def test_complexity():
from lego.base import BaseLegoFunction
from lego.hybrid import ConvBNReLULego
from tools.complexity import get_complexity
n = caffe.NetSpec()
params = dict(name='data', batch_size=16, ntop=2,
transform_param=dict(crop_size=224),
memory_data_param=dict(batch_size=16, channels=3,
height=224, width=224))
n.data, n.label = BaseLegoFunction('MemoryData', params).attach(n, [])
params = dict(name='1', num_output=64, kernel_size=7,
use_global_stats=True, pad=3, stride=2)
stage1 = ConvBNReLULego(params).attach(n, [n.data])
params = dict(kernel_size=3, stride=2, pool=P.Pooling.MAX, name='pool1')
pool1 = BaseLegoFunction('Pooling', params).attach(n, [stage1])
ip_params = dict(name='fc10', num_output=10,
param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)])
ip = BaseLegoFunction('InnerProduct', ip_params).attach(n, [pool1])
smax_loss = BaseLegoFunction('SoftmaxWithLoss', dict(name='loss')).attach(n, [ip, n.label])
params, conn = get_complexity(netspec=n)
print >> sys.stderr, params, conn
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])
ip_params = dict(name='fc8', num_output=1000)
fc8 = BaseLegoFunction('InnerProduct', ip_params).attach(netspec, [drop7])
# Loss and accuracy
smax_loss = BaseLegoFunction('SoftmaxWithLoss', dict(name='loss')).attach(netspec, [fc8, netspec.label])
if include == 'test':
BaseLegoFunction('Accuracy', dict(name='accuracy')).attach(netspec, [fc8, netspec.label])
filename = 'train.prototxt' if is_train else 'test.prototxt'
filepath = output_folder + '/' + filename
fp = open(filepath, 'w')
print >> fp, netspec.to_proto()
fp.close()
if __name__ == '__main__':
args = parser.parse_args()
write_prototxt(True, 'train', args.output_folder)
write_prototxt(False, 'test', args.output_folder)
filepath = args.output_folder + '/train.prototxt'
params, flops = get_complexity(prototxt_file=filepath)
print 'Number of params: ', (1.0 * params) / 1000000.0, ' Million'
print 'Number of flops: ', (1.0 * flops) / 1000000.0, ' Million'
drop_params = dict(name='drop7', dropout_param=dict(dropout_ratio=0.5))
drop7 = BaseLegoFunction('Dropout', drop_params).attach(netspec, [relu7])
ip_params = dict(name='fc8', num_output=1000)
fc8 = BaseLegoFunction('InnerProduct', ip_params).attach(netspec, [drop7])
# Loss and accuracy
smax_loss = BaseLegoFunction('SoftmaxWithLoss', dict(name='loss')).attach(
netspec, [fc8, netspec.label])
if include == 'test':
BaseLegoFunction('Accuracy',
dict(name='accuracy')).attach(netspec,
[fc8, netspec.label])
filename = 'train.prototxt' if is_train else 'test.prototxt'
filepath = output_folder + '/' + filename
fp = open(filepath, 'w')
print >> fp, netspec.to_proto()
fp.close()
if __name__ == '__main__':
args = parser.parse_args()
write_prototxt(True, 'train', args.output_folder)
write_prototxt(False, 'test', args.output_folder)
filepath = args.output_folder + '/train.prototxt'
params, flops = get_complexity(prototxt_file=filepath)
print 'Number of params: ', (1.0 * params) / 1000000.0, ' Million'
print 'Number of flops: ', (1.0 * flops) / 1000000.0, ' Million'
fc_layers=args.fc_layers,
extra_blocks=args.extra_blocks,
extra_num_outputs=args.extra_num_outputs,
mbox_source_layers=args.mbox_source_layers,
extra_layer_attach=args.extra_layer_attach,
num_classes=args.num_classes)
print args.type
if args.type == 'VGG':
netspec = get_vgg_ssdnet(is_train=True)
else:
res_params['is_train'] = True
netspec = get_resnet_ssdnet(res_params)
from tools.complexity import get_complexity
params, flops = get_complexity(netspec=netspec)
print 'Number of params: ', (1.0 * params) / 1000000.0, ' Million'
print 'Number of flops: ', (1.0 * flops) / 1000000.0, ' Million'
fp = open(args.output_folder + '/train.prototxt', 'w')
print >> fp, netspec.to_proto()
fp.close()
if args.type == 'VGG':
netspec = get_vgg_ssdnet(is_train=False)
else:
res_params['is_train'] = False
netspec = get_resnet_ssdnet(res_params)
fp = open(args.output_folder + '/test.prototxt', 'w')
print >> fp, netspec.to_proto()
extra_blocks=args.extra_blocks,
extra_num_outputs=args.extra_num_outputs,
mbox_source_layers=args.mbox_source_layers,
extra_layer_attach=args.extra_layer_attach,
num_classes=args.num_classes)
print args.type
if args.type == 'VGG':
netspec = get_vgg_ssdnet(is_train=True)
else:
res_params['is_train'] = True
netspec = get_resnet_ssdnet(res_params)
from tools.complexity import get_complexity
params, flops = get_complexity(netspec=netspec)
print 'Number of params: ', (1.0 * params) / 1000000.0, ' Million'
print 'Number of flops: ', (1.0 * flops) / 1000000.0, ' Million'
fp = open(args.output_folder + '/train.prototxt', 'w')
print >> fp, netspec.to_proto()
fp.close()
if args.type == 'VGG':
netspec = get_vgg_ssdnet(is_train=False)
else:
res_params['is_train'] = False
netspec = get_resnet_ssdnet(res_params)
fp = open(args.output_folder + '/test.prototxt', 'w')
print >> fp, netspec.to_proto()