in classy_vision/optim/rmsprop_tf.py [0:0]
def step(self, closure=None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError("RMSprop does not support sparse gradients")
state = self.state[p]
# State initialization
if len(state) == 0:
state["step"] = 0
state["square_avg"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
if group["momentum"] > 0:
state["momentum_buffer"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
if group["centered"]:
state["grad_avg"] = torch.zeros_like(
p.data, memory_format=torch.preserve_format
)
square_avg = state["square_avg"]
alpha = group["alpha"]
state["step"] += 1
if group["weight_decay"] != 0:
grad = grad.add(group["weight_decay"], p.data)
square_avg.mul_(alpha).addcmul_(1 - alpha, grad, grad)
if group["centered"]:
grad_avg = state["grad_avg"]
grad_avg.mul_(alpha).add_(1 - alpha, grad)
avg = (
square_avg.addcmul(-1, grad_avg, grad_avg)
.add_(group["eps"])
.sqrt_()
)
else:
avg = square_avg.add_(group["eps"]).sqrt_()
if group["momentum"] > 0:
buf = state["momentum_buffer"]
buf.mul_(group["momentum"]).addcdiv_(grad, avg)
p.data.add_(-group["lr"], buf)
else:
p.data.addcdiv_(-group["lr"], grad, avg)
return loss