openpose/3rdparty/caffe/examples/pycaffe/caffenet.py

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()