19_learner.ipynb

Kernel: Python 3

#hide
! [ -e /content ] && pip install -Uqq fastbook
import fastbook
fastbook.setup_book()

#hide
from fastbook import *

A fastai Learner from Scratch

Data

path = untar_data(URLs.IMAGENETTE_160)

t = get_image_files(path)
t[0]

from glob import glob
files = L(glob(f'{path}/**/*.JPEG', recursive=True)).map(Path)
files[0]

im = Image.open(files[0])
im

im_t = tensor(im)
im_t.shape

lbls = files.map(Self.parent.name()).unique(); lbls

v2i = lbls.val2idx(); v2i

Dataset

class Dataset:
    def __init__(self, fns): self.fns=fns
    def __len__(self): return len(self.fns)
    def __getitem__(self, i):
        im = Image.open(self.fns[i]).resize((64,64)).convert('RGB')
        y = v2i[self.fns[i].parent.name]
        return tensor(im).float()/255, tensor(y)

train_filt = L(o.parent.parent.name=='train' for o in files)
train,valid = files[train_filt],files[~train_filt]
len(train),len(valid)

train_ds,valid_ds = Dataset(train),Dataset(valid)
x,y = train_ds[0]
x.shape,y

show_image(x, title=lbls[y]);

def collate(idxs, ds): 
    xb,yb = zip(*[ds[i] for i in idxs])
    return torch.stack(xb),torch.stack(yb)

x,y = collate([1,2], train_ds)
x.shape,y

class DataLoader:
    def __init__(self, ds, bs=128, shuffle=False, n_workers=1):
        self.ds,self.bs,self.shuffle,self.n_workers = ds,bs,shuffle,n_workers

    def __len__(self): return (len(self.ds)-1)//self.bs+1

    def __iter__(self):
        idxs = L.range(self.ds)
        if self.shuffle: idxs = idxs.shuffle()
        chunks = [idxs[n:n+self.bs] for n in range(0, len(self.ds), self.bs)]
        with ProcessPoolExecutor(self.n_workers) as ex:
            yield from ex.map(collate, chunks, ds=self.ds)

n_workers = min(16, defaults.cpus)
train_dl = DataLoader(train_ds, bs=128, shuffle=True, n_workers=n_workers)
valid_dl = DataLoader(valid_ds, bs=256, shuffle=False, n_workers=n_workers)
xb,yb = first(train_dl)
xb.shape,yb.shape,len(train_dl)

stats = [xb.mean((0,1,2)),xb.std((0,1,2))]
stats

class Normalize:
    def __init__(self, stats): self.stats=stats
    def __call__(self, x):
        if x.device != self.stats[0].device:
            self.stats = to_device(self.stats, x.device)
        return (x-self.stats[0])/self.stats[1]

norm = Normalize(stats)
def tfm_x(x): return norm(x).permute((0,3,1,2))

t = tfm_x(x)
t.mean((0,2,3)),t.std((0,2,3))

Module and Parameter

class Parameter(Tensor):
    def __new__(self, x): return Tensor._make_subclass(Parameter, x, True)
    def __init__(self, *args, **kwargs): self.requires_grad_()

Parameter(tensor(3.))

class Module:
    def __init__(self):
        self.hook,self.params,self.children,self._training = None,[],[],False
        
    def register_parameters(self, *ps): self.params += ps
    def register_modules   (self, *ms): self.children += ms
        
    @property
    def training(self): return self._training
    @training.setter
    def training(self,v):
        self._training = v
        for m in self.children: m.training=v
            
    def parameters(self):
        return self.params + sum([m.parameters() for m in self.children], [])

    def __setattr__(self,k,v):
        super().__setattr__(k,v)
        if isinstance(v,Parameter): self.register_parameters(v)
        if isinstance(v,Module):    self.register_modules(v)
        
    def __call__(self, *args, **kwargs):
        res = self.forward(*args, **kwargs)
        if self.hook is not None: self.hook(res, args)
        return res
    
    def cuda(self):
        for p in self.parameters(): p.data = p.data.cuda()

class ConvLayer(Module):
    def __init__(self, ni, nf, stride=1, bias=True, act=True):
        super().__init__()
        self.w = Parameter(torch.zeros(nf,ni,3,3))
        self.b = Parameter(torch.zeros(nf)) if bias else None
        self.act,self.stride = act,stride
        init = nn.init.kaiming_normal_ if act else nn.init.xavier_normal_
        init(self.w)
    
    def forward(self, x):
        x = F.conv2d(x, self.w, self.b, stride=self.stride, padding=1)
        if self.act: x = F.relu(x)
        return x

l = ConvLayer(3, 4)
len(l.parameters())

xbt = tfm_x(xb)
r = l(xbt)
r.shape

class Linear(Module):
    def __init__(self, ni, nf):
        super().__init__()
        self.w = Parameter(torch.zeros(nf,ni))
        self.b = Parameter(torch.zeros(nf))
        nn.init.xavier_normal_(self.w)
    
    def forward(self, x): return x@self.w.t() + self.b

l = Linear(4,2)
r = l(torch.ones(3,4))
r.shape

class T(Module):
    def __init__(self):
        super().__init__()
        self.c,self.l = ConvLayer(3,4),Linear(4,2)

t = T()
len(t.parameters())

t.cuda()
t.l.w.device

Simple CNN

class Sequential(Module):
    def __init__(self, *layers):
        super().__init__()
        self.layers = layers
        self.register_modules(*layers)

    def forward(self, x):
        for l in self.layers: x = l(x)
        return x

class AdaptivePool(Module):
    def forward(self, x): return x.mean((2,3))

def simple_cnn():
    return Sequential(
        ConvLayer(3 ,16 ,stride=2), #32
        ConvLayer(16,32 ,stride=2), #16
        ConvLayer(32,64 ,stride=2), # 8
        ConvLayer(64,128,stride=2), # 4
        AdaptivePool(),
        Linear(128, 10)
    )

m = simple_cnn()
len(m.parameters())

def print_stats(outp, inp): print (outp.mean().item(),outp.std().item())
for i in range(4): m.layers[i].hook = print_stats

r = m(xbt)
r.shape

Loss

def nll(input, target): return -input[range(target.shape[0]), target].mean()

def log_softmax(x): return (x.exp()/(x.exp().sum(-1,keepdim=True))).log()

sm = log_softmax(r); sm[0][0]

loss = nll(sm, yb)
loss

def log_softmax(x): return x - x.exp().sum(-1,keepdim=True).log()
sm = log_softmax(r); sm[0][0]

x = torch.rand(5)
a = x.max()
x.exp().sum().log() == a + (x-a).exp().sum().log()

def logsumexp(x):
    m = x.max(-1)[0]
    return m + (x-m[:,None]).exp().sum(-1).log()

logsumexp(r)[0]

def log_softmax(x): return x - x.logsumexp(-1,keepdim=True)

sm = log_softmax(r); sm[0][0]

def cross_entropy(preds, yb): return nll(log_softmax(preds), yb).mean()

Learner

class SGD:
    def __init__(self, params, lr, wd=0.): store_attr()
    def step(self):
        for p in self.params:
            p.data -= (p.grad.data + p.data*self.wd) * self.lr
            p.grad.data.zero_()

class DataLoaders:
    def __init__(self, *dls): self.train,self.valid = dls

dls = DataLoaders(train_dl,valid_dl)

class Learner:
    def __init__(self, model, dls, loss_func, lr, cbs, opt_func=SGD):
        store_attr()
        for cb in cbs: cb.learner = self

    def one_batch(self):
        self('before_batch')
        xb,yb = self.batch
        self.preds = self.model(xb)
        self.loss = self.loss_func(self.preds, yb)
        if self.model.training:
            self.loss.backward()
            self.opt.step()
        self('after_batch')

    def one_epoch(self, train):
        self.model.training = train
        self('before_epoch')
        dl = self.dls.train if train else self.dls.valid
        for self.num,self.batch in enumerate(progress_bar(dl, leave=False)):
            self.one_batch()
        self('after_epoch')
    
    def fit(self, n_epochs):
        self('before_fit')
        self.opt = self.opt_func(self.model.parameters(), self.lr)
        self.n_epochs = n_epochs
        try:
            for self.epoch in range(n_epochs):
                self.one_epoch(True)
                self.one_epoch(False)
        except CancelFitException: pass
        self('after_fit')
        
    def __call__(self,name):
        for cb in self.cbs: getattr(cb,name,noop)()

Callbacks

class Callback(GetAttr): _default='learner'

class SetupLearnerCB(Callback):
    def before_batch(self):
        xb,yb = to_device(self.batch)
        self.learner.batch = tfm_x(xb),yb

    def before_fit(self): self.model.cuda()

class TrackResults(Callback):
    def before_epoch(self): self.accs,self.losses,self.ns = [],[],[]
        
    def after_epoch(self):
        n = sum(self.ns)
        print(self.epoch, self.model.training,
              sum(self.losses).item()/n, sum(self.accs).item()/n)
        
    def after_batch(self):
        xb,yb = self.batch
        acc = (self.preds.argmax(dim=1)==yb).float().sum()
        self.accs.append(acc)
        n = len(xb)
        self.losses.append(self.loss*n)
        self.ns.append(n)

cbs = [SetupLearnerCB(),TrackResults()]
learn = Learner(simple_cnn(), dls, cross_entropy, lr=0.1, cbs=cbs)
learn.fit(1)

Scheduling the Learning Rate

class LRFinder(Callback):
    def before_fit(self):
        self.losses,self.lrs = [],[]
        self.learner.lr = 1e-6
        
    def before_batch(self):
        if not self.model.training: return
        self.opt.lr *= 1.2

    def after_batch(self):
        if not self.model.training: return
        if self.opt.lr>10 or torch.isnan(self.loss): raise CancelFitException
        self.losses.append(self.loss.item())
        self.lrs.append(self.opt.lr)

lrfind = LRFinder()
learn = Learner(simple_cnn(), dls, cross_entropy, lr=0.1, cbs=cbs+[lrfind])
learn.fit(2)

plt.plot(lrfind.lrs[:-2],lrfind.losses[:-2])
plt.xscale('log')

class OneCycle(Callback):
    def __init__(self, base_lr): self.base_lr = base_lr
    def before_fit(self): self.lrs = []

    def before_batch(self):
        if not self.model.training: return
        n = len(self.dls.train)
        bn = self.epoch*n + self.num
        mn = self.n_epochs*n
        pct = bn/mn
        pct_start,div_start = 0.25,10
        if pct<pct_start:
            pct /= pct_start
            lr = (1-pct)*self.base_lr/div_start + pct*self.base_lr
        else:
            pct = (pct-pct_start)/(1-pct_start)
            lr = (1-pct)*self.base_lr
        self.opt.lr = lr
        self.lrs.append(lr)

onecyc = OneCycle(0.1)
learn = Learner(simple_cnn(), dls, cross_entropy, lr=0.1, cbs=cbs+[onecyc])

learn.fit(8)

plt.plot(onecyc.lrs);

Conclusion

Questionnaire

tip: Experiments: For the questions here that ask you to explain what some function or class is, you should also complete your own code experiments.

1. What is glob?

2. How do you open an image with the Python imaging library?

3. What does L.map do?

4. What does Self do?

5. What is L.val2idx?

6. What methods do you need to implement to create your own Dataset?

7. Why do we call convert when we open an image from Imagenette?

8. What does ~ do? How is it useful for splitting training and validation sets?

9. Does ~ work with the L or Tensor classes? What about NumPy arrays, Python lists, or pandas DataFrames?

10. What is ProcessPoolExecutor?

11. How does L.range(self.ds) work?

12. What is __iter__?

13. What is first?

14. What is permute? Why is it needed?

15. What is a recursive function? How does it help us define the parameters method?

16. Write a recursive function that returns the first 20 items of the Fibonacci sequence.

17. What is super?

18. Why do subclasses of Module need to override forward instead of defining __call__?

19. In ConvLayer, why does init depend on act?

20. Why does Sequential need to call register_modules?

21. Write a hook that prints the shape of every layer's activations.

22. What is "LogSumExp"?

23. Why is log_softmax useful?

24. What is GetAttr? How is it helpful for callbacks?

25. Reimplement one of the callbacks in this chapter without inheriting from Callback or GetAttr.

26. What does Learner.__call__ do?

27. What is getattr? (Note the case difference to GetAttr!)

28. Why is there a try block in fit?

29. Why do we check for model.training in one_batch?

30. What is store_attr?

31. What is the purpose of TrackResults.before_epoch?

32. What does model.cuda do? How does it work?

33. Why do we need to check model.training in LRFinder and OneCycle?

34. Use cosine annealing in OneCycle.

Further Research

1. Write resnet18 from scratch (refer to <<chapter_resnet>> as needed), and train it with the Learner in this chapter.

2. Implement a batchnorm layer from scratch and use it in your resnet18.

3. Write a Mixup callback for use in this chapter.

4. Add momentum to SGD.

5. Pick a few features that you're interested in from fastai (or any other library) and implement them in this chapter.

6. Pick a research paper that's not yet implemented in fastai or PyTorch and implement it in this chapter.

• Port it over to fastai.

• Submit a pull request to fastai, or create your own extension module and release it.

• Hint: you may find it helpful to use nbdev to create and deploy your package.