Shell 在 hexagon nnlib 中的 Standalone graph_app 为 运行 时卡住
Shell gets stuck when Standalone graph_app in hexagon nnlib is ran
Shell 当我发出这个命令时没有响应。
/data/local/graph_app --flag 299 299 3 1 0 0 1 NULL 0 1 0 inputfile /data/local/tmp/img_299x299.bmp
从助手 optargs 获得了描述信息
Usage: testapp [--flag flagopt] [inputfile [inputfile...]]
flag name type default function
height int 0 Height of the input data. 0 == autodetect-square
width int 0 Width of the input data. 0 == autodetect-square
depth int 3 Depth of the input data
iters int 1 Number of times to run each input
perfdump int 0 Generate performance dump
pmu int 0 Get Performance Monitor Unit information
elementsize int 1 Element Size (uint8==1,float==4)
layer_reorder string NULL Reorder depth layers. ("210" changes RGB to BGR)
pprint_floats int 0 Pretty-Print output as floats
pprint_imagenet int 1 Pretty-print output, getting top 5 values and use imagenet categories
debug int 0 Debug verbosity level. Higher numbers get more verbosity
我有没有遗漏什么，让我知道，我使用 graphinit_med.c 只是为了检查它的工作情况，没有描述这个模型的作用。
谢谢，
没有关于使用单机版的文档graph_app，在完成代码后使其工作：
data/hvx_tf/graph_app --height 299 --width 299 --depth 3 --iters 1 --perfdump 0 --pmu 0 --elementsize 1 --pprint_floats 0 --pprint_imagenet 1 --debug 0 /data/local/tmp/keyboard_299x299.dat
>> Generate *.dat from *.jpg using `./scripts/imagedump.py`
仍然存在警告，您可以在下面的日志中看到：
return value from dspCV_initQ6() : 0
const node 1000b success
const node 1000c success
const node 1000d success
const node 1000e success
const node 1000f success
const node 10010 success
const node 10011 success
const node 10012 success
const node 10250 success
nn @ fc72cf80: id=0x0 debug_level=0
node @ fc733970: id=0x1000b type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733a20: id=0x1000c type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733a70: id=0x1000d type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733b20: id=0x1000e type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733c20: id=0x1000f type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733c70: id=0x10010 type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733d30: id=0x10011 type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733e20: id=0x10012 type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733e70: id=0x10250 type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733ec0: id=0x1024a type=0x0(INPUT) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733f60: id=0x1024b type=0xe(Flatten) n_inputs=2 n_outputs=1 padding=0(WHATEVER)
node @ fc734040: id=0x1024c type=0x29(Min_f) n_inputs=2 n_outputs=1 padding=0(WHATEVER)
node @ fc734120: id=0x1024d type=0x2b(Max_f) n_inputs=2 n_outputs=1 padding=0(WHATEVER)
node @ fc734200: id=0x1024e type=0x2d(Quantize) n_inputs=3 n_outputs=3 padding=0(WHATEVER)
node @ fc734350: id=0x1024f type=0xf(QuantizedConv2d_8x8to32) n_inputs=7 n_outputs=3 padding=2(VALID)
node @ fc7344d0: id=0x10251 type=0x13(QuantizeDownAndShrinkRange_32to8) n_inputs=3 n_outputs=3 padding=0(WHATEVER)
node @ fc734620: id=0x10252 type=0x23(QuantizedBiasAdd_8p8to32) n_inputs=6 n_outputs=3 padding=0(WHATEVER)
node @ fc734790: id=0x10253 type=0x13(QuantizeDownAndShrinkRange_32to8) n_inputs=3 n_outputs=3 padding=0(WHATEVER)
node @ fc7348e0: id=0x10254 type=0x15(QuantizedRelu_8) n_inputs=3 n_outputs=3 padding=0(WHATEVER)
node @ fc734a30: id=0x10442 type=0x2f(Dequantize) n_inputs=3 n_outputs=1 padding=0(WHATEVER)
node @ fc734b20: id=0x1044d type=0x1(OUTPUT) n_inputs=1 n_outputs=0 padding=0(WHATEVER)
21 nodes total.
Init graph done.Prepare fc72cf80 success!
Using </data/local/tmp/keyboard_299x299.dat>
filesize=268203 elementsize=1 height=299 width=299 depth=3
Run!
sum=37845659
Executing!
**execute got err: -1**
hexagon/ops/src/op_output.c:58:output 0 too small
output size=4096
Rank,Softmax,index,string
0,303036629674309094288042513882152960.000000,575,pick
1,303036292954618408664607741320364032.000000,461,terrapin
2,303036292954618408664607741320364032.000000,445,electric ray
3,79327539388858010780491752432205824.000000,833,bulletproof vest
4,78902425827607254052570293577187328.000000,936,volleyball
AppReported: 4294967296
一旦我获得独立应用程序以最高精度预测示例图像，我将更新答案。
Shell 当我发出这个命令时没有响应。
/data/local/graph_app --flag 299 299 3 1 0 0 1 NULL 0 1 0 inputfile /data/local/tmp/img_299x299.bmp
从助手 optargs 获得了描述信息
Usage: testapp [--flag flagopt] [inputfile [inputfile...]]
flag name type default function
height int 0 Height of the input data. 0 == autodetect-square
width int 0 Width of the input data. 0 == autodetect-square
depth int 3 Depth of the input data
iters int 1 Number of times to run each input
perfdump int 0 Generate performance dump
pmu int 0 Get Performance Monitor Unit information
elementsize int 1 Element Size (uint8==1,float==4)
layer_reorder string NULL Reorder depth layers. ("210" changes RGB to BGR)
pprint_floats int 0 Pretty-Print output as floats
pprint_imagenet int 1 Pretty-print output, getting top 5 values and use imagenet categories
debug int 0 Debug verbosity level. Higher numbers get more verbosity
我有没有遗漏什么，让我知道，我使用 graphinit_med.c 只是为了检查它的工作情况，没有描述这个模型的作用。
谢谢，
没有关于使用单机版的文档graph_app，在完成代码后使其工作：
data/hvx_tf/graph_app --height 299 --width 299 --depth 3 --iters 1 --perfdump 0 --pmu 0 --elementsize 1 --pprint_floats 0 --pprint_imagenet 1 --debug 0 /data/local/tmp/keyboard_299x299.dat
>> Generate *.dat from *.jpg using `./scripts/imagedump.py`
仍然存在警告，您可以在下面的日志中看到：
return value from dspCV_initQ6() : 0
const node 1000b success
const node 1000c success
const node 1000d success
const node 1000e success
const node 1000f success
const node 10010 success
const node 10011 success
const node 10012 success
const node 10250 success
nn @ fc72cf80: id=0x0 debug_level=0
node @ fc733970: id=0x1000b type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733a20: id=0x1000c type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733a70: id=0x1000d type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733b20: id=0x1000e type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733c20: id=0x1000f type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733c70: id=0x10010 type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733d30: id=0x10011 type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733e20: id=0x10012 type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733e70: id=0x10250 type=0x3(Const) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733ec0: id=0x1024a type=0x0(INPUT) n_inputs=0 n_outputs=1 padding=0(WHATEVER)
node @ fc733f60: id=0x1024b type=0xe(Flatten) n_inputs=2 n_outputs=1 padding=0(WHATEVER)
node @ fc734040: id=0x1024c type=0x29(Min_f) n_inputs=2 n_outputs=1 padding=0(WHATEVER)
node @ fc734120: id=0x1024d type=0x2b(Max_f) n_inputs=2 n_outputs=1 padding=0(WHATEVER)
node @ fc734200: id=0x1024e type=0x2d(Quantize) n_inputs=3 n_outputs=3 padding=0(WHATEVER)
node @ fc734350: id=0x1024f type=0xf(QuantizedConv2d_8x8to32) n_inputs=7 n_outputs=3 padding=2(VALID)
node @ fc7344d0: id=0x10251 type=0x13(QuantizeDownAndShrinkRange_32to8) n_inputs=3 n_outputs=3 padding=0(WHATEVER)
node @ fc734620: id=0x10252 type=0x23(QuantizedBiasAdd_8p8to32) n_inputs=6 n_outputs=3 padding=0(WHATEVER)
node @ fc734790: id=0x10253 type=0x13(QuantizeDownAndShrinkRange_32to8) n_inputs=3 n_outputs=3 padding=0(WHATEVER)
node @ fc7348e0: id=0x10254 type=0x15(QuantizedRelu_8) n_inputs=3 n_outputs=3 padding=0(WHATEVER)
node @ fc734a30: id=0x10442 type=0x2f(Dequantize) n_inputs=3 n_outputs=1 padding=0(WHATEVER)
node @ fc734b20: id=0x1044d type=0x1(OUTPUT) n_inputs=1 n_outputs=0 padding=0(WHATEVER)
21 nodes total.
Init graph done.Prepare fc72cf80 success!
Using </data/local/tmp/keyboard_299x299.dat>
filesize=268203 elementsize=1 height=299 width=299 depth=3
Run!
sum=37845659
Executing!
**execute got err: -1**
hexagon/ops/src/op_output.c:58:output 0 too small
output size=4096
Rank,Softmax,index,string
0,303036629674309094288042513882152960.000000,575,pick
1,303036292954618408664607741320364032.000000,461,terrapin
2,303036292954618408664607741320364032.000000,445,electric ray
3,79327539388858010780491752432205824.000000,833,bulletproof vest
4,78902425827607254052570293577187328.000000,936,volleyball
AppReported: 4294967296
一旦我获得独立应用程序以最高精度预测示例图像，我将更新答案。