Regular refactoring

1 files changed, 0 insertions, 70 deletions

diff --git a/supervised/lars_optimizer.py b/supervised/lars_optimizer.py
deleted file mode 100644
index 1904e8d..0000000
--- a/supervised/lars_optimizer.py
+++ /dev/null
@@ -1,70 +0,0 @@
-import torch
-
-
-class LARS(object):
-    """
-    Slight modification of LARC optimizer from https://github.com/NVIDIA/apex/blob/d74fda260c403f775817470d87f810f816f3d615/apex/parallel/LARC.py
-    Matches one from SimCLR implementation https://github.com/google-research/simclr/blob/master/lars_optimizer.py
-
-    Args:
-        optimizer: Pytorch optimizer to wrap and modify learning rate for.
-        trust_coefficient: Trust coefficient for calculating the adaptive lr. See https://arxiv.org/abs/1708.03888
-    """
-
-    def __init__(self,
-                 optimizer,
-                 trust_coefficient=0.001,
-                 ):
-        self.param_groups = optimizer.param_groups
-        self.optim = optimizer
-        self.trust_coefficient = trust_coefficient
-
-    def __getstate__(self):
-        return self.optim.__getstate__()
-
-    def __setstate__(self, state):
-        self.optim.__setstate__(state)
-
-    def __repr__(self):
-        return self.optim.__repr__()
-
-    def state_dict(self):
-        return self.optim.state_dict()
-
-    def load_state_dict(self, state_dict):
-        self.optim.load_state_dict(state_dict)
-
-    def zero_grad(self):
-        self.optim.zero_grad()
-
-    def add_param_group(self, param_group):
-        self.optim.add_param_group(param_group)
-
-    def step(self):
-        with torch.no_grad():
-            weight_decays = []
-            for group in self.optim.param_groups:
-                # absorb weight decay control from optimizer
-                weight_decay = group['weight_decay'] if 'weight_decay' in group else 0
-                weight_decays.append(weight_decay)
-                group['weight_decay'] = 0
-                for p in group['params']:
-                    if p.grad is None:
-                        continue
-
-                    if weight_decay != 0:
-                        p.grad.data += weight_decay * p.data
-
-                    param_norm = torch.norm(p.data)
-                    grad_norm = torch.norm(p.grad.data)
-                    adaptive_lr = 1.
-
-                    if param_norm != 0 and grad_norm != 0 and group['layer_adaptation']:
-                        adaptive_lr = self.trust_coefficient * param_norm / grad_norm
-
-                    p.grad.data *= adaptive_lr
-
-        self.optim.step()
-        # return weight decay control to optimizer
-        for i, group in enumerate(self.optim.param_groups):
-            group['weight_decay'] = weight_decays[i]