (float) w; x_p5.channel(1)[h * pw + w] = -1.f + step_h * (float) h; } }
将其作为输入：
ncnn::Extractor ex = solov2.create_extractor(); ex.input("input", in); ex.input("p3_input", x_p3); ex.input("p4_input", x_p4); ex.input("p5_input", x_p5);
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抽取feature_pred、kernerl_pred、cate_pred特征：
ncnn::Mat feature_pred, cate_pred1, cate_pred2, cate_pred3, cate_pred4, cate_pred5, kernel_pred1, kernel_pred2, kernel_pred3, kernel_pred4, kernel_pred5;
ex.extract("cate_pred1", cate_pred1); ex.extract("cate_pred2", cate_pred2); ex.extract("cate_pred3", cate_pred3); ex.extract("cate_pred4", cate_pred4); ex.extract("cate_pred5", cate_pred5); ex.extract("kernel_pred1", kernel_pred1); ex.extract("kernel_pred2", kernel_pred2); ex.extract("kernel_pred3", kernel_pred3); ex.extract("kernel_pred4", kernel_pred4); ex.extract("kernel_pred5", kernel_pred5); ex.extract("feature_pred", feature_pred);
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生成分割mask图
前面已经有了kernel_pred和feature_pred，接下来就是生成mask图，这就用到了low-level op api<这里说一句ncnn真灵活，谁用谁知道>，目的是实现torch中的动态conv2d，对应torch实现为：
ins_i = F.conv2d(feature_pred, kernel, groups=N).view(N, self.seg_num_grids[i] ** 2, h, w)
ncnn的low-level op api使用wiki：
https://github.com/Tencent/ncnn/wiki
创建ncnn::Option
ncnn::Option opt;
opt.num_threads = num_threads;
opt.use_fp16_storage = false;
opt.use_packing_layout = false;
实现分割的预测功能函数
static void ins_decode(const ncnn::Mat &kernel_pred, const ncnn::Mat &feature_pred, ncnn::Mat *ins_pred, int c_in, int c_out,
ncnn::Option &opt) {
ncnn::Layer *op = ncnn::create_layer("Convolution");
ncnn::ParamDict pd;
pd.set(0, c_out);
pd.set(1, 1);
pd.set(6, c_in * c_out);
op->load_param(pd);
ncnn::Mat weights[1];
weights[0].create(c_in * c_out);
float *kernel_pred_data = (float *) kernel_pred.data;
for (int i = 0; i < c_in * c_out; i++) {
weights[0][i] = kernel_pred_data[i];
}
op->load_model(ncnn::ModelBinFromMatArray(weights));
op->create_pipeline(opt);
ncnn::Mat temp_ins;
op->forward(feature_pred, temp_ins, opt);
*ins_pred = temp_ins;
op->destroy_pipeline(opt);
delete op;
}
将每个kernel_pre转换为分割图：
ncnn::Mat ins_pred1, ins_pred2, ins_pred3, ins_pred4, ins_pred5;
int c_in = feature_pred.c;
ins_decode(kernel_pred1, feature_pred, &ins_pred1, c_in, 40 * 40,opt);
ins_decode(kernel_pred2, feature_pred, &ins_pred2, c_in, 36 * 36,opt);
ins_decode(kernel_pred3, feature_pred, &ins_pred3, c_in, 24 * 24,opt);
ins_decode(kernel_pred4, feature_pred, &ins_pred4, c_in, 16 * 16,opt);
ins_decode(kernel_pred5, feature_pred, &ins_pred5, c_in, 12 * 12,opt);
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后处理
有了这一切后就是后处理啦，贴代码：
功能函数
void generate_res(ncnn::Mat &cate_pred, ncnn::Mat &ins_pred, std::vector&objects, float cate_thresh, float conf_thresh, int img_w, int img_h, int num_class, float stride) { int w = cate_pred.w; int h = cate_pred.h; int w_ins = ins_pred.w; int h_ins = ins_pred.h; for (int q = 0; q < num_class; q++) { const float *cate_ptr = cate_pred.channel(q); for (int i = 0; i < h; i++) { for (int j = 0; j < w; j++) { int index = i * w + j; float cate_socre = cate_ptr[index]; if (cate_socre < cate_thresh) { continue; } const float *ins_ptr = ins_pred.channel(index); cv::Mat mask(h_ins, w_ins, CV_32FC1); float sum_mask = 0.f; int count_mask = 0; { mask = cv::Scalar(0.f); float *mp = (float *) mask.data; for (int m = 0; m < w_ins * h_ins; m++) { float mask_score = sigmoid(ins_ptr[m]);
if (mask_score > 0.5) { mp[m] += mask_score; sum_mask += mask_score; count_mask++; } } } if (count_mask < stride) { continue; } float mask_score = sum_mask / (float(count_mask) + 1e-6);
// float socre = mask_score * cate_socre; float socre = mask_score * cate_socre;
if (socre < conf_thresh) { continue; }
cv::Mat mask2; cv::resize(mask, mask2, cv::Size(img_w, img_h)); Object obj; obj.mask = cv::Mat(img_h, img_w, CV_8UC1); float sum_mask_y = 0.f; float sum_mask_x = 0.f; int area = 0; { obj.mask = cv::Scalar(0); for (int y = 0; y < img_h; y++) { const float *mp2 = mask2.ptr(y); uchar *bmp = obj.mask.ptr(y); for (int x = 0; x < img_w; x++) {
if (mp2[x] > 0.5f) { bmp[x] = 255; sum_mask_y += (float) y; sum_mask_x += (float) x; area++;
} else bmp[x] = 0; } } } obj.cx = int(sum_mask_x / area); obj.cy = int(sum_mask_y / area); obj.label = q + 1; obj.prob = socre; objects[q].push_back(obj);
} } }
}
每个head的解码：
std::vectorclass_candidates; class_candidates.resize(num_class); generate_res(cate_pred1,ins_pred1,class_candidates,cate_thresh,confidence_thresh,img_w,img_h,num_class,8.f); generate_res(cate_pred2,ins_pred2,class_candidates,cate_thresh,confidence_thresh,img_w,img_h,num_class,8.f); generate_res(cate_pred3,ins_pred3,class_candidates,cate_thresh,confidence_thresh,img_w,img_h,num_class,16.f); generate_res(cate_pred4,ins_pred4,class_candidates,cate_thresh,confidence_thresh,img_w,img_h,num_class,32.f); generate_res(cate_pred5,ins_pred5,class_candidates,cate_thresh,confidence_thresh,img_w,img_h,num_class,32.f);
最后就是nms了，nms实现的比较低效：
objects.clear(); for (int i = 0; i < (int) class_candidates.size(); i++) { std::vector&candidates = class_candidates[i];
qsort_descent_inplace(candidates);
std::vectorpicked; nms_sorted_segs(candidates, picked, nms_threshold,img_w,img_h);
for (int j = 0; j < (int) picked.size(); j++) { int z = picked[j]; objects.push_back(candidates[z]); } }
qsort_descent_inplace(objects);
至此，已经基本完成了所有工作啦：来预测一张图看看：
可以看出来结果还是非常好的QAQ
速度优化重中之重来啦，本人针对SOLO的特点做了一处优化，就是kernel_pred的部分，在kernel_pred1部分输出特征图为256x40x40，转换为cond2d的核就为1600x256，1600的通道在stride=4的特征图上耗时巨大，那怎么来优化呢。
SOLO预测的cate_pred1分支预测了40x40=1600个网格，但并不是所有网格都是需要的，只有大于预值得才需要，那么在kernel_pred1的输出部分仅仅只需要cate_pred1大于阈值对应的核就好，基于这个思想就能减少非常多的通道，进而减少计算量。
话不多说，具体这样实现：
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首先获取cate_pred大于阈值的index：
功能函数为：
static void kernel_pick(const ncnn::Mat &cate_pred, std::vector&picked, int num_class, float cate_thresh){ int w = cate_pred.w; int h = cate_pred.h; for (int q = 0; q < num_class; q++) { const float *cate_ptr = cate_pred.channel(q); for (int i = 0; i < h; i++) { for (int j = 0; j < w; j++) { int index = i * w + j; float cate_score = cate_ptr[index]; if (cate_score < cate_thresh) { continue; } else picked.push_back(index); } } }}
获取每个cate_pred分支的index结果：
std::vectorkernel_picked1, kernel_picked2, kernel_picked3, kernel_picked4, kernel_picked5; kernel_pick(cate_pred1, kernel_picked1, num_class, cate_thresh); kernel_pick(cate_pred2, kernel_picked2, num_class, cate_thresh); kernel_pick(cate_pred3, kernel_picked3, num_class, cate_thresh); kernel_pick(cate_pred4, kernel_picked4, num_class, cate_thresh); kernel_pick(cate_pred5, kernel_picked5, num_class, cate_thresh);
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在ins_decode功能函数中加入map，来保存index和ins_pred的通道索引的映射关系
static void ins_decode(const ncnn::Mat &kernel_pred, const ncnn::Mat &feature_pred, std::vector&kernel_picked, std::map &kernel_map, ncnn::Mat *ins_pred, int c_in, ncnn::Option &opt) { std::setkernel_pick_set; kernel_pick_set.insert(kernel_picked.begin(), kernel_picked.end()); int c_out = kernel_pick_set.size(); if (c_out > 0) { ncnn::Layer *op = ncnn::create_layer("Convolution"); ncnn::ParamDict pd; pd.set(0, c_out); pd.set(1, 1); pd.set(6, c_in * c_out); op->load_param(pd); ncnn::Mat weights[1]; weights[0].create(c_in * c_out); float *kernel_pred_data = (float *) kernel_pred.data; std::set::iterator pick_c; int count_c = 0; for (pick_c = kernel_pick_set.begin(); pick_c != kernel_pick_set.end(); pick_c++) { kernel_map[*pick_c] = count_c; for (int j = 0; j < c_in; j++) { weights[0][count_c * c_in + j] = kernel_pred_data[c_in * (*pick_c) + j]; }
count_c++; }
op->load_model(ncnn::ModelBinFromMatArray(weights)); op->create_pipeline(opt); ncnn::Mat temp_ins; op->forward(feature_pred, temp_ins, opt); *ins_pred = temp_ins; op->destroy_pipeline(opt); delete op; }}
这样后面generate_res里面通过map来获取ins_pred的特征：
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速度对比
先来打印一下ins_pred的通道
std::cout << ins_pred1.h << "," << ins_pred1.w << "," << ins_pred1.c << std::endl;
std::cout << ins_pred2.h << "," << ins_pred2.w << "," << ins_pred2.c << std::endl;
std::cout << ins_pred3.h << "," << ins_pred3.w << "," << ins_pred3.c << std::endl;
std::cout << ins_pred4.h << "," << ins_pred4.w << "," << ins_pred4.c << std::endl;
std::cout << ins_pred5.h << "," << ins_pred5.w << "," << ins_pred5.c << std::endl;
没做通道筛选之前：
做了通道筛选后：
从1600+1296+576+256+144 = 3872 -> 62+40 = 102 ,对于stride=4的特征图这计算量减少了许多，如果在cate_pred的输出做maxpool的nms，通道数还会减少几倍。
resnet_r101的backbone下，耗时6825.4ms ->5226.95ms(backbone太厚了，本人训练一个mbv2版本，已经上传百度云，700ms左右的样子，14年老mac cpu单线程)
总结实践下如何在ncnn上使用动态op和corrd_conv，以及后处理后处理优化的一些策略，给需要的人一点借鉴。
项目地址
https://github.com/DayBreak-u/SOLOV2_ncnn
