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Long-term Recurrent Convolutional Networks紙を再現しようとしています。事前にトレーニングしたカフェモデルをラザニアにロードしますか?
私はtheano
で使用したいと思う事前トレーニングされたカフェモデルを持っています。 私はこのファイルに.caffemodel
、prototxt
を持っています。 私はlasagne exampleを使って、カフェウェイトをカフェモデルにロードしました。 これはcode I usedですが、データはラセーンモデルに読み込まれません。 このエラーをスローするlasagne.layers.get_all_param_values(net)
コマンドを使用してチェックします。
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'
トライアル/テストコード: -
# -*- 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 !!!!!!!!!!")
質問自体にコードを含める必要があります。 [最小限の完全かつ検証可能なサンプルを作成する方法](http://stackoverflow.com/help/mcve)を参照してください。 –