Lazanyaya önceden hazırlanmış bir caffe modeli mi yüklüyor?

Question

Long-term Recurrent Convolutional Networks belgesini çoğaltmaya çalışıyorum.

theano'da kullanmak istediğim önceden hazırlanmış bir caffe modelim var. Bu dosya için .caffemodel ve prototxt var. Caffe ağırlıklarını caffe modeline yüklemek için lasagne example'u kullandım. Bu code I used, ancak veriler lazanya modeline yüklenmemiş. Bu hatayı atan lasagne.layers.get_all_param_values(net) komutunu kullanarak denetleyin.

Traceback (most recent call last): 
    File "/home/anilil/projects/pycharm-community-5.0.4/helpers/pydev/pydevd.py", line 2411, in <module> 
    globals = debugger.run(setup['file'], None, None, is_module) 
    File "/home/anilil/projects/pycharm-community-5.0.4/helpers/pydev/pydevd.py", line 1802, in run 
    launch(file, globals, locals) # execute the script 
    File "/media/anilil/Data/charm/mv_clean/Vgg_las.py", line 218, in <module> 
    x=lasagne.layers.get_all_param_values(net) 
    File "/usr/local/lib/python2.7/dist-packages/lasagne/layers/helper.py", line 439, in get_all_param_values 
    params = get_all_params(layer, **tags) 
    File "/usr/local/lib/python2.7/dist-packages/lasagne/layers/helper.py", line 353, in get_all_params 
    return utils.unique(params) 
    File "/usr/local/lib/python2.7/dist-packages/lasagne/utils.py", line 157, in unique 
    for el in l: 
    File "/usr/local/lib/python2.7/dist-packages/lasagne/layers/helper.py", line 352, in <genexpr> 
    params = chain.from_iterable(l.get_params(**tags) for l in layers) 
AttributeError: 'str' object has no attribute 'get_params' 

DENEME/Test Kodu: -

# -*- coding: utf-8 -*- 
import os 
import sys 
import lasagne 
from lasagne.layers import InputLayer 
from lasagne.layers import DenseLayer 
from lasagne.layers import NonlinearityLayer 
from lasagne.nonlinearities import rectify 
from lasagne.layers import DropoutLayer 
from lasagne.layers import Pool2DLayer as PoolLayer 
from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer 
from lasagne.nonlinearities import softmax 
import theano as T 
from lasagne.layers import LocalResponseNormalization2DLayer as LRN 
sys.path.append('/home/anilil/projects/lstm/lisa-caffe-public/python/') 
import caffe 
from lasagne.utils import floatX 
import numpy as np 

def build_model(): 
    net = {} 
    # Input layer 
    net['input'] = InputLayer((None, 3, 227, 227)) 
    # First Conv Layer 
    net['conv1'] = ConvLayer(net['input'], num_filters=96,filter_size=7, pad=0, flip_filters=False,stride=2,nonlinearity=rectify) 
    net['pool1'] = PoolLayer(net['conv1'], pool_size=3,stride=2,mode='max') 
    net['norm1'] = LRN(net['pool1'],alpha=0.0001,beta=0.75,n=5) 
    # 2nd Conv Layer 
    net['conv2'] = ConvLayer(net['norm1'], num_filters=384,filter_size=5, pad=0, flip_filters=False,stride=2,nonlinearity=rectify) 
    net['pool2'] = PoolLayer(net['conv2'], pool_size=3,stride=2,mode='max') 
    net['norm2'] = LRN(net['pool2'],alpha=0.0001,beta=0.75,n=5) 
    # 3rd Conv Layer 
    net['conv3'] = ConvLayer(net['norm2'], num_filters=512,filter_size=3, pad=1, flip_filters=False,nonlinearity=rectify) 
    net['conv4'] = ConvLayer(net['conv3'], num_filters=512,filter_size=3, pad=1, flip_filters=False,nonlinearity=rectify) 
    net['conv5'] = ConvLayer(net['conv4'], num_filters=384,filter_size=3, pad=1, flip_filters=False,nonlinearity=rectify) 
    net['pool5'] = PoolLayer(net['conv5'], pool_size=3,stride=2,mode='max') 
    net['fc6'] = DenseLayer(net['pool5'], num_units=4096,nonlinearity=rectify) 
    net['fc6_dropout'] = DropoutLayer(net['fc6'], p=0.5) 
    net['fc7'] = DenseLayer(net['fc6_dropout'], num_units=4096) 
    net['fc7_dropout'] = DropoutLayer(net['fc7'], p=0.5) 
    net['fc8-ucf'] = DenseLayer(net['fc7_dropout'], num_units=101, nonlinearity=None) 
    net['prob'] = NonlinearityLayer(net['fc8-ucf'], softmax) 

    return net 

if __name__=="__main__": 
    net = build_model() 
    #net= load_caffe_weights(net,'/home/anilil/projects/lstm/lisa-caffe-public/examples/LRCN_activity_recognition/deploy_singleFrame.prototxt','/home/anilil/projects/lstm/lisa-caffe-public/examples/LRCN_activity_recognition/singleframe_flow/snaps/snapshots_singleFrame_flow_v2_iter_50000.caffemodel') 
    caffe.set_device(0) 
    caffe.set_mode_gpu() 
    net_caffe = caffe.Net('/home/anilil/projects/lstm/lisa-caffe-public/examples/LRCN_activity_recognition/deploy_singleFrame.prototxt', '/home/anilil/projects/lstm/lisa-caffe-public/examples/LRCN_activity_recognition/singleframe_flow/snaps/snapshots_singleFrame_flow_v2_iter_50000.caffemodel', caffe.TEST) 
    layers_caffe = dict(zip(list(net_caffe._layer_names), net_caffe.layers)) 

    for name, layer in net.items(): 
     try: 
      layer.W.set_value(layers_caffe[name].blobs[0].data,borrow=True) 
      layer.b.set_value(layers_caffe[name].blobs[1].data,borrow=True) 
     except AttributeError: 
      continue 

    print ("Loaded the files without issues !!!!!!!!!!") 
    x=lasagne.layers.get_all_param_values(net) 
    print ("Saved Weights to the file without issues !!!!!!!!!!") 

Answer 1

deneyin:

x = lasagne.layers.get_all_param_values ​​(net [ 'prob'])

veya Bu şekilde ağınızı yapın:

def build_model(): 
    net = {} 
    # Input layer 
    net = InputLayer((None, 3, 227, 227)) 
    # First Conv Layer 
    net = ConvLayer(net, num_filters=96,filter_size=7, pad=0,  flip_filters=False,stride=2,nonlinearity=rectify) 
    net = PoolLayer(net, pool_size=3,stride=2,mode='max') 
    .... 
    net= NonlinearityLayer(net, softmax) 
    return net