def get_model(args):
global reg
reg = args.reg
input_img = Input((3, 32, 32))
x = input_img
x = SCL(BN(x))
x = C(x, 128, 7, 2)
x = SCRES(x, 128, 16)
x = SCL(BN(x))
x = C(x, 128, 3, 2)
x = SCRES(x, 128, 16)
x = SCL(BN(x))
x = C(x, 128, 3, 2)
x = SCRES(x, 128, 16)
x = SCL(BN(x))
x = C(x, 128, 3, 2)
x = SCRES(x, 128, 16)
x = SCL(BN(x))
x = C(x, 128, 3, 2)
x = SCRES(x, 128, 16)
x = SCL(BN(x))
x = D(x, 128, 3, 2)
x = SCRES(x, 128, 16)
x = SCL(BN(x))
x = D(x, 128, 3, 2)
x = SCRES(x, 128, 16)
x = SCL(BN(x))
x = D(x, 128, 3, 2)
x = SCRES(x, 128, 16)
x = SCL(BN(x))
x = D(x, 128, 3, 2)
x = SCRES(x, 128, 16)
x = SCL(BN(x))
x = D(x, 3, 7, 2)
x = SCL(x)
return Model(input_img, x)
def get_model(args):
global reg
reg = args.reg
def RB(x):
for _ in range(4):
x = SCRES(x, 128, 64)
return x
input_img = Input((3,32,32))
x = input_img
x = SCL(BN(x))
x = C(x, 128, 7, 2)
x = RB(x)
x = SCL(BN(x))
x = C(x, 128, 3, 2)
x = RB(x)
x = SCL(BN(x))
x = C(x, 128, 3, 2)
x = RB(x)
x = SCL(BN(x))
x = C(x, 128, 3, 2)
x = SCL(BN(x))
x = C(x, 128, 3, 2)
x = SCL(BN(x), name="bottleneck")
x = D(x, 128, 3, 2)
x = SCL(BN(x))
x = D(x, 128, 3, 2)
x = RB(x)
x = SCL(BN(x))
x = D(x, 128, 3, 2)
x = RB(x)
x = SCL(BN(x))
x = D(x, 128, 3, 2)
x = RB(x)
x = SCL(BN(x))
x = D(x, 3, 7, 2)
x = SCL(x)
return Model(input_img, x)
def __init__(self, classes=20, p=5, q=3):
'''
:param classes: number of classes in the dataset. Default is 20 for the cityscapes
:param p: depth multiplier
:param q: depth multiplier
'''
super(ESPNetEncoder, self).__init__()
self.level1 = CBR(3, 16, 3, 2)
self.sample1 = InputProjectionA(1)
self.sample2 = InputProjectionA(2)
self.b1 = BR(16 + 3)
self.level2_0 = DownSamplerB(16 + 3, 64)
self.level2 = nn.ModuleList()
for i in range(0, p):
self.level2.append(DilatedParallelResidualBlockB(64, 64))
self.b2 = BR(128 + 3)
self.level3_0 = DownSamplerB(128 + 3, 128)
self.level3 = nn.ModuleList()
for i in range(0, q):
self.level3.append(DilatedParallelResidualBlockB(128, 128))
self.b3 = BR(256)
self.classifier = C(256, classes, 1, 1)
def get_model(args):
global reg
reg = args.reg
input_img = Input((3, 32, 32))
x = input_img
x = R(BN(x))
x = C(x, 128, 7, 2)
x = R(BN(x))
x = C(x, 128, 3, 2)
x = R(BN(x))
x = C(x, 128, 3, 2)
x = R(BN(x))
x = C(x, 128, 3, 2)
x = R(BN(x))
x = C(x, 128, 3, 2)
x = R(BN(x))
x = D(x, 128, 3, 2)
x = R(BN(x))
x = D(x, 128, 3, 2)
x = R(BN(x))
x = D(x, 128, 3, 2)
x = R(BN(x))
x = D(x, 128, 3, 2)
x = R(BN(x))
x = D(x, 3, 7, 2)
x = Lambda(lambda x: clip(x, -2.5, 2.5) + 0.001 * x)(x)
return Model(input_img, x)