what tool did you use to convert mxnet to caffe ?

[wsx276166228]

@GarrickLin I use MXNet2Caffe to convert mobilefacenet layers from mxnet to caffe. I use the transfered prototxt and caffemodel to extract feature, but I found all the pic's feature are the same. I don't know what's the problem, Does the MXNet2Caffe have problem when transfor mxnet to caffe? The prototxt I transfored as below.
model-0.prototxt.txt

[duweidai]

@GarrickLin I tested twice the resnet50_caffe.model in megaface, first time，I use the insightface‘s resnet50_caffe.model，The second time, I used the method you provided to transform the caffe model。The two results are the same：Allocating ranks (1000080), Rank 1: 0.877983. This result is quite different from that of the mxnet-model. I don't kown where is wrong?can you give me some advice?

[GarrickLin]

@ddwktv caffemodel is not including preprocessing.

[duweidai]

@GarrickLin thank you very much. Can you tell me what preprocessing is required in caffemodel?
transform_param { mean_value: 127.5 mean_value: 127.5 mean_value: 127.5 scale: 0.0078125 } The way I input the image is RGB.

[Yangel-hide]

@GarrickLin I use MXNet2Caffe to convert LResNet34E-IR, and replace eps from 0.001 to 2e-05 in prototxt file, but i get different output when I run check_results.py，（I comment out_caffe = netcaffe.blob_by_name("fc2") ） The output of the first line is below： MXNET OUTPUT [-0.88368684 -0.04408775 -0.07892119 -0.3554067 Caffe OUTPUT [-0.42465356 -0.17522429 0.08665871 0.11202835 can you tell me where is wrong? thx~

[zuoqing1988]

Does anyone success to convert the MobileFaceNet model?

[JackyWang-001]

@zuoqing1988 Convert ok, but can not be used.

[zuoqing1988]

@Jacky3213 all images return the same feature and it's very very slow in matcaffe.

[mariolew]

@GarrickLin Hi , I convert the LResNet50 model to caffe, and when I run check_results.py, the fc1 output is totally different, I've done preprocess for caffe. So, any problems? I found the output of any layer after conv0 is wrong..

[jimeffry]

First thank you for your share. I get the same accuracy rate on my own image datas, after converted to caffe model. I add some changes in json2prototxt.py file, and add "ewrfcas" codes. The code is as follows： < for input_idx_i in node_i['inputs']: input_i = jdata['nodes'][input_idx_i[0]] if str(input_i['op']) == '_mul_scalar': info['bottom'].append('data') elif str(input_i['op']) == 'Dropout': current_input = input_i['inputs'] current_input = current_input[0] input_idx = jdata['nodes'][current_input[0]] info['bottom'].append(str(input_idx['name'])) elif str(input_i['op']) != 'null' or (str(input_i['name']) == 'data'): info['bottom'].append(str(input_i['name'])) if str(input_i['op']) == 'null': info['params'].append(str(input_i['name'])) if not str(input_i['name']).startswith(str(node_i['name'])): print(' use shared weight -> %s'% str(input_i['name'])) info['share'] = True >

[jimeffry]

@GarrickLin @nttstar I convert the mobileNet model to caffe successfully, when add a line in Convolution func included in prototxt_basic.py file.
<if info[attrstr].has_key('num_group') and int(info[attrstr]['num_group']) >1: txt_file.write(' group: %s\n' % info[attrstr]['num_group']) txt_file.write(' engine: CAFFE\n')>

[duweidai]

@Yangel-hide I have the same mistake with you .I use MXNet2Caffe to convert LResNet50E-IR, but i get different output when I run check_results.py. are you find where is wrong ?

[duweidai]

@jimeffry I use MXNet2Caffe to convert LResNet50E-IR, but i get different output when I run check_results.py. can you give me some advice? thx.

[jimeffry]

@ddwktv The mxnet model, converted to caffe, is the nttstar's opensource model-100. I did not run check_results.py. I have compared the results of caffe-100 model with the rsults of mxnet-100 model, using the same input image. There is a little different between the 2 results. But the distance compared between 2 different input images, is the same, for 2 models.

[jimeffry]

@GarrickLin @nttstar I get the same accuracy rate on my own image datas, after converted to mobilenet model of yours (the one fmobilefacenet ). I add some changes in mxnet2caffe.py file, because you have given a name of pre_fc1 weights called "fc1_wight". The add codes are below: < if 'fc' in key_i: print key_i print arg_params[key_i].shape key_caffe = 'pre_fc1' print net.params[key_caffe][0].data.shape>

[duweidai]

I converted mxnet50 to resnet50, and run the check_results. I got two same results. because, caffe prototxt not have preprocess, so if you want to got a right check_results ,you have to revise a few the .py

[yang0817manman]

hi, @GarrickLin @nttstar .Thank you for your sharing.When I trained se-resnet50-arcloss with ms1m datasets then I want to converted mxnetmodel to caffemodel but in the stage of "json2prototxt.py" the code have yet not worked well. Can you help me?The jsonfile and the error are as following: model-symbol.json.tar.gz the command lines : "parser = argparse.ArgumentParser(description='Convert MXNet jason to Caffe prototxt') parser.add_argument('--mx-json', type=str, default='model_mxnet/model-r50-softmax1e3/model-symbol.json') parser.add_argument('--cf-prototxt', type=str, default='model_caffe/model-r50_deploy.prototxt') args = parser.parse_args()" the error is: "/usr/bin/python2.7 /home/algorithm-5/MXNet2Caffe/json2prototxt.py Warning! Unknown mxnet op:_copy 0, op:null , name:data -> data 1, op:_copy , name:id -> id use shared weight -> data Process finished with exit code 1"

[jimeffry]

@yang0817manman I have modified the code, according to GarrickLin‘s coding. https://github.com/jimeffry/MXNet2Caffe This maybe help you.

[yang0817manman]

Thanks for your reply. But when I run the code of "json2prototxt.py" and the many unknown layers appeared. How to modify the code I should ?Can you help me ?Thanks a lot. @jimeffry " Warning! Skip Unknown mxnet op:broadcast_mul 799, op:elemwise_add , name:_plus22 -> _plus22 804, op:BatchNorm , name:stage4_unit3_bn1 -> stage4_unit3_bn1 806, op:Convolution , name:stage4_unit3_conv1 -> stage4_unit3_conv1 811, op:BatchNorm , name:stage4_unit3_bn2 -> stage4_unit3_bn2 813, op:LeakyReLU , name:stage4_unit3_relu1 -> stage4_unit3_relu1 815, op:Convolution , name:stage4_unit3_conv2 -> stage4_unit3_conv2 820, op:BatchNorm , name:stage4_unit3_bn3 -> stage4_unit3_bn3 821, op:Pooling , name:stage4_unit3_se_pool1 -> stage4_unit3_se_pool1 824, op:Convolution , name:stage4_unit3_se_conv1 -> stage4_unit3_se_conv1 826, op:LeakyReLU , name:stage4_unit3_se_relu1 -> stage4_unit3_se_relu1 829, op:Convolution , name:stage4_unit3_se_conv2 -> stage4_unit3_se_conv2 830, op:Activation , name:stage4_unit3_se_sigmoid -> stage4_unit3_se_sigmoid 831, op:broadcast_mul , name:broadcast_mul23 -> broadcast_mul23 Warning! Skip Unknown mxnet op:broadcast_mul 832, op:elemwise_add , name:_plus23 -> _plus23 837, op:BatchNorm , name:bn1 -> bn1 841, op:FullyConnected , name:pre_fc1 -> pre_fc1 ('fullconnect: ', u'512', ['pre_fc1_weight', 'pre_fc1_bias']) 846, op:BatchNorm , name:fc1 -> fc1 847, op:BlockGrad , name:blockgrad0 -> blockgrad0 Warning! Skip Unknown mxnet op:BlockGrad 848, op:L2Normalization , name:fc1n -> fc1n Warning! Skip Unknown mxnet op:L2Normalization 851, op:L2Normalization , name:l2normalization0 -> l2normalization0 use shared weight -> fc7_weight Warning! Skip Unknown mxnet op:L2Normalization 852, op:FullyConnected , name:fc7 -> fc7 ('fullconnect: ', u'85164', []) 854, op:one_hot , name:one_hot0 -> one_hot0 use shared weight -> softmax_label Warning! Skip Unknown mxnet op:one_hot 855, op:pick , name:pick0 -> pick0 use shared weight -> softmax_label Warning! Skip Unknown mxnet op:pick 856, op:_div_scalar , name:_divscalar0 -> _divscalar0 Warning! Skip Unknown mxnet op:_div_scalar 858, op:Activation , name:activation0 -> activation0 860, op:elemwise_mul , name:_mul0 -> _mul0 Warning! Skip Unknown mxnet op:elemwise_mul 861, op:_rminus_scalar , name:_rminusscalar0 -> _rminusscalar0 Warning! Skip Unknown mxnet op:_rminus_scalar 862, op:sqrt , name:sqrt0 -> sqrt0 Warning! Skip Unknown mxnet op:sqrt 864, op:elemwise_sub , name:_minus0 -> _minus0 Warning! Skip Unknown mxnet op:elemwise_sub 867, op:where , name:where0 -> where0 Warning! Skip Unknown mxnet op:where 868, op:elemwise_sub , name:_minus1 -> _minus1 Warning! Skip Unknown mxnet op:elemwise_sub 869, op:expand_dims , name:expand_dims0 -> expand_dims0 Warning! Skip Unknown mxnet op:expand_dims 870, op:broadcast_mul , name:broadcast_mul24 -> broadcast_mul24 Warning! Skip Unknown mxnet op:broadcast_mul 871, op:elemwise_add , name:_plus24 -> _plus24 872, op:SoftmaxOutput , name:softmax -> softmax 873, op:SoftmaxActivation , name:softmaxactivation0 -> softmaxactivation0 Warning! Skip Unknown mxnet op:SoftmaxActivation 874, op:log , name:log0 -> log0 Warning! Skip Unknown mxnet op:log 875, op:one_hot , name:one_hot1 -> one_hot1 use shared weight -> softmax_label Warning! Skip Unknown mxnet op:one_hot 876, op:elemwise_mul , name:_mul1 -> _mul1 Warning! Skip Unknown mxnet op:elemwise_mul 877, op:sum , name:sum0 -> sum0 Warning! Skip Unknown mxnet op:sum 878, op:_div_scalar , name:_divscalar1 -> _divscalar1 Warning! Skip Unknown mxnet op:_div_scalar 879, op:BlockGrad , name:blockgrad1 -> blockgrad1 Warning! Skip Unknown mxnet op:BlockGrad "

[liu1004010308]

I converted mxnet50 to resnet50, and run the check_results. I got two same results. because, caffe prototxt not have preprocess, so if you want to got a right check_results ,you have to revise a few the .py

@ddwktv can you tell me how to modify a few the .py. I am worried. thanks.

[liu1004010308]

I converted mxnet50 to resnet50, and run the check_results. I got two same results. because, caffe prototxt not have preprocess, so if you want to got a right check_results ,you have to revise a few the .py

@ddwktv can you tell me how to modify a few the .py. I am worried. thanks.

i have finished it and result is ok. you should change caffe's input nimg = (nimg-127.5) / 128

[yang0817manman]

I want to converted mxnetmodel into caffemodel all the time but some error of "Warning! Skip Unknown mxnet op" always appeared. Can you tell me how to modify the code of "json2prototxt.py" or "mxnet2caffe.py"? Thanks a lot. @liu1004010308

[liu1004010308]

I want to converted mxnetmodel into caffemodel all the time but some error of "Warning! Skip Unknown mxnet op" always appeared. Can you tell me how to modify the code of "json2prototxt.py" or "mxnet2caffe.py"? Thanks a lot. @liu1004010308

https://blog.csdn.net/Wuzebiao2016/article/details/81735487 this paper can tell you what to do. ofcourse this paper isn't the best one but it maybe helpful for you.

[yang0817manman]

@liu1004010308,Thanks. I have tried to do it according to this blog and modify the prototxt file some times but it doesn't work.

[liu1004010308]

@liu1004010308,Thanks. I have tried to do it according to this blog and modify the prototxt file some times but it doesn't work. i have finished it and result is ok. you should change caffe's input :nimg = (nimg-127.5) / 128

[jiankang1991]

Does anyone know how to transfer from mxnet to caffe of MobileFacenet model? For the transformed caffe model, I also get the same feature results of different tensor input. How to solve this problem? Thank you.

[77h2l]

I converted mxnet50 to resnet50, and run the check_results. I got two same results. because, caffe prototxt not have preprocess, so if you want to got a right check_results ,you have to revise a few the .py

@ddwktv can you tell me how to modify a few the .py. I am worried. thanks.

i have finished it and result is ok. you should change caffe's input nimg = (nimg-127.5) / 128

hi,did you modify the mxnet's json file to run the checkout_result.py,and how did you modify the prototxt file,can you share,thx.

[ddfabbro]

what tool did you use to convert mxnet to caffe ?

I used MMdnn to convert from MXNet to Caffe and it was very effortless. Their README explains on how to get started, however the dependencies can be pretty messy, so if it doesn't work, I suggest using Docker as follows:

---

1. With Docker, you can pull an image of MMdnn and start a container with all dependencies installed

   docker run -it -v /path/to/local/folder:/path/to/container/folder mmdnn/mmdnn:cpu.small

Important: You need to specify a shared folder between your local filesystem and the container with -v /path/to/local/folder:/path/to/container/folder. Also, /path/to/container/folder doesn't really matter as Docker will create the folders that doesn't exist.

2. Inside the container, I installed git

   apt update && apt install git

and upgraded mmdnn package with

pip install -U git+https://github.com/Microsoft/MMdnn.git@master

3. Finally, you should place the models inside the container through the shared folder and convert to Caffe using

   mmconvert -sf mxnet -iw model-0000.params -in model-symbol.json --inputShape 3,112,112 -df caffe -o caffe_model -dw caffe_model.npy

[77h2l]

I converted mxnet50 to resnet50, and run the check_results. I got two same results. because, caffe prototxt not have preprocess, so if you want to got a right check_results ,you have to revise a few the .py

@ddwktv can you tell me how to modify a few the .py. I am worried. thanks.

i have finished it and result is ok. you should change caffe's input nimg = (nimg-127.5) / 128

hi,did you modify the mxnet's json file to run the checkout_result.py,and how did you modify the prototxt file,can you share,thx.

I figure out that the version of caffe matters.When using NVIDIA'caffe, the result of the converted model seems incorrect,since NVIDIA's caffe implements BN and scale layer together,so you'll find that the blobs mismatch.BTW,can anyone improve the tools so that people need not to manually modify the pt file.

[meanmee]

@ddfabbro It seems like using mmdnn to cvt model from mxnet to caffe will loss some precisions, I tested it by getting the feature of the same pic using mxnet and the converted caffe model, the similarity between the two features is 97.7%. And also I tested it with my own testset, the top 1 acc using the converted caffe model is 1.5% lower than the mxnet model.

[EtheneXiang]

@GarrickLin I use MXNet2Caffe to convert mobilefacenet layers from mxnet to caffe. I use the transfered prototxt and caffemodel to extract feature, but I found all the pic's feature are the same. I don't know what's the problem, Does the MXNet2Caffe have problem when transfor mxnet to caffe? The prototxt I transfored as below. model-0.prototxt.txt

@wsx276166228 i have met the same probloms as you said,all pic's feature are the same

[meanmee]

The MMDNN works for me with no precision decline. But you have to modify something because of the minus and the multiply operation on the top of the network.

[Laulian]

I used to convert mobilefacenet from mxnet to caffe with MXNet2Caffe https://github.com/GarrickLin/MXNet2Caffe,but the output features totally different. I check some layer‘s’weights and features ,they remain the same. Finally,I found the keypoint is the eps param in the bn layer. The default value in caffe is 1.0e-3,but 1.0e-5 in mxnet, when I changed it,the pic feature's difference became extremely small (differ only after the decimal point six digits ) MxNet2Caffe-mobilefacenet

[shiyuanyin]

@

I converted mxnet50 to resnet50, and run the check_results. I got two same results. because, caffe prototxt not have preprocess, so if you want to got a right check_results ,you have to revise a few the .py

@ddwktv can you tell me how to modify a few the .py. I am worried. thanks.

i have finished it and result is ok. you should change caffe's input nimg = (nimg-127.5)

@liu1004010308 Hi, I transform mxnet res50,but ,check,reult not same ,could you help me

[shiyuanyin]

I converted mxnet50 to resnet50, and run the check_results. I got two same results. because, caffe prototxt not have preprocess, so if you want to got a right check_results ,you have to revise a few the .py

@ddwktv Hi , I transform mxnet res50, but the check result is differrent ,and I -127.5/128, Could I ask your help? def compare_models(prefix_mxnet, prefix_caffe, size): netmx = PredictorMxNet(prefix_mxnet, 0, size)
model_file = prefix_caffe + ".prototxt" pretrained_file = prefix_caffe + ".caffemodel" netcaffe = PredictorCaffe(model_file, pretrained_file, size) tensor = np.ones(size, dtype=np.float32) out_mx = netmx.forward(tensor) print out_mx netcaffe.forward((tensor-127.5)*0.0078125) out_caffe = netcaffe.blob_by_name("pre_fc1") print out_caffe.data

print("*****")

# print softmax(out_caffe.data)
# out_caffe = netcaffe.blob_by_name("fc2")
# print out_caffe.data
# #print softmax(out_caffe.data)     
print "done"

[shiyuanyin]

@GarrickLin Hi , I convert the LResNet50 model to caffe, and when I run check_results.py, the fc1 output is totally different, I've done preprocess for caffe. So, any problems? I found the output of any layer after conv0 is wrong..

@marcosly Hi I also have different out in the mxnet to caffe res50IR and mxnet resIR50, Ihave modify the caffe model processing ,Have you solved it?

[Laulian]

@shiyuanyin you can try this,https://github.com/Laulian/MxNet2Caffe-mobilefacenet，I just tested with resnet-50

[qingchenwuhou]

I realize the accurate conversion from MXNet model to Caffe Model by MMdnn. In order to ensure the same preprocess before input into both networks, in verification exp I use the MXNet preprocess and convert mx-NDArray to numpy, then input the 224*224 numpy into caffe network. The process obtains the accurate result. The same preprocess is important in check exp, where formerly I used caffe preprocess by mistake and was confused by the wrong results. And you will find MXNet preprocess contains steps(subtract mean, divide std, imresize realized by opencv not cv2, crop) which are different from Caffe preprocess. So In Caffe prediction code, you should execute the same preprocess. The command I use in mmdnn is:

mmtoir -f mxnet -n downed_mxnet_model/resnext-101-symbol.json -w downed_mxnet_model/resnext-101-0000.params -d converted_caffe_model/resnext101 --inputSh ape 3,224,224 mmtocode -f caffe -n converted_caffe_model/resnext101.pb -w converted_caffe_model/resnext101.npy -d converted_caffe_model/resnext101_caffe_converted.py - dw converted_caffe_model/resnext101_caffe_converted.npy mmtomodel -f caffe -in converted_caffe_model/resnext101_caffe_converted.py -iw converted_caffe_model/resnext101_caffe_converted.npy -o converted_caffe_mo del/resnext101_final

[shiyuanyin]

@Laulian , Thank you ,Yes, I try the author's res50 model is right ,same out ,but I use differ dataset finutune，model，the out not the same?, I compare two mxnet transform prototxt is the same ,so is caffemdel,have some different. but I dont know why ?

[Laulian]

@shiyuanyin check the last layer's output of each bottleneck in resnet-50,that is the Eltwise layer.And you will figure out where the difference generated.

[shiyuanyin]

@ Laulian ,Thank you your reply ,now transform my model is ok ,but I m not sure ,is wd or save model_0000 problem, because I use wd=0.0005 ,get the model is OK, before save model I use include fc model to save no fc model model-0000

[xiakj]

@jimeffry 我也用的MxNet2Caffe进行的模型转换，在caffe上跑的识别结果非常离谱，可能是什么原因，谢谢

[xiakj]

@jeehyun100 @jimeffry I test resnet50 caffemodel using lfw face dataset, the result is unnormal: the same face pair has higher cosine similarity, the value is around 0.8-0.9, and the different face pair also has high cosing similarity, the value is around 0.6. I thought this is not correct, the different face pair should not get so high similarity. Can anyone explain this question? thanks.