BoghdadyJR/codesearch_python · Datasets at Hugging Face

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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':'ift:ico'} if size not in _img_sizes: raise RuntimeError(...
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=webhp&source=lnms&tbm=isch' + _url_params(size, fo...
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 NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'} html = r...
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': 'rg_meta'}) metadata_dicts = (json.loads(e.text) for e in img_tags) ...
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 metadata as tuples (fname, url). Use this for downloads of >100 images. Requires `selenium`. """ try: from selenium import webdriver ...
def _download_images(label_path:PathOrStr, img_tuples:list, max_workers:int=defaults.cpus, timeout:int=4) -> FilePathList: """ 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 s...
def _download_single_image(label_path:Path, img_tuple:tuple, i:int, timeout:int=4) -> None: """ 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`. """ suffix = re.f...
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, layout=Layo...
def clear_imgs(self) -> None: "Clear the widget's images preview pane." self._preview_header.value = self._heading self._img_pane.children = tuple()
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() return input.value != str()
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_input.value if not self.validate_search_input(): return self....
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)
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 self.stop,self.best_loss = False,0. re...
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.step() if self.sched.is_done or (self.stop_div...
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 in self.callbacks: if hasattr(cb, 'reset'): cb.reset() ...
def dropout_mask(x, sz, dropout): """ 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 use...
def _setup(self): """ 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 _setweights(self): """ Uses pytorch's built-in dropout function to apply dropout to the parameters of the wrapped module. Args: None Returns: None """ for name_w in self.weights: raw_w = getattr(self.module, name_w + '_raw') ...
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:Callable=data_collate, no_check:bool=False, **kwargs)->DataBunch: "Load ...
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_tfms:Optional[Collection[Callable]]=None, device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False, *...
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.Train else self.test_dl if ds_type == DatasetType.Test else ...
def dls(self)->List[DeviceDataLoader]: "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." res = [self.train_dl, self.fix_dl, self.single_...
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("Serializing the `DataBunch` only works when you created it using the data block API.") ...
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 = self.dl(ds_type) w = self.num_workers self.num_workers = 0 ...
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=DatasetType.Single, detach=detach, denorm=denorm, cpu=cpu)
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) n_items = rows 2 if self.train_ds.x._square_...
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(normalize=self.norm.keywords) if getattr(self, 'norm', False) else {} try_save(self.valid_ds.get_state(**xtra),...
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.train_ds.items) == 0 or not hasattr(self.train_dl, 'batch_sampler'): re...
def one_cycle_scheduler(lr_max:float, kwargs:Any)->OneCycleScheduler: "Instantiate a `OneCycleScheduler` with `lr_max`." return partial(OneCycleScheduler, lr_max=lr_max, kwargs)
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, tot_epochs:int=None, start_epoch:int=None)...
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 `stop_div`, stops when loss diverges." start_lr = learn.lr_range(start_lr) start_lr = np.array(start_lr) if i...
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) learn.data.add_tfm(batch_to_half) ...
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
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)) return learn
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
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, ds_type=ds_type)
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 = range_of(rec.losses) val_iter = np.array(rec...
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)
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'")
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
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_grad: p.grad.div_(self.acc_samples) self.acc_samples = 0 ...
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_last: self.learn.opt.step() self.learn.opt.zero_grad()
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)
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(self.data.c, self.data.c, dtype=x.dtype)...
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 confusion matrix, with `title` and using `cmap`." # T...
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 actual, predicted, number of occurrences." cm = self.confusion_matrix(slice_size=slice_size) np.fill_diagonal(cm, 0)...
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)
def fbeta(log_preds, targs, beta, thresh=0.5, epsilon=1e-8): """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). ...
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 + epsilon)
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-1 dirname = 'imagenet' gpu = setup_distrib(gpu) if gpu is None: bs *= torch.cud...
def cnn_config(arch): "Get the metadata associated with `arch`." torch.backends.cudnn.benchmark = True return model_meta.get(arch, _default_meta)
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:...
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] ...
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_pool:bool=True): "Create custom c...
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_norm...
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[float,float]]=None, last_cross:bool=True, ...
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 Augmentation." preds = learn.TTA(ds_type=ds_type, with_loss=True) if tta else learn.get_preds(ds_type=ds_type, with_l...
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` along with their prediction, actual, loss, and probability of actual class." tl_val,tl_idx = self.to...
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") retu...
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
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_losses = learn.get_preds(ds_type=DatasetType.Fix, with_loss=True) idxs = to...
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`." return ll_input.transform(tfms=crop_pad(), size=size, resize_method=resize_method, padding_mode=p...
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
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_actns = cls.get_actns(learn, hook=hook, dl=dl, kwargs) si...
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" print('Getting activations...') actns = [] learn.model.eval() with torch.no...
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('Computing similarities...') w1 = t1.norm(p=2, dim=1, keepdim=True) w2 ...
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.argpartition(flat, -n)[-n:] indices = indices[np.argso...
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]) for i in range(len(idxs[0]))] return [e for l in idxs for e in l]
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)
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_click(handler) if style is not None: btn.but...
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.Dropdown(description=description, options=options, value...
def make_horizontal_box(cls, children, layout=Layout()): "Make a horizontal box with `children` and `layout`." return widgets.HBox(children, layout=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]], layout=layout)
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 if self._duplicates: chunked_idxs = chunks(fns_idxs, 2) chunked_idxs = [chunk for chunk ...
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,change.owner.file_path fp = Path(file_path) parent = fp.parents[1] self._csv_dict[fp] =...
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 if (delete_btn.flagged_for_delete == True): self.delete_image(fp) ...
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
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')) dropdown = self.make_dropdown_wid...
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 any(img in self._deleted_fns for img in imgs)
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 since at least one of the images was deleted ...
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_shift = x_pad.view(bs,nh,p + 1,n)[:,:,1:].view_as(x) if mask: x_shift.mul_(torch.tril(x.n...
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:2n]), list(encoder.layers[2n:])] return groups + [[encoder.encoder, model...
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(encoder.layers[n:2n]), list(encoder.layers[2n:])] return group...
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), ParameterModule(encoder.v)]] return groups + [list(encoder.layers...
def reset(self): "Reset the internal memory." self.hidden = [next(self.parameters()).data.new(0) for i in range(self.n_layers+1)]
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 cells. """ failures = self.getreports('failed') if not failures: return for rep in failures: ...
def batchnorm_to_fp32(module): ''' 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 ...
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 = [group['params'] for group in optim.param_groups] iter_fp32_params...
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.startswith('python'): # Get the pytest-cov coverage object cov = get_cov(config) ...
def teardown_coverage(config, kernel, output_loc=None): """Finish coverage reporting in kernel. The coverage should previously have been started with setup_coverage. """ language = kernel.language if language.startswith('python'): # Teardown code does not require any input, simply execu...
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_controller: return plugin.cov_contr...
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 coverage.py. # The suffixed data files will be automatically com...
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 will be generated. This will cause some warnings ...
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]
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)
def is1d(a:Collection)->bool: "Return `True` if `a` is one-dimensional" return len(a.shape) == 1 if hasattr(a, 'shape') else True
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
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(classes, key=lambda d: d.name)
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 in arrs]), 'All arrays should have same length' mask = array(mask) return list(zip([(a[m...
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 be between 0 and 1' is_train = np.random.uniform(size=(len(arrs[0]),)) > valid...

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':'ift:ico'} if size not in _img_sizes: raise RuntimeError(...

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=webhp&source=lnms&tbm=isch' + _url_params(size, fo...

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 NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.36'} html = r...

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': 'rg_meta'}) metadata_dicts = (json.loads(e.text) for e in img_tags) ...

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 metadata as tuples (fname, url). Use this for downloads of >100 images. Requires `selenium`. """ try: from selenium import webdriver ...

def _download_images(label_path:PathOrStr, img_tuples:list, max_workers:int=defaults.cpus, timeout:int=4) -> FilePathList: """ 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 s...

def _download_single_image(label_path:Path, img_tuple:tuple, i:int, timeout:int=4) -> None: """ 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`. """ suffix = re.f...

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, layout=Layo...

def clear_imgs(self) -> None: "Clear the widget's images preview pane." self._preview_header.value = self._heading self._img_pane.children = tuple()

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() return input.value != str()

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_input.value if not self.validate_search_input(): return self....

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)

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 self.stop,self.best_loss = False,0. re...

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.step() if self.sched.is_done or (self.stop_div...

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 in self.callbacks: if hasattr(cb, 'reset'): cb.reset() ...

def dropout_mask(x, sz, dropout): """ 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 use...

def _setup(self): """ 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 _setweights(self): """ Uses pytorch's built-in dropout function to apply dropout to the parameters of the wrapped module. Args: None Returns: None """ for name_w in self.weights: raw_w = getattr(self.module, name_w + '_raw') ...

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:Callable=data_collate, no_check:bool=False, **kwargs)->DataBunch: "Load ...

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_tfms:Optional[Collection[Callable]]=None, device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False, *...

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.Train else self.test_dl if ds_type == DatasetType.Test else ...

def dls(self)->List[DeviceDataLoader]: "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." res = [self.train_dl, self.fix_dl, self.single_...

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("Serializing the `DataBunch` only works when you created it using the data block API.") ...

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 = self.dl(ds_type) w = self.num_workers self.num_workers = 0 ...

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=DatasetType.Single, detach=detach, denorm=denorm, cpu=cpu)

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) n_items = rows **2 if self.train_ds.x._square_...

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(normalize=self.norm.keywords) if getattr(self, 'norm', False) else {} try_save(self.valid_ds.get_state(**xtra),...

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.train_ds.items) == 0 or not hasattr(self.train_dl, 'batch_sampler'): re...

def one_cycle_scheduler(lr_max:float, **kwargs:Any)->OneCycleScheduler: "Instantiate a `OneCycleScheduler` with `lr_max`." return partial(OneCycleScheduler, lr_max=lr_max, **kwargs)

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, tot_epochs:int=None, start_epoch:int=None)...

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 `stop_div`, stops when loss diverges." start_lr = learn.lr_range(start_lr) start_lr = np.array(start_lr) if i...

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) learn.data.add_tfm(batch_to_half) ...

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

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)) return learn

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

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, ds_type=ds_type)

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 = range_of(rec.losses) val_iter = np.array(rec...

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)

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'")

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

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_grad: p.grad.div_(self.acc_samples) self.acc_samples = 0 ...

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_last: self.learn.opt.step() self.learn.opt.zero_grad()

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)

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(self.data.c, self.data.c, dtype=x.dtype)...

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 confusion matrix, with `title` and using `cmap`." # T...

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 actual, predicted, number of occurrences." cm = self.confusion_matrix(slice_size=slice_size) np.fill_diagonal(cm, 0)...

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)

def fbeta(log_preds, targs, beta, thresh=0.5, epsilon=1e-8): """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). ...

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 + epsilon)

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-1 dirname = 'imagenet' gpu = setup_distrib(gpu) if gpu is None: bs *= torch.cud...

def cnn_config(arch): "Get the metadata associated with `arch`." torch.backends.cudnn.benchmark = True return model_meta.get(arch, _default_meta)

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:...

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] ...

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_pool:bool=True): "Create custom c...

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_norm...

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[float,float]]=None, last_cross:bool=True, ...

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 Augmentation." preds = learn.TTA(ds_type=ds_type, with_loss=True) if tta else learn.get_preds(ds_type=ds_type, with_l...

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` along with their prediction, actual, loss, and probability of actual class." tl_val,tl_idx = self.to...

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") retu...

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

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_losses = learn.get_preds(ds_type=DatasetType.Fix, with_loss=True) idxs = to...

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`." return ll_input.transform(tfms=crop_pad(), size=size, resize_method=resize_method, padding_mode=p...

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

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_actns = cls.get_actns(learn, hook=hook, dl=dl, **kwargs) si...

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" print('Getting activations...') actns = [] learn.model.eval() with torch.no...

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('Computing similarities...') w1 = t1.norm(p=2, dim=1, keepdim=True) w2 ...

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.argpartition(flat, -n)[-n:] indices = indices[np.argso...

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]) for i in range(len(idxs[0]))] return [e for l in idxs for e in l]

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)

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_click(handler) if style is not None: btn.but...

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.Dropdown(description=description, options=options, value...

def make_horizontal_box(cls, children, layout=Layout()): "Make a horizontal box with `children` and `layout`." return widgets.HBox(children, layout=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]], layout=layout)

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 if self._duplicates: chunked_idxs = chunks(fns_idxs, 2) chunked_idxs = [chunk for chunk ...

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,change.owner.file_path fp = Path(file_path) parent = fp.parents[1] self._csv_dict[fp] =...

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 if (delete_btn.flagged_for_delete == True): self.delete_image(fp) ...

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

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')) dropdown = self.make_dropdown_wid...

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 any(img in self._deleted_fns for img in imgs)

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 since at least one of the images was deleted ...

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_shift = x_pad.view(bs,nh,p + 1,n)[:,:,1:].view_as(x) if mask: x_shift.mul_(torch.tril(x.n...

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(encoder.layers[2*n:])] return groups + [[encoder.encoder, model...

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(encoder.layers[n:2*n]), list(encoder.layers[2*n:])] return group...

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), ParameterModule(encoder.v)]] return groups + [list(encoder.layers...

def reset(self): "Reset the internal memory." self.hidden = [next(self.parameters()).data.new(0) for i in range(self.n_layers+1)]

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 cells. """ failures = self.getreports('failed') if not failures: return for rep in failures: ...

def batchnorm_to_fp32(module): ''' 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 ...

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 = [group['params'] for group in optim.param_groups] iter_fp32_params...

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.startswith('python'): # Get the pytest-cov coverage object cov = get_cov(config) ...

def teardown_coverage(config, kernel, output_loc=None): """Finish coverage reporting in kernel. The coverage should previously have been started with setup_coverage. """ language = kernel.language if language.startswith('python'): # Teardown code does not require any input, simply execu...

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_controller: return plugin.cov_contr...

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 coverage.py. # The suffixed data files will be automatically com...

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 will be generated. This will cause some warnings ...

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]

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)

def is1d(a:Collection)->bool: "Return `True` if `a` is one-dimensional" return len(a.shape) == 1 if hasattr(a, 'shape') else True

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

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(classes, key=lambda d: d.name)

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 in arrs]), 'All arrays should have same length' mask = array(mask) return list(zip(*[(a[m...

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 be between 0 and 1' is_train = np.random.uniform(size=(len(arrs[0]),)) > valid...