INSTRUCTION stringlengths 1 8.43k | RESPONSE stringlengths 75 104k |
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Factory method to convert a batch of model images to a list of ModelImageSet. | def get_list_from_model(learn:Learner, ds_type:DatasetType, batch:Tuple)->[]:
"Factory method to convert a batch of model images to a list of ModelImageSet."
image_sets = []
x,y = batch[0],batch[1]
preds = learn.pred_batch(ds_type=ds_type, batch=(x,y), reconstruct=True)
for ori... |
Writes single model histogram to Tensorboard. | def _write_histogram(self, param_name:str, values)->None:
"Writes single model histogram to Tensorboard."
tag = self.name + '/weights/' + param_name
self.tbwriter.add_histogram(tag=tag, values=values, global_step=self.iteration) |
Writes model histograms to Tensorboard. | def write(self)->None:
"Writes model histograms to Tensorboard."
for param_name, values in self.params: self._write_histogram(param_name=param_name, values=values) |
Writes model histograms to Tensorboard. | def write(self, model:nn.Module, iteration:int, tbwriter:SummaryWriter, name:str='model')->None:
"Writes model histograms to Tensorboard."
request = HistogramTBRequest(model=model, iteration=iteration, tbwriter=tbwriter, name=name)
asyncTBWriter.request_write(request) |
Writes a single scalar value for a gradient statistic to Tensorboard. | def _add_gradient_scalar(self, name:str, scalar_value)->None:
"Writes a single scalar value for a gradient statistic to Tensorboard."
tag = self.name + '/gradients/' + name
self.tbwriter.add_scalar(tag=tag, scalar_value=scalar_value, global_step=self.iteration) |
Writes the average norm of the gradients to Tensorboard. | def _write_avg_norm(self, norms:[])->None:
"Writes the average norm of the gradients to Tensorboard."
avg_norm = sum(norms)/len(self.gradients)
self._add_gradient_scalar('avg_norm', scalar_value=avg_norm) |
Writes the median norm of the gradients to Tensorboard. | def _write_median_norm(self, norms:[])->None:
"Writes the median norm of the gradients to Tensorboard."
median_norm = statistics.median(norms)
self._add_gradient_scalar('median_norm', scalar_value=median_norm) |
Writes the maximum norm of the gradients to Tensorboard. | def _write_max_norm(self, norms:[])->None:
"Writes the maximum norm of the gradients to Tensorboard."
max_norm = max(norms)
self._add_gradient_scalar('max_norm', scalar_value=max_norm) |
Writes the minimum norm of the gradients to Tensorboard. | def _write_min_norm(self, norms:[])->None:
"Writes the minimum norm of the gradients to Tensorboard."
min_norm = min(norms)
self._add_gradient_scalar('min_norm', scalar_value=min_norm) |
Writes the number of zeroes in the gradients to Tensorboard. | def _write_num_zeros(self)->None:
"Writes the number of zeroes in the gradients to Tensorboard."
gradient_nps = [to_np(x.data) for x in self.gradients]
num_zeros = sum((np.asarray(x) == 0.0).sum() for x in gradient_nps)
self._add_gradient_scalar('num_zeros', scalar_value=num_zeros) |
Writes the average of the gradients to Tensorboard. | def _write_avg_gradient(self)->None:
"Writes the average of the gradients to Tensorboard."
avg_gradient = sum(x.data.mean() for x in self.gradients)/len(self.gradients)
self._add_gradient_scalar('avg_gradient', scalar_value=avg_gradient) |
Writes the median of the gradients to Tensorboard. | def _write_median_gradient(self)->None:
"Writes the median of the gradients to Tensorboard."
median_gradient = statistics.median(x.data.median() for x in self.gradients)
self._add_gradient_scalar('median_gradient', scalar_value=median_gradient) |
Writes the maximum of the gradients to Tensorboard. | def _write_max_gradient(self)->None:
"Writes the maximum of the gradients to Tensorboard."
max_gradient = max(x.data.max() for x in self.gradients)
self._add_gradient_scalar('max_gradient', scalar_value=max_gradient) |
Writes the minimum of the gradients to Tensorboard. | def _write_min_gradient(self)->None:
"Writes the minimum of the gradients to Tensorboard."
min_gradient = min(x.data.min() for x in self.gradients)
self._add_gradient_scalar('min_gradient', scalar_value=min_gradient) |
Writes model gradient statistics to Tensorboard. | def write(self)->None:
"Writes model gradient statistics to Tensorboard."
if len(self.gradients) == 0: return
norms = [x.data.norm() for x in self.gradients]
self._write_avg_norm(norms=norms)
self._write_median_norm(norms=norms)
self._write_max_norm(norms=norms)
s... |
Writes list of images as tensors to Tensorboard. | def _write_images(self, name:str, images:[Tensor])->None:
"Writes list of images as tensors to Tensorboard."
tag = self.ds_type.name + ' ' + name
self.tbwriter.add_image(tag=tag, img_tensor=vutils.make_grid(images, normalize=True), global_step=self.iteration) |
Gets list of image tensors from lists of Image objects as a tuple of original generated and real ( target ) images. | def _get_image_tensors(self)->([Tensor], [Tensor], [Tensor]):
"Gets list of image tensors from lists of Image objects, as a tuple of original, generated and real(target) images."
orig_images, gen_images, real_images = [], [], []
for image_set in self.image_sets:
orig_images.append(im... |
Writes original generated and real ( target ) images to Tensorboard. | def write(self)->None:
"Writes original, generated and real(target) images to Tensorboard."
orig_images, gen_images, real_images = self._get_image_tensors()
self._write_images(name='orig images', images=orig_images)
self._write_images(name='gen images', images=gen_images)
self._... |
Writes training and validation batch images to Tensorboard. | def write(self, learn:Learner, trn_batch:Tuple, val_batch:Tuple, iteration:int, tbwriter:SummaryWriter)->None:
"Writes training and validation batch images to Tensorboard."
self._write_for_dstype(learn=learn, batch=val_batch, iteration=iteration, tbwriter=tbwriter, ds_type=DatasetType.Valid)
sel... |
Writes batch images of specified DatasetType to Tensorboard. | def _write_for_dstype(self, learn:Learner, batch:Tuple, iteration:int, tbwriter:SummaryWriter, ds_type:DatasetType)->None:
"Writes batch images of specified DatasetType to Tensorboard."
request = ImageTBRequest(learn=learn, batch=batch, iteration=iteration, tbwriter=tbwriter, ds_type=ds_type)
as... |
Writes single model graph to Tensorboard. | def write(self)->None:
"Writes single model graph to Tensorboard."
self.tbwriter.add_graph(model=self.model, input_to_model=self.input_to_model) |
Writes model graph to Tensorboard. | def write(self, model:nn.Module, tbwriter:SummaryWriter, input_to_model:torch.Tensor)->None:
"Writes model graph to Tensorboard."
request = GraphTBRequest(model=model, tbwriter=tbwriter, input_to_model=input_to_model)
asyncTBWriter.request_write(request) |
During training batch norm layers keep track of a running mean and variance of the previous layer s activations. Because the parameters of the SWA model are computed as the average of other models parameters the SWA model never sees the training data itself and therefore has no opportunity to compute the correct batch ... | def fix_batchnorm(swa_model, train_dl):
"""
During training, batch norm layers keep track of a running mean and
variance of the previous layer's activations. Because the parameters
of the SWA model are computed as the average of other models' parameters,
the SWA model never sees the training data it... |
Wraps h in new Variables to detach them from their history. | def repackage_var(h):
"""Wraps h in new Variables, to detach them from their history."""
if IS_TORCH_04: return h.detach() if type(h) == torch.Tensor else tuple(repackage_var(v) for v in h)
else: return Variable(h.data) if type(h) == Variable else tuple(repackage_var(v) for v in h) |
Returns a SequentialRNN model. | def get_language_model(n_tok, emb_sz, n_hid, n_layers, pad_token,
dropout=0.4, dropouth=0.3, dropouti=0.5, dropoute=0.1, wdrop=0.5, tie_weights=True, qrnn=False, bias=False):
"""Returns a SequentialRNN model.
A RNN_Encoder layer is instantiated using the parameters provided.
This is follo... |
Invoked during the forward propagation of the RNN_Encoder module. Args: input ( Tensor ): input of shape ( sentence length x batch_size ) | def forward(self, input):
""" Invoked during the forward propagation of the RNN_Encoder module.
Args:
input (Tensor): input of shape (sentence length x batch_size)
Returns:
raw_outputs (tuple(list (Tensor), list(Tensor)): list of tensors evaluated from each RNN layer wit... |
Replace repetitions at the character level in t. | def replace_rep(t:str) -> str:
"Replace repetitions at the character level in `t`."
def _replace_rep(m:Collection[str]) -> str:
c,cc = m.groups()
return f' {TK_REP} {len(cc)+1} {c} '
re_rep = re.compile(r'(\S)(\1{3,})')
return re_rep.sub(_replace_rep, t) |
Replace word repetitions in t. | def replace_wrep(t:str) -> str:
"Replace word repetitions in `t`."
def _replace_wrep(m:Collection[str]) -> str:
c,cc = m.groups()
return f' {TK_WREP} {len(cc.split())+1} {c} '
re_wrep = re.compile(r'(\b\w+\W+)(\1{3,})')
return re_wrep.sub(_replace_wrep, t) |
List of replacements from html strings in x. | def fix_html(x:str) -> str:
"List of replacements from html strings in `x`."
re1 = re.compile(r' +')
x = x.replace('#39;', "'").replace('amp;', '&').replace('#146;', "'").replace(
'nbsp;', ' ').replace('#36;', '$').replace('\\n', "\n").replace('quot;', "'").replace(
'<br />', "\n").replace(... |
Replace tokens in ALL CAPS in x by their lower version and add TK_UP before. | def replace_all_caps(x:Collection[str]) -> Collection[str]:
"Replace tokens in ALL CAPS in `x` by their lower version and add `TK_UP` before."
res = []
for t in x:
if t.isupper() and len(t) > 1: res.append(TK_UP); res.append(t.lower())
else: res.append(t)
return res |
Replace all Capitalized tokens in x by their lower version and add TK_MAJ before. | def deal_caps(x:Collection[str]) -> Collection[str]:
"Replace all Capitalized tokens in `x` by their lower version and add `TK_MAJ` before."
res = []
for t in x:
if t == '': continue
if t[0].isupper() and len(t) > 1 and t[1:].islower(): res.append(TK_MAJ)
res.append(t.lower())
re... |
Process one text t with tokenizer tok. | def process_text(self, t:str, tok:BaseTokenizer) -> List[str]:
"Process one text `t` with tokenizer `tok`."
for rule in self.pre_rules: t = rule(t)
toks = tok.tokenizer(t)
for rule in self.post_rules: toks = rule(toks)
return toks |
Process a list of texts in one process. | def _process_all_1(self, texts:Collection[str]) -> List[List[str]]:
"Process a list of `texts` in one process."
tok = self.tok_func(self.lang)
if self.special_cases: tok.add_special_cases(self.special_cases)
return [self.process_text(str(t), tok) for t in texts] |
Process a list of texts. | def process_all(self, texts:Collection[str]) -> List[List[str]]:
"Process a list of `texts`."
if self.n_cpus <= 1: return self._process_all_1(texts)
with ProcessPoolExecutor(self.n_cpus) as e:
return sum(e.map(self._process_all_1, partition_by_cores(texts, self.n_cpus)), []) |
Convert a list of tokens t to their ids. | def numericalize(self, t:Collection[str]) -> List[int]:
"Convert a list of tokens `t` to their ids."
return [self.stoi[w] for w in t] |
Convert a list of nums to their tokens. | def textify(self, nums:Collection[int], sep=' ') -> List[str]:
"Convert a list of `nums` to their tokens."
return sep.join([self.itos[i] for i in nums]) if sep is not None else [self.itos[i] for i in nums] |
Create a vocabulary from a set of tokens. | def create(cls, tokens:Tokens, max_vocab:int, min_freq:int) -> 'Vocab':
"Create a vocabulary from a set of `tokens`."
freq = Counter(p for o in tokens for p in o)
itos = [o for o,c in freq.most_common(max_vocab) if c >= min_freq]
for o in reversed(defaults.text_spec_tok):
if ... |
Load the Vocab contained in path | def load(cls, path):
"Load the `Vocab` contained in `path`"
itos = pickle.load(open(path, 'rb'))
return cls(itos) |
plots loss function as function of iterations. When used in Jupyternotebook plot will be displayed in notebook. Else plot will be displayed in console and both plot and loss are saved in save_path. | def plot_loss(self, n_skip=10, n_skip_end=5):
'''
plots loss function as function of iterations.
When used in Jupyternotebook, plot will be displayed in notebook. Else, plot will be displayed in console and both plot and loss are saved in save_path.
'''
if not in_ipynb(): plt.s... |
Plots learning rate in jupyter notebook or console depending on the enviroment of the learner. | def plot_lr(self):
'''Plots learning rate in jupyter notebook or console, depending on the enviroment of the learner.'''
if not in_ipynb():
plt.switch_backend('agg')
if self.record_mom:
fig, axs = plt.subplots(1,2,figsize=(12,4))
for i in range(0,2): axs[i].se... |
Plots the loss function with respect to learning rate in log scale. | def plot(self, n_skip=10, n_skip_end=5):
'''
Plots the loss function with respect to learning rate, in log scale.
'''
plt.ylabel("validation loss")
plt.xlabel("learning rate (log scale)")
plt.plot(self.lrs[n_skip:-(n_skip_end+1)], self.losses[n_skip:-(n_skip_end+1)])
... |
Plots the lr rate/ momentum schedule | def plot_lr(self, show_text=True, show_moms=True):
"""Plots the lr rate/momentum schedule"""
phase_limits = [0]
for nb_batch, phase in zip(self.nb_batches, self.phases):
phase_limits.append(phase_limits[-1] + nb_batch * phase.epochs)
if not in_ipynb():
plt.switch_... |
Distributed training of Imagenette. | def main(
gpu:Param("GPU to run on", str)=None,
woof: Param("Use imagewoof (otherwise imagenette)", int)=0,
lr: Param("Learning rate", float)=1e-3,
size: Param("Size (px: 128,192,224)", int)=128,
alpha: Param("Alpha", float)=0.99,
mom: Param("Momentum", float)=0.9,
... |
Test if last_loss is NaN and interrupts training. | def on_batch_end(self, last_loss, epoch, num_batch, **kwargs:Any)->None:
"Test if `last_loss` is NaN and interrupts training."
if self.stop: return True #to skip validation after stopping during training
if torch.isnan(last_loss):
print (f'Epoch/Batch ({epoch}/{num_batch}): Invalid l... |
Initializes the best value. | def on_train_begin(self, **kwargs:Any)->None:
"Initializes the best value."
self.best = float('inf') if self.operator == np.less else -float('inf') |
Pick the monitored value. | def get_monitor_value(self):
"Pick the monitored value."
if self.monitor=='trn_loss' and len(self.learn.recorder.losses) == 0: return None
elif len(self.learn.recorder.val_losses) == 0: return None
values = {'train_loss':self.learn.recorder.losses[-1].cpu().numpy(),
'va... |
Compare the value monitored to its best score and maybe save the model. | def on_epoch_end(self, epoch:int, **kwargs:Any)->None:
"Compare the value monitored to its best score and maybe save the model."
if self.every=="epoch": self.learn.save(f'{self.name}_{epoch}')
else: #every="improvement"
current = self.get_monitor_value()
if current is not... |
Load the best model. | def on_train_end(self, **kwargs):
"Load the best model."
if self.every=="improvement" and (self.learn.path/f'{self.learn.model_dir}/{self.name}.pth').is_file():
self.learn.load(f'{self.name}', purge=False) |
Initialize inner arguments. | def on_train_begin(self, **kwargs:Any)->None:
"Initialize inner arguments."
self.wait, self.opt = 0, self.learn.opt
super().on_train_begin(**kwargs) |
Compare the value monitored to its best and maybe reduce lr. | def on_epoch_end(self, epoch, **kwargs:Any)->None:
"Compare the value monitored to its best and maybe reduce lr."
current = self.get_monitor_value()
if current is None: return
if self.operator(current - self.min_delta, self.best): self.best,self.wait = current,0
else:
... |
Convert a notebook fname to html file in dest_path. | def convert_nb(fname, dest_path='.'):
"Convert a notebook `fname` to html file in `dest_path`."
from .gen_notebooks import remove_undoc_cells, remove_code_cell_jupyter_widget_state_elem
nb = read_nb(fname)
nb['cells'] = remove_undoc_cells(nb['cells'])
nb['cells'] = remove_code_cell_jupyter_widget_st... |
Convert modified notebooks in folder to html pages in dest_path. | def convert_all(folder, dest_path='.', force_all=False):
"Convert modified notebooks in `folder` to html pages in `dest_path`."
path = Path(folder)
changed_cnt = 0
for fname in path.glob("*.ipynb"):
# only rebuild modified files
fname_out = Path(dest_path)/fname.with_suffix('.html').nam... |
Function that collect samples and adds padding. Flips token order if needed | def pad_collate(samples:BatchSamples, pad_idx:int=1, pad_first:bool=True, backwards:bool=False) -> Tuple[LongTensor, LongTensor]:
"Function that collect samples and adds padding. Flips token order if needed"
samples = to_data(samples)
max_len = max([len(s[0]) for s in samples])
res = torch.zeros(len(sam... |
Read the text in fn. | def open_text(fn:PathOrStr, enc='utf-8'):
"Read the text in `fn`."
with open(fn,'r', encoding = enc) as f: return ''.join(f.readlines()) |
Create the ragged array that will be filled when we ask for items. | def allocate_buffers(self):
"Create the ragged array that will be filled when we ask for items."
if self.ite_len is None: len(self)
self.idx = LanguageModelPreLoader.CircularIndex(len(self.dataset.x.items), not self.backwards)
self.batch = np.zeros((self.bs, self.bptt+1), dtype=np.int6... |
Fill the row with tokens from the ragged array. -- OBS -- overlap ! = 1 has not been implemented | def fill_row(self, forward, items, idx, row, ro, ri, overlap,lengths):
"Fill the row with tokens from the ragged array. --OBS-- overlap != 1 has not been implemented"
ibuf = n = 0
ro -= 1
while ibuf < row.size:
ro += 1
ix = idx[ro]
rag = items[... |
Create a TextDataBunch from ids labels and a vocab. kwargs are passed to the dataloader creation. | def from_ids(cls, path:PathOrStr, vocab:Vocab, train_ids:Collection[Collection[int]], valid_ids:Collection[Collection[int]],
test_ids:Collection[Collection[int]]=None, train_lbls:Collection[Union[int,float]]=None,
valid_lbls:Collection[Union[int,float]]=None, classes:Collection[Any]=No... |
Load a TextDataBunch from path/ cache_name. kwargs are passed to the dataloader creation. | def load(cls, path:PathOrStr, cache_name:PathOrStr='tmp', processor:PreProcessor=None, **kwargs):
"Load a `TextDataBunch` from `path/cache_name`. `kwargs` are passed to the dataloader creation."
warn("""This method is deprecated and only kept to load data serialized in v1.0.43 or earlier.
... |
Create a TextDataBunch from tokens and labels. kwargs are passed to the dataloader creation. | def from_tokens(cls, path:PathOrStr, trn_tok:Collection[Collection[str]], trn_lbls:Collection[Union[int,float]],
val_tok:Collection[Collection[str]], val_lbls:Collection[Union[int,float]], vocab:Vocab=None,
tst_tok:Collection[Collection[str]]=None, classes:Collection[Any]=None, max_voc... |
Create a TextDataBunch from DataFrames. kwargs are passed to the dataloader creation. | def from_df(cls, path:PathOrStr, train_df:DataFrame, valid_df:DataFrame, test_df:Optional[DataFrame]=None,
tokenizer:Tokenizer=None, vocab:Vocab=None, classes:Collection[str]=None, text_cols:IntsOrStrs=1,
label_cols:IntsOrStrs=0, label_delim:str=None, chunksize:int=10000, max_vocab:int=6... |
Create a TextDataBunch from texts in csv files. kwargs are passed to the dataloader creation. | def from_csv(cls, path:PathOrStr, csv_name, valid_pct:float=0.2, test:Optional[str]=None,
tokenizer:Tokenizer=None, vocab:Vocab=None, classes:Collection[str]=None, delimiter:str=None, header='infer',
text_cols:IntsOrStrs=1, label_cols:IntsOrStrs=0, label_delim:str=None,
... |
Create a TextDataBunch from text files in folders. | def from_folder(cls, path:PathOrStr, train:str='train', valid:str='valid', test:Optional[str]=None,
classes:Collection[Any]=None, tokenizer:Tokenizer=None, vocab:Vocab=None, chunksize:int=10000, max_vocab:int=60000,
min_freq:int=2, mark_fields:bool=False, include_bos:bool=True, i... |
Create a TextDataBunch in path from the datasets for language modelling. Passes ** dl_kwargs on to DataLoader () | def create(cls, train_ds, valid_ds, test_ds=None, path:PathOrStr='.', no_check:bool=False, bs=64, val_bs:int=None,
num_workers:int=0, device:torch.device=None, collate_fn:Callable=data_collate,
dl_tfms:Optional[Collection[Callable]]=None, bptt:int=70, backwards:bool=False, **dl_kwargs) -> ... |
Function that transform the datasets in a DataBunch for classification. Passes ** dl_kwargs on to DataLoader () | def create(cls, train_ds, valid_ds, test_ds=None, path:PathOrStr='.', bs:int=32, val_bs:int=None, pad_idx=1,
pad_first=True, device:torch.device=None, no_check:bool=False, backwards:bool=False, **dl_kwargs) -> DataBunch:
"Function that transform the `datasets` in a `DataBunch` for classification.... |
A special labelling method for language models. | def label_for_lm(self, **kwargs):
"A special labelling method for language models."
self.__class__ = LMTextList
kwargs['label_cls'] = LMLabelList
return self.label_const(0, **kwargs) |
Get the list of files in path that have a text suffix. recurse determines if we search subfolders. | def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=text_extensions, vocab:Vocab=None,
processor:PreProcessor=None, **kwargs)->'TextList':
"Get the list of files in `path` that have a text suffix. `recurse` determines if we search subfolders."
processor = ifnone(proce... |
Show the xs ( inputs ) and ys ( targets ). max_len is the maximum number of tokens displayed. | def show_xys(self, xs, ys, max_len:int=70)->None:
"Show the `xs` (inputs) and `ys` (targets). `max_len` is the maximum number of tokens displayed."
from IPython.display import display, HTML
names = ['idx','text'] if self._is_lm else ['text','target']
items = []
for i, (x,y) in en... |
r InceptionV4 model architecture from the Inception - v4 Inception - ResNet... <https:// arxiv. org/ abs/ 1602. 07261 > _ paper. | def inceptionv4(pretrained=True):
r"""InceptionV4 model architecture from the
`"Inception-v4, Inception-ResNet..." <https://arxiv.org/abs/1602.07261>`_ paper.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = InceptionV4()
if pretrained:
model... |
This over - ride is necessary because otherwise the learner method accesses the wrong model when it is called with precompute set to true | def predict_array(self, arr):
"""
This over-ride is necessary because otherwise the learner method accesses the wrong model when it is called
with precompute set to true
Args:
arr: a numpy array to be used as input to the model for prediction purposes
Returns:
... |
Distrubuted training of CIFAR - 10. Fastest speed is if you run as follows: python - m fastai. launch train_cifar. py | def main( gpu:Param("GPU to run on", str)=None ):
"""Distrubuted training of CIFAR-10.
Fastest speed is if you run as follows:
python -m fastai.launch train_cifar.py"""
gpu = setup_distrib(gpu)
n_gpus = num_distrib()
path = url2path(URLs.CIFAR)
ds_tfms = ([*rand_pad(4, 32), flip_lr(p=0.5... |
Set default values for options. | def initialize_options(self):
"""Set default values for options."""
self.dep_groups = ''
self.dep_quote = False
self.dep_conda = False |
Run command. | def run(self):
"""Run command."""
wanted_groups = self.parse()
deps = []
invalid_groups = []
for grp in wanted_groups:
if grp in dep_groups: deps.extend(dep_groups[grp])
else: invalid_groups.append(grp)
if invalid_groups or not wa... |
Return the saved feature indexes that will be concatenated Inputs: sfs ( list ): saved features by hook function in other words intermediate activations last ( bool ): whether to concatenate only last different activation or all from the encoder model | def get_sfs_idxs(sfs, last=True):
"""
Return the saved feature indexes that will be concatenated
Inputs:
sfs (list): saved features by hook function, in other words intermediate activations
last (bool): whether to concatenate only last different activation, or all from the encoder model
... |
Return a Hook that stores activations of module in self. stored | def hook_output (module:nn.Module, detach:bool=True, grad:bool=False)->Hook:
"Return a `Hook` that stores activations of `module` in `self.stored`"
return Hook(module, _hook_inner, detach=detach, is_forward=not grad) |
Return Hooks that store activations of all modules in self. stored | def hook_outputs(modules:Collection[nn.Module], detach:bool=True, grad:bool=False)->Hooks:
"Return `Hooks` that store activations of all `modules` in `self.stored`"
return Hooks(modules, _hook_inner, detach=detach, is_forward=not grad) |
Create a dummy batch to go through m with size. | def dummy_batch(m: nn.Module, size:tuple=(64,64))->Tensor:
"Create a dummy batch to go through `m` with `size`."
ch_in = in_channels(m)
return one_param(m).new(1, ch_in, *size).requires_grad_(False).uniform_(-1.,1.) |
Pass a dummy_batch in evaluation mode in m with size. | def dummy_eval(m:nn.Module, size:tuple=(64,64)):
"Pass a `dummy_batch` in evaluation mode in `m` with `size`."
return m.eval()(dummy_batch(m, size)) |
Pass a dummy input through the model m to get the various sizes of activations. | def model_sizes(m:nn.Module, size:tuple=(64,64))->Tuple[Sizes,Tensor,Hooks]:
"Pass a dummy input through the model `m` to get the various sizes of activations."
with hook_outputs(m) as hooks:
x = dummy_eval(m, size)
return [o.stored.shape for o in hooks] |
Return the number of output features for model. | def num_features_model(m:nn.Module)->int:
"Return the number of output features for `model`."
sz = 64
while True:
try: return model_sizes(m, size=(sz,sz))[-1][1]
except Exception as e:
sz *= 2
if sz > 2048: raise |
Pass a dummy input through the model to get the various sizes. Returns ( res x hooks ) if full | def params_size(m: Union[nn.Module,Learner], size: tuple = (3, 64, 64))->Tuple[Sizes, Tensor, Hooks]:
"Pass a dummy input through the model to get the various sizes. Returns (res,x,hooks) if `full`"
if isinstance(m, Learner):
if m.data.is_empty:
raise Exception("This is an empty `Learner` an... |
Print a summary of m using a output text width of n chars | def model_summary(m:Learner, n:int=70):
"Print a summary of `m` using a output text width of `n` chars"
info = layers_info(m)
header = ["Layer (type)", "Output Shape", "Param #", "Trainable"]
res = "=" * n + "\n"
res += f"{header[0]:<20} {header[1]:<20} {header[2]:<10} {header[3]:<10}\n"
res += ... |
Applies hook_func to module input output. | def hook_fn(self, module:nn.Module, input:Tensors, output:Tensors):
"Applies `hook_func` to `module`, `input`, `output`."
if self.detach:
input = (o.detach() for o in input ) if is_listy(input ) else input.detach()
output = (o.detach() for o in output) if is_listy(output) else o... |
Remove the hook from the model. | def remove(self):
"Remove the hook from the model."
if not self.removed:
self.hook.remove()
self.removed=True |
Register the Hooks on self. modules. | def on_train_begin(self, **kwargs):
"Register the `Hooks` on `self.modules`."
if not self.modules:
self.modules = [m for m in flatten_model(self.learn.model)
if hasattr(m, 'weight')]
self.hooks = Hooks(self.modules, self.hook) |
Take the mean and std of o. | def hook(self, m:nn.Module, i:Tensors, o:Tensors)->Tuple[Rank0Tensor,Rank0Tensor]:
"Take the mean and std of `o`."
return o.mean().item(),o.std().item() |
Take the stored results and puts it in self. stats | def on_batch_end(self, train, **kwargs):
"Take the stored results and puts it in `self.stats`"
if train: self.stats.append(self.hooks.stored) |
Plots images given image files. | def plots_from_files(imspaths, figsize=(10,5), rows=1, titles=None, maintitle=None):
"""Plots images given image files.
Arguments:
im_paths (list): list of paths
figsize (tuple): figure size
rows (int): number of rows
titles (list): list of titles
maintitle (string): mai... |
Displays the images and their probabilities of belonging to a certain class | def plot_val_with_title(self, idxs, y):
""" Displays the images and their probabilities of belonging to a certain class
Arguments:
idxs (numpy.ndarray): indexes of the image samples from the dataset
y (int): the selected class
Returns:
Pl... |
Extracts the first 4 most correct/ incorrect indexes from the ordered list of probabilities | def most_by_mask(self, mask, y, mult):
""" Extracts the first 4 most correct/incorrect indexes from the ordered list of probabilities
Arguments:
mask (numpy.ndarray): the mask of probabilities specific to the selected class; a boolean array with shape (num_of_samples,) which contain... |
Extracts the first 4 most uncertain indexes from the ordered list of probabilities | def most_uncertain_by_mask(self, mask, y):
""" Extracts the first 4 most uncertain indexes from the ordered list of probabilities
Arguments:
mask (numpy.ndarray): the mask of probabilities specific to the selected class; a boolean array with shape (num_of_samples,) which contains Tr... |
Extracts the predicted classes which correspond to the selected class ( y ) and to the specific case ( prediction is correct - is_true = True prediction is wrong - is_true = False ) | def most_by_correct(self, y, is_correct):
""" Extracts the predicted classes which correspond to the selected class (y) and to the specific case (prediction is correct - is_true=True, prediction is wrong - is_true=False)
Arguments:
y (int): the selected class
is_corr... |
Plots the images which correspond to the selected class ( y ) and to the specific case ( prediction is correct - is_true = True prediction is wrong - is_true = False ) | def plot_by_correct(self, y, is_correct):
""" Plots the images which correspond to the selected class (y) and to the specific case (prediction is correct - is_true=True, prediction is wrong - is_true=False)
Arguments:
y (int): the selected class
is_correct (boolean):... |
Extracts the predicted classes which correspond to the selected class ( y ) and have probabilities nearest to 1/ number_of_classes ( eg. 0. 5 for 2 classes 0. 33 for 3 classes ) for the selected class. | def most_by_uncertain(self, y):
""" Extracts the predicted classes which correspond to the selected class (y) and have probabilities nearest to 1/number_of_classes (eg. 0.5 for 2 classes, 0.33 for 3 classes) for the selected class.
Arguments:
y (int): the selected class
... |
PyTorch distributed training launch helper that spawns multiple distributed processes | def main(
gpus:Param("The GPUs to use for distributed training", str)='all',
script:Param("Script to run", str, opt=False)='',
args:Param("Args to pass to script", nargs='...', opt=False)=''
):
"PyTorch distributed training launch helper that spawns multiple distributed processes"
# Loosely based on... |
Add the metrics names to the Recorder. | def on_train_begin(self, **kwargs):
"Add the metrics names to the `Recorder`."
self.names = ifnone(self.learn.loss_func.metric_names, [])
if not self.names: warn('LossMetrics requested but no loss_func.metric_names provided')
self.learn.recorder.add_metric_names(self.names) |
Initialize the metrics for this epoch. | def on_epoch_begin(self, **kwargs):
"Initialize the metrics for this epoch."
self.metrics = {name:0. for name in self.names}
self.nums = 0 |
Update the metrics if not train | def on_batch_end(self, last_target, train, **kwargs):
"Update the metrics if not `train`"
if train: return
bs = last_target.size(0)
for name in self.names:
self.metrics[name] += bs * self.learn.loss_func.metrics[name].detach().cpu()
self.nums += bs |
Finish the computation and sends the result to the Recorder. | def on_epoch_end(self, last_metrics, **kwargs):
"Finish the computation and sends the result to the Recorder."
if not self.nums: return
metrics = [self.metrics[name]/self.nums for name in self.names]
return {'last_metrics': last_metrics+metrics} |
Create the various optimizers. | def on_train_begin(self, **kwargs):
"Create the various optimizers."
self.G_A,self.G_B = self.learn.model.G_A,self.learn.model.G_B
self.D_A,self.D_B = self.learn.model.D_A,self.learn.model.D_B
self.crit = self.learn.loss_func.crit
self.opt_G = self.learn.opt.new([nn.Sequential(*f... |
Steps through the generators then each of the critics. | def on_batch_end(self, last_input, last_output, **kwargs):
"Steps through the generators then each of the critics."
self.G_A.zero_grad(); self.G_B.zero_grad()
fake_A, fake_B = last_output[0].detach(), last_output[1].detach()
real_A, real_B = last_input
self._set_trainable(D_A=Tru... |
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