mirrors
/
fast-reid
mirror of https://github.com/JDAI-CV/fast-reid.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

feat($solver): change scheduler call methods 

using name of lr scheduler in config to call
pull/44/head
liaoxingyu 2020-04-27 15:12:01 +08:00
parent 9e3f2c1e7a
commit 325d9abb76
3 changed files with 203 additions and 38 deletions

37
fastreid/solver/build.py
View File

@ -34,21 +34,22 @@ def build_optimizer(cfg, model):
def build_lr_scheduler(cfg, optimizer):
if cfg.SOLVER.SCHED == "warmup":
return lr_scheduler.WarmupMultiStepLR(
optimizer,
cfg.SOLVER.STEPS,
cfg.SOLVER.GAMMA,
warmup_factor=cfg.SOLVER.WARMUP_FACTOR,
warmup_iters=cfg.SOLVER.WARMUP_ITERS,
warmup_method=cfg.SOLVER.WARMUP_METHOD
)
elif cfg.SOLVER.SCHED == "delay":
return lr_scheduler.DelayedCosineAnnealingLR(
optimizer,
cfg.SOLVER.DELAY_ITERS,
cfg.SOLVER.COS_ANNEAL_ITERS,
warmup_factor=cfg.SOLVER.WARMUP_FACTOR,
warmup_iters=cfg.SOLVER.WARMUP_ITERS,
warmup_method=cfg.SOLVER.WARMUP_METHOD
)
scheduler_args = {
"optimizer": optimizer,
# warmup options
"warmup_factor": cfg.SOLVER.WARMUP_FACTOR,
"warmup_iters": cfg.SOLVER.WARMUP_ITERS,
"warmup_method": cfg.SOLVER.WARMUP_METHOD,
# multi-step lr scheduler options
"milestones": cfg.SOLVER.STEPS,
"gamma": cfg.SOLVER.GAMMA,
# cosine annealing lr scheduler options
"max_iters": cfg.SOLVER.MAX_ITER,
"delay_iters": cfg.SOLVER.DELAY_ITERS,
"eta_min_lr": cfg.SOLVER.ETA_MIN_LR,
}
return getattr(lr_scheduler, cfg.SOLVER.SCHED)(**scheduler_args)

23
fastreid/solver/lr_scheduler.py
View File

@ -10,7 +10,7 @@ from typing import List
import torch
from torch.optim.lr_scheduler import _LRScheduler, CosineAnnealingLR
__all__ = ["WarmupMultiStepLR", "DelayerScheduler"]
__all__ = ["WarmupMultiStepLR", "DelayedScheduler"]
class WarmupMultiStepLR(_LRScheduler):
@ -23,6 +23,7 @@ class WarmupMultiStepLR(_LRScheduler):
warmup_iters: int = 1000,
warmup_method: str = "linear",
last_epoch: int = -1,
**kwargs,
):
if not list(milestones) == sorted(milestones):
raise ValueError(
@ -76,16 +77,16 @@ def _get_warmup_factor_at_iter(
raise ValueError("Unknown warmup method: {}".format(method))
class DelayerScheduler(_LRScheduler):
class DelayedScheduler(_LRScheduler):
""" Starts with a flat lr schedule until it reaches N epochs the applies a scheduler
Args:
optimizer (Optimizer): Wrapped optimizer.
delay_epochs: number of epochs to keep the initial lr until starting aplying the scheduler
delay_iters: number of epochs to keep the initial lr until starting applying the scheduler
after_scheduler: after target_epoch, use this scheduler(eg. ReduceLROnPlateau)
"""
def __init__(self, optimizer, delay_epochs, after_scheduler, warmup_factor, warmup_iters, warmup_method):
self.delay_epochs = delay_epochs
def __init__(self, optimizer, delay_iters, after_scheduler, warmup_factor, warmup_iters, warmup_method):
self.delay_epochs = delay_iters
self.after_scheduler = after_scheduler
self.finished = False
self.warmup_factor = warmup_factor
@ -94,7 +95,6 @@ class DelayerScheduler(_LRScheduler):
super().__init__(optimizer)
def get_lr(self):
if self.last_epoch >= self.delay_epochs:
if not self.finished:
self.after_scheduler.base_lrs = self.base_lrs
@ -113,10 +113,11 @@ class DelayerScheduler(_LRScheduler):
else:
self.after_scheduler.step(epoch - self.delay_epochs)
else:
return super(DelayerScheduler, self).step(epoch)
return super(DelayedScheduler, self).step(epoch)
def DelayedCosineAnnealingLR(optimizer, delay_epochs, cosine_annealing_epochs, warmup_factor,
warmup_iters, warmup_method):
base_scheduler = CosineAnnealingLR(optimizer, cosine_annealing_epochs, eta_min=0)
return DelayerScheduler(optimizer, delay_epochs, base_scheduler, warmup_factor, warmup_iters, warmup_method)
def DelayedCosineAnnealingLR(optimizer, delay_iters, max_iters, eta_min_lr, warmup_factor,
warmup_iters, warmup_method, **kwargs, ):
cosine_annealing_iters = max_iters - delay_iters
base_scheduler = CosineAnnealingLR(optimizer, cosine_annealing_iters, eta_min_lr)
return DelayedScheduler(optimizer, delay_iters, base_scheduler, warmup_factor, warmup_iters, warmup_method)

181
fastreid/solver/optim/ranger.py
View File

@ -1,14 +1,177 @@
####
# CODE TAKEN FROM https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# Blog post: https://medium.com/@lessw/new-deep-learning-optimizer-ranger-synergistic-combination-of-radam-lookahead-for-the-best-of-2dc83f79a48d
####
# Ranger deep learning optimizer - RAdam + Lookahead + Gradient Centralization, combined into one optimizer.
# https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
# and/or
# https://github.com/lessw2020/Best-Deep-Learning-Optimizers
# Ranger has now been used to capture 12 records on the FastAI leaderboard.
# This version = 20.4.11
# Credits:
# Gradient Centralization --> https://arxiv.org/abs/2004.01461v2 (a new optimization technique for DNNs), github: https://github.com/Yonghongwei/Gradient-Centralization
# RAdam --> https://github.com/LiyuanLucasLiu/RAdam
# Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.
# Lookahead paper --> MZhang,G Hinton https://arxiv.org/abs/1907.08610
# summary of changes:
# 4/11/20 - add gradient centralization option. Set new testing benchmark for accuracy with it, toggle with use_gc flag at init.
# full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights),
# supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.
# changes 8/31/19 - fix references to *self*.N_sma_threshold;
# changed eps to 1e-5 as better default than 1e-8.
import math
import torch
from .lookahead import Lookahead
from .radam import RAdam
from torch.optim.optimizer import Optimizer
def Ranger(params, alpha=0.5, k=6, betas=(.95, 0.999), *args, **kwargs):
radam = RAdam(params, betas=betas, *args, **kwargs)
return Lookahead(radam, alpha, k)
class Ranger(Optimizer):
def __init__(self, params, lr=1e-3, # lr
alpha=0.5, k=6, N_sma_threshhold=5, # Ranger options
betas=(.95, 0.999), eps=1e-5, weight_decay=0, # Adam options
use_gc=True, gc_conv_only=False
# Gradient centralization on or off, applied to conv layers only or conv + fc layers
):
# parameter checks
if not 0.0 <= alpha <= 1.0:
raise ValueError(f'Invalid slow update rate: {alpha}')
if not 1 <= k:
raise ValueError(f'Invalid lookahead steps: {k}')
if not lr > 0:
raise ValueError(f'Invalid Learning Rate: {lr}')
if not eps > 0:
raise ValueError(f'Invalid eps: {eps}')
# parameter comments:
# beta1 (momentum) of .95 seems to work better than .90...
# N_sma_threshold of 5 seems better in testing than 4.
# In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.
# prep defaults and init torch.optim base
defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold,
eps=eps, weight_decay=weight_decay)
super().__init__(params, defaults)
# adjustable threshold
self.N_sma_threshhold = N_sma_threshhold
# look ahead params
self.alpha = alpha
self.k = k
# radam buffer for state
self.radam_buffer = [[None, None, None] for ind in range(10)]
# gc on or off
self.use_gc = use_gc
# level of gradient centralization
self.gc_gradient_threshold = 3 if gc_conv_only else 1
print(f"Ranger optimizer loaded. \nGradient Centralization usage = {self.use_gc}")
if (self.use_gc and self.gc_gradient_threshold == 1):
print(f"GC applied to both conv and fc layers")
elif (self.use_gc and self.gc_gradient_threshold == 3):
print(f"GC applied to conv layers only")
def __setstate__(self, state):
print("set state called")
super(Ranger, self).__setstate__(state)
def step(self, closure=None):
loss = None
# note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.
# Uncomment if you need to use the actual closure...
# if closure is not None:
# loss = closure()
# Evaluate averages and grad, update param tensors
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data.float()
if grad.is_sparse:
raise RuntimeError('Ranger optimizer does not support sparse gradients')
p_data_fp32 = p.data.float()
state = self.state[p] # get state dict for this param
if len(state) == 0: # if first time to run...init dictionary with our desired entries
# if self.first_run_check==0:
# self.first_run_check=1
# print("Initializing slow buffer...should not see this at load from saved model!")
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p_data_fp32)
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
# look ahead weight storage now in state dict
state['slow_buffer'] = torch.empty_like(p.data)
state['slow_buffer'].copy_(p.data)
else:
state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
# begin computations
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
beta1, beta2 = group['betas']
# GC operation for Conv layers and FC layers
if grad.dim() > self.gc_gradient_threshold:
grad.add_(-grad.mean(dim=tuple(range(1, grad.dim())), keepdim=True))
state['step'] += 1
# compute variance mov avg
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
# compute mean moving avg
exp_avg.mul_(beta1).add_(1 - beta1, grad)
buffered = self.radam_buffer[int(state['step'] % 10)]
if state['step'] == buffered[0]:
N_sma, step_size = buffered[1], buffered[2]
else:
buffered[0] = state['step']
beta2_t = beta2 ** state['step']
N_sma_max = 2 / (1 - beta2) - 1
N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
buffered[1] = N_sma
if N_sma > self.N_sma_threshhold:
step_size = math.sqrt(
(1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
N_sma_max - 2)) / (1 - beta1 ** state['step'])
else:
step_size = 1.0 / (1 - beta1 ** state['step'])
buffered[2] = step_size
if group['weight_decay'] != 0:
p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
# apply lr
if N_sma > self.N_sma_threshhold:
denom = exp_avg_sq.sqrt().add_(group['eps'])
p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
else:
p_data_fp32.add_(-step_size * group['lr'], exp_avg)
p.data.copy_(p_data_fp32)
# integrated look ahead...
# we do it at the param level instead of group level
if state['step'] % group['k'] == 0:
slow_p = state['slow_buffer'] # get access to slow param tensor
slow_p.add_(self.alpha, p.data - slow_p) # (fast weights - slow weights) * alpha
p.data.copy_(slow_p) # copy interpolated weights to RAdam param tensor
return loss