Sha*_*hai 10 python neural-network deep-learning caffe pycaffe
我在caffe中创建了一个"Python"图层"myLayer",并在网中使用它train_val.prototxt我像这样插入图层:
layer {
name: "my_py_layer"
type: "Python"
bottom: "in"
top: "out"
python_param {
module: "my_module_name"
layer: "myLayer"
}
include { phase: TRAIN } # THIS IS THE TRICKY PART!
}
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现在,我的图层只参与网络的TRAINing阶段,
我怎么知道在我的图层的setup功能?
class myLayer(caffe.Layer):
def setup(self, bottom, top):
# I want to know here what is the phase?!!
...
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PS,
我在"Caffe Users"谷歌小组上发布了这个问题.如果有什么东西在那里,我会更新.
正如galloguille所指出的,caffe现在暴露phase给python层类.这个新功能使这个答案有点多余.了解param_str用于将其他参数传递给图层的caffe python图层仍然很有用.
AFAIK没有琐碎的方式来获得阶段.但是,可以将任意参数从net prototxt传递给python.这可以使用的param_str参数来完成python_param.
以下是它的完成方式:
layer {
type: "Python"
...
python_param {
...
param_str: '{"phase":"TRAIN","numeric_arg":5}' # passing params as a STRING
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在python中,你进入param_str了图层的setup功能:
import caffe, json
class myLayer(caffe.Layer):
def setup(self, bottom, top):
param = json.loads( self.param_str ) # use JSON to convert string to dict
self.phase = param['phase']
self.other_param = int( param['numeric_arg'] ) # I might want to use this as well...
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这是一个非常好的解决方法,但如果您只想将phase参数作为参数传递,那么现在您可以将该阶段作为图层的属性进行访问.此功能仅在6天前合并https://github.com/BVLC/caffe/pull/3995.
具体提交:https://github.com/BVLC/caffe/commit/de8ac32a02f3e324b0495f1729bff2446d402c2c
使用此新功能,您只需使用该属性即可self.phase.例如,您可以执行以下操作:
class PhaseLayer(caffe.Layer):
"""A layer for checking attribute `phase`"""
def setup(self, bottom, top):
pass
def reshape(self, bootom, top):
top[0].reshape()
def forward(self, bottom, top):
top[0].data[()] = self.phase
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