更新faq文档 (#2831)

* update faq.md about ncnn-produce-wrong-result

* update faq.md  about print Mat content and visualize Mat content

* update faq.md about efficient roi resize rotate

* update faq.md about convert to fp16

* update faq.md about use ncnn with pytorch or onnx

* update faq.md about add the url of netron

* update faq.md about add the url of convertmodel

Co-authored-by: runrunrun1994 <runrunrun1994@163.com>

docs/faq.md

@@ -96,6 +96,8 @@ opencv rtti -> opencv-mobile
 
 ## pytorch - onnx
 
+[use ncnn with pytorch or onnx](https://github.com/Tencent/ncnn/wiki/use-ncnn-with-pytorch-or-onnx)
+
 ## tensorflow 1.x/2.x - keras
 
 ## tensorflow 2.x - mlir
@@ -106,8 +108,12 @@ onnx-simplifier 静态shape
 
 ## convertmodel
 
+[https://convertmodel.com/](https://convertmodel.com/) **[@大老师](https://github.com/daquexian)**
+
 ## netron
 
+[https://github.com/lutzroeder/netron](https://github.com/lutzroeder/netron)
+
 ## 怎么生成有固定 shape 信息的模型？
 
 Input      0=w 1=h 2=c
@@ -116,6 +122,8 @@ why gpu能更快
 
 ## ncnnoptimize 怎么转成 fp16 模型
 
+`ncnnoptimize model.param model.bin yolov5s-opt.param yolov5s-opt.bin 65536`
+
 ## ncnnoptimize 怎样查看模型的 FLOPS / 内存占用情况
 
 ## 怎么修改模型支持动态 shape？
@@ -216,6 +224,8 @@ https://github.com/Tencent/ncnn/wiki/use-ncnn-with-pytorch-or-onnx
 
 ## 如何 resize crop rotate 图片
 
+[efficient roi resize rotate](https://github.com/Tencent/ncnn/wiki/efficient-roi-resize-rotate)
+
 ## 如何人脸5点对齐
 
 get_affine_transform
@@ -301,13 +311,92 @@ for msvc++:
 for g++:
 
     setenv("OMP_WAIT_POLICY", "passive", 1)
-
+    
     reference: https://stackoverflow.com/questions/34439956/vc-crash-when-freeing-a-dll-built-with-openmp
 
 # 跑出来的结果对不上
 
+[ncnn-produce-wrong-result](https://github.com/Tencent/ncnn/wiki/FAQ-ncnn-produce-wrong-result)
+
 ## 如何打印 ncnn::Mat 的值？
 
+```C++
+void pretty_print(const ncnn::Mat& m)
+{
+    for (int q=0; q<m.c; q++)
+    {
+        const float* ptr = m.channel(q);
+        for (int y=0; y<m.h; y++)
+        {
+            for (int x=0; x<m.w; x++)
+            {
+                printf("%f ", ptr[x]);
+            }
+            ptr += m.w;
+            printf("\n");
+        }
+        printf("------------------------\n");
+    }
+}
+```
+
+## 如何可视化 ncnn::Mat 的值？
+
+```
+void visualize(const char* title, const ncnn::Mat& m)
+{
+    std::vector<cv::Mat> normed_feats(m.c);
+
+    for (int i=0; i<m.c; i++)
+    {
+        cv::Mat tmp(m.h, m.w, CV_32FC1, (void*)(const float*)m.channel(i));
+
+        cv::normalize(tmp, normed_feats[i], 0, 255, cv::NORM_MINMAX, CV_8U);
+
+        cv::cvtColor(normed_feats[i], normed_feats[i], cv::COLOR_GRAY2BGR);
+
+        // check NaN
+        for (int y=0; y<m.h; y++)
+        {
+            const float* tp = tmp.ptr<float>(y);
+            uchar* sp = normed_feats[i].ptr<uchar>(y);
+            for (int x=0; x<m.w; x++)
+            {
+                float v = tp[x];
+                if (v != v)
+                {
+                    sp[0] = 0;
+                    sp[1] = 0;
+                    sp[2] = 255;
+                }
+
+                sp += 3;
+            }
+        }
+    }
+
+    int tw = m.w < 10 ? 32 : m.w < 20 ? 16 : m.w < 40 ? 8 : m.w < 80 ? 4 : m.w < 160 ? 2 : 1;
+    int th = (m.c - 1) / tw + 1;
+
+    cv::Mat show_map(m.h * th, m.w * tw, CV_8UC3);
+    show_map = cv::Scalar(127);
+
+    // tile
+    for (int i=0; i<m.c; i++)
+    {
+        int ty = i / tw;
+        int tx = i % tw;
+
+        normed_feats[i].copyTo(show_map(cv::Rect(tx * m.w, ty * m.h, m.w, m.h)));
+    }
+
+    cv::resize(show_map, show_map, cv::Size(0,0), 2, 2, cv::INTER_NEAREST);
+    cv::imshow(title, show_map);
+}
+```
+
+
+
 
 ## 总是输出第一张图的结果
 复用 Extractor？！