BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def add_metrics(last_metrics:Collection[Rank0Tensor], mets:Union[Rank0Tensor, Collection[Rank0Tensor]]): "Return a dictionary for updating `last_metrics` with `mets`." last_metrics,mets = listify(last_metrics),listify(mets) return {'last_metrics': last_metrics + mets}
def map(self, fn, *iterables, timeout=None, chunksize=1, prefetch=None): """ Collects iterables lazily, rather than immediately. Docstring same as parent: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Executor Implmentation taken from this PR: https://githu...
def gen_ascii_docs(src='fastai'): """Generate documentation for fastai library in HTML (asciidoctor required) :param str src: The absolute/relative path of source file/dir """ os.chdir(Path(__file__).absolute().parent) with working_directory('..'): path = Path(src) if path.is_dir(): ...
def _get_new_batch(self, ds_type:DatasetType)->Collection[Tensor]: "Retrieves new batch of DatasetType, and detaches it." return self.learn.data.one_batch(ds_type=ds_type, detach=True, denorm=False, cpu=False)
def _update_batches_if_needed(self)->None: "one_batch function is extremely slow with large datasets. This is caching the result as an optimization." if self.learn.data.valid_dl is None: return # Running learning rate finder, so return update_batches = self.data is not self.learn.data i...
def _write_model_stats(self, iteration:int)->None: "Writes gradient statistics to Tensorboard." self.stats_writer.write(model=self.learn.model, iteration=iteration, tbwriter=self.tbwriter)
def _write_training_loss(self, iteration:int, last_loss:Tensor)->None: "Writes training loss to Tensorboard." scalar_value = to_np(last_loss) tag = self.metrics_root + 'train_loss' self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)
def _write_weight_histograms(self, iteration:int)->None: "Writes model weight histograms to Tensorboard." self.hist_writer.write(model=self.learn.model, iteration=iteration, tbwriter=self.tbwriter)
def _write_scalar(self, name:str, scalar_value, iteration:int)->None: "Writes single scalar value to Tensorboard." tag = self.metrics_root + name self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)
def _write_metrics(self, iteration:int, last_metrics:MetricsList, start_idx:int=2)->None: "Writes training metrics to Tensorboard." recorder = self.learn.recorder for i, name in enumerate(recorder.names[start_idx:]): if last_metrics is None or len(last_metrics) < i+1: return ...
def on_batch_end(self, last_loss:Tensor, iteration:int, **kwargs)->None: "Callback function that writes batch end appropriate data to Tensorboard." if iteration == 0: return self._update_batches_if_needed() if iteration % self.loss_iters == 0: self._write_training_loss(iteration=iteratio...
def on_backward_end(self, iteration:int, **kwargs)->None: "Callback function that writes backward end appropriate data to Tensorboard." if iteration == 0: return self._update_batches_if_needed() if iteration % self.stats_iters == 0: self._write_model_stats(iteration=iteration)
def on_epoch_end(self, last_metrics:MetricsList, iteration:int, **kwargs)->None: "Callback function that writes epoch end appropriate data to Tensorboard." self._write_metrics(iteration=iteration, last_metrics=last_metrics)
def _write_weight_histograms(self, iteration:int)->None: "Writes model weight histograms to Tensorboard." generator, critic = self.learn.gan_trainer.generator, self.learn.gan_trainer.critic self.hist_writer.write(model=generator, iteration=iteration, tbwriter=self.tbwriter, name='generator') ...
def _write_gen_model_stats(self, iteration:int)->None: "Writes gradient statistics for generator to Tensorboard." generator = self.learn.gan_trainer.generator self.stats_writer.write(model=generator, iteration=iteration, tbwriter=self.tbwriter, name='gen_model_stats') self.gen_stats_upda...
def _write_critic_model_stats(self, iteration:int)->None: "Writes gradient statistics for critic to Tensorboard." critic = self.learn.gan_trainer.critic self.stats_writer.write(model=critic, iteration=iteration, tbwriter=self.tbwriter, name='crit_model_stats') self.crit_stats_updated = T...
def _write_model_stats(self, iteration:int)->None: "Writes gradient statistics to Tensorboard." # We don't want to write stats when model is not iterated on and hence has zeroed out gradients gen_mode = self.learn.gan_trainer.gen_mode if gen_mode and not self.gen_stats_updated: self._wri...
def _write_training_loss(self, iteration:int, last_loss:Tensor)->None: "Writes training loss to Tensorboard." recorder = self.learn.gan_trainer.recorder if len(recorder.losses) == 0: return scalar_value = to_np((recorder.losses[-1:])[0]) tag = self.metrics_root + 'train_loss' ...
def _write_images(self, iteration:int)->None: "Writes model generated, original and real images to Tensorboard." trainer = self.learn.gan_trainer #TODO: Switching gen_mode temporarily seems a bit hacky here. Certainly not a good side-effect. Is there a better way? gen_mode = trainer.g...
def on_batch_end(self, iteration:int, kwargs)->None: "Callback function that writes batch end appropriate data to Tensorboard." super().on_batch_end(iteration=iteration, kwargs) if iteration == 0: return if iteration % self.visual_iters == 0: self._write_images(iteration=iteration)
def on_backward_end(self, iteration:int, **kwargs)->None: "Callback function that writes backward end appropriate data to Tensorboard." if iteration == 0: return self._update_batches_if_needed() #TODO: This could perhaps be implemented as queues of requests instead but that seemed like ...
def _write_images(self, iteration:int)->None: "Writes model generated, original and real images to Tensorboard" self.img_gen_vis.write(learn=self.learn, trn_batch=self.trn_batch, val_batch=self.val_batch, iteration=iteration, tbwriter=self.tbwriter)
def request_write(self, request: TBWriteRequest)->None: "Queues up an asynchronous write request to Tensorboard." if self.stop_request.isSet(): return self.queue.put(request)
def _queue_processor(self)->None: "Processes queued up write requests asynchronously to Tensorboard." while not self.stop_request.isSet(): while not self.queue.empty(): if self.stop_request.isSet(): return request = self.queue.get() request.wri...
def get_list_from_model(learn:Learner, ds_type:DatasetType, batch:Tuple)->[]: "Factory method to convert a batch of model images to a list of ModelImageSet." image_sets = [] x,y = batch[0],batch[1] preds = learn.pred_batch(ds_type=ds_type, batch=(x,y), reconstruct=True) for ori...
def _write_histogram(self, param_name:str, values)->None: "Writes single model histogram to Tensorboard." tag = self.name + '/weights/' + param_name self.tbwriter.add_histogram(tag=tag, values=values, global_step=self.iteration)
def write(self)->None: "Writes model histograms to Tensorboard." for param_name, values in self.params: self._write_histogram(param_name=param_name, values=values)
def write(self, model:nn.Module, iteration:int, tbwriter:SummaryWriter, name:str='model')->None: "Writes model histograms to Tensorboard." request = HistogramTBRequest(model=model, iteration=iteration, tbwriter=tbwriter, name=name) asyncTBWriter.request_write(request)
def _add_gradient_scalar(self, name:str, scalar_value)->None: "Writes a single scalar value for a gradient statistic to Tensorboard." tag = self.name + '/gradients/' + name self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=self.iteration)
def _write_avg_norm(self, norms:[])->None: "Writes the average norm of the gradients to Tensorboard." avg_norm = sum(norms)/len(self.gradients) self._add_gradient_scalar('avg_norm', scalar_value=avg_norm)
def _write_median_norm(self, norms:[])->None: "Writes the median norm of the gradients to Tensorboard." median_norm = statistics.median(norms) self._add_gradient_scalar('median_norm', scalar_value=median_norm)
def _write_max_norm(self, norms:[])->None: "Writes the maximum norm of the gradients to Tensorboard." max_norm = max(norms) self._add_gradient_scalar('max_norm', scalar_value=max_norm)
def _write_min_norm(self, norms:[])->None: "Writes the minimum norm of the gradients to Tensorboard." min_norm = min(norms) self._add_gradient_scalar('min_norm', scalar_value=min_norm)
def _write_num_zeros(self)->None: "Writes the number of zeroes in the gradients to Tensorboard." gradient_nps = [to_np(x.data) for x in self.gradients] num_zeros = sum((np.asarray(x) == 0.0).sum() for x in gradient_nps) self._add_gradient_scalar('num_zeros', scalar_value=num_zeros)
def _write_avg_gradient(self)->None: "Writes the average of the gradients to Tensorboard." avg_gradient = sum(x.data.mean() for x in self.gradients)/len(self.gradients) self._add_gradient_scalar('avg_gradient', scalar_value=avg_gradient)
def _write_median_gradient(self)->None: "Writes the median of the gradients to Tensorboard." median_gradient = statistics.median(x.data.median() for x in self.gradients) self._add_gradient_scalar('median_gradient', scalar_value=median_gradient)
def _write_max_gradient(self)->None: "Writes the maximum of the gradients to Tensorboard." max_gradient = max(x.data.max() for x in self.gradients) self._add_gradient_scalar('max_gradient', scalar_value=max_gradient)
def _write_min_gradient(self)->None: "Writes the minimum of the gradients to Tensorboard." min_gradient = min(x.data.min() for x in self.gradients) self._add_gradient_scalar('min_gradient', scalar_value=min_gradient)
def write(self)->None: "Writes model gradient statistics to Tensorboard." if len(self.gradients) == 0: return norms = [x.data.norm() for x in self.gradients] self._write_avg_norm(norms=norms) self._write_median_norm(norms=norms) self._write_max_norm(norms=norms) s...
def _write_images(self, name:str, images:[Tensor])->None: "Writes list of images as tensors to Tensorboard." tag = self.ds_type.name + ' ' + name self.tbwriter.add_image(tag=tag, img_tensor=vutils.make_grid(images, normalize=True), global_step=self.iteration)
def _get_image_tensors(self)->([Tensor], [Tensor], [Tensor]): "Gets list of image tensors from lists of Image objects, as a tuple of original, generated and real(target) images." orig_images, gen_images, real_images = [], [], [] for image_set in self.image_sets: orig_images.append(im...
def write(self)->None: "Writes original, generated and real(target) images to Tensorboard." orig_images, gen_images, real_images = self._get_image_tensors() self._write_images(name='orig images', images=orig_images) self._write_images(name='gen images', images=gen_images) self._...
def write(self, learn:Learner, trn_batch:Tuple, val_batch:Tuple, iteration:int, tbwriter:SummaryWriter)->None: "Writes training and validation batch images to Tensorboard." self._write_for_dstype(learn=learn, batch=val_batch, iteration=iteration, tbwriter=tbwriter, ds_type=DatasetType.Valid) sel...
def _write_for_dstype(self, learn:Learner, batch:Tuple, iteration:int, tbwriter:SummaryWriter, ds_type:DatasetType)->None: "Writes batch images of specified DatasetType to Tensorboard." request = ImageTBRequest(learn=learn, batch=batch, iteration=iteration, tbwriter=tbwriter, ds_type=ds_type) as...
def write(self)->None: "Writes single model graph to Tensorboard." self.tbwriter.add_graph(model=self.model, input_to_model=self.input_to_model)
def write(self, model:nn.Module, tbwriter:SummaryWriter, input_to_model:torch.Tensor)->None: "Writes model graph to Tensorboard." request = GraphTBRequest(model=model, tbwriter=tbwriter, input_to_model=input_to_model) asyncTBWriter.request_write(request)
def fix_batchnorm(swa_model, train_dl): """ During training, batch norm layers keep track of a running mean and variance of the previous layer's activations. Because the parameters of the SWA model are computed as the average of other models' parameters, the SWA model never sees the training data it...
def repackage_var(h): """Wraps h in new Variables, to detach them from their history.""" if IS_TORCH_04: return h.detach() if type(h) == torch.Tensor else tuple(repackage_var(v) for v in h) else: return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)
def get_language_model(n_tok, emb_sz, n_hid, n_layers, pad_token, dropout=0.4, dropouth=0.3, dropouti=0.5, dropoute=0.1, wdrop=0.5, tie_weights=True, qrnn=False, bias=False): """Returns a SequentialRNN model. A RNN_Encoder layer is instantiated using the parameters provided. This is follo...
def forward(self, input): """ Invoked during the forward propagation of the RNN_Encoder module. Args: input (Tensor): input of shape (sentence length x batch_size) Returns: raw_outputs (tuple(list (Tensor), list(Tensor)): list of tensors evaluated from each RNN layer wit...
def replace_rep(t:str) -> str: "Replace repetitions at the character level in `t`." def _replace_rep(m:Collection[str]) -> str: c,cc = m.groups() return f' {TK_REP} {len(cc)+1} {c} ' re_rep = re.compile(r'(\S)(\1{3,})') return re_rep.sub(_replace_rep, t)
def replace_wrep(t:str) -> str: "Replace word repetitions in `t`." def _replace_wrep(m:Collection[str]) -> str: c,cc = m.groups() return f' {TK_WREP} {len(cc.split())+1} {c} ' re_wrep = re.compile(r'(\b\w+\W+)(\1{3,})') return re_wrep.sub(_replace_wrep, t)
def fix_html(x:str) -> str: "List of replacements from html strings in `x`." re1 = re.compile(r' +') x = x.replace('#39;', "'").replace('amp;', '&').replace('#146;', "'").replace( 'nbsp;', ' ').replace('#36;', '$').replace('\\n', "\n").replace('quot;', "'").replace( '<br />', "\n").replace(...
def replace_all_caps(x:Collection[str]) -> Collection[str]: "Replace tokens in ALL CAPS in `x` by their lower version and add `TK_UP` before." res = [] for t in x: if t.isupper() and len(t) > 1: res.append(TK_UP); res.append(t.lower()) else: res.append(t) return res
def deal_caps(x:Collection[str]) -> Collection[str]: "Replace all Capitalized tokens in `x` by their lower version and add `TK_MAJ` before." res = [] for t in x: if t == '': continue if t[0].isupper() and len(t) > 1 and t[1:].islower(): res.append(TK_MAJ) res.append(t.lower()) re...
def process_text(self, t:str, tok:BaseTokenizer) -> List[str]: "Process one text `t` with tokenizer `tok`." for rule in self.pre_rules: t = rule(t) toks = tok.tokenizer(t) for rule in self.post_rules: toks = rule(toks) return toks
def _process_all_1(self, texts:Collection[str]) -> List[List[str]]: "Process a list of `texts` in one process." tok = self.tok_func(self.lang) if self.special_cases: tok.add_special_cases(self.special_cases) return [self.process_text(str(t), tok) for t in texts]
def process_all(self, texts:Collection[str]) -> List[List[str]]: "Process a list of `texts`." if self.n_cpus <= 1: return self._process_all_1(texts) with ProcessPoolExecutor(self.n_cpus) as e: return sum(e.map(self._process_all_1, partition_by_cores(texts, self.n_cpus)), [])
def numericalize(self, t:Collection[str]) -> List[int]: "Convert a list of tokens `t` to their ids." return [self.stoi[w] for w in t]
def textify(self, nums:Collection[int], sep=' ') -> List[str]: "Convert a list of `nums` to their tokens." return sep.join([self.itos[i] for i in nums]) if sep is not None else [self.itos[i] for i in nums]
def create(cls, tokens:Tokens, max_vocab:int, min_freq:int) -> 'Vocab': "Create a vocabulary from a set of `tokens`." freq = Counter(p for o in tokens for p in o) itos = [o for o,c in freq.most_common(max_vocab) if c >= min_freq] for o in reversed(defaults.text_spec_tok): if ...
def load(cls, path): "Load the `Vocab` contained in `path`" itos = pickle.load(open(path, 'rb')) return cls(itos)
def plot_loss(self, n_skip=10, n_skip_end=5): ''' plots loss function as function of iterations. When used in Jupyternotebook, plot will be displayed in notebook. Else, plot will be displayed in console and both plot and loss are saved in save_path. ''' if not in_ipynb(): plt.s...
def plot_lr(self): '''Plots learning rate in jupyter notebook or console, depending on the enviroment of the learner.''' if not in_ipynb(): plt.switch_backend('agg') if self.record_mom: fig, axs = plt.subplots(1,2,figsize=(12,4)) for i in range(0,2): axs[i].se...
def plot(self, n_skip=10, n_skip_end=5): ''' Plots the loss function with respect to learning rate, in log scale. ''' plt.ylabel("validation loss") plt.xlabel("learning rate (log scale)") plt.plot(self.lrs[n_skip:-(n_skip_end+1)], self.losses[n_skip:-(n_skip_end+1)]) ...
def plot_lr(self, show_text=True, show_moms=True): """Plots the lr rate/momentum schedule""" phase_limits = [0] for nb_batch, phase in zip(self.nb_batches, self.phases): phase_limits.append(phase_limits[-1] + nb_batch * phase.epochs) if not in_ipynb(): plt.switch_...
def main( gpu:Param("GPU to run on", str)=None, woof: Param("Use imagewoof (otherwise imagenette)", int)=0, lr: Param("Learning rate", float)=1e-3, size: Param("Size (px: 128,192,224)", int)=128, alpha: Param("Alpha", float)=0.99, mom: Param("Momentum", float)=0.9, ...
def on_batch_end(self, last_loss, epoch, num_batch, **kwargs:Any)->None: "Test if `last_loss` is NaN and interrupts training." if self.stop: return True #to skip validation after stopping during training if torch.isnan(last_loss): print (f'Epoch/Batch ({epoch}/{num_batch}): Invalid l...
def on_train_begin(self, **kwargs:Any)->None: "Initializes the best value." self.best = float('inf') if self.operator == np.less else -float('inf')
def get_monitor_value(self): "Pick the monitored value." if self.monitor=='trn_loss' and len(self.learn.recorder.losses) == 0: return None elif len(self.learn.recorder.val_losses) == 0: return None values = {'train_loss':self.learn.recorder.losses[-1].cpu().numpy(), 'va...
def on_epoch_end(self, epoch:int, **kwargs:Any)->None: "Compare the value monitored to its best score and maybe save the model." if self.every=="epoch": self.learn.save(f'{self.name}_{epoch}') else: #every="improvement" current = self.get_monitor_value() if current is not...
def on_train_end(self, **kwargs): "Load the best model." if self.every=="improvement" and (self.learn.path/f'{self.learn.model_dir}/{self.name}.pth').is_file(): self.learn.load(f'{self.name}', purge=False)
def on_train_begin(self, kwargs:Any)->None: "Initialize inner arguments." self.wait, self.opt = 0, self.learn.opt super().on_train_begin(kwargs)
def on_epoch_end(self, epoch, **kwargs:Any)->None: "Compare the value monitored to its best and maybe reduce lr." current = self.get_monitor_value() if current is None: return if self.operator(current - self.min_delta, self.best): self.best,self.wait = current,0 else: ...
def convert_nb(fname, dest_path='.'): "Convert a notebook `fname` to html file in `dest_path`." from .gen_notebooks import remove_undoc_cells, remove_code_cell_jupyter_widget_state_elem nb = read_nb(fname) nb['cells'] = remove_undoc_cells(nb['cells']) nb['cells'] = remove_code_cell_jupyter_widget_st...
def convert_all(folder, dest_path='.', force_all=False): "Convert modified notebooks in `folder` to html pages in `dest_path`." path = Path(folder) changed_cnt = 0 for fname in path.glob("*.ipynb"): # only rebuild modified files fname_out = Path(dest_path)/fname.with_suffix('.html').nam...
def pad_collate(samples:BatchSamples, pad_idx:int=1, pad_first:bool=True, backwards:bool=False) -> Tuple[LongTensor, LongTensor]: "Function that collect samples and adds padding. Flips token order if needed" samples = to_data(samples) max_len = max([len(s[0]) for s in samples]) res = torch.zeros(len(sam...
def open_text(fn:PathOrStr, enc='utf-8'): "Read the text in `fn`." with open(fn,'r', encoding = enc) as f: return ''.join(f.readlines())
def allocate_buffers(self): "Create the ragged array that will be filled when we ask for items." if self.ite_len is None: len(self) self.idx = LanguageModelPreLoader.CircularIndex(len(self.dataset.x.items), not self.backwards) self.batch = np.zeros((self.bs, self.bptt+1), dtype=np.int6...
def fill_row(self, forward, items, idx, row, ro, ri, overlap,lengths): "Fill the row with tokens from the ragged array. --OBS-- overlap != 1 has not been implemented" ibuf = n = 0 ro -= 1 while ibuf < row.size: ro += 1 ix = idx[ro] rag = items[...
def from_ids(cls, path:PathOrStr, vocab:Vocab, train_ids:Collection[Collection[int]], valid_ids:Collection[Collection[int]], test_ids:Collection[Collection[int]]=None, train_lbls:Collection[Union[int,float]]=None, valid_lbls:Collection[Union[int,float]]=None, classes:Collection[Any]=No...
def load(cls, path:PathOrStr, cache_name:PathOrStr='tmp', processor:PreProcessor=None, **kwargs): "Load a `TextDataBunch` from `path/cache_name`. `kwargs` are passed to the dataloader creation." warn("""This method is deprecated and only kept to load data serialized in v1.0.43 or earlier. ...
def from_tokens(cls, path:PathOrStr, trn_tok:Collection[Collection[str]], trn_lbls:Collection[Union[int,float]], val_tok:Collection[Collection[str]], val_lbls:Collection[Union[int,float]], vocab:Vocab=None, tst_tok:Collection[Collection[str]]=None, classes:Collection[Any]=None, max_voc...
def from_df(cls, path:PathOrStr, train_df:DataFrame, valid_df:DataFrame, test_df:Optional[DataFrame]=None, tokenizer:Tokenizer=None, vocab:Vocab=None, classes:Collection[str]=None, text_cols:IntsOrStrs=1, label_cols:IntsOrStrs=0, label_delim:str=None, chunksize:int=10000, max_vocab:int=6...
def from_csv(cls, path:PathOrStr, csv_name, valid_pct:float=0.2, test:Optional[str]=None, tokenizer:Tokenizer=None, vocab:Vocab=None, classes:Collection[str]=None, delimiter:str=None, header='infer', text_cols:IntsOrStrs=1, label_cols:IntsOrStrs=0, label_delim:str=None, ...
def from_folder(cls, path:PathOrStr, train:str='train', valid:str='valid', test:Optional[str]=None, classes:Collection[Any]=None, tokenizer:Tokenizer=None, vocab:Vocab=None, chunksize:int=10000, max_vocab:int=60000, min_freq:int=2, mark_fields:bool=False, include_bos:bool=True, i...
def create(cls, train_ds, valid_ds, test_ds=None, path:PathOrStr='.', no_check:bool=False, bs=64, val_bs:int=None, num_workers:int=0, device:torch.device=None, collate_fn:Callable=data_collate, dl_tfms:Optional[Collection[Callable]]=None, bptt:int=70, backwards:bool=False, **dl_kwargs) -> ...
def create(cls, train_ds, valid_ds, test_ds=None, path:PathOrStr='.', bs:int=32, val_bs:int=None, pad_idx=1, pad_first=True, device:torch.device=None, no_check:bool=False, backwards:bool=False, **dl_kwargs) -> DataBunch: "Function that transform the `datasets` in a `DataBunch` for classification....
def label_for_lm(self, kwargs): "A special labelling method for language models." self.__class__ = LMTextList kwargs['label_cls'] = LMLabelList return self.label_const(0, kwargs)
def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=text_extensions, vocab:Vocab=None, processor:PreProcessor=None, **kwargs)->'TextList': "Get the list of files in `path` that have a text suffix. `recurse` determines if we search subfolders." processor = ifnone(proce...
def show_xys(self, xs, ys, max_len:int=70)->None: "Show the `xs` (inputs) and `ys` (targets). `max_len` is the maximum number of tokens displayed." from IPython.display import display, HTML names = ['idx','text'] if self._is_lm else ['text','target'] items = [] for i, (x,y) in en...
def inceptionv4(pretrained=True): r"""InceptionV4 model architecture from the `"Inception-v4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = InceptionV4() if pretrained: model...
def predict_array(self, arr): """ This over-ride is necessary because otherwise the learner method accesses the wrong model when it is called with precompute set to true Args: arr: a numpy array to be used as input to the model for prediction purposes Returns: ...
def main( gpu:Param("GPU to run on", str)=None ): """Distrubuted training of CIFAR-10. Fastest speed is if you run as follows: python -m fastai.launch train_cifar.py""" gpu = setup_distrib(gpu) n_gpus = num_distrib() path = url2path(URLs.CIFAR) ds_tfms = ([*rand_pad(4, 32), flip_lr(p=0.5...
def initialize_options(self): """Set default values for options.""" self.dep_groups = '' self.dep_quote = False self.dep_conda = False
def run(self): """Run command.""" wanted_groups = self.parse() deps = [] invalid_groups = [] for grp in wanted_groups: if grp in dep_groups: deps.extend(dep_groups[grp]) else: invalid_groups.append(grp) if invalid_groups or not wa...
def get_sfs_idxs(sfs, last=True): """ Return the saved feature indexes that will be concatenated Inputs: sfs (list): saved features by hook function, in other words intermediate activations last (bool): whether to concatenate only last different activation, or all from the encoder model ...
def hook_output (module:nn.Module, detach:bool=True, grad:bool=False)->Hook: "Return a `Hook` that stores activations of `module` in `self.stored`" return Hook(module, _hook_inner, detach=detach, is_forward=not grad)
def hook_outputs(modules:Collection[nn.Module], detach:bool=True, grad:bool=False)->Hooks: "Return `Hooks` that store activations of all `modules` in `self.stored`" return Hooks(modules, _hook_inner, detach=detach, is_forward=not grad)
def dummy_batch(m: nn.Module, size:tuple=(64,64))->Tensor: "Create a dummy batch to go through `m` with `size`." ch_in = in_channels(m) return one_param(m).new(1, ch_in, *size).requires_grad_(False).uniform_(-1.,1.)

def add_metrics(last_metrics:Collection[Rank0Tensor], mets:Union[Rank0Tensor, Collection[Rank0Tensor]]): "Return a dictionary for updating `last_metrics` with `mets`." last_metrics,mets = listify(last_metrics),listify(mets) return {'last_metrics': last_metrics + mets}

def map(self, fn, *iterables, timeout=None, chunksize=1, prefetch=None): """ Collects iterables lazily, rather than immediately. Docstring same as parent: https://docs.python.org/3/library/concurrent.futures.html#concurrent.futures.Executor Implmentation taken from this PR: https://githu...

def gen_ascii_docs(src='fastai'): """Generate documentation for fastai library in HTML (asciidoctor required) :param str src: The absolute/relative path of source file/dir """ os.chdir(Path(__file__).absolute().parent) with working_directory('..'): path = Path(src) if path.is_dir(): ...

def _get_new_batch(self, ds_type:DatasetType)->Collection[Tensor]: "Retrieves new batch of DatasetType, and detaches it." return self.learn.data.one_batch(ds_type=ds_type, detach=True, denorm=False, cpu=False)

def _update_batches_if_needed(self)->None: "one_batch function is extremely slow with large datasets. This is caching the result as an optimization." if self.learn.data.valid_dl is None: return # Running learning rate finder, so return update_batches = self.data is not self.learn.data i...

def _write_model_stats(self, iteration:int)->None: "Writes gradient statistics to Tensorboard." self.stats_writer.write(model=self.learn.model, iteration=iteration, tbwriter=self.tbwriter)

def _write_training_loss(self, iteration:int, last_loss:Tensor)->None: "Writes training loss to Tensorboard." scalar_value = to_np(last_loss) tag = self.metrics_root + 'train_loss' self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)

def _write_weight_histograms(self, iteration:int)->None: "Writes model weight histograms to Tensorboard." self.hist_writer.write(model=self.learn.model, iteration=iteration, tbwriter=self.tbwriter)

def _write_scalar(self, name:str, scalar_value, iteration:int)->None: "Writes single scalar value to Tensorboard." tag = self.metrics_root + name self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=iteration)

def _write_metrics(self, iteration:int, last_metrics:MetricsList, start_idx:int=2)->None: "Writes training metrics to Tensorboard." recorder = self.learn.recorder for i, name in enumerate(recorder.names[start_idx:]): if last_metrics is None or len(last_metrics) < i+1: return ...

def on_batch_end(self, last_loss:Tensor, iteration:int, **kwargs)->None: "Callback function that writes batch end appropriate data to Tensorboard." if iteration == 0: return self._update_batches_if_needed() if iteration % self.loss_iters == 0: self._write_training_loss(iteration=iteratio...

def on_backward_end(self, iteration:int, **kwargs)->None: "Callback function that writes backward end appropriate data to Tensorboard." if iteration == 0: return self._update_batches_if_needed() if iteration % self.stats_iters == 0: self._write_model_stats(iteration=iteration)

def on_epoch_end(self, last_metrics:MetricsList, iteration:int, **kwargs)->None: "Callback function that writes epoch end appropriate data to Tensorboard." self._write_metrics(iteration=iteration, last_metrics=last_metrics)

def _write_weight_histograms(self, iteration:int)->None: "Writes model weight histograms to Tensorboard." generator, critic = self.learn.gan_trainer.generator, self.learn.gan_trainer.critic self.hist_writer.write(model=generator, iteration=iteration, tbwriter=self.tbwriter, name='generator') ...

def _write_gen_model_stats(self, iteration:int)->None: "Writes gradient statistics for generator to Tensorboard." generator = self.learn.gan_trainer.generator self.stats_writer.write(model=generator, iteration=iteration, tbwriter=self.tbwriter, name='gen_model_stats') self.gen_stats_upda...

def _write_critic_model_stats(self, iteration:int)->None: "Writes gradient statistics for critic to Tensorboard." critic = self.learn.gan_trainer.critic self.stats_writer.write(model=critic, iteration=iteration, tbwriter=self.tbwriter, name='crit_model_stats') self.crit_stats_updated = T...

def _write_model_stats(self, iteration:int)->None: "Writes gradient statistics to Tensorboard." # We don't want to write stats when model is not iterated on and hence has zeroed out gradients gen_mode = self.learn.gan_trainer.gen_mode if gen_mode and not self.gen_stats_updated: self._wri...

def _write_training_loss(self, iteration:int, last_loss:Tensor)->None: "Writes training loss to Tensorboard." recorder = self.learn.gan_trainer.recorder if len(recorder.losses) == 0: return scalar_value = to_np((recorder.losses[-1:])[0]) tag = self.metrics_root + 'train_loss' ...

def _write_images(self, iteration:int)->None: "Writes model generated, original and real images to Tensorboard." trainer = self.learn.gan_trainer #TODO: Switching gen_mode temporarily seems a bit hacky here. Certainly not a good side-effect. Is there a better way? gen_mode = trainer.g...

def on_batch_end(self, iteration:int, **kwargs)->None: "Callback function that writes batch end appropriate data to Tensorboard." super().on_batch_end(iteration=iteration, **kwargs) if iteration == 0: return if iteration % self.visual_iters == 0: self._write_images(iteration=iteration)

def on_backward_end(self, iteration:int, **kwargs)->None: "Callback function that writes backward end appropriate data to Tensorboard." if iteration == 0: return self._update_batches_if_needed() #TODO: This could perhaps be implemented as queues of requests instead but that seemed like ...

def _write_images(self, iteration:int)->None: "Writes model generated, original and real images to Tensorboard" self.img_gen_vis.write(learn=self.learn, trn_batch=self.trn_batch, val_batch=self.val_batch, iteration=iteration, tbwriter=self.tbwriter)

def request_write(self, request: TBWriteRequest)->None: "Queues up an asynchronous write request to Tensorboard." if self.stop_request.isSet(): return self.queue.put(request)

def _queue_processor(self)->None: "Processes queued up write requests asynchronously to Tensorboard." while not self.stop_request.isSet(): while not self.queue.empty(): if self.stop_request.isSet(): return request = self.queue.get() request.wri...

def get_list_from_model(learn:Learner, ds_type:DatasetType, batch:Tuple)->[]: "Factory method to convert a batch of model images to a list of ModelImageSet." image_sets = [] x,y = batch[0],batch[1] preds = learn.pred_batch(ds_type=ds_type, batch=(x,y), reconstruct=True) for ori...

def _write_histogram(self, param_name:str, values)->None: "Writes single model histogram to Tensorboard." tag = self.name + '/weights/' + param_name self.tbwriter.add_histogram(tag=tag, values=values, global_step=self.iteration)

def write(self)->None: "Writes model histograms to Tensorboard." for param_name, values in self.params: self._write_histogram(param_name=param_name, values=values)

def write(self, model:nn.Module, iteration:int, tbwriter:SummaryWriter, name:str='model')->None: "Writes model histograms to Tensorboard." request = HistogramTBRequest(model=model, iteration=iteration, tbwriter=tbwriter, name=name) asyncTBWriter.request_write(request)

def _add_gradient_scalar(self, name:str, scalar_value)->None: "Writes a single scalar value for a gradient statistic to Tensorboard." tag = self.name + '/gradients/' + name self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=self.iteration)

def _write_avg_norm(self, norms:[])->None: "Writes the average norm of the gradients to Tensorboard." avg_norm = sum(norms)/len(self.gradients) self._add_gradient_scalar('avg_norm', scalar_value=avg_norm)

def _write_median_norm(self, norms:[])->None: "Writes the median norm of the gradients to Tensorboard." median_norm = statistics.median(norms) self._add_gradient_scalar('median_norm', scalar_value=median_norm)

def _write_max_norm(self, norms:[])->None: "Writes the maximum norm of the gradients to Tensorboard." max_norm = max(norms) self._add_gradient_scalar('max_norm', scalar_value=max_norm)

def _write_min_norm(self, norms:[])->None: "Writes the minimum norm of the gradients to Tensorboard." min_norm = min(norms) self._add_gradient_scalar('min_norm', scalar_value=min_norm)

def _write_num_zeros(self)->None: "Writes the number of zeroes in the gradients to Tensorboard." gradient_nps = [to_np(x.data) for x in self.gradients] num_zeros = sum((np.asarray(x) == 0.0).sum() for x in gradient_nps) self._add_gradient_scalar('num_zeros', scalar_value=num_zeros)

def _write_avg_gradient(self)->None: "Writes the average of the gradients to Tensorboard." avg_gradient = sum(x.data.mean() for x in self.gradients)/len(self.gradients) self._add_gradient_scalar('avg_gradient', scalar_value=avg_gradient)

def _write_median_gradient(self)->None: "Writes the median of the gradients to Tensorboard." median_gradient = statistics.median(x.data.median() for x in self.gradients) self._add_gradient_scalar('median_gradient', scalar_value=median_gradient)

def _write_max_gradient(self)->None: "Writes the maximum of the gradients to Tensorboard." max_gradient = max(x.data.max() for x in self.gradients) self._add_gradient_scalar('max_gradient', scalar_value=max_gradient)

def _write_min_gradient(self)->None: "Writes the minimum of the gradients to Tensorboard." min_gradient = min(x.data.min() for x in self.gradients) self._add_gradient_scalar('min_gradient', scalar_value=min_gradient)

def write(self)->None: "Writes model gradient statistics to Tensorboard." if len(self.gradients) == 0: return norms = [x.data.norm() for x in self.gradients] self._write_avg_norm(norms=norms) self._write_median_norm(norms=norms) self._write_max_norm(norms=norms) s...

def _write_images(self, name:str, images:[Tensor])->None: "Writes list of images as tensors to Tensorboard." tag = self.ds_type.name + ' ' + name self.tbwriter.add_image(tag=tag, img_tensor=vutils.make_grid(images, normalize=True), global_step=self.iteration)

def _get_image_tensors(self)->([Tensor], [Tensor], [Tensor]): "Gets list of image tensors from lists of Image objects, as a tuple of original, generated and real(target) images." orig_images, gen_images, real_images = [], [], [] for image_set in self.image_sets: orig_images.append(im...

def write(self)->None: "Writes original, generated and real(target) images to Tensorboard." orig_images, gen_images, real_images = self._get_image_tensors() self._write_images(name='orig images', images=orig_images) self._write_images(name='gen images', images=gen_images) self._...

def write(self, learn:Learner, trn_batch:Tuple, val_batch:Tuple, iteration:int, tbwriter:SummaryWriter)->None: "Writes training and validation batch images to Tensorboard." self._write_for_dstype(learn=learn, batch=val_batch, iteration=iteration, tbwriter=tbwriter, ds_type=DatasetType.Valid) sel...

def _write_for_dstype(self, learn:Learner, batch:Tuple, iteration:int, tbwriter:SummaryWriter, ds_type:DatasetType)->None: "Writes batch images of specified DatasetType to Tensorboard." request = ImageTBRequest(learn=learn, batch=batch, iteration=iteration, tbwriter=tbwriter, ds_type=ds_type) as...

def write(self)->None: "Writes single model graph to Tensorboard." self.tbwriter.add_graph(model=self.model, input_to_model=self.input_to_model)

def write(self, model:nn.Module, tbwriter:SummaryWriter, input_to_model:torch.Tensor)->None: "Writes model graph to Tensorboard." request = GraphTBRequest(model=model, tbwriter=tbwriter, input_to_model=input_to_model) asyncTBWriter.request_write(request)

def fix_batchnorm(swa_model, train_dl): """ During training, batch norm layers keep track of a running mean and variance of the previous layer's activations. Because the parameters of the SWA model are computed as the average of other models' parameters, the SWA model never sees the training data it...

def repackage_var(h): """Wraps h in new Variables, to detach them from their history.""" if IS_TORCH_04: return h.detach() if type(h) == torch.Tensor else tuple(repackage_var(v) for v in h) else: return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h)

def get_language_model(n_tok, emb_sz, n_hid, n_layers, pad_token, dropout=0.4, dropouth=0.3, dropouti=0.5, dropoute=0.1, wdrop=0.5, tie_weights=True, qrnn=False, bias=False): """Returns a SequentialRNN model. A RNN_Encoder layer is instantiated using the parameters provided. This is follo...

def forward(self, input): """ Invoked during the forward propagation of the RNN_Encoder module. Args: input (Tensor): input of shape (sentence length x batch_size) Returns: raw_outputs (tuple(list (Tensor), list(Tensor)): list of tensors evaluated from each RNN layer wit...

def replace_rep(t:str) -> str: "Replace repetitions at the character level in `t`." def _replace_rep(m:Collection[str]) -> str: c,cc = m.groups() return f' {TK_REP} {len(cc)+1} {c} ' re_rep = re.compile(r'(\S)(\1{3,})') return re_rep.sub(_replace_rep, t)

def replace_wrep(t:str) -> str: "Replace word repetitions in `t`." def _replace_wrep(m:Collection[str]) -> str: c,cc = m.groups() return f' {TK_WREP} {len(cc.split())+1} {c} ' re_wrep = re.compile(r'(\b\w+\W+)(\1{3,})') return re_wrep.sub(_replace_wrep, t)

def fix_html(x:str) -> str: "List of replacements from html strings in `x`." re1 = re.compile(r' +') x = x.replace('#39;', "'").replace('amp;', '&').replace('#146;', "'").replace( 'nbsp;', ' ').replace('#36;', '$').replace('\\n', "\n").replace('quot;', "'").replace( '<br />', "\n").replace(...

def replace_all_caps(x:Collection[str]) -> Collection[str]: "Replace tokens in ALL CAPS in `x` by their lower version and add `TK_UP` before." res = [] for t in x: if t.isupper() and len(t) > 1: res.append(TK_UP); res.append(t.lower()) else: res.append(t) return res

def deal_caps(x:Collection[str]) -> Collection[str]: "Replace all Capitalized tokens in `x` by their lower version and add `TK_MAJ` before." res = [] for t in x: if t == '': continue if t[0].isupper() and len(t) > 1 and t[1:].islower(): res.append(TK_MAJ) res.append(t.lower()) re...

def process_text(self, t:str, tok:BaseTokenizer) -> List[str]: "Process one text `t` with tokenizer `tok`." for rule in self.pre_rules: t = rule(t) toks = tok.tokenizer(t) for rule in self.post_rules: toks = rule(toks) return toks

def _process_all_1(self, texts:Collection[str]) -> List[List[str]]: "Process a list of `texts` in one process." tok = self.tok_func(self.lang) if self.special_cases: tok.add_special_cases(self.special_cases) return [self.process_text(str(t), tok) for t in texts]

def process_all(self, texts:Collection[str]) -> List[List[str]]: "Process a list of `texts`." if self.n_cpus <= 1: return self._process_all_1(texts) with ProcessPoolExecutor(self.n_cpus) as e: return sum(e.map(self._process_all_1, partition_by_cores(texts, self.n_cpus)), [])

def numericalize(self, t:Collection[str]) -> List[int]: "Convert a list of tokens `t` to their ids." return [self.stoi[w] for w in t]

def textify(self, nums:Collection[int], sep=' ') -> List[str]: "Convert a list of `nums` to their tokens." return sep.join([self.itos[i] for i in nums]) if sep is not None else [self.itos[i] for i in nums]

def create(cls, tokens:Tokens, max_vocab:int, min_freq:int) -> 'Vocab': "Create a vocabulary from a set of `tokens`." freq = Counter(p for o in tokens for p in o) itos = [o for o,c in freq.most_common(max_vocab) if c >= min_freq] for o in reversed(defaults.text_spec_tok): if ...

def load(cls, path): "Load the `Vocab` contained in `path`" itos = pickle.load(open(path, 'rb')) return cls(itos)

def plot_loss(self, n_skip=10, n_skip_end=5): ''' plots loss function as function of iterations. When used in Jupyternotebook, plot will be displayed in notebook. Else, plot will be displayed in console and both plot and loss are saved in save_path. ''' if not in_ipynb(): plt.s...

def plot_lr(self): '''Plots learning rate in jupyter notebook or console, depending on the enviroment of the learner.''' if not in_ipynb(): plt.switch_backend('agg') if self.record_mom: fig, axs = plt.subplots(1,2,figsize=(12,4)) for i in range(0,2): axs[i].se...

def plot(self, n_skip=10, n_skip_end=5): ''' Plots the loss function with respect to learning rate, in log scale. ''' plt.ylabel("validation loss") plt.xlabel("learning rate (log scale)") plt.plot(self.lrs[n_skip:-(n_skip_end+1)], self.losses[n_skip:-(n_skip_end+1)]) ...

def plot_lr(self, show_text=True, show_moms=True): """Plots the lr rate/momentum schedule""" phase_limits = [0] for nb_batch, phase in zip(self.nb_batches, self.phases): phase_limits.append(phase_limits[-1] + nb_batch * phase.epochs) if not in_ipynb(): plt.switch_...

def main( gpu:Param("GPU to run on", str)=None, woof: Param("Use imagewoof (otherwise imagenette)", int)=0, lr: Param("Learning rate", float)=1e-3, size: Param("Size (px: 128,192,224)", int)=128, alpha: Param("Alpha", float)=0.99, mom: Param("Momentum", float)=0.9, ...

def on_batch_end(self, last_loss, epoch, num_batch, **kwargs:Any)->None: "Test if `last_loss` is NaN and interrupts training." if self.stop: return True #to skip validation after stopping during training if torch.isnan(last_loss): print (f'Epoch/Batch ({epoch}/{num_batch}): Invalid l...

def on_train_begin(self, **kwargs:Any)->None: "Initializes the best value." self.best = float('inf') if self.operator == np.less else -float('inf')

def get_monitor_value(self): "Pick the monitored value." if self.monitor=='trn_loss' and len(self.learn.recorder.losses) == 0: return None elif len(self.learn.recorder.val_losses) == 0: return None values = {'train_loss':self.learn.recorder.losses[-1].cpu().numpy(), 'va...

def on_epoch_end(self, epoch:int, **kwargs:Any)->None: "Compare the value monitored to its best score and maybe save the model." if self.every=="epoch": self.learn.save(f'{self.name}_{epoch}') else: #every="improvement" current = self.get_monitor_value() if current is not...

def on_train_end(self, **kwargs): "Load the best model." if self.every=="improvement" and (self.learn.path/f'{self.learn.model_dir}/{self.name}.pth').is_file(): self.learn.load(f'{self.name}', purge=False)

def on_train_begin(self, **kwargs:Any)->None: "Initialize inner arguments." self.wait, self.opt = 0, self.learn.opt super().on_train_begin(**kwargs)

def on_epoch_end(self, epoch, **kwargs:Any)->None: "Compare the value monitored to its best and maybe reduce lr." current = self.get_monitor_value() if current is None: return if self.operator(current - self.min_delta, self.best): self.best,self.wait = current,0 else: ...

def convert_nb(fname, dest_path='.'): "Convert a notebook `fname` to html file in `dest_path`." from .gen_notebooks import remove_undoc_cells, remove_code_cell_jupyter_widget_state_elem nb = read_nb(fname) nb['cells'] = remove_undoc_cells(nb['cells']) nb['cells'] = remove_code_cell_jupyter_widget_st...

def convert_all(folder, dest_path='.', force_all=False): "Convert modified notebooks in `folder` to html pages in `dest_path`." path = Path(folder) changed_cnt = 0 for fname in path.glob("*.ipynb"): # only rebuild modified files fname_out = Path(dest_path)/fname.with_suffix('.html').nam...

def pad_collate(samples:BatchSamples, pad_idx:int=1, pad_first:bool=True, backwards:bool=False) -> Tuple[LongTensor, LongTensor]: "Function that collect samples and adds padding. Flips token order if needed" samples = to_data(samples) max_len = max([len(s[0]) for s in samples]) res = torch.zeros(len(sam...

def open_text(fn:PathOrStr, enc='utf-8'): "Read the text in `fn`." with open(fn,'r', encoding = enc) as f: return ''.join(f.readlines())

def allocate_buffers(self): "Create the ragged array that will be filled when we ask for items." if self.ite_len is None: len(self) self.idx = LanguageModelPreLoader.CircularIndex(len(self.dataset.x.items), not self.backwards) self.batch = np.zeros((self.bs, self.bptt+1), dtype=np.int6...

def fill_row(self, forward, items, idx, row, ro, ri, overlap,lengths): "Fill the row with tokens from the ragged array. --OBS-- overlap != 1 has not been implemented" ibuf = n = 0 ro -= 1 while ibuf < row.size: ro += 1 ix = idx[ro] rag = items[...

def from_ids(cls, path:PathOrStr, vocab:Vocab, train_ids:Collection[Collection[int]], valid_ids:Collection[Collection[int]], test_ids:Collection[Collection[int]]=None, train_lbls:Collection[Union[int,float]]=None, valid_lbls:Collection[Union[int,float]]=None, classes:Collection[Any]=No...

def load(cls, path:PathOrStr, cache_name:PathOrStr='tmp', processor:PreProcessor=None, **kwargs): "Load a `TextDataBunch` from `path/cache_name`. `kwargs` are passed to the dataloader creation." warn("""This method is deprecated and only kept to load data serialized in v1.0.43 or earlier. ...

def from_tokens(cls, path:PathOrStr, trn_tok:Collection[Collection[str]], trn_lbls:Collection[Union[int,float]], val_tok:Collection[Collection[str]], val_lbls:Collection[Union[int,float]], vocab:Vocab=None, tst_tok:Collection[Collection[str]]=None, classes:Collection[Any]=None, max_voc...

def from_df(cls, path:PathOrStr, train_df:DataFrame, valid_df:DataFrame, test_df:Optional[DataFrame]=None, tokenizer:Tokenizer=None, vocab:Vocab=None, classes:Collection[str]=None, text_cols:IntsOrStrs=1, label_cols:IntsOrStrs=0, label_delim:str=None, chunksize:int=10000, max_vocab:int=6...

def from_csv(cls, path:PathOrStr, csv_name, valid_pct:float=0.2, test:Optional[str]=None, tokenizer:Tokenizer=None, vocab:Vocab=None, classes:Collection[str]=None, delimiter:str=None, header='infer', text_cols:IntsOrStrs=1, label_cols:IntsOrStrs=0, label_delim:str=None, ...

def from_folder(cls, path:PathOrStr, train:str='train', valid:str='valid', test:Optional[str]=None, classes:Collection[Any]=None, tokenizer:Tokenizer=None, vocab:Vocab=None, chunksize:int=10000, max_vocab:int=60000, min_freq:int=2, mark_fields:bool=False, include_bos:bool=True, i...

def create(cls, train_ds, valid_ds, test_ds=None, path:PathOrStr='.', no_check:bool=False, bs=64, val_bs:int=None, num_workers:int=0, device:torch.device=None, collate_fn:Callable=data_collate, dl_tfms:Optional[Collection[Callable]]=None, bptt:int=70, backwards:bool=False, **dl_kwargs) -> ...

def create(cls, train_ds, valid_ds, test_ds=None, path:PathOrStr='.', bs:int=32, val_bs:int=None, pad_idx=1, pad_first=True, device:torch.device=None, no_check:bool=False, backwards:bool=False, **dl_kwargs) -> DataBunch: "Function that transform the `datasets` in a `DataBunch` for classification....

def label_for_lm(self, **kwargs): "A special labelling method for language models." self.__class__ = LMTextList kwargs['label_cls'] = LMLabelList return self.label_const(0, **kwargs)

def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=text_extensions, vocab:Vocab=None, processor:PreProcessor=None, **kwargs)->'TextList': "Get the list of files in `path` that have a text suffix. `recurse` determines if we search subfolders." processor = ifnone(proce...

def show_xys(self, xs, ys, max_len:int=70)->None: "Show the `xs` (inputs) and `ys` (targets). `max_len` is the maximum number of tokens displayed." from IPython.display import display, HTML names = ['idx','text'] if self._is_lm else ['text','target'] items = [] for i, (x,y) in en...

def inceptionv4(pretrained=True): r"""InceptionV4 model architecture from the `"Inception-v4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = InceptionV4() if pretrained: model...

def predict_array(self, arr): """ This over-ride is necessary because otherwise the learner method accesses the wrong model when it is called with precompute set to true Args: arr: a numpy array to be used as input to the model for prediction purposes Returns: ...

def main( gpu:Param("GPU to run on", str)=None ): """Distrubuted training of CIFAR-10. Fastest speed is if you run as follows: python -m fastai.launch train_cifar.py""" gpu = setup_distrib(gpu) n_gpus = num_distrib() path = url2path(URLs.CIFAR) ds_tfms = ([*rand_pad(4, 32), flip_lr(p=0.5...

def initialize_options(self): """Set default values for options.""" self.dep_groups = '' self.dep_quote = False self.dep_conda = False

def run(self): """Run command.""" wanted_groups = self.parse() deps = [] invalid_groups = [] for grp in wanted_groups: if grp in dep_groups: deps.extend(dep_groups[grp]) else: invalid_groups.append(grp) if invalid_groups or not wa...

def get_sfs_idxs(sfs, last=True): """ Return the saved feature indexes that will be concatenated Inputs: sfs (list): saved features by hook function, in other words intermediate activations last (bool): whether to concatenate only last different activation, or all from the encoder model ...

def hook_output (module:nn.Module, detach:bool=True, grad:bool=False)->Hook: "Return a `Hook` that stores activations of `module` in `self.stored`" return Hook(module, _hook_inner, detach=detach, is_forward=not grad)

def hook_outputs(modules:Collection[nn.Module], detach:bool=True, grad:bool=False)->Hooks: "Return `Hooks` that store activations of all `modules` in `self.stored`" return Hooks(modules, _hook_inner, detach=detach, is_forward=not grad)

def dummy_batch(m: nn.Module, size:tuple=(64,64))->Tensor: "Create a dummy batch to go through `m` with `size`." ch_in = in_channels(m) return one_param(m).new(1, ch_in, *size).requires_grad_(False).uniform_(-1.,1.)