Comments (8)
继续用经典的模型efficientnetv2_b0试了试
模型的下载地址为:https://github.com/onnx/models/raw/main/Computer_Vision/tf_efficientnetv2_b0_Opset16_timm/tf_efficientnetv2_b0_Opset16.onnx
得到的量化完的运行结果:
./pictureRecognition.out tf_efficientnetv2_b0_Opset16.mnn daisy.jpg
Load Cache file error.
The device support i8sdot:1, support fp16:1, support i8mm: 0
Create execution error : 101
Create execution error : 101
Session Info: memory use 0.005398 MB, flops is 463.154877 M, backendType is 0, batch size = 1
input: w:192 , h:192, bpp: 3
origin size: 2100, 1500
Can't run session because not resized
For Image: daisy.jpg
21, 250908922840517956672101818055251722240.000000
971, 155706892288681663873593671155795886080.000000
558, 144564517325205176731988373268433207296.000000
952, 112714106392544388862589291026817482752.000000
485, 72253792300723863246554176667036680192.000000
280, 70477894939442528461738991265310048256.000000
255, 42867207357076265693574510722641035264.000000
930, 40476864538057105379101055316830191616.000000
234, 39606622401000425894973568566339567616.000000
39, 33638683099101550629631678460033236992.000000
这结果多少是有些抽象了
而未量化前的模型结果
./pictureRecognition.out tmp.mnn daisy.jpg
Load Cache file error.
The device support i8sdot:1, support fp16:1, support i8mm: 0
Session Info: memory use 34.192852 MB, flops is 537.623291 M, backendType is 0, batch size = 1
input: w:192 , h:192, bpp: 3
origin size: 2100, 1500
For Image: daisy.jpg
985, 9.512913
89, 2.403510
322, 2.085096
108, 2.013274
883, 1.951369
309, 1.885692
113, 1.817128
968, 1.690546
770, 1.643703
738, 1.622023
看着就正常很多
from mnn.
我测试了结果没有不对啊,你用pictureRecognition_module.out 测试看看
from mnn.
@v0jiuqi 您确实是测试了量化后的模型了吗,能否贴一下您运行的分类结果让我看看,感谢!
然后我是在arm64的开发板jetson orin (不是在x86的机器上,这一点也请注意)上进行编译运行的
from mnn.
具体的量化流程可以参考#2614
from mnn.
另外我想问一下,官方是打算放弃Session接口,改用Module接口了吗
from mnn.
我测试了结果没有不对啊,你用pictureRecognition_module.out 测试看看
@v0jiuqi 可否提供一下你用pictureRecognition_module.out 测试的config.json文件,感谢
from mnn.
这个是我们的量化工具和后端不一致导致的,你等我们更新吧
from mnn.
好的,感谢
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from mnn.