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19.8k
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19.8k
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21,000
|
fastai/fastai
|
fastai/text/learner.py
|
language_model_learner
|
def language_model_learner(data:DataBunch, arch, config:dict=None, drop_mult:float=1., pretrained:bool=True,
pretrained_fnames:OptStrTuple=None, **learn_kwargs) -> 'LanguageLearner':
"Create a `Learner` with a language model from `data` and `arch`."
model = get_language_model(arch, len(data.vocab.itos), config=config, drop_mult=drop_mult)
meta = _model_meta[arch]
learn = LanguageLearner(data, model, split_func=meta['split_lm'], **learn_kwargs)
if pretrained:
if 'url' not in meta:
warn("There are no pretrained weights for that architecture yet!")
return learn
model_path = untar_data(meta['url'], data=False)
fnames = [list(model_path.glob(f'*.{ext}'))[0] for ext in ['pth', 'pkl']]
learn.load_pretrained(*fnames)
learn.freeze()
if pretrained_fnames is not None:
fnames = [learn.path/learn.model_dir/f'{fn}.{ext}' for fn,ext in zip(pretrained_fnames, ['pth', 'pkl'])]
learn.load_pretrained(*fnames)
learn.freeze()
return learn
|
python
|
def language_model_learner(data:DataBunch, arch, config:dict=None, drop_mult:float=1., pretrained:bool=True,
pretrained_fnames:OptStrTuple=None, **learn_kwargs) -> 'LanguageLearner':
"Create a `Learner` with a language model from `data` and `arch`."
model = get_language_model(arch, len(data.vocab.itos), config=config, drop_mult=drop_mult)
meta = _model_meta[arch]
learn = LanguageLearner(data, model, split_func=meta['split_lm'], **learn_kwargs)
if pretrained:
if 'url' not in meta:
warn("There are no pretrained weights for that architecture yet!")
return learn
model_path = untar_data(meta['url'], data=False)
fnames = [list(model_path.glob(f'*.{ext}'))[0] for ext in ['pth', 'pkl']]
learn.load_pretrained(*fnames)
learn.freeze()
if pretrained_fnames is not None:
fnames = [learn.path/learn.model_dir/f'{fn}.{ext}' for fn,ext in zip(pretrained_fnames, ['pth', 'pkl'])]
learn.load_pretrained(*fnames)
learn.freeze()
return learn
|
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Create a `Learner` with a language model from `data` and `arch`.
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L201-L219
|
21,001
|
fastai/fastai
|
fastai/text/learner.py
|
get_text_classifier
|
def get_text_classifier(arch:Callable, vocab_sz:int, n_class:int, bptt:int=70, max_len:int=20*70, config:dict=None,
drop_mult:float=1., lin_ftrs:Collection[int]=None, ps:Collection[float]=None,
pad_idx:int=1) -> nn.Module:
"Create a text classifier from `arch` and its `config`, maybe `pretrained`."
meta = _model_meta[arch]
config = ifnone(config, meta['config_clas'].copy())
for k in config.keys():
if k.endswith('_p'): config[k] *= drop_mult
if lin_ftrs is None: lin_ftrs = [50]
if ps is None: ps = [0.1]*len(lin_ftrs)
layers = [config[meta['hid_name']] * 3] + lin_ftrs + [n_class]
ps = [config.pop('output_p')] + ps
init = config.pop('init') if 'init' in config else None
encoder = MultiBatchEncoder(bptt, max_len, arch(vocab_sz, **config), pad_idx=pad_idx)
model = SequentialRNN(encoder, PoolingLinearClassifier(layers, ps))
return model if init is None else model.apply(init)
|
python
|
def get_text_classifier(arch:Callable, vocab_sz:int, n_class:int, bptt:int=70, max_len:int=20*70, config:dict=None,
drop_mult:float=1., lin_ftrs:Collection[int]=None, ps:Collection[float]=None,
pad_idx:int=1) -> nn.Module:
"Create a text classifier from `arch` and its `config`, maybe `pretrained`."
meta = _model_meta[arch]
config = ifnone(config, meta['config_clas'].copy())
for k in config.keys():
if k.endswith('_p'): config[k] *= drop_mult
if lin_ftrs is None: lin_ftrs = [50]
if ps is None: ps = [0.1]*len(lin_ftrs)
layers = [config[meta['hid_name']] * 3] + lin_ftrs + [n_class]
ps = [config.pop('output_p')] + ps
init = config.pop('init') if 'init' in config else None
encoder = MultiBatchEncoder(bptt, max_len, arch(vocab_sz, **config), pad_idx=pad_idx)
model = SequentialRNN(encoder, PoolingLinearClassifier(layers, ps))
return model if init is None else model.apply(init)
|
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Create a text classifier from `arch` and its `config`, maybe `pretrained`.
|
[
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"classifier",
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"maybe",
"pretrained",
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L269-L284
|
21,002
|
fastai/fastai
|
fastai/text/learner.py
|
text_classifier_learner
|
def text_classifier_learner(data:DataBunch, arch:Callable, bptt:int=70, max_len:int=70*20, config:dict=None,
pretrained:bool=True, drop_mult:float=1., lin_ftrs:Collection[int]=None,
ps:Collection[float]=None, **learn_kwargs) -> 'TextClassifierLearner':
"Create a `Learner` with a text classifier from `data` and `arch`."
model = get_text_classifier(arch, len(data.vocab.itos), data.c, bptt=bptt, max_len=max_len,
config=config, drop_mult=drop_mult, lin_ftrs=lin_ftrs, ps=ps)
meta = _model_meta[arch]
learn = RNNLearner(data, model, split_func=meta['split_clas'], **learn_kwargs)
if pretrained:
if 'url' not in meta:
warn("There are no pretrained weights for that architecture yet!")
return learn
model_path = untar_data(meta['url'], data=False)
fnames = [list(model_path.glob(f'*.{ext}'))[0] for ext in ['pth', 'pkl']]
learn.load_pretrained(*fnames, strict=False)
learn.freeze()
return learn
|
python
|
def text_classifier_learner(data:DataBunch, arch:Callable, bptt:int=70, max_len:int=70*20, config:dict=None,
pretrained:bool=True, drop_mult:float=1., lin_ftrs:Collection[int]=None,
ps:Collection[float]=None, **learn_kwargs) -> 'TextClassifierLearner':
"Create a `Learner` with a text classifier from `data` and `arch`."
model = get_text_classifier(arch, len(data.vocab.itos), data.c, bptt=bptt, max_len=max_len,
config=config, drop_mult=drop_mult, lin_ftrs=lin_ftrs, ps=ps)
meta = _model_meta[arch]
learn = RNNLearner(data, model, split_func=meta['split_clas'], **learn_kwargs)
if pretrained:
if 'url' not in meta:
warn("There are no pretrained weights for that architecture yet!")
return learn
model_path = untar_data(meta['url'], data=False)
fnames = [list(model_path.glob(f'*.{ext}'))[0] for ext in ['pth', 'pkl']]
learn.load_pretrained(*fnames, strict=False)
learn.freeze()
return learn
|
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Create a `Learner` with a text classifier from `data` and `arch`.
|
[
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"text",
"classifier",
"from",
"data",
"and",
"arch",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L286-L302
|
21,003
|
fastai/fastai
|
fastai/text/learner.py
|
RNNLearner.save_encoder
|
def save_encoder(self, name:str):
"Save the encoder to `name` inside the model directory."
encoder = get_model(self.model)[0]
if hasattr(encoder, 'module'): encoder = encoder.module
torch.save(encoder.state_dict(), self.path/self.model_dir/f'{name}.pth')
|
python
|
def save_encoder(self, name:str):
"Save the encoder to `name` inside the model directory."
encoder = get_model(self.model)[0]
if hasattr(encoder, 'module'): encoder = encoder.module
torch.save(encoder.state_dict(), self.path/self.model_dir/f'{name}.pth')
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L57-L61
|
21,004
|
fastai/fastai
|
fastai/text/learner.py
|
RNNLearner.load_encoder
|
def load_encoder(self, name:str, device:torch.device=None):
"Load the encoder `name` from the model directory."
encoder = get_model(self.model)[0]
if device is None: device = self.data.device
if hasattr(encoder, 'module'): encoder = encoder.module
encoder.load_state_dict(torch.load(self.path/self.model_dir/f'{name}.pth'))
encoder.load_state_dict(torch.load(self.path/self.model_dir/f'{name}.pth', map_location=device))
self.freeze()
|
python
|
def load_encoder(self, name:str, device:torch.device=None):
"Load the encoder `name` from the model directory."
encoder = get_model(self.model)[0]
if device is None: device = self.data.device
if hasattr(encoder, 'module'): encoder = encoder.module
encoder.load_state_dict(torch.load(self.path/self.model_dir/f'{name}.pth'))
encoder.load_state_dict(torch.load(self.path/self.model_dir/f'{name}.pth', map_location=device))
self.freeze()
|
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[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L63-L70
|
21,005
|
fastai/fastai
|
fastai/text/learner.py
|
RNNLearner.load_pretrained
|
def load_pretrained(self, wgts_fname:str, itos_fname:str, strict:bool=True):
"Load a pretrained model and adapts it to the data vocabulary."
old_itos = pickle.load(open(itos_fname, 'rb'))
old_stoi = {v:k for k,v in enumerate(old_itos)}
wgts = torch.load(wgts_fname, map_location=lambda storage, loc: storage)
if 'model' in wgts: wgts = wgts['model']
wgts = convert_weights(wgts, old_stoi, self.data.train_ds.vocab.itos)
self.model.load_state_dict(wgts, strict=strict)
|
python
|
def load_pretrained(self, wgts_fname:str, itos_fname:str, strict:bool=True):
"Load a pretrained model and adapts it to the data vocabulary."
old_itos = pickle.load(open(itos_fname, 'rb'))
old_stoi = {v:k for k,v in enumerate(old_itos)}
wgts = torch.load(wgts_fname, map_location=lambda storage, loc: storage)
if 'model' in wgts: wgts = wgts['model']
wgts = convert_weights(wgts, old_stoi, self.data.train_ds.vocab.itos)
self.model.load_state_dict(wgts, strict=strict)
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L72-L79
|
21,006
|
fastai/fastai
|
fastai/text/learner.py
|
RNNLearner.get_preds
|
def get_preds(self, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False, n_batch:Optional[int]=None, pbar:Optional[PBar]=None,
ordered:bool=False) -> List[Tensor]:
"Return predictions and targets on the valid, train, or test set, depending on `ds_type`."
self.model.reset()
if ordered: np.random.seed(42)
preds = super().get_preds(ds_type=ds_type, with_loss=with_loss, n_batch=n_batch, pbar=pbar)
if ordered and hasattr(self.dl(ds_type), 'sampler'):
np.random.seed(42)
sampler = [i for i in self.dl(ds_type).sampler]
reverse_sampler = np.argsort(sampler)
preds = [p[reverse_sampler] for p in preds]
return(preds)
|
python
|
def get_preds(self, ds_type:DatasetType=DatasetType.Valid, with_loss:bool=False, n_batch:Optional[int]=None, pbar:Optional[PBar]=None,
ordered:bool=False) -> List[Tensor]:
"Return predictions and targets on the valid, train, or test set, depending on `ds_type`."
self.model.reset()
if ordered: np.random.seed(42)
preds = super().get_preds(ds_type=ds_type, with_loss=with_loss, n_batch=n_batch, pbar=pbar)
if ordered and hasattr(self.dl(ds_type), 'sampler'):
np.random.seed(42)
sampler = [i for i in self.dl(ds_type).sampler]
reverse_sampler = np.argsort(sampler)
preds = [p[reverse_sampler] for p in preds]
return(preds)
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L81-L92
|
21,007
|
fastai/fastai
|
fastai/text/learner.py
|
LanguageLearner.predict
|
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(text)
new_idx = []
for _ in range(n_words): #progress_bar(range(n_words), leave=False):
res = self.pred_batch(batch=(xb,yb))[0][-1]
#if len(new_idx) == 0: self.model[0].select_hidden([0])
if no_unk: res[self.data.vocab.stoi[UNK]] = 0.
if min_p is not None:
if (res >= min_p).float().sum() == 0:
warn(f"There is no item with probability >= {min_p}, try a lower value.")
else: res[res < min_p] = 0.
if temperature != 1.: res.pow_(1 / temperature)
idx = torch.multinomial(res, 1).item()
new_idx.append(idx)
xb = xb.new_tensor([idx])[None]
return text + sep + sep.join(decoder(self.data.vocab.textify(new_idx, sep=None)))
|
python
|
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(text)
new_idx = []
for _ in range(n_words): #progress_bar(range(n_words), leave=False):
res = self.pred_batch(batch=(xb,yb))[0][-1]
#if len(new_idx) == 0: self.model[0].select_hidden([0])
if no_unk: res[self.data.vocab.stoi[UNK]] = 0.
if min_p is not None:
if (res >= min_p).float().sum() == 0:
warn(f"There is no item with probability >= {min_p}, try a lower value.")
else: res[res < min_p] = 0.
if temperature != 1.: res.pow_(1 / temperature)
idx = torch.multinomial(res, 1).item()
new_idx.append(idx)
xb = xb.new_tensor([idx])[None]
return text + sep + sep.join(decoder(self.data.vocab.textify(new_idx, sep=None)))
|
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Return the `n_words` that come after `text`.
|
[
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"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L116-L135
|
21,008
|
fastai/fastai
|
fastai/text/learner.py
|
LanguageLearner.beam_search
|
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()
xb, yb = self.data.one_item(text)
nodes = None
xb = xb.repeat(top_k, 1)
nodes = xb.clone()
scores = xb.new_zeros(1).float()
with torch.no_grad():
for k in progress_bar(range(n_words), leave=False):
out = F.log_softmax(self.model(xb)[0][:,-1], dim=-1)
if no_unk: out[:,self.data.vocab.stoi[UNK]] = -float('Inf')
values, indices = out.topk(top_k, dim=-1)
scores = (-values + scores[:,None]).view(-1)
indices_idx = torch.arange(0,nodes.size(0))[:,None].expand(nodes.size(0), top_k).contiguous().view(-1)
sort_idx = scores.argsort()[:beam_sz]
scores = scores[sort_idx]
nodes = torch.cat([nodes[:,None].expand(nodes.size(0),top_k,nodes.size(1)),
indices[:,:,None].expand(nodes.size(0),top_k,1),], dim=2)
nodes = nodes.view(-1, nodes.size(2))[sort_idx]
self.model[0].select_hidden(indices_idx[sort_idx])
xb = nodes[:,-1][:,None]
if temperature != 1.: scores.div_(temperature)
node_idx = torch.multinomial(torch.exp(-scores), 1).item()
return text + sep + sep.join(decoder(self.data.vocab.textify([i.item() for i in nodes[node_idx][1:] ], sep=None)))
|
python
|
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()
xb, yb = self.data.one_item(text)
nodes = None
xb = xb.repeat(top_k, 1)
nodes = xb.clone()
scores = xb.new_zeros(1).float()
with torch.no_grad():
for k in progress_bar(range(n_words), leave=False):
out = F.log_softmax(self.model(xb)[0][:,-1], dim=-1)
if no_unk: out[:,self.data.vocab.stoi[UNK]] = -float('Inf')
values, indices = out.topk(top_k, dim=-1)
scores = (-values + scores[:,None]).view(-1)
indices_idx = torch.arange(0,nodes.size(0))[:,None].expand(nodes.size(0), top_k).contiguous().view(-1)
sort_idx = scores.argsort()[:beam_sz]
scores = scores[sort_idx]
nodes = torch.cat([nodes[:,None].expand(nodes.size(0),top_k,nodes.size(1)),
indices[:,:,None].expand(nodes.size(0),top_k,1),], dim=2)
nodes = nodes.view(-1, nodes.size(2))[sort_idx]
self.model[0].select_hidden(indices_idx[sort_idx])
xb = nodes[:,-1][:,None]
if temperature != 1.: scores.div_(temperature)
node_idx = torch.multinomial(torch.exp(-scores), 1).item()
return text + sep + sep.join(decoder(self.data.vocab.textify([i.item() for i in nodes[node_idx][1:] ], sep=None)))
|
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] |
Return the `n_words` that come after `text` using beam search.
|
[
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"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L137-L163
|
21,009
|
fastai/fastai
|
fastai/text/learner.py
|
LanguageLearner.show_results
|
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 = self.pred_batch(batch=(x,y))
y = y.view(*x.size())
z = preds.view(*x.size(),-1).argmax(dim=2)
xs = [ds.x.reconstruct(grab_idx(x, i)) for i in range(rows)]
ys = [ds.x.reconstruct(grab_idx(y, i)) for i in range(rows)]
zs = [ds.x.reconstruct(grab_idx(z, i)) for i in range(rows)]
items,names = [],['text', 'target', 'pred']
for i, (x,y,z) in enumerate(zip(xs,ys,zs)):
txt_x = ' '.join(x.text.split(' ')[:max_len])
txt_y = ' '.join(y.text.split(' ')[max_len-1:2*max_len-1])
txt_z = ' '.join(z.text.split(' ')[max_len-1:2*max_len-1])
items.append([txt_x, txt_y, txt_z])
items = np.array(items)
df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)
with pd.option_context('display.max_colwidth', -1):
display(HTML(df.to_html(index=False)))
|
python
|
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 = self.pred_batch(batch=(x,y))
y = y.view(*x.size())
z = preds.view(*x.size(),-1).argmax(dim=2)
xs = [ds.x.reconstruct(grab_idx(x, i)) for i in range(rows)]
ys = [ds.x.reconstruct(grab_idx(y, i)) for i in range(rows)]
zs = [ds.x.reconstruct(grab_idx(z, i)) for i in range(rows)]
items,names = [],['text', 'target', 'pred']
for i, (x,y,z) in enumerate(zip(xs,ys,zs)):
txt_x = ' '.join(x.text.split(' ')[:max_len])
txt_y = ' '.join(y.text.split(' ')[max_len-1:2*max_len-1])
txt_z = ' '.join(z.text.split(' ')[max_len-1:2*max_len-1])
items.append([txt_x, txt_y, txt_z])
items = np.array(items)
df = pd.DataFrame({n:items[:,i] for i,n in enumerate(names)}, columns=names)
with pd.option_context('display.max_colwidth', -1):
display(HTML(df.to_html(index=False)))
|
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"to_html",
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] |
Show `rows` result of predictions on `ds_type` dataset.
|
[
"Show",
"rows",
"result",
"of",
"predictions",
"on",
"ds_type",
"dataset",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L165-L185
|
21,010
|
fastai/fastai
|
fastai/text/learner.py
|
MultiBatchEncoder.concat
|
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])]
|
python
|
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])]
|
[
"def",
"concat",
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")",
"for",
"si",
"in",
"range_of",
"(",
"arrs",
"[",
"0",
"]",
")",
"]"
] |
Concatenate the `arrs` along the batch dimension.
|
[
"Concatenate",
"the",
"arrs",
"along",
"the",
"batch",
"dimension",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/text/learner.py#L250-L252
|
21,011
|
fastai/fastai
|
fastai/layers.py
|
batchnorm_2d
|
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
|
python
|
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
|
[
"def",
"batchnorm_2d",
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":",
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",",
"norm_type",
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"NormType",
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"NormType",
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"==",
"NormType",
".",
"BatchZero",
"else",
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] |
A batchnorm2d layer with `nf` features initialized depending on `norm_type`.
|
[
"A",
"batchnorm2d",
"layer",
"with",
"nf",
"features",
"initialized",
"depending",
"on",
"norm_type",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L52-L58
|
21,012
|
fastai/fastai
|
fastai/layers.py
|
conv1d
|
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_()
return spectral_norm(conv)
|
python
|
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_()
return spectral_norm(conv)
|
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] |
Create and initialize a `nn.Conv1d` layer with spectral normalization.
|
[
"Create",
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"initialize",
"a",
"nn",
".",
"Conv1d",
"layer",
"with",
"spectral",
"normalization",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L68-L73
|
21,013
|
fastai/fastai
|
fastai/layers.py
|
conv2d_trans
|
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)
|
python
|
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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Create `nn.ConvTranspose2d` layer.
|
[
"Create",
"nn",
".",
"ConvTranspose2d",
"layer",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L120-L122
|
21,014
|
fastai/fastai
|
fastai/layers.py
|
relu
|
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)
|
python
|
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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] |
Return a relu activation, maybe `leaky` and `inplace`.
|
[
"Return",
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"relu",
"activation",
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"leaky",
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"inplace",
"."
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L124-L126
|
21,015
|
fastai/fastai
|
fastai/layers.py
|
res_block
|
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 bottle else nf
return SequentialEx(conv_layer(nf, nf_inner, norm_type=norm_type, **conv_kwargs),
conv_layer(nf_inner, nf, norm_type=norm2, **conv_kwargs),
MergeLayer(dense))
|
python
|
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 bottle else nf
return SequentialEx(conv_layer(nf, nf_inner, norm_type=norm_type, **conv_kwargs),
conv_layer(nf_inner, nf, norm_type=norm2, **conv_kwargs),
MergeLayer(dense))
|
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Resnet block of `nf` features. `conv_kwargs` are passed to `conv_layer`.
|
[
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L174-L181
|
21,016
|
fastai/fastai
|
fastai/layers.py
|
icnr
|
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([nf,ni,h,w]).transpose(0, 1)
x.data.copy_(k)
|
python
|
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([nf,ni,h,w]).transpose(0, 1)
x.data.copy_(k)
|
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ICNR init of `x`, with `scale` and `init` function.
|
[
"ICNR",
"init",
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"x",
"with",
"scale",
"and",
"init",
"function",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L221-L229
|
21,017
|
fastai/fastai
|
fastai/layers.py
|
CrossEntropyFlat
|
def CrossEntropyFlat(*args, axis:int=-1, **kwargs):
"Same as `nn.CrossEntropyLoss`, but flattens input and target."
return FlattenedLoss(nn.CrossEntropyLoss, *args, axis=axis, **kwargs)
|
python
|
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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Same as `nn.CrossEntropyLoss`, but flattens input and target.
|
[
"Same",
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".",
"CrossEntropyLoss",
"but",
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"input",
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L269-L271
|
21,018
|
fastai/fastai
|
fastai/layers.py
|
BCEWithLogitsFlat
|
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)
|
python
|
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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Same as `nn.BCEWithLogitsLoss`, but flattens input and target.
|
[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L273-L275
|
21,019
|
fastai/fastai
|
fastai/layers.py
|
BCEFlat
|
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)
|
python
|
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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Same as `nn.BCELoss`, but flattens input and target.
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L277-L279
|
21,020
|
fastai/fastai
|
fastai/layers.py
|
MSELossFlat
|
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)
|
python
|
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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Same as `nn.MSELoss`, but flattens input and target.
|
[
"Same",
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"MSELoss",
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"input",
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L281-L283
|
21,021
|
fastai/fastai
|
fastai/layers.py
|
simple_cnn
|
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)
strides = ifnone(strides , [2]*nl)
layers = [conv_layer(actns[i], actns[i+1], kernel_szs[i], stride=strides[i],
norm_type=(NormType.Batch if bn and i<(len(strides)-1) else None)) for i in range_of(strides)]
layers.append(PoolFlatten())
return nn.Sequential(*layers)
|
python
|
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)
strides = ifnone(strides , [2]*nl)
layers = [conv_layer(actns[i], actns[i+1], kernel_szs[i], stride=strides[i],
norm_type=(NormType.Batch if bn and i<(len(strides)-1) else None)) for i in range_of(strides)]
layers.append(PoolFlatten())
return nn.Sequential(*layers)
|
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CNN with `conv_layer` defined by `actns`, `kernel_szs` and `strides`, plus batchnorm if `bn`.
|
[
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L293-L302
|
21,022
|
fastai/fastai
|
fastai/layers.py
|
trunc_normal_
|
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)
|
python
|
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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Truncated normal initialization.
|
[
"Truncated",
"normal",
"initialization",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L304-L307
|
21,023
|
fastai/fastai
|
fastai/layers.py
|
embedding
|
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
|
python
|
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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Create an embedding layer.
|
[
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/layers.py#L309-L314
|
21,024
|
fastai/fastai
|
fastai/callbacks/mlflow.py
|
MLFlowTracker.on_train_begin
|
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.experiment_id
run = self.client.create_run(experiment_id=self.exp_id)
self.run = run.info.run_uuid
for k,v in self.params.items():
self.client.log_param(run_id=self.run, key=k, value=v)
|
python
|
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.experiment_id
run = self.client.create_run(experiment_id=self.exp_id)
self.run = run.info.run_uuid
for k,v in self.params.items():
self.client.log_param(run_id=self.run, key=k, value=v)
|
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Prepare MLflow experiment and log params
|
[
"Prepare",
"MLflow",
"experiment",
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/mlflow.py#L16-L24
|
21,025
|
fastai/fastai
|
fastai/callbacks/mlflow.py
|
MLFlowTracker.on_epoch_end
|
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, metrics):
self.client.log_metric(self.run, name, np.float(val))
|
python
|
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, metrics):
self.client.log_metric(self.run, name, np.float(val))
|
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|
[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/mlflow.py#L26-L31
|
21,026
|
fastai/fastai
|
fastai/callbacks/mlflow.py
|
MLFlowTracker.on_train_end
|
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)
|
python
|
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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Store the notebook and stop run
|
[
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/mlflow.py#L33-L36
|
21,027
|
fastai/fastai
|
fastai/vision/image.py
|
pil2tensor
|
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(dtype, copy=False) )
|
python
|
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(dtype, copy=False) )
|
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[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L14-L20
|
21,028
|
fastai/fastai
|
fastai/vision/image.py
|
_draw_outline
|
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()])
|
python
|
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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Outline bounding box onto image `Patch`.
|
[
"Outline",
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"onto",
"image",
"Patch",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L36-L39
|
21,029
|
fastai/fastai
|
fastai/vision/image.py
|
_draw_rect
|
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, verticalalignment='top', color=color, fontsize=text_size, weight='bold')
_draw_outline(patch,1)
|
python
|
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, verticalalignment='top', color=color, fontsize=text_size, weight='bold')
_draw_outline(patch,1)
|
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Draw bounding box on `ax`.
|
[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L41-L47
|
21,030
|
fastai/fastai
|
fastai/vision/image.py
|
open_image
|
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 = PIL.Image.open(fn).convert(convert_mode)
if after_open: x = after_open(x)
x = pil2tensor(x,np.float32)
if div: x.div_(255)
return cls(x)
|
python
|
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 = PIL.Image.open(fn).convert(convert_mode)
if after_open: x = after_open(x)
x = pil2tensor(x,np.float32)
if div: x.div_(255)
return cls(x)
|
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[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L388-L397
|
21,031
|
fastai/fastai
|
fastai/vision/image.py
|
open_mask
|
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)
|
python
|
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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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L399-L401
|
21,032
|
fastai/fastai
|
fastai/vision/image.py
|
open_mask_rle
|
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.permute(2,0,1))
|
python
|
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.permute(2,0,1))
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L403-L407
|
21,033
|
fastai/fastai
|
fastai/vision/image.py
|
rle_encode
|
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)
|
python
|
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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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L409-L414
|
21,034
|
fastai/fastai
|
fastai/vision/image.py
|
rle_decode
|
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(shape[0]*shape[1], dtype=np.uint)
for low, up in zip(starts, ends): img[low:up] = 1
return img.reshape(shape)
|
python
|
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(shape[0]*shape[1], dtype=np.uint)
for low, up in zip(starts, ends): img[low:up] = 1
return img.reshape(shape)
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L416-L424
|
21,035
|
fastai/fastai
|
fastai/vision/image.py
|
show_image
|
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)
if hide_axis: ax.axis('off')
return ax
|
python
|
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)
if hide_axis: ax.axis('off')
return ax
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L426-L432
|
21,036
|
fastai/fastai
|
fastai/vision/image.py
|
_affine_mult
|
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()).view(size)
return c
|
python
|
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()).view(size)
return c
|
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Multiply `c` by `m` - can adjust for rectangular shaped `c`.
|
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"for",
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"shaped",
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"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L547-L556
|
21,037
|
fastai/fastai
|
fastai/vision/image.py
|
_affine_inv_mult
|
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
|
python
|
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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|
[
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"to",
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"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L558-L567
|
21,038
|
fastai/fastai
|
fastai/vision/image.py
|
_round_multiple
|
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
|
python
|
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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Calc `x` to nearest multiple of `mult`.
|
[
"Calc",
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"multiple",
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"mult",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L585-L587
|
21,039
|
fastai/fastai
|
fastai/vision/image.py
|
_get_crop_target
|
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)
|
python
|
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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Calc crop shape of `target_px` to nearest multiple of `mult`.
|
[
"Calc",
"crop",
"shape",
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"target_px",
"to",
"nearest",
"multiple",
"of",
"mult",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L589-L592
|
21,040
|
fastai/fastai
|
fastai/vision/image.py
|
_get_resize_target
|
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)
return ch,int(round(r/ratio)),int(round(c/ratio))
|
python
|
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)
return ch,int(round(r/ratio)),int(round(c/ratio))
|
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|
[
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"-",
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L594-L600
|
21,041
|
fastai/fastai
|
fastai/vision/image.py
|
plot_multi
|
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])
|
python
|
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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Call `func` for every combination of `r,c` on a subplot
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[
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"c",
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L606-L610
|
21,042
|
fastai/fastai
|
fastai/vision/image.py
|
show_all
|
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)
|
python
|
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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Show all `imgs` using `r` rows
|
[
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L616-L620
|
21,043
|
fastai/fastai
|
fastai/vision/image.py
|
Image.apply_tfms
|
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:
"Apply all `tfms` to the `Image`, if `do_resolve` picks value for random args."
if not (tfms or xtra or size): return self
tfms = listify(tfms)
xtra = ifnone(xtra, {})
default_rsz = ResizeMethod.SQUISH if (size is not None and is_listy(size)) else ResizeMethod.CROP
resize_method = ifnone(resize_method, default_rsz)
if resize_method <= 2 and size is not None: tfms = self._maybe_add_crop_pad(tfms)
tfms = sorted(tfms, key=lambda o: o.tfm.order)
if do_resolve: _resolve_tfms(tfms)
x = self.clone()
x.set_sample(padding_mode=padding_mode, mode=mode, remove_out=remove_out)
if size is not None:
crop_target = _get_crop_target(size, mult=mult)
if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
target = _get_resize_target(x, crop_target, do_crop=(resize_method==ResizeMethod.CROP))
x.resize(target)
elif resize_method==ResizeMethod.SQUISH: x.resize((x.shape[0],) + crop_target)
else: size = x.size
size_tfms = [o for o in tfms if isinstance(o.tfm,TfmCrop)]
for tfm in tfms:
if tfm.tfm in xtra: x = tfm(x, **xtra[tfm.tfm])
elif tfm in size_tfms:
if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
x = tfm(x, size=_get_crop_target(size,mult=mult), padding_mode=padding_mode)
else: x = tfm(x)
return x.refresh()
|
python
|
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:
"Apply all `tfms` to the `Image`, if `do_resolve` picks value for random args."
if not (tfms or xtra or size): return self
tfms = listify(tfms)
xtra = ifnone(xtra, {})
default_rsz = ResizeMethod.SQUISH if (size is not None and is_listy(size)) else ResizeMethod.CROP
resize_method = ifnone(resize_method, default_rsz)
if resize_method <= 2 and size is not None: tfms = self._maybe_add_crop_pad(tfms)
tfms = sorted(tfms, key=lambda o: o.tfm.order)
if do_resolve: _resolve_tfms(tfms)
x = self.clone()
x.set_sample(padding_mode=padding_mode, mode=mode, remove_out=remove_out)
if size is not None:
crop_target = _get_crop_target(size, mult=mult)
if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
target = _get_resize_target(x, crop_target, do_crop=(resize_method==ResizeMethod.CROP))
x.resize(target)
elif resize_method==ResizeMethod.SQUISH: x.resize((x.shape[0],) + crop_target)
else: size = x.size
size_tfms = [o for o in tfms if isinstance(o.tfm,TfmCrop)]
for tfm in tfms:
if tfm.tfm in xtra: x = tfm(x, **xtra[tfm.tfm])
elif tfm in size_tfms:
if resize_method in (ResizeMethod.CROP,ResizeMethod.PAD):
x = tfm(x, size=_get_crop_target(size,mult=mult), padding_mode=padding_mode)
else: x = tfm(x)
return x.refresh()
|
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Apply all `tfms` to the `Image`, if `do_resolve` picks value for random args.
|
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"Image",
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"do_resolve",
"picks",
"value",
"for",
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L96-L124
|
21,044
|
fastai/fastai
|
fastai/vision/image.py
|
Image.refresh
|
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._px = _grid_sample(self._px, self.flow, **self.sample_kwargs)
self.sample_kwargs = {}
self._flow = None
return self
|
python
|
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._px = _grid_sample(self._px, self.flow, **self.sample_kwargs)
self.sample_kwargs = {}
self._flow = None
return self
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L126-L135
|
21,045
|
fastai/fastai
|
fastai/vision/image.py
|
Image.save
|
def save(self, fn:PathOrStr):
"Save the image to `fn`."
x = image2np(self.data*255).astype(np.uint8)
PIL.Image.fromarray(x).save(fn)
|
python
|
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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"Image",
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] |
Save the image to `fn`.
|
[
"Save",
"the",
"image",
"to",
"fn",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L137-L140
|
21,046
|
fastai/fastai
|
fastai/vision/image.py
|
Image.flow
|
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_mat = None
return self._flow
|
python
|
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_mat = None
return self._flow
|
[
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"affine",
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L153-L160
|
21,047
|
fastai/fastai
|
fastai/vision/image.py
|
Image.affine
|
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
|
python
|
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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"affine_mat",
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"self",
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"affine_mat",
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] |
Equivalent to `image.affine_mat = image.affine_mat @ func()`.
|
[
"Equivalent",
"to",
"image",
".",
"affine_mat",
"=",
"image",
".",
"affine_mat"
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L180-L184
|
21,048
|
fastai/fastai
|
fastai/vision/image.py
|
Image.affine_mat
|
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
|
python
|
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
|
[
"def",
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"to",
"(",
"self",
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"device",
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"return",
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] |
Get the affine matrix that will be applied by `refresh`.
|
[
"Get",
"the",
"affine",
"matrix",
"that",
"will",
"be",
"applied",
"by",
"refresh",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L195-L199
|
21,049
|
fastai/fastai
|
fastai/vision/image.py
|
Image.show
|
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_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize)
if y is not None: y.show(ax=ax, **kwargs)
if title is not None: ax.set_title(title)
|
python
|
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_image(self, ax=ax, hide_axis=hide_axis, cmap=cmap, figsize=figsize)
if y is not None: y.show(ax=ax, **kwargs)
if title is not None: ax.set_title(title)
|
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Show image on `ax` with `title`, using `cmap` if single-channel, overlaid with optional `y`
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[
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L216-L222
|
21,050
|
fastai/fastai
|
fastai/vision/image.py
|
ImageSegment.show
|
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,
interpolation='nearest', alpha=alpha, vmin=0)
if title: ax.set_title(title)
|
python
|
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,
interpolation='nearest', alpha=alpha, vmin=0)
if title: ax.set_title(title)
|
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Show the `ImageSegment` on `ax`.
|
[
"Show",
"the",
"ImageSegment",
"on",
"ax",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L237-L242
|
21,051
|
fastai/fastai
|
fastai/vision/image.py
|
ImagePoints.clone
|
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)
|
python
|
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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Mimic the behavior of torch.clone for `ImagePoints` objects.
|
[
"Mimic",
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"of",
"torch",
".",
"clone",
"for",
"ImagePoints",
"objects",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L258-L260
|
21,052
|
fastai/fastai
|
fastai/vision/image.py
|
ImagePoints.flow
|
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.flow_func) != 0:
for f in self.flow_func[::-1]: self._flow = f(self._flow)
self.transformed = True
self.flow_func = []
return self._flow
|
python
|
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.flow_func) != 0:
for f in self.flow_func[::-1]: self._flow = f(self._flow)
self.transformed = True
self.flow_func = []
return self._flow
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L275-L285
|
21,053
|
fastai/fastai
|
fastai/vision/image.py
|
ImagePoints.coord
|
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, *args, **kwargs))
return self
|
python
|
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, *args, **kwargs))
return self
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L290-L295
|
21,054
|
fastai/fastai
|
fastai/vision/image.py
|
ImagePoints.data
|
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_points_out(flow)
self.transformed=False
return flow.flow.flip(1)
|
python
|
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_points_out(flow)
self.transformed=False
return flow.flow.flip(1)
|
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Return the points associated to this object.
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L315-L322
|
21,055
|
fastai/fastai
|
fastai/vision/image.py
|
ImagePoints.show
|
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': 10, 'marker': '.', 'c': 'r', **kwargs}
ax.scatter(pnt[:, 0], pnt[:, 1], **params)
if hide_axis: ax.axis('off')
if title: ax.set_title(title)
|
python
|
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': 10, 'marker': '.', 'c': 'r', **kwargs}
ax.scatter(pnt[:, 0], pnt[:, 1], **params)
if hide_axis: ax.axis('off')
if title: ax.set_title(title)
|
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Show the `ImagePoints` on `ax`.
|
[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L324-L331
|
21,056
|
fastai/fastai
|
fastai/vision/image.py
|
ImageBBox.clone
|
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)
|
python
|
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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[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L343-L346
|
21,057
|
fastai/fastai
|
fastai/vision/image.py
|
ImageBBox.create
|
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 bb in bboxes])
bboxes = tensor(bboxes).float()
tr_corners = torch.cat([bboxes[:,0][:,None], bboxes[:,3][:,None]], 1)
bl_corners = bboxes[:,1:3].flip(1)
bboxes = torch.cat([bboxes[:,:2], tr_corners, bl_corners, bboxes[:,2:]], 1)
flow = FlowField((h,w), bboxes.view(-1,2))
return cls(flow, labels=labels, classes=classes, pad_idx=pad_idx, y_first=True, scale=scale)
|
python
|
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 bb in bboxes])
bboxes = tensor(bboxes).float()
tr_corners = torch.cat([bboxes[:,0][:,None], bboxes[:,3][:,None]], 1)
bl_corners = bboxes[:,1:3].flip(1)
bboxes = torch.cat([bboxes[:,:2], tr_corners, bl_corners, bboxes[:,2:]], 1)
flow = FlowField((h,w), bboxes.view(-1,2))
return cls(flow, labels=labels, classes=classes, pad_idx=pad_idx, y_first=True, scale=scale)
|
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Create an ImageBBox object from `bboxes`.
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[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L349-L358
|
21,058
|
fastai/fastai
|
fastai/vision/image.py
|
ImageBBox.show
|
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.flow.size
bboxes.add_(1).mul_(torch.tensor([h/2, w/2, h/2, w/2])).long()
for i, bbox in enumerate(bboxes):
if lbls is not None: text = str(lbls[i])
else: text=None
_draw_rect(ax, bb2hw(bbox), text=text, color=color)
|
python
|
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.flow.size
bboxes.add_(1).mul_(torch.tensor([h/2, w/2, h/2, w/2])).long()
for i, bbox in enumerate(bboxes):
if lbls is not None: text = str(lbls[i])
else: text=None
_draw_rect(ax, bb2hw(bbox), text=text, color=color)
|
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[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L376-L386
|
21,059
|
fastai/fastai
|
fastai/vision/image.py
|
Transform.calc
|
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)
|
python
|
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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|
[
"Apply",
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"wrapping",
"it",
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/vision/image.py#L467-L470
|
21,060
|
fastai/fastai
|
fastai/datasets.py
|
url2path
|
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)
|
python
|
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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Change `url` to a path.
|
[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/datasets.py#L186-L189
|
21,061
|
fastai/fastai
|
fastai/datasets.py
|
modelpath4file
|
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
|
python
|
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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"exists",
"(",
")",
"or",
"local_path",
".",
"with_suffix",
"(",
"ext",
")",
".",
"exists",
"(",
")",
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"return",
"local_path",
"else",
":",
"return",
"Config",
".",
"model_path",
"(",
")",
"/",
"filename"
] |
Return model path to `filename`, checking locally first then in the config file.
|
[
"Return",
"model",
"path",
"to",
"filename",
"checking",
"locally",
"first",
"then",
"in",
"the",
"config",
"file",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/datasets.py#L193-L197
|
21,062
|
fastai/fastai
|
fastai/datasets.py
|
datapath4file
|
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_archive_path() / filename
else: return Config.data_path() / filename
|
python
|
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_archive_path() / filename
else: return Config.data_path() / filename
|
[
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"datapath4file",
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"'.tgz'",
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"data_path",
"(",
")",
"/",
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] |
Return data path to `filename`, checking locally first then in the config file.
|
[
"Return",
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"path",
"to",
"filename",
"checking",
"locally",
"first",
"then",
"in",
"the",
"config",
"file",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/datasets.py#L199-L204
|
21,063
|
fastai/fastai
|
fastai/datasets.py
|
download_data
|
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}')
download_url(f'{url}{ext}', fname)
return fname
|
python
|
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}')
download_url(f'{url}{ext}', fname)
return fname
|
[
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")",
"download_url",
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"f'{url}{ext}'",
",",
"fname",
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"return",
"fname"
] |
Download `url` to destination `fname`.
|
[
"Download",
"url",
"to",
"destination",
"fname",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/datasets.py#L206-L213
|
21,064
|
fastai/fastai
|
fastai/datasets.py
|
untar_data
|
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, data)))
if force_download or (fname.exists() and url in _checks and _check_file(fname) != _checks[url]):
print(f"A new version of the {'dataset' if data else 'model'} is available.")
if fname.exists(): os.remove(fname)
if dest.exists(): shutil.rmtree(dest)
if not dest.exists():
fname = download_data(url, fname=fname, data=data)
if url in _checks:
assert _check_file(fname) == _checks[url], f"Downloaded file {fname} does not match checksum expected! Remove that file from {Config().data_archive_path()} and try your code again."
tarfile.open(fname, 'r:gz').extractall(dest.parent)
return dest
|
python
|
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, data)))
if force_download or (fname.exists() and url in _checks and _check_file(fname) != _checks[url]):
print(f"A new version of the {'dataset' if data else 'model'} is available.")
if fname.exists(): os.remove(fname)
if dest.exists(): shutil.rmtree(dest)
if not dest.exists():
fname = download_data(url, fname=fname, data=data)
if url in _checks:
assert _check_file(fname) == _checks[url], f"Downloaded file {fname} does not match checksum expected! Remove that file from {Config().data_archive_path()} and try your code again."
tarfile.open(fname, 'r:gz').extractall(dest.parent)
return dest
|
[
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",",
"'r:gz'",
")",
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"extractall",
"(",
"dest",
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"parent",
")",
"return",
"dest"
] |
Download `url` to `fname` if `dest` doesn't exist, and un-tgz to folder `dest`.
|
[
"Download",
"url",
"to",
"fname",
"if",
"dest",
"doesn",
"t",
"exist",
"and",
"un",
"-",
"tgz",
"to",
"folder",
"dest",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/datasets.py#L221-L234
|
21,065
|
fastai/fastai
|
fastai/datasets.py
|
Config.get_key
|
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))
|
python
|
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))
|
[
"def",
"get_key",
"(",
"cls",
",",
"key",
")",
":",
"return",
"cls",
".",
"get",
"(",
")",
".",
"get",
"(",
"key",
",",
"cls",
".",
"DEFAULT_CONFIG",
".",
"get",
"(",
"key",
",",
"None",
")",
")"
] |
Get the path to `key` in the config file.
|
[
"Get",
"the",
"path",
"to",
"key",
"in",
"the",
"config",
"file",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/datasets.py#L140-L142
|
21,066
|
fastai/fastai
|
fastai/datasets.py
|
Config.get
|
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 open(fpath, 'r') as yaml_file: return yaml.safe_load(yaml_file)
|
python
|
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 open(fpath, 'r') as yaml_file: return yaml.safe_load(yaml_file)
|
[
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"cls",
",",
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] |
Retrieve the `Config` in `fpath`.
|
[
"Retrieve",
"the",
"Config",
"in",
"fpath",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/datasets.py#L165-L170
|
21,067
|
fastai/fastai
|
fastai/datasets.py
|
Config.create
|
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, yaml_file, default_flow_style=False)
|
python
|
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, yaml_file, default_flow_style=False)
|
[
"def",
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"cls",
".",
"DEFAULT_CONFIG",
",",
"yaml_file",
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"default_flow_style",
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"False",
")"
] |
Creates a `Config` from `fpath`.
|
[
"Creates",
"a",
"Config",
"from",
"fpath",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/datasets.py#L173-L180
|
21,068
|
fastai/fastai
|
fastai/callbacks/mixup.py
|
MixUpCallback.on_batch_begin
|
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)
lambd = last_input.new(lambd)
shuffle = torch.randperm(last_target.size(0)).to(last_input.device)
x1, y1 = last_input[shuffle], last_target[shuffle]
if self.stack_x:
new_input = [last_input, last_input[shuffle], lambd]
else:
new_input = (last_input * lambd.view(lambd.size(0),1,1,1) + x1 * (1-lambd).view(lambd.size(0),1,1,1))
if self.stack_y:
new_target = torch.cat([last_target[:,None].float(), y1[:,None].float(), lambd[:,None].float()], 1)
else:
if len(last_target.shape) == 2:
lambd = lambd.unsqueeze(1).float()
new_target = last_target.float() * lambd + y1.float() * (1-lambd)
return {'last_input': new_input, 'last_target': new_target}
|
python
|
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)
lambd = last_input.new(lambd)
shuffle = torch.randperm(last_target.size(0)).to(last_input.device)
x1, y1 = last_input[shuffle], last_target[shuffle]
if self.stack_x:
new_input = [last_input, last_input[shuffle], lambd]
else:
new_input = (last_input * lambd.view(lambd.size(0),1,1,1) + x1 * (1-lambd).view(lambd.size(0),1,1,1))
if self.stack_y:
new_target = torch.cat([last_target[:,None].float(), y1[:,None].float(), lambd[:,None].float()], 1)
else:
if len(last_target.shape) == 2:
lambd = lambd.unsqueeze(1).float()
new_target = last_target.float() * lambd + y1.float() * (1-lambd)
return {'last_input': new_input, 'last_target': new_target}
|
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Applies mixup to `last_input` and `last_target` if `train`.
|
[
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"last_input",
"and",
"last_target",
"if",
"train",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/callbacks/mixup.py#L15-L33
|
21,069
|
fastai/fastai
|
fastai/tabular/transform.py
|
add_datepart
|
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$', '', field_name))
attr = ['Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start',
'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']
if time: attr = attr + ['Hour', 'Minute', 'Second']
for n in attr: df[prefix + n] = getattr(field.dt, n.lower())
df[prefix + 'Elapsed'] = field.astype(np.int64) // 10 ** 9
if drop: df.drop(field_name, axis=1, inplace=True)
return df
|
python
|
def add_datepart(df:DataFrame, field_name:str, prefix:str=None, drop:bool=True, time:bool=False):
"Helper function that adds columns relevant to a date in the column `field_name` of `df`."
make_date(df, field_name)
field = df[field_name]
prefix = ifnone(prefix, re.sub('[Dd]ate$', '', field_name))
attr = ['Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear', 'Is_month_end', 'Is_month_start',
'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']
if time: attr = attr + ['Hour', 'Minute', 'Second']
for n in attr: df[prefix + n] = getattr(field.dt, n.lower())
df[prefix + 'Elapsed'] = field.astype(np.int64) // 10 ** 9
if drop: df.drop(field_name, axis=1, inplace=True)
return df
|
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Helper function that adds columns relevant to a date in the column `field_name` of `df`.
|
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"of",
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"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/tabular/transform.py#L55-L66
|
21,070
|
fastai/fastai
|
fastai/tabular/transform.py
|
cont_cat_split
|
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] > max_card or df[label].dtype == float: cont_names.append(label)
else: cat_names.append(label)
return cont_names, cat_names
|
python
|
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] > max_card or df[label].dtype == float: cont_names.append(label)
else: cat_names.append(label)
return cont_names, cat_names
|
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Helper function that returns column names of cont and cat variables from given df.
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/tabular/transform.py#L106-L113
|
21,071
|
fastai/fastai
|
fastai/tabular/transform.py
|
Categorify.apply_train
|
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
|
python
|
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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|
[
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"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/tabular/transform.py#L135-L140
|
21,072
|
fastai/fastai
|
fastai/tabular/transform.py
|
Normalize.apply_train
|
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_names:
assert is_numeric_dtype(df[n]), (f"""Cannot normalize '{n}' column as it isn't numerical.
Are you sure it doesn't belong in the categorical set of columns?""")
self.means[n],self.stds[n] = df[n].mean(),df[n].std()
df[n] = (df[n]-self.means[n]) / (1e-7 + self.stds[n])
|
python
|
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_names:
assert is_numeric_dtype(df[n]), (f"""Cannot normalize '{n}' column as it isn't numerical.
Are you sure it doesn't belong in the categorical set of columns?""")
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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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/tabular/transform.py#L183-L190
|
21,073
|
fastai/fastai
|
fastai/tabular/data.py
|
def_emb_sz
|
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
|
python
|
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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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/tabular/data.py#L17-L22
|
21,074
|
fastai/fastai
|
fastai/tabular/data.py
|
tabular_learner
|
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 params."
emb_szs = data.get_emb_szs(ifnone(emb_szs, {}))
model = TabularModel(emb_szs, len(data.cont_names), out_sz=data.c, layers=layers, ps=ps, emb_drop=emb_drop,
y_range=y_range, use_bn=use_bn)
return Learner(data, model, metrics=metrics, **learn_kwargs)
|
python
|
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 params."
emb_szs = data.get_emb_szs(ifnone(emb_szs, {}))
model = TabularModel(emb_szs, len(data.cont_names), out_sz=data.c, layers=layers, ps=ps, emb_drop=emb_drop,
y_range=y_range, use_bn=use_bn)
return Learner(data, model, metrics=metrics, **learn_kwargs)
|
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/tabular/data.py#L170-L176
|
21,075
|
fastai/fastai
|
fastai/tabular/data.py
|
TabularDataBunch.from_df
|
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[Callable]]=None,
device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False)->DataBunch:
"Create a `DataBunch` from `df` and `valid_idx` with `dep_var`. `kwargs` are passed to `DataBunch.create`."
cat_names = ifnone(cat_names, []).copy()
cont_names = ifnone(cont_names, list(set(df)-set(cat_names)-{dep_var}))
procs = listify(procs)
src = (TabularList.from_df(df, path=path, cat_names=cat_names, cont_names=cont_names, procs=procs)
.split_by_idx(valid_idx))
src = src.label_from_df(cols=dep_var) if classes is None else src.label_from_df(cols=dep_var, classes=classes)
if test_df is not None: src.add_test(TabularList.from_df(test_df, cat_names=cat_names, cont_names=cont_names,
processor = src.train.x.processor))
return src.databunch(path=path, bs=bs, val_bs=val_bs, num_workers=num_workers, device=device,
collate_fn=collate_fn, no_check=no_check)
|
python
|
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[Callable]]=None,
device:torch.device=None, collate_fn:Callable=data_collate, no_check:bool=False)->DataBunch:
"Create a `DataBunch` from `df` and `valid_idx` with `dep_var`. `kwargs` are passed to `DataBunch.create`."
cat_names = ifnone(cat_names, []).copy()
cont_names = ifnone(cont_names, list(set(df)-set(cat_names)-{dep_var}))
procs = listify(procs)
src = (TabularList.from_df(df, path=path, cat_names=cat_names, cont_names=cont_names, procs=procs)
.split_by_idx(valid_idx))
src = src.label_from_df(cols=dep_var) if classes is None else src.label_from_df(cols=dep_var, classes=classes)
if test_df is not None: src.add_test(TabularList.from_df(test_df, cat_names=cat_names, cont_names=cont_names,
processor = src.train.x.processor))
return src.databunch(path=path, bs=bs, val_bs=val_bs, num_workers=num_workers, device=device,
collate_fn=collate_fn, no_check=no_check)
|
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Create a `DataBunch` from `df` and `valid_idx` with `dep_var`. `kwargs` are passed to `DataBunch.create`.
|
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"DataBunch",
".",
"create",
"."
] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/tabular/data.py#L87-L101
|
21,076
|
fastai/fastai
|
fastai/tabular/data.py
|
TabularList.get_emb_szs
|
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]
|
python
|
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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Return the default embedding sizes suitable for this data or takes the ones in `sz_dict`.
|
[
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] |
9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/fastai/tabular/data.py#L129-L131
|
21,077
|
fastai/fastai
|
courses/dl2/imdb_scripts/predict_with_classifier.py
|
load_model
|
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 int to string mapping file (usually called itos.pkl)
classifier_filename (str): The filename of the trained classifier
Returns:
string to int mapping, trained classifer model
"""
# 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 to get a model
bptt,em_sz,nh,nl = 70,400,1150,3
dps = np.array([0.4,0.5,0.05,0.3,0.4])*0.5
vs = len(itos)
model = get_rnn_classifer(bptt, 20*70, num_classes, vs, emb_sz=em_sz, n_hid=nh, n_layers=nl, pad_token=1,
layers=[em_sz*3, 50, num_classes], drops=[dps[4], 0.1],
dropouti=dps[0], wdrop=dps[1], dropoute=dps[2], dropouth=dps[3])
# load the trained classifier
model.load_state_dict(torch.load(classifier_filename, map_location=lambda storage, loc: storage))
# put the classifier into evaluation mode
model.reset()
model.eval()
return stoi, model
|
python
|
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 int to string mapping file (usually called itos.pkl)
classifier_filename (str): The filename of the trained classifier
Returns:
string to int mapping, trained classifer model
"""
# 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 to get a model
bptt,em_sz,nh,nl = 70,400,1150,3
dps = np.array([0.4,0.5,0.05,0.3,0.4])*0.5
vs = len(itos)
model = get_rnn_classifer(bptt, 20*70, num_classes, vs, emb_sz=em_sz, n_hid=nh, n_layers=nl, pad_token=1,
layers=[em_sz*3, 50, num_classes], drops=[dps[4], 0.1],
dropouti=dps[0], wdrop=dps[1], dropoute=dps[2], dropouth=dps[3])
# load the trained classifier
model.load_state_dict(torch.load(classifier_filename, map_location=lambda storage, loc: storage))
# put the classifier into evaluation mode
model.reset()
model.eval()
return stoi, model
|
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Load the classifier and int to string mapping
Args:
itos_filename (str): The filename of the int to string mapping file (usually called itos.pkl)
classifier_filename (str): The filename of the trained classifier
Returns:
string to int mapping, trained classifer model
|
[
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/courses/dl2/imdb_scripts/predict_with_classifier.py#L6-L38
|
21,078
|
fastai/fastai
|
courses/dl2/imdb_scripts/predict_with_classifier.py
|
predict_text
|
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 fastai wrapper around spacy
tok = Tokenizer().proc_all_mp(partition_by_cores(texts))
# turn into integers for each word
encoded = [stoi[p] for p in tok[0]]
# we want a [x,1] array where x is the number
# of words inputted (including the prefix tokens)
ary = np.reshape(np.array(encoded),(-1,1))
# turn this array into a tensor
tensor = torch.from_numpy(ary)
# wrap in a torch Variable
variable = Variable(tensor)
# do the predictions
predictions = model(variable)
# convert back to numpy
numpy_preds = predictions[0].data.numpy()
return softmax(numpy_preds[0])[0]
|
python
|
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 fastai wrapper around spacy
tok = Tokenizer().proc_all_mp(partition_by_cores(texts))
# turn into integers for each word
encoded = [stoi[p] for p in tok[0]]
# we want a [x,1] array where x is the number
# of words inputted (including the prefix tokens)
ary = np.reshape(np.array(encoded),(-1,1))
# turn this array into a tensor
tensor = torch.from_numpy(ary)
# wrap in a torch Variable
variable = Variable(tensor)
# do the predictions
predictions = model(variable)
# convert back to numpy
numpy_preds = predictions[0].data.numpy()
return softmax(numpy_preds[0])[0]
|
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Do the actual prediction on the text using the
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9fb84a5cdefe5a766cdb792b8f5d8971737b7e67
|
https://github.com/fastai/fastai/blob/9fb84a5cdefe5a766cdb792b8f5d8971737b7e67/courses/dl2/imdb_scripts/predict_with_classifier.py#L64-L100
|
21,079
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
_make_w3c_caps
|
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 reject requests containing unknown capability names.
Moves the Firefox profile, if present, from the old location to the new Firefox
options object.
:Args:
- caps - A dictionary of capabilities requested by the caller.
"""
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_match[_OSS_W3C_CONVERSION[k]] = v.lower() if k == 'platform' else v
if k in _W3C_CAPABILITY_NAMES or ':' in k:
always_match[k] = v
if profile:
moz_opts = always_match.get('moz:firefoxOptions', {})
# If it's already present, assume the caller did that intentionally.
if 'profile' not in moz_opts:
# Don't mutate the original capabilities.
new_opts = copy.deepcopy(moz_opts)
new_opts['profile'] = profile
always_match['moz:firefoxOptions'] = new_opts
return {"firstMatch": [{}], "alwaysMatch": always_match}
|
python
|
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 reject requests containing unknown capability names.
Moves the Firefox profile, if present, from the old location to the new Firefox
options object.
:Args:
- caps - A dictionary of capabilities requested by the caller.
"""
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_match[_OSS_W3C_CONVERSION[k]] = v.lower() if k == 'platform' else v
if k in _W3C_CAPABILITY_NAMES or ':' in k:
always_match[k] = v
if profile:
moz_opts = always_match.get('moz:firefoxOptions', {})
# If it's already present, assume the caller did that intentionally.
if 'profile' not in moz_opts:
# Don't mutate the original capabilities.
new_opts = copy.deepcopy(moz_opts)
new_opts['profile'] = profile
always_match['moz:firefoxOptions'] = new_opts
return {"firstMatch": [{}], "alwaysMatch": always_match}
|
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Filters out capability names that are not in the W3C spec. Spec-compliant
drivers will reject requests containing unknown capability names.
Moves the Firefox profile, if present, from the old location to the new Firefox
options object.
:Args:
- caps - A dictionary of capabilities requested by the caller.
|
[
"Makes",
"a",
"W3C",
"alwaysMatch",
"capabilities",
"object",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L65-L95
|
21,080
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.start_session
|
def start_session(self, capabilities, browser_profile=None):
"""
Creates a new session with the desired capabilities.
:Args:
- browser_name - The name of the browser to request.
- version - Which browser version to request.
- platform - Which platform to request the browser on.
- javascript_enabled - Whether the new session should support JavaScript.
- browser_profile - A selenium.webdriver.firefox.firefox_profile.FirefoxProfile object. Only used if Firefox is requested.
"""
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:
capabilities.update({'firefox_profile': browser_profile.encoded})
w3c_caps = _make_w3c_caps(capabilities)
parameters = {"capabilities": w3c_caps,
"desiredCapabilities": capabilities}
response = self.execute(Command.NEW_SESSION, parameters)
if 'sessionId' not in response:
response = response['value']
self.session_id = response['sessionId']
self.capabilities = response.get('value')
# if capabilities is none we are probably speaking to
# a W3C endpoint
if self.capabilities is None:
self.capabilities = response.get('capabilities')
# Double check to see if we have a W3C Compliant browser
self.w3c = response.get('status') is None
self.command_executor.w3c = self.w3c
|
python
|
def start_session(self, capabilities, browser_profile=None):
"""
Creates a new session with the desired capabilities.
:Args:
- browser_name - The name of the browser to request.
- version - Which browser version to request.
- platform - Which platform to request the browser on.
- javascript_enabled - Whether the new session should support JavaScript.
- browser_profile - A selenium.webdriver.firefox.firefox_profile.FirefoxProfile object. Only used if Firefox is requested.
"""
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:
capabilities.update({'firefox_profile': browser_profile.encoded})
w3c_caps = _make_w3c_caps(capabilities)
parameters = {"capabilities": w3c_caps,
"desiredCapabilities": capabilities}
response = self.execute(Command.NEW_SESSION, parameters)
if 'sessionId' not in response:
response = response['value']
self.session_id = response['sessionId']
self.capabilities = response.get('value')
# if capabilities is none we are probably speaking to
# a W3C endpoint
if self.capabilities is None:
self.capabilities = response.get('capabilities')
# Double check to see if we have a W3C Compliant browser
self.w3c = response.get('status') is None
self.command_executor.w3c = self.w3c
|
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] |
Creates a new session with the desired capabilities.
:Args:
- browser_name - The name of the browser to request.
- version - Which browser version to request.
- platform - Which platform to request the browser on.
- javascript_enabled - Whether the new session should support JavaScript.
- browser_profile - A selenium.webdriver.firefox.firefox_profile.FirefoxProfile object. Only used if Firefox is requested.
|
[
"Creates",
"a",
"new",
"session",
"with",
"the",
"desired",
"capabilities",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L228-L262
|
21,081
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.create_web_element
|
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)
|
python
|
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)
|
[
"def",
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"(",
"self",
",",
"element_id",
",",
"w3c",
"=",
"self",
".",
"w3c",
")"
] |
Creates a web element with the specified `element_id`.
|
[
"Creates",
"a",
"web",
"element",
"with",
"the",
"specified",
"element_id",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L277-L279
|
21,082
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.execute
|
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 to execute as a string.
- params: A dictionary of named parameters to send with the command.
:Returns:
The command's JSON response loaded into a dictionary object.
"""
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_command, params)
if response:
self.error_handler.check_response(response)
response['value'] = self._unwrap_value(
response.get('value', None))
return response
# If the server doesn't send a response, assume the command was
# a success
return {'success': 0, 'value': None, 'sessionId': self.session_id}
|
python
|
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 to execute as a string.
- params: A dictionary of named parameters to send with the command.
:Returns:
The command's JSON response loaded into a dictionary object.
"""
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_command, params)
if response:
self.error_handler.check_response(response)
response['value'] = self._unwrap_value(
response.get('value', None))
return response
# If the server doesn't send a response, assume the command was
# a success
return {'success': 0, 'value': None, 'sessionId': self.session_id}
|
[
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"0",
",",
"'value'",
":",
"None",
",",
"'sessionId'",
":",
"self",
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"session_id",
"}"
] |
Sends a command to be executed by a command.CommandExecutor.
:Args:
- driver_command: The name of the command to execute as a string.
- params: A dictionary of named parameters to send with the command.
:Returns:
The command's JSON response loaded into a dictionary object.
|
[
"Sends",
"a",
"command",
"to",
"be",
"executed",
"by",
"a",
"command",
".",
"CommandExecutor",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L298-L324
|
21,083
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_element_by_link_text
|
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:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_link_text('Sign In')
"""
return self.find_element(by=By.LINK_TEXT, value=link_text)
|
python
|
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:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_link_text('Sign In')
"""
return self.find_element(by=By.LINK_TEXT, value=link_text)
|
[
"def",
"find_element_by_link_text",
"(",
"self",
",",
"link_text",
")",
":",
"return",
"self",
".",
"find_element",
"(",
"by",
"=",
"By",
".",
"LINK_TEXT",
",",
"value",
"=",
"link_text",
")"
] |
Finds an element by link text.
:Args:
- link_text: The text of the element to be found.
:Returns:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_link_text('Sign In')
|
[
"Finds",
"an",
"element",
"by",
"link",
"text",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L419-L437
|
21,084
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_elements_by_link_text
|
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 webelement - a list with elements if any was found. an
empty list if not
:Usage:
::
elements = driver.find_elements_by_link_text('Sign In')
"""
return self.find_elements(by=By.LINK_TEXT, value=text)
|
python
|
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 webelement - a list with elements if any was found. an
empty list if not
:Usage:
::
elements = driver.find_elements_by_link_text('Sign In')
"""
return self.find_elements(by=By.LINK_TEXT, value=text)
|
[
"def",
"find_elements_by_link_text",
"(",
"self",
",",
"text",
")",
":",
"return",
"self",
".",
"find_elements",
"(",
"by",
"=",
"By",
".",
"LINK_TEXT",
",",
"value",
"=",
"text",
")"
] |
Finds elements by link text.
:Args:
- link_text: The text of the elements to be found.
:Returns:
- list of webelement - a list with elements if any was found. an
empty list if not
:Usage:
::
elements = driver.find_elements_by_link_text('Sign In')
|
[
"Finds",
"elements",
"by",
"link",
"text",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L439-L455
|
21,085
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_element_by_partial_link_text
|
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 partially match on.
:Returns:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_partial_link_text('Sign')
"""
return self.find_element(by=By.PARTIAL_LINK_TEXT, value=link_text)
|
python
|
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 partially match on.
:Returns:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_partial_link_text('Sign')
"""
return self.find_element(by=By.PARTIAL_LINK_TEXT, value=link_text)
|
[
"def",
"find_element_by_partial_link_text",
"(",
"self",
",",
"link_text",
")",
":",
"return",
"self",
".",
"find_element",
"(",
"by",
"=",
"By",
".",
"PARTIAL_LINK_TEXT",
",",
"value",
"=",
"link_text",
")"
] |
Finds an element by a partial match of its link text.
:Args:
- link_text: The text of the element to partially match on.
:Returns:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_partial_link_text('Sign')
|
[
"Finds",
"an",
"element",
"by",
"a",
"partial",
"match",
"of",
"its",
"link",
"text",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L457-L475
|
21,086
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_elements_by_partial_link_text
|
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 partial match on.
:Returns:
- list of webelement - a list with elements if any was found. an
empty list if not
:Usage:
::
elements = driver.find_elements_by_partial_link_text('Sign')
"""
return self.find_elements(by=By.PARTIAL_LINK_TEXT, value=link_text)
|
python
|
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 partial match on.
:Returns:
- list of webelement - a list with elements if any was found. an
empty list if not
:Usage:
::
elements = driver.find_elements_by_partial_link_text('Sign')
"""
return self.find_elements(by=By.PARTIAL_LINK_TEXT, value=link_text)
|
[
"def",
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"(",
"self",
",",
"link_text",
")",
":",
"return",
"self",
".",
"find_elements",
"(",
"by",
"=",
"By",
".",
"PARTIAL_LINK_TEXT",
",",
"value",
"=",
"link_text",
")"
] |
Finds elements by a partial match of their link text.
:Args:
- link_text: The text of the element to partial match on.
:Returns:
- list of webelement - a list with elements if any was found. an
empty list if not
:Usage:
::
elements = driver.find_elements_by_partial_link_text('Sign')
|
[
"Finds",
"elements",
"by",
"a",
"partial",
"match",
"of",
"their",
"link",
"text",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L477-L493
|
21,087
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_elements_by_name
|
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 elements if any was found. an
empty list if not
:Usage:
::
elements = driver.find_elements_by_name('foo')
"""
return self.find_elements(by=By.NAME, value=name)
|
python
|
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 elements if any was found. an
empty list if not
:Usage:
::
elements = driver.find_elements_by_name('foo')
"""
return self.find_elements(by=By.NAME, value=name)
|
[
"def",
"find_elements_by_name",
"(",
"self",
",",
"name",
")",
":",
"return",
"self",
".",
"find_elements",
"(",
"by",
"=",
"By",
".",
"NAME",
",",
"value",
"=",
"name",
")"
] |
Finds elements by name.
:Args:
- name: The name of the elements to find.
:Returns:
- list of webelement - a list with elements if any was found. an
empty list if not
:Usage:
::
elements = driver.find_elements_by_name('foo')
|
[
"Finds",
"elements",
"by",
"name",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L515-L531
|
21,088
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_element_by_tag_name
|
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 element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_tag_name('h1')
"""
return self.find_element(by=By.TAG_NAME, value=name)
|
python
|
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 element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_tag_name('h1')
"""
return self.find_element(by=By.TAG_NAME, value=name)
|
[
"def",
"find_element_by_tag_name",
"(",
"self",
",",
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")",
":",
"return",
"self",
".",
"find_element",
"(",
"by",
"=",
"By",
".",
"TAG_NAME",
",",
"value",
"=",
"name",
")"
] |
Finds an element by tag name.
:Args:
- name - name of html tag (eg: h1, a, span)
:Returns:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_tag_name('h1')
|
[
"Finds",
"an",
"element",
"by",
"tag",
"name",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L533-L551
|
21,089
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_elements_by_tag_name
|
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 list with elements if any was found. An
empty list if not
:Usage:
::
elements = driver.find_elements_by_tag_name('h1')
"""
return self.find_elements(by=By.TAG_NAME, value=name)
|
python
|
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 list with elements if any was found. An
empty list if not
:Usage:
::
elements = driver.find_elements_by_tag_name('h1')
"""
return self.find_elements(by=By.TAG_NAME, value=name)
|
[
"def",
"find_elements_by_tag_name",
"(",
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"name",
")",
":",
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"self",
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"find_elements",
"(",
"by",
"=",
"By",
".",
"TAG_NAME",
",",
"value",
"=",
"name",
")"
] |
Finds elements by tag name.
:Args:
- name - name of html tag (eg: h1, a, span)
:Returns:
- list of WebElement - a list with elements if any was found. An
empty list if not
:Usage:
::
elements = driver.find_elements_by_tag_name('h1')
|
[
"Finds",
"elements",
"by",
"tag",
"name",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L553-L569
|
21,090
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_element_by_class_name
|
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 - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_class_name('foo')
"""
return self.find_element(by=By.CLASS_NAME, value=name)
|
python
|
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 - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_class_name('foo')
"""
return self.find_element(by=By.CLASS_NAME, value=name)
|
[
"def",
"find_element_by_class_name",
"(",
"self",
",",
"name",
")",
":",
"return",
"self",
".",
"find_element",
"(",
"by",
"=",
"By",
".",
"CLASS_NAME",
",",
"value",
"=",
"name",
")"
] |
Finds an element by class name.
:Args:
- name: The class name of the element to find.
:Returns:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_class_name('foo')
|
[
"Finds",
"an",
"element",
"by",
"class",
"name",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L571-L589
|
21,091
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_elements_by_class_name
|
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 WebElement - a list with elements if any was found. An
empty list if not
:Usage:
::
elements = driver.find_elements_by_class_name('foo')
"""
return self.find_elements(by=By.CLASS_NAME, value=name)
|
python
|
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 WebElement - a list with elements if any was found. An
empty list if not
:Usage:
::
elements = driver.find_elements_by_class_name('foo')
"""
return self.find_elements(by=By.CLASS_NAME, value=name)
|
[
"def",
"find_elements_by_class_name",
"(",
"self",
",",
"name",
")",
":",
"return",
"self",
".",
"find_elements",
"(",
"by",
"=",
"By",
".",
"CLASS_NAME",
",",
"value",
"=",
"name",
")"
] |
Finds elements by class name.
:Args:
- name: The class name of the elements to find.
:Returns:
- list of WebElement - a list with elements if any was found. An
empty list if not
:Usage:
::
elements = driver.find_elements_by_class_name('foo')
|
[
"Finds",
"elements",
"by",
"class",
"name",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L591-L607
|
21,092
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_element_by_css_selector
|
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'
:Returns:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_css_selector('#foo')
"""
return self.find_element(by=By.CSS_SELECTOR, value=css_selector)
|
python
|
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'
:Returns:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_css_selector('#foo')
"""
return self.find_element(by=By.CSS_SELECTOR, value=css_selector)
|
[
"def",
"find_element_by_css_selector",
"(",
"self",
",",
"css_selector",
")",
":",
"return",
"self",
".",
"find_element",
"(",
"by",
"=",
"By",
".",
"CSS_SELECTOR",
",",
"value",
"=",
"css_selector",
")"
] |
Finds an element by css selector.
:Args:
- css_selector - CSS selector string, ex: 'a.nav#home'
:Returns:
- WebElement - the element if it was found
:Raises:
- NoSuchElementException - if the element wasn't found
:Usage:
::
element = driver.find_element_by_css_selector('#foo')
|
[
"Finds",
"an",
"element",
"by",
"css",
"selector",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L609-L627
|
21,093
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.find_elements_by_css_selector
|
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:
- list of WebElement - a list with elements if any was found. An
empty list if not
:Usage:
::
elements = driver.find_elements_by_css_selector('.foo')
"""
return self.find_elements(by=By.CSS_SELECTOR, value=css_selector)
|
python
|
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:
- list of WebElement - a list with elements if any was found. An
empty list if not
:Usage:
::
elements = driver.find_elements_by_css_selector('.foo')
"""
return self.find_elements(by=By.CSS_SELECTOR, value=css_selector)
|
[
"def",
"find_elements_by_css_selector",
"(",
"self",
",",
"css_selector",
")",
":",
"return",
"self",
".",
"find_elements",
"(",
"by",
"=",
"By",
".",
"CSS_SELECTOR",
",",
"value",
"=",
"css_selector",
")"
] |
Finds elements by css selector.
:Args:
- css_selector - CSS selector string, ex: 'a.nav#home'
:Returns:
- list of WebElement - a list with elements if any was found. An
empty list if not
:Usage:
::
elements = driver.find_elements_by_css_selector('.foo')
|
[
"Finds",
"elements",
"by",
"css",
"selector",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L629-L645
|
21,094
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.quit
|
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()
|
python
|
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()
|
[
"def",
"quit",
"(",
"self",
")",
":",
"try",
":",
"self",
".",
"execute",
"(",
"Command",
".",
"QUIT",
")",
"finally",
":",
"self",
".",
"stop_client",
"(",
")",
"self",
".",
"command_executor",
".",
"close",
"(",
")"
] |
Quits the driver and closes every associated window.
:Usage:
::
driver.quit()
|
[
"Quits",
"the",
"driver",
"and",
"closes",
"every",
"associated",
"window",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L731-L744
|
21,095
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.current_window_handle
|
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']
|
python
|
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']
|
[
"def",
"current_window_handle",
"(",
"self",
")",
":",
"if",
"self",
".",
"w3c",
":",
"return",
"self",
".",
"execute",
"(",
"Command",
".",
"W3C_GET_CURRENT_WINDOW_HANDLE",
")",
"[",
"'value'",
"]",
"else",
":",
"return",
"self",
".",
"execute",
"(",
"Command",
".",
"GET_CURRENT_WINDOW_HANDLE",
")",
"[",
"'value'",
"]"
] |
Returns the handle of the current window.
:Usage:
::
driver.current_window_handle
|
[
"Returns",
"the",
"handle",
"of",
"the",
"current",
"window",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L747-L759
|
21,096
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.window_handles
|
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']
|
python
|
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']
|
[
"def",
"window_handles",
"(",
"self",
")",
":",
"if",
"self",
".",
"w3c",
":",
"return",
"self",
".",
"execute",
"(",
"Command",
".",
"W3C_GET_WINDOW_HANDLES",
")",
"[",
"'value'",
"]",
"else",
":",
"return",
"self",
".",
"execute",
"(",
"Command",
".",
"GET_WINDOW_HANDLES",
")",
"[",
"'value'",
"]"
] |
Returns the handles of all windows within the current session.
:Usage:
::
driver.window_handles
|
[
"Returns",
"the",
"handles",
"of",
"all",
"windows",
"within",
"the",
"current",
"session",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L762-L774
|
21,097
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.maximize_window
|
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)
|
python
|
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)
|
[
"def",
"maximize_window",
"(",
"self",
")",
":",
"params",
"=",
"None",
"command",
"=",
"Command",
".",
"W3C_MAXIMIZE_WINDOW",
"if",
"not",
"self",
".",
"w3c",
":",
"command",
"=",
"Command",
".",
"MAXIMIZE_WINDOW",
"params",
"=",
"{",
"'windowHandle'",
":",
"'current'",
"}",
"self",
".",
"execute",
"(",
"command",
",",
"params",
")"
] |
Maximizes the current window that webdriver is using
|
[
"Maximizes",
"the",
"current",
"window",
"that",
"webdriver",
"is",
"using"
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L776-L785
|
21,098
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.get_cookie
|
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'] == name:
return cookie
return None
|
python
|
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'] == name:
return cookie
return None
|
[
"def",
"get_cookie",
"(",
"self",
",",
"name",
")",
":",
"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'",
"]",
"==",
"name",
":",
"return",
"cookie",
"return",
"None"
] |
Get a single cookie by name. Returns the cookie if found, None if not.
:Usage:
::
driver.get_cookie('my_cookie')
|
[
"Get",
"a",
"single",
"cookie",
"by",
"name",
".",
"Returns",
"the",
"cookie",
"if",
"found",
"None",
"if",
"not",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L865-L884
|
21,099
|
SeleniumHQ/selenium
|
py/selenium/webdriver/remote/webdriver.py
|
WebDriver.implicitly_wait
|
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 needs to be called one
time per session. To set the timeout for calls to
execute_async_script, see set_script_timeout.
:Args:
- time_to_wait: Amount of time to wait (in seconds)
:Usage:
::
driver.implicitly_wait(30)
"""
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})
|
python
|
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 needs to be called one
time per session. To set the timeout for calls to
execute_async_script, see set_script_timeout.
:Args:
- time_to_wait: Amount of time to wait (in seconds)
:Usage:
::
driver.implicitly_wait(30)
"""
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})
|
[
"def",
"implicitly_wait",
"(",
"self",
",",
"time_to_wait",
")",
":",
"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",
"}",
")"
] |
Sets a sticky timeout to implicitly wait for an element to be found,
or a command to complete. This method only needs to be called one
time per session. To set the timeout for calls to
execute_async_script, see set_script_timeout.
:Args:
- time_to_wait: Amount of time to wait (in seconds)
:Usage:
::
driver.implicitly_wait(30)
|
[
"Sets",
"a",
"sticky",
"timeout",
"to",
"implicitly",
"wait",
"for",
"an",
"element",
"to",
"be",
"found",
"or",
"a",
"command",
"to",
"complete",
".",
"This",
"method",
"only",
"needs",
"to",
"be",
"called",
"one",
"time",
"per",
"session",
".",
"To",
"set",
"the",
"timeout",
"for",
"calls",
"to",
"execute_async_script",
"see",
"set_script_timeout",
"."
] |
df40c28b41d4b3953f90eaff84838a9ac052b84a
|
https://github.com/SeleniumHQ/selenium/blob/df40c28b41d4b3953f90eaff84838a9ac052b84a/py/selenium/webdriver/remote/webdriver.py#L925-L945
|
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