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Build Google Images Search Url params and return them as a string.
def _url_params(size:str='>400*300', format:str='jpg') -> str:
"Build Google Images Search Url params and return them as a string."
_fmts = {'jpg':'ift:jpg','gif':'ift:gif','png':'ift:png','bmp':'ift:bmp', 'svg':'ift:svg','webp':'webp','ico':'i... |
Return a Google Images Search URL for a given search term.
def _search_url(search_term:str, size:str='>400*300', format:str='jpg') -> str:
"Return a Google Images Search URL for a given search term."
return ('https://www.google.com/search?q=' + quote(search_term) +
'&espv=2&biw=1366&bih=667&site=we... |
Parse the Google Images Search for urls and return the image metadata as tuples (fname, url).
def _fetch_img_tuples(url:str, format:str='jpg', n_images:int=10) -> list:
"Parse the Google Images Search for urls and return the image metadata as tuples (fname, url)."
headers = {'User-Agent': 'Mozilla/5.0 (Windows... |
Parse the google images html to img tuples containining `(fname, url)`
def _html_to_img_tuples(html:str, format:str='jpg', n_images:int=10) -> list:
"Parse the google images html to img tuples containining `(fname, url)`"
bs = BeautifulSoup(html, 'html.parser')
img_tags = bs.find_all('div', {'class': '... |
Parse the Google Images Search for urls and return the image metadata as tuples (fname, url).
Use this for downloads of >100 images. Requires `selenium`.
def _fetch_img_tuples_webdriver(url:str, format:str='jpg', n_images:int=150) -> list:
"""
Parse the Google Images Search for urls and return the image me... |
Downloads images in `img_tuples` to `label_path`.
If the directory doesn't exist, it'll be created automatically.
Uses `parallel` to speed things up in `max_workers` when the system has enough CPU cores.
If something doesn't work, try setting up `max_workers=0` to debug.
def _download_images(label_path:Pa... |
Downloads a single image from Google Search results to `label_path`
given an `img_tuple` that contains `(fname, url)` of an image to download.
`i` is just an iteration number `int`.
def _download_single_image(label_path:Path, img_tuple:tuple, i:int, timeout:int=4) -> None:
"""
Downloads a single image ... |
Initialize the widget UI and return the UI.
def _init_ui(self) -> VBox:
"Initialize the widget UI and return the UI."
self._search_input = Text(placeholder="What images to search for?")
self._count_input = BoundedIntText(placeholder="How many pics?", value=10, min=1, max=5000, step=1,
... |
Clear the widget's images preview pane.
def clear_imgs(self) -> None:
"Clear the widget's images preview pane."
self._preview_header.value = self._heading
self._img_pane.children = tuple() |
Check if input value is empty.
def validate_search_input(self) -> bool:
"Check if input value is empty."
input = self._search_input
if input.value == str(): input.layout = Layout(border="solid 2px red", height='auto')
else: self._search_input.layout = Layout()
... |
Download button click handler: validate search term and download images.
def on_download_button_click(self, btn) -> None:
"Download button click handler: validate search term and download images."
term = self._search_input.value
limit = int(self._count_input.value)
size = self._size_inp... |
Display a few preview images in the notebook
def display_images_widgets(self, fnames:list) -> None:
"Display a few preview images in the notebook"
imgs = [widgets.Image(value=open(f, 'rb').read(), width='200px') for f in fnames]
self._img_pane.children = tuple(imgs) |
Initialize optimizer and learner hyperparameters.
def on_train_begin(self, pbar, **kwargs:Any)->None:
"Initialize optimizer and learner hyperparameters."
setattr(pbar, 'clean_on_interrupt', True)
self.learn.save('tmp')
self.opt = self.learn.opt
self.opt.lr = self.sched.start
... |
Determine if loss has runaway and we should stop.
def on_batch_end(self, iteration:int, smooth_loss:TensorOrNumber, **kwargs:Any)->None:
"Determine if loss has runaway and we should stop."
if iteration==0 or smooth_loss < self.best_loss: self.best_loss = smooth_loss
self.opt.lr = self.sched.ste... |
Cleanup learn model weights disturbed during LRFinder exploration.
def on_train_end(self, **kwargs:Any)->None:
"Cleanup learn model weights disturbed during LRFinder exploration."
self.learn.load('tmp', purge=False)
if hasattr(self.learn.model, 'reset'): self.learn.model.reset()
for cb ... |
Applies a dropout mask whose size is determined by passed argument 'sz'.
Args:
x (nn.Variable): A torch Variable object
sz (tuple(int, int, int)): The expected size of the new tensor
dropout (float): The dropout fraction to apply
This method uses the bernoulli distribution to decide whi... |
for each string defined in self.weights, the corresponding
attribute in the wrapped module is referenced, then deleted, and subsequently
registered as a new parameter with a slightly modified name.
Args:
None
Returns:
None
def _setup(self):
""" for ea... |
Uses pytorch's built-in dropout function to apply dropout to the parameters of
the wrapped module.
Args:
None
Returns:
None
def _setweights(self):
""" Uses pytorch's built-in dropout function to apply dropout to the parameters of
the wrapped module.
... |
Load a saved `DataBunch` from `path/file`. `file` can be file-like (file or buffer)
def load_data(path:PathOrStr, file:PathLikeOrBinaryStream='data_save.pkl', bs:int=64, val_bs:int=None, num_workers:int=defaults.cpus,
dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None, collate_fn:Calla... |
Create a `DataBunch` from `train_ds`, `valid_ds` and maybe `test_ds` with a batch size of `bs`. Passes `**dl_kwargs` to `DataLoader()`
def create(cls, train_ds:Dataset, valid_ds:Dataset, test_ds:Optional[Dataset]=None, path:PathOrStr='.', bs:int=64,
val_bs:int=None, num_workers:int=defaults.cpus, dl_tfm... |
Returns appropriate `Dataset` for validation, training, or test (`ds_type`).
def dl(self, ds_type:DatasetType=DatasetType.Valid)->DeviceDataLoader:
"Returns appropriate `Dataset` for validation, training, or test (`ds_type`)."
#TODO: refactor
return (self.train_dl if ds_type == DatasetType.Trai... |
Returns a list of all DeviceDataLoaders. If you need a specific DeviceDataLoader, access via the relevant property (`train_dl`, `valid_dl`, etc) as the index of DLs in this list is not guaranteed to remain constant.
def dls(self)->List[DeviceDataLoader]:
"Returns a list of all DeviceDataLoaders. If you need a ... |
Save the `DataBunch` in `self.path/file`. `file` can be file-like (file or buffer)
def save(self, file:PathLikeOrBinaryStream= 'data_save.pkl')->None:
"Save the `DataBunch` in `self.path/file`. `file` can be file-like (file or buffer)"
if not getattr(self, 'label_list', False):
warn("Serial... |
Get one batch from the data loader of `ds_type`. Optionally `detach` and `denorm`.
def one_batch(self, ds_type:DatasetType=DatasetType.Train, detach:bool=True, denorm:bool=True, cpu:bool=True)->Collection[Tensor]:
"Get one batch from the data loader of `ds_type`. Optionally `detach` and `denorm`."
dl =... |
Get `item` into a batch. Optionally `detach` and `denorm`.
def one_item(self, item, detach:bool=False, denorm:bool=False, cpu:bool=False):
"Get `item` into a batch. Optionally `detach` and `denorm`."
ds = self.single_ds
with ds.set_item(item):
return self.one_batch(ds_type=DatasetTy... |
Show a batch of data in `ds_type` on a few `rows`.
def show_batch(self, rows:int=5, ds_type:DatasetType=DatasetType.Train, reverse:bool=False, **kwargs)->None:
"Show a batch of data in `ds_type` on a few `rows`."
x,y = self.one_batch(ds_type, True, True)
if reverse: x,y = x.flip(0),y.flip(0)
... |
Export the minimal state of `self` for inference in `self.path/file`. `file` can be file-like (file or buffer)
def export(self, file:PathLikeOrBinaryStream='export.pkl'):
"Export the minimal state of `self` for inference in `self.path/file`. `file` can be file-like (file or buffer)"
xtra = dict(normali... |
Check the underlying data in the training set can be properly loaded.
def sanity_check(self):
"Check the underlying data in the training set can be properly loaded."
final_message = "You can deactivate this warning by passing `no_check=True`."
if not hasattr(self.train_ds, 'items') or len(self.... |
Instantiate a `OneCycleScheduler` with `lr_max`.
def one_cycle_scheduler(lr_max:float, **kwargs:Any)->OneCycleScheduler:
"Instantiate a `OneCycleScheduler` with `lr_max`."
return partial(OneCycleScheduler, lr_max=lr_max, **kwargs) |
Fit a model following the 1cycle policy.
def fit_one_cycle(learn:Learner, cyc_len:int, max_lr:Union[Floats,slice]=defaults.lr,
moms:Tuple[float,float]=(0.95,0.85), div_factor:float=25., pct_start:float=0.3, final_div:float=None,
wd:float=None, callbacks:Optional[CallbackList]=None, ... |
Explore lr from `start_lr` to `end_lr` over `num_it` iterations in `learn`. If `stop_div`, stops when loss diverges.
def lr_find(learn:Learner, start_lr:Floats=1e-7, end_lr:Floats=10, num_it:int=100, stop_div:bool=True, wd:float=None):
"Explore lr from `start_lr` to `end_lr` over `num_it` iterations in `learn`. If... |
Put `learn` in FP16 precision mode.
def to_fp16(learn:Learner, loss_scale:float=None, max_noskip:int=1000, dynamic:bool=True, clip:float=None,
flat_master:bool=False, max_scale:float=2**24)->Learner:
"Put `learn` in FP16 precision mode."
learn.to_fp32()
learn.model = model2half(learn.model)
... |
Put `learn` back to FP32 precision mode.
def to_fp32(learn:Learner):
"Put `learn` back to FP32 precision mode."
learn.data.remove_tfm(batch_to_half)
for cb in learn.callbacks:
if isinstance(cb, MixedPrecision): learn.callbacks.remove(cb)
learn.model = learn.model.float()
return learn |
Add mixup https://arxiv.org/abs/1710.09412 to `learn`.
def mixup(learn:Learner, alpha:float=0.4, stack_x:bool=False, stack_y:bool=True) -> Learner:
"Add mixup https://arxiv.org/abs/1710.09412 to `learn`."
learn.callback_fns.append(partial(MixUpCallback, alpha=alpha, stack_x=stack_x, stack_y=stack_y))
retur... |
Add gradient clipping of `clip` during training.
def clip_grad(learn:Learner, clip:float=0.1)->Learner:
"Add gradient clipping of `clip` during training."
learn.callback_fns.append(partial(GradientClipping, clip=clip))
return learn |
Create a `ClassificationInterpretation` object from `learner` on `ds_type` with `tta`.
def _learner_interpret(learn:Learner, ds_type:DatasetType=DatasetType.Valid):
"Create a `ClassificationInterpretation` object from `learner` on `ds_type` with `tta`."
return ClassificationInterpretation.from_learner(learn, d... |
If we have `last_metrics` plot them in our pbar graph
def on_epoch_end(self, n_epochs:int, last_metrics:MetricsList, **kwargs)->bool:
"If we have `last_metrics` plot them in our pbar graph"
if last_metrics is not None and np.any(last_metrics):
rec = self.learn.recorder
iters = r... |
Clip the gradient before the optimizer step.
def on_backward_end(self, **kwargs):
"Clip the gradient before the optimizer step."
if self.clip: nn.utils.clip_grad_norm_(self.learn.model.parameters(), self.clip) |
check if loss is reduction
def on_train_begin(self, **kwargs):
"check if loss is reduction"
if hasattr(self.loss_func, "reduction") and (self.loss_func.reduction != "sum"):
warn("For better gradients consider 'reduction=sum'") |
accumulate samples and batches
def on_batch_begin(self, last_input, last_target, **kwargs):
"accumulate samples and batches"
self.acc_samples += last_input.shape[0]
self.acc_batches += 1 |
accumulated step and reset samples, True will result in no stepping
def on_backward_end(self, **kwargs):
"accumulated step and reset samples, True will result in no stepping"
if (self.acc_batches % self.n_step) == 0:
for p in (self.learn.model.parameters()):
if p.requires_gr... |
step the rest of the accumulated grads if not perfectly divisible
def on_epoch_end(self, **kwargs):
"step the rest of the accumulated grads if not perfectly divisible"
for p in (self.learn.model.parameters()):
if p.requires_grad: p.grad.div_(self.acc_samples)
if not self.drop_la... |
Create an instance of `ClassificationInterpretation`
def from_learner(cls, learn: Learner, ds_type:DatasetType=DatasetType.Valid):
"Create an instance of `ClassificationInterpretation`"
preds = learn.get_preds(ds_type=ds_type, with_loss=True)
return cls(learn, *preds) |
Confusion matrix as an `np.ndarray`.
def confusion_matrix(self, slice_size:int=1):
"Confusion matrix as an `np.ndarray`."
x=torch.arange(0,self.data.c)
if slice_size is None: cm = ((self.pred_class==x[:,None]) & (self.y_true==x[:,None,None])).sum(2)
else:
cm = torch.zeros(se... |
Plot the confusion matrix, with `title` and using `cmap`.
def plot_confusion_matrix(self, normalize:bool=False, title:str='Confusion matrix', cmap:Any="Blues", slice_size:int=1,
norm_dec:int=2, plot_txt:bool=True, return_fig:bool=None, **kwargs)->Optional[plt.Figure]:
"Plot the co... |
Sorted descending list of largest non-diagonal entries of confusion matrix, presented as actual, predicted, number of occurrences.
def most_confused(self, min_val:int=1, slice_size:int=1)->Collection[Tuple[str,str,int]]:
"Sorted descending list of largest non-diagonal entries of confusion matrix, presented as ... |
`k` largest(/smallest) losses and indexes, defaulting to all losses (sorted by `largest`).
def top_losses(self, k:int=None, largest=True):
"`k` largest(/smallest) losses and indexes, defaulting to all losses (sorted by `largest`)."
return self.losses.topk(ifnone(k, len(self.losses)), largest=largest) |
Calculates the F-beta score (the weighted harmonic mean of precision and recall).
This is the micro averaged version where the true positives, false negatives and
false positives are calculated globally (as opposed to on a per label basis).
beta == 1 places equal weight on precision and recall, b < 1 empha... |
see fbeta
def fbeta_np(preds, targs, beta, thresh=0.5, epsilon=1e-8):
""" see fbeta """
assert beta > 0, 'beta needs to be greater than 0'
beta2 = beta ** 2
rec = recall_np(preds, targs, thresh)
prec = precision_np(preds, targs, thresh)
return (1 + beta2) * prec * rec / (beta2 * prec + rec + ep... |
Distributed training of Imagenet. Fastest speed is if you run with: python -m fastai.launch
def main( gpu:Param("GPU to run on", str)=None ):
"""Distributed training of Imagenet. Fastest speed is if you run with: python -m fastai.launch"""
path = Path('/mnt/fe2_disk/')
tot_epochs,size,bs,lr = 60,224,256,3e... |
Get the metadata associated with `arch`.
def cnn_config(arch):
"Get the metadata associated with `arch`."
torch.backends.cudnn.benchmark = True
return model_meta.get(arch, _default_meta) |
Cut off the body of a typically pretrained `model` at `cut` (int) or cut the model as specified by `cut(model)` (function).
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... |
Model head that takes `nf` features, runs through `lin_ftrs`, and about `nc` classes.
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` cl... |
Create custom convnet architecture
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,
split_on:Optional[SplitFuncOrIdxList]=None, bn_final:bool=False, concat_p... |
Build convnet style learner.
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=Fa... |
Build Unet learner from `data` and `arch`.
def unet_learner(data:DataBunch, arch:Callable, pretrained:bool=True, blur_final:bool=True,
norm_type:Optional[NormType]=NormType, split_on:Optional[SplitFuncOrIdxList]=None, blur:bool=False,
self_attention:bool=False, y_range:Optional[Tuple[... |
Create an instance of `ClassificationInterpretation`. `tta` indicates if we want to use Test Time Augmentation.
def _cl_int_from_learner(cls, learn:Learner, ds_type:DatasetType=DatasetType.Valid, tta=False):
"Create an instance of `ClassificationInterpretation`. `tta` indicates if we want to use Test Time Augmenta... |
Show images in `top_losses` along with their prediction, actual, loss, and probability of actual class.
def _cl_int_plot_top_losses(self, k, largest=True, figsize=(12,12), heatmap:bool=True, heatmap_thresh:int=16,
return_fig:bool=None)->Optional[plt.Figure]:
"Show images in `top_losses`... |
Show images in `top_losses` along with their prediction, actual, loss, and probability of predicted class in a multilabeled dataset.
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,... |
Gets indices with top losses.
def from_toplosses(cls, learn, n_imgs=None, **kwargs):
"Gets indices with top losses."
train_ds, train_idxs = cls.get_toplosses_idxs(learn, n_imgs, **kwargs)
return train_ds, train_idxs |
Sorts `ds_type` dataset by top losses and returns dataset and sorted indices.
def get_toplosses_idxs(cls, learn, n_imgs, **kwargs):
"Sorts `ds_type` dataset by top losses and returns dataset and sorted indices."
dl = learn.data.fix_dl
if not n_imgs: n_imgs = len(dl.dataset)
_,_,top_loss... |
For a LabelList `ll_input`, resize each image to `size` using `resize_method` and `padding_mode`.
def padded_ds(ll_input, size=(250, 300), resize_method=ResizeMethod.CROP, padding_mode='zeros', **kwargs):
"For a LabelList `ll_input`, resize each image to `size` using `resize_method` and `padding_mode`."
... |
Gets the indices for the most similar images.
def from_similars(cls, learn, layer_ls:list=[0, 7, 2], **kwargs):
"Gets the indices for the most similar images."
train_ds, train_idxs = cls.get_similars_idxs(learn, layer_ls, **kwargs)
return train_ds, train_idxs |
Gets the indices for the most similar images in `ds_type` dataset
def get_similars_idxs(cls, learn, layer_ls, **kwargs):
"Gets the indices for the most similar images in `ds_type` dataset"
hook = hook_output(learn.model[layer_ls[0]][layer_ls[1]][layer_ls[2]])
dl = learn.data.fix_dl
ds_... |
Gets activations at the layer specified by `hook`, applies `pool` of dim `pool_dim` and concatenates
def get_actns(learn, hook:Hook, dl:DataLoader, pool=AdaptiveConcatPool2d, pool_dim:int=4, **kwargs):
"Gets activations at the layer specified by `hook`, applies `pool` of dim `pool_dim` and concatenates"
... |
Computes the similarity function between each embedding of `t1` and `t2` matrices.
def comb_similarity(t1: torch.Tensor, t2: torch.Tensor, **kwargs):
# https://github.com/pytorch/pytorch/issues/11202
"Computes the similarity function between each embedding of `t1` and `t2` matrices."
print('Com... |
Returns the `n` largest indices from a numpy array `arr`.
def largest_indices(arr, n):
"Returns the `n` largest indices from a numpy array `arr`."
#https://stackoverflow.com/questions/6910641/how-do-i-get-indices-of-n-maximum-values-in-a-numpy-array
flat = arr.flatten()
indices = np.arg... |
Sorts `similarities` and return the indexes in pairs ordered by highest similarity.
def sort_idxs(cls, similarities):
"Sorts `similarities` and return the indexes in pairs ordered by highest similarity."
idxs = cls.largest_indices(similarities, len(similarities))
idxs = [(idxs[0][i], idxs[1][i]... |
Returns an image widget for specified file name `img`.
def make_img_widget(cls, img, layout=Layout(), format='jpg'):
"Returns an image widget for specified file name `img`."
return widgets.Image(value=img, format=format, layout=layout) |
Return a Button widget with specified `handler`.
def make_button_widget(cls, label, file_path=None, handler=None, style=None, layout=Layout(width='auto')):
"Return a Button widget with specified `handler`."
btn = widgets.Button(description=label, layout=layout)
if handler is not None: btn.on_cl... |
Return a Dropdown widget with specified `handler`.
def make_dropdown_widget(cls, description='Description', options=['Label 1', 'Label 2'], value='Label 1',
file_path=None, layout=Layout(), handler=None):
"Return a Dropdown widget with specified `handler`."
dd = widgets.Drop... |
Make a horizontal box with `children` and `layout`.
def make_horizontal_box(cls, children, layout=Layout()):
"Make a horizontal box with `children` and `layout`."
return widgets.HBox(children, layout=layout) |
Make a vertical box with `children` and `layout`.
def make_vertical_box(cls, children, layout=Layout(), duplicates=False):
"Make a vertical box with `children` and `layout`."
if not duplicates: return widgets.VBox(children, layout=layout)
else: return widgets.VBox([children[0], children[2]], la... |
Create a list of images, filenames and labels but first removing files that are not supposed to be displayed.
def create_image_list(self, dataset, fns_idxs):
"Create a list of images, filenames and labels but first removing files that are not supposed to be displayed."
items = dataset.x.items
i... |
Relabel images by moving from parent dir with old label `class_old` to parent dir with new label `class_new`.
def relabel(self, change):
"Relabel images by moving from parent dir with old label `class_old` to parent dir with new label `class_new`."
class_new,class_old,file_path = change.new,change.old,... |
Handler for 'Next Batch' button click. Delete all flagged images and renders next batch.
def next_batch(self, _):
"Handler for 'Next Batch' button click. Delete all flagged images and renders next batch."
for img_widget, delete_btn, fp, in self._batch:
fp = delete_btn.file_path
... |
Flag this image as delete or keep.
def on_delete(self, btn):
"Flag this image as delete or keep."
btn.button_style = "" if btn.flagged_for_delete else "danger"
btn.flagged_for_delete = not btn.flagged_for_delete |
Create and format widget set.
def get_widgets(self, duplicates):
"Create and format widget set."
widgets = []
for (img,fp,human_readable_label) in self._all_images[:self._batch_size]:
img_widget = self.make_img_widget(img, layout=Layout(height='250px', width='300px'))
dr... |
Check if current batch contains already deleted images.
def batch_contains_deleted(self):
"Check if current batch contains already deleted images."
if not self._duplicates: return False
imgs = [self._all_images[:self._batch_size][0][1], self._all_images[:self._batch_size][1][1]]
return ... |
Re-render Jupyter cell for batch of images.
def render(self):
"Re-render Jupyter cell for batch of images."
clear_output()
self.write_csv()
if self.empty() and self._skipped>0:
return display(f'No images to show :). {self._skipped} pairs were '
f'skipped ... |
Shift the line i of `x` by p-i elements to the left, is `mask` puts 0s on the diagonal.
def _line_shift(x:Tensor, mask:bool=False):
"Shift the line i of `x` by p-i elements to the left, is `mask` puts 0s on the diagonal."
bs,nh,n,p = x.size()
x_pad = torch.cat([x.new_zeros(bs,nh,n,1), x], dim=3)
x_shif... |
Split a RNN `model` in groups for differential learning rates.
def tfmer_lm_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0]
n = len(encoder.layers)//3
groups = [list(encoder.layers[:n]), list(encoder.layers[n:2*n]), list(enc... |
Split a RNN `model` in groups for differential learning rates.
def tfmer_clas_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0].module
n = len(encoder.layers)//3
groups = [[encoder.encoder], list(encoder.layers[:n]), list(enco... |
Split a RNN `model` in groups for differential learning rates.
def tfmerXL_lm_split(model:nn.Module) -> List[nn.Module]:
"Split a RNN `model` in groups for differential learning rates."
encoder = model[0]
n = len(encoder.layers)//3
groups = [list(encoder.layers[:n]) + [ParameterModule(encoder.u), Param... |
Reset the internal memory.
def reset(self):
"Reset the internal memory."
self.hidden = [next(self.parameters()).data.new(0) for i in range(self.n_layers+1)] |
Make report in form of two notebooks.
Use nbdime diff-web to present the difference between reference
cells and test cells.
def make_report(self, outcome):
"""Make report in form of two notebooks.
Use nbdime diff-web to present the difference between reference
cells and test c... |
BatchNorm layers to have parameters in single precision.
Find all layers and convert them back to float. This can't
be done with built in .apply as that function will apply
fn to all modules, parameters, and buffers. Thus we wouldn't
be able to guard the float conversion based on the module type.
def b... |
Creates a fp32 copy of model parameters and sets optimizer parameters
def copy_model_to_fp32(m, optim):
""" Creates a fp32 copy of model parameters and sets optimizer parameters
"""
fp32_params = [m_param.clone().type(torch.cuda.FloatTensor).detach() for m_param in trainable_params_(m)]
optim_groups =... |
Start coverage reporting in kernel.
Currently supported kernel languages are:
- Python
def setup_coverage(config, kernel, floc, output_loc=None):
"""Start coverage reporting in kernel.
Currently supported kernel languages are:
- Python
"""
language = kernel.language
if language.sta... |
Finish coverage reporting in kernel.
The coverage should previously have been started with
setup_coverage.
def teardown_coverage(config, kernel, output_loc=None):
"""Finish coverage reporting in kernel.
The coverage should previously have been started with
setup_coverage.
"""
language = k... |
Returns the coverage object of pytest-cov.
def get_cov(config):
"""Returns the coverage object of pytest-cov."""
# Check with hasplugin to avoid getplugin exception in older pytest.
if config.pluginmanager.hasplugin('_cov'):
plugin = config.pluginmanager.getplugin('_cov')
if plugin.cov_con... |
Create a suffix for nbval data file depending on pytest-cov config.
def _make_suffix(cov):
"""Create a suffix for nbval data file depending on pytest-cov config."""
# Check if coverage object has data_suffix:
if cov and cov.data_suffix is not None:
# If True, the suffix will be autogenerated by cov... |
Merge nbval coverage data into pytest-cov data.
def _merge_nbval_coverage_data(cov):
"""Merge nbval coverage data into pytest-cov data."""
if not cov:
return
suffix = _make_suffix(cov)
if suffix is True:
# Note: If suffix is true, we are running in parallel, so several
# files ... |
Yield successive `n`-sized chunks from `l`.
def chunks(l:Collection, n:int)->Iterable:
"Yield successive `n`-sized chunks from `l`."
for i in range(0, len(l), n): yield l[i:i+n] |
Convert `b` to an int or list of ints (if `is_listy`); raises exception if not convertible
def to_int(b:Any)->Union[int,List[int]]:
"Convert `b` to an int or list of ints (if `is_listy`); raises exception if not convertible"
if is_listy(b): return [to_int(x) for x in b]
else: return int(b) |
Return `True` if `a` is one-dimensional
def is1d(a:Collection)->bool:
"Return `True` if `a` is one-dimensional"
return len(a.shape) == 1 if hasattr(a, 'shape') else True |
Return sorted unique values of `x`.
def uniqueify(x:Series, sort:bool=False)->List:
"Return sorted unique values of `x`."
res = list(OrderedDict.fromkeys(x).keys())
if sort: res.sort()
return res |
List of label subdirectories in imagenet-style `folder`.
def find_classes(folder:Path)->FilePathList:
"List of label subdirectories in imagenet-style `folder`."
classes = [d for d in folder.iterdir()
if d.is_dir() and not d.name.startswith('.')]
assert(len(classes)>0)
return sorted(class... |
Given `arrs` is [a,b,...] and `mask`index - return[(a[mask],a[~mask]),(b[mask],b[~mask]),...].
def arrays_split(mask:NPArrayMask, *arrs:NPArrayableList)->SplitArrayList:
"Given `arrs` is [a,b,...] and `mask`index - return[(a[mask],a[~mask]),(b[mask],b[~mask]),...]."
assert all([len(arr)==len(arrs[0]) for arr i... |
Randomly split `arrs` with `valid_pct` ratio. good for creating validation set.
def random_split(valid_pct:float, *arrs:NPArrayableList)->SplitArrayList:
"Randomly split `arrs` with `valid_pct` ratio. good for creating validation set."
assert (valid_pct>=0 and valid_pct<=1), 'Validation set percentage should b... |
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