class Large(nn.Module):
def __init__(self, eps=0):
super().__init__()
self.conv1 = Conv2dInterval(3, 64, 3, 1, input_layer=True)
self.conv2 = Conv2dInterval(64, 64, 3, 1)
self.conv3 = Conv2dInterval(64, 128, 3, 2)
self.conv4 = Conv2dInterval(128, 128, 3, 1)
self.conv5 = Conv2dInterval(128, 128, 3, 1)
self.fc1 = LinearInterval(128 * 9 * 9, 200)
self.last = LinearInterval(200, 10)
self.a = nn.Parameter(torch.zeros(7), requires_grad=True)
self.e = None
self.eps = eps
self.bounds = None
def save_bounds(self, x):
s = x.size(1) // 3
self.bounds = x[:, s:2 * s], x[:, 2 * s:]
def calc_eps(self, r):
exp = self.a.exp()
self.e = r * exp / exp.sum()
def print_eps(self):
for c in (self.conv1, self.conv2, self.conv3, self.conv4, self.conv5,
self.fc1):
e1 = c.eps.detach()
print(f"sum: {e1.sum()} - mean: {e1.mean()} - std: {e1.std()}")
for name, layer in self.last.items():
l = layer.eps.detach()
print(
f"last-{name} sum: {l.sum()} - mean: {l.mean()} - std: {l.std()}"
)
def reset_importance(self):
pass
# self.conv1.reset_importance()
# self.conv2.reset_importance()
# self.conv3.reset_importance()
# self.conv4.reset_importance()
# self.conv5.reset_importance()
# self.fc1.reset_importance()
# for _, layer in self.last.items():
# layer.reset_importance()
def set_eps(self, eps, trainable=False):
if trainable:
self.calc_eps(eps)
self.conv1.calc_eps(self.e[0])
self.conv2.calc_eps(self.e[1])
self.conv3.calc_eps(self.e[2])
self.conv4.calc_eps(self.e[3])
self.conv5.calc_eps(self.e[4])
self.fc1.calc_eps(self.e[5])
for _, layer in self.last.items():
layer.calc_eps(self.e[6])
else:
self.conv1.calc_eps(eps)
self.conv2.calc_eps(eps)
self.conv3.calc_eps(eps)
self.conv4.calc_eps(eps)
self.conv5.calc_eps(eps)
self.fc1.calc_eps(eps)
for _, layer in self.last.items():
layer.calc_eps(eps)
def features(self, x):
x = f.relu(self.conv1(x))
x = f.relu(self.conv2(x))
x = f.relu(self.conv3(x))
x = f.relu(self.conv4(x))
x = f.relu(self.conv5(x))
x = torch.flatten(x, 1)
x = f.relu(self.fc1(x))
self.save_bounds(x)
return x
def logits(self, x):
return self.last(x)
def forward(self, x):
x = self.features(x)
x = self.logits(x)
return {k: v[:, :v.size(1) // 3] for k, v in x.items()}
class IntervalMLP(nn.Module):
def __init__(self, out_dim=10, in_channel=1, img_sz=32, hidden_dim=256):
super(IntervalMLP, self).__init__()
self.in_dim = in_channel*img_sz*img_sz
self.fc1 = LinearInterval(self.in_dim, hidden_dim, input_layer=True)
self.fc2 = LinearInterval(hidden_dim, hidden_dim)
# Subject to be replaced dependent on task
self.last = LinearInterval(hidden_dim, out_dim)
self.a = nn.Parameter(torch.zeros(3), requires_grad=True)
self.e = None
self.bounds = None
def save_bounds(self, x):
s = x.size(1) // 3
self.bounds = x[:, s:2*s], x[:, 2*s:]
def calc_eps(self, r):
exp = self.a.exp()
self.e = r * exp / exp.sum()
def print_eps(self):
e1 = self.fc1.eps.detach()
e2 = self.fc2.eps.detach()
print(f"sum: {e1.sum()} - mean: {e1.mean()} - std: {e1.std()}")
print(f"sum: {e2.sum()} - mean: {e2.mean()} - std: {e2.std()}")
# print(100 * "=")
# print(e1)
# print(100 * "+")
# print(e2)
# print(100 * "+")
for name, layer in self.last.items():
l = layer.eps.detach()
print(f"last-{name} sum: {l.sum()} - mean: {l.mean()} - std: {l.std()}")
# print(100 * "+")
# print(l)
# print(100 * "+")
def reset_importance(self):
self.fc1.rest_importance()
self.fc2.rest_importance()
for _, layer in self.last.items():
layer.rest_importance()
def set_eps(self, eps, trainable=False):
if trainable:
self.calc_eps(eps)
self.fc1.calc_eps(self.e[0])
self.fc2.calc_eps(self.e[1])
for _, layer in self.last.items():
layer.calc_eps(self.e[2])
else:
self.fc1.calc_eps(eps)
self.fc2.calc_eps(eps)
for _, layer in self.last.items():
layer.calc_eps(eps)
def features(self, x):
x = x.view(-1, self.in_dim)
x = f.relu(self.fc1(x))
x = f.relu(self.fc2(x))
self.save_bounds(x)
return x
def logits(self, x):
return self.last(x)
def forward(self, x):
x = self.features(x)
x = self.logits(x)
return {k: v[:, :v.size(1)//3] for k, v in x.items()}
class IntervalCNN(nn.Module):
def __init__(self, in_channel=3, out_dim=10, pooling=MaxPool2dInterval):
super(IntervalCNN, self).__init__()
self.input = Conv2dInterval(in_channel,
32,
kernel_size=3,
stride=1,
padding=1,
input_layer=True)
self.c1 = nn.Sequential(
Conv2dInterval(32, 32, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
Conv2dInterval(32, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(), pooling(2, stride=2, padding=0), IntervalDropout(0.25))
self.c2 = nn.Sequential(
Conv2dInterval(64, 64, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
Conv2dInterval(64, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(), pooling(2, stride=2, padding=0), IntervalDropout(0.25))
self.c3 = nn.Sequential(
Conv2dInterval(128, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(),
Conv2dInterval(128, 128, kernel_size=3, stride=1, padding=1),
nn.ReLU(), pooling(2, stride=2, padding=1), IntervalDropout(0.25))
self.fc1 = nn.Sequential(LinearInterval(128 * 5 * 5, 256), nn.ReLU())
self.last = LinearInterval(256, out_dim)
self.a = nn.Parameter(torch.zeros(9), requires_grad=True)
self.e = None
self.bounds = None
def save_bounds(self, x):
s = x.size(1) // 3
self.bounds = x[:, s:2 * s], x[:, 2 * s:]
def print_eps(self):
e = self.input.eps.detach()
print(f"sum: {e.sum()} - mean: {e.mean()} - std: {e.std()}")
print(f"min: {e.min()} - max: {e.max()}")
for c in (self.c1, self.c2, self.c3):
e1 = c[0].eps.detach()
e2 = c[2].eps.detach()
print(f"sum: {e1.sum()} - mean: {e1.mean()} - std: {e1.std()}")
print(f"min: {e1.min()} - max: {e1.max()}")
print(f"sum: {e2.sum()} - mean: {e2.mean()} - std: {e2.std()}")
print(f"min: {e2.min()} - max: {e2.max()}")
e = self.fc1[0].eps.detach()
print(f"sum: {e.sum()} - mean: {e.mean()} - std: {e.std()}")
print(f"min: {e.min()} - max: {e.max()}")
for name, layer in self.last.items():
l = layer.eps.detach()
print(
f"last-{name} sum: {l.sum()} - mean: {l.mean()} - std: {l.std()}"
)
print(f"min: {l.min()} - max: {l.max()}")
def calc_eps(self, r):
exp = self.a.exp()
self.e = r * exp / exp.sum()
def reset_importance(self):
self.input.rest_importance()
self.c1[0].rest_importance()
self.c1[2].rest_importance()
self.c2[0].rest_importance()
self.c2[2].rest_importance()
self.c3[0].rest_importance()
self.c3[2].rest_importance()
self.fc1[0].rest_importance()
for _, layer in self.last.items():
layer.rest_importance()
def set_eps(self, eps, trainable=False):
if trainable:
self.calc_eps(eps)
self.input.calc_eps(self.e[0])
self.c1[0].calc_eps(self.e[1])
self.c1[2].calc_eps(self.e[2])
self.c2[0].calc_eps(self.e[3])
self.c2[2].calc_eps(self.e[4])
self.c3[0].calc_eps(self.e[5])
self.c3[2].calc_eps(self.e[6])
self.fc1[0].calc_eps(self.e[7])
for _, layer in self.last.items():
layer.calc_eps(self.e[8])
else:
self.input.calc_eps(eps)
self.c1[0].calc_eps(eps)
self.c1[2].calc_eps(eps)
self.c2[0].calc_eps(eps)
self.c2[2].calc_eps(eps)
self.c3[0].calc_eps(eps)
self.c3[2].calc_eps(eps)
self.fc1[0].calc_eps(eps)
for _, layer in self.last.items():
layer.calc_eps(eps)
def features(self, x):
x = self.input(x)
x = self.c1(x)
x = self.c2(x)
x = self.c3(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
self.save_bounds(x)
return x
def logits(self, x):
return self.last(x)
def forward(self, x):
x = self.features(x)
x = self.logits(x)
return {k: v[:, :v.size(1) // 3] for k, v in x.items()}