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resnext.py
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resnext.py
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import caffe
from caffe import layers as L
from caffe import params as P
def resnext_block(bottom, base_output=64, card=32):
"""
input:4*base_output x n x n
output:4*base_output x n x n
:param base_output: base num_output of branch2
:param bottom: bottom layer
:return: layers
Args:
card:
"""
conv1 = L.Convolution(bottom, num_output=base_output * (card / 16), kernel_size=1, stride=1, pad=0, bias_term=False,
param=[dict(lr_mult=1, decay_mult=1)], weight_filler=dict(type='xavier'))
conv1_bn = L.BatchNorm(conv1, use_global_stats=False, in_place=True)
conv1_scale = L.Scale(conv1, scale_param=dict(bias_term=True), in_place=True)
conv1_relu = L.ReLU(conv1, in_place=True)
conv2 = L.Convolution(conv1, num_output=base_output * (card / 16), kernel_size=3, stride=1, pad=1, group=card,
bias_term=False, param=[dict(lr_mult=1, decay_mult=1)], weight_filler=dict(type='xavier'))
conv2_bn = L.BatchNorm(conv2, use_global_stats=False, in_place=True)
conv2_scale = L.Scale(conv2, scale_param=dict(bias_term=True), in_place=True)
conv2_relu = L.ReLU(conv2, in_place=True)
conv3 = L.Convolution(conv2, num_output=base_output * 4, kernel_size=1, stride=1, pad=0, bias_term=False,
param=[dict(lr_mult=1, decay_mult=1)], weight_filler=dict(type='xavier'))
conv3_bn = L.BatchNorm(conv3, use_global_stats=False, in_place=True)
conv3_scale = L.Scale(conv3, scale_param=dict(bias_term=True), in_place=True)
eltwise = L.Eltwise(bottom, conv3, eltwise_param=dict(operation=1))
eltwise_relu = L.ReLU(eltwise, in_place=True)
return conv1, conv1_bn, conv1_scale, conv1_relu, conv2, conv2_bn, conv2_scale, conv2_relu, \
conv3, conv3_bn, conv3_scale, eltwise, eltwise_relu
def match_block(bottom, base_output=64, stride=2, card=32):
"""
input:4*base_output x n x n
output:4*base_output x n x n
:param base_output: base num_output of branch2
:param bottom: bottom layer
:return: layers
"""
conv1 = L.Convolution(bottom, num_output=base_output * (card / 16), kernel_size=1, stride=1, pad=0, bias_term=False,
param=[dict(lr_mult=1, decay_mult=1)], weight_filler=dict(type='xavier'))
conv1_bn = L.BatchNorm(conv1, use_global_stats=False, in_place=True)
conv1_scale = L.Scale(conv1, scale_param=dict(bias_term=True), in_place=True)
conv1_relu = L.ReLU(conv1, in_place=True)
conv2 = L.Convolution(conv1, num_output=base_output * (card / 16), kernel_size=3, stride=stride, pad=1, group=card,
bias_term=False, param=[dict(lr_mult=1, decay_mult=1)], weight_filler=dict(type='xavier'))
conv2_bn = L.BatchNorm(conv2, use_global_stats=False, in_place=True)
conv2_scale = L.Scale(conv2, scale_param=dict(bias_term=True), in_place=True)
conv2_relu = L.ReLU(conv2, in_place=True)
conv3 = L.Convolution(conv2, num_output=base_output * 4, kernel_size=1, stride=1, pad=0, bias_term=False,
param=[dict(lr_mult=1, decay_mult=1)], weight_filler=dict(type='xavier'))
conv3_bn = L.BatchNorm(conv3, use_global_stats=False, in_place=True)
conv3_scale = L.Scale(conv3, scale_param=dict(bias_term=True), in_place=True)
match = L.Convolution(bottom, num_output=base_output * 4, kernel_size=1, stride=stride, pad=0, bias_term=False,
param=[dict(lr_mult=1, decay_mult=1)], weight_filler=dict(type='xavier'))
match_bn = L.BatchNorm(match, use_global_stats=False, in_place=True)
match_scale = L.Scale(match, scale_param=dict(bias_term=True), in_place=True)
eltwise = L.Eltwise(match, conv3, eltwise_param=dict(operation=1))
eltwise_relu = L.ReLU(eltwise, in_place=True)
return conv1, conv1_bn, conv1_scale, conv1_relu, conv2, conv2_bn, conv2_scale, conv2_relu, \
conv3, conv3_bn, conv3_scale, match, match_bn, match_scale, eltwise, eltwise_relu
resnext_string = 'n.resx(n)_conv1, n.resx(n)_conv1_bn, n.resx(n)_conv1_scale, n.resx(n)_conv1_relu, \
n.resx(n)_conv2, n.resx(n)_conv2_bn, n.resx(n)_conv2_scale, n.resx(n)_conv2_relu, n.resx(n)_conv3, \
n.resx(n)_conv3_bn, n.resx(n)_conv3_scale, n.resx(n)_elewise, n.resx(n)_elewise_relu = \
resnext_block((bottom), base_output=(base), card=(c))'
match_string = 'n.resx(n)_conv1, n.resx(n)_conv1_bn, n.resx(n)_conv1_scale, n.resx(n)_conv1_relu, \
n.resx(n)_conv2, n.resx(n)_conv2_bn, n.resx(n)_conv2_scale, n.resx(n)_conv2_relu, n.resx(n)_conv3, \
n.resx(n)_conv3_bn, n.resx(n)_conv3_scale, n.resx(n)_match_conv, n.resx(n)_match_conv_bn, n.resx(n)_match_conv_scale,\
n.resx(n)_elewise, n.resx(n)_elewise_relu = match_block((bottom), base_output=(base), stride=(s), card=(c))'
class ResNeXt(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 resnext_layers_proto(self, batch_size, card=32, phase='TRAIN', stages=(3, 4, 6, 3)):
"""
:param batch_size: the batch_size of train and test phase
:param phase: TRAIN or TEST
:param stages: the num of layers = 2 + 3*sum(stages), layers would better be chosen from [50, 101, 152]
{every stage is composed of 1 residual_branch_shortcut module and stage[i]-1 residual_branch
modules, each module consists of 3 conv layers}
(3, 4, 6, 3) for 50 layers; (3, 4, 23, 3) for 101 layers; (3, 8, 36, 3) for 152 layers
"""
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=224, mean_value=[104, 117, 123], mirror=mirror))
n.conv1 = L.Convolution(n.data, num_output=64, kernel_size=7, stride=2, pad=3, bias_term=False,
param=[dict(lr_mult=1, decay_mult=1)], weight_filler=dict(type='xavier'))
n.conv1_bn = L.BatchNorm(n.conv1, use_global_stats=False, in_place=True)
n.conv1_scale = L.Scale(n.conv1, scale_param=dict(bias_term=True), in_place=True)
n.conv1_relu = L.ReLU(n.conv1, in_place=True) # 64x112x112
n.pool1 = L.Pooling(n.conv1, kernel_size=3, stride=2, pad=1, ceil_mode=False, pool=P.Pooling.MAX) # 64x56x56
for num in xrange(len(stages)): # num = 0, 1, 2, 3
for i in xrange(stages[num]):
if i == 0:
stage_string = match_string
bottom_string = ['n.pool1', 'n.resx{}_elewise'.format(str(sum(stages[:1]))),
'n.resx{}_elewise'.format(str(sum(stages[:2]))),
'n.resx{}_elewise'.format(str(sum(stages[:3])))][num]
else:
stage_string = resnext_string
bottom_string = 'n.resx{}_elewise'.format(str(sum(stages[:num]) + i))
print num, i
exec (stage_string.replace('(bottom)', bottom_string).
replace('(base)', str(2 ** num * 64)).
replace('(n)', str(sum(stages[:num]) + i + 1)).
replace('(s)', str(int(num > 0) + 1)).
replace('(c)', str(card)))
exec 'n.pool_ave = L.Pooling(n.resx{}_elewise, pool=P.Pooling.AVE, global_pooling=True)'.format(
str(sum(stages)))
n.classifier = L.InnerProduct(n.pool_ave, num_output=self.classifier_num,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)],
weight_filler=dict(type='xavier'),
bias_filler=dict(type='constant', value=0))
n.loss = L.SoftmaxWithLoss(n.classifier, n.label)
if phase == 'TRAIN':
pass
else:
n.accuracy_top1 = L.Accuracy(n.classifier, n.label, include=dict(phase=1))
n.accuracy_top5 = L.Accuracy(n.classifier, n.label, include=dict(phase=1),
accuracy_param=dict(top_k=5))
return n.to_proto()