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alexnet.py
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import caffe
from caffe import layers as L
from caffe import params as P
def conv_relu(bottom, num_output=64, kernel_size=3, stride=1, pad=0, group=1):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad, group=group,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
relu = L.ReLU(conv, in_place=True)
return conv, relu
def factorization_conv_bn_scale_relu(bottom, num_output=64, kernel_size=3, stride=1, pad=0):
conv = L.Convolution(bottom, num_output=num_output, kernel_size=kernel_size, stride=stride, pad=pad,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', std=1),
bias_filler=dict(type='constant', value=0.2))
conv_bn = L.BatchNorm(conv, use_global_stats=False, in_place=True)
conv_scale = L.Scale(conv, scale_param=dict(bias_term=True), in_place=True)
conv_relu = L.ReLU(conv, in_place=True)
return conv, conv_bn, conv_scale, conv_relu
def fc_relu_drop(bottom, num_output=1024, dropout_ratio=0.5):
fc = L.InnerProduct(bottom, num_output=num_output,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier', std=1),
bias_filler=dict(type='constant', value=0.2))
relu = L.ReLU(fc, in_place=True)
drop = L.Dropout(fc, in_place=True,
dropout_param=dict(dropout_ratio=dropout_ratio))
return fc, relu, drop
def accuracy_top1_top5(bottom, label):
accuracy_top1 = L.Accuracy(bottom, label, include=dict(phase=1))
accuracy_top5 = L.Accuracy(bottom, label, include=dict(phase=1), accuracy_param=dict(top_k=5))
return accuracy_top1, accuracy_top5
class AlexNet(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 alexnet_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
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(crop_size=227, mean_value=[104, 117, 123], mirror=mirror))
n.conv1, n.relu1 = conv_relu(n.data, num_output=96, kernel_size=11, stride=4) # 96x55x55
n.norm1 = L.LRN(n.conv1, local_size=5, alpha=0.0001, beta=0.75)
n.pool1 = L.Pooling(n.norm1, kernel_size=3, stride=2, pool=P.Pooling.MAX) # 96x27x27
n.conv2, n.relu2 = conv_relu(n.pool1, num_output=256, kernel_size=5, pad=2, group=2) # 256x27x27
n.norm2 = L.LRN(n.conv2, local_size=5, alpha=0.0001, beta=0.75)
n.pool2 = L.Pooling(n.norm2, kernel_size=3, stride=2, pool=P.Pooling.MAX) # 256x13x13
n.conv3, n.relu3 = conv_relu(n.pool2, num_output=384, kernel_size=3, pad=1) # 384x13x13
n.conv4, n.relu4 = conv_relu(n.conv3, num_output=384, kernel_size=3, pad=1, group=2) # 384x13x13
n.conv5, n.relu5 = conv_relu(n.conv4, num_output=256, kernel_size=3, pad=1, group=2) # 256x13x13
n.pool5 = L.Pooling(n.conv5, kernel_size=3, stride=2, pool=P.Pooling.MAX) # 256x6x16
n.fc6, n.relu6, n.drop6 = fc_relu_drop(n.pool5, num_output=4096) # 4096x1x1
n.fc7, n.relu7, n.drop7 = fc_relu_drop(n.fc6, num_output=4096) # 4096x1x1
n.fc8 = L.InnerProduct(n.fc7, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
n.loss = L.SoftmaxWithLoss(n.fc8, n.label)
if phase == 'TRAIN':
pass
else:
n.accuracy_top1, n.accuracy_top5 = accuracy_top1_top5(n.fc8, n.label)
return n.to_proto()
def alexnet_bn_proto(self, batch_size, phase='TRAIN'):
n = caffe.NetSpec()
if phase == 'TRAIN':
source_data = self.train_data
mirror = True
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(crop_size=227, mean_value=[104, 117, 123], mirror=mirror))
n.conv1, n.conv1_bn, n.conv1_scale, n.conv1_relu = \
factorization_conv_bn_scale_relu(n.data, num_output=96, kernel_size=11, stride=4) # 96x55x55
n.pool1 = L.Pooling(n.conv1, kernel_size=3, stride=2, pool=P.Pooling.MAX) # 96x27x27
n.conv2, n.conv2_bn, n.conv2_scale, n.conv2_relu = \
factorization_conv_bn_scale_relu(n.pool1, num_output=256, kernel_size=5, pad=2) # 256x27x27
n.pool2 = L.Pooling(n.conv2, kernel_size=3, stride=2, pool=P.Pooling.MAX) # 256x13x13
n.conv3, n.conv3_bn, n.conv3_scale, n.conv3_relu = \
factorization_conv_bn_scale_relu(n.pool2, num_output=384, kernel_size=3, pad=1) # 384x13x13
n.conv4, n.conv4_bn, n.conv4_scale, n.conv4_relu = \
factorization_conv_bn_scale_relu(n.conv3, num_output=384, kernel_size=3, pad=1) # 384x13x13
n.conv5, n.conv5_bn, n.conv5_scale, n.conv5_relu = \
factorization_conv_bn_scale_relu(n.conv4, num_output=256, kernel_size=3, pad=1) # 256x13x13
n.pool5 = L.Pooling(n.conv5, kernel_size=3, stride=2, pool=P.Pooling.MAX) # 256x6x16
n.fc6, n.relu6, n.drop6 = fc_relu_drop(n.pool5, num_output=4096) # 4096x1x1
n.fc7, n.relu7, n.drop7 = fc_relu_drop(n.fc6, num_output=4096) # 4096x1x1
n.fc8 = L.InnerProduct(n.fc7, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='gaussian', std=0.01),
bias_filler=dict(type='constant', value=0))
n.loss = L.SoftmaxWithLoss(n.fc8, n.label)
if phase == 'TRAIN':
pass
else:
n.accuracy_top1, n.accuracy_top5 = accuracy_top1_top5(n.fc8, n.label)
return n.to_proto()