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lenet.py
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lenet.py
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from caffe import layers as L
from caffe import params as P
import caffe
class LeNet(object):
def __init__(self, lmdb_train, lmdb_test, num_output):
self.train_data = lmdb_train
self.test_data = lmdb_test
self.classifier_num = num_output
def lenet_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = False
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
transform_param=dict(scale=0.00390625, mirror=mirror))
n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, stride=1,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
n.pool1 = L.Pooling(n.conv1, pool=P.Pooling.MAX, kernel_size=2, stride=2)
n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, stride=1,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
n.pool2 = L.Pooling(n.conv2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
n.ip1 = L.InnerProduct(n.pool2, num_output=500,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
n.relu1 = L.ReLU(n.ip1, in_place=True)
n.ip2 = L.InnerProduct(n.relu1, num_output=self.classifier_num,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
if phase == 'TRAIN':
pass
else:
n.accuracy = L.Accuracy(n.ip2, n.label, include=dict(phase=1))
return n.to_proto()
def lenet_bn_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = False
else:
source_data = self.test_data
mirror = False
n.data, n.label = L.Data(source=source_data, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
transform_param=dict(scale=0.00390625, mirror=mirror))
n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, stride=1,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
n.bn1 = L.BatchNorm(n.conv1, use_global_stats=False)
n.pool1 = L.Pooling(n.bn1, pool=P.Pooling.MAX, kernel_size=2, stride=2)
n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, stride=1,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
n.bn2 = L.BatchNorm(n.conv2, use_global_stats=False)
n.pool2 = L.Pooling(n.bn2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
n.ip1 = L.InnerProduct(n.pool2, num_output=500,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
n.relu1 = L.ReLU(n.ip1, in_place=True)
n.ip2 = L.InnerProduct(n.relu1, num_output=self.classifier_num,
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant'))
n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
if phase == 'TRAIN':
pass
else:
n.accuracy = L.Accuracy(n.ip2, n.label, include=dict(phase=1))
return n.to_proto()