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Description:
| def predict(self, text:str, n_words:int=1, no_unk:bool=True, temperature:float=1., min_p:float=None, sep:str=' ',
decoder=decode_spec_tokens):
"Return the `n_words` that come after `text`."
ds = self.data.single_dl.dataset
self.model.reset()
xb,yb = self.data.one_item(tex... |
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| def beam_search(self, text:str, n_words:int, no_unk:bool=True, top_k:int=10, beam_sz:int=1000, temperature:float=1.,
sep:str=' ', decoder=decode_spec_tokens):
"Return the `n_words` that come after `text` using beam search."
ds = self.data.single_dl.dataset
self.model.reset()
... |
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| def show_results(self, ds_type=DatasetType.Valid, rows:int=5, max_len:int=20):
from IPython.display import display, HTML
"Show `rows` result of predictions on `ds_type` dataset."
ds = self.dl(ds_type).dataset
x,y = self.data.one_batch(ds_type, detach=False, denorm=False)
preds = ... |
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| def concat(self, arrs:Collection[Tensor])->Tensor:
"Concatenate the `arrs` along the batch dimension."
return [torch.cat([l[si] for l in arrs], dim=1) for si in range_of(arrs[0])] |
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| def batchnorm_2d(nf:int, norm_type:NormType=NormType.Batch):
"A batchnorm2d layer with `nf` features initialized depending on `norm_type`."
bn = nn.BatchNorm2d(nf)
with torch.no_grad():
bn.bias.fill_(1e-3)
bn.weight.fill_(0. if norm_type==NormType.BatchZero else 1.)
return bn |
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| def conv1d(ni:int, no:int, ks:int=1, stride:int=1, padding:int=0, bias:bool=False):
"Create and initialize a `nn.Conv1d` layer with spectral normalization."
conv = nn.Conv1d(ni, no, ks, stride=stride, padding=padding, bias=bias)
nn.init.kaiming_normal_(conv.weight)
if bias: conv.bias.data.zero_()
re... |
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| def conv2d_trans(ni:int, nf:int, ks:int=2, stride:int=2, padding:int=0, bias=False) -> nn.ConvTranspose2d:
"Create `nn.ConvTranspose2d` layer."
return nn.ConvTranspose2d(ni, nf, kernel_size=ks, stride=stride, padding=padding, bias=bias) |
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| def relu(inplace:bool=False, leaky:float=None):
"Return a relu activation, maybe `leaky` and `inplace`."
return nn.LeakyReLU(inplace=inplace, negative_slope=leaky) if leaky is not None else nn.ReLU(inplace=inplace) |
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| def res_block(nf, dense:bool=False, norm_type:Optional[NormType]=NormType.Batch, bottle:bool=False, **conv_kwargs):
"Resnet block of `nf` features. `conv_kwargs` are passed to `conv_layer`."
norm2 = norm_type
if not dense and (norm_type==NormType.Batch): norm2 = NormType.BatchZero
nf_inner = nf//2 if bo... |
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| def icnr(x, scale=2, init=nn.init.kaiming_normal_):
"ICNR init of `x`, with `scale` and `init` function."
ni,nf,h,w = x.shape
ni2 = int(ni/(scale**2))
k = init(torch.zeros([ni2,nf,h,w])).transpose(0, 1)
k = k.contiguous().view(ni2, nf, -1)
k = k.repeat(1, 1, scale**2)
k = k.contiguous().view... |
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| def CrossEntropyFlat(*args, axis:int=-1, **kwargs):
"Same as `nn.CrossEntropyLoss`, but flattens input and target."
return FlattenedLoss(nn.CrossEntropyLoss, *args, axis=axis, **kwargs) |
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| def BCEWithLogitsFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
"Same as `nn.BCEWithLogitsLoss`, but flattens input and target."
return FlattenedLoss(nn.BCEWithLogitsLoss, *args, axis=axis, floatify=floatify, is_2d=False, **kwargs) |
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| def BCEFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
"Same as `nn.BCELoss`, but flattens input and target."
return FlattenedLoss(nn.BCELoss, *args, axis=axis, floatify=floatify, is_2d=False, **kwargs) |
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| def MSELossFlat(*args, axis:int=-1, floatify:bool=True, **kwargs):
"Same as `nn.MSELoss`, but flattens input and target."
return FlattenedLoss(nn.MSELoss, *args, axis=axis, floatify=floatify, is_2d=False, **kwargs) |
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| def simple_cnn(actns:Collection[int], kernel_szs:Collection[int]=None,
strides:Collection[int]=None, bn=False) -> nn.Sequential:
"CNN with `conv_layer` defined by `actns`, `kernel_szs` and `strides`, plus batchnorm if `bn`."
nl = len(actns)-1
kernel_szs = ifnone(kernel_szs, [3]*nl)
stride... |
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| def trunc_normal_(x:Tensor, mean:float=0., std:float=1.) -> Tensor:
"Truncated normal initialization."
# From https://discuss.pytorch.org/t/implementing-truncated-normal-initializer/4778/12
return x.normal_().fmod_(2).mul_(std).add_(mean) |
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| def embedding(ni:int,nf:int) -> nn.Module:
"Create an embedding layer."
emb = nn.Embedding(ni, nf)
# See https://arxiv.org/abs/1711.09160
with torch.no_grad(): trunc_normal_(emb.weight, std=0.01)
return emb |
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| def on_train_begin(self, **kwargs: Any) -> None:
"Prepare MLflow experiment and log params"
self.client = mlflow.tracking.MlflowClient(self.uri)
exp = self.client.get_experiment_by_name(self.exp_name)
self.exp_id = self.client.create_experiment(self.exp_name) if exp is None else exp.expe... |
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| def on_epoch_end(self, epoch, **kwargs:Any)->None:
"Send loss and metrics values to MLFlow after each epoch"
if kwargs['smooth_loss'] is None or kwargs["last_metrics"] is None: return
metrics = [kwargs['smooth_loss']] + kwargs["last_metrics"]
for name, val in zip(self.metrics_names, metr... |
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| def on_train_end(self, **kwargs: Any) -> None:
"Store the notebook and stop run"
self.client.log_artifact(run_id=self.run, local_path=self.nb_path)
self.client.set_terminated(run_id=self.run) |
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| def pil2tensor(image:Union[NPImage,NPArray],dtype:np.dtype)->TensorImage:
"Convert PIL style `image` array to torch style image tensor."
a = np.asarray(image)
if a.ndim==2 : a = np.expand_dims(a,2)
a = np.transpose(a, (1, 0, 2))
a = np.transpose(a, (2, 1, 0))
return torch.from_numpy(a.astype(dty... |
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| def _draw_outline(o:Patch, lw:int):
"Outline bounding box onto image `Patch`."
o.set_path_effects([patheffects.Stroke(
linewidth=lw, foreground='black'), patheffects.Normal()]) |
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| def _draw_rect(ax:plt.Axes, b:Collection[int], color:str='white', text=None, text_size=14):
"Draw bounding box on `ax`."
patch = ax.add_patch(patches.Rectangle(b[:2], *b[-2:], fill=False, edgecolor=color, lw=2))
_draw_outline(patch, 4)
if text is not None:
patch = ax.text(*b[:2], text, verticala... |
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| def open_image(fn:PathOrStr, div:bool=True, convert_mode:str='RGB', cls:type=Image,
after_open:Callable=None)->Image:
"Return `Image` object created from image in file `fn`."
with warnings.catch_warnings():
warnings.simplefilter("ignore", UserWarning) # EXIF warning from TiffPlugin
x = P... |
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| def open_mask(fn:PathOrStr, div=False, convert_mode='L', after_open:Callable=None)->ImageSegment:
"Return `ImageSegment` object create from mask in file `fn`. If `div`, divides pixel values by 255."
return open_image(fn, div=div, convert_mode=convert_mode, cls=ImageSegment, after_open=after_open) |
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| def open_mask_rle(mask_rle:str, shape:Tuple[int, int])->ImageSegment:
"Return `ImageSegment` object create from run-length encoded string in `mask_lre` with size in `shape`."
x = FloatTensor(rle_decode(str(mask_rle), shape).astype(np.uint8))
x = x.view(shape[1], shape[0], -1)
return ImageSegment(x.permu... |
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| def rle_encode(img:NPArrayMask)->str:
"Return run-length encoding string from `img`."
pixels = np.concatenate([[0], img.flatten() , [0]])
runs = np.where(pixels[1:] != pixels[:-1])[0] + 1
runs[1::2] -= runs[::2]
return ' '.join(str(x) for x in runs) |
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| def rle_decode(mask_rle:str, shape:Tuple[int,int])->NPArrayMask:
"Return an image array from run-length encoded string `mask_rle` with `shape`."
s = mask_rle.split()
starts, lengths = [np.asarray(x, dtype=int) for x in (s[0:][::2], s[1:][::2])]
starts -= 1
ends = starts + lengths
img = np.zeros(... |
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| def show_image(img:Image, ax:plt.Axes=None, figsize:tuple=(3,3), hide_axis:bool=True, cmap:str='binary',
alpha:float=None, **kwargs)->plt.Axes:
"Display `Image` in notebook."
if ax is None: fig,ax = plt.subplots(figsize=figsize)
ax.imshow(image2np(img.data), cmap=cmap, alpha=alpha, **kwargs)... |
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| def _affine_mult(c:FlowField,m:AffineMatrix)->FlowField:
"Multiply `c` by `m` - can adjust for rectangular shaped `c`."
if m is None: return c
size = c.flow.size()
h,w = c.size
m[0,1] *= h/w
m[1,0] *= w/h
c.flow = c.flow.view(-1,2)
c.flow = torch.addmm(m[:2,2], c.flow, m[:2,:2].t()).vie... |
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| def _affine_inv_mult(c, m):
"Applies the inverse affine transform described in `m` to `c`."
size = c.flow.size()
h,w = c.size
m[0,1] *= h/w
m[1,0] *= w/h
c.flow = c.flow.view(-1,2)
a = torch.inverse(m[:2,:2].t())
c.flow = torch.mm(c.flow - m[:2,2], a).view(size)
return c |
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| def _round_multiple(x:int, mult:int=None)->int:
"Calc `x` to nearest multiple of `mult`."
return (int(x/mult+0.5)*mult) if mult is not None else x |
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| def _get_crop_target(target_px:Union[int,TensorImageSize], mult:int=None)->Tuple[int,int]:
"Calc crop shape of `target_px` to nearest multiple of `mult`."
target_r,target_c = tis2hw(target_px)
return _round_multiple(target_r,mult),_round_multiple(target_c,mult) |
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| def _get_resize_target(img, crop_target, do_crop=False)->TensorImageSize:
"Calc size of `img` to fit in `crop_target` - adjust based on `do_crop`."
if crop_target is None: return None
ch,r,c = img.shape
target_r,target_c = crop_target
ratio = (min if do_crop else max)(r/target_r, c/target_c)
ret... |
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| def plot_multi(func:Callable[[int,int,plt.Axes],None], r:int=1, c:int=1, figsize:Tuple=(12,6)):
"Call `func` for every combination of `r,c` on a subplot"
axes = plt.subplots(r, c, figsize=figsize)[1]
for i in range(r):
for j in range(c): func(i,j,axes[i,j]) |
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| def show_all(imgs:Collection[Image], r:int=1, c:Optional[int]=None, figsize=(12,6)):
"Show all `imgs` using `r` rows"
imgs = listify(imgs)
if c is None: c = len(imgs)//r
for i,ax in plot_flat(r,c,figsize): imgs[i].show(ax) |
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| def apply_tfms(self, tfms:TfmList, do_resolve:bool=True, xtra:Optional[Dict[Callable,dict]]=None,
size:Optional[Union[int,TensorImageSize]]=None, resize_method:ResizeMethod=None,
mult:int=None, padding_mode:str='reflection', mode:str='bilinear', remove_out:bool=True)->TensorImage:
... |
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| def refresh(self)->None:
"Apply any logit, flow, or affine transfers that have been sent to the `Image`."
if self._logit_px is not None:
self._px = self._logit_px.sigmoid_()
self._logit_px = None
if self._affine_mat is not None or self._flow is not None:
self.... |
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| def save(self, fn:PathOrStr):
"Save the image to `fn`."
x = image2np(self.data*255).astype(np.uint8)
PIL.Image.fromarray(x).save(fn) |
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| def flow(self)->FlowField:
"Access the flow-field grid after applying queued affine transforms."
if self._flow is None:
self._flow = _affine_grid(self.shape)
if self._affine_mat is not None:
self._flow = _affine_mult(self._flow,self._affine_mat)
self._affine_m... |
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| def affine(self, func:AffineFunc, *args, **kwargs)->'Image':
"Equivalent to `image.affine_mat = image.affine_mat @ func()`."
m = tensor(func(*args, **kwargs)).to(self.device)
self.affine_mat = self.affine_mat @ m
return self |
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| def affine_mat(self)->AffineMatrix:
"Get the affine matrix that will be applied by `refresh`."
if self._affine_mat is None:
self._affine_mat = torch.eye(3).to(self.device)
return self._affine_mat |
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| def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
cmap:str=None, y:Any=None, **kwargs):
"Show image on `ax` with `title`, using `cmap` if single-channel, overlaid with optional `y`"
cmap = ifnone(cmap, defaults.cmap)
ax = show_imag... |
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| def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
cmap:str='tab20', alpha:float=0.5, **kwargs):
"Show the `ImageSegment` on `ax`."
ax = show_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize,
interpolatio... |
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| def clone(self):
"Mimic the behavior of torch.clone for `ImagePoints` objects."
return self.__class__(FlowField(self.size, self.flow.flow.clone()), scale=False, y_first=False) |
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| def flow(self)->FlowField:
"Access the flow-field grid after applying queued affine and coord transforms."
if self._affine_mat is not None:
self._flow = _affine_inv_mult(self._flow, self._affine_mat)
self._affine_mat = None
self.transformed = True
if len(self.... |
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| def coord(self, func:CoordFunc, *args, **kwargs)->'ImagePoints':
"Put `func` with `args` and `kwargs` in `self.flow_func` for later."
if 'invert' in kwargs: kwargs['invert'] = True
else: warn(f"{func.__name__} isn't implemented for {self.__class__}.")
self.flow_func.append(partial(func, ... |
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| def data(self)->Tensor:
"Return the points associated to this object."
flow = self.flow #This updates flow before we test if some transforms happened
if self.transformed:
if 'remove_out' not in self.sample_kwargs or self.sample_kwargs['remove_out']:
flow = _remove_poi... |
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| def show(self, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True, **kwargs):
"Show the `ImagePoints` on `ax`."
if ax is None: _,ax = plt.subplots(figsize=figsize)
pnt = scale_flow(FlowField(self.size, self.data), to_unit=False).flow.flip(1)
params = {'s... |
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| def clone(self) -> 'ImageBBox':
"Mimic the behavior of torch.clone for `Image` objects."
flow = FlowField(self.size, self.flow.flow.clone())
return self.__class__(flow, scale=False, y_first=False, labels=self.labels, pad_idx=self.pad_idx) |
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| def create(cls, h:int, w:int, bboxes:Collection[Collection[int]], labels:Collection=None, classes:dict=None,
pad_idx:int=0, scale:bool=True)->'ImageBBox':
"Create an ImageBBox object from `bboxes`."
if isinstance(bboxes, np.ndarray) and bboxes.dtype == np.object: bboxes = np.array([bb for... |
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| def show(self, y:Image=None, ax:plt.Axes=None, figsize:tuple=(3,3), title:Optional[str]=None, hide_axis:bool=True,
color:str='white', **kwargs):
"Show the `ImageBBox` on `ax`."
if ax is None: _,ax = plt.subplots(figsize=figsize)
bboxes, lbls = self._compute_boxes()
h,w = self.flo... |
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| def calc(self, x:Image, *args:Any, **kwargs:Any)->Image:
"Apply to image `x`, wrapping it if necessary."
if self._wrap: return getattr(x, self._wrap)(self.func, *args, **kwargs)
else: return self.func(x, *args, **kwargs) |
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| def url2path(url, data=True, ext:str='.tgz'):
"Change `url` to a path."
name = url2name(url)
return datapath4file(name, ext=ext, archive=False) if data else modelpath4file(name, ext=ext) |
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| def modelpath4file(filename, ext:str='.tgz'):
"Return model path to `filename`, checking locally first then in the config file."
local_path = URLs.LOCAL_PATH/'models'/filename
if local_path.exists() or local_path.with_suffix(ext).exists(): return local_path
else: return Config.model_path()/filename |
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| def datapath4file(filename, ext:str='.tgz', archive=True):
"Return data path to `filename`, checking locally first then in the config file."
local_path = URLs.LOCAL_PATH/'data'/filename
if local_path.exists() or local_path.with_suffix(ext).exists(): return local_path
elif archive: return Config.data_arc... |
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| def download_data(url:str, fname:PathOrStr=None, data:bool=True, ext:str='.tgz') -> Path:
"Download `url` to destination `fname`."
fname = Path(ifnone(fname, _url2tgz(url, data, ext=ext)))
os.makedirs(fname.parent, exist_ok=True)
if not fname.exists():
print(f'Downloading {url}')
downloa... |
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| def untar_data(url:str, fname:PathOrStr=None, dest:PathOrStr=None, data=True, force_download=False) -> Path:
"Download `url` to `fname` if `dest` doesn't exist, and un-tgz to folder `dest`."
dest = url2path(url, data) if dest is None else Path(dest)/url2name(url)
fname = Path(ifnone(fname, _url2tgz(url, dat... |
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| def get_key(cls, key):
"Get the path to `key` in the config file."
return cls.get().get(key, cls.DEFAULT_CONFIG.get(key,None)) |
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| def get(cls, fpath=None, create_missing=True):
"Retrieve the `Config` in `fpath`."
fpath = _expand_path(fpath or cls.DEFAULT_CONFIG_PATH)
if not fpath.exists() and create_missing: cls.create(fpath)
assert fpath.exists(), f'Could not find config at: {fpath}. Please create'
with op... |
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Description:
| def create(cls, fpath):
"Creates a `Config` from `fpath`."
fpath = _expand_path(fpath)
assert(fpath.suffix == '.yml')
if fpath.exists(): return
fpath.parent.mkdir(parents=True, exist_ok=True)
with open(fpath, 'w') as yaml_file:
yaml.dump(cls.DEFAULT_CONFIG, ya... |
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Description:
| def on_batch_begin(self, last_input, last_target, train, **kwargs):
"Applies mixup to `last_input` and `last_target` if `train`."
if not train: return
lambd = np.random.beta(self.alpha, self.alpha, last_target.size(0))
lambd = np.concatenate([lambd[:,None], 1-lambd[:,None]], 1).max(1)
... |
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| def add_datepart(df:DataFrame, field_name:str, prefix:str=None, drop:bool=True, time:bool=False):
"Helper function that adds columns relevant to a date in the column `field_name` of `df`."
make_date(df, field_name)
field = df[field_name]
prefix = ifnone(prefix, re.sub('[Dd]ate$', '', field_name))
at... |
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| def cont_cat_split(df, max_card=20, dep_var=None)->Tuple[List,List]:
"Helper function that returns column names of cont and cat variables from given df."
cont_names, cat_names = [], []
for label in df:
if label == dep_var: continue
if df[label].dtype == int and df[label].unique().shape[0] > ... |
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| def apply_train(self, df:DataFrame):
"Transform `self.cat_names` columns in categorical."
self.categories = {}
for n in self.cat_names:
df.loc[:,n] = df.loc[:,n].astype('category').cat.as_ordered()
self.categories[n] = df[n].cat.categories |
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Description:
def apply_train(self, df:DataFrame):
"Compute the means and stds of `self.cont_names` columns to normalize them." self.means,self.stds = {},{} for n in self.cont... | )
self.means[n],self.stds[n] = df[n].mean(),df[n].std()
df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n]) |
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| def def_emb_sz(classes, n, sz_dict=None):
"Pick an embedding size for `n` depending on `classes` if not given in `sz_dict`."
sz_dict = ifnone(sz_dict, {})
n_cat = len(classes[n])
sz = sz_dict.get(n, int(emb_sz_rule(n_cat))) # rule of thumb
return n_cat,sz |
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| def tabular_learner(data:DataBunch, layers:Collection[int], emb_szs:Dict[str,int]=None, metrics=None,
ps:Collection[float]=None, emb_drop:float=0., y_range:OptRange=None, use_bn:bool=True, **learn_kwargs):
"Get a `Learner` using `data`, with `metrics`, including a `TabularModel` created using the remaining ... |
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| def from_df(cls, path, df:DataFrame, dep_var:str, valid_idx:Collection[int], procs:OptTabTfms=None,
cat_names:OptStrList=None, cont_names:OptStrList=None, classes:Collection=None,
test_df=None, bs:int=64, val_bs:int=None, num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callab... |
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| def get_emb_szs(self, sz_dict=None):
"Return the default embedding sizes suitable for this data or takes the ones in `sz_dict`."
return [def_emb_sz(self.classes, n, sz_dict) for n in self.cat_names] |
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def load_model(itos_filename, classifier_filename, num_classes):
"""Load the classifier and int to string mapping Args: itos_filename (str):
The filename of the... |
# load the int to string mapping file
itos = pickle.load(Path(itos_filename).open('rb'))
# turn it into a string to int mapping (which is what we need)
stoi = collections.defaultdict(lambda:0, {str(v):int(k) for k,v in enumerate(itos)})
# these parameters aren't used, but this is the easiest way ... |
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def predict_text(stoi, model, text):
"""Do the actual prediction on the text using the model and mapping files passed """ |
# prefix text with tokens:
# xbos: beginning of sentence
# xfld 1: we are using a single field here
input_str = 'xbos xfld 1 ' + text
# predictions are done on arrays of input.
# We only have a single input, so turn it into a 1x1 array
texts = [input_str]
# tokenize using the fas... |
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def _make_w3c_caps(caps):
"""Makes a W3C alwaysMatch capabilities object. Filters out capability names that are not in the W3C spec. Spec-compliant drivers will ... |
caps = copy.deepcopy(caps)
profile = caps.get('firefox_profile')
always_match = {}
if caps.get('proxy') and caps['proxy'].get('proxyType'):
caps['proxy']['proxyType'] = caps['proxy']['proxyType'].lower()
for k, v in caps.items():
if v and k in _OSS_W3C_CONVERSION:
always... |
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def start_session(self, capabilities, browser_profile=None):
""" Creates a new session with the desired capabilities. :Args: - browser_name - The name of the bro... |
if not isinstance(capabilities, dict):
raise InvalidArgumentException("Capabilities must be a dictionary")
if browser_profile:
if "moz:firefoxOptions" in capabilities:
capabilities["moz:firefoxOptions"]["profile"] = browser_profile.encoded
else:
... |
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def create_web_element(self, element_id):
"""Creates a web element with the specified `element_id`.""" |
return self._web_element_cls(self, element_id, w3c=self.w3c) |
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def execute(self, driver_command, params=None):
""" Sends a command to be executed by a command.CommandExecutor. :Args: - driver_command: The name of the command... |
if self.session_id is not None:
if not params:
params = {'sessionId': self.session_id}
elif 'sessionId' not in params:
params['sessionId'] = self.session_id
params = self._wrap_value(params)
response = self.command_executor.execute(driver... |
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def find_element_by_link_text(self, link_text):
""" Finds an element by link text. :Args: - link_text: The text of the element to be found. :Returns: - WebElemen... |
return self.find_element(by=By.LINK_TEXT, value=link_text) |
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def find_elements_by_link_text(self, text):
""" Finds elements by link text. :Args: - link_text: The text of the elements to be found. :Returns: - list of webele... |
return self.find_elements(by=By.LINK_TEXT, value=text) |
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def find_element_by_partial_link_text(self, link_text):
""" Finds an element by a partial match of its link text. :Args: - link_text: The text of the element to ... |
return self.find_element(by=By.PARTIAL_LINK_TEXT, value=link_text) |
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def find_elements_by_partial_link_text(self, link_text):
""" Finds elements by a partial match of their link text. :Args: - link_text: The text of the element to... |
return self.find_elements(by=By.PARTIAL_LINK_TEXT, value=link_text) |
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def find_elements_by_name(self, name):
""" Finds elements by name. :Args: - name: The name of the elements to find. :Returns: - list of webelement - a list with ... |
return self.find_elements(by=By.NAME, value=name) |
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def find_element_by_tag_name(self, name):
""" Finds an element by tag name. :Args: - name - name of html tag (eg: h1, a, span) :Returns: - WebElement - the eleme... |
return self.find_element(by=By.TAG_NAME, value=name) |
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def find_elements_by_tag_name(self, name):
""" Finds elements by tag name. :Args: - name - name of html tag (eg: h1, a, span) :Returns: - list of WebElement - a ... |
return self.find_elements(by=By.TAG_NAME, value=name) |
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def find_element_by_class_name(self, name):
""" Finds an element by class name. :Args: - name: The class name of the element to find. :Returns: - WebElement - th... |
return self.find_element(by=By.CLASS_NAME, value=name) |
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def find_elements_by_class_name(self, name):
""" Finds elements by class name. :Args: - name: The class name of the elements to find. :Returns: - list of WebElem... |
return self.find_elements(by=By.CLASS_NAME, value=name) |
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def find_element_by_css_selector(self, css_selector):
""" Finds an element by css selector. :Args: - css_selector - CSS selector string, ex: 'a.nav#home' :Return... |
return self.find_element(by=By.CSS_SELECTOR, value=css_selector) |
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def find_elements_by_css_selector(self, css_selector):
""" Finds elements by css selector. :Args: - css_selector - CSS selector string, ex: 'a.nav#home' :Returns... |
return self.find_elements(by=By.CSS_SELECTOR, value=css_selector) |
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def quit(self):
""" Quits the driver and closes every associated window. :Usage: :: driver.quit() """ |
try:
self.execute(Command.QUIT)
finally:
self.stop_client()
self.command_executor.close() |
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def current_window_handle(self):
""" Returns the handle of the current window. :Usage: :: driver.current_window_handle """ |
if self.w3c:
return self.execute(Command.W3C_GET_CURRENT_WINDOW_HANDLE)['value']
else:
return self.execute(Command.GET_CURRENT_WINDOW_HANDLE)['value'] |
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def window_handles(self):
""" Returns the handles of all windows within the current session. :Usage: :: driver.window_handles """ |
if self.w3c:
return self.execute(Command.W3C_GET_WINDOW_HANDLES)['value']
else:
return self.execute(Command.GET_WINDOW_HANDLES)['value'] |
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def maximize_window(self):
""" Maximizes the current window that webdriver is using """ |
params = None
command = Command.W3C_MAXIMIZE_WINDOW
if not self.w3c:
command = Command.MAXIMIZE_WINDOW
params = {'windowHandle': 'current'}
self.execute(command, params) |
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def get_cookie(self, name):
""" Get a single cookie by name. Returns the cookie if found, None if not. :Usage: :: driver.get_cookie('my_cookie') """ |
if self.w3c:
try:
return self.execute(Command.GET_COOKIE, {'name': name})['value']
except NoSuchCookieException:
return None
else:
cookies = self.get_cookies()
for cookie in cookies:
if cookie['name'] == nam... |
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def implicitly_wait(self, time_to_wait):
""" Sets a sticky timeout to implicitly wait for an element to be found, or a command to complete. This method only need... |
if self.w3c:
self.execute(Command.SET_TIMEOUTS, {
'implicit': int(float(time_to_wait) * 1000)})
else:
self.execute(Command.IMPLICIT_WAIT, {
'ms': float(time_to_wait) * 1000}) |
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def set_script_timeout(self, time_to_wait):
""" Set the amount of time that the script should wait during an execute_async_script call before throwing an error. ... |
if self.w3c:
self.execute(Command.SET_TIMEOUTS, {
'script': int(float(time_to_wait) * 1000)})
else:
self.execute(Command.SET_SCRIPT_TIMEOUT, {
'ms': float(time_to_wait) * 1000}) |
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def set_page_load_timeout(self, time_to_wait):
""" Set the amount of time to wait for a page load to complete before throwing an error. :Args: - time_to_wait: Th... |
try:
self.execute(Command.SET_TIMEOUTS, {
'pageLoad': int(float(time_to_wait) * 1000)})
except WebDriverException:
self.execute(Command.SET_TIMEOUTS, {
'ms': float(time_to_wait) * 1000,
'type': 'page load'}) |
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def get_screenshot_as_file(self, filename):
""" Saves a screenshot of the current window to a PNG image file. Returns False if there is any IOError, else returns... |
if not filename.lower().endswith('.png'):
warnings.warn("name used for saved screenshot does not match file "
"type. It should end with a `.png` extension", UserWarning)
png = self.get_screenshot_as_png()
try:
with open(filename, 'wb') as f:
... |
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def get_window_size(self, windowHandle='current'):
""" Gets the width and height of the current window. :Usage: :: driver.get_window_size() """ |
command = Command.GET_WINDOW_SIZE
if self.w3c:
if windowHandle != 'current':
warnings.warn("Only 'current' window is supported for W3C compatibile browsers.")
size = self.get_window_rect()
else:
size = self.execute(command, {'windowHandle': wi... |
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def get_window_position(self, windowHandle='current'):
""" Gets the x,y position of the current window. :Usage: :: driver.get_window_position() """ |
if self.w3c:
if windowHandle != 'current':
warnings.warn("Only 'current' window is supported for W3C compatibile browsers.")
position = self.get_window_rect()
else:
position = self.execute(Command.GET_WINDOW_POSITION,
... |
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def set_window_rect(self, x=None, y=None, width=None, height=None):
""" Sets the x, y coordinates of the window as well as height and width of the current window... |
if not self.w3c:
raise UnknownMethodException("set_window_rect is only supported for W3C compatible browsers")
if (x is None and y is None) and (height is None and width is None):
raise InvalidArgumentException("x and y or height and width need values")
return self.exe... |
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Description:
def file_detector(self, detector):
""" Set the file detector to be used when sending keyboard input. By default, this is set to a file detector that does nothing... |
if detector is None:
raise WebDriverException("You may not set a file detector that is null")
if not isinstance(detector, FileDetector):
raise WebDriverException("Detector has to be instance of FileDetector")
self._file_detector = detector |
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