def setUp(self):
self.size = 3
self.link = links.GroupNormalization(self.groups)
self.shape = (1, self.size, 1)
def __init__(self, n_layers, class_labels=None):
super(ResNet, self).__init__()
w = chainer.initializers.HeNormal()
# expected insize = 224
if n_layers == 18:
block = [2, 2, 2, 2]
elif n_layers == 19:
block = [2, 2, 2, 2]
elif n_layers == 20:
block = [2, 2, 2, 2, 2]
elif n_layers == 21:
block = [2, 2, 2, 2, 2, 2]
elif n_layers == 34:
block = [3, 4, 6, 3]
elif n_layers == 50:
block = [3, 4, 6, 3]
elif n_layers == 101:
block = [3, 4, 23, 3]
elif n_layers == 152:
block = [3, 4, 36, 3]
# expected insize = 32
elif n_layers == 32:
block = [5, 5, 5]
elif n_layers == 44:
block = [7, 7, 7]
elif n_layers == 56:
block = [9, 9, 9]
elif n_layers == 110:
block = [18, 18, 18]
else:
raise ValueError(
"You tried to create a ResNet variant that does not exist")
with self.init_scope():
if n_layers == 16:
self.conv1 = L.Convolution2D(3,
64,
7,
2,
3,
initialW=w,
nobias=True)
self.bn1 = L.GroupNormalization(32)
self.res2 = BasicBlock(block[0], 128, 1)
self.res3 = BasicBlock(block[1], 256)
self.res4 = BasicBlock(block[2], 512)
elif n_layers in [18, 20, 21, 34]:
self.conv1 = L.Convolution2D(3,
64,
7,
2,
3,
initialW=w,
nobias=True)
self.bn1 = L.GroupNormalization(16)
self.res2 = BasicBlock(block[0], 64, 1, num_groups=16)
self.res3 = BasicBlock(block[1], 128)
self.res4 = BasicBlock(block[2], 256)
self.res5 = BasicBlock(block[3], 512)
elif n_layers in [32, 44, 56, 110]:
self.conv1 = L.Convolution2D(3,
16,
7,
2,
3,
initialW=w,
nobias=True)
self.bn1 = L.GroupNormalization(8)
self.res2 = BasicBlock(block[0], 16, 1)
self.res3 = BasicBlock(block[1], 32)
self.res4 = BasicBlock(block[2], 64)
elif n_layers in [19, 50, 101, 152]:
self.conv1 = L.Convolution2D(3,
64,
7,
2,
3,
initialW=w,
nobias=True)
self.bn1 = L.GroupNormalization(32)
self.res2 = BottleNeckBlock(block[0], 64, 64, 256, 1)
self.res3 = BottleNeckBlock(block[1], 256, 128, 512)
self.res4 = BottleNeckBlock(block[2], 512, 256, 1024)
self.res5 = BottleNeckBlock(block[3], 1024, 512, 2048)
if n_layers in [20, 21]:
self.res6 = BasicBlock(block[4], 512)
if n_layers in [21]:
self.res7 = BasicBlock(block[5], 512)
if class_labels is not None:
self.fc = L.Linear(None, class_labels)
self.n_layers = n_layers
self.class_labels = class_labels
def test_invalid_groups(self):
self.link = links.GroupNormalization(groups=3)
with self.assertRaises(ValueError):
self.link(self.x)
def test_invalid_type_groups(self):
self.link = links.GroupNormalization(groups=3.5)
with self.assertRaises(TypeError):
self.link(self.x)
def setUp(self):
self.link = links.GroupNormalization(groups=3)