#hide
#skip
! [ -e /content ] && pip install -Uqq fastai # upgrade fastai on colab
#export
from fastai.basics import *
from fastai.vision.core import *
from fastai.vision.data import *
from fastai.vision.augment import *
from fastai.vision import models
#default_exp vision.learner
#hide
from nbdev.showdoc import *
All the functions necessary to build
Learner
suitable for transfer learning in computer vision
The most important functions of this module are cnn_learner
and unet_learner
. They will help you define a Learner
using a pretrained model. See the vision tutorial for examples of use.
# export
def _is_pool_type(l): return re.search(r'Pool[123]d$', l.__class__.__name__)
#hide
m = nn.Sequential(nn.AdaptiveAvgPool2d(5), nn.Linear(2,3), nn.Conv2d(2,3,1), nn.MaxPool3d(5))
test_eq([bool(_is_pool_type(m_)) for m_ in m.children()], [True,False,False,True])
By default, the fastai library cuts a pretrained model at the pooling layer. This function helps detecting it.
# export
def has_pool_type(m):
"Return `True` if `m` is a pooling layer or has one in its children"
if _is_pool_type(m): return True
for l in m.children():
if has_pool_type(l): return True
return False
m = nn.Sequential(nn.AdaptiveAvgPool2d(5), nn.Linear(2,3), nn.Conv2d(2,3,1), nn.MaxPool3d(5))
assert has_pool_type(m)
test_eq([has_pool_type(m_) for m_ in m.children()], [True,False,False,True])
# export
def _get_first_layer(m):
"Access first layer of a model"
c,p,n = m,None,None # child, parent, name
for n in next(m.named_parameters())[0].split('.')[:-1]:
p,c=c,getattr(c,n)
return c,p,n
#export
def _load_pretrained_weights(new_layer, previous_layer):
"Load pretrained weights based on number of input channels"
n_in = getattr(new_layer, 'in_channels')
if n_in==1:
# we take the sum
new_layer.weight.data = previous_layer.weight.data.sum(dim=1, keepdim=True)
elif n_in==2:
# we take first 2 channels + 50%
new_layer.weight.data = previous_layer.weight.data[:,:2] * 1.5
else:
# keep 3 channels weights and set others to null
new_layer.weight.data[:,:3] = previous_layer.weight.data
new_layer.weight.data[:,3:].zero_()
#export
def _update_first_layer(model, n_in, pretrained):
"Change first layer based on number of input channels"
if n_in == 3: return
first_layer, parent, name = _get_first_layer(model)
assert isinstance(first_layer, nn.Conv2d), f'Change of input channels only supported with Conv2d, found {first_layer.__class__.__name__}'
assert getattr(first_layer, 'in_channels') == 3, f'Unexpected number of input channels, found {getattr(first_layer, "in_channels")} while expecting 3'
params = {attr:getattr(first_layer, attr) for attr in 'out_channels kernel_size stride padding dilation groups padding_mode'.split()}
params['bias'] = getattr(first_layer, 'bias') is not None
params['in_channels'] = n_in
new_layer = nn.Conv2d(**params)
if pretrained:
_load_pretrained_weights(new_layer, first_layer)
setattr(parent, name, new_layer)
#export
def create_body(arch, n_in=3, pretrained=True, cut=None):
"Cut off the body of a typically pretrained `arch` as determined by `cut`"
model = arch(pretrained=pretrained)
_update_first_layer(model, n_in, pretrained)
#cut = ifnone(cut, cnn_config(arch)['cut'])
if cut is None:
ll = list(enumerate(model.children()))
cut = next(i for i,o in reversed(ll) if has_pool_type(o))
if isinstance(cut, int): return nn.Sequential(*list(model.children())[:cut])
elif callable(cut): return cut(model)
else: raise NamedError("cut must be either integer or a function")
cut
can either be an integer, in which case we cut the model at the corresponding layer, or a function, in which case, this function returns cut(model)
. It defaults to the first layer that contains some pooling otherwise.
tst = lambda pretrained : nn.Sequential(nn.Conv2d(3,5,3), nn.BatchNorm2d(5), nn.AvgPool2d(1), nn.Linear(3,4))
m = create_body(tst)
test_eq(len(m), 2)
m = create_body(tst, cut=3)
test_eq(len(m), 3)
m = create_body(tst, cut=noop)
test_eq(len(m), 4)
for n in range(1,5):
m = create_body(tst, n_in=n)
test_eq(_get_first_layer(m)[0].in_channels, n)
#export
def create_head(nf, n_out, lin_ftrs=None, ps=0.5, concat_pool=True, first_bn=True, bn_final=False,
lin_first=False, y_range=None):
"Model head that takes `nf` features, runs through `lin_ftrs`, and out `n_out` classes."
if concat_pool: nf *= 2
lin_ftrs = [nf, 512, n_out] if lin_ftrs is None else [nf] + lin_ftrs + [n_out]
bns = [first_bn] + [True]*len(lin_ftrs[1:])
ps = L(ps)
if len(ps) == 1: ps = [ps[0]/2] * (len(lin_ftrs)-2) + ps
actns = [nn.ReLU(inplace=True)] * (len(lin_ftrs)-2) + [None]
pool = AdaptiveConcatPool2d() if concat_pool else nn.AdaptiveAvgPool2d(1)
layers = [pool, Flatten()]
if lin_first: layers.append(nn.Dropout(ps.pop(0)))
for ni,no,bn,p,actn in zip(lin_ftrs[:-1], lin_ftrs[1:], bns, ps, actns):
layers += LinBnDrop(ni, no, bn=bn, p=p, act=actn, lin_first=lin_first)
if lin_first: layers.append(nn.Linear(lin_ftrs[-2], n_out))
if bn_final: layers.append(nn.BatchNorm1d(lin_ftrs[-1], momentum=0.01))
if y_range is not None: layers.append(SigmoidRange(*y_range))
return nn.Sequential(*layers)
The head begins with fastai's AdaptiveConcatPool2d
if concat_pool=True
otherwise, it uses traditional average pooling. Then it uses a Flatten
layer before going on blocks of BatchNorm
, Dropout
and Linear
layers (if lin_first=True
, those are Linear
, BatchNorm
, Dropout
).
Those blocks start at nf
, then every element of lin_ftrs
(defaults to [512]
) and end at n_out
. ps
is a list of probabilities used for the dropouts (if you only pass 1, it will use half the value then that value as many times as necessary).
If first_bn=True
, a BatchNorm
added just after the pooling operations. If bn_final=True
, a final BatchNorm
layer is added. If y_range
is passed, the function adds a SigmoidRange
to that range.
tst = create_head(5, 10)
tst
Sequential( (0): AdaptiveConcatPool2d( (ap): AdaptiveAvgPool2d(output_size=1) (mp): AdaptiveMaxPool2d(output_size=1) ) (1): Flatten(full=False) (2): BatchNorm1d(10, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (3): Dropout(p=0.25, inplace=False) (4): Linear(in_features=10, out_features=512, bias=False) (5): ReLU(inplace=True) (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (7): Dropout(p=0.5, inplace=False) (8): Linear(in_features=512, out_features=10, bias=False) )
#hide
mods = list(tst.children())
test_eq(len(mods), 9)
assert isinstance(mods[2], nn.BatchNorm1d)
assert isinstance(mods[-1], nn.Linear)
tst = create_head(5, 10, lin_first=True)
mods = list(tst.children())
test_eq(len(mods), 8)
assert isinstance(mods[2], nn.Dropout)
tst = create_head(5, 10, first_bn=False)
mods = list(tst.children())
test_eq(len(mods), 8)
assert isinstance(mods[2], nn.Dropout)
tst = create_head(5, 10, concat_pool=True)
modes = list(tst.children())
test_eq(modes[4].in_features, 10)
tst = create_head(5, 10, concat_pool=False)
modes = list(tst.children())
test_eq(modes[4].in_features, 5)
#export
from fastai.callback.hook import num_features_model
#TODO: refactor, i.e. something like this?
# class ModelSplitter():
# def __init__(self, idx): self.idx = idx
# def split(self, m): return L(m[:self.idx], m[self.idx:]).map(params)
# def __call__(self,): return {'cut':self.idx, 'split':self.split}
#export
def default_split(m):
"Default split of a model between body and head"
return L(m[0], m[1:]).map(params)
To do transfer learning, you need to pass a splitter
to Learner
. This should be a function taking the model and returning a collection of parameter groups, e.g. a list of list of parameters.
#export
def _xresnet_split(m): return L(m[0][:3], m[0][3:], m[1:]).map(params)
def _resnet_split(m): return L(m[0][:6], m[0][6:], m[1:]).map(params)
def _squeezenet_split(m:nn.Module): return L(m[0][0][:5], m[0][0][5:], m[1:]).map(params)
def _densenet_split(m:nn.Module): return L(m[0][0][:7],m[0][0][7:], m[1:]).map(params)
def _vgg_split(m:nn.Module): return L(m[0][0][:22], m[0][0][22:], m[1:]).map(params)
def _alexnet_split(m:nn.Module): return L(m[0][0][:6], m[0][0][6:], m[1:]).map(params)
_default_meta = {'cut':None, 'split':default_split}
_xresnet_meta = {'cut':-4, 'split':_xresnet_split, 'stats':imagenet_stats}
_resnet_meta = {'cut':-2, 'split':_resnet_split, 'stats':imagenet_stats}
_squeezenet_meta = {'cut':-1, 'split': _squeezenet_split, 'stats':imagenet_stats}
_densenet_meta = {'cut':-1, 'split':_densenet_split, 'stats':imagenet_stats}
_vgg_meta = {'cut':-2, 'split':_vgg_split, 'stats':imagenet_stats}
_alexnet_meta = {'cut':-2, 'split':_alexnet_split, 'stats':imagenet_stats}
#export
model_meta = {
models.xresnet.xresnet18 :{**_xresnet_meta}, models.xresnet.xresnet34: {**_xresnet_meta},
models.xresnet.xresnet50 :{**_xresnet_meta}, models.xresnet.xresnet101:{**_xresnet_meta},
models.xresnet.xresnet152:{**_xresnet_meta},
models.resnet18 :{**_resnet_meta}, models.resnet34: {**_resnet_meta},
models.resnet50 :{**_resnet_meta}, models.resnet101:{**_resnet_meta},
models.resnet152:{**_resnet_meta},
models.squeezenet1_0:{**_squeezenet_meta},
models.squeezenet1_1:{**_squeezenet_meta},
models.densenet121:{**_densenet_meta}, models.densenet169:{**_densenet_meta},
models.densenet201:{**_densenet_meta}, models.densenet161:{**_densenet_meta},
models.vgg11_bn:{**_vgg_meta}, models.vgg13_bn:{**_vgg_meta}, models.vgg16_bn:{**_vgg_meta}, models.vgg19_bn:{**_vgg_meta},
models.alexnet:{**_alexnet_meta}}
#export
@delegates(create_head)
def create_cnn_model(arch, n_out, pretrained=True, cut=None, n_in=3, init=nn.init.kaiming_normal_, custom_head=None,
concat_pool=True, **kwargs):
"Create custom convnet architecture"
meta = model_meta.get(arch, _default_meta)
body = create_body(arch, n_in, pretrained, ifnone(cut, meta['cut']))
if custom_head is None:
nf = num_features_model(nn.Sequential(*body.children()))
head = create_head(nf, n_out, concat_pool=concat_pool, **kwargs)
else: head = custom_head
model = nn.Sequential(body, head)
if init is not None: apply_init(model[1], init)
return model
show_doc(create_cnn_model)
create_cnn_model
[source]
create_cnn_model
(arch
,n_out
,pretrained
=True
,cut
=None
,n_in
=3
,init
=kaiming_normal_
,custom_head
=None
,concat_pool
=True
,lin_ftrs
=None
,ps
=0.5
,first_bn
=True
,bn_final
=False
,lin_first
=False
,y_range
=None
)
Create custom convnet architecture
The model is cut according to cut
and it may be pretrained
, in which case, the proper set of weights is downloaded then loaded. init
is applied to the head of the model, which is either created by create_head
(with lin_ftrs
, ps
, concat_pool
, bn_final
, lin_first
and y_range
) or is custom_head
.
tst = create_cnn_model(models.resnet18, 10, True)
tst = create_cnn_model(models.resnet18, 10, True, n_in=1)
pets = DataBlock(blocks=(ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(),
get_y=RegexLabeller(pat = r'/([^/]+)_\d+.jpg$'))
dls = pets.dataloaders(untar_data(URLs.PETS)/"images", item_tfms=RandomResizedCrop(300, min_scale=0.5), bs=64,
batch_tfms=[*aug_transforms(size=224)])
Learner
convenience functions¶#export
def _add_norm(dls, meta, pretrained):
if not pretrained: return
stats = meta.get('stats')
if stats is None: return
dls.add_tfms([Normalize.from_stats(*stats)],'after_batch')
#export
@delegates(create_cnn_model)
def cnn_learner(dls, arch, normalize=True, n_out=None, pretrained=True, config=None,
# learner args
loss_func=None, opt_func=Adam, lr=defaults.lr, splitter=None, cbs=None, metrics=None, path=None,
model_dir='models', wd=None, wd_bn_bias=False, train_bn=True, moms=(0.95,0.85,0.95),
# other model args
**kwargs):
"Build a convnet style learner from `dls` and `arch`"
if config:
warnings.warn('config param is deprecated. Pass your args directly to cnn_learner.')
kwargs = {**config, **kwargs}
meta = model_meta.get(arch, _default_meta)
if normalize: _add_norm(dls, meta, pretrained)
if n_out is None: n_out = get_c(dls)
assert n_out, "`n_out` is not defined, and could not be inferred from data, set `dls.c` or pass `n_out`"
model = create_cnn_model(arch, n_out, pretrained=pretrained, **kwargs)
splitter=ifnone(splitter, meta['split'])
learn = Learner(dls=dls, model=model, loss_func=loss_func, opt_func=opt_func, lr=lr, splitter=splitter, cbs=cbs,
metrics=metrics, path=path, model_dir=model_dir, wd=wd, wd_bn_bias=wd_bn_bias, train_bn=train_bn,
moms=moms)
if pretrained: learn.freeze()
# keep track of args for loggers
store_attr('arch,normalize,n_out,pretrained', self=learn, **kwargs)
return learn
The model is built from arch
using the number of final activations inferred from dls
if possible (otherwise pass a value to n_out
). It might be pretrained
and the architecture is cut and split using the default metadata of the model architecture (this can be customized by passing a cut
or a splitter
).
If normalize
and pretrained
are True
, this function adds a Normalization
transform to the dls
(if there is not already one) using the statistics of the pretrained model. That way, you won't ever forget to normalize your data in transfer learning.
All other arguments are passed to Learner
.
path = untar_data(URLs.PETS)
fnames = get_image_files(path/"images")
pat = r'^(.*)_\d+.jpg$'
dls = ImageDataLoaders.from_name_re(path, fnames, pat, item_tfms=Resize(224))
learn = cnn_learner(dls, models.resnet34, loss_func=CrossEntropyLossFlat(), ps=0.25)
#hide
learn = cnn_learner(dls, models.resnet34, loss_func=CrossEntropyLossFlat(), ps=0.25, concat_pool=False)
test_ne(learn.cbs, None)
#hide
test_eq(to_cpu(dls.after_batch[1].mean[0].squeeze()), tensor(imagenet_stats[0]))
test_eq(to_cpu(dls.valid.after_batch[1].mean[0].squeeze()), tensor(imagenet_stats[0]))
#export
@delegates(models.unet.DynamicUnet.__init__)
def create_unet_model(arch, n_out, img_size, pretrained=True, cut=None, n_in=3, **kwargs):
"Create custom unet architecture"
meta = model_meta.get(arch, _default_meta)
body = create_body(arch, n_in, pretrained, ifnone(cut, meta['cut']))
model = models.unet.DynamicUnet(body, n_out, img_size, **kwargs)
return model
show_doc(create_unet_model)
create_unet_model
[source]
create_unet_model
(arch
,n_out
,img_size
,pretrained
=True
,cut
=None
,n_in
=3
,blur
=False
,blur_final
=True
,self_attention
=False
,y_range
=None
,last_cross
=True
,bottle
=False
,act_cls
=ReLU
,init
=kaiming_normal_
,norm_type
=None
)
Create custom unet architecture
tst = create_unet_model(models.resnet18, 10, (24,24), True, n_in=1)
#export
@delegates(create_unet_model)
def unet_learner(dls, arch, normalize=True, n_out=None, pretrained=True, config=None,
# learner args
loss_func=None, opt_func=Adam, lr=defaults.lr, splitter=None, cbs=None, metrics=None, path=None,
model_dir='models', wd=None, wd_bn_bias=False, train_bn=True, moms=(0.95,0.85,0.95),
# other model args
**kwargs):
"Build a unet learner from `dls` and `arch`"
if config:
warnings.warn('config param is deprecated. Pass your args directly to unet_learner.')
kwargs = {**config, **kwargs}
meta = model_meta.get(arch, _default_meta)
if normalize: _add_norm(dls, meta, pretrained)
n_out = ifnone(n_out, get_c(dls))
assert n_out, "`n_out` is not defined, and could not be inferred from data, set `dls.c` or pass `n_out`"
img_size = dls.one_batch()[0].shape[-2:]
assert img_size, "image size could not be inferred from data"
model = create_unet_model(arch, n_out, img_size, pretrained=pretrained, **kwargs)
splitter=ifnone(splitter, meta['split'])
learn = Learner(dls=dls, model=model, loss_func=loss_func, opt_func=opt_func, lr=lr, splitter=splitter, cbs=cbs,
metrics=metrics, path=path, model_dir=model_dir, wd=wd, wd_bn_bias=wd_bn_bias, train_bn=train_bn,
moms=moms)
if pretrained: learn.freeze()
# keep track of args for loggers
store_attr('arch,normalize,n_out,pretrained', self=learn, **kwargs)
return learn
The model is built from arch
using the number of final filters inferred from dls
if possible (otherwise pass a value to n_out
). It might be pretrained
and the architecture is cut and split using the default metadata of the model architecture (this can be customized by passing a cut
or a splitter
).
If normalize
and pretrained
are True
, this function adds a Normalization
transform to the dls
(if there is not already one) using the statistics of the pretrained model. That way, you won't ever forget to normalize your data in transfer learning.
All other arguments are passed to Learner
.
path = untar_data(URLs.CAMVID_TINY)
fnames = get_image_files(path/'images')
def label_func(x): return path/'labels'/f'{x.stem}_P{x.suffix}'
codes = np.loadtxt(path/'codes.txt', dtype=str)
dls = SegmentationDataLoaders.from_label_func(path, fnames, label_func, codes=codes)
learn = unet_learner(dls, models.resnet34, loss_func=CrossEntropyLossFlat(axis=1), y_range=(0,1))
#hide
test_ne(learn.cbs, None)
#export
@typedispatch
def show_results(x:TensorImage, y, samples, outs, ctxs=None, max_n=10, nrows=None, ncols=None, figsize=None, **kwargs):
if ctxs is None: ctxs = get_grid(min(len(samples), max_n), nrows=nrows, ncols=ncols, add_vert=1, figsize=figsize)
ctxs = show_results[object](x, y, samples, outs, ctxs=ctxs, max_n=max_n, **kwargs)
return ctxs
#export
@typedispatch
def show_results(x:TensorImage, y:TensorCategory, samples, outs, ctxs=None, max_n=10, nrows=None, ncols=None, figsize=None, **kwargs):
if ctxs is None: ctxs = get_grid(min(len(samples), max_n), nrows=nrows, ncols=ncols, add_vert=1, figsize=figsize)
for i in range(2):
ctxs = [b.show(ctx=c, **kwargs) for b,c,_ in zip(samples.itemgot(i),ctxs,range(max_n))]
ctxs = [r.show(ctx=c, color='green' if b==r else 'red', **kwargs)
for b,r,c,_ in zip(samples.itemgot(1),outs.itemgot(0),ctxs,range(max_n))]
return ctxs
#export
@typedispatch
def show_results(x:TensorImage, y:(TensorMask, TensorPoint, TensorBBox), samples, outs, ctxs=None, max_n=6,
nrows=None, ncols=1, figsize=None, **kwargs):
if ctxs is None: ctxs = get_grid(min(len(samples), max_n), nrows=nrows, ncols=ncols, add_vert=1, figsize=figsize, double=True,
title='Target/Prediction')
for i in range(2):
ctxs[::2] = [b.show(ctx=c, **kwargs) for b,c,_ in zip(samples.itemgot(i),ctxs[::2],range(2*max_n))]
for o in [samples,outs]:
ctxs[1::2] = [b.show(ctx=c, **kwargs) for b,c,_ in zip(o.itemgot(0),ctxs[1::2],range(2*max_n))]
return ctxs
#export
@typedispatch
def show_results(x:TensorImage, y:TensorImage, samples, outs, ctxs=None, max_n=10, figsize=None, **kwargs):
if ctxs is None: ctxs = get_grid(3*min(len(samples), max_n), ncols=3, figsize=figsize, title='Input/Target/Prediction')
for i in range(2):
ctxs[i::3] = [b.show(ctx=c, **kwargs) for b,c,_ in zip(samples.itemgot(i),ctxs[i::3],range(max_n))]
ctxs[2::3] = [b.show(ctx=c, **kwargs) for b,c,_ in zip(outs.itemgot(0),ctxs[2::3],range(max_n))]
return ctxs
#export
@typedispatch
def plot_top_losses(x: TensorImage, y:TensorCategory, samples, outs, raws, losses, nrows=None, ncols=None, figsize=None, **kwargs):
axs = get_grid(len(samples), nrows=nrows, ncols=ncols, add_vert=1, figsize=figsize, title='Prediction/Actual/Loss/Probability')
for ax,s,o,r,l in zip(axs, samples, outs, raws, losses):
s[0].show(ctx=ax, **kwargs)
ax.set_title(f'{o[0]}/{s[1]} / {l.item():.2f} / {r.max().item():.2f}')
#export
@typedispatch
def plot_top_losses(x: TensorImage, y:TensorMultiCategory, samples, outs, raws, losses, nrows=None, ncols=None, figsize=None, **kwargs):
axs = get_grid(len(samples), nrows=nrows, ncols=ncols, add_vert=1, figsize=figsize)
for i,(ax,s) in enumerate(zip(axs, samples)): s[0].show(ctx=ax, title=f'Image {i}', **kwargs)
rows = get_empty_df(len(samples))
outs = L(s[1:] + o + (TitledStr(r), TitledFloat(l.item())) for s,o,r,l in zip(samples, outs, raws, losses))
for i,l in enumerate(["target", "predicted", "probabilities", "loss"]):
rows = [b.show(ctx=r, label=l, **kwargs) for b,r in zip(outs.itemgot(i),rows)]
display_df(pd.DataFrame(rows))
#export
@typedispatch
def plot_top_losses(x:TensorImage, y:TensorMask, samples, outs, raws, losses, nrows=None, ncols=None, figsize=None, **kwargs):
axes = get_grid(len(samples)*3, nrows=len(samples), ncols=3, add_vert=1, figsize=figsize, flatten=False, title="Input | Target | Prediction")
if axes.ndim == 1: axes = (axes,)
titles = ["input", "target", "pred"]
for axs,s,o,l in zip(axes, samples, outs, losses):
imgs = (s[0], s[1], o[0])
for ax,im,title in zip(axs, imgs, titles):
if title=="pred": title += f"; loss = {l:.4f}"
im.show(ctx=ax)
ax.set_title(title)
#hide
from nbdev.export import notebook2script
notebook2script()
Converted 00_torch_core.ipynb. Converted 01_layers.ipynb. Converted 01a_losses.ipynb. Converted 02_data.load.ipynb. Converted 03_data.core.ipynb. Converted 04_data.external.ipynb. Converted 05_data.transforms.ipynb. Converted 06_data.block.ipynb. Converted 07_vision.core.ipynb. Converted 08_vision.data.ipynb. Converted 09_vision.augment.ipynb. Converted 09b_vision.utils.ipynb. Converted 09c_vision.widgets.ipynb. Converted 10_tutorial.pets.ipynb. Converted 10b_tutorial.albumentations.ipynb. Converted 11_vision.models.xresnet.ipynb. Converted 12_optimizer.ipynb. Converted 13_callback.core.ipynb. Converted 13a_learner.ipynb. Converted 13b_metrics.ipynb. Converted 14_callback.schedule.ipynb. Converted 14a_callback.data.ipynb. Converted 15_callback.hook.ipynb. Converted 15a_vision.models.unet.ipynb. Converted 16_callback.progress.ipynb. Converted 17_callback.tracker.ipynb. Converted 18_callback.fp16.ipynb. Converted 18a_callback.training.ipynb. Converted 18b_callback.preds.ipynb. Converted 19_callback.mixup.ipynb. Converted 20_interpret.ipynb. Converted 20a_distributed.ipynb. Converted 21_vision.learner.ipynb. Converted 22_tutorial.imagenette.ipynb. Converted 23_tutorial.vision.ipynb. Converted 24_tutorial.siamese.ipynb. Converted 24_vision.gan.ipynb. Converted 30_text.core.ipynb. Converted 31_text.data.ipynb. Converted 32_text.models.awdlstm.ipynb. Converted 33_text.models.core.ipynb. Converted 34_callback.rnn.ipynb. Converted 35_tutorial.wikitext.ipynb. Converted 36_text.models.qrnn.ipynb. Converted 37_text.learner.ipynb. Converted 38_tutorial.text.ipynb. Converted 39_tutorial.transformers.ipynb. Converted 40_tabular.core.ipynb. Converted 41_tabular.data.ipynb. Converted 42_tabular.model.ipynb. Converted 43_tabular.learner.ipynb. Converted 44_tutorial.tabular.ipynb. Converted 45_collab.ipynb. Converted 46_tutorial.collab.ipynb. Converted 50_tutorial.datablock.ipynb. Converted 60_medical.imaging.ipynb. Converted 61_tutorial.medical_imaging.ipynb. Converted 65_medical.text.ipynb. Converted 70_callback.wandb.ipynb. Converted 71_callback.tensorboard.ipynb. Converted 72_callback.neptune.ipynb. Converted 73_callback.captum.ipynb. Converted 74_callback.azureml.ipynb. Converted 97_test_utils.ipynb. Converted 99_pytorch_doc.ipynb. Converted dev-setup.ipynb. Converted index.ipynb. Converted quick_start.ipynb. Converted tutorial.ipynb.