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from __future__ import print_function
from caffe import layers as L, params as P, to_proto
from caffe.proto import caffe_pb2
# helper function for common structures
def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
num_output=nout, pad=pad, group=group)
return conv, L.ReLU(conv, in_place=True)
def fc_relu(bottom, nout):
fc = L.InnerProduct(bottom, num_output=nout)
return fc, L.ReLU(fc, in_place=True)
def max_pool(bottom, ks, stride=1):
return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
def caffenet(lmdb, batch_size=256, include_acc=False):
data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
# the net itself
conv1, relu1 = conv_relu(data, 11, 96, stride=4)
pool1 = max_pool(relu1, 3, stride=2)
norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
pool2 = max_pool(relu2, 3, stride=2)
norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
pool5 = max_pool(relu5, 3, stride=2)
fc6, relu6 = fc_relu(pool5, 4096)
drop6 = L.Dropout(relu6, in_place=True)
fc7, relu7 = fc_relu(drop6, 4096)
drop7 = L.Dropout(relu7, in_place=True)
fc8 = L.InnerProduct(drop7, num_output=1000)
loss = L.SoftmaxWithLoss(fc8, label)
if include_acc:
acc = L.Accuracy(fc8, label)
return to_proto(loss, acc)
else:
return to_proto(loss)
def make_net():
with open('train.prototxt', 'w') as f:
print(caffenet('/path/to/caffe-train-lmdb'), file=f)
with open('test.prototxt', 'w') as f:
print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)
if __name__ == '__main__':
make_net()
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