class ConvThenLinear(_Net):
def __init__(self, start_shape, stop_shape, depth=2, conv_part=Conv1dAC):
_Net.__init__(self, start_shape, stop_shape, depth)
self.conv_part = conv_part(start_shape=start_shape,
stop_shape=stop_shape,
depth=depth,
bias_prob=0.3)
self.lin_part = LinearAC(start_shape=None,
stop_shape=(10, ),
depth=depth,
bias_prob=0.3)
self.change_num = IntParam(name="")
def change_when(self, x):
self.change_num.unrandomize(val=x)
def change_random(self):
self.change_num.randomize(limits=(1, self.depth - 1))
def generate(self):
self.change_random()
t = self.change_num.value
self.lin_part.depth = self.depth - t
cp = self.conv_part.generate()[:t]
self.lin_part.start_shape = cp[-1].out_shape
lp = self.lin_part.generate()
return cp + lp
class ResNetStyle(_Net):
def __init__(self, start_shape, stop_shape, depth=3):
_Net.__init__(self, start_shape, stop_shape, depth)
self.layers.append(Conv2dAC(start_shape, [10, 1, 1], depth))
self.layers.append(Conv2dThenLinear(start_shape, [10, 1, 1], depth))
self.change_num = IntParam("", default=1)
def generate(self):
z1 = Conv2dAC.ac.val
Conv2dAC.ac.val = Conv2dAC.ac.choices[2]
z2 = LinearAC.ac.val
LinearAC.ac.val = LinearAC.ac.choices[2]
self.change_num.randomize(limits=(1, self.depth - 2 * self.depth // 3))
t = self.change_num.value
cp = self.layers[0].generate(resnet_cover=20)[:t]
skip_gen = BasicBlock()
self.change_num.randomize(
limits=(1, max([1, self.depth - (t + self.depth // 3)]))
)
t2 = self.change_num.value
sp = []
for i in range(t2):
sp.append(skip_gen(_in_shape=cp[-1].out_shape))
self.change_num.randomize(limits=(2, max([2, self.depth - (t + t2)])))
t3 = self.change_num.value
self.layers[1].start_shape = sp[-1].out_shape
self.layers[1].conv_part.start_shape = sp[-1].out_shape
self.layers[1].depth = t3
lp = self.layers[1].generate()
Conv2dAC.ac.val = z1
LinearAC.ac.val = z2
return cp + sp + lp