问题描述
我未能尝试使用Caffe在Python中实现简单的损失层.作为参考，我发现了用Python实现的几层，包括 ，此处和此处.
I am unsuccessfully trying to implement a simple loss layer in Python using Caffe. As reference, I found several layers implemented in Python, including here, here and here.
从Caffe文档/示例提供的EuclideanLossLayer开始，我无法使其正常运行并开始调试.即使使用这个简单的TestLayer:
Starting with the EuclideanLossLayer as provided by the Caffe documentation/examples, I was not able to get it working and startd debugging. Even using this simple TestLayer:
def setup(self, bottom, top):
"""
Checks the correct number of bottom inputs.
:param bottom: bottom inputs
:type bottom: [numpy.ndarray]
:param top: top outputs
:type top: [numpy.ndarray]
"""
print 'setup'
def reshape(self, bottom, top):
"""
Make sure all involved blobs have the right dimension.
:param bottom: bottom inputs
:type bottom: caffe._caffe.RawBlobVec
:param top: top outputs
:type top: caffe._caffe.RawBlobVec
"""
print 'reshape'
top[0].reshape(bottom[0].data.shape[0], bottom[0].data.shape[1], bottom[0].data.shape[2], bottom[0].data.shape[3])
def forward(self, bottom, top):
"""
Forward propagation.
:param bottom: bottom inputs
:type bottom: caffe._caffe.RawBlobVec
:param top: top outputs
:type top: caffe._caffe.RawBlobVec
"""
print 'forward'
top[0].data[...] = bottom[0].data
def backward(self, top, propagate_down, bottom):
"""
Backward pass.
:param bottom: bottom inputs
:type bottom: caffe._caffe.RawBlobVec
:param propagate_down:
:type propagate_down:
:param top: top outputs
:type top: caffe._caffe.RawBlobVec
"""
print 'backward'
bottom[0].diff[...] = top[0].diff[...]
我无法使Python层正常工作.学习任务非常简单，因为我只是在尝试预测实数值是正数还是负数.相应的数据如下生成并写入LMDB:
I am not able to get the Python layer working. The learning task is rather simple, as I am merely trying to predict whether a real-valued number is positive or negative. The corresponding data is generated as follows and written to LMDBs:
N = 10000
N_train = int(0.8*N)
images = []
labels = []
for n in range(N):
image = (numpy.random.rand(1, 1, 1)*2 - 1).astype(numpy.float)
label = int(numpy.sign(image))
images.append(image)
labels.append(label)
将数据写入LMDB应该是正确的，因为使用Caffe提供的MNIST数据集进行的测试没有问题.网络定义如下:
Writing the data to LMDB should be correct as tests with the MNIST dataset provided by Caffe show no problems. The network is defined as follows:
net.data, net.labels = caffe.layers.Data(batch_size = batch_size, backend = caffe.params.Data.LMDB,
source = lmdb_path, ntop = 2)
net.fc1 = caffe.layers.Python(net.data, python_param = dict(module = 'tools.layers', layer = 'TestLayer'))
net.score = caffe.layers.TanH(net.fc1)
net.loss = caffe.layers.EuclideanLoss(net.score, net.labels)
使用以下方法手动完成:
Solving is done manually using:
for iteration in range(iterations):
solver.step(step)
相应的prototxt文件如下:
The corresponding prototxt files are below:
solver.prototxt:
weight_decay: 0.0005
test_net: "tests/test.prototxt"
snapshot_prefix: "tests/snapshot_"
max_iter: 1000
stepsize: 1000
base_lr: 0.01
snapshot: 0
gamma: 0.01
solver_mode: CPU
train_net: "tests/train.prototxt"
test_iter: 0
test_initialization: false
lr_policy: "step"
momentum: 0.9
display: 100
test_interval: 100000
train.prototxt:
layer {
name: "data"
type: "Data"
top: "data"
top: "labels"
data_param {
source: "tests/train_lmdb"
batch_size: 64
backend: LMDB
}
}
layer {
name: "fc1"
type: "Python"
bottom: "data"
top: "fc1"
python_param {
module: "tools.layers"
layer: "TestLayer"
}
}
layer {
name: "score"
type: "TanH"
bottom: "fc1"
top: "score"
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "score"
bottom: "labels"
top: "loss"
}
test.prototxt:
layer {
name: "data"
type: "Data"
top: "data"
top: "labels"
data_param {
source: "tests/test_lmdb"
batch_size: 64
backend: LMDB
}
}
layer {
name: "fc1"
type: "Python"
bottom: "data"
top: "fc1"
python_param {
module: "tools.layers"
layer: "TestLayer"
}
}
layer {
name: "score"
type: "TanH"
bottom: "fc1"
top: "score"
}
layer {
name: "loss"
type: "EuclideanLoss"
bottom: "score"
bottom: "labels"
top: "loss"
}
我尝试对其进行跟踪，在TestLayer的backward和foward方法中添加了调试消息，在求解过程中仅调用了forward方法(请注意，未执行测试，调用只能相关的OT解决方案).同样，我在python_layer.hpp中添加了调试消息:
I tried tracking it down, adding debug messages in the backward and foward methods of TestLayer, only the forward methods gets called during solving (note that NO testing is performed, the calls can only be related ot solving). Similarly I added debug messages in python_layer.hpp:
virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
const vector<Blob<Dtype>*>& top) {
LOG(INFO) << "cpp forward";
self_.attr("forward")(bottom, top);
}
virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
LOG(INFO) << "cpp backward";
self_.attr("backward")(top, propagate_down, bottom);
}
同样，仅执行前向通过.当我删除TestLayer中的backward方法时，求解仍然有效.删除forward方法时，由于未实现forward，因此会引发错误.我希望对于backward也是一样，因此似乎完全不执行向后传递.切换回常规层并添加调试消息，一切都会按预期进行.
Again, only the forward pass is executed. When I remove the backward method in TestLayer, solving still works. When removing the forward method, an error is thrown as forward is not implemented. I would expect the same for backward, so it seems that the backward pass does not get executed at all. Switching back to regular layers and adding debug messages, everything works as expected.
我感觉自己缺少一些简单或基本的东西，但是现在已经有好几天无法解决问题了.因此，任何帮助或提示都将受到赞赏.
I have the feeling that I am missing something simple or fundamental, but I was not able to resolve the problem for several days now. So any helps or hints are appreciated.
谢谢！
推荐答案
除了 Erik B. 的答案，您可以通过指定
In addition to Erik B.'s answer, you can force caffe to backprob by specifying
force_backward: true
在您的网络原型中.
请参见 caffe.proto 有关更多信息.
In your net prototxt.
See comments in caffe.proto for more information.
这篇关于在Caffe Python层中向后传递不被调用/起作用吗?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！