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# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/14_callback.schedule.ipynb (unless otherwise specified).
__all__ = ['annealer', 'SchedLin', 'SchedCos', 'SchedNo', 'SchedExp', 'SchedPoly', 'combine_scheds', 'combined_cos',
'ParamScheduler', 'LRFinder']
# Cell
from ..basics import *
# Cell
def annealer(f):
"Decorator to make `f` return itself partially applied."
@functools.wraps(f)
def _inner(start, end): return partial(f, start, end)
return _inner
# Cell
@annealer
def SchedLin(start, end, pos): return start + pos*(end-start)
@annealer
def SchedCos(start, end, pos): return start + (1 + math.cos(math.pi*(1-pos))) * (end-start) / 2
@annealer
def SchedNo (start, end, pos): return start
@annealer
def SchedExp(start, end, pos): return start * (end/start) ** pos
SchedLin.__doc__ = "Linear schedule function from `start` to `end`"
SchedCos.__doc__ = "Cosine schedule function from `start` to `end`"
SchedNo .__doc__ = "Constant schedule function with `start` value"
SchedExp.__doc__ = "Exponential schedule function from `start` to `end`"
# Cell
def SchedPoly(start, end, power):
"Polynomial schedule (of `power`) function from `start` to `end`"
def _inner(pos): return start + (end - start) * pos ** power
return _inner
# Cell
def combine_scheds(pcts, scheds):
"Combine `scheds` according to `pcts` in one function"
assert sum(pcts) == 1.
pcts = tensor([0] + L(pcts))
assert torch.all(pcts >= 0)
pcts = torch.cumsum(pcts, 0)
def _inner(pos):
if pos == 1.: return scheds[-1](1.)
idx = (pos >= pcts).nonzero().max()
actual_pos = (pos-pcts[idx]) / (pcts[idx+1]-pcts[idx])
return scheds[idx](actual_pos.item())
return _inner
# Cell
def combined_cos(pct, start, middle, end):
"Return a scheduler with cosine annealing from `start`→`middle` & `middle`→`end`"
return combine_scheds([pct,1-pct], [SchedCos(start, middle), SchedCos(middle, end)])
# Cell
@docs
class ParamScheduler(Callback):
"Schedule hyper-parameters according to `scheds`"
run_after,run_valid = TrainEvalCallback,False
def __init__(self, scheds): self.scheds = scheds
def begin_fit(self): self.hps = {p:[] for p in self.scheds.keys()}
def begin_batch(self): self._update_val(self.pct_train)
def _update_val(self, pct):
for n,f in self.scheds.items(): self.opt.set_hyper(n, f(pct))
def after_batch(self):
for p in self.scheds.keys(): self.hps[p].append(self.opt.hypers[-1][p])
def after_fit(self):
if hasattr(self.learn, 'recorder'): self.recorder.hps = self.hps
_docs = {"begin_fit": "Initialize container for hyper-parameters",
"begin_batch": "Set the proper hyper-parameters in the optimizer",
"after_batch": "Record hyper-parameters of this batch",
"after_fit": "Save the hyper-parameters in the recorder if there is one"}
# Cell
@patch
def fit_one_cycle(self:Learner, n_epoch, lr_max=None, div=25., div_final=1e5, pct_start=0.25, wd=defaults.wd,
moms=None, cbs=None, reset_opt=False):
"Fit `self.model` for `n_epoch` using the 1cycle policy."
if self.opt is None: self.create_opt()
self.opt.set_hyper('lr', self.lr if lr_max is None else lr_max)
lr_max = np.array([h['lr'] for h in self.opt.hypers])
scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
# Cell
@patch
def plot_sched(self:Recorder, keys=None, figsize=None):
keys = self.hps.keys() if keys is None else L(keys)
rows,cols = (len(keys)+1)//2, min(2, len(keys))
figsize = figsize or (6*cols,4*rows)
_, axs = plt.subplots(rows, cols, figsize=figsize)
axs = axs.flatten() if len(keys) > 1 else L(axs)
for p,ax in zip(keys, axs):
ax.plot(self.hps[p])
ax.set_ylabel(p)
# Cell
@patch
def fit_flat_cos(self:Learner, n_epoch, lr=None, div_final=1e5, pct_start=0.75, wd=defaults.wd,
cbs=None, reset_opt=False):
"Fit `self.model` for `n_epoch` at flat `lr` before a cosine annealing."
if self.opt is None: self.create_opt()
self.opt.set_hyper('lr', self.lr if lr is None else lr)
lr = np.array([h['lr'] for h in self.opt.hypers])
scheds = {'lr': combined_cos(pct_start, lr, lr, lr/div_final)}
self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
# Cell
@patch
def fit_sgdr(self:Learner, n_cycles, cycle_len, lr_max=None, cycle_mult=2, cbs=None, reset_opt=False, wd=defaults.wd):
"Fit `self.model` for `n_cycles` of `cycle_len` using SGDR."
if self.opt is None: self.create_opt()
self.opt.set_hyper('lr', self.lr if lr_max is None else lr_max)
lr_max = np.array([h['lr'] for h in self.opt.hypers])
n_epoch = cycle_len * (cycle_mult**n_cycles-1)//(cycle_mult-1)
pcts = [cycle_len * cycle_mult**i / n_epoch for i in range(n_cycles)]
scheds = [SchedCos(lr_max, 0) for _ in range(n_cycles)]
scheds = {'lr': combine_scheds(pcts, scheds)}
self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
# Cell
@delegates(Learner.fit_one_cycle)
@patch
def fine_tune(self:Learner, epochs, base_lr=1e-3, freeze_epochs=1, lr_mult=100,
pct_start=0.3, div=5.0, **kwargs):
"Fine tune with `freeze` for `freeze_epochs` then with `unfreeze` from `epochs` using discriminative LR"
self.freeze()
self.fit_one_cycle(freeze_epochs, slice(base_lr*2), pct_start=0.99, **kwargs)
self.unfreeze()
self.fit_one_cycle(epochs, slice(base_lr/lr_mult, base_lr), pct_start=pct_start, div=div, **kwargs)
# Cell
@docs
class LRFinder(ParamScheduler):
"Training with exponentially growing learning rate"
run_after=Recorder
def __init__(self, start_lr=1e-7, end_lr=10, num_it=100, stop_div=True):
if is_listy(start_lr):
self.scheds = {'lr': [SchedExp(s, e) for (s,e) in zip(start_lr,end_lr)]}
else: self.scheds = {'lr': SchedExp(start_lr, end_lr)}
self.num_it,self.stop_div = num_it,stop_div
def begin_fit(self):
super().begin_fit()
self.learn.save('_tmp')
self.best_loss = float('inf')
def begin_batch(self):
self._update_val(self.train_iter/self.num_it)
def after_batch(self):
super().after_batch()
if self.smooth_loss < self.best_loss: self.best_loss = self.smooth_loss
if self.smooth_loss > 4*self.best_loss and self.stop_div: raise CancelFitException()
if self.train_iter >= self.num_it: raise CancelFitException()
def begin_validate(self): raise CancelValidException()
def after_fit(self):
tmp_f = self.path/self.model_dir/'_tmp.pth'
if tmp_f.exists():
self.learn.load('_tmp')
os.remove(tmp_f)
_docs = {"begin_fit": "Initialize container for hyper-parameters and save the model",
"begin_batch": "Set the proper hyper-parameters in the optimizer",
"after_batch": "Record hyper-parameters of this batch and potentially stop training",
"after_fit": "Save the hyper-parameters in the recorder if there is one and load the original model",
"begin_validate": "Skip the validation part of training"}
# Cell
@patch
def plot_lr_find(self:Recorder, skip_end=5):
"Plot the result of an LR Finder test (won't work if you didn't do `learn.lr_find()` before)"
lrs = self.lrs if skip_end==0 else self.lrs [:-skip_end]
losses = self.losses if skip_end==0 else self.losses[:-skip_end]
fig, ax = plt.subplots(1,1)
ax.plot(lrs, losses)
ax.set_ylabel("Loss")
ax.set_xlabel("Learning Rate")
ax.set_xscale('log')
# Cell
@patch
def lr_find(self:Learner, start_lr=1e-7, end_lr=10, num_it=100, stop_div=True, show_plot=True, suggestions=True):
"Launch a mock training to find a good learning rate, return lr_min, lr_steep if `suggestions` is True"
n_epoch = num_it//len(self.dls.train) + 1
cb=LRFinder(start_lr=start_lr, end_lr=end_lr, num_it=num_it, stop_div=stop_div)
with self.no_logging(): self.fit(n_epoch, cbs=cb)
if show_plot: self.recorder.plot_lr_find()
if suggestions:
lrs,losses = tensor(self.recorder.lrs[5:-5]),tensor(self.recorder.losses[5:-5])
lr_min = lrs[losses.argmin()].item()
grads = (losses[1:]-losses[:-1]) / (lrs[1:].log()-lrs[:-1].log())
lr_steep = lrs[grads.argmin()].item()
return lr_min/10.,lr_steep
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