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License: Apache License 2.0
An Open Source Deep Learning Inference Engine Based on FPGA
License: Apache License 2.0
1.代码位置:(模型转换部分) TF2/TransForm_Kit/ModelConvert/caffe2fpga/src/
问题一:其中所有的C++和头文件中版权信息注释使用方式有问题。
问题二:其中的caffe2fpga.cpp中 99行 float weight = blob.data()[j];
请问blob是做什么用的?这个地方使用方式是不是有点问题?
2.代码位置:(量化部分) TF2/TransForm_Kit/Quantization/quantization.py
问题一:QuantizeChannel函数中使用到了QuantizeLinear函数,但是QuantizeLinear函数并没有重载为两参的?或者说这个函数本来就是两参的函数,上边的这个函数就定义错了。
问题二:在上述两个函数中都使用到了n这个参数,但是并没有作相关定义?参数n的作用是什么?
没找到RTL代码???
./bin/host ../host/model/googlenet/param.bin ../host/model/googlenet_Q ../host/test_images/googlenet_data_label_391.bin 1
1)其中googlenet_data_label_391.bin 1是如何生成的?因为想用自己的图片测试,不知道如何生成。
2)cnn/srs/test_images有两个.bin文件,但是我没有在代码库中找到生成代码文件。
May I evaluation your CNN application on an Intel PAC or Nallatech 385A FPGA board ? Is it board specific ?
In addition, could you please provide a tutorial on how users can convert, quantize, and run a Squeezenet model on an FPGA board ?
Thank you!
caffe2fpga是如何把caffe模型转换成FPGA计算需要的配置参数文件
在Run_Engine下面的手册说明了如何在evaluation mode和running mode下运行。我想请问您一下:
1、#CL_CONTEXT_EMULATOR_DEVICE_ALTERA=1 ./bin/host ../host/model/<your_model_file_path> ../host/model/<your_quantization_file_path> ../host/test_images/<your_test_file_path> 这条指令是用于传递参数的吗? 为什么我的文件夹里没有 ./bin/host路径
2、除了按照手册上面的进入两种mode外,需要运行run.sh文件吗?希望您可以将Run_Enigne的运行手册写的详细一点。谢谢
Now I am reading the code of TF2, I found in Runtime_Engine/cnn/host/src/network.cpp will call quantization function, but I also found in TransForm_Kit will do quantization,
what's the difference between them and what's the difference in the way they quantify?
我运行pytorch2caffee的main.py文件,使用的是resnet50和训练好的pth模型文件。
出现问题:① convert_caffe函数部分,Unknown layer type: MkldnnConvolution0, 将模型和参数放到GPU上可以解决。
② 解决①后,又出现Unknown layer type: ToCopy0的问题。
请问有没有完整的部署流程的教程。
你好 关于这个项目我大四毕业设计想参考一下 但还是看不太懂 我能跟您联系吗 或者有直接的工程可以参考下吗
使用19.1版本的BSP运行时遇到如下错误
error:use of type '_global channel PoolTailOutput' requires cl_intel_channels extension to be enabled
channel PoolTailOutput retriever_input_channel___attribute((depth256))
final_c16 = round(float(nz_c)/16)*16 if round(float(nz_c)/16)*16>0 else 16
if final_c16 > nz_c: #change 0 to 1
ichange_c = final_c16 - nz_c
for c_index in range(c_num):
if sumMask_C[c_index]==0 and ichange_c>0:
param_mask[:,c_index,:,:]=1
ichange_c=ichange_c-1
if final_c16 < nz_c: #change 1 to 0
ichange_c = nz_c - final_c16
for c_index in range(c_num):
if sumMask_C[c_index]== 1 and ichange_c>0:
param_mask[:,c_index,:,:]=0
ichange_c=ichange_c-1
The pruning is not in the papers and documents. What is the use?
Hi,
in resnet50 has batch nomalization layer, but I can not find bn kernel in device client, why?
thank you
Now, I want to test TF2, but has not a docs to show how to build test system, and what are the specific implementation steps, who can help me ?
thank you
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