def test_config():
from lego.base import Config
conv_default = Config.get_default_params('Convolution')
assert conv_default is not None
Config.set_default_params('Convolution', 'param', [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)])
conv_default = Config.get_default_params('Convolution')
assert conv_default is not None
def test_config():
from lego.base import Config
conv_default = Config.get_default_params('Convolution')
assert conv_default is not None
Config.set_default_params(
'Convolution', 'param',
[dict(lr_mult=0, decay_mult=0),
dict(lr_mult=0, decay_mult=0)])
conv_default = Config.get_default_params('Convolution')
assert conv_default is not None
def stitch(self, is_train, source, main_branch, num_output_stage1, fc_layers, blocks):
from lego.hybrid import ConvBNReLULego, EltwiseReLULego, ShortcutLego, ConvBNLego
from lego.data import ImageDataLego
from lego.base import BaseLegoFunction
netspec = caffe.NetSpec()
if is_train:
include = 'train'
use_global_stats = False
batch_size = 256
else:
include = 'test'
use_global_stats = True
batch_size = 1
# Freeze 1st 2 stages and dont update batch norm stats
Config.set_default_params('Convolution', 'param', [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)])
if is_train:
use_global_stats = True
# Data layer, its dummy, you need to replace this with Annotated data layer
params = dict(name='data', batch_size=1, ntop=2,
memory_data_param=dict(batch_size=1, channels=3, height=300, width=300)
)
netspec.data, netspec.label = BaseLegoFunction('MemoryData', params).attach(netspec, [])
# Stage 1
params = dict(name='1', num_output=64, kernel_size=7,
use_global_stats=use_global_stats, pad=3, stride=2)
stage1 = ConvBNReLULego(params).attach(netspec, [netspec.data])
params = dict(kernel_size=3, stride=2, pool=P.Pooling.MAX, name='pool1')
pool1 = BaseLegoFunction('Pooling', params).attach(netspec, [stage1])
num_output = num_output_stage1
# Stages 2 - 5
last = pool1
for stage in range(4):
name = str(stage + 1)
for block in range(blocks[stage]):
if block == 0:
shortcut = 'projection'
if stage > 0:
stride = 2
else:
stride = 1
else:
shortcut = 'identity'
stride = 1
# this is for resnet 18 / 34, where the first block of stage
# 0 does not need projection shortcut
if block == 0 and stage == 0 and main_branch == 'normal':
shortcut = 'identity'
# This is for not downsampling while creating detection
# network
# if block == 0 and stage == 1:
# stride = 1
name = 'stage' + str(stage) + '_block' + str(block)
curr_num_output = num_output * (2 ** (stage))
params = dict(name=name, num_output=curr_num_output,
shortcut=shortcut, main_branch=main_branch,
stride=stride, use_global_stats=use_global_stats,)
last = ShortcutLego(params).attach(netspec, [last])
# TODO: Note this should be configurable
if stage == 0:
Config.set_default_params('Convolution', 'param', [dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
if is_train:
use_global_stats = False
'''
You should modify these layers in order to experiment with different
architectures specific for detection
'''
if not fc_layers:
# Last stage
pool_params = dict(kernel_size=7, stride=1, pool=P.Pooling.AVE, name='pool', pad=3)
pool = BaseLegoFunction('Pooling', pool_params).attach(netspec, [last])
else:
'''pool_params = dict(name='pool_before1024', kernel_size=3, stride=1, pool=P.Pooling.MAX, pad=1)
pool = BaseLegoFunction('Pooling', pool_params).attach(netspec, [last])
conv_last1_params = dict(name='3by3_1024', num_output=1024, use_global_stats=use_global_stats,
# pad=0, kernel_size=1)
pad=1, kernel_size=3, dilation=3)
conv_last1 = ConvBNReLULego(conv_last1_params).attach(netspec, [pool])
'''
conv_last1_params = dict(name='1by1_2048', num_output=2048, use_global_stats=use_global_stats,
pad=1, kernel_size=1, dilation=1)
# pad=3, kernel_size=7)
conv_last1 = ConvBNReLULego(conv_last1_params).attach(netspec, [last])
pool_last_params = dict(kernel_size=7, stride=1, pool=P.Pooling.AVE, name='pool', pad=3)
pool_last = BaseLegoFunction('Pooling', pool_last_params).attach(netspec, [conv_last1])
conv_last2_params = dict(name='1by1_4096', kernel_size=1, num_output=4096, use_global_stats=use_global_stats,
stride=1, pad=0)
conv_last2 = ConvBNReLULego(conv_last2_params).attach(netspec, [pool_last])
return netspec
def stitch(self, is_train=True, reduced=True, fully_conv=True, dilated=True, dropout=False):
netspec = caffe.NetSpec()
if is_train:
include = 'train'
use_global_stats = False
batch_size = 128
else:
include = 'test'
use_global_stats = True
batch_size = 1
Config.set_default_params('Convolution', 'param', [dict(lr_mult=0, decay_mult=0), dict(lr_mult=0, decay_mult=0)])
# Data layer
params = dict(name='data', batch_size=1, ntop=2,
transform_param=dict(crop_size=300),
memory_data_param=dict(batch_size=1, channels=3, height=300, width=300)
)
netspec.data, netspec.label = BaseLegoFunction('MemoryData', params).attach(netspec, [])
last = netspec.data
num_outputs = [64, 128, 256, 512, 512]
# Conv layers stages
for stage in range(1, 6):
blocks = 2 if stage < 3 else 3
for b in range(1, blocks + 1):
name = str(stage) + '_' + str(b)
params = dict(name=name, num_output=num_outputs[stage - 1], kernel_size=3, pad=1, stride=1)
last = ConvReLULego(params).attach(netspec, [last])
if dilated and stage == 5:
pool_params = dict(name='pool' + str(stage), kernel_size=3, stride=1, pool=P.Pooling.MAX, pad=1)
else:
pool_params = dict(name='pool' + str(stage), kernel_size=2, stride=2, pool=P.Pooling.MAX)
last = BaseLegoFunction('Pooling', pool_params).attach(netspec, [last])
if stage == 2:
Config.set_default_params('Convolution', 'param', [dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
# FC layers
if fully_conv:
# FC6 fully convolutional
if dilated:
if reduced:
ip_params = dict(name='fc6', num_output=1024, pad=6, kernel_size=3, dilation=6)
else:
ip_params = dict(name='fc6', num_output=4096, pad=6, kernel_size=7, dilation=2)
else:
if reduced:
ip_params = dict(name='fc6', num_output=1024, pad=3, kernel_size=3, dilation=3)
else:
ip_params = dict(name='fc6', num_output=4096, pad=3, kernel_size=7)
fc6 = BaseLegoFunction('Convolution', ip_params).attach(netspec, [last])
relu_params = dict(name='relu6', in_place=True)
out6 = BaseLegoFunction('ReLU', relu_params).attach(netspec, [fc6])
if dropout:
drop_params = dict(name='drop6', dropout_param=dict(dropout_ratio=0.5), in_place=True)
out6 = BaseLegoFunction('Dropout', drop_params).attach(netspec, [out6])
# FC7 fully convolutional
if reduced:
ip_params = dict(name='fc7', num_output=1024, kernel_size=1)
else:
ip_params = dict(name='fc7', num_output=4096, kernel_size=1)
fc7 = BaseLegoFunction('Convolution', ip_params).attach(netspec, [out6])
relu_params = dict(name='relu7', in_place=True)
out7 = BaseLegoFunction('ReLU', relu_params).attach(netspec, [fc7])
if dropout:
drop_params = dict(name='drop6', dropout_param=dict(dropout_ratio=0.5), in_place=True)
out7 = BaseLegoFunction('Dropout', drop_params).attach(netspec, [out7])
else:
ip_params = dict(name='fc6', num_output=4096)
fc6 = BaseLegoFunction('InnerProduct', ip_params).attach(netspec, [last])
relu_params = dict(name='relu6')
relu6 = BaseLegoFunction('ReLU', relu_params).attach(netspec, [fc6])
drop_params = dict(name='drop6', dropout_param=dict(dropout_ratio=0.5))
drop6 = BaseLegoFunction('Dropout', drop_params).attach(netspec, [relu6])
ip_params = dict(name='fc7', num_output=4096)
fc7 = BaseLegoFunction('InnerProduct', ip_params).attach(netspec, [drop6])
relu_params = dict(name='relu7')
relu7 = BaseLegoFunction('ReLU', relu_params).attach(netspec, [fc7])
drop_params = dict(name='drop7', dropout_param=dict(dropout_ratio=0.5))
drop7 = BaseLegoFunction('Dropout', drop_params).attach(netspec, [relu7])
return netspec
def stitch(self, is_train, source, main_branch, num_output_stage1,
fc_layers, blocks):
from lego.hybrid import ConvBNReLULego, EltwiseReLULego, ShortcutLego, ConvBNLego
from lego.data import ImageDataLego
from lego.base import BaseLegoFunction
netspec = caffe.NetSpec()
if is_train:
include = 'train'
use_global_stats = False
batch_size = 256
else:
include = 'test'
use_global_stats = True
batch_size = 1
# Freeze 1st 2 stages and dont update batch norm stats
Config.set_default_params(
'Convolution', 'param',
[dict(lr_mult=0, decay_mult=0),
dict(lr_mult=0, decay_mult=0)])
if is_train:
use_global_stats = True
# Data layer, its dummy, you need to replace this with Annotated data layer
params = dict(name='data',
batch_size=1,
ntop=2,
memory_data_param=dict(batch_size=1,
channels=3,
height=300,
width=300))
netspec.data, netspec.label = BaseLegoFunction('MemoryData',
params).attach(
netspec, [])
# Stage 1
params = dict(name='1',
num_output=64,
kernel_size=7,
use_global_stats=use_global_stats,
pad=3,
stride=2)
stage1 = ConvBNReLULego(params).attach(netspec, [netspec.data])
params = dict(kernel_size=3,
stride=2,
pool=P.Pooling.MAX,
name='pool1')
pool1 = BaseLegoFunction('Pooling', params).attach(netspec, [stage1])
num_output = num_output_stage1
# Stages 2 - 5
last = pool1
for stage in range(4):
name = str(stage + 1)
for block in range(blocks[stage]):
if block == 0:
shortcut = 'projection'
if stage > 0:
stride = 2
else:
stride = 1
else:
shortcut = 'identity'
stride = 1
# this is for resnet 18 / 34, where the first block of stage
# 0 does not need projection shortcut
if block == 0 and stage == 0 and main_branch == 'normal':
shortcut = 'identity'
# This is for not downsampling while creating detection
# network
# if block == 0 and stage == 1:
# stride = 1
name = 'stage' + str(stage) + '_block' + str(block)
curr_num_output = num_output * (2**(stage))
params = dict(
name=name,
num_output=curr_num_output,
shortcut=shortcut,
main_branch=main_branch,
stride=stride,
use_global_stats=use_global_stats,
)
last = ShortcutLego(params).attach(netspec, [last])
# TODO: Note this should be configurable
if stage == 0:
Config.set_default_params('Convolution', 'param', [
dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)
])
if is_train:
use_global_stats = False
'''
You should modify these layers in order to experiment with different
architectures specific for detection
'''
if not fc_layers:
# Last stage
pool_params = dict(kernel_size=7,
stride=1,
pool=P.Pooling.AVE,
name='pool',
pad=3)
pool = BaseLegoFunction('Pooling',
pool_params).attach(netspec, [last])
else:
'''pool_params = dict(name='pool_before1024', kernel_size=3, stride=1, pool=P.Pooling.MAX, pad=1)
pool = BaseLegoFunction('Pooling', pool_params).attach(netspec, [last])
conv_last1_params = dict(name='3by3_1024', num_output=1024, use_global_stats=use_global_stats,
# pad=0, kernel_size=1)
pad=1, kernel_size=3, dilation=3)
conv_last1 = ConvBNReLULego(conv_last1_params).attach(netspec, [pool])
'''
conv_last1_params = dict(name='1by1_2048',
num_output=2048,
use_global_stats=use_global_stats,
pad=1,
kernel_size=1,
dilation=1)
# pad=3, kernel_size=7)
conv_last1 = ConvBNReLULego(conv_last1_params).attach(
netspec, [last])
pool_last_params = dict(kernel_size=7,
stride=1,
pool=P.Pooling.AVE,
name='pool',
pad=3)
pool_last = BaseLegoFunction('Pooling', pool_last_params).attach(
netspec, [conv_last1])
conv_last2_params = dict(name='1by1_4096',
kernel_size=1,
num_output=4096,
use_global_stats=use_global_stats,
stride=1,
pad=0)
conv_last2 = ConvBNReLULego(conv_last2_params).attach(
netspec, [pool_last])
return netspec
def stitch(self,
is_train=True,
reduced=True,
fully_conv=True,
dilated=True,
dropout=False):
netspec = caffe.NetSpec()
if is_train:
include = 'train'
use_global_stats = False
batch_size = 128
else:
include = 'test'
use_global_stats = True
batch_size = 1
Config.set_default_params(
'Convolution', 'param',
[dict(lr_mult=0, decay_mult=0),
dict(lr_mult=0, decay_mult=0)])
# Data layer
params = dict(name='data',
batch_size=1,
ntop=2,
transform_param=dict(crop_size=300),
memory_data_param=dict(batch_size=1,
channels=3,
height=300,
width=300))
netspec.data, netspec.label = BaseLegoFunction('MemoryData',
params).attach(
netspec, [])
last = netspec.data
num_outputs = [64, 128, 256, 512, 512]
# Conv layers stages
for stage in range(1, 6):
blocks = 2 if stage < 3 else 3
for b in range(1, blocks + 1):
name = str(stage) + '_' + str(b)
params = dict(name=name,
num_output=num_outputs[stage - 1],
kernel_size=3,
pad=1,
stride=1)
last = ConvReLULego(params).attach(netspec, [last])
if dilated and stage == 5:
pool_params = dict(name='pool' + str(stage),
kernel_size=3,
stride=1,
pool=P.Pooling.MAX,
pad=1)
else:
pool_params = dict(name='pool' + str(stage),
kernel_size=2,
stride=2,
pool=P.Pooling.MAX)
last = BaseLegoFunction('Pooling',
pool_params).attach(netspec, [last])
if stage == 2:
Config.set_default_params('Convolution', 'param', [
dict(lr_mult=1, decay_mult=1),
dict(lr_mult=2, decay_mult=0)
])
# FC layers
if fully_conv:
# FC6 fully convolutional
if dilated:
if reduced:
ip_params = dict(name='fc6',
num_output=1024,
pad=6,
kernel_size=3,
dilation=6)
else:
ip_params = dict(name='fc6',
num_output=4096,
pad=6,
kernel_size=7,
dilation=2)
else:
if reduced:
ip_params = dict(name='fc6',
num_output=1024,
pad=3,
kernel_size=3,
dilation=3)
else:
ip_params = dict(name='fc6',
num_output=4096,
pad=3,
kernel_size=7)
fc6 = BaseLegoFunction('Convolution',
ip_params).attach(netspec, [last])
relu_params = dict(name='relu6', in_place=True)
out6 = BaseLegoFunction('ReLU', relu_params).attach(netspec, [fc6])
if dropout:
drop_params = dict(name='drop6',
dropout_param=dict(dropout_ratio=0.5),
in_place=True)
out6 = BaseLegoFunction('Dropout',
drop_params).attach(netspec, [out6])
# FC7 fully convolutional
if reduced:
ip_params = dict(name='fc7', num_output=1024, kernel_size=1)
else:
ip_params = dict(name='fc7', num_output=4096, kernel_size=1)
fc7 = BaseLegoFunction('Convolution',
ip_params).attach(netspec, [out6])
relu_params = dict(name='relu7', in_place=True)
out7 = BaseLegoFunction('ReLU', relu_params).attach(netspec, [fc7])
if dropout:
drop_params = dict(name='drop6',
dropout_param=dict(dropout_ratio=0.5),
in_place=True)
out7 = BaseLegoFunction('Dropout',
drop_params).attach(netspec, [out7])
else:
ip_params = dict(name='fc6', num_output=4096)
fc6 = BaseLegoFunction('InnerProduct',
ip_params).attach(netspec, [last])
relu_params = dict(name='relu6')
relu6 = BaseLegoFunction('ReLU',
relu_params).attach(netspec, [fc6])
drop_params = dict(name='drop6',
dropout_param=dict(dropout_ratio=0.5))
drop6 = BaseLegoFunction('Dropout',
drop_params).attach(netspec, [relu6])
ip_params = dict(name='fc7', num_output=4096)
fc7 = BaseLegoFunction('InnerProduct',
ip_params).attach(netspec, [drop6])
relu_params = dict(name='relu7')
relu7 = BaseLegoFunction('ReLU',
relu_params).attach(netspec, [fc7])
drop_params = dict(name='drop7',
dropout_param=dict(dropout_ratio=0.5))
drop7 = BaseLegoFunction('Dropout',
drop_params).attach(netspec, [relu7])
return netspec
