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Minor tweaks #2

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4a63c12
Update SGDB.py
Richienb Mar 14, 2024
034e64a
Update AdamB.py
Richienb Mar 14, 2024
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9 changes: 4 additions & 5 deletions AdamB.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,13 +6,13 @@ class AdamB(Optimizer):
def __init__(
self, params, lr=1e-4, betas=(0.9, 0.999), eps=1e-8, pretrained=True,
back_level_max=1, back_level_min=0, back_pow=2):
assert 0<= back_level_max <= 1, "back_level_max should be in [0, 1]"
assert 0 <= back_level_max <= 1, "back_level_max should be in [0, 1]"
defaults = dict(
lr=lr, betas=betas, eps=eps, back_level_max=back_level_max, back_level_min=back_level_min,
pretrained=pretrained, back_pow=back_pow)
super().__init__(params, defaults)
self.init_all()

@torch.no_grad()
def init_all(self):
for group in self.param_groups:
Expand All @@ -28,7 +28,7 @@ def init_all(self):
else:
state['exp_avg'] = torch.zeros_like(p)
state['exp_avg_sq'] = torch.zeros_like(p)

@torch.no_grad()
def step(self):
for group in self.param_groups:
Expand All @@ -53,7 +53,7 @@ def step(self):
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) # v_t
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
update = (exp_avg / bias_correction1).div_(denom)

if pretrained:
D_value = state['D_value']
p_index, num_layers, back_level_max, back_level_min = state['p_index'], group['num_layers'], group['back_level_max'], group['back_level_min']
Expand All @@ -64,4 +64,3 @@ def step(self):
D_value.add_(update, alpha=-group['lr'])
else:
p.add_(update, alpha=-group['lr'])
return
11 changes: 5 additions & 6 deletions SGDB.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,13 +6,13 @@ class SGDB(Optimizer):
def __init__(
self, params, lr=1e-3, beta=0.9, dampening=0.0, eps=1e-8, pretrained=True,
back_level_max=1, back_level_min=0, back_pow=2):
assert 0<= back_level_max <= 1, "back_level_max should be in [0, 1]"
assert 0 <= back_level_max <= 1, "back_level_max should be in [0, 1]"
defaults = dict(
lr=lr, beta=beta, dampening=dampening, eps=eps, back_level_max=back_level_max, back_level_min=back_level_min,
pretrained=pretrained, back_pow=back_pow)
super().__init__(params, defaults)
self.init_all()

@torch.no_grad()
def init_all(self):
for group in self.param_groups:
Expand All @@ -26,7 +26,7 @@ def init_all(self):
state['momentum'] = torch.zeros_like(p)
else:
state['momentum'] = torch.zeros_like(p)

@torch.no_grad()
def step(self):
for group in self.param_groups:
Expand All @@ -38,7 +38,7 @@ def step(self):
group['step'] += 1
else:
group['step'] = 1

for p in group['params']:
if p.grad is None:
continue
Expand All @@ -54,7 +54,6 @@ def step(self):
update.add_(D_value, alpha=back_level)
p.add_(update, alpha=-group['lr'])
D_value.add_(update, alpha=-group['lr'])

else:
p.add_(momentum, alpha=-group['lr'])
return