Hi Yihui,

I tried your code and met some problems.

After make -j8 and make pycaffe, I tried to python3 train.py, but found something wrong with protobuf. So I change the protobuf version but the problem was still not solved.

Here is the problem: When I tried protobuf 3.0.0(b1,b2,b3,b4) or 3.1.0 , the error message is:

Failed to include caffe_pb2, things might go wrong!
Traceback (most recent call last):
  File "/home/dutianyuan/anaconda3/lib/python3.5/site-packages/google/protobuf/internal/python_message.py", line 1087, in MergeFromString
    if self._InternalParse(serialized, 0, length) != length:
  File "/home/dutianyuan/anaconda3/lib/python3.5/site-packages/google/protobuf/internal/python_message.py", line 1109, in InternalParse
    (tag_bytes, new_pos) = local_ReadTag(buffer, pos)
  File "/home/dutianyuan/anaconda3/lib/python3.5/site-packages/google/protobuf/internal/decoder.py", line 181, in ReadTag
    while six.indexbytes(buffer, pos) & 0x80:
TypeError: unsupported operand type(s) for &: 'str' and 'int'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "train.py", line 19, in <module>
    from lib.net import Net, load_layer, caffe_test
  File "/mnt/lustre/dutianyuan/channel-pruning/lib/net.py", line 7, in <module>
    import caffe
  File "/mnt/lustre/dutianyuan/channel-pruning/caffe/python/caffe/__init__.py", line 4, in <module>
    from .proto.caffe_pb2 import TRAIN, TEST
  File "/mnt/lustre/dutianyuan/channel-pruning/caffe/python/caffe/proto/caffe_pb2.py", line 799, in <module>
    options=_descriptor._ParseOptions(descriptor_pb2.FieldOptions(), '\020\001')),
  File "/home/dutianyuan/anaconda3/lib/python3.5/site-packages/google/protobuf/descriptor.py", line 869, in _ParseOptions
    message.ParseFromString(string)
  File "/home/dutianyuan/anaconda3/lib/python3.5/site-packages/google/protobuf/message.py", line 185, in ParseFromString
    self.MergeFromString(serialized)
  File "/home/dutianyuan/anaconda3/lib/python3.5/site-packages/google/protobuf/internal/python_message.py", line 1093, in MergeFromString
    raise message_mod.DecodeError('Truncated message.')
google.protobuf.message.DecodeError: Truncated message.

and when I change protobuf to 3.2.0 / 3.3.0 / 3.4.0, the error message is

Traceback (most recent call last):
File "train.py", line 19, in <module>
  from lib.net import Net, load_layer, caffe_test 
File "/mnt/lustre/dutianyuan/channel-pruning/lib/net.py", line 7, in <module>
  import caffe
File "/mnt/lustre/dutianyuan/channel-pruning/caffe/python/caffe/__init__.py", line 4, in <module>
  from .proto.caffe_pb2 import TRAIN, TEST
File "/mnt/lustre/dutianyuan/channel-pruning/caffe/python/caffe/proto/caffe_pb2.py", line 17, in <module>
  serialized_pb='\n\x0b\x63\x61\x66\x66\x65.proto\x12\x05\x63\x61\x66\x66\x65\"\x1c\n\tBlobShape\x12\x0f\n\x03\x64im\x18\x01 
.....with a lot of \x......
  File "/home/dutianyuan/anaconda3/lib/python3.5/site-packages/google/protobuf/descriptor.py", line 824, in __new__
    return _message.default_pool.AddSerializedFile(serialized_pb)
TypeError: expected bytes, str found

When I first met this problem, my Python version is 3.4 and your default setting is with Python 3.5. So I install Python3.5 and Python3.6 by Anaconda, Python 3.6 by apt-get. No matter what version is, the problem was still not solved.

Hope you can show me the specific version of your coding environment! Thanks!

Sorry to trouble when conv = ‘conv2_1’ and convnext = 'conv2_2' In CR step , X_name='conv2_1' and convnext = 'conv2_2' My problem is why don't use X_name = self.bottom_names['conv2_2'][0] and X_name = 'relu2_1' ?

Thanks again for sharing your code and releasing the VGG-16 5X pruned caffemodel. May I ask how much should the code be changed in order to produce this model (pruning only, no 3C included) ?

Can this be done by simply passing a different flag instead of "-action c3"? Thanks!!!! And sorry for asking but I'm sure a lot of people will want to know about this.

I'm truely sorry to ask but I 'm not sure why I'm unable to overwrite weights of vgg.caffemodel with the final WPQ. I simplified R3() to use your channel pruning algorithm only:

def stepR1(net,conv,convnext,d_c):   
  if conv in net.selection:
      net.WPQ[(conv,0)] =  net.param_data(conv)[:,net.selection[conv],:,:]
      net.WPQ[(conv,1)] =  net.param_b_data(conv)
  else:
      net.WPQ[(conv,0)] =  net.param_data(conv)
      net.WPQ[(conv,1)] =  net.param_b_data(conv)
  if conv in pooldic:
      X_name = net.bottom_names[convnext][0]
  else: 
      X_name = conv

  idxs, W2, B2 = net.dictionary_kernel(X_name, None, d_c, convnext, None, DEBUG=True)
  # W2
  net.selection[convnext] = idxs
  net.param_data(convnext)[:, ~idxs, ...] = 0
  net.param_data(convnext)[:, idxs, ...] = W2.copy()
  net.set_param_b(convnext,B2)
  # W1
  net.WPQ[(conv,0)] = net.WPQ[(conv,0)][idxs]
  net.WPQ[(conv,1)] = net.WPQ[(conv,1)][idxs]
  net.set_conv(conv, num_output=sum(idxs))
  return net  

The code executes properly (all weights in WPQ have the same shape of your VGG-16_5x prototxt release). But when running stepend(), the instance of net that takes the new pt 3C4x_mem_bn_vgg.prototxt and the original model vgg.caffemodel fails ( the command is net = Net(new_pt, model=model)):

net.cpp:757] Cannot copy param 0 weights from layer 'conv1_2'; shape mismatch. Source param shape is 64 64 3 3 (36864); target param shape is 24 64 3 3 (13824). To learn this layer's parameters from scratch rather than copying from a saved net, rename the layer. *** Check failure stack trace: ***

Have you ever experienced this type of issue? I'm sure I'm missing a line in my stepR1() function, but don;t know what. Maybe I need to use net.insert?? Please, any help would be appreciated. And once again thank you!!!

https://github.com/yihui-he/channel-pruning/blob/master/lib/net.py#L1301

The rankdic is set beforehand and it looks like only for VGG16.

it seems that any formula or theorem about calculating rankdic does not mention in paper.

is it the experimental outcome?

if true, what's the rank for ResNet-50? Since the rankdic in the release source code of ResNet-50 is same as VGG16 https://github.com/yihui-he/channel-pruning/releases/tag/ResNet-50-2X

thanks.

Hi,I am using your open source model faster-rcnn VGGx2 and VGGx4 code, the results did not achieve the effect of acceleration, your accuracy in the form is the actual accuracy of it? Why is it so much worse than the original VGG? Or I ignored what the details, look forward to your answer, thank you

e.g., your resnet prototxt : ResNet-50 | 2X | 90.8 (Top-5), it sounded impossible.

Due to the structure constraints of ResNet-50, non-tensor layers (e.g., batch normalization and pooling layers) take up more than 40% of the inference time on GPU.

first, I really appreciated for your help! Then I have some quesqions How can i set single channel pruning? how much is channel prunning accerate ration ? why channel_pruning_VGG-16_3C4x.caffemodel is so big ?

HI.I'm sorry to bother you.When I run python train.py -action c3 -caffe 0 ,I got the error like this:

no lighting pack [libprotobuf INFO google/protobuf/io/coded_stream.cc:610] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h. [libprotobuf WARNING google/protobuf/io/coded_stream.cc:81] The total number of bytes read was 553432081 Process Process-1: Traceback (most recent call last): File "/home/tang/anaconda3/lib/python3.6/multiprocessing/process.py", line 249, in _bootstrap self.run() File "/home/tang/anaconda3/lib/python3.6/multiprocessing/process.py", line 93, in run self._target(*self._args, **self._kwargs) File "/home/tang/channel-pruning/lib/worker.py", line 21, in job ret = target(**kwargs) File "train.py", line 26, in step0 net = Net(pt, model=model, noTF=1) File "/home/tang/channel-pruning/lib/net.py", line 67, in init self.net_param = NetBuilder(pt=pt) File "/home/tang/channel-pruning/lib/builder.py", line 131, in init pb2.text_format.Merge(f.read(), self.net) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 476, in Merge descriptor_pool=descriptor_pool) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 526, in MergeLines return parser.MergeLines(lines, message) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 559, in MergeLines self._ParseOrMerge(lines, message) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 574, in _ParseOrMerge self._MergeField(tokenizer, message) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 675, in _MergeField merger(tokenizer, message, field) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 764, in _MergeMessageField self._MergeField(tokenizer, sub_message) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 675, in _MergeField merger(tokenizer, message, field) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 764, in _MergeMessageField self._MergeField(tokenizer, sub_message) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 675, in _MergeField merger(tokenizer, message, field) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 809, in _MergeScalarField value = tokenizer.ConsumeString() File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 1151, in ConsumeString the_bytes = self.ConsumeByteString() File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 1166, in ConsumeByteString the_list = [self._ConsumeSingleByteString()] File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_format.py", line 1191, in _ConsumeSingleByteString result = text_encoding.CUnescape(text[1:-1]) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_encoding.py", line 103, in CUnescape result = ''.join(_cescape_highbit_to_str[ord(c)] for c in result) File "/home/tang/anaconda3/lib/python3.6/site-packages/protobuf-3.2.0-py3.6.egg/google/protobuf/text_encoding.py", line 103, in result = ''.join(_cescape_highbit_to_str[ord(c)] for c in result) IndexError: list index out of range

Can you show me what is the problem? Thanks!

@yihui-he All are welcome to create issues, but please google the problem first, and make sure it has not already been reported.

What steps reproduce the bug?

make clean && make -j32 && make pycaffe -j32

What hardware and operating system/distribution are you running?

Operating system: Ubuntu14.04 CUDA version: cuda-8.0 CUDNN version: 5.1 openCV version: 2.4.9 BLAS: mkl Python version: python3.4

If the bug is a crash, provide the backtrace.

part error log: AR -o .build_release/lib/libcaffe.a LD -o .build_release/lib/libcaffe.so.1.0.0 /usr/bin/ld: cannot find -lboost_python3 collect2: error: ld returned 1 exit status make: *** [.build_release/lib/libcaffe.so.1.0.0] Error 1

you only give the pruned .prototxt for VOC datasets, but i want to get the pruned model of faster_vgg2x for my own datasets, what should I do??? if i also use this network structure of prototxt, i need the pre-trained ImageNet models of network structure of prototxt(vgg 2x).

I cannot find in your code the normalization step (W_i \leftarrow W_i/||W_i||_F) described after Eq (4) in the ICCV2017 paper. I expected to find it in dictionary() in decompose.py. There is some normalization, involving W2_std and nowstd variables in the nested function updateW2, but it seems to be different from what is described in the paper. Any comment?

(from email)

Extracting conv1_2 (50000, 64)
Acc 100.000
Reconstruction Err 0.013643407047130757
channel_decomposition 461.0681805610657
Extracting X pool1 From Y conv2_1 stride 1
Acc 100.000
rMSE 0.01639283918160166
channel_pruning 340.1220703125
Extracting X pool1 From Y conv2_1 stride 1
Acc 100.000
spatial_decomposition 1991.869223356247
run for 500 batches nFeatsPerBatch 100
Extracting conv2_1 (50000, 128)
Acc 100.000
Reconstruction Err 0.02323069786987915

hello,I'm learning from your channel-pruning-master. I have a problem running the problem.Do you have any solutions? Here are my problem: /home/linux/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/externals/joblib/init.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be

removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models,

you may need to re-serialize those models with scikit-learn 0.21+. warnings.warn(msg, category=FutureWarning) /home/linux/anaconda3/envs/py35/lib/python3.5/site-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.linear_model.base module is deprecated in version

0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.linear_model. Anything that cannot be imported from

sklearn.linear_model is now part of the private API. warnings.warn(message, FutureWarning) no lighting pack using CPU caffe [libprotobuf INFO google/protobuf/io/coded_stream.cc:610] Reading dangerously large protocol message. If the message turns out to be larger than 2147483647 bytes, parsing

will be halted for security reasons. To increase the limit (or to disable these warnings), see CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h. [libprotobuf WARNING google/protobuf/io/coded_stream.cc:81] The total number of bytes read was 553432081

stage0 freeze

temp/bn_vgg.prototxt using CPU caffe including last conv layer! run for 100 batches nFeatsPerBatch 100 Extracting conv1_1 (10000, 64) Extracting conv1_2_V (10000, 22) Extracting conv1_2_H (10000, 22) Extracting conv1_2_P (10000, 59) Extracting conv2_1_V (10000, 37) Extracting conv2_1_H (10000, 37) Extracting conv2_1_P (10000, 118) Extracting conv2_2_V (10000, 47) Extracting conv2_2_H (10000, 47) Extracting conv2_2_P (10000, 119) Extracting conv3_1_V (10000, 83) Extracting conv3_1_H (10000, 83) Extracting conv3_1_P (10000, 226) Extracting conv3_2_V (10000, 89) Extracting conv3_2_H (10000, 89) Extracting conv3_2_P (10000, 243) Extracting conv3_3_V (10000, 106) Extracting conv3_3_H (10000, 106) Extracting conv3_3_P (10000, 256) Extracting conv4_1_V (10000, 175) Extracting conv4_1_H (10000, 175) Extracting conv4_1_P (10000, 482) Extracting conv4_2_V (10000, 192) Extracting conv4_2_H (10000, 192) Extracting conv4_2_P (10000, 457) Extracting conv4_3_V (10000, 227) Extracting conv4_3_H (10000, 227) Extracting conv4_3_P (10000, 512) Extracting conv5_1_V (10000, 398) Extracting conv5_1_H (10000, 512) Extracting conv5_2_V (10000, 390) Extracting conv5_2_H (10000, 512) Extracting conv5_3_V (10000, 379) Extracting conv5_3_H (10000, 512) Acc 0.000 wrote memory data layer to temp/mem_bn_vgg.prototxt freezing imgs to temp/frozen100.pickle

stage1 speed3.0

using CPU caffe loading imgs from temp/frozen100.pickle loaded Process Process-3: Traceback (most recent call last): File "/home/linux/anaconda3/envs/py35/lib/python3.5/multiprocessing/process.py", line 315, in _bootstrap self.run() File "/home/linux/anaconda3/envs/py35/lib/python3.5/multiprocessing/process.py", line 108, in run self._target(*self._args, **self._kwargs) File "/home/linux/channel-pruning-master/lib/worker.py", line 21, in job ret = target(**kwargs) File "train.py", line 75, in solve WPQ, new_pt = net.R3() File "/home/linux/channel-pruning-master/lib/net.py", line 1348, in R3 rank = rankdic[conv] KeyError: 'conv1_2_V'

您好，我在做测试的时候发现一个问题，模型是剪枝后的模型VGG-16_3C4x.prototxt和VGG-16_3C4x.caffemodel。应该是boost的问题 Operating system:ubuntu16
CUDA version: 8.0 Python version: 3.6 请看一下是boost还是多线程读取数据的问题