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learner.py
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"`Learner` support for computer vision"
from ..torch_core import *
from ..basic_train import *
from ..basic_data import *
from .image import *
from . import models
from ..callback import *
from ..layers import *
from ..callbacks.hooks import *
from ..train import ClassificationInterpretation
__all__ = ['cnn_learner', 'create_cnn', 'create_cnn_model', 'create_body', 'create_head', 'unet_learner']
# By default split models between first and second layer
def _default_split(m:nn.Module): return (m[1],)
# Split a resnet style model
def _resnet_split(m:nn.Module): return (m[0][6],m[1])
# Split squeezenet model on maxpool layers
def _squeezenet_split(m:nn.Module): return (m[0][0][5], m[0][0][8], m[1])
def _densenet_split(m:nn.Module): return (m[0][0][7],m[1])
def _vgg_split(m:nn.Module): return (m[0][0][22],m[1])
def _alexnet_split(m:nn.Module): return (m[0][0][6],m[1])
def _mobilenetv2_split(m:nn.Module): return (m[0][0][10],m[1])
_default_meta = {'cut':None, 'split':_default_split}
_resnet_meta = {'cut':-2, 'split':_resnet_split }
_squeezenet_meta = {'cut':-1, 'split': _squeezenet_split}
_densenet_meta = {'cut':-1, 'split':_densenet_split}
_vgg_meta = {'cut':-1, 'split':_vgg_split}
_alexnet_meta = {'cut':-1, 'split':_alexnet_split}
_mobilenetv2_meta = {'cut':-1, 'split':_mobilenetv2_split}
model_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},
models.mobilenet_v2:{**_mobilenetv2_meta}}
def cnn_config(arch):
"Get the metadata associated with `arch`."
torch.backends.cudnn.benchmark = True
return model_meta.get(arch, _default_meta)
def has_pool_type(m):
if is_pool_type(m): return True
for l in m.children():
if has_pool_type(l): return True
return False
def create_body(arch:Callable, pretrained:bool=True, cut:Optional[Union[int, Callable]]=None):
"Cut off the body of a typically pretrained `model` at `cut` (int) or cut the model as specified by `cut(model)` (function)."
model = arch(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 isinstance(cut, Callable): return cut(model)
else: raise NamedError("cut must be either integer or a function")
def create_head(nf:int, nc:int, lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5,
concat_pool:bool=True, bn_final:bool=False):
"Model head that takes `nf` features, runs through `lin_ftrs`, and about `nc` classes."
lin_ftrs = [nf, 512, nc] if lin_ftrs is None else [nf] + lin_ftrs + [nc]
ps = listify(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()]
for ni,no,p,actn in zip(lin_ftrs[:-1], lin_ftrs[1:], ps, actns):
layers += bn_drop_lin(ni, no, True, p, actn)
if bn_final: layers.append(nn.BatchNorm1d(lin_ftrs[-1], momentum=0.01))
return nn.Sequential(*layers)
def create_cnn_model(base_arch:Callable, nc:int, cut:Union[int,Callable]=None, pretrained:bool=True,
lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5, custom_head:Optional[nn.Module]=None,
bn_final:bool=False, concat_pool:bool=True):
"Create custom convnet architecture"
body = create_body(base_arch, pretrained, cut)
if custom_head is None:
nf = num_features_model(nn.Sequential(*body.children())) * (2 if concat_pool else 1)
head = create_head(nf, nc, lin_ftrs, ps=ps, concat_pool=concat_pool, bn_final=bn_final)
else: head = custom_head
return nn.Sequential(body, head)
def cnn_learner(data:DataBunch, base_arch:Callable, cut:Union[int,Callable]=None, pretrained:bool=True,
lin_ftrs:Optional[Collection[int]]=None, ps:Floats=0.5, custom_head:Optional[nn.Module]=None,
split_on:Optional[SplitFuncOrIdxList]=None, bn_final:bool=False, init=nn.init.kaiming_normal_,
concat_pool:bool=True, **kwargs:Any)->Learner:
"Build convnet style learner."
meta = cnn_config(base_arch)
model = create_cnn_model(base_arch, data.c, cut, pretrained, lin_ftrs, ps=ps, custom_head=custom_head,
bn_final=bn_final, concat_pool=concat_pool)
learn = Learner(data, model, **kwargs)
learn.split(split_on or meta['split'])
if pretrained: learn.freeze()
if init: apply_init(model[1], init)
return learn
def create_cnn(data, base_arch, **kwargs):
warn("`create_cnn` is deprecated and is now named `cnn_learner`.")
return cnn_learner(data, base_arch, **kwargs)
def unet_learner(data:DataBunch, arch:Callable, pretrained:bool=True, blur_final:bool=True,
norm_type:Optional[NormType]=None, split_on:Optional[SplitFuncOrIdxList]=None, blur:bool=False,
self_attention:bool=False, y_range:Optional[Tuple[float,float]]=None, last_cross:bool=True,
bottle:bool=False, cut:Union[int,Callable]=None, **learn_kwargs:Any)->Learner:
"Build Unet learner from `data` and `arch`."
meta = cnn_config(arch)
body = create_body(arch, pretrained, cut)
try: size = data.train_ds[0][0].size
except: size = next(iter(data.train_dl))[0].shape[-2:]
model = to_device(models.unet.DynamicUnet(body, n_classes=data.c, img_size=size, blur=blur, blur_final=blur_final,
self_attention=self_attention, y_range=y_range, norm_type=norm_type, last_cross=last_cross,
bottle=bottle), data.device)
learn = Learner(data, model, **learn_kwargs)
learn.split(ifnone(split_on, meta['split']))
if pretrained: learn.freeze()
apply_init(model[2], nn.init.kaiming_normal_)
return learn
@classmethod
def _cl_int_from_learner(cls, learn:Learner, ds_type:DatasetType=DatasetType.Valid, activ:nn.Module=None, tta=False):
"Create an instance of `ClassificationInterpretation`. `tta` indicates if we want to use Test Time Augmentation."
preds = learn.TTA(ds_type=ds_type, with_loss=True) if tta else learn.get_preds(ds_type=ds_type, activ=activ, with_loss=True)
return cls(learn, *preds, ds_type=ds_type)
def _test_cnn(m):
if not isinstance(m, nn.Sequential) or not len(m) == 2: return False
return isinstance(m[1][0], (AdaptiveConcatPool2d, nn.AdaptiveAvgPool2d))
def _cl_int_gradcam(self, idx, ds_type:DatasetType=DatasetType.Valid, heatmap_thresh:int=16, image:bool=True):
m = self.learn.model.eval()
im,cl = self.learn.data.dl(ds_type).dataset[idx]
cl = int(cl)
xb,_ = self.data.one_item(im, detach=False, denorm=False) #put into a minibatch of batch size = 1
with hook_output(m[0]) as hook_a:
with hook_output(m[0], grad=True) as hook_g:
preds = m(xb)
preds[0,int(cl)].backward()
acts = hook_a.stored[0].cpu() #activation maps
if (acts.shape[-1]*acts.shape[-2]) >= heatmap_thresh:
grad = hook_g.stored[0][0].cpu()
grad_chan = grad.mean(1).mean(1)
mult = F.relu(((acts*grad_chan[...,None,None])).sum(0))
if image:
xb_im = Image(xb[0])
_,ax = plt.subplots()
sz = list(xb_im.shape[-2:])
xb_im.show(ax,title=f"pred. class: {self.pred_class[idx]}, actual class: {self.learn.data.classes[cl]}")
ax.imshow(mult, alpha=0.4, extent=(0,*sz[::-1],0),
interpolation='bilinear', cmap='magma')
return mult
ClassificationInterpretation.GradCAM =_cl_int_gradcam
def _cl_int_plot_top_losses(self, k, largest=True, figsize=(12,12), heatmap:bool=False, heatmap_thresh:int=16,
alpha:float=0.6, cmap:str="magma", show_text:bool=True,
return_fig:bool=None)->Optional[plt.Figure]:
"Show images in `top_losses` along with their prediction, actual, loss, and probability of actual class."
assert not heatmap or _test_cnn(self.learn.model), "`heatmap=True` requires a model like `cnn_learner` produces."
if heatmap is None: heatmap = _test_cnn(self.learn.model)
tl_val,tl_idx = self.top_losses(k, largest)
classes = self.data.classes
cols = math.ceil(math.sqrt(k))
rows = math.ceil(k/cols)
fig,axes = plt.subplots(rows, cols, figsize=figsize)
if show_text: fig.suptitle('Prediction/Actual/Loss/Probability', weight='bold', size=14)
for i,idx in enumerate(tl_idx):
im,cl = self.data.dl(self.ds_type).dataset[idx]
cl = int(cl)
title = f'{classes[self.pred_class[idx]]}/{classes[cl]} / {self.losses[idx]:.2f} / {self.preds[idx][cl]:.2f}' if show_text else None
im.show(ax=axes.flat[i], title=title)
if heatmap:
mult = self.GradCAM(idx,self.ds_type,heatmap_thresh,image=False)
if mult is not None:
sz = list(im.shape[-2:])
axes.flat[i].imshow(mult, alpha=alpha, extent=(0,*sz[::-1],0), interpolation='bilinear', cmap=cmap)
if ifnone(return_fig, defaults.return_fig): return fig
def _cl_int_plot_multi_top_losses(self, samples:int=3, figsize:Tuple[int,int]=(8,8), save_misclassified:bool=False):
"Show images in `top_losses` along with their prediction, actual, loss, and probability of predicted class in a multilabeled dataset."
if samples >20:
print("Max 20 samples")
return
losses, idxs = self.top_losses(self.data.c)
l_dim = len(losses.size())
if l_dim == 1: losses, idxs = self.top_losses()
infolist, ordlosses_idxs, mismatches_idxs, mismatches, losses_mismatches, mismatchescontainer = [],[],[],[],[],[]
truthlabels = np.asarray(self.y_true, dtype=int)
classes_ids = [k for k in enumerate(self.data.classes)]
predclass = np.asarray(self.pred_class)
for i,pred in enumerate(predclass):
where_truth = np.nonzero((truthlabels[i]>0))[0]
mismatch = np.all(pred!=where_truth)
if mismatch:
mismatches_idxs.append(i)
if l_dim > 1 : losses_mismatches.append((losses[i][pred], i))
else: losses_mismatches.append((losses[i], i))
if l_dim > 1: infotup = (i, pred, where_truth, losses[i][pred], np.round(self.preds[i], decimals=3)[pred], mismatch)
else: infotup = (i, pred, where_truth, losses[i], np.round(self.preds[i], decimals=3)[pred], mismatch)
infolist.append(infotup)
ds = self.data.dl(self.ds_type).dataset
mismatches = ds[mismatches_idxs]
ordlosses = sorted(losses_mismatches, key = lambda x: x[0], reverse=True)
for w in ordlosses: ordlosses_idxs.append(w[1])
mismatches_ordered_byloss = ds[ordlosses_idxs]
print(f'{str(len(mismatches))} misclassified samples over {str(len(self.data.valid_ds))} samples in the validation set.')
samples = min(samples, len(mismatches))
for ima in range(len(mismatches_ordered_byloss)):
mismatchescontainer.append(mismatches_ordered_byloss[ima][0])
for sampleN in range(samples):
actualclasses = ''
for clas in infolist[ordlosses_idxs[sampleN]][2]:
actualclasses = f'{actualclasses} -- {str(classes_ids[clas][1])}'
imag = mismatches_ordered_byloss[sampleN][0]
imag = show_image(imag, figsize=figsize)
imag.set_title(f"""Predicted: {classes_ids[infolist[ordlosses_idxs[sampleN]][1]][1]} \nActual: {actualclasses}\nLoss: {infolist[ordlosses_idxs[sampleN]][3]}\nProbability: {infolist[ordlosses_idxs[sampleN]][4]}""",
loc='left')
plt.show()
if save_misclassified: return mismatchescontainer
ClassificationInterpretation.from_learner = _cl_int_from_learner
ClassificationInterpretation.plot_top_losses = _cl_int_plot_top_losses
ClassificationInterpretation.plot_multi_top_losses = _cl_int_plot_multi_top_losses
def _learner_interpret(learn:Learner, ds_type:DatasetType=DatasetType.Valid, tta=False):
"Create a `ClassificationInterpretation` object from `learner` on `ds_type` with `tta`."
return ClassificationInterpretation.from_learner(learn, ds_type=ds_type, tta=tta)
Learner.interpret = _learner_interpret