def test_add_unmatch_prev(self):
''' Modifier add unmatch prevs. '''
network = Network('test_net')
network.set_input(InputLayer(3, 224))
network.add('c1', ConvLayer(3, 64, 224, 3))
with self.assertRaisesRegexp(ValueError,
'Network: .*c1.*p1.*mismatch fmap.*'):
network.add('p1', PoolingLayer(64, 7, 2))
self.assertEqual(len(network), 1)
with self.assertRaisesRegexp(ValueError,
'Network: .*c1.*c2.*mismatch fmap.*'):
network.add('c2', ConvLayer(64, 128, 220, 3))
self.assertEqual(len(network), 1)
with self.assertRaisesRegexp(ValueError, 'Network: .*merge.*c1.*p1.*'):
network.add('p1', PoolingLayer(32, 7, 32))
self.assertEqual(len(network), 1)
with self.assertRaisesRegexp(ValueError, 'Network: .*merge.*c1.*c2.*'):
network.add('c2', ConvLayer(32, 128, 224, 3))
self.assertEqual(len(network), 1)
network.add('c2', ConvLayer(64, 128, 224, 3))
with self.assertRaisesRegexp(ValueError,
r'Network: .*merge.*c1\s*c2.*p1.*'):
network.add('p1', PoolingLayer(128, 7, 32), prevs=('c1', 'c2'))
self.assertEqual(len(network), 2)
def test_data_loops(self):
''' Get data_loops. '''
dls = LocalRegionLayer.data_loops()
self.assertEqual(dls[de.FIL], DataDimLoops())
self.assertEqual(dls[de.IFM], DataDimLoops(le.OFM, le.BAT))
self.assertEqual(dls[de.OFM], DataDimLoops(le.OFM, le.BAT))
llayer = LocalRegionLayer(64, 28, 2, 1)
player = PoolingLayer(64, 28, 2)
self.assertTupleEqual(PoolingLayer.data_loops(), dls)
self.assertTupleEqual(llayer.data_loops(), dls)
self.assertTupleEqual(player.data_loops(), dls)
def setUp(self):
self.layers = {}
self.layers['BASE'] = ConvLayer(64, 64, 28, 3)
self.layers['FC'] = FCLayer(4096, 1000, 6)
self.layers['POOL'] = PoolingLayer(32, 7, 3, strd=2)
self.layers['LR'] = LocalRegionLayer(32, 7, nreg=5, sreg=1)
# With irregular nifm/nofm.
self.layers['IRR'] = ConvLayer(255, 383, 13, 3)
# With small numbers of fmaps.
self.layers['SM'] = ConvLayer(5, 3, 13, 3)
# Super small networks. No partitioning schemes.
self.layers['SSM1'] = ConvLayer(1, 1, 2, 3)
self.layers['SSM2'] = FCLayer(2, 2)
self.layers['SSM3'] = PoolingLayer(1, 2, 2)
self.batch_size = 8
self.dim_nodes = {}
self.dim_nodes['BASE'] = PhyDim2(4, 4)
self.dim_nodes['LG'] = PhyDim2(10, 10)
self.dim_nodes['PRIME'] = PhyDim2(3, 3)
self.options = {}
# Irrelevant options.
optdict = {'ntops': 10000}
self.options['BASE'] = Option(partition_hybrid=True,
partition_batch=True,
partition_ifmaps=True,
**optdict)
self.options['NOBATP'] = Option(partition_hybrid=True,
partition_batch=False,
partition_ifmaps=True,
**optdict)
self.options['NOINPP'] = Option(partition_hybrid=True,
partition_batch=True,
partition_ifmaps=False,
**optdict)
self.options['NOHYB'] = Option(partition_hybrid=False,
partition_batch=True,
partition_ifmaps=False,
**optdict)
self.options['ACCFWD'] = Option(partition_hybrid=True,
partition_batch=True,
partition_ifmaps=True,
hw_access_forwarding=True,
**optdict)
self.options['BUFSHR'] = Option(partition_hybrid=True,
partition_batch=True,
partition_ifmaps=True,
hw_gbuf_sharing=True,
**optdict)
def setUp(self):
super(TestPipelineSegmentTiming, self).setUp()
self.net1 = self.net['net1']
self.net4 = self.net['net4']
self.netlr = Network('net1')
self.netlr.set_input_layer(InputLayer(10, 1))
self.netlr.add('0p1', PoolingLayer(10, 1, 1))
self.netlr.add('0p2', PoolingLayer(10, 1, 1))
self.netlr.add('0p3', PoolingLayer(10, 1, 1))
self.netlr.add('1', FCLayer(10, 20))
def test_hash(self):
''' Get hash. '''
l1 = Layer(2, 12)
l2 = Layer(2, 12)
self.assertEqual(hash(l1), hash(l2))
l1 = ConvLayer(2, 12, 56, 3)
l2 = ConvLayer(2, 12, 56, 3)
self.assertEqual(hash(l1), hash(l2))
l1 = PoolingLayer(12, 14, 2)
l2 = PoolingLayer(12, 14, 2)
self.assertEqual(hash(l1), hash(l2))
def setUp(self):
# AlexNet.
self.convlayers = OrderedDict()
self.convlayers['conv1'] = ConvLayer(3, 96, 55, 11, 4)
self.convlayers['conv2'] = ConvLayer(48, 256, 27, 5)
self.convlayers['conv3'] = ConvLayer(256, 384, 13, 3)
self.convlayers['conv4'] = ConvLayer(192, 384, 13, 3)
self.convlayers['conv5'] = ConvLayer(192, 256, 13, 3)
self.fclayers = {}
self.fclayers['fc1'] = FCLayer(256, 4096, 6)
self.fclayers['fc2'] = FCLayer(4096, 4096)
self.fclayers['fc3'] = FCLayer(4096, 1000)
# LocalRegionLayer.
self.lrlayers = {}
self.lrlayers['pool1'] = PoolingLayer(64, 7, 2)
self.lrlayers['pool2'] = PoolingLayer(29, 13, 3)
self.lrlayers['pool3'] = PoolingLayer(32, 7, 2, strd=3)
self.lrlayers['lr1'] = LocalRegionLayer(32, 7, nreg=5, sreg=1)
self.lrlayers['lr2'] = LocalRegionLayer(32, 7, nreg=5, sreg=1, strd=2)
# Fake layers.
self.fake_layers = {}
# With irregular nifm/nofm.
self.fake_layers['IRR'] = ConvLayer(255, 383, 13, 3)
# With small numbers of fmaps.
self.fake_layers['SM'] = ConvLayer(5, 3, 13, 3)
# With large FIL height.
self.fake_layers['LGFIL'] = ConvLayer(64, 64, 13, 22)
# Resource.
self.resource = {}
proc_region = NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.PROC)
data_region = NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.DRAM)
# Eyeriss, ISSCC'16, JSSC'17.
self.resource['BASE'] = Resource(proc_region=proc_region,
dram_region=data_region,
src_data_region=data_region,
dst_data_region=data_region,
dim_array=PhyDim2(12, 14),
size_gbuf=108 * 1024,
size_regf=520,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
def test_eq(self):
''' Whether eq. '''
l1 = Layer(2, 12)
l2 = Layer(2, 12)
self.assertEqual(l1, l2)
l1 = ConvLayer(2, 12, 56, 3)
l2 = ConvLayer(2, 12, 56, 3)
self.assertEqual(l1, l2)
l1 = PoolingLayer(12, 14, 2)
l2 = PoolingLayer(12, 14, 2)
self.assertEqual(l1, l2)
_ = l1 == 4
def test_poolinglayer(self):
''' PoolingLayer init. '''
player = PoolingLayer(64, 28, 2)
self.assertEqual(player.ops_per_neuron(), 4)
self.assertEqual(player.total_ifmap_size(),
player.total_ofmap_size() * 4)
player = PoolingLayer(64, 28, 3, strd=2)
self.assertEqual(player.ops_per_neuron(), 9)
def setUp(self):
self.layers = {}
self.layers['BASE'] = ConvLayer(8, 16, 28, 3)
self.layers['POOL'] = PoolingLayer(16, 28, 2)
self.layers['LR'] = LocalRegionLayer(16, 28, nreg=3, sreg=1)
self.batch_size = 4
self.cost = Cost(mac_op=1, mem_hier=(200, 6, 2, 1),
noc_hop=50, unit_static=50)
self.resource = Resource(
proc_region=NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(4, 4),
type=NodeRegion.PROC),
data_regions=(NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(4, 1),
type=NodeRegion.DATA),),
dim_array=PhyDim2(16, 16), size_gbuf=65536, size_regf=64)
self.options = Option(partition_hybrid=True, partition_batch=True,
partition_ifmaps=True, ntops=10)
self.ifmap_layouts = {}
part = PartitionScheme(order=(pe.INPP, pe.BATP, pe.OUTP, pe.OFMP),
pdims=((1, 2), (2, 1), (1, 2), (2, 1)))
for wlkey in self.layers:
self.ifmap_layouts[wlkey] = partition.get_ofmap_layout(
self.layers[wlkey].input_layer(), self.batch_size, part,
self.resource.src_data_region())
def test_part_layer(self):
''' Get part_layer. '''
batch_size = 16
layer = ConvLayer(32, 128, 28, 3)
p_layer, p_batch_size, p_occ = self.ps1.part_layer(layer, batch_size)
self.assertGreaterEqual(p_layer.hofm * self.ps1.dim(pe.OFMP).h,
layer.hofm, 'part_layer: Conv: hofm')
self.assertGreaterEqual(p_layer.wofm * self.ps1.dim(pe.OFMP).w,
layer.wofm, 'part_layer: Conv: wofm')
self.assertGreaterEqual(p_layer.nofm * self.ps1.size(pe.OUTP),
layer.nofm, 'part_layer: Conv: nofm')
self.assertGreaterEqual(p_layer.nifm * self.ps1.size(pe.INPP),
layer.nifm, 'part_layer: Conv: nifm')
self.assertGreaterEqual(p_batch_size * self.ps1.size(pe.BATP),
16, 'part_layer: Conv: batch_size')
self.assertAlmostEqual(p_occ, 1. * (32 * 128 * 28 * 28 * 16)
/ (4 * 22 * 10 * 28 * 4 * self.ps1.size()))
layer = PoolingLayer(128, 112, 2)
p_layer, p_batch_size, p_occ = self.ps2.part_layer(layer, batch_size)
self.assertGreaterEqual(p_layer.hofm * self.ps2.dim(pe.OFMP).h,
layer.hofm, 'part_layer: Pooling: hofm')
self.assertGreaterEqual(p_layer.wofm * self.ps2.dim(pe.OFMP).w,
layer.wofm, 'part_layer: Pooling: wofm')
self.assertGreaterEqual(p_layer.nofm * self.ps2.size(pe.OUTP),
layer.nofm, 'part_layer: Pooling: nofm')
self.assertGreaterEqual(p_layer.nifm, p_layer.nofm,
'part_layer: Pooling: nifm')
self.assertGreaterEqual(p_batch_size * self.ps2.size(pe.BATP),
16, 'part_layer: Pooling: batch_size')
self.assertAlmostEqual(p_occ, 1. * (128 * 112 * 112 * 16)
/ (32 * 23 * 23 * 2 * self.ps2.size()))
def setUp(self):
''' Set up. '''
self.network = Network('test_net')
self.network.set_input(InputLayer(3, 224))
self.network.add('c1', ConvLayer(3, 64, 224, 3))
self.network.add('p1', PoolingLayer(64, 7, 32))
self.network.add('f1', FCLayer(64, 1000, 7))
def test_add_no_prev(self):
''' Modifier add no prevs. '''
network = Network('test_net')
network.set_input(InputLayer(3, 224))
network.add('c1', ConvLayer(3, 64, 224, 3))
with self.assertRaisesRegexp(KeyError, 'Network: .*prev.*p1.*'):
network.add('p1', PoolingLayer(64, 7, 32), prevs='p1')
def test_vertex_no_merge_lr(self):
''' LocalRegionLayer has no previous layer to merge with. '''
net = Network('tmp_net')
net.set_input_layer(InputLayer(30, 1))
net.add('0', PoolingLayer(30, 1, 1))
net.add('1', FCLayer(30, 40))
net.add('1p', PoolingLayer(40, 1, 1))
ilp = self._make_ilp(net)
for layer in net:
vidx = ilp.dag_vertex_dict[layer]
self.assertIn(layer, ilp.dag_vertex_list[vidx])
# Layer is named by topological order.
self.assertTrue(layer.startswith(str(vidx)))
def setUp(self):
self.network = Network('test_net')
self.network.set_input(InputLayer(3, 224))
self.network.add('c1', ConvLayer(3, 64, 224, 3))
self.network.add('p1', PoolingLayer(64, 7, 32), prevs='c1')
self.network.add('p2', PoolingLayer(64, 7, 32), prevs='c1')
self.network.add('f1', FCLayer(64, 1000, 7), prevs=['p1', 'p2'])
self.batch_size = 4
self.input_layout = partition.get_ofmap_layout(
self.network.input_layer(), self.batch_size,
PartitionScheme(order=range(pe.NUM), pdims=[(1, 1)] * pe.NUM),
NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(2, 1),
type=NodeRegion.DATA))
self.c1res = SchedulingResult(
dict_loop=OrderedDict([('cost', 1.), ('time', 2.), ('ops', 4.),
('access', [[7, 8, 9]] * me.NUM),
]),
dict_part=OrderedDict([('cost', 0.5), ('total_nhops', [4, 5, 6]),
('num_nodes', 4),
]),
ofmap_layout=partition.get_ofmap_layout(
self.network['c1'], self.batch_size,
PartitionScheme(order=range(pe.NUM), pdims=[(1, 1)] * pe.NUM),
NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(1, 2),
type=NodeRegion.DATA)))
self.pres = SchedulingResult(
dict_loop=OrderedDict([('cost', 0.1), ('time', 0.05), ('ops', 0.1),
('access', [[.7, .8, .9]] * me.NUM),
]),
dict_part=OrderedDict([('cost', 0.5), ('total_nhops', [.4, .5, .6]),
('num_nodes', 2),
]),
ofmap_layout=partition.get_ofmap_layout(
self.network['p1'], self.batch_size,
PartitionScheme(order=range(pe.NUM), pdims=[(1, 1)] * pe.NUM),
NodeRegion(origin=PhyDim2(0, 0), dim=PhyDim2(1, 2),
type=NodeRegion.DATA)))
self.dtfl = NNDataflowScheme(self.network, self.input_layout)
self.dtfl['c1'] = self.c1res
self.dtfl['p1'] = self.pres
self.dtfl['p2'] = self.pres
def setUp(self):
self.layers = {}
self.layers['BASE'] = ConvLayer(8, 16, 28, 3)
self.layers['POOL'] = PoolingLayer(16, 28, 2)
self.layers['LR'] = LocalRegionLayer(16, 28, nreg=3, sreg=1)
self.batch_size = 4
self.cost = Cost(mac_op=1,
mem_hier=(200, 6, 2, 1),
noc_hop=50,
idl_unit=50)
self.none_cstr = SchedulingConstraint()
self.cstr = SchedulingConstraint(topofm=1, topbat=self.batch_size)
self.resource = Resource(
proc_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 4),
type=NodeRegion.PROC),
dram_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 1),
type=NodeRegion.DRAM),
src_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 1),
type=NodeRegion.DRAM),
dst_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(4, 1),
type=NodeRegion.DRAM),
dim_array=PhyDim2(16, 16),
size_gbuf=65536,
size_regf=64,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
self.options = Option(partition_hybrid=True,
partition_batch=True,
partition_ifmaps=True,
ntops=10)
self.ifmap_layouts = {}
part = PartitionScheme(order=(pe.INPP, pe.BATP, pe.OUTP, pe.OFMP),
pdims=((1, 2), (2, 1), (1, 2), (2, 1)))
for wlkey in self.layers:
input_layer = self.layers[wlkey].input_layer()
self.ifmap_layouts[wlkey] = DataLayout(
frngs=(FmapRange((0, 0, 0, 0),
FmapPosition(b=self.batch_size,
n=input_layer.nofm,
h=input_layer.hofm,
w=input_layer.wofm)), ),
regions=(self.resource.src_data_region, ),
parts=(part.projection(self.resource.src_data_region,
appl2frng=True), ))
self.sched_seq = (2, 0, 1)
def test_repr(self):
''' __repr__. '''
# pylint: disable=eval-used
for l in [
LocalRegionLayer(64, 28, 2, 1),
LocalRegionLayer(64, [28, 14], 1, [2, 4]),
LocalRegionLayer(64, [28, 14], 1, [2, 4], 7),
LocalRegionLayer(64, 28, 1, 4, 7)
]:
self.assertIn('LocalRegionLayer', repr(l))
self.assertEqual(eval(repr(l)), l)
for l in [
PoolingLayer(64, 28, 2),
PoolingLayer(64, 28, 3, strd=2),
PoolingLayer(64, [28, 14], [3, 4], strd=[2, 3])
]:
self.assertIn('PoolingLayer', repr(l))
self.assertEqual(eval(repr(l)), l)
def test_is_valid_padding_sifm(self):
''' is_valid_padding_sifm. '''
clayer = ConvLayer(3, 64, [28, 14], [3, 1], [2, 4])
self.assertTrue(clayer.is_valid_padding_sifm([28 * 2, 14 * 4]))
self.assertTrue(clayer.is_valid_padding_sifm([27 * 2 + 3, 13 * 4 + 1]))
self.assertFalse(clayer.is_valid_padding_sifm([28, 14]))
self.assertFalse(clayer.is_valid_padding_sifm([28 * 2, 14]))
self.assertTrue(clayer.is_valid_padding_sifm([27 * 2 + 3, 13 * 4 + 3]))
flayer = FCLayer(2048, 4096, sfil=2)
self.assertTrue(flayer.is_valid_padding_sifm(2))
self.assertTrue(flayer.is_valid_padding_sifm(1))
self.assertTrue(flayer.is_valid_padding_sifm([1, 2]))
llayer = LocalRegionLayer(64, 28, 2, 1)
self.assertTrue(llayer.is_valid_padding_sifm(28))
self.assertFalse(llayer.is_valid_padding_sifm(28 - 1))
self.assertFalse(llayer.is_valid_padding_sifm(28 + 1))
player = PoolingLayer(64, 28, [2, 3], strd=[3, 2])
self.assertTrue(player.is_valid_padding_sifm([28 * 3, 28 * 2]))
self.assertTrue(player.is_valid_padding_sifm([27 * 3 + 2, 27 * 2 + 3]))
program. If not, see <https://opensource.org/licenses/BSD-3-Clause>.
"""
from nn_dataflow.core import Network
from nn_dataflow.core import InputLayer, ConvLayer, FCLayer, PoolingLayer
'''
ZFNet
Zeiler and Fergus, 2013
'''
NN = Network('ZFNet')
NN.set_input(InputLayer(3, 224))
NN.add('conv1', ConvLayer(3, 96, 110, 7, 2))
NN.add('pool1', PoolingLayer(96, 55, 3, strd=2))
# Norm layer is ignored.
NN.add('conv2', ConvLayer(96, 256, 26, 5, 2))
NN.add('pool2', PoolingLayer(256, 13, 3, strd=2))
# Norm layer is ignored.
NN.add('conv3', ConvLayer(256, 512, 13, 3))
NN.add('conv4', ConvLayer(512, 1024, 13, 3))
NN.add('conv5', ConvLayer(1024, 512, 13, 3))
NN.add('pool3', PoolingLayer(512, 6, 3, strd=2))
NN.add('fc1', FCLayer(512, 4096, 6))
NN.add('fc2', FCLayer(4096, 4096))
NN.add('fc3', FCLayer(4096, 1000))
def setUp(self):
self.net = {}
net = Network('net1')
# Linear.
net.set_input_layer(InputLayer(10, 1))
net.add('0', FCLayer(10, 20))
net.add('1', FCLayer(20, 30))
net.add('1p', PoolingLayer(30, 1, 1))
net.add('2', FCLayer(30, 40))
net.add('3', FCLayer(40, 50))
self.net[net.net_name] = net
net = Network('net2')
# Long linear.
net.set_input_layer(InputLayer(1, 1))
for idx in range(16):
net.add(str(idx), FCLayer(1, 1))
self.net[net.net_name] = net
net = Network('net3')
# Fork.
# /0-2\ /6- 7- 8\
# x 4-5 12
# \1-3/ \9-10-11/
net.set_input_layer(InputLayer(1, 1))
net.add('0', FCLayer(1, 1), prevs=net.INPUT_LAYER_KEY)
net.add('1', FCLayer(1, 1), prevs=net.INPUT_LAYER_KEY)
net.add('2', FCLayer(2, 1), prevs=('0', '1'))
net.add('2p', PoolingLayer(1, 1, 1))
net.add('3', FCLayer(2, 1), prevs=('0', '1'))
net.add('4', FCLayer(2, 1), prevs=('2p', '3'))
net.add('5', FCLayer(1, 1))
net.add('5p', PoolingLayer(1, 1, 1))
net.add('6', FCLayer(1, 1), prevs='5p')
net.add('7', FCLayer(1, 1))
net.add('8', FCLayer(1, 1))
net.add('9', FCLayer(1, 1), prevs='5p')
net.add('10', FCLayer(1, 1))
net.add('11', FCLayer(1, 1))
net.add('12', FCLayer(2, 1), prevs=('8', '11'))
self.net[net.net_name] = net
net = Network('net4')
# Complex fork.
# /5 \
# 0-1-2-3-4-6-7-8-10-14
# \9/
# \11-12 /
# \13 /
net.set_input_layer(InputLayer(1, 1))
net.add('0', FCLayer(1, 1))
net.add('1', FCLayer(1, 1))
net.add('2', FCLayer(1, 1))
net.add('3', FCLayer(1, 1))
net.add('4', FCLayer(1, 1))
net.add('5', FCLayer(1, 1), prevs='4')
net.add('6', FCLayer(1, 1), prevs='4')
net.add('7', FCLayer(1, 1))
net.add('8', FCLayer(1, 1), prevs='7')
net.add('9', FCLayer(1, 1), prevs='7')
net.add('10', FCLayer(1, 1))
net.add('10p', PoolingLayer(2, 1, 1), prevs=('8', '10'))
net.add('11', PoolingLayer(1, 1, 1), prevs='4')
net.add('12', FCLayer(1, 1))
net.add('13', PoolingLayer(1, 1, 1), prevs='4')
net.add('14', FCLayer(5, 1), prevs=('5', '10p', '12', '13'))
self.net[net.net_name] = net
net = Network('net5')
# Corner cases.
# ----\
# //1-2\ 7-8\
# 0-3-4-x 10-11-12
# \ \5/ 9 / \__/
# 6--/
net.set_input_layer(InputLayer(1, 1))
net.add('0', FCLayer(1, 1))
net.add('1', FCLayer(1, 1), prevs='0')
net.add('2', FCLayer(1, 1))
net.add('3', FCLayer(1, 1), prevs='0')
net.add('4', FCLayer(1, 1), prevs='3')
net.add('5', FCLayer(1, 1), prevs='3')
net.add('6', FCLayer(1, 1), prevs='0')
net.add('7', FCLayer(5, 1), prevs=('0', '2', '4', '5', '6'))
net.add('8', FCLayer(1, 1))
net.add('9', FCLayer(5, 1), prevs=('0', '2', '4', '5', '6'))
net.add('10', FCLayer(2, 1), prevs=('8', '9'))
net.add('11', FCLayer(1, 1))
net.add('12', FCLayer(2, 1), prevs=('10', '11'))
self.net[net.net_name] = net
net = Network('net6')
# Fmap sizes.
net.set_input_layer(InputLayer(1, 24))
net.add('0', ConvLayer(1, 1, 24, 3))
net.add('1', ConvLayer(1, 1, 12, 3, strd=2))
net.add('1p', PoolingLayer(1, 6, 2))
net.add('2', ConvLayer(1, 1, 6, 3))
net.add('3', ConvLayer(1, 1, 6, 3, strd=4), prevs=('0'))
self.net[net.net_name] = net
net = Network('net7')
# Topological order: see a visited vertex again.
# /---
# 0-1-\\
# \2--2p
net.set_input_layer(InputLayer(1, 1))
net.add('0', FCLayer(1, 1))
net.add('1', FCLayer(1, 1), prevs='0')
net.add('2', FCLayer(1, 1), prevs='0')
net.add('2p', PoolingLayer(3, 1, 1), prevs=('0', '1', '2'))
self.net[net.net_name] = net
net = Network('net8')
# Forward to the middle.
# /-\
# 0-1-2-2p-4-4p
# \-3------/
net.set_input_layer(InputLayer(1, 1))
net.add('0', FCLayer(1, 1))
net.add('1', FCLayer(1, 1), prevs='0')
net.add('2', FCLayer(1, 1), prevs='1')
net.add('2p', PoolingLayer(2, 1, 1), prevs=('1', '2'))
net.add('3', FCLayer(1, 1), prevs='0')
net.add('4', FCLayer(2, 1), prevs='2p')
net.add('4p', PoolingLayer(2, 1, 1), prevs=('3', '4'))
self.net[net.net_name] = net
net = Network('net9')
# Previous layers include input and others.
net.set_input_layer(InputLayer(1, 1))
net.add('0', FCLayer(1, 1))
net.add('1', FCLayer(2, 1), prevs=(net.INPUT_LAYER_KEY, '0'))
self.net[net.net_name] = net
# Real networks.
for net_name in all_networks():
self.net[net_name] = import_network(net_name)
self.batch_size = 16
self.resource = Resource(
proc_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(8, 8),
type=NodeRegion.PROC),
dram_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(8, 8),
type=NodeRegion.DRAM),
src_data_region=NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(8, 4),
type=NodeRegion.DRAM),
dst_data_region=NodeRegion(origin=PhyDim2(0, 4),
dim=PhyDim2(8, 4),
type=NodeRegion.DRAM),
dim_array=PhyDim2(16, 16),
size_gbuf=65536,
size_regf=64,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
part = PartitionScheme(order=range(pe.NUM), pdims=[(1, 1)] * pe.NUM)
self.ofmap_layout = DataLayout(
frngs=(FmapRange((0, 0, 0, 0), (2, 4, 16, 16)), ),
regions=(NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.DRAM), ),
parts=(part, ))
def setUp(self):
self.network = Network('test_net')
self.network.set_input_layer(InputLayer(3, 224))
self.network.add('c1', ConvLayer(3, 64, 224, 3))
self.network.add('p1', PoolingLayer(64, 7, 32), prevs='c1')
self.network.add('p2', PoolingLayer(64, 7, 32), prevs='c1')
self.network.add('f1', FCLayer(128, 1000, 7), prevs=['p1', 'p2'])
self.batch_size = 4
input_layer = self.network.input_layer()
self.input_layout = DataLayout(
frngs=(FmapRange((0, 0, 0, 0),
FmapPosition(b=self.batch_size,
n=input_layer.nofm,
h=input_layer.hofm,
w=input_layer.wofm)), ),
regions=(NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(2, 1),
type=NodeRegion.DRAM), ),
parts=(PartitionScheme(order=range(pe.NUM),
pdims=[(1, 1)] * pe.NUM), ))
c1_layer = self.network['c1']
self.c1res = SchedulingResult(
scheme=OrderedDict([
('cost', 1.5),
('time', 200.),
('ops', 4.),
('num_nodes', 4),
('cost_op', 0.5),
('cost_access', 1.),
('cost_noc', 0),
('cost_static', 0),
('proc_time', 200),
('bus_time', 0),
('dram_time', 0),
('access', [[7, 8, 9]] * me.NUM),
('remote_gbuf_access', [0] * 3),
('total_nhops', [4, 5, 6]),
('fetch', [[1, 1, 1], [2, 2, 2]]),
('ti', [2, 2, 3]),
('to', [1, 2, 3]),
('tb', [1, 2, 3]),
('tvals', [[2, 1, 1], [2, 2, 2], [3, 3, 3]]),
('orders', [range(3)] * 2),
]),
ofmap_layout=DataLayout(
frngs=(FmapRange(
(0, 0, 0, 0),
FmapPosition(b=self.batch_size,
n=c1_layer.nofm,
h=c1_layer.hofm,
w=c1_layer.wofm)), ),
regions=(NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 2),
type=NodeRegion.DRAM), ),
parts=(PartitionScheme(order=range(pe.NUM),
pdims=[(1, 1)] * pe.NUM), )),
sched_seq=(0, 0, 0))
p1_layer = self.network['p1']
self.p1res = SchedulingResult(
scheme=OrderedDict([
('cost', 0.6),
('time', 5),
('ops', 0.1),
('num_nodes', 2),
('cost_op', 0.1),
('cost_access', 0.5),
('cost_noc', 0),
('cost_static', 0),
('proc_time', 5),
('bus_time', 0),
('dram_time', 0),
('access', [[.7, .8, .9]] * me.NUM),
('remote_gbuf_access', [0] * 3),
('total_nhops', [.4, .5, .6]),
('fetch', [[1, 1, 1], [2, 2, 2]]),
('ti', [2, 2, 3]),
('to', [1, 2, 3]),
('tb', [1, 2, 3]),
('tvals', [[2, 1, 1], [2, 2, 2], [3, 3, 3]]),
('orders', [range(3)] * 2),
]),
ofmap_layout=DataLayout(
frngs=(FmapRange(
(0, 0, 0, 0),
FmapPosition(b=self.batch_size,
n=p1_layer.nofm,
h=p1_layer.hofm,
w=p1_layer.wofm)), ),
regions=(NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 2),
type=NodeRegion.DRAM), ),
parts=(PartitionScheme(order=range(pe.NUM),
pdims=[(1, 1)] * pe.NUM), )),
sched_seq=(0, 1, 0))
self.p2res = SchedulingResult(scheme=self.p1res.scheme,
ofmap_layout=self.p1res.ofmap_layout,
sched_seq=(0, 2, 0))
self.dtfl = NNDataflowScheme(self.network, self.input_layout)
self.dtfl['c1'] = self.c1res
self.dtfl['p1'] = self.p1res
self.dtfl['p2'] = self.p2res
def setUp(self):
# Workload.
self.layer = {}
self.layer['BASE'] = ConvLayer(12, 10, 28, 3)
self.layer['LGFIL'] = ConvLayer(2, 4, 28, 20)
self.layer['POOL'] = PoolingLayer(32, 28, 2)
self.batch_size = 4
# Resource.
self.resource = {}
dim_array = PhyDim2(16, 16)
proc_region = NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.PROC)
data_regions = (NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.DATA), )
# Typical resource.
self.resource['BASE'] = Resource(proc_region=proc_region,
data_regions=data_regions,
dim_array=dim_array,
size_gbuf=65536,
size_regf=64)
# Larger resource with sufficient capacity, to make all schemes valid.
self.resource['LG'] = Resource(proc_region=proc_region,
data_regions=data_regions,
dim_array=dim_array,
size_gbuf=1024**3,
size_regf=1024**3)
# Small resource.
self.resource['SM'] = Resource(proc_region=proc_region,
data_regions=data_regions,
dim_array=dim_array,
size_gbuf=4096,
size_regf=16)
# Nested loop description after mapping.
self.nld = {}
self.nld['BASE'] = next(
MapStrategyEyeriss(self.layer['BASE'], self.batch_size,
dim_array).gen_nested_loop_desc())
self.nld['LGFIL'] = next(
MapStrategyEyeriss(self.layer['LGFIL'], self.batch_size,
dim_array).gen_nested_loop_desc())
self.nld['POOL'] = next(
MapStrategyEyeriss(self.layer['POOL'], self.batch_size,
dim_array).gen_nested_loop_desc())
# Fake nested loop, with zero filter size.
self.nld['ZERO_FIL'] = NestedLoopDesc(
loopcnt=(12, 10, 4),
usize_gbuf=(0, 1000, 800),
usize_regf=(0, 3, 1),
unit_access=((0, 1000, 800), (0, 1000, 800), (3, 9, 7), (1, 1, 1)),
data_loops=(DataDimLoops(le.IFM,
le.OFM), DataDimLoops(le.IFM, le.BAT),
DataDimLoops(le.OFM, le.BAT)),
unit_ops=1,
unit_time=1)
# Fake nested loop, with zero ifmap size.
self.nld['ZERO_IFM'] = NestedLoopDesc(
loopcnt=(12, 10, 4),
usize_gbuf=(9, 0, 800),
usize_regf=(3, 0, 1),
unit_access=((9, 0, 800), (9, 0, 800), (3, 9, 7), (1, 1, 1)),
data_loops=(DataDimLoops(le.IFM,
le.OFM), DataDimLoops(le.IFM, le.BAT),
DataDimLoops(le.OFM, le.BAT)),
unit_ops=1,
unit_time=1)
# Options.
self.options = {}
# Basic.
self.options['BASE'] = Option(ntops=2**30)
# Multiprocessing.
self.options['MP'] = Option(ntops=2**30, nprocesses=8)
# Limited top schemes.
self.options['NTOPS'] = Option(ntops=10)
# Bypass.
self.options['BYP'] = Option(sw_gbuf_bypass=(True, ) * 3, ntops=2**30)
# Bypass solver.
self.options['BYPSOL'] = Option(sw_gbuf_bypass=(True, ) * 3,
sw_solve_loopblocking=True,
ntops=2**30)
# Cost.
self.cost = Cost(mac_op=1,
mem_hier=(200, 6, 2, 1),
noc_hop=50,
unit_static=50)
# Partition occupation.
self.part_occ = 0.91
from nn_dataflow.core import InputLayer, ConvLayer, PoolingLayer
'''
ResNet-152
He, Zhang, Ren, and Sun, 2015
'''
NN = Network('ResNet')
NN.set_input(InputLayer(3, 224))
_PREVS = None
NN.add('conv1', ConvLayer(3, 64, 112, 7, 2))
NN.add('pool1', PoolingLayer(64, 56, 2))
for i in range(1, 4):
NN.add('conv2_{}_a'.format(i),
ConvLayer(64, 64, 56, 1) if i == 1 else ConvLayer(256, 64, 56, 1),
prevs=_PREVS)
NN.add('conv2_{}_b'.format(i), ConvLayer(64, 64, 56, 3))
NN.add('conv2_{}_c'.format(i), ConvLayer(64, 256, 56, 1))
# With residual shortcut.
if i == 1:
# Residual does not cross module.
_PREVS = None
else:
_PREVS = ('conv2_{}_c'.format(i), 'conv2_{}_c'.format(i - 1))
from nn_dataflow.core import Network
from nn_dataflow.core import InputLayer, ConvLayer, FCLayer, PoolingLayer
'''
AlexNet
Krizhevsky, Sutskever, and Hinton, 2012
'''
NN = Network('AlexNet')
NN.set_input(InputLayer(3, 224))
NN.add('conv1_a', ConvLayer(3, 48, 55, 11, 4), prevs=(NN.INPUT_LAYER_KEY,))
NN.add('conv1_b', ConvLayer(3, 48, 55, 11, 4), prevs=(NN.INPUT_LAYER_KEY,))
NN.add('pool1_a', PoolingLayer(48, 27, 3, strd=2), prevs=('conv1_a',))
NN.add('pool1_b', PoolingLayer(48, 27, 3, strd=2), prevs=('conv1_b',))
# Norm layer is ignored.
NN.add('conv2_a', ConvLayer(48, 128, 27, 5), prevs=('pool1_a',))
NN.add('conv2_b', ConvLayer(48, 128, 27, 5), prevs=('pool1_b',))
NN.add('pool2_a', PoolingLayer(128, 13, 3, strd=2), prevs=('conv2_a',))
NN.add('pool2_b', PoolingLayer(128, 13, 3, strd=2), prevs=('conv2_b',))
# Norm layer is ignored.
NN.add('conv3_a', ConvLayer(256, 192, 13, 3), prevs=('pool2_a', 'pool2_b'))
NN.add('conv3_b', ConvLayer(256, 192, 13, 3), prevs=('pool2_a', 'pool2_b'))
NN.add('conv4_a', ConvLayer(192, 192, 13, 3), prevs=('conv3_a',))
NN.add('conv4_b', ConvLayer(192, 192, 13, 3), prevs=('conv3_b',))
NN.add('conv5_a', ConvLayer(192, 128, 13, 3), prevs=('conv4_a',))
NN.add('conv5_b', ConvLayer(192, 128, 13, 3), prevs=('conv4_b',))
"""
from nn_dataflow.core import Network
from nn_dataflow.core import InputLayer, ConvLayer, FCLayer, PoolingLayer
'''
GoogLeNet
ILSVRC 2014
'''
NN = Network('GoogLeNet')
NN.set_input_layer(InputLayer(3, 224))
NN.add('conv1', ConvLayer(3, 64, 112, 7, 2))
NN.add('pool1', PoolingLayer(64, 56, 3, strd=2))
# Norm layer is ignored.
NN.add('conv2_3x3_reduce', ConvLayer(64, 64, 56, 1))
NN.add('conv2_3x3', ConvLayer(64, 192, 56, 3))
# Norm layer is ignored.
NN.add('pool2', PoolingLayer(192, 28, 3, strd=2))
def add_inception(network, incp_id, sfmap, nfmaps_in, nfmaps_1, nfmaps_3r,
nfmaps_3, nfmaps_5r, nfmaps_5, nfmaps_pool, prevs):
''' Add an inception module to the network. '''
pfx = 'inception_{}_'.format(incp_id)
# 1x1 branch.
network.add(pfx + '1x1',
ConvLayer(nfmaps_in, nfmaps_1, sfmap, 1),
def setUp(self):
# Workload.
self.layer = {}
self.layer['BASE'] = ConvLayer(12, 10, 28, 3)
self.layer['LGFIL'] = ConvLayer(2, 4, 28, 20)
self.layer['POOL'] = PoolingLayer(32, 28, 2)
self.layer['PAR'] = ConvLayer(24, 36, 56, 3)
self.batch_size = 4
# Resource.
self.resource = {}
dim_array = PhyDim2(16, 16)
proc_region = NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.PROC)
data_region = NodeRegion(origin=PhyDim2(0, 0),
dim=PhyDim2(1, 1),
type=NodeRegion.DRAM)
# Typical resource.
self.resource['BASE'] = Resource(proc_region=proc_region,
dram_region=data_region,
src_data_region=data_region,
dst_data_region=data_region,
dim_array=dim_array,
size_gbuf=65536,
size_regf=64,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
# Larger resource with sufficient capacity, to make all schemes valid.
self.resource['LG'] = Resource(proc_region=proc_region,
dram_region=data_region,
src_data_region=data_region,
dst_data_region=data_region,
dim_array=dim_array,
size_gbuf=1024**3,
size_regf=1024**3,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
# Small resource.
self.resource['SM'] = Resource(proc_region=proc_region,
dram_region=data_region,
src_data_region=data_region,
dst_data_region=data_region,
dim_array=dim_array,
size_gbuf=4096,
size_regf=16,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
# Multi-node parallel resource.
self.resource['PAR'] = Resource(proc_region=NodeRegion(
origin=PhyDim2(0, 0), dim=PhyDim2(4, 2), type=NodeRegion.PROC),
dram_region=data_region,
src_data_region=data_region,
dst_data_region=data_region,
dim_array=dim_array,
size_gbuf=25000,
size_regf=64,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
# Resource with no data regions.
proc_data_region = NodeRegion(origin=PhyDim2(1, 1),
dim=PhyDim2(1, 1),
type=NodeRegion.PROC)
self.resource['SRCNOTDATA'] = Resource(
proc_region=proc_region,
dram_region=data_region,
src_data_region=proc_data_region,
dst_data_region=data_region,
dim_array=dim_array,
size_gbuf=1024**3,
size_regf=1024**3,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
self.resource['DSTNOTDATA'] = Resource(
proc_region=proc_region,
dram_region=data_region,
src_data_region=data_region,
dst_data_region=proc_data_region,
dim_array=dim_array,
size_gbuf=1024**3,
size_regf=1024**3,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
self.resource['DATALOCAL'] = Resource(proc_region=proc_region,
dram_region=data_region,
src_data_region=proc_region,
dst_data_region=proc_region,
dim_array=dim_array,
size_gbuf=1024**3,
size_regf=1024**3,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=False)
# Filter pinning.
self.resource['FILPIN'] = Resource(proc_region=proc_region,
dram_region=data_region,
src_data_region=data_region,
dst_data_region=data_region,
dim_array=dim_array,
size_gbuf=1024**3,
size_regf=1024**3,
array_bus_width=float('inf'),
dram_bandwidth=float('inf'),
no_time_mux=True)
# Nested loop description after mapping.
self.nld = {}
self.nld['BASE'] = next(
MapStrategyEyeriss(self.layer['BASE'], self.batch_size, 1,
dim_array).gen_nested_loop_desc())
self.nld['LGFIL'] = next(
MapStrategyEyeriss(self.layer['LGFIL'], self.batch_size, 1,
dim_array).gen_nested_loop_desc())
self.nld['POOL'] = next(
MapStrategyEyeriss(self.layer['POOL'], self.batch_size, 1,
dim_array).gen_nested_loop_desc())
# Fake nested loop, with zero filter size.
self.nld['ZERO_FIL'] = NestedLoopDesc(
loopcnt=(12, 10, 4),
usize_gbuf=(0, 1000, 800),
usize_regf=(0, 3, 1),
unit_access=((0, 1000, 800), (0, 1000, 800), (3, 9, 7), (1, 1, 1)),
data_loops=(DataDimLoops(le.IFM,
le.OFM), DataDimLoops(le.IFM, le.BAT),
DataDimLoops(le.OFM, le.BAT)),
unit_ops=1,
unit_time=1)
# Fake nested loop, with zero ifmap size.
self.nld['ZERO_IFM'] = NestedLoopDesc(
loopcnt=(12, 10, 4),
usize_gbuf=(9, 0, 800),
usize_regf=(3, 0, 1),
unit_access=((9, 0, 800), (9, 0, 800), (3, 9, 7), (1, 1, 1)),
data_loops=(DataDimLoops(le.IFM,
le.OFM), DataDimLoops(le.IFM, le.BAT),
DataDimLoops(le.OFM, le.BAT)),
unit_ops=1,
unit_time=1)
# Fake partition scheme.
self.part = PartitionScheme(range(pe.NUM), ((1, 1), ) * pe.NUM)
# Fake buffer sharing scheme.
self.bufshr = BufShrScheme(proc_region, self.part)
# Options.
self.options = {}
# Basic.
self.options['BASE'] = Option(ntops=2**30)
# Multiprocessing.
self.options['MP'] = Option(ntops=2**30, nprocesses=8)
# Limited top schemes.
self.options['NTOPS'] = Option(ntops=10)
# Bypass.
self.options['BYP'] = Option(sw_gbuf_bypass=(True, ) * 3, ntops=2**30)
# Bypass solver.
self.options['BYPSOL'] = Option(sw_gbuf_bypass=(True, ) * 3,
sw_solve_loopblocking=True,
ntops=2**30)
# Access forwarding.
self.options['ACCFWD'] = Option(hw_access_forwarding=True, ntops=2**30)
# Buffer sharing.
self.options['BUFSHR'] = Option(hw_gbuf_sharing=True, ntops=2**30)
# Buffer sharing with bypassing.
self.options['BUFSHR-BYP'] = Option(sw_gbuf_bypass=(True, ) * 3,
hw_gbuf_sharing=True,
ntops=2**30)
# Constraint.
self.none_cstr = SchedulingConstraint()
self.cstr = SchedulingConstraint(topifm=1, topbat=1)
# Cost.
self.cost = Cost(mac_op=1,
mem_hier=(200, 6, 2, 1),
noc_hop=50,
idl_unit=50)
from nn_dataflow.core import Network
from nn_dataflow.core import InputLayer, ConvLayer, FCLayer, \
PoolingLayer, EltwiseLayer
'''
ResNet-152
He, Zhang, Ren, and Sun, 2015
'''
NN = Network('ResNet')
NN.set_input_layer(InputLayer(3, 224))
NN.add('conv1', ConvLayer(3, 64, 112, 7, 2))
NN.add('pool1', PoolingLayer(64, 56, 3, 2))
RES_PREV = 'pool1'
for i in range(3):
NN.add('conv2_{}_a'.format(i), ConvLayer(64 if i == 0 else 256, 64, 56, 1))
NN.add('conv2_{}_b'.format(i), ConvLayer(64, 64, 56, 3))
NN.add('conv2_{}_c'.format(i), ConvLayer(64, 256, 56, 1))
# With residual shortcut.
if i == 0:
NN.add('conv2_br', ConvLayer(64, 256, 56, 1), prevs=(RES_PREV, ))
RES_PREV = 'conv2_br'
NN.add('conv2_{}_res'.format(i),
EltwiseLayer(256, 56, 2),
prevs=(RES_PREV, 'conv2_{}_c'.format(i)))
def test_part_layer_invalid_inpart(self):
''' Get part_layer invalid INPP. '''
with self.assertRaisesRegexp(ValueError, 'PartitionScheme: .*input.*'):
_ = self.ps1.part_layer(PoolingLayer(self.ps1.size(pe.OUTP),
self.ps1.size(pe.OFMP), 2),
self.ps1.size(pe.BATP))
from nn_dataflow.core import Network
from nn_dataflow.core import InputLayer, ConvLayer, FCLayer, PoolingLayer
'''
VGGNet-16
Simonyan and Zisserman, 2014
'''
NN = Network('VGG')
NN.set_input_layer(InputLayer(3, 224))
NN.add('conv1', ConvLayer(3, 64, 224, 3))
NN.add('conv2', ConvLayer(64, 64, 224, 3))
NN.add('pool1', PoolingLayer(64, 112, 2))
NN.add('conv3', ConvLayer(64, 128, 112, 3))
NN.add('conv4', ConvLayer(128, 128, 112, 3))
NN.add('pool2', PoolingLayer(128, 56, 2))
NN.add('conv5', ConvLayer(128, 256, 56, 3))
NN.add('conv6', ConvLayer(256, 256, 56, 3))
NN.add('conv7', ConvLayer(256, 256, 56, 3))
NN.add('pool3', PoolingLayer(256, 28, 2))
NN.add('conv8', ConvLayer(256, 512, 28, 3))
NN.add('conv9', ConvLayer(512, 512, 28, 3))
NN.add('conv10', ConvLayer(512, 512, 28, 3))
NN.add('pool4', PoolingLayer(512, 14, 2))
""" $lic$
Copyright (C) 2016-2019 by The Board of Trustees of Stanford University
This program is free software: you can redistribute it and/or modify it under
the terms of the Modified BSD-3 License as published by the Open Source
Initiative.
This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the BSD-3 License for more details.
You should have received a copy of the Modified BSD-3 License along with this
program. If not, see <https://opensource.org/licenses/BSD-3-Clause>.
"""
from nn_dataflow.core import Network
from nn_dataflow.core import InputLayer, ConvLayer, FCLayer, PoolingLayer
'''
net1
'''
NN = Network('net1')
# Linear.
NN.set_input_layer(InputLayer(10, 1))
NN.add('0', FCLayer(10, 20))
NN.add('1', FCLayer(20, 30))
NN.add('1p', PoolingLayer(30, 1, 1))
NN.add('2', FCLayer(30, 40))
NN.add('3', FCLayer(40, 50))