Code stringlengths 103 85.9k | Summary listlengths 0 94 |
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Please provide a description of the function:def filter_by_func(self, func:Callable)->'ItemList':
"Only keep elements for which `func` returns `True`."
self.items = array([o for o in self.items if func(o)])
return self | [] |
Please provide a description of the function:def filter_by_folder(self, include=None, exclude=None):
"Only keep filenames in `include` folder or reject the ones in `exclude`."
include,exclude = listify(include),listify(exclude)
def _inner(o):
if isinstance(o, Path): n = o.relative_to... | [] |
Please provide a description of the function:def filter_by_rand(self, p:float, seed:int=None):
"Keep random sample of `items` with probability `p` and an optional `seed`."
if seed is not None: np.random.seed(seed)
return self.filter_by_func(lambda o: rand_bool(p)) | [] |
Please provide a description of the function:def split_none(self):
"Don't split the data and create an empty validation set."
val = self[[]]
val.ignore_empty = True
return self._split(self.path, self, val) | [] |
Please provide a description of the function:def split_by_list(self, train, valid):
"Split the data between `train` and `valid`."
return self._split(self.path, train, valid) | [] |
Please provide a description of the function:def split_by_idxs(self, train_idx, valid_idx):
"Split the data between `train_idx` and `valid_idx`."
return self.split_by_list(self[train_idx], self[valid_idx]) | [] |
Please provide a description of the function:def split_by_idx(self, valid_idx:Collection[int])->'ItemLists':
"Split the data according to the indexes in `valid_idx`."
#train_idx = [i for i in range_of(self.items) if i not in valid_idx]
train_idx = np.setdiff1d(arange_of(self.items), valid_idx)
... | [] |
Please provide a description of the function:def split_by_folder(self, train:str='train', valid:str='valid')->'ItemLists':
"Split the data depending on the folder (`train` or `valid`) in which the filenames are."
return self.split_by_idxs(self._get_by_folder(train), self._get_by_folder(valid)) | [] |
Please provide a description of the function:def split_by_rand_pct(self, valid_pct:float=0.2, seed:int=None)->'ItemLists':
"Split the items randomly by putting `valid_pct` in the validation set, optional `seed` can be passed."
if valid_pct==0.: return self.split_none()
if seed is not None: np.ra... | [] |
Please provide a description of the function:def split_subsets(self, train_size:float, valid_size:float, seed=None) -> 'ItemLists':
"Split the items into train set with size `train_size * n` and valid set with size `valid_size * n`."
assert 0 < train_size < 1
assert 0 < valid_size < 1
as... | [] |
Please provide a description of the function:def split_by_valid_func(self, func:Callable)->'ItemLists':
"Split the data by result of `func` (which returns `True` for validation set)."
valid_idx = [i for i,o in enumerate(self.items) if func(o)]
return self.split_by_idx(valid_idx) | [] |
Please provide a description of the function:def split_by_files(self, valid_names:'ItemList')->'ItemLists':
"Split the data by using the names in `valid_names` for validation."
if isinstance(self.items[0], Path): return self.split_by_valid_func(lambda o: o.name in valid_names)
else: return self.... | [] |
Please provide a description of the function:def split_by_fname_file(self, fname:PathOrStr, path:PathOrStr=None)->'ItemLists':
"Split the data by using the names in `fname` for the validation set. `path` will override `self.path`."
path = Path(ifnone(path, self.path))
valid_names = loadtxt_str(p... | [] |
Please provide a description of the function:def split_from_df(self, col:IntsOrStrs=2):
"Split the data from the `col` in the dataframe in `self.inner_df`."
valid_idx = np.where(self.inner_df.iloc[:,df_names_to_idx(col, self.inner_df)])[0]
return self.split_by_idx(valid_idx) | [] |
Please provide a description of the function:def get_label_cls(self, labels, label_cls:Callable=None, label_delim:str=None, **kwargs):
"Return `label_cls` or guess one from the first element of `labels`."
if label_cls is not None: return label_cls
if self.label_cls is not None: ... | [] |
Please provide a description of the function:def _label_from_list(self, labels:Iterator, label_cls:Callable=None, from_item_lists:bool=False, **kwargs)->'LabelList':
"Label `self.items` with `labels`."
if not from_item_lists:
raise Exception("Your data isn't split, if you don't want a valida... | [] |
Please provide a description of the function:def label_from_df(self, cols:IntsOrStrs=1, label_cls:Callable=None, **kwargs):
"Label `self.items` from the values in `cols` in `self.inner_df`."
labels = self.inner_df.iloc[:,df_names_to_idx(cols, self.inner_df)]
assert labels.isna().sum().sum() == 0... | [] |
Please provide a description of the function:def label_const(self, const:Any=0, label_cls:Callable=None, **kwargs)->'LabelList':
"Label every item with `const`."
return self.label_from_func(func=lambda o: const, label_cls=label_cls, **kwargs) | [] |
Please provide a description of the function:def label_empty(self, **kwargs):
"Label every item with an `EmptyLabel`."
kwargs['label_cls'] = EmptyLabelList
return self.label_from_func(func=lambda o: 0., **kwargs) | [] |
Please provide a description of the function:def label_from_func(self, func:Callable, label_cls:Callable=None, **kwargs)->'LabelList':
"Apply `func` to every input to get its label."
return self._label_from_list([func(o) for o in self.items], label_cls=label_cls, **kwargs) | [] |
Please provide a description of the function:def label_from_folder(self, label_cls:Callable=None, **kwargs)->'LabelList':
"Give a label to each filename depending on its folder."
return self.label_from_func(func=lambda o: (o.parts if isinstance(o, Path) else o.split(os.path.sep))[-2],
... | [] |
Please provide a description of the function:def label_from_re(self, pat:str, full_path:bool=False, label_cls:Callable=None, **kwargs)->'LabelList':
"Apply the re in `pat` to determine the label of every filename. If `full_path`, search in the full name."
pat = re.compile(pat)
def _inner(o):
... | [] |
Please provide a description of the function:def generate_classes(self, items):
"Generate classes from `items` by taking the sorted unique values."
classes = set()
for c in items: classes = classes.union(set(c))
classes = list(classes)
classes.sort()
return classes | [] |
Please provide a description of the function:def label_from_lists(self, train_labels:Iterator, valid_labels:Iterator, label_cls:Callable=None, **kwargs)->'LabelList':
"Use the labels in `train_labels` and `valid_labels` to label the data. `label_cls` will overwrite the default."
label_cls = self.train.g... | [] |
Please provide a description of the function:def transform(self, tfms:Optional[Tuple[TfmList,TfmList]]=(None,None), **kwargs):
"Set `tfms` to be applied to the xs of the train and validation set."
if not tfms: tfms=(None,None)
assert is_listy(tfms) and len(tfms) == 2, "Please pass a list of two ... | [] |
Please provide a description of the function:def transform_y(self, tfms:Optional[Tuple[TfmList,TfmList]]=(None,None), **kwargs):
"Set `tfms` to be applied to the ys of the train and validation set."
if not tfms: tfms=(None,None)
self.train.transform_y(tfms[0], **kwargs)
self.valid.transf... | [] |
Please provide a description of the function:def get_processors(self):
"Read the default class processors if none have been set."
procs_x,procs_y = listify(self.train.x._processor),listify(self.train.y._processor)
xp = ifnone(self.train.x.processor, [p(ds=self.train.x) for p in procs_x])
... | [] |
Please provide a description of the function:def process(self):
"Process the inner datasets."
xp,yp = self.get_processors()
for ds,n in zip(self.lists, ['train','valid','test']): ds.process(xp, yp, name=n)
#progress_bar clear the outputs so in some case warnings issued during processing ... | [] |
Please provide a description of the function:def databunch(self, path:PathOrStr=None, 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)->'D... | [] |
Please provide a description of the function:def load_state(cls, path:PathOrStr, state:dict):
"Create a `LabelLists` with empty sets from the serialized `state`."
path = Path(path)
train_ds = LabelList.load_state(path, state)
valid_ds = LabelList.load_state(path, state)
return La... | [] |
Please provide a description of the function:def load_empty(cls, path:PathOrStr, fn:PathOrStr='export.pkl'):
"Create a `LabelLists` with empty sets from the serialized file in `path/fn`."
path = Path(path)
state = torch.load(open(path/fn, 'rb'))
return LabelLists.load_state(path, state) | [] |
Please provide a description of the function:def set_item(self,item):
"For inference, will briefly replace the dataset with one that only contains `item`."
self.item = self.x.process_one(item)
yield None
self.item = None | [] |
Please provide a description of the function:def to_df(self)->None:
"Create `pd.DataFrame` containing `items` from `self.x` and `self.y`."
return pd.DataFrame(dict(x=self.x._relative_item_paths(), y=[str(o) for o in self.y])) | [] |
Please provide a description of the function:def to_csv(self, dest:str)->None:
"Save `self.to_df()` to a CSV file in `self.path`/`dest`."
self.to_df().to_csv(self.path/dest, index=False) | [] |
Please provide a description of the function:def get_state(self, **kwargs):
"Return the minimal state for export."
state = {'x_cls':self.x.__class__, 'x_proc':self.x.processor,
'y_cls':self.y.__class__, 'y_proc':self.y.processor,
'tfms':self.tfms, 'tfm_y':self.tfm_y, 't... | [] |
Please provide a description of the function:def export(self, fn:PathOrStr, **kwargs):
"Export the minimal state and save it in `fn` to load an empty version for inference."
pickle.dump(self.get_state(**kwargs), open(fn, 'wb')) | [] |
Please provide a description of the function:def load_empty(cls, path:PathOrStr, fn:PathOrStr):
"Load the state in `fn` to create an empty `LabelList` for inference."
return cls.load_state(path, pickle.load(open(Path(path)/fn, 'rb'))) | [] |
Please provide a description of the function:def load_state(cls, path:PathOrStr, state:dict) -> 'LabelList':
"Create a `LabelList` from `state`."
x = state['x_cls']([], path=path, processor=state['x_proc'], ignore_empty=True)
y = state['y_cls']([], path=path, processor=state['y_proc'], ignore_em... | [] |
Please provide a description of the function:def process(self, xp:PreProcessor=None, yp:PreProcessor=None, name:str=None):
"Launch the processing on `self.x` and `self.y` with `xp` and `yp`."
self.y.process(yp)
if getattr(self.y, 'filter_missing_y', False):
filt = array([o is None fo... | [] |
Please provide a description of the function:def transform(self, tfms:TfmList, tfm_y:bool=None, **kwargs):
"Set the `tfms` and `tfm_y` value to be applied to the inputs and targets."
_check_kwargs(self.x, tfms, **kwargs)
if tfm_y is None: tfm_y = self.tfm_y
if tfm_y: _check_kwargs(self.y... | [] |
Please provide a description of the function:def transform_y(self, tfms:TfmList=None, **kwargs):
"Set `tfms` to be applied to the targets only."
_check_kwargs(self.y, tfms, **kwargs)
self.tfm_y=True
if tfms is None:
self.tfms_y = list(filter(lambda t: t.use_on_y, listify(self... | [] |
Please provide a description of the function:def new(self, item_lists, processor:PreProcessor=None, **kwargs)->'ItemList':
"Create a new `ItemList` from `items`, keeping the same attributes."
processor = ifnone(processor, self.processor)
copy_d = {o:getattr(self,o) for o in self.copy_new}
... | [] |
Please provide a description of the function:def parse_docstring(docstring):
short_description = long_description = return_str = ""
args = []
if docstring:
docstring = trim(docstring.lstrip("\n"))
lines = docstring.split("\n", 1)
short_description = lines[0]
if len(l... | [
"Parse the docstring into its components.\n\n :return: a dictionary of form\n {\n \"short_description\": ...,\n \"long_description\": ...,\n \"params\": [{\"name\": ..., \"doc\": ...}, ...],\n \"vals\": [{\"name\": ..., \"doc\": ...... |
Please provide a description of the function:def get_env(name):
"Return env var value if it's defined and not an empty string, or return Unknown"
res = os.environ.get(name,'')
return res if len(res) else "Unknown" | [] |
Please provide a description of the function:def show_install(show_nvidia_smi:bool=False):
"Print user's setup information"
import platform, fastai.version
rep = []
opt_mods = []
rep.append(["=== Software ===", None])
rep.append(["python", platform.python_version()])
rep.append(["fastai",... | [] |
Please provide a description of the function:def pypi_module_version_is_available(module, version):
"Check whether module==version is available on pypi"
# returns True/False (or None if failed to execute the check)
# using a hack that when passing "module==" w/ no version number to pip
# it "fails" and... | [] |
Please provide a description of the function:def check_perf():
"Suggest how to improve the setup to speed things up"
from PIL import features, Image
from packaging import version
print("Running performance checks.")
# libjpeg_turbo check
print("\n*** libjpeg-turbo status")
if version.pars... | [] |
Please provide a description of the function:def annealing_linear(start:Number, end:Number, pct:float)->Number:
"Linearly anneal from `start` to `end` as pct goes from 0.0 to 1.0."
return start + pct * (end-start) | [] |
Please provide a description of the function:def annealing_exp(start:Number, end:Number, pct:float)->Number:
"Exponentially anneal from `start` to `end` as pct goes from 0.0 to 1.0."
return start * (end/start) ** pct | [] |
Please provide a description of the function:def annealing_cos(start:Number, end:Number, pct:float)->Number:
"Cosine anneal from `start` to `end` as pct goes from 0.0 to 1.0."
cos_out = np.cos(np.pi * pct) + 1
return end + (start-end)/2 * cos_out | [] |
Please provide a description of the function:def do_annealing_poly(start:Number, end:Number, pct:float, degree:Number)->Number:
"Helper function for `anneal_poly`."
return end + (start-end) * (1-pct)**degree | [] |
Please provide a description of the function:def create(cls, opt_func:Union[type,Callable], lr:Union[float,Tuple,List], layer_groups:ModuleList, wd:Floats=0.,
true_wd:bool=False, bn_wd:bool=True)->optim.Optimizer:
"Create an `optim.Optimizer` from `opt_func` with `lr`. Set lr on `layer_groups`."... | [] |
Please provide a description of the function:def new(self, layer_groups:Collection[nn.Module], split_no_wd:bool=True):
"Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters."
opt_func = getattr(self, 'opt_func', self.opt.__class__)
res = self.create(o... | [] |
Please provide a description of the function:def new_with_params(self, param_groups:Collection[Collection[nn.Parameter]]):
"Create a new `OptimWrapper` from `self` with another `layer_groups` but the same hyper-parameters."
opt_func = getattr(self, 'opt_func', self.opt.__class__)
opt = opt_func(... | [] |
Please provide a description of the function:def step(self)->None:
"Set weight decay and step optimizer."
# weight decay outside of optimizer step (AdamW)
if self.true_wd:
for lr,wd,pg1,pg2 in zip(self._lr,self._wd,self.opt.param_groups[::2],self.opt.param_groups[1::2]):
... | [] |
Please provide a description of the function:def beta(self, val:float)->None:
"Set beta (or alpha as makes sense for given optimizer)."
if val is None: return
if 'betas' in self.opt_keys: self.set_val('betas', (self._mom, listify(val, self._beta)))
elif 'alpha' in self.opt_keys: self... | [] |
Please provide a description of the function:def wd(self, val:float)->None:
"Set weight decay."
if not self.true_wd: self.set_val('weight_decay', listify(val, self._wd), bn_groups=self.bn_wd)
self._wd = listify(val, self._wd) | [] |
Please provide a description of the function:def read_defaults(self)->None:
"Read the values inside the optimizer for the hyper-parameters."
self._beta = None
if 'lr' in self.opt_keys: self._lr = self.read_val('lr')
if 'momentum' in self.opt_keys: self._mom = self.read_val('momentum')
... | [] |
Please provide a description of the function:def set_val(self, key:str, val:Any, bn_groups:bool=True)->Any:
"Set `val` inside the optimizer dictionary at `key`."
if is_tuple(val): val = [(v1,v2) for v1,v2 in zip(*val)]
for v,pg1,pg2 in zip(val,self.opt.param_groups[::2],self.opt.param_groups[1::... | [] |
Please provide a description of the function:def read_val(self, key:str) -> Union[List[float],Tuple[List[float],List[float]]]:
"Read a hyperparameter `key` in the optimizer dictionary."
val = [pg[key] for pg in self.opt.param_groups[::2]]
if is_tuple(val[0]): val = [o[0] for o in val], [o[1] for... | [] |
Please provide a description of the function:def get_state(self):
"Return the inner state minus the layer groups."
return {'opt_state':self.opt.state_dict(), 'lr':self._lr, 'wd':self._wd, 'beta':self._beta, 'mom':self._mom,
'opt_func':self.opt_func, 'true_wd':self.true_wd, 'bn_wd':self.b... | [] |
Please provide a description of the function:def get_state(self, minimal:bool=True):
"Return the inner state of the `Callback`, `minimal` or not."
to_remove = ['exclude', 'not_min'] + getattr(self, 'exclude', []).copy()
if minimal: to_remove += getattr(self, 'not_min', []).copy()
return ... | [] |
Please provide a description of the function:def add_value(self, val:float)->None:
"Add `val` to calculate updated smoothed value."
self.n += 1
self.mov_avg = self.beta * self.mov_avg + (1 - self.beta) * val
self.smooth = self.mov_avg / (1 - self.beta ** self.n) | [] |
Please provide a description of the function:def on_batch_end(self, last_output, last_target, **kwargs):
"Update metric computation with `last_output` and `last_target`."
if not is_listy(last_target): last_target=[last_target]
self.count += last_target[0].size(0)
val = self.func(last_out... | [] |
Please provide a description of the function:def on_epoch_end(self, last_metrics, **kwargs):
"Set the final result in `last_metrics`."
return add_metrics(last_metrics, self.val/self.count) | [] |
Please provide a description of the function:def step(self)->Number:
"Return next value along annealed schedule."
self.n += 1
return self.func(self.start, self.end, self.n/self.n_iter) | [] |
Please provide a description of the function:def steps(self, *steps_cfg:StartOptEnd):
"Build anneal schedule for all of the parameters."
return [Scheduler(step, n_iter, func=func)
for (step,(n_iter,func)) in zip(steps_cfg, self.phases)] | [] |
Please provide a description of the function:def on_train_begin(self, n_epochs:int, epoch:int, **kwargs:Any)->None:
"Initialize our optimization params based on our annealing schedule."
res = {'epoch':self.start_epoch} if self.start_epoch is not None else None
self.start_epoch = ifnone(self.star... | [] |
Please provide a description of the function:def on_batch_end(self, train, **kwargs:Any)->None:
"Take one step forward on the annealing schedule for the optim params."
if train:
if self.idx_s >= len(self.lr_scheds): return {'stop_training': True, 'stop_epoch': True}
self.opt.lr =... | [] |
Please provide a description of the function:def main(
gpu:Param("GPU to run on", str)=None,
lr: Param("Learning rate", float)=1e-3,
size: Param("Size (px: 128,192,224)", int)=128,
debias_mom: Param("Debias statistics", bool)=False,
debias_sqr: Param("Debias statistics", bool)=Fa... | [
"Distributed training of Imagenette.\n Fastest multi-gpu speed is if you run with: python -m fastai.launch"
] |
Please provide a description of the function:def basic_critic(in_size:int, n_channels:int, n_features:int=64, n_extra_layers:int=0, **conv_kwargs):
"A basic critic for images `n_channels` x `in_size` x `in_size`."
layers = [conv_layer(n_channels, n_features, 4, 2, 1, leaky=0.2, norm_type=None, **conv_kwargs)]#n... | [] |
Please provide a description of the function:def basic_generator(in_size:int, n_channels:int, noise_sz:int=100, n_features:int=64, n_extra_layers=0, **conv_kwargs):
"A basic generator from `noise_sz` to images `n_channels` x `in_size` x `in_size`."
cur_size, cur_ftrs = 4, n_features//2
while cur_size < in_s... | [] |
Please provide a description of the function:def gan_loss_from_func(loss_gen, loss_crit, weights_gen:Tuple[float,float]=None):
"Define loss functions for a GAN from `loss_gen` and `loss_crit`."
def _loss_G(fake_pred, output, target, weights_gen=weights_gen):
ones = fake_pred.new_ones(fake_pred.shape[0])... | [] |
Please provide a description of the function:def gan_critic(n_channels:int=3, nf:int=128, n_blocks:int=3, p:int=0.15):
"Critic to train a `GAN`."
layers = [
_conv(n_channels, nf, ks=4, stride=2),
nn.Dropout2d(p/2),
res_block(nf, dense=True,**_conv_args)]
nf *= 2 # after dense block
... | [] |
Please provide a description of the function:def accuracy_thresh_expand(y_pred:Tensor, y_true:Tensor, thresh:float=0.5, sigmoid:bool=True)->Rank0Tensor:
"Compute accuracy after expanding `y_true` to the size of `y_pred`."
if sigmoid: y_pred = y_pred.sigmoid()
return ((y_pred>thresh)==y_true[:,None].expand_a... | [] |
Please provide a description of the function:def switch(self, gen_mode:bool=None):
"Put the model in generator mode if `gen_mode`, in critic mode otherwise."
self.gen_mode = (not self.gen_mode) if gen_mode is None else gen_mode | [] |
Please provide a description of the function:def generator(self, output, target):
"Evaluate the `output` with the critic then uses `self.loss_funcG` to combine it with `target`."
fake_pred = self.gan_model.critic(output)
return self.loss_funcG(fake_pred, target, output) | [] |
Please provide a description of the function:def critic(self, real_pred, input):
"Create some `fake_pred` with the generator from `input` and compare them to `real_pred` in `self.loss_funcD`."
fake = self.gan_model.generator(input.requires_grad_(False)).requires_grad_(True)
fake_pred = self.gan_... | [] |
Please provide a description of the function:def on_train_begin(self, **kwargs):
"Create the optimizers for the generator and critic if necessary, initialize smootheners."
if not getattr(self,'opt_gen',None):
self.opt_gen = self.opt.new([nn.Sequential(*flatten_model(self.generator))])
... | [] |
Please provide a description of the function:def on_batch_begin(self, last_input, last_target, **kwargs):
"Clamp the weights with `self.clip` if it's not None, return the correct input."
if self.clip is not None:
for p in self.critic.parameters(): p.data.clamp_(-self.clip, self.clip)
... | [] |
Please provide a description of the function:def on_backward_begin(self, last_loss, last_output, **kwargs):
"Record `last_loss` in the proper list."
last_loss = last_loss.detach().cpu()
if self.gen_mode:
self.smoothenerG.add_value(last_loss)
self.glosses.append(self.smoot... | [] |
Please provide a description of the function:def on_epoch_end(self, pbar, epoch, last_metrics, **kwargs):
"Put the various losses in the recorder and show a sample image."
if not hasattr(self, 'last_gen') or not self.show_img: return
data = self.learn.data
img = self.last_gen[0]
... | [] |
Please provide a description of the function:def switch(self, gen_mode:bool=None):
"Switch the model, if `gen_mode` is provided, in the desired mode."
self.gen_mode = (not self.gen_mode) if gen_mode is None else gen_mode
self.opt.opt = self.opt_gen.opt if self.gen_mode else self.opt_critic.opt
... | [] |
Please provide a description of the function:def on_batch_end(self, iteration, **kwargs):
"Switch the model if necessary."
if self.learn.gan_trainer.gen_mode:
self.n_g += 1
n_iter,n_in,n_out = self.n_gen,self.n_c,self.n_g
else:
self.n_c += 1
n_iter... | [] |
Please provide a description of the function:def from_learners(cls, learn_gen:Learner, learn_crit:Learner, switcher:Callback=None,
weights_gen:Tuple[float,float]=None, **learn_kwargs):
"Create a GAN from `learn_gen` and `learn_crit`."
losses = gan_loss_from_func(learn_gen.loss_func... | [] |
Please provide a description of the function:def wgan(cls, data:DataBunch, generator:nn.Module, critic:nn.Module, switcher:Callback=None, clip:float=0.01, **learn_kwargs):
"Create a WGAN from `data`, `generator` and `critic`."
return cls(data, generator, critic, NoopLoss(), WassersteinLoss(), switcher=s... | [] |
Please provide a description of the function:def show_xys(self, xs, ys, imgsize:int=4, figsize:Optional[Tuple[int,int]]=None, **kwargs):
"Shows `ys` (target images) on a figure of `figsize`."
super().show_xys(ys, xs, imgsize=imgsize, figsize=figsize, **kwargs) | [] |
Please provide a description of the function:def on_batch_begin(self, train, **kwargs):
"Multiply the current lr if necessary."
if not self.learn.gan_trainer.gen_mode and train: self.learn.opt.lr *= self.mult_lr | [] |
Please provide a description of the function:def on_step_end(self, **kwargs):
"Put the LR back to its value if necessary."
if not self.learn.gan_trainer.gen_mode: self.learn.opt.lr /= self.mult_lr | [] |
Please provide a description of the function:def _get_sfs_idxs(sizes:Sizes) -> List[int]:
"Get the indexes of the layers where the size of the activation changes."
feature_szs = [size[-1] for size in sizes]
sfs_idxs = list(np.where(np.array(feature_szs[:-1]) != np.array(feature_szs[1:]))[0])
if feature_... | [] |
Please provide a description of the function:def download_google_images(path:PathOrStr, search_term:str, size:str='>400*300', n_images:int=10, format:str='jpg',
max_workers:int=defaults.cpus, timeout:int=4) -> FilePathList:
label_path = Path(path)/search_term
search_url = _searc... | [
"\n Search for `n_images` images on Google, matching `search_term` and `size` requirements,\n download them into `path`/`search_term` and verify them, using `max_workers` threads.\n "
] |
Please provide a description of the function: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 no... | [
"Unexpected size argument value: {size}.\n See `widgets.image_downloader._img_sizes` for supported sizes."
] |
Please provide a description of the function: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... | [] |
Please provide a description of the function: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) Ch... | [] |
Please provide a description of the function: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_dic... | [] |
Please provide a description of the function:def _fetch_img_tuples_webdriver(url:str, format:str='jpg', n_images:int=150) -> list:
try:
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
except:
print()
options = webdriver.ChromeOptions()
options.... | [
"\n Parse the Google Images Search for urls and return the image metadata as tuples (fname, url).\n Use this for downloads of >100 images. Requires `selenium`.\n ",
"Looks like you're trying to download > 100 images and `selenium`\n is not installed. Try running `pip install selenium` to f... |
Please provide a description of the function:def _download_images(label_path:PathOrStr, img_tuples:list, max_workers:int=defaults.cpus, timeout:int=4) -> FilePathList:
os.makedirs(Path(label_path), exist_ok=True)
parallel( partial(_download_single_image, label_path, timeout=timeout), img_tuples, max_worker... | [
"\n Downloads images in `img_tuples` to `label_path`. \n If the directory doesn't exist, it'll be created automatically.\n Uses `parallel` to speed things up in `max_workers` when the system has enough CPU cores.\n If something doesn't work, try setting up `max_workers=0` to debug.\n "
] |
Please provide a description of the function:def _download_single_image(label_path:Path, img_tuple:tuple, i:int, timeout:int=4) -> None:
suffix = re.findall(r'\.\w+?(?=(?:\?|$))', img_tuple[1])
suffix = suffix[0].lower() if len(suffix)>0 else '.jpg'
fname = f"{i:08d}{suffix}"
download_url(img_tupl... | [
"\n Downloads a single image from Google Search results to `label_path`\n given an `img_tuple` that contains `(fname, url)` of an image to download.\n `i` is just an iteration number `int`. \n "
] |
Please provide a description of the function: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,
... | [] |
Please provide a description of the function:def clear_imgs(self) -> None:
"Clear the widget's images preview pane."
self._preview_header.value = self._heading
self._img_pane.children = tuple() | [] |
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