In [ ]:

#| default_exp learner

In [ ]:

#|export
import math,torch,matplotlib.pyplot as plt
import fastcore.all as fc
from collections.abc import Mapping
from operator import attrgetter
from functools import partial
from copy import copy

from torch import optim
import torch.nn.functional as F

from miniai.conv import *

from fastprogress import progress_bar,master_bar

In [ ]:

import matplotlib as mpl
import torchvision.transforms.functional as TF
from contextlib import contextmanager
from torch import nn,tensor
from datasets import load_dataset,load_dataset_builder
from miniai.datasets import *
from miniai.conv import *
import logging
from fastcore.test import test_close

In [ ]:

torch.set_printoptions(precision=2, linewidth=140, sci_mode=False)
torch.manual_seed(1)
mpl.rcParams['image.cmap'] = 'gray'

In [ ]:

logging.disable(logging.WARNING)

Learner¶

In [ ]:

x,y = 'image','label'
name = "fashion_mnist"
dsd = load_dataset(name)

  0%|          | 0/2 [00:00<?, ?it/s]

In [ ]:

@inplace
def transformi(b): b[x] = [torch.flatten(TF.to_tensor(o)) for o in b[x]]

In [ ]:

bs = 1024
tds = dsd.with_transform(transformi)

In [ ]:

dls = DataLoaders.from_dd(tds, bs, num_workers=4)
dt = dls.train
xb,yb = next(iter(dt))
xb.shape,yb[:10]

Out[ ]:

(torch.Size([1024, 784]), tensor([5, 4, 9, 4, 3, 0, 6, 5, 7, 6]))

In [ ]:

class Learner:
    def __init__(self, model, dls, loss_func, lr, opt_func=optim.SGD): fc.store_attr()

    def one_batch(self):
        self.xb,self.yb = to_device(self.batch)
        self.preds = self.model(self.xb)
        self.loss = self.loss_func(self.preds, self.yb)
        if self.model.training:
            self.loss.backward()
            self.opt.step()
            self.opt.zero_grad()
        with torch.no_grad(): self.calc_stats()

    def calc_stats(self):
        acc = (self.preds.argmax(dim=1)==self.yb).float().sum()
        self.accs.append(acc)
        n = len(self.xb)
        self.losses.append(self.loss*n)
        self.ns.append(n)

    def one_epoch(self, train):
        self.model.training = train
        dl = self.dls.train if train else self.dls.valid
        for self.num,self.batch in enumerate(dl): self.one_batch()
        n = sum(self.ns)
        print(self.epoch, self.model.training, sum(self.losses).item()/n, sum(self.accs).item()/n)
    
    def fit(self, n_epochs):
        self.accs,self.losses,self.ns = [],[],[]
        self.model.to(def_device)
        self.opt = self.opt_func(self.model.parameters(), self.lr)
        self.n_epochs = n_epochs
        for self.epoch in range(n_epochs):
            self.one_epoch(True)
            with torch.no_grad(): self.one_epoch(False)

In [ ]:

m,nh = 28*28,50
model = nn.Sequential(nn.Linear(m,nh), nn.ReLU(), nn.Linear(nh,10))

In [ ]:

learn = Learner(model, dls, F.cross_entropy, lr=0.2)
learn.fit(1)

0 True 1.1753044270833333 0.5986833333333333
0 False 1.120328794642857 0.6135285714285714

Basic Callbacks Learner¶

In [ ]:

#|export
class CancelFitException(Exception): pass
class CancelBatchException(Exception): pass
class CancelEpochException(Exception): pass

In [ ]:

#|export
class Callback(): order = 0

In [ ]:

#|export
def run_cbs(cbs, method_nm, learn=None):
    for cb in sorted(cbs, key=attrgetter('order')):
        method = getattr(cb, method_nm, None)
        if method is not None: method(learn)

In [ ]:

class CompletionCB(Callback):
    def before_fit(self, learn): self.count = 0
    def after_batch(self, learn): self.count += 1
    def after_fit(self, learn): print(f'Completed {self.count} batches')

In [ ]:

cbs = [CompletionCB()]
run_cbs(cbs, 'before_fit')
run_cbs(cbs, 'after_batch')
run_cbs(cbs, 'after_fit')

Completed 1 batches

In [ ]:

class Learner():
    def __init__(self, model, dls, loss_func, lr, cbs, opt_func=optim.SGD): fc.store_attr()

    def one_batch(self):
        self.preds = self.model(self.batch[0])
        self.loss = self.loss_func(self.preds, self.batch[1])
        if self.model.training:
            self.loss.backward()
            self.opt.step()
            self.opt.zero_grad()

    def one_epoch(self, train):
        self.model.train(train)
        self.dl = self.dls.train if train else self.dls.valid
        try:
            self.callback('before_epoch')
            for self.iter,self.batch in enumerate(self.dl):
                try:
                    self.callback('before_batch')
                    self.one_batch()
                    self.callback('after_batch')
                except CancelBatchException: pass
            self.callback('after_epoch')
        except CancelEpochException: pass
    
    def fit(self, n_epochs):
        self.n_epochs = n_epochs
        self.epochs = range(n_epochs)
        self.opt = self.opt_func(self.model.parameters(), self.lr)
        try:
            self.callback('before_fit')
            for self.epoch in self.epochs:
                self.one_epoch(True)
                self.one_epoch(False)
            self.callback('after_fit')
        except CancelFitException: pass

    def callback(self, method_nm): run_cbs(self.cbs, method_nm, self)

In [ ]:

m,nh = 28*28,50
def get_model(): return nn.Sequential(nn.Linear(m,nh), nn.ReLU(), nn.Linear(nh,10))

In [ ]:

model = get_model()
learn = Learner(model, dls, F.cross_entropy, lr=0.2, cbs=[CompletionCB()])
learn.fit(1)

Completed 64 batches

In [ ]:

#| export
class SingleBatchCB(Callback):
    order = 1
    def after_batch(self, learn): raise CancelFitException()

In [ ]:

learn = Learner(get_model(), dls, F.cross_entropy, lr=0.2, cbs=[SingleBatchCB(), CompletionCB()])
learn.fit(1)

Metrics¶

In [ ]:

class Metric:
    def __init__(self): self.reset()
    def reset(self): self.vals,self.ns = [],[]
    def add(self, inp, targ=None, n=1):
        self.last = self.calc(inp, targ)
        self.vals.append(self.last)
        self.ns.append(n)
    @property
    def value(self):
        ns = tensor(self.ns)
        return (tensor(self.vals)*ns).sum()/ns.sum()
    def calc(self, inps, targs): return inps

In [ ]:

class Accuracy(Metric):
    def calc(self, inps, targs): return (inps==targs).float().mean()

In [ ]:

acc = Accuracy()
acc.add(tensor([0, 1, 2, 0, 1, 2]), tensor([0, 1, 1, 2, 1, 0]))
acc.add(tensor([1, 1, 2, 0, 1]), tensor([0, 1, 1, 2, 1]))
acc.value

Out[ ]:

tensor(0.45)

In [ ]:

loss = Metric()
loss.add(0.6, n=32)
loss.add(0.9, n=2)
loss.value, round((0.6*32+0.9*2)/(32+2), 2)

Out[ ]:

(tensor(0.62), 0.62)

Some callbacks¶

pip install torcheval

In [ ]:

#|export
from torcheval.metrics import MulticlassAccuracy,Mean

In [ ]:

metric = MulticlassAccuracy()
metric.update(tensor([0, 2, 1, 3]), tensor([0, 1, 2, 3]))
metric.compute()

Out[ ]:

tensor(0.50)

In [ ]:

metric.reset()
metric.compute()

Out[ ]:

tensor(nan)

In [ ]:

#|export
def to_cpu(x):
    if isinstance(x, Mapping): return {k:to_cpu(v) for k,v in x.items()}
    if isinstance(x, list): return [to_cpu(o) for o in x]
    if isinstance(x, tuple): return tuple(to_cpu(list(x)))
    res = x.detach().cpu()
    return res.float() if res.dtype==torch.float16 else res

In [ ]:

#|export
class MetricsCB(Callback):
    def __init__(self, *ms, **metrics):
        for o in ms: metrics[type(o).__name__] = o
        self.metrics = metrics
        self.all_metrics = copy(metrics)
        self.all_metrics['loss'] = self.loss = Mean()

    def _log(self, d): print(d)
    def before_fit(self, learn): learn.metrics = self
    def before_epoch(self, learn): [o.reset() for o in self.all_metrics.values()]

    def after_epoch(self, learn):
        log = {k:f'{v.compute():.3f}' for k,v in self.all_metrics.items()}
        log['epoch'] = learn.epoch
        log['train'] = 'train' if learn.model.training else 'eval'
        self._log(log)

    def after_batch(self, learn):
        x,y,*_ = to_cpu(learn.batch)
        for m in self.metrics.values(): m.update(to_cpu(learn.preds), y)
        self.loss.update(to_cpu(learn.loss), weight=len(x))

In [ ]:

#|export
class DeviceCB(Callback):
    def __init__(self, device=def_device): fc.store_attr()
    def before_fit(self, learn):
        if hasattr(learn.model, 'to'): learn.model.to(self.device)
    def before_batch(self, learn): learn.batch = to_device(learn.batch, device=self.device)

In [ ]:

model = get_model()
metrics = MetricsCB(accuracy=MulticlassAccuracy())
learn = Learner(model, dls, F.cross_entropy, lr=0.2, cbs=[DeviceCB(), metrics])
learn.fit(1)

{'accuracy': '0.602', 'loss': '1.183', 'epoch': 0, 'train': 'train'}
{'accuracy': '0.700', 'loss': '0.847', 'epoch': 0, 'train': 'eval'}

Flexible learner¶

In [ ]:

class Learner():
    def __init__(self, model, dls=(0,), loss_func=F.mse_loss, lr=0.1, cbs=None, opt_func=optim.SGD):
        cbs = fc.L(cbs)
        fc.store_attr()

    @contextmanager
    def cb_ctx(self, nm):
        try:
            self.callback(f'before_{nm}')
            yield
            self.callback(f'after_{nm}')
        except globals()[f'Cancel{nm.title()}Exception']: pass
        finally: self.callback(f'cleanup_{nm}')
                
    def one_epoch(self, train):
        self.model.train(train)
        self.dl = self.dls.train if train else self.dls.valid
        with self.cb_ctx('epoch'):
            for self.iter,self.batch in enumerate(self.dl):
                with self.cb_ctx('batch'):
                    self.predict()
                    self.get_loss()
                    if self.training:
                        self.backward()
                        self.step()
                        self.zero_grad()
    
    def fit(self, n_epochs=1, train=True, valid=True, cbs=None, lr=None):
        cbs = fc.L(cbs)
        # `add_cb` and `rm_cb` were added in lesson 18
        for cb in cbs: self.cbs.append(cb)
        try:
            self.n_epochs = n_epochs
            self.epochs = range(n_epochs)
            self.opt = self.opt_func(self.model.parameters(), self.lr if lr is None else lr)
            with self.cb_ctx('fit'):
                for self.epoch in self.epochs:
                    if train: self.one_epoch(True)
                    if valid: torch.no_grad()(self.one_epoch)(False)
        finally:
            for cb in cbs: self.cbs.remove(cb)

    def __getattr__(self, name):
        if name in ('predict','get_loss','backward','step','zero_grad'): return partial(self.callback, name)
        raise AttributeError(name)

    def callback(self, method_nm): run_cbs(self.cbs, method_nm, self)
    
    @property
    def training(self): return self.model.training

In [ ]:

#|export
class TrainCB(Callback):
    def __init__(self, n_inp=1): self.n_inp = n_inp
    def predict(self, learn): learn.preds = learn.model(*learn.batch[:self.n_inp])
    def get_loss(self, learn): learn.loss = learn.loss_func(learn.preds, *learn.batch[self.n_inp:])
    def backward(self, learn): learn.loss.backward()
    def step(self, learn): learn.opt.step()
    def zero_grad(self, learn): learn.opt.zero_grad()

NB: I added self.n_inp after the lesson. This allows us to train models with more than one input or output.

In [ ]:

#|export
class ProgressCB(Callback):
    order = MetricsCB.order+1
    def __init__(self, plot=False): self.plot = plot
    def before_fit(self, learn):
        learn.epochs = self.mbar = master_bar(learn.epochs)
        self.first = True
        if hasattr(learn, 'metrics'): learn.metrics._log = self._log
        self.losses = []
        self.val_losses = []

    def _log(self, d):
        if self.first:
            self.mbar.write(list(d), table=True)
            self.first = False
        self.mbar.write(list(d.values()), table=True)

    def before_epoch(self, learn): learn.dl = progress_bar(learn.dl, leave=False, parent=self.mbar)
    def after_batch(self, learn):
        learn.dl.comment = f'{learn.loss:.3f}'
        if self.plot and hasattr(learn, 'metrics') and learn.training:
            self.losses.append(learn.loss.item())
            if self.val_losses: self.mbar.update_graph([[fc.L.range(self.losses), self.losses],[fc.L.range(learn.epoch).map(lambda x: (x+1)*len(learn.dls.train)), self.val_losses]])
    
    def after_epoch(self, learn): 
        if not learn.training:
            if self.plot and hasattr(learn, 'metrics'): 
                self.val_losses.append(learn.metrics.all_metrics['loss'].compute())
                self.mbar.update_graph([[fc.L.range(self.losses), self.losses],[fc.L.range(learn.epoch+1).map(lambda x: (x+1)*len(learn.dls.train)), self.val_losses]])

NB: Added validation loss plotting after the lesson.

In [ ]:

model = get_model()

In [ ]:

metrics = MetricsCB(accuracy=MulticlassAccuracy())
cbs = [TrainCB(), DeviceCB(), metrics, ProgressCB(plot=True)]
learn = Learner(model, dls, F.cross_entropy, lr=0.2, cbs=cbs)
learn.fit(2)

accuracy	loss	epoch	train
0.596	1.167	0	train
0.729	0.794	0	eval
0.744	0.710	1	train
0.764	0.654	1	eval

Updated versions since the lesson¶

After the lesson we noticed that contextlib.context_manager has a surprising "feature" which doesn't let us raise an exception before the yield. Therefore we've replaced the context manager with a decorator in this updated version of Learner. We have also added a few more callbacks in one_epoch().

In [ ]:

#|export
class with_cbs:
    def __init__(self, nm): self.nm = nm
    def __call__(self, f):
        def _f(o, *args, **kwargs):
            try:
                o.callback(f'before_{self.nm}')
                f(o, *args, **kwargs)
                o.callback(f'after_{self.nm}')
            except globals()[f'Cancel{self.nm.title()}Exception']: pass
            finally: o.callback(f'cleanup_{self.nm}')
        return _f

In [ ]:

#|export
class Learner():
    def __init__(self, model, dls=(0,), loss_func=F.mse_loss, lr=0.1, cbs=None, opt_func=optim.SGD):
        cbs = fc.L(cbs)
        fc.store_attr()

    @with_cbs('batch')
    def _one_batch(self):
        self.predict()
        self.callback('after_predict')
        self.get_loss()
        self.callback('after_loss')
        if self.training:
            self.backward()
            self.callback('after_backward')
            self.step()
            self.callback('after_step')
            self.zero_grad()

    @with_cbs('epoch')
    def _one_epoch(self):
        for self.iter,self.batch in enumerate(self.dl): self._one_batch()

    def one_epoch(self, training):
        self.model.train(training)
        self.dl = self.dls.train if training else self.dls.valid
        self._one_epoch()

    @with_cbs('fit')
    def _fit(self, train, valid):
        for self.epoch in self.epochs:
            if train: self.one_epoch(True)
            if valid: torch.no_grad()(self.one_epoch)(False)

    def fit(self, n_epochs=1, train=True, valid=True, cbs=None, lr=None):
        cbs = fc.L(cbs)
        # `add_cb` and `rm_cb` were added in lesson 18
        for cb in cbs: self.cbs.append(cb)
        try:
            self.n_epochs = n_epochs
            self.epochs = range(n_epochs)
            if lr is None: lr = self.lr
            if self.opt_func: self.opt = self.opt_func(self.model.parameters(), lr)
            self._fit(train, valid)
        finally:
            for cb in cbs: self.cbs.remove(cb)

    def __getattr__(self, name):
        if name in ('predict','get_loss','backward','step','zero_grad'): return partial(self.callback, name)
        raise AttributeError(name)

    def callback(self, method_nm): run_cbs(self.cbs, method_nm, self)
    
    @property
    def training(self): return self.model.training

In [ ]:

model = get_model()

metrics = MetricsCB(accuracy=MulticlassAccuracy())
cbs = [TrainCB(), DeviceCB(), metrics, ProgressCB(plot=True)]
learn = Learner(model, dls, F.cross_entropy, lr=0.2, cbs=cbs)
learn.fit(1)

accuracy	loss	epoch	train
0.606	1.176	0	train
0.702	0.796	0	eval

TrainLearner and MomentumLearner¶

In [ ]:

#|export
class TrainLearner(Learner):
    def predict(self): self.preds = self.model(self.batch[0])
    def get_loss(self): self.loss = self.loss_func(self.preds, self.batch[1])
    def backward(self): self.loss.backward()
    def step(self): self.opt.step()
    def zero_grad(self): self.opt.zero_grad()

In [ ]:

#|export
class MomentumLearner(TrainLearner):
    def __init__(self, model, dls, loss_func, lr=None, cbs=None, opt_func=optim.SGD, mom=0.85):
        self.mom = mom
        super().__init__(model, dls, loss_func, lr, cbs, opt_func)

    def zero_grad(self):
        with torch.no_grad():
            for p in self.model.parameters(): p.grad *= self.mom

In [ ]:

# NB: No TrainCB
metrics = MetricsCB(accuracy=MulticlassAccuracy())
cbs = [DeviceCB(), metrics, ProgressCB(plot=True)]
learn = MomentumLearner(get_model(), dls, F.cross_entropy, lr=0.1, cbs=cbs)
learn.fit(1)

accuracy	loss	epoch	train
0.674	0.976	0	train
0.789	0.588	0	eval

LRFinderCB¶

In [ ]:

class LRFinderCB(Callback):
    def __init__(self, lr_mult=1.3): fc.store_attr()
    
    def before_fit(self, learn):
        self.lrs,self.losses = [],[]
        self.min = math.inf

    def after_batch(self, learn):
        if not learn.training: raise CancelEpochException()
        self.lrs.append(learn.opt.param_groups[0]['lr'])
        loss = to_cpu(learn.loss)
        self.losses.append(loss)
        if loss < self.min: self.min = loss
        if loss > self.min*3: raise CancelFitException()
        for g in learn.opt.param_groups: g['lr'] *= self.lr_mult

In [ ]:

lrfind = LRFinderCB()
cbs = [DeviceCB(), lrfind]
learn = MomentumLearner(get_model(), dls, F.cross_entropy, lr=1e-4, cbs=cbs)
learn.fit(1)
plt.plot(lrfind.lrs, lrfind.losses)
plt.xscale('log')

In [ ]:

#|export
from torch.optim.lr_scheduler import ExponentialLR

ExponentialLR

In [ ]:

#|export
class LRFinderCB(Callback):
    def __init__(self, gamma=1.3, max_mult=3): fc.store_attr()
    
    def before_fit(self, learn):
        self.sched = ExponentialLR(learn.opt, self.gamma)
        self.lrs,self.losses = [],[]
        self.min = math.inf

    def after_batch(self, learn):
        if not learn.training: raise CancelEpochException()
        self.lrs.append(learn.opt.param_groups[0]['lr'])
        loss = to_cpu(learn.loss)
        self.losses.append(loss)
        if loss < self.min: self.min = loss
        if math.isnan(loss) or (loss > self.min*self.max_mult):
            raise CancelFitException()
        self.sched.step()

    def cleanup_fit(self, learn):
        plt.plot(self.lrs, self.losses)
        plt.xscale('log')

In [ ]:

cbs = [DeviceCB()]
learn = MomentumLearner(get_model(), dls, F.cross_entropy, lr=1e-5, cbs=cbs)
learn.fit(3, cbs=LRFinderCB())

In [ ]:

#|export
@fc.patch
def lr_find(self:Learner, gamma=1.3, max_mult=3, start_lr=1e-5, max_epochs=10):
    self.fit(max_epochs, lr=start_lr, cbs=LRFinderCB(gamma=gamma, max_mult=max_mult))

lr_find was added in lesson 18. It's just a shorter way of using LRFinderCB.

In [ ]:

MomentumLearner(get_model(), dls, F.cross_entropy, cbs=cbs).lr_find()

Export -¶

In [ ]:

import nbdev; nbdev.nbdev_export()

/home/tmabraham/anaconda3/envs/course22p2/lib/python3.10/site-packages/nbdev/export.py:54: UserWarning: Notebook '/home/tmabraham/course22p2/nbs/horse2zebra-latents_AB.ipynb' uses `#|export` without `#|default_exp` cell.
Note nbdev2 no longer supports nbdev1 syntax. Run `nbdev_migrate` to upgrade.
See https://nbdev.fast.ai/getting_started.html for more information.
  warn(f"Notebook '{nbname}' uses `#|export` without `#|default_exp` cell.\n"
/home/tmabraham/anaconda3/envs/course22p2/lib/python3.10/site-packages/nbdev/export.py:54: UserWarning: Notebook '/home/tmabraham/course22p2/nbs/FIBItoHE-latents_AB.ipynb' uses `#|export` without `#|default_exp` cell.
Note nbdev2 no longer supports nbdev1 syntax. Run `nbdev_migrate` to upgrade.
See https://nbdev.fast.ai/getting_started.html for more information.
  warn(f"Notebook '{nbname}' uses `#|export` without `#|default_exp` cell.\n"

In [ ]: