update DFQ/EQ/Evaluate int8 perchannel quant tool (#1112)

* update DFQ/EQ/Evaluate int8 perchannel quant tool

* apply code-format changes

Co-authored-by: Your Name <you@example.com>
Co-authored-by: BowShotDS <BowShotDS@users.noreply.github.com>

tools/quantize/CMakeLists.txt

@@ -30,6 +30,8 @@ IF (${TENGINE_TARGET_PROCESSOR} MATCHES "X86")
         ADD_EXECUTABLE(
             ${name}
             ./quant_save_graph.cpp
+            ./algorithm/quant_dfq.cpp
+            ./algorithm/quant_eq.cpp
             ./quant_utils.cpp
             ../save_graph/save_graph.cpp
             ../save_graph/tm2_op_save.cpp

tools/quantize/README.md

@@ -76,7 +76,7 @@ Status      : int8, per-channel, symmetric
 Before use the quant tool, **you need Float32 tmfile and Calibration Dataset**, the image num of calibration dataset we suggest to use 500-1000.
 
 ```
-$ .quant_tool_int8  -m ./mobilenet_fp32.tmfile -i ./dataset -o ./mobilenet_int8.tmfile -g 3,224,224 -w 104.007,116.669,122.679 -s 0.017,0.017,0.017
+$ .quant_tool_int8  -m ./mobilenet_fp32.tmfile -i ./dataset -o ./mobilenet_int8.tmfile -g 3,224,224 -w 104.007,116.669,122.679 -s 0.017,0.017,0.017 -z 1
 
 ---- Tengine Post Training Quantization Tool ----
 
@@ -111,6 +111,38 @@ Thread num  : 1
 [Quant Tools Info]: Step 4, quantize activation tensor done.
 [Quant Tools Info]: Step 5, quantize weight tensor done.
 [Quant Tools Info]: Step 6, save Int8 tmfile done, ./mobilenet_int8.tmfile
+[Quant Tools Info]: Step Evaluate, evaluate quantitative losses
+cosin   0    32  avg  0.995317  ### 0.000000 0.953895 0.998249 0.969256 ...
+cosin   1    32  avg  0.982403  ### 0.000000 0.902383 0.964436 0.873998 ...
+cosin   2    64  avg  0.976753  ### 0.952854 0.932301 0.982766 0.958503 ...
+cosin   3    64  avg  0.981889  ### 0.976637 0.981754 0.987276 0.970671 ...
+cosin   4   128  avg  0.979728  ### 0.993999 0.991858 0.990438 0.992766 ...
+cosin   5   128  avg  0.970351  ### 0.772556 0.989541 0.986996 0.989563 ...
+cosin   6   128  avg  0.954545  ### 0.950125 0.922964 0.946804 0.972852 ...
+cosin   7   128  avg  0.977192  ### 0.994728 0.972071 0.995353 0.992700 ...
+cosin   8   256  avg  0.977426  ### 0.968429 0.991248 0.991274 0.994450 ...
+cosin   9   256  avg  0.962224  ### 0.985255 0.969171 0.958762 0.967461 ...
+cosin  10   256  avg  0.954253  ### 0.984353 0.935643 0.656188 0.929778 ...
+cosin  11   256  avg  0.971987  ### 0.997596 0.967681 0.476525 0.999115 ...
+cosin  12   512  avg  0.972861  ### 0.968920 0.905907 0.993918 0.622953 ...
+cosin  13   512  avg  0.959161  ### 0.935686 0.000000 0.642560 0.994388 ...
+cosin  14   512  avg  0.963903  ### 0.979613 0.957169 0.976440 0.902512 ...
+cosin  15   512  avg  0.963226  ### 0.977065 0.965819 0.998149 0.905297 ...
+cosin  16   512  avg  0.960935  ### 0.861674 0.972926 0.950579 0.987609 ...
+cosin  17   512  avg  0.961057  ### 0.738472 0.987884 0.999124 0.995397 ...
+cosin  18   512  avg  0.960127  ### 0.935455 0.968909 0.970831 0.981240 ...
+cosin  19   512  avg  0.963755  ### 0.972628 0.992305 0.999518 0.799737 ...
+cosin  20   512  avg  0.949364  ### 0.922776 0.896038 0.945079 0.971338 ...
+cosin  21   512  avg  0.961256  ### 0.902256 0.896438 0.923361 0.973974 ...
+cosin  22   512  avg  0.946552  ### 0.963806 0.982075 0.878965 0.929992 ...
+cosin  23   512  avg  0.953677  ### 0.953880 0.996364 0.936540 0.930796 ...
+cosin  24  1024  avg  0.941197  ### 0.000000 0.992507 1.000000 0.994460 ...
+cosin  25  1024  avg  0.973546  ### 1.000000 0.889181 0.000000 0.998084 ...
+cosin  26  1024  avg  0.869351  ### 0.522966 0.000000 0.987009 0.000000 ...
+cosin  27     1  avg  0.974982  ### 0.974982 
+cosin  28     1  avg  0.974982  ### 0.974982 
+cosin  29     1  avg  0.974982  ### 0.974982 
+cosin  30     1  avg  0.978486  ### 0.978486 
 
 ---- Tengine Int8 tmfile create success, best wish for your INT8 inference has a low accuracy loss...\(^0^)/ ----
 ```