Add supervised baseline on CIFAR10

1 files changed, 70 insertions, 0 deletions

diff --git a/supervised/lars_optimizer.py b/supervised/lars_optimizer.py
new file mode 100644
index 0000000..1904e8d
--- /dev/null
+++ b/supervised/lars_optimizer.py
@@ -0,0 +1,70 @@
+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]