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import os
import caffe
from caffe import layers as L
from caffe import params as P
from caffe.proto import caffe_pb2
def check_if_exist(path):
return os.path.exists(path)
def make_if_not_exist(path):
if not os.path.exists(path):
os.makedirs(path)
def UnpackVariable(var, num):
assert len > 0
if type(var) is list and len(var) == num:
return var
else:
ret = []
if type(var) is list:
assert len(var) == 1
for i in xrange(0, num):
ret.append(var[0])
else:
for i in xrange(0, num):
ret.append(var)
return ret
def ConvBNLayer(net, from_layer, out_layer, use_bn, use_relu, num_output,
kernel_size, pad, stride, dilation=1, use_scale=True, eps=0.001,
conv_prefix='', conv_postfix='', bn_prefix='', bn_postfix='_bn',
scale_prefix='', scale_postfix='_scale', bias_prefix='', bias_postfix='_bias'):
if use_bn:
# parameters for convolution layer with batchnorm.
kwargs = {
'param': [dict(lr_mult=1, decay_mult=1)],
'weight_filler': dict(type='gaussian', std=0.01),
'bias_term': False,
}
# parameters for batchnorm layer.
bn_kwargs = {
'param': [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)],
'eps': eps,
}
# parameters for scale bias layer after batchnorm.
if use_scale:
sb_kwargs = {
'bias_term': True,
'param': [dict(lr_mult=1, decay_mult=0), dict(lr_mult=1, decay_mult=0)],
'filler': dict(type='constant', value=1.0),
'bias_filler': dict(type='constant', value=0.0),
}
else:
bias_kwargs = {
'param': [dict(lr_mult=1, decay_mult=0)],
'filler': dict(type='constant', value=0.0),
}
else:
kwargs = {
'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)
}
conv_name = '{}{}{}'.format(conv_prefix, out_layer, conv_postfix)
[kernel_h, kernel_w] = UnpackVariable(kernel_size, 2)
[pad_h, pad_w] = UnpackVariable(pad, 2)
[stride_h, stride_w] = UnpackVariable(stride, 2)
if kernel_h == kernel_w:
net[conv_name] = L.Convolution(net[from_layer], num_output=num_output,
kernel_size=kernel_h, pad=pad_h, stride=stride_h, **kwargs)
else:
net[conv_name] = L.Convolution(net[from_layer], num_output=num_output,
kernel_h=kernel_h, kernel_w=kernel_w, pad_h=pad_h, pad_w=pad_w,
stride_h=stride_h, stride_w=stride_w, **kwargs)
if dilation > 1:
net.update(conv_name, {'dilation': dilation})
if use_bn:
bn_name = '{}{}{}'.format(bn_prefix, out_layer, bn_postfix)
net[bn_name] = L.BatchNorm(net[conv_name], in_place=True, **bn_kwargs)
if use_scale:
sb_name = '{}{}{}'.format(scale_prefix, out_layer, scale_postfix)
net[sb_name] = L.Scale(net[bn_name], in_place=True, **sb_kwargs)
else:
bias_name = '{}{}{}'.format(bias_prefix, out_layer, bias_postfix)
net[bias_name] = L.Bias(net[bn_name], in_place=True, **bias_kwargs)
if use_relu:
relu_name = '{}_relu'.format(conv_name)
net[relu_name] = L.ReLU(net[conv_name], in_place=True)
def ResBody(net, from_layer, block_name, out2a, out2b, out2c, stride, use_branch1, dilation=1):
# ResBody(net, 'pool1', '2a', 64, 64, 256, 1, True)
conv_prefix = 'res{}_'.format(block_name)
conv_postfix = ''
bn_prefix = 'bn{}_'.format(block_name)
bn_postfix = ''
scale_prefix = 'scale{}_'.format(block_name)
scale_postfix = ''
use_scale = True
if use_branch1:
branch_name = 'branch1'
ConvBNLayer(net, from_layer, branch_name, use_bn=True, use_relu=False,
num_output=out2c, kernel_size=1, pad=0, stride=stride, use_scale=use_scale,
conv_prefix=conv_prefix, conv_postfix=conv_postfix,
bn_prefix=bn_prefix, bn_postfix=bn_postfix,
scale_prefix=scale_prefix, scale_postfix=scale_postfix)
branch1 = '{}{}'.format(conv_prefix, branch_name)
else:
branch1 = from_layer
branch_name = 'branch2a'
ConvBNLayer(net, from_layer, branch_name, use_bn=True, use_relu=True,
num_output=out2a, kernel_size=1, pad=0, stride=stride, use_scale=use_scale,
conv_prefix=conv_prefix, conv_postfix=conv_postfix,
bn_prefix=bn_prefix, bn_postfix=bn_postfix,
scale_prefix=scale_prefix, scale_postfix=scale_postfix)
out_name = '{}{}'.format(conv_prefix, branch_name)
branch_name = 'branch2b'
if dilation == 1:
ConvBNLayer(net, out_name, branch_name, use_bn=True, use_relu=True,
num_output=out2b, kernel_size=3, pad=1, stride=1, use_scale=use_scale,
conv_prefix=conv_prefix, conv_postfix=conv_postfix,
bn_prefix=bn_prefix, bn_postfix=bn_postfix,
scale_prefix=scale_prefix, scale_postfix=scale_postfix)
else:
pad = int((3 + (dilation - 1) * 2) - 1) / 2
ConvBNLayer(net, out_name, branch_name, use_bn=True, use_relu=True,
num_output=out2b, kernel_size=3, pad=pad, stride=1, use_scale=use_scale,
dilation=dilation, conv_prefix=conv_prefix, conv_postfix=conv_postfix,
bn_prefix=bn_prefix, bn_postfix=bn_postfix,
scale_prefix=scale_prefix, scale_postfix=scale_postfix)
out_name = '{}{}'.format(conv_prefix, branch_name)
branch_name = 'branch2c'
ConvBNLayer(net, out_name, branch_name, use_bn=True, use_relu=False,
num_output=out2c, kernel_size=1, pad=0, stride=1, use_scale=use_scale,
conv_prefix=conv_prefix, conv_postfix=conv_postfix,
bn_prefix=bn_prefix, bn_postfix=bn_postfix,
scale_prefix=scale_prefix, scale_postfix=scale_postfix)
branch2 = '{}{}'.format(conv_prefix, branch_name)
res_name = 'res{}'.format(block_name)
net[res_name] = L.Eltwise(net[branch1], net[branch2])
relu_name = '{}_relu'.format(res_name)
net[relu_name] = L.ReLU(net[res_name], in_place=True)
def InceptionTower(net, from_layer, tower_name, layer_params):
use_scale = False
for param in layer_params:
tower_layer = '{}/{}'.format(tower_name, param['name'])
del param['name']
if 'pool' in tower_layer:
net[tower_layer] = L.Pooling(net[from_layer], **param)
else:
ConvBNLayer(net, from_layer, tower_layer, use_bn=True, use_relu=True,
use_scale=use_scale, **param)
from_layer = tower_layer
return net[from_layer]
def CreateAnnotatedDataLayer(source, batch_size=32, backend=P.Data.LMDB,
output_label=True, train=True, label_map_file='',
transform_param={}, batch_sampler=[{}]):
if train:
kwargs = {
'include': dict(phase=caffe_pb2.Phase.Value('TRAIN')),
'transform_param': transform_param,
}
else:
kwargs = {
'include': dict(phase=caffe_pb2.Phase.Value('TEST')),
'transform_param': transform_param,
}
if output_label:
data, label = L.AnnotatedData(name="data",
annotated_data_param=dict(label_map_file=label_map_file,
batch_sampler=batch_sampler),
data_param=dict(batch_size=batch_size, backend=backend, source=source),
ntop=2, **kwargs)
return [data, label]
else:
data = L.AnnotatedData(name="data",
annotated_data_param=dict(label_map_file=label_map_file,
batch_sampler=batch_sampler),
data_param=dict(batch_size=batch_size, backend=backend, source=source),
ntop=1, **kwargs)
return data
def VGGNetBody(net, from_layer, need_fc=True, fully_conv=False, reduced=False,
dilated=False, nopool=False, dropout=True, freeze_layers=[]):
kwargs = {
'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)}
assert from_layer in net.keys()
net.conv1_1 = L.Convolution(net[from_layer], num_output=64, pad=1, kernel_size=3, **kwargs)
net.relu1_1 = L.ReLU(net.conv1_1, in_place=True)
net.conv1_2 = L.Convolution(net.relu1_1, num_output=64, pad=1, kernel_size=3, **kwargs)
net.relu1_2 = L.ReLU(net.conv1_2, in_place=True)
if nopool:
name = 'conv1_3'
net[name] = L.Convolution(net.relu1_2, num_output=64, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool1'
net.pool1 = L.Pooling(net.relu1_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv2_1 = L.Convolution(net[name], num_output=128, pad=1, kernel_size=3, **kwargs)
net.relu2_1 = L.ReLU(net.conv2_1, in_place=True)
net.conv2_2 = L.Convolution(net.relu2_1, num_output=128, pad=1, kernel_size=3, **kwargs)
net.relu2_2 = L.ReLU(net.conv2_2, in_place=True)
if nopool:
name = 'conv2_3'
net[name] = L.Convolution(net.relu2_2, num_output=128, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool2'
net[name] = L.Pooling(net.relu2_2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv3_1 = L.Convolution(net[name], num_output=256, pad=1, kernel_size=3, **kwargs)
net.relu3_1 = L.ReLU(net.conv3_1, in_place=True)
net.conv3_2 = L.Convolution(net.relu3_1, num_output=256, pad=1, kernel_size=3, **kwargs)
net.relu3_2 = L.ReLU(net.conv3_2, in_place=True)
net.conv3_3 = L.Convolution(net.relu3_2, num_output=256, pad=1, kernel_size=3, **kwargs)
net.relu3_3 = L.ReLU(net.conv3_3, in_place=True)
if nopool:
name = 'conv3_4'
net[name] = L.Convolution(net.relu3_3, num_output=256, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool3'
net[name] = L.Pooling(net.relu3_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv4_1 = L.Convolution(net[name], num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu4_1 = L.ReLU(net.conv4_1, in_place=True)
net.conv4_2 = L.Convolution(net.relu4_1, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu4_2 = L.ReLU(net.conv4_2, in_place=True)
net.conv4_3 = L.Convolution(net.relu4_2, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu4_3 = L.ReLU(net.conv4_3, in_place=True)
if nopool:
name = 'conv4_4'
net[name] = L.Convolution(net.relu4_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool4'
net[name] = L.Pooling(net.relu4_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
net.conv5_1 = L.Convolution(net[name], num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu5_1 = L.ReLU(net.conv5_1, in_place=True)
net.conv5_2 = L.Convolution(net.relu5_1, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu5_2 = L.ReLU(net.conv5_2, in_place=True)
net.conv5_3 = L.Convolution(net.relu5_2, num_output=512, pad=1, kernel_size=3, **kwargs)
net.relu5_3 = L.ReLU(net.conv5_3, in_place=True)
if need_fc:
if dilated:
if nopool:
name = 'conv5_4'
net[name] = L.Convolution(net.relu5_3, num_output=512, pad=1, kernel_size=3, stride=1, **kwargs)
else:
name = 'pool5'
net[name] = L.Pooling(net.relu5_3, pool=P.Pooling.MAX, pad=1, kernel_size=3, stride=1)
else:
if nopool:
name = 'conv5_4'
net[name] = L.Convolution(net.relu5_3, num_output=512, pad=1, kernel_size=3, stride=2, **kwargs)
else:
name = 'pool5'
net[name] = L.Pooling(net.relu5_3, pool=P.Pooling.MAX, kernel_size=2, stride=2)
if fully_conv:
if dilated:
if reduced:
net.fc6 = L.Convolution(net[name], num_output=1024, pad=6, kernel_size=3, dilation=6, **kwargs)
else:
net.fc6 = L.Convolution(net[name], num_output=4096, pad=6, kernel_size=7, dilation=2, **kwargs)
else:
if reduced:
net.fc6 = L.Convolution(net[name], num_output=1024, pad=3, kernel_size=3, dilation=3, **kwargs)
else:
net.fc6 = L.Convolution(net[name], num_output=4096, pad=3, kernel_size=7, **kwargs)
net.relu6 = L.ReLU(net.fc6, in_place=True)
if dropout:
net.drop6 = L.Dropout(net.relu6, dropout_ratio=0.5, in_place=True)
if reduced:
net.fc7 = L.Convolution(net.relu6, num_output=1024, kernel_size=1, **kwargs)
else:
net.fc7 = L.Convolution(net.relu6, num_output=4096, kernel_size=1, **kwargs)
net.relu7 = L.ReLU(net.fc7, in_place=True)
if dropout:
net.drop7 = L.Dropout(net.relu7, dropout_ratio=0.5, in_place=True)
else:
net.fc6 = L.InnerProduct(net.pool5, num_output=4096)
net.relu6 = L.ReLU(net.fc6, in_place=True)
if dropout:
net.drop6 = L.Dropout(net.relu6, dropout_ratio=0.5, in_place=True)
net.fc7 = L.InnerProduct(net.relu6, num_output=4096)
net.relu7 = L.ReLU(net.fc7, in_place=True)
if dropout:
net.drop7 = L.Dropout(net.relu7, dropout_ratio=0.5, in_place=True)
# Update freeze layers.
kwargs['param'] = [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)]
layers = net.keys()
for freeze_layer in freeze_layers:
if freeze_layer in layers:
net.update(freeze_layer, kwargs)
return net
def ResNet101Body(net, from_layer, use_pool5=True, use_dilation_conv5=False):
conv_prefix = ''
conv_postfix = ''
bn_prefix = 'bn_'
bn_postfix = ''
scale_prefix = 'scale_'
scale_postfix = ''
ConvBNLayer(net, from_layer, 'conv1', use_bn=True, use_relu=True,
num_output=64, kernel_size=7, pad=3, stride=2,
conv_prefix=conv_prefix, conv_postfix=conv_postfix,
bn_prefix=bn_prefix, bn_postfix=bn_postfix,
scale_prefix=scale_prefix, scale_postfix=scale_postfix)
net.pool1 = L.Pooling(net.conv1, pool=P.Pooling.MAX, kernel_size=3, stride=2)
ResBody(net, 'pool1', '2a', out2a=64, out2b=64, out2c=256, stride=1, use_branch1=True)
ResBody(net, 'res2a', '2b', out2a=64, out2b=64, out2c=256, stride=1, use_branch1=False)
ResBody(net, 'res2b', '2c', out2a=64, out2b=64, out2c=256, stride=1, use_branch1=False)
ResBody(net, 'res2c', '3a', out2a=128, out2b=128, out2c=512, stride=2, use_branch1=True)
from_layer = 'res3a'
for i in xrange(1, 4):
block_name = '3b{}'.format(i)
ResBody(net, from_layer, block_name, out2a=128, out2b=128, out2c=512, stride=1, use_branch1=False)
from_layer = 'res{}'.format(block_name)
ResBody(net, from_layer, '4a', out2a=256, out2b=256, out2c=1024, stride=2, use_branch1=True)
from_layer = 'res4a'
for i in xrange(1, 23):
block_name = '4b{}'.format(i)
ResBody(net, from_layer, block_name, out2a=256, out2b=256, out2c=1024, stride=1, use_branch1=False)
from_layer = 'res{}'.format(block_name)
stride = 2
dilation = 1
if use_dilation_conv5:
stride = 1
dilation = 2
ResBody(net, from_layer, '5a', out2a=512, out2b=512, out2c=2048, stride=stride, use_branch1=True, dilation=dilation)
ResBody(net, 'res5a', '5b', out2a=512, out2b=512, out2c=2048, stride=1, use_branch1=False, dilation=dilation)
ResBody(net, 'res5b', '5c', out2a=512, out2b=512, out2c=2048, stride=1, use_branch1=False, dilation=dilation)
if use_pool5:
net.pool5 = L.Pooling(net.res5c, pool=P.Pooling.AVE, global_pooling=True)
return net
def ResNet152Body(net, from_layer, use_pool5=True, use_dilation_conv5=False):
conv_prefix = ''
conv_postfix = ''
bn_prefix = 'bn_'
bn_postfix = ''
scale_prefix = 'scale_'
scale_postfix = ''
ConvBNLayer(net, from_layer, 'conv1', use_bn=True, use_relu=True,
num_output=64, kernel_size=7, pad=3, stride=2,
conv_prefix=conv_prefix, conv_postfix=conv_postfix,
bn_prefix=bn_prefix, bn_postfix=bn_postfix,
scale_prefix=scale_prefix, scale_postfix=scale_postfix)
net.pool1 = L.Pooling(net.conv1, pool=P.Pooling.MAX, kernel_size=3, stride=2)
ResBody(net, 'pool1', '2a', out2a=64, out2b=64, out2c=256, stride=1, use_branch1=True)
ResBody(net, 'res2a', '2b', out2a=64, out2b=64, out2c=256, stride=1, use_branch1=False)
ResBody(net, 'res2b', '2c', out2a=64, out2b=64, out2c=256, stride=1, use_branch1=False)
ResBody(net, 'res2c', '3a', out2a=128, out2b=128, out2c=512, stride=2, use_branch1=True)
from_layer = 'res3a'
for i in xrange(1, 8):
block_name = '3b{}'.format(i)
ResBody(net, from_layer, block_name, out2a=128, out2b=128, out2c=512, stride=1, use_branch1=False)
from_layer = 'res{}'.format(block_name)
ResBody(net, from_layer, '4a', out2a=256, out2b=256, out2c=1024, stride=2, use_branch1=True)
from_layer = 'res4a'
for i in xrange(1, 36):
block_name = '4b{}'.format(i)
ResBody(net, from_layer, block_name, out2a=256, out2b=256, out2c=1024, stride=1, use_branch1=False)
from_layer = 'res{}'.format(block_name)
stride = 2
dilation = 1
if use_dilation_conv5:
stride = 1
dilation = 2
ResBody(net, from_layer, '5a', out2a=512, out2b=512, out2c=2048, stride=stride, use_branch1=True, dilation=dilation)
ResBody(net, 'res5a', '5b', out2a=512, out2b=512, out2c=2048, stride=1, use_branch1=False, dilation=dilation)
ResBody(net, 'res5b', '5c', out2a=512, out2b=512, out2c=2048, stride=1, use_branch1=False, dilation=dilation)
if use_pool5:
net.pool5 = L.Pooling(net.res5c, pool=P.Pooling.AVE, global_pooling=True)
return net
def InceptionV3Body(net, from_layer, output_pred=False):
# scale is fixed to 1, thus we ignore it.
use_scale = False
out_layer = 'conv'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=32, kernel_size=3, pad=0, stride=2, use_scale=use_scale)
from_layer = out_layer
out_layer = 'conv_1'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=32, kernel_size=3, pad=0, stride=1, use_scale=use_scale)
from_layer = out_layer
out_layer = 'conv_2'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=64, kernel_size=3, pad=1, stride=1, use_scale=use_scale)
from_layer = out_layer
out_layer = 'pool'
net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.MAX,
kernel_size=3, stride=2, pad=0)
from_layer = out_layer
out_layer = 'conv_3'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=80, kernel_size=1, pad=0, stride=1, use_scale=use_scale)
from_layer = out_layer
out_layer = 'conv_4'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True,
num_output=192, kernel_size=3, pad=0, stride=1, use_scale=use_scale)
from_layer = out_layer
out_layer = 'pool_1'
net[out_layer] = L.Pooling(net[from_layer], pool=P.Pooling.MAX,
kernel_size=3, stride=2, pad=0)
from_layer = out_layer
# inceptions with 1x1, 3x3, 5x5 convolutions
for inception_id in xrange(0, 3):
if inception_id == 0:
out_layer = 'mixed'
tower_2_conv_num_output = 32
else:
out_layer = 'mixed_{}'.format(inception_id)
tower_2_conv_num_output = 64
towers = []
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=64, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=48, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=64, kernel_size=5, pad=2, stride=1),
])
towers.append(tower)
tower_name = '{}/tower_1'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=64, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=96, kernel_size=3, pad=1, stride=1),
dict(name='conv_2', num_output=96, kernel_size=3, pad=1, stride=1),
])
towers.append(tower)
tower_name = '{}/tower_2'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=P.Pooling.AVE, kernel_size=3, pad=1, stride=1),
dict(name='conv', num_output=tower_2_conv_num_output, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
# inceptions with 1x1, 3x3(in sequence) convolutions
out_layer = 'mixed_3'
towers = []
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=384, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=64, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=96, kernel_size=3, pad=1, stride=1),
dict(name='conv_2', num_output=96, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=P.Pooling.MAX, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
# inceptions with 1x1, 7x1, 1x7 convolutions
for inception_id in xrange(4, 8):
if inception_id == 4:
num_output = 128
elif inception_id == 5 or inception_id == 6:
num_output = 160
elif inception_id == 7:
num_output = 192
out_layer = 'mixed_{}'.format(inception_id)
towers = []
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=num_output, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=num_output, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]),
dict(name='conv_2', num_output=192, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]),
])
towers.append(tower)
tower_name = '{}/tower_1'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=num_output, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=num_output, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]),
dict(name='conv_2', num_output=num_output, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]),
dict(name='conv_3', num_output=num_output, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]),
dict(name='conv_4', num_output=192, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]),
])
towers.append(tower)
tower_name = '{}/tower_2'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=P.Pooling.AVE, kernel_size=3, pad=1, stride=1),
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
# inceptions with 1x1, 3x3, 1x7, 7x1 filters
out_layer = 'mixed_8'
towers = []
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=320, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
tower_name = '{}/tower_1'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=192, kernel_size=[1, 7], pad=[0, 3], stride=[1, 1]),
dict(name='conv_2', num_output=192, kernel_size=[7, 1], pad=[3, 0], stride=[1, 1]),
dict(name='conv_3', num_output=192, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=P.Pooling.MAX, kernel_size=3, pad=0, stride=2),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
for inception_id in xrange(9, 11):
num_output = 384
num_output2 = 448
if inception_id == 9:
pool = P.Pooling.AVE
else:
pool = P.Pooling.MAX
out_layer = 'mixed_{}'.format(inception_id)
towers = []
tower_name = '{}'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=320, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
tower_name = '{}/tower'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=num_output, kernel_size=1, pad=0, stride=1),
])
subtowers = []
subtower_name = '{}/mixed'.format(tower_name)
subtower = InceptionTower(net, '{}/conv'.format(tower_name), subtower_name, [
dict(name='conv', num_output=num_output, kernel_size=[1, 3], pad=[0, 1], stride=[1, 1]),
])
subtowers.append(subtower)
subtower = InceptionTower(net, '{}/conv'.format(tower_name), subtower_name, [
dict(name='conv_1', num_output=num_output, kernel_size=[3, 1], pad=[1, 0], stride=[1, 1]),
])
subtowers.append(subtower)
net[subtower_name] = L.Concat(*subtowers, axis=1)
towers.append(net[subtower_name])
tower_name = '{}/tower_1'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='conv', num_output=num_output2, kernel_size=1, pad=0, stride=1),
dict(name='conv_1', num_output=num_output, kernel_size=3, pad=1, stride=1),
])
subtowers = []
subtower_name = '{}/mixed'.format(tower_name)
subtower = InceptionTower(net, '{}/conv_1'.format(tower_name), subtower_name, [
dict(name='conv', num_output=num_output, kernel_size=[1, 3], pad=[0, 1], stride=[1, 1]),
])
subtowers.append(subtower)
subtower = InceptionTower(net, '{}/conv_1'.format(tower_name), subtower_name, [
dict(name='conv_1', num_output=num_output, kernel_size=[3, 1], pad=[1, 0], stride=[1, 1]),
])
subtowers.append(subtower)
net[subtower_name] = L.Concat(*subtowers, axis=1)
towers.append(net[subtower_name])
tower_name = '{}/tower_2'.format(out_layer)
tower = InceptionTower(net, from_layer, tower_name, [
dict(name='pool', pool=pool, kernel_size=3, pad=1, stride=1),
dict(name='conv', num_output=192, kernel_size=1, pad=0, stride=1),
])
towers.append(tower)
out_layer = '{}/join'.format(out_layer)
net[out_layer] = L.Concat(*towers, axis=1)
from_layer = out_layer
if output_pred:
net.pool_3 = L.Pooling(net[from_layer], pool=P.Pooling.AVE, kernel_size=8, pad=0, stride=1)
net.softmax = L.InnerProduct(net.pool_3, num_output=1008)
net.softmax_prob = L.Softmax(net.softmax)
return net
def CreateMultiBoxHead(net, data_layer="data", num_classes=[], from_layers=[],
use_objectness=False, normalizations=[], use_batchnorm=True,
min_sizes=[], max_sizes=[], prior_variance = [0.1],
aspect_ratios=[], share_location=True, flip=True, clip=True,
inter_layer_depth=0, kernel_size=1, pad=0, conf_postfix='', loc_postfix=''):
assert num_classes, "must provide num_classes"
assert num_classes > 0, "num_classes must be positive number"
if normalizations:
assert len(from_layers) == len(normalizations), "from_layers and normalizations should have same length"
assert len(from_layers) == len(min_sizes), "from_layers and min_sizes should have same length"
if max_sizes:
assert len(from_layers) == len(max_sizes), "from_layers and max_sizes should have same length"
net_layers = net.keys()
assert data_layer in net_layers, "data_layer is not in net's layers"
num = len(from_layers)
priorbox_layers = []
loc_layers = []
conf_layers = []
objectness_layers = []
for i in range(0, num):
from_layer = from_layers[i]
# Get the normalize value.
if normalizations:
if normalizations[i] != -1:
norm_name = "{}_norm".format(from_layer)
net[norm_name] = L.Normalize(net[from_layer], scale_filler=dict(type="constant", value=normalizations[i]),
across_spatial=False, channel_shared=False)
from_layer = norm_name
# Add intermediate layers.
if inter_layer_depth > 0:
inter_name = "{}_inter".format(from_layer)
ConvBNLayer(net, from_layer, inter_name, use_bn=use_batchnorm, use_relu=True,
num_output=inter_layer_depth, kernel_size=3, pad=1, stride=1)
from_layer = inter_name
# Estimate number of priors per location given provided parameters.
aspect_ratio = []
if len(aspect_ratios) > i:
aspect_ratio = aspect_ratios[i]
if type(aspect_ratio) is not list:
aspect_ratio = [aspect_ratio]
if max_sizes and max_sizes[i]:
num_priors_per_location = 2 + len(aspect_ratio)
else:
num_priors_per_location = 1 + len(aspect_ratio)
if flip:
num_priors_per_location += len(aspect_ratio)
num_priors_per_location=2*num_priors_per_location
# Create location prediction layer.
name = "{}_mbox_loc{}".format(from_layer, loc_postfix)
num_loc_output = num_priors_per_location * 4;
if not share_location:
num_loc_output *= num_classes
ConvBNLayer(net, from_layer, name, use_bn=use_batchnorm, use_relu=False,
num_output=num_loc_output, kernel_size=kernel_size, pad=pad, stride=1)
permute_name = "{}_perm".format(name)
net[permute_name] = L.Permute(net[name], order=[0, 2, 3, 1])
flatten_name = "{}_flat".format(name)
net[flatten_name] = L.Flatten(net[permute_name], axis=1)
loc_layers.append(net[flatten_name])
# Create confidence prediction layer.
name = "{}_mbox_conf{}".format(from_layer, conf_postfix)
num_conf_output = num_priors_per_location * num_classes;
ConvBNLayer(net, from_layer, name, use_bn=use_batchnorm, use_relu=False,
num_output=num_conf_output, kernel_size=kernel_size, pad=pad, stride=1)
permute_name = "{}_perm".format(name)
net[permute_name] = L.Permute(net[name], order=[0, 2, 3, 1])
flatten_name = "{}_flat".format(name)
net[flatten_name] = L.Flatten(net[permute_name], axis=1)
conf_layers.append(net[flatten_name])
# Create prior generation layer.
name = "{}_mbox_priorbox".format(from_layer)
if max_sizes and max_sizes[i]:
if aspect_ratio:
net[name] = L.PriorBox(net[from_layer], net[data_layer], min_size=min_sizes[i], max_size=max_sizes[i],
aspect_ratio=aspect_ratio, flip=flip, clip=clip, variance=prior_variance)
else:
net[name] = L.PriorBox(net[from_layer], net[data_layer], min_size=min_sizes[i], max_size=max_sizes[i],
clip=clip, variance=prior_variance)
else:
if aspect_ratio:
net[name] = L.PriorBox(net[from_layer], net[data_layer], min_size=min_sizes[i],
aspect_ratio=aspect_ratio, flip=flip, clip=clip, variance=prior_variance)
else:
net[name] = L.PriorBox(net[from_layer], net[data_layer], min_size=min_sizes[i],
clip=clip, variance=prior_variance)
priorbox_layers.append(net[name])
# Create objectness prediction layer.
if use_objectness:
name = "{}_mbox_objectness".format(from_layer)
num_obj_output = num_priors_per_location * 2;
ConvBNLayer(net, from_layer, name, use_bn=use_batchnorm, use_relu=False,
num_output=num_obj_output, kernel_size=kernel_size, pad=pad, stride=1)
permute_name = "{}_perm".format(name)
net[permute_name] = L.Permute(net[name], order=[0, 2, 3, 1])
flatten_name = "{}_flat".format(name)
net[flatten_name] = L.Flatten(net[permute_name], axis=1)
objectness_layers.append(net[flatten_name])
# Concatenate priorbox, loc, and conf layers.
mbox_layers = []
name = "mbox_loc"
net[name] = L.Concat(*loc_layers, axis=1)
mbox_layers.append(net[name])
name = "mbox_conf"
net[name] = L.Concat(*conf_layers, axis=1)
mbox_layers.append(net[name])
name = "mbox_priorbox"
net[name] = L.Concat(*priorbox_layers, axis=2)
mbox_layers.append(net[name])
if use_objectness:
name = "mbox_objectness"
net[name] = L.Concat(*objectness_layers, axis=1)
mbox_layers.append(net[name])
return mbox_layers