None has type NoneType, but expected one of: bytes, unicode #26

cswwp · 2019-07-09T10:57:58Z

convert from pytorch to caffe with own model,

WARNING: CANNOT FOUND blob 140437991457808
Traceback (most recent call last):
File "conver_rgb.py", line 49, in
convert(args)
File "conver_rgb.py", line 42, in convert
pytorch_to_caffe.trans_net(net, input_var, name)
File "/home/wangwenpeng/work/FaceAntiSpoofing_readsense_pytorch_patch/pytorch2caffe_MGN/convert/pytorch_to_caffe.py", line 459, in trans_net
out = net.forward(input_var)
File "convert/patch_attention_net.py", line 58, in forward
logit = logit.view(batch_size, -1)
File "/home/wangwenpeng/work/FaceAntiSpoofing_readsense_pytorch_patch/pytorch2caffe_MGN/convert/pytorch_to_caffe.py", line 297, in _view
bottom=[log.blobs(input)],top=top_blobs)
File "/home/wangwenpeng/work/FaceAntiSpoofing_readsense_pytorch_patch/pytorch2caffe_MGN/Caffe/layer_param.py", line 33, in init
self.bottom.extend(bottom)
TypeError: None has type NoneType, but expected one of: bytes, unicode

Anyone help? seems input blob error, but i show input shape ok before NET_INITTED=True in trans_net(), and error after NET_INITTED=True

xingyueye · 2019-11-05T08:17:29Z

hi, I have met same problem, can you tell how to solve it, thank you

ivylinden · 2019-11-05T08:50:42Z

Hi ,I also have met this problem,Can you tell me how to solve it. thank you

114153 · 2019-11-06T07:53:09Z

Haaaaaa ,I also have met this problem.So, what can we do?

cswwp · 2019-11-06T08:10:36Z

please check your pytorch layer, confirm whether the layer you used is supported in this tool, some operation need change your writing, such as change torch.add to0 "torch.add"

xingyueye · 2019-11-06T09:25:04Z

That's right, I have found some layers that are not supported.
The core reason for this problem is that some layers are not registered and the blobs can not be found.
You must accurately locating the unsuitable layers/operations, Unfortunately ,This work would be disgusting...

peyer · 2019-11-10T09:52:32Z

@xingyueye I want to convert a model containing DCN, how should I add DCN layer register on this tools?

xingyueye · 2019-11-13T03:10:33Z

@peyer Sorry, I have no experience in converting DCN and can't give you advice.

zchrissirhcz · 2019-11-18T09:32:45Z

Same problem message here...

torch 1.3.1
torchvision 0.4.2

What I ran is the official example

python example/alexnet_pytorch_to_caffe.py

Got result output:

⚡ python example/alexnet_pytorch_to_caffe.py
Starting Transform, This will take a while
140624258807272:blob1 was added to blobs
torch ops name: {AlexNet(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): ReLU(inplace=True)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(6, 6))
(classifier): Sequential(
(0): Dropout(p=0.5, inplace=False)
(1): Linear(in_features=9216, out_features=4096, bias=True)
(2): ReLU(inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Linear(in_features=4096, out_features=4096, bias=True)
(5): ReLU(inplace=True)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
): '', Sequential(
(0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))
(1): ReLU(inplace=True)
(2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(4): ReLU(inplace=True)
(5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU(inplace=True)
(8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(9): ReLU(inplace=True)
(10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)
): 'features', Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2)): 'features.0', ReLU(inplace=True): 'features.1', MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False): 'features.2', Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2)): 'features.3', ReLU(inplace=True): 'features.4', MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False): 'features.5', Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)): 'features.6', ReLU(inplace=True): 'features.7', Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)): 'features.8', ReLU(inplace=True): 'features.9', Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)): 'features.10', ReLU(inplace=True): 'features.11', MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False): 'features.12', AdaptiveAvgPool2d(output_size=(6, 6)): 'avgpool', Sequential(
(0): Dropout(p=0.5, inplace=False)
(1): Linear(in_features=9216, out_features=4096, bias=True)
(2): ReLU(inplace=True)
(3): Dropout(p=0.5, inplace=False)
(4): Linear(in_features=4096, out_features=4096, bias=True)
(5): ReLU(inplace=True)
(6): Linear(in_features=4096, out_features=1000, bias=True)
): 'classifier', Dropout(p=0.5, inplace=False): 'classifier.0', Linear(in_features=9216, out_features=4096, bias=True): 'classifier.1', ReLU(inplace=True): 'classifier.2', Dropout(p=0.5, inplace=False): 'classifier.3', Linear(in_features=4096, out_features=4096, bias=True): 'classifier.4', ReLU(inplace=True): 'classifier.5', Linear(in_features=4096, out_features=1000, bias=True): 'classifier.6'}
features.0
conv: blob1
conv1 was added to layers
140624258809792:conv_blob1 was added to blobs
features.1
relu1 was added to layers
140624258809576:relu_blob1 was added to blobs
features.2
max_pool1 was added to layers
140624258809432:max_pool_blob1 was added to blobs
features.3
conv: max_pool_blob1
conv2 was added to layers
140624258807200:conv_blob2 was added to blobs
features.4
relu2 was added to layers
140624258809504:relu_blob2 was added to blobs
features.5
max_pool2 was added to layers
140624258809648:max_pool_blob2 was added to blobs
features.6
conv: max_pool_blob2
conv3 was added to layers
140624258809720:conv_blob3 was added to blobs
features.7
relu3 was added to layers
140624258890920:relu_blob3 was added to blobs
features.8
conv: relu_blob3
conv4 was added to layers
140624258890704:conv_blob4 was added to blobs
features.9
relu4 was added to layers
140624258890776:relu_blob4 was added to blobs
features.10
conv: relu_blob4
conv5 was added to layers
140624258890560:conv_blob5 was added to blobs
features.11
relu5 was added to layers
140624258890632:relu_blob5 was added to blobs
features.12
max_pool3 was added to layers
140624258890416:max_pool_blob3 was added to blobs
classifier.0
WARNING: CANNOT FOUND blob 140624258890272
dropout1 was added to layers
140624258890488:None was added to blobs
Traceback (most recent call last):
File "example/alexnet_pytorch_to_caffe.py", line 12, in
pytorch_to_caffe.trans_net(net,input,name)
File "./pytorch_to_caffe.py", line 759, in trans_net
out = net.forward(input_var)
File "/home/zz/soft/miniconda3/lib/python3.7/site-packages/torchvision/models/alexnet.py", line 48, in forward
x = self.classifier(x)
File "/home/zz/soft/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/zz/soft/miniconda3/lib/python3.7/site-packages/torch/nn/modules/container.py", line 92, in forward
input = module(input)
File "/home/zz/soft/miniconda3/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call
result = self.forward(*input, **kwargs)
File "/home/zz/soft/miniconda3/lib/python3.7/site-packages/torch/nn/modules/dropout.py", line 54, in forward
return F.dropout(input, self.p, self.training, self.inplace)
File "./pytorch_to_caffe.py", line 662, in call
out=self.obj(self.raw,*args,**kwargs)
File "./pytorch_to_caffe.py", line 230, in _dropout
bottom=bottom_blobs,top=top_blobs)
File "./Caffe/layer_param.py", line 33, in init
self.bottom.extend(bottom)
TypeError: None has type NoneType, but expected one of: bytes, unicode

zchrissirhcz · 2019-11-18T11:20:40Z

By switching to python3.6 and related packages, I can run it corectly.

You may just conda env create -f environment.yml. Its content:

name: py36_pytorch041
channels:
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - ca-certificates=2019.10.16=0
  - certifi=2019.9.11=py36_0
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hd88cf55_4
  - libgcc-ng=9.1.0=hdf63c60_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - ncurses=6.1=he6710b0_1
  - openssl=1.1.1d=h7b6447c_3
  - pip=19.3.1=py36_0
  - python=3.6.9=h265db76_0
  - readline=7.0=h7b6447c_5
  - setuptools=41.6.0=py36_0
  - sqlite=3.30.1=h7b6447c_0
  - tk=8.6.8=hbc83047_0
  - wheel=0.33.6=py36_0
  - xz=5.2.4=h14c3975_4
  - zlib=1.2.11=h7b6447c_3
  - pip:
    - cycler==0.10.0
    - decorator==4.4.1
    - imageio==2.6.1
    - kiwisolver==1.1.0
    - matplotlib==3.1.1
    - networkx==2.4
    - numpy==1.17.2
    - opencv-python==3.3.1.11
    - pillow==6.2.1
    - protobuf==3.10.0
    - pyparsing==2.4.5
    - python-dateutil==2.8.1
    - pywavelets==1.1.1
    - scikit-image==0.15.0
    - scipy==1.3.1
    - six==1.13.0
    - torch==0.4.1
    - torchvision==0.2.1
prefix: /media/data1/hl/software/miniconda3/envs/py36_pytorch041

maithstartup · 2019-11-20T06:40:53Z

By switching to python3.6 and related packages, I can run it corectly.

You may just conda env create -f environment.yml. Its content:

name: py36_pytorch041
channels:
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - ca-certificates=2019.10.16=0
  - certifi=2019.9.11=py36_0
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hd88cf55_4
  - libgcc-ng=9.1.0=hdf63c60_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - ncurses=6.1=he6710b0_1
  - openssl=1.1.1d=h7b6447c_3
  - pip=19.3.1=py36_0
  - python=3.6.9=h265db76_0
  - readline=7.0=h7b6447c_5
  - setuptools=41.6.0=py36_0
  - sqlite=3.30.1=h7b6447c_0
  - tk=8.6.8=hbc83047_0
  - wheel=0.33.6=py36_0
  - xz=5.2.4=h14c3975_4
  - zlib=1.2.11=h7b6447c_3
  - pip:
    - cycler==0.10.0
    - decorator==4.4.1
    - imageio==2.6.1
    - kiwisolver==1.1.0
    - matplotlib==3.1.1
    - networkx==2.4
    - numpy==1.17.2
    - opencv-python==3.3.1.11
    - pillow==6.2.1
    - protobuf==3.10.0
    - pyparsing==2.4.5
    - python-dateutil==2.8.1
    - pywavelets==1.1.1
    - scikit-image==0.15.0
    - scipy==1.3.1
    - six==1.13.0
    - torch==0.4.1
    - torchvision==0.2.1
prefix: /media/data1/hl/software/miniconda3/envs/py36_pytorch041

I created a virtual environment with your environment.yml file, I used python 3.6.0,is there anything else i need to convert the model.

CASIATumingfei · 2019-12-24T16:49:06Z

convert from pytorch to caffe with own model,
WARNING: CANNOT FOUND blob 140437991457808
Traceback (most recent call last):
File "conver_rgb.py", line 49, in
convert(args)
File "conver_rgb.py", line 42, in convert
pytorch_to_caffe.trans_net(net, input_var, name)
File "/home/wangwenpeng/work/FaceAntiSpoofing_readsense_pytorch_patch/pytorch2caffe_MGN/convert/pytorch_to_caffe.py", line 459, in trans_net
out = net.forward(input_var)
File "convert/patch_attention_net.py", line 58, in forward
logit = logit.view(batch_size, -1)
File "/home/wangwenpeng/work/FaceAntiSpoofing_readsense_pytorch_patch/pytorch2caffe_MGN/convert/pytorch_to_caffe.py", line 297, in _view
bottom=[log.blobs(input)],top=top_blobs)
File "/home/wangwenpeng/work/FaceAntiSpoofing_readsense_pytorch_patch/pytorch2caffe_MGN/Caffe/layer_param.py", line 33, in init
self.bottom.extend(bottom)
TypeError: None has type NoneType, but expected one of: bytes, unicode
Anyone help? seems input blob error, but i show input shape ok before NET_INITTED=True in trans_net(), and error after NET_INITTED=True

I met the same problem when using .view(), WARNING: CANNOT FOUND blob 140538514487648, should I change view() to something else? want to know how did you solve it

NickShen4Github · 2020-01-07T08:32:18Z

By switching to python3.6 and related packages, I can run it corectly.
You may just conda env create -f environment.yml. Its content:

name: py36_pytorch041
channels:
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - ca-certificates=2019.10.16=0
  - certifi=2019.9.11=py36_0
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hd88cf55_4
  - libgcc-ng=9.1.0=hdf63c60_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - ncurses=6.1=he6710b0_1
  - openssl=1.1.1d=h7b6447c_3
  - pip=19.3.1=py36_0
  - python=3.6.9=h265db76_0
  - readline=7.0=h7b6447c_5
  - setuptools=41.6.0=py36_0
  - sqlite=3.30.1=h7b6447c_0
  - tk=8.6.8=hbc83047_0
  - wheel=0.33.6=py36_0
  - xz=5.2.4=h14c3975_4
  - zlib=1.2.11=h7b6447c_3
  - pip:
    - cycler==0.10.0
    - decorator==4.4.1
    - imageio==2.6.1
    - kiwisolver==1.1.0
    - matplotlib==3.1.1
    - networkx==2.4
    - numpy==1.17.2
    - opencv-python==3.3.1.11
    - pillow==6.2.1
    - protobuf==3.10.0
    - pyparsing==2.4.5
    - python-dateutil==2.8.1
    - pywavelets==1.1.1
    - scikit-image==0.15.0
    - scipy==1.3.1
    - six==1.13.0
    - torch==0.4.1
    - torchvision==0.2.1
prefix: /media/data1/hl/software/miniconda3/envs/py36_pytorch041

I created a virtual environment with your environment.yml file, I used python 3.6.0,is there anything else i need to convert the model.

If you are using pytorch version above or equal to 1.1. The conda solution wont work. In my conputer pytorch <=1.0.0 works perfectly while I update to 1.3 it gives an identical error shown above.

xiexu666 · 2020-01-17T08:41:06Z

By switching to python3.6 and related packages, I can run it corectly.
You may just conda env create -f environment.yml. Its content:

name: py36_pytorch041
channels:
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - ca-certificates=2019.10.16=0
  - certifi=2019.9.11=py36_0
  - libedit=3.1.20181209=hc058e9b_0
  - libffi=3.2.1=hd88cf55_4
  - libgcc-ng=9.1.0=hdf63c60_0
  - libstdcxx-ng=9.1.0=hdf63c60_0
  - ncurses=6.1=he6710b0_1
  - openssl=1.1.1d=h7b6447c_3
  - pip=19.3.1=py36_0
  - python=3.6.9=h265db76_0
  - readline=7.0=h7b6447c_5
  - setuptools=41.6.0=py36_0
  - sqlite=3.30.1=h7b6447c_0
  - tk=8.6.8=hbc83047_0
  - wheel=0.33.6=py36_0
  - xz=5.2.4=h14c3975_4
  - zlib=1.2.11=h7b6447c_3
  - pip:
    - cycler==0.10.0
    - decorator==4.4.1
    - imageio==2.6.1
    - kiwisolver==1.1.0
    - matplotlib==3.1.1
    - networkx==2.4
    - numpy==1.17.2
    - opencv-python==3.3.1.11
    - pillow==6.2.1
    - protobuf==3.10.0
    - pyparsing==2.4.5
    - python-dateutil==2.8.1
    - pywavelets==1.1.1
    - scikit-image==0.15.0
    - scipy==1.3.1
    - six==1.13.0
    - torch==0.4.1
    - torchvision==0.2.1
prefix: /media/data1/hl/software/miniconda3/envs/py36_pytorch041

I created a virtual environment with your environment.yml file, I used python 3.6.0,is there anything else i need to convert the model.

If you are using pytorch version above or equal to 1.1. The conda solution wont work. In my conputer pytorch <=1.0.0 works perfectly while I update to 1.3 it gives an identical error shown above.

Have you tried other environments? Such as 1.2 ，1.4

imistyrain · 2020-05-28T06:17:57Z

torchvision 0.3-0.5 doesn't work, roll backto 0.2
Note: You can use torch==1.4 and torchvision==0.2 sametime.

leoluopy · 2020-10-09T06:19:06Z

please check your pytorch layer, confirm whether the layer you used is supported in this tool, some operation need change your writing, such as change torch.add to0 "torch.add"

such as change torch.add to0 "torch.add" ?? some details ?? change torch.add to ????
@cswwp

applefishsky009 · 2020-11-19T05:38:34Z

torchvision 0.3-0.5 doesn't work, roll backto 0.2
Note: You can use torch==1.4 and torchvision==0.2 sametime.

谢谢，这完美的解决了我的问题。

XinyingZheng · 2020-12-14T12:58:26Z

That's right, I have found some layers that are not supported.
The core reason for this problem is that some layers are not registered and the blobs can not be found.
You must accurately locating the unsuitable layers/operations, Unfortunately ,This work would be disgusting...

How do you determine which layer operations are not supported，looking forward to your reply,thank you

XinyingZheng · 2020-12-15T02:22:54Z

torchvision 0.3-0.5 doesn't work, roll backto 0.2
Note: You can use torch==1.4 and torchvision==0.2 sametime.

谢谢，这完美的解决了我的问题。

我torch改成1.4 ，torchvision改成0.2 ，还是报一样的错误....

imistyrain · 2020-12-15T02:47:44Z

That's right, I have found some layers that are not supported.
The core reason for this problem is that some layers are not registered and the blobs can not be found.
You must accurately locating the unsuitable layers/operations, Unfortunately ,This work would be disgusting...

How do you determine which layer operations are not supported，looking forward to your reply,thank you

出现这个错误的原因一般是当前层的上一层未做转换，blob未加入列表所致，调试下断点那就能找到.

imistyrain · 2020-12-15T06:50:11Z

主要是0.2之后的版本将view改成了flatten操作，以alexnet为例
0.2的实现为

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        return x

到了0.5变成了

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

其中的flatten并没有实现，搞清楚原因后解决就简单多了
新建一_flatten函数

def _flatten(raw , input, * args):
    x = raw(input, *args)
    if not NET_INITTED:
        return x
    layer_name=log.add_layer(name='flatten')
    top_blobs=log.add_blobs([x],name='flatten_blob')
    layer=caffe_net.Layer_param(name=layer_name,type='Reshape',
                                bottom=[log.blobs(input)],top=top_blobs)
    start_dim = args[0]
    end_dim = len(x.shape)
    if len(args) > 1:
        end_dim = args[1]
    dims = []
    for i in range(start_dim):
        dims.append(x.shape[i])
    cum = 1
    for i in range(start_dim, end_dim):
        cum = cum * x.shape[i]
    dims.append(cum)
    if end_dim != len(x.shape):
        cum = 1
        for i in range(end_dim, len(x.shape)):
            cum = cum * x.shape[i]
        dims.append(cum)
    layer.param.reshape_param.shape.CopyFrom(caffe_net.pb.BlobShape(dim=dims))
    log.cnet.add_layer(layer)
    return x

然后替换pytorch中的实现，在那一堆torch.max=Rp(torch.max,_max)等骚操作后加上
torch.flatten = Rp(torch.flatten,_flatten)

Adhithya-tech · 2021-03-30T08:59:55Z

Still getting that error with torch 1.8, torchvision 0.9

Ronales · 2021-10-27T05:48:33Z

may be you should add net.eval()

example:
net = resnet()
net.eval()

YarrDOpanas · 2023-01-19T10:13:57Z

@Ronales 's advice worked for me. Added net.eval() and it worked. Have torch==1.8.1 and torchvision==0.9.1

zengfanyi · 2023-01-19T10:14:20Z

您的来信已收到，将会尽早做出答复，谢谢！

cc-wangxin2020 · 2024-06-24T08:12:02Z

主要是0.2之后的版本将view改成了flatten操作，以alexnet为例 0.2的实现为

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), 256 * 6 * 6)
        x = self.classifier(x)
        return x

到了0.5变成了

    def forward(self, x):
        x = self.features(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

其中的flatten并没有实现，搞清楚原因后解决就简单多了新建一_flatten函数

def _flatten(raw , input, * args):
    x = raw(input, *args)
    if not NET_INITTED:
        return x
    layer_name=log.add_layer(name='flatten')
    top_blobs=log.add_blobs([x],name='flatten_blob')
    layer=caffe_net.Layer_param(name=layer_name,type='Reshape',
                                bottom=[log.blobs(input)],top=top_blobs)
    start_dim = args[0]
    end_dim = len(x.shape)
    if len(args) > 1:
        end_dim = args[1]
    dims = []
    for i in range(start_dim):
        dims.append(x.shape[i])
    cum = 1
    for i in range(start_dim, end_dim):
        cum = cum * x.shape[i]
    dims.append(cum)
    if end_dim != len(x.shape):
        cum = 1
        for i in range(end_dim, len(x.shape)):
            cum = cum * x.shape[i]
        dims.append(cum)
    layer.param.reshape_param.shape.CopyFrom(caffe_net.pb.BlobShape(dim=dims))
    log.cnet.add_layer(layer)
    return x

然后替换pytorch中的实现，在那一堆torch.max=Rp(torch.max,_max)等骚操作后加上 torch.flatten = Rp(torch.flatten,_flatten)

解决了！！太感谢了

zengfanyi · 2024-06-24T08:12:35Z

您的来信已收到，将会尽早做出答复，谢谢！

imistyrain mentioned this issue Dec 16, 2020

Example fails #68

Open

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

None has type NoneType, but expected one of: bytes, unicode #26

None has type NoneType, but expected one of: bytes, unicode #26

cswwp commented Jul 9, 2019 •

edited

Loading

xingyueye commented Nov 5, 2019

ivylinden commented Nov 5, 2019

114153 commented Nov 6, 2019

cswwp commented Nov 6, 2019 •

edited

Loading

xingyueye commented Nov 6, 2019

peyer commented Nov 10, 2019

xingyueye commented Nov 13, 2019

zchrissirhcz commented Nov 18, 2019 •

edited

Loading

zchrissirhcz commented Nov 18, 2019

maithstartup commented Nov 20, 2019

CASIATumingfei commented Dec 24, 2019

NickShen4Github commented Jan 7, 2020

xiexu666 commented Jan 17, 2020

imistyrain commented May 28, 2020

leoluopy commented Oct 9, 2020

applefishsky009 commented Nov 19, 2020

XinyingZheng commented Dec 14, 2020

XinyingZheng commented Dec 15, 2020

imistyrain commented Dec 15, 2020

imistyrain commented Dec 15, 2020

Adhithya-tech commented Mar 30, 2021

Ronales commented Oct 27, 2021

YarrDOpanas commented Jan 19, 2023

zengfanyi commented Jan 19, 2023 via email

cc-wangxin2020 commented Jun 24, 2024

zengfanyi commented Jun 24, 2024 via email

None has type NoneType, but expected one of: bytes, unicode #26

None has type NoneType, but expected one of: bytes, unicode #26

Comments

cswwp commented Jul 9, 2019 • edited Loading

xingyueye commented Nov 5, 2019

ivylinden commented Nov 5, 2019

114153 commented Nov 6, 2019

cswwp commented Nov 6, 2019 • edited Loading

xingyueye commented Nov 6, 2019

peyer commented Nov 10, 2019

xingyueye commented Nov 13, 2019

zchrissirhcz commented Nov 18, 2019 • edited Loading

zchrissirhcz commented Nov 18, 2019

maithstartup commented Nov 20, 2019

CASIATumingfei commented Dec 24, 2019

NickShen4Github commented Jan 7, 2020

xiexu666 commented Jan 17, 2020

imistyrain commented May 28, 2020

leoluopy commented Oct 9, 2020

applefishsky009 commented Nov 19, 2020

XinyingZheng commented Dec 14, 2020

XinyingZheng commented Dec 15, 2020

imistyrain commented Dec 15, 2020

imistyrain commented Dec 15, 2020

Adhithya-tech commented Mar 30, 2021

Ronales commented Oct 27, 2021

YarrDOpanas commented Jan 19, 2023

zengfanyi commented Jan 19, 2023 via email

cc-wangxin2020 commented Jun 24, 2024

zengfanyi commented Jun 24, 2024 via email

cswwp commented Jul 9, 2019 •

edited

Loading

cswwp commented Nov 6, 2019 •

edited

Loading

zchrissirhcz commented Nov 18, 2019 •

edited

Loading