id int32 0 252k | repo stringlengths 7 55 | path stringlengths 4 127 | func_name stringlengths 1 88 | original_string stringlengths 75 19.8k | language stringclasses 1
value | code stringlengths 75 19.8k | code_tokens list | docstring stringlengths 3 17.3k | docstring_tokens list | sha stringlengths 40 40 | url stringlengths 87 242 |
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20,800 | fastai/fastai | old/fastai/sgdr.py | LossRecorder.plot_loss | 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... | python | 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... | [
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20,801 | fastai/fastai | old/fastai/sgdr.py | LossRecorder.plot_lr | 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... | python | 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... | [
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20,802 | fastai/fastai | old/fastai/sgdr.py | LR_Finder.plot | 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)])
... | python | 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)])
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20,803 | fastai/fastai | examples/train_imagenette.py | main | 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,
... | python | 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,
... | [
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20,804 | fastai/fastai | fastai/callbacks/tracker.py | TerminateOnNaNCallback.on_batch_end | 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... | python | 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... | [
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20,805 | fastai/fastai | fastai/callbacks/tracker.py | TrackerCallback.on_train_begin | def on_train_begin(self, **kwargs:Any)->None:
"Initializes the best value."
self.best = float('inf') if self.operator == np.less else -float('inf') | python | def on_train_begin(self, **kwargs:Any)->None:
"Initializes the best value."
self.best = float('inf') if self.operator == np.less else -float('inf') | [
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20,806 | fastai/fastai | fastai/callbacks/tracker.py | TrackerCallback.get_monitor_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... | python | 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(),
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20,807 | fastai/fastai | fastai/callbacks/tracker.py | SaveModelCallback.on_epoch_end | 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... | python | 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... | [
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20,808 | fastai/fastai | fastai/callbacks/tracker.py | ReduceLROnPlateauCallback.on_train_begin | def on_train_begin(self, **kwargs:Any)->None:
"Initialize inner arguments."
self.wait, self.opt = 0, self.learn.opt
super().on_train_begin(**kwargs) | python | def on_train_begin(self, **kwargs:Any)->None:
"Initialize inner arguments."
self.wait, self.opt = 0, self.learn.opt
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20,809 | fastai/fastai | fastai/callbacks/tracker.py | ReduceLROnPlateauCallback.on_epoch_end | 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:
... | python | 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:
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20,810 | fastai/fastai | fastai/gen_doc/convert2html.py | convert_nb | 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... | python | 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... | [
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20,811 | fastai/fastai | fastai/gen_doc/convert2html.py | convert_all | 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... | python | 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... | [
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20,812 | fastai/fastai | fastai/text/data.py | pad_collate | 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... | python | 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... | [
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20,813 | fastai/fastai | fastai/text/data.py | open_text | 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()) | python | 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()) | [
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20,814 | fastai/fastai | fastai/text/data.py | LanguageModelPreLoader.allocate_buffers | 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... | python | 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... | [
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20,815 | fastai/fastai | fastai/text/data.py | TextDataBunch.from_ids | 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... | python | 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... | [
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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... | python | 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,
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20,817 | fastai/fastai | fastai/text/data.py | TextDataBunch.from_df | 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... | python | 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,
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20,818 | fastai/fastai | fastai/text/data.py | TextDataBunch.from_csv | 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,
... | python | 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,
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20,819 | fastai/fastai | fastai/text/data.py | TextDataBunch.from_folder | 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... | python | 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... | [
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20,820 | fastai/fastai | fastai/text/data.py | TextList.label_for_lm | 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) | python | 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) | [
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20,821 | fastai/fastai | fastai/text/data.py | TextList.from_folder | 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... | python | def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=text_extensions, vocab:Vocab=None,
processor:PreProcessor=None, **kwargs)->'TextList':
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20,822 | fastai/fastai | old/fastai/conv_learner.py | ConvLearner.predict_array | def predict_array(self, arr):
"""
This over-ride is necessary because otherwise the learner method accesses the wrong model when it is called
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Args:
arr: a numpy array to be used as input to the model for prediction purposes
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... | python | def predict_array(self, arr):
"""
This over-ride is necessary because otherwise the learner method accesses the wrong model when it is called
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arr: a numpy array to be used as input to the model for prediction purposes
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20,823 | fastai/fastai | fastai/callbacks/hooks.py | hook_output | 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) | python | def hook_output (module:nn.Module, detach:bool=True, grad:bool=False)->Hook:
"Return a `Hook` that stores activations of `module` in `self.stored`"
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20,824 | fastai/fastai | fastai/callbacks/hooks.py | hook_outputs | 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) | python | 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`"
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20,825 | fastai/fastai | fastai/callbacks/hooks.py | dummy_batch | 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.) | python | 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.) | [
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20,826 | fastai/fastai | fastai/callbacks/hooks.py | dummy_eval | 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)) | python | def dummy_eval(m:nn.Module, size:tuple=(64,64)):
"Pass a `dummy_batch` in evaluation mode in `m` with `size`."
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20,827 | fastai/fastai | fastai/callbacks/hooks.py | model_sizes | 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] | python | 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] | [
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20,828 | fastai/fastai | fastai/callbacks/hooks.py | num_features_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 | python | 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 | [
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20,829 | fastai/fastai | fastai/callbacks/hooks.py | model_summary | def model_summary(m:Learner, n:int=70):
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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 += ... | python | 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"
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20,833 | fastai/fastai | fastai/callbacks/hooks.py | ActivationStats.hook | def hook(self, m:nn.Module, i:Tensors, o:Tensors)->Tuple[Rank0Tensor,Rank0Tensor]:
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20,835 | fastai/fastai | old/fastai/plots.py | plots_from_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... | python | def plots_from_files(imspaths, figsize=(10,5), rows=1, titles=None, maintitle=None):
"""Plots images given image files.
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im_paths (list): list of paths
figsize (tuple): figure size
rows (int): number of rows
titles (list): list of titles
maintitle (string): mai... | [
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20,836 | fastai/fastai | old/fastai/plots.py | ImageModelResults.plot_val_with_title | 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... | python | def plot_val_with_title(self, idxs, y):
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idxs (numpy.ndarray): indexes of the image samples from the dataset
y (int): the selected class
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20,837 | fastai/fastai | old/fastai/plots.py | ImageModelResults.most_uncertain_by_mask | 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... | python | def most_uncertain_by_mask(self, mask, y):
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20,838 | fastai/fastai | fastai/launch.py | main | 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... | python | 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)=''
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20,839 | fastai/fastai | fastai/callbacks/loss_metrics.py | LossMetrics.on_train_begin | def on_train_begin(self, **kwargs):
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self.names = ifnone(self.learn.loss_func.metric_names, [])
if not self.names: warn('LossMetrics requested but no loss_func.metric_names provided')
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"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')
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20,840 | fastai/fastai | fastai/callbacks/loss_metrics.py | LossMetrics.on_epoch_begin | def on_epoch_begin(self, **kwargs):
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20,841 | fastai/fastai | fastai/callbacks/loss_metrics.py | LossMetrics.on_batch_end | def on_batch_end(self, last_target, train, **kwargs):
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"Update the metrics if not `train`"
if train: return
bs = last_target.size(0)
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self.metrics[name] += bs * self.learn.loss_func.metrics[name].detach().cpu()
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20,842 | fastai/fastai | fastai/callbacks/loss_metrics.py | LossMetrics.on_epoch_end | def on_epoch_end(self, last_metrics, **kwargs):
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20,843 | fastai/fastai | fastai/vision/cyclegan.py | CycleGANTrainer.on_train_begin | def on_train_begin(self, **kwargs):
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self.D_A,self.D_B = self.learn.model.D_A,self.learn.model.D_B
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self.opt_G = self.learn.opt.new([nn.Sequential(*f... | python | 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
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20,844 | fastai/fastai | fastai/vision/cyclegan.py | CycleGANTrainer.on_batch_end | def on_batch_end(self, last_input, last_output, **kwargs):
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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... | python | 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()
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20,848 | fastai/fastai | fastai/callbacks/fp16.py | get_master | def get_master(layer_groups:ModuleList, flat_master:bool=False) -> Tuple[List[List[Tensor]], List[List[Tensor]]]:
"Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32."
split_params = split_no_wd_params(layer_groups)
model_params = [[param for param in pg if para... | python | def get_master(layer_groups:ModuleList, flat_master:bool=False) -> Tuple[List[List[Tensor]], List[List[Tensor]]]:
"Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32."
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"Copy the `model_params` gradients to `master_params` for the optimizer step."
if flat_master:
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20,850 | fastai/fastai | fastai/callbacks/fp16.py | master2model | def master2model(model_params:Sequence[Tensor], master_params:Sequence[Tensor], flat_master:bool=False)->None:
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if flat_master:
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for model, maste... | python | def master2model(model_params:Sequence[Tensor], master_params:Sequence[Tensor], flat_master:bool=False)->None:
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20,851 | fastai/fastai | fastai/callbacks/fp16.py | MixedPrecision.on_train_begin | def on_train_begin(self, **kwargs:Any)->None:
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#Get a copy of the model params in FP32
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20,852 | fastai/fastai | fastai/callbacks/fp16.py | MixedPrecision.on_backward_begin | def on_backward_begin(self, last_loss:Rank0Tensor, **kwargs:Any) -> Rank0Tensor:
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20,854 | fastai/fastai | fastai/callbacks/fp16.py | MixedPrecision.on_step_end | def on_step_end(self, **kwargs:Any)->None:
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20,855 | fastai/fastai | old/fastai/transforms.py | dihedral | def dihedral(x, dih):
""" Perform any of 8 permutations of 90-degrees rotations or flips for image x. """
x = np.rot90(x, dih%4)
return x if dih<4 else np.fliplr(x) | python | def dihedral(x, dih):
""" Perform any of 8 permutations of 90-degrees rotations or flips for image x. """
x = np.rot90(x, dih%4)
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20,856 | fastai/fastai | old/fastai/transforms.py | lighting | def lighting(im, b, c):
""" Adjust image balance and contrast """
if b==0 and c==1: return im
mu = np.average(im)
return np.clip((im-mu)*c+mu+b,0.,1.).astype(np.float32) | python | def lighting(im, b, c):
""" Adjust image balance and contrast """
if b==0 and c==1: return im
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20,857 | fastai/fastai | old/fastai/transforms.py | scale_to | def scale_to(x, ratio, targ):
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20,858 | fastai/fastai | old/fastai/transforms.py | to_bb | def to_bb(YY, y="deprecated"):
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right_col = np.max(cols)
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"""Convert mask YY to a bounding box, assumes 0 as background nonzero object"""
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20,859 | fastai/fastai | old/fastai/transforms.py | image_gen | def image_gen(normalizer, denorm, sz, tfms=None, max_zoom=None, pad=0, crop_type=None,
tfm_y=None, sz_y=None, pad_mode=cv2.BORDER_REFLECT, scale=None):
"""
Generate a standard set of transformations
Arguments
---------
normalizer :
image normalizing function
denorm :
... | python | def image_gen(normalizer, denorm, sz, tfms=None, max_zoom=None, pad=0, crop_type=None,
tfm_y=None, sz_y=None, pad_mode=cv2.BORDER_REFLECT, scale=None):
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Generate a standard set of transformations
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normalizer :
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20,860 | fastai/fastai | old/fastai/transforms.py | tfms_from_stats | def tfms_from_stats(stats, sz, aug_tfms=None, max_zoom=None, pad=0, crop_type=CropType.RANDOM,
tfm_y=None, sz_y=None, pad_mode=cv2.BORDER_REFLECT, norm_y=True, scale=None):
""" Given the statistics of the training image sets, returns separate training and validation transform functions
"""
... | python | def tfms_from_stats(stats, sz, aug_tfms=None, max_zoom=None, pad=0, crop_type=CropType.RANDOM,
tfm_y=None, sz_y=None, pad_mode=cv2.BORDER_REFLECT, norm_y=True, scale=None):
""" Given the statistics of the training image sets, returns separate training and validation transform functions
"""
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20,861 | fastai/fastai | fastai/vision/data.py | get_image_files | def get_image_files(c:PathOrStr, check_ext:bool=True, recurse=False)->FilePathList:
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return get_files(c, extensions=(image_extensions if check_ext else None), recurse=recurse) | python | def get_image_files(c:PathOrStr, check_ext:bool=True, recurse=False)->FilePathList:
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20,862 | fastai/fastai | fastai/vision/data.py | bb_pad_collate | def bb_pad_collate(samples:BatchSamples, pad_idx:int=0) -> Tuple[FloatTensor, Tuple[LongTensor, LongTensor]]:
"Function that collect `samples` of labelled bboxes and adds padding with `pad_idx`."
if isinstance(samples[0][1], int): return data_collate(samples)
max_len = max([len(s[1].data[1]) for s in sample... | python | def bb_pad_collate(samples:BatchSamples, pad_idx:int=0) -> Tuple[FloatTensor, Tuple[LongTensor, LongTensor]]:
"Function that collect `samples` of labelled bboxes and adds padding with `pad_idx`."
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20,863 | fastai/fastai | fastai/vision/data.py | normalize | def normalize(x:TensorImage, mean:FloatTensor,std:FloatTensor)->TensorImage:
"Normalize `x` with `mean` and `std`."
return (x-mean[...,None,None]) / std[...,None,None] | python | def normalize(x:TensorImage, mean:FloatTensor,std:FloatTensor)->TensorImage:
"Normalize `x` with `mean` and `std`."
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"Denormalize `x` with `mean` and `std`."
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"Denormalize `x` with `mean` and `std`."
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20,865 | fastai/fastai | fastai/vision/data.py | _normalize_batch | def _normalize_batch(b:Tuple[Tensor,Tensor], mean:FloatTensor, std:FloatTensor, do_x:bool=True, do_y:bool=False)->Tuple[Tensor,Tensor]:
"`b` = `x`,`y` - normalize `x` array of imgs and `do_y` optionally `y`."
x,y = b
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if do_x: x = normalize(x,mean,std)
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"`b` = `x`,`y` - normalize `x` array of imgs and `do_y` optionally `y`."
x,y = b
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20,866 | fastai/fastai | fastai/vision/data.py | channel_view | def channel_view(x:Tensor)->Tensor:
"Make channel the first axis of `x` and flatten remaining axes"
return x.transpose(0,1).contiguous().view(x.shape[1],-1) | python | def channel_view(x:Tensor)->Tensor:
"Make channel the first axis of `x` and flatten remaining axes"
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20,867 | fastai/fastai | fastai/vision/data.py | download_images | def download_images(urls:Collection[str], dest:PathOrStr, max_pics:int=1000, max_workers:int=8, timeout=4):
"Download images listed in text file `urls` to path `dest`, at most `max_pics`"
urls = open(urls).read().strip().split("\n")[:max_pics]
dest = Path(dest)
dest.mkdir(exist_ok=True)
parallel(par... | python | def download_images(urls:Collection[str], dest:PathOrStr, max_pics:int=1000, max_workers:int=8, timeout=4):
"Download images listed in text file `urls` to path `dest`, at most `max_pics`"
urls = open(urls).read().strip().split("\n")[:max_pics]
dest = Path(dest)
dest.mkdir(exist_ok=True)
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20,868 | fastai/fastai | fastai/vision/data.py | verify_image | def verify_image(file:Path, idx:int, delete:bool, max_size:Union[int,Tuple[int,int]]=None, dest:Path=None, n_channels:int=3,
interp=PIL.Image.BILINEAR, ext:str=None, img_format:str=None, resume:bool=False, **kwargs):
"Check if the image in `file` exists, maybe resize it and copy it in `dest`."
... | python | def verify_image(file:Path, idx:int, delete:bool, max_size:Union[int,Tuple[int,int]]=None, dest:Path=None, n_channels:int=3,
interp=PIL.Image.BILINEAR, ext:str=None, img_format:str=None, resume:bool=False, **kwargs):
"Check if the image in `file` exists, maybe resize it and copy it in `dest`."
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20,869 | fastai/fastai | fastai/vision/data.py | verify_images | def verify_images(path:PathOrStr, delete:bool=True, max_workers:int=4, max_size:Union[int]=None, recurse:bool=False,
dest:PathOrStr='.', n_channels:int=3, interp=PIL.Image.BILINEAR, ext:str=None, img_format:str=None,
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dest:PathOrStr='.', n_channels:int=3, interp=PIL.Image.BILINEAR, ext:str=None, img_format:str=None,
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20,870 | fastai/fastai | fastai/vision/data.py | _presize | def _presize(self, size:int, val_xtra_size:int=32, scale:Tuple[float]=(0.08, 1.0), ratio:Tuple[float]=(0.75, 4./3.),
interpolation:int=2):
"Resize images to `size` using `RandomResizedCrop`, passing along `kwargs` to train transform"
return self.pre_transform(
tvt.RandomResizedCrop(size, sc... | python | def _presize(self, size:int, val_xtra_size:int=32, scale:Tuple[float]=(0.08, 1.0), ratio:Tuple[float]=(0.75, 4./3.),
interpolation:int=2):
"Resize images to `size` using `RandomResizedCrop`, passing along `kwargs` to train transform"
return self.pre_transform(
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20,871 | fastai/fastai | fastai/vision/data.py | ImageDataBunch.create_from_ll | def create_from_ll(cls, lls:LabelLists, bs:int=64, val_bs:int=None, ds_tfms:Optional[TfmList]=None,
num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None,
test:Optional[PathOrStr]=None, collate_fn:Callable=data_collate, size:int=None, no_che... | python | def create_from_ll(cls, lls:LabelLists, bs:int=64, val_bs:int=None, ds_tfms:Optional[TfmList]=None,
num_workers:int=defaults.cpus, dl_tfms:Optional[Collection[Callable]]=None, device:torch.device=None,
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20,872 | fastai/fastai | fastai/vision/data.py | ImageDataBunch.from_df | def from_df(cls, path:PathOrStr, df:pd.DataFrame, folder:PathOrStr=None, label_delim:str=None, valid_pct:float=0.2,
fn_col:IntsOrStrs=0, label_col:IntsOrStrs=1, suffix:str='', **kwargs:Any)->'ImageDataBunch':
"Create from a `DataFrame` `df`."
src = (ImageList.from_df(df, path=path, folde... | python | def from_df(cls, path:PathOrStr, df:pd.DataFrame, folder:PathOrStr=None, label_delim:str=None, valid_pct:float=0.2,
fn_col:IntsOrStrs=0, label_col:IntsOrStrs=1, suffix:str='', **kwargs:Any)->'ImageDataBunch':
"Create from a `DataFrame` `df`."
src = (ImageList.from_df(df, path=path, folde... | [
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20,873 | fastai/fastai | fastai/vision/data.py | ImageDataBunch.from_lists | def from_lists(cls, path:PathOrStr, fnames:FilePathList, labels:Collection[str], valid_pct:float=0.2,
item_cls:Callable=None, **kwargs):
"Create from list of `fnames` in `path`."
item_cls = ifnone(item_cls, ImageList)
fname2label = {f:l for (f,l) in zip(fnames, labels)}
... | python | def from_lists(cls, path:PathOrStr, fnames:FilePathList, labels:Collection[str], valid_pct:float=0.2,
item_cls:Callable=None, **kwargs):
"Create from list of `fnames` in `path`."
item_cls = ifnone(item_cls, ImageList)
fname2label = {f:l for (f,l) in zip(fnames, labels)}
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20,874 | fastai/fastai | fastai/vision/data.py | ImageDataBunch.from_name_func | def from_name_func(cls, path:PathOrStr, fnames:FilePathList, label_func:Callable, valid_pct:float=0.2, **kwargs):
"Create from list of `fnames` in `path` with `label_func`."
src = ImageList(fnames, path=path).split_by_rand_pct(valid_pct)
return cls.create_from_ll(src.label_from_func(label_func),... | python | def from_name_func(cls, path:PathOrStr, fnames:FilePathList, label_func:Callable, valid_pct:float=0.2, **kwargs):
"Create from list of `fnames` in `path` with `label_func`."
src = ImageList(fnames, path=path).split_by_rand_pct(valid_pct)
return cls.create_from_ll(src.label_from_func(label_func),... | [
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20,875 | fastai/fastai | fastai/vision/data.py | ImageDataBunch.from_name_re | def from_name_re(cls, path:PathOrStr, fnames:FilePathList, pat:str, valid_pct:float=0.2, **kwargs):
"Create from list of `fnames` in `path` with re expression `pat`."
pat = re.compile(pat)
def _get_label(fn):
if isinstance(fn, Path): fn = fn.as_posix()
res = pat.search(st... | python | def from_name_re(cls, path:PathOrStr, fnames:FilePathList, pat:str, valid_pct:float=0.2, **kwargs):
"Create from list of `fnames` in `path` with re expression `pat`."
pat = re.compile(pat)
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20,876 | fastai/fastai | fastai/vision/data.py | ImageDataBunch.batch_stats | def batch_stats(self, funcs:Collection[Callable]=None, ds_type:DatasetType=DatasetType.Train)->Tensor:
"Grab a batch of data and call reduction function `func` per channel"
funcs = ifnone(funcs, [torch.mean,torch.std])
x = self.one_batch(ds_type=ds_type, denorm=False)[0].cpu()
return [fu... | python | def batch_stats(self, funcs:Collection[Callable]=None, ds_type:DatasetType=DatasetType.Train)->Tensor:
"Grab a batch of data and call reduction function `func` per channel"
funcs = ifnone(funcs, [torch.mean,torch.std])
x = self.one_batch(ds_type=ds_type, denorm=False)[0].cpu()
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20,877 | fastai/fastai | fastai/vision/data.py | ImageList.open | def open(self, fn):
"Open image in `fn`, subclass and overwrite for custom behavior."
return open_image(fn, convert_mode=self.convert_mode, after_open=self.after_open) | python | def open(self, fn):
"Open image in `fn`, subclass and overwrite for custom behavior."
return open_image(fn, convert_mode=self.convert_mode, after_open=self.after_open) | [
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20,878 | fastai/fastai | fastai/vision/data.py | ImageList.from_folder | def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=None, **kwargs)->ItemList:
"Get the list of files in `path` that have an image suffix. `recurse` determines if we search subfolders."
extensions = ifnone(extensions, image_extensions)
return super().from_folder(path=path, extens... | python | def from_folder(cls, path:PathOrStr='.', extensions:Collection[str]=None, **kwargs)->ItemList:
"Get the list of files in `path` that have an image suffix. `recurse` determines if we search subfolders."
extensions = ifnone(extensions, image_extensions)
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20,879 | fastai/fastai | fastai/vision/data.py | ImageList.from_df | def from_df(cls, df:DataFrame, path:PathOrStr, cols:IntsOrStrs=0, folder:PathOrStr=None, suffix:str='', **kwargs)->'ItemList':
"Get the filenames in `cols` of `df` with `folder` in front of them, `suffix` at the end."
suffix = suffix or ''
res = super().from_df(df, path=path, cols=cols, **kwargs... | python | def from_df(cls, df:DataFrame, path:PathOrStr, cols:IntsOrStrs=0, folder:PathOrStr=None, suffix:str='', **kwargs)->'ItemList':
"Get the filenames in `cols` of `df` with `folder` in front of them, `suffix` at the end."
suffix = suffix or ''
res = super().from_df(df, path=path, cols=cols, **kwargs... | [
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20,880 | fastai/fastai | fastai/vision/data.py | ObjectCategoryProcessor.generate_classes | def generate_classes(self, items):
"Generate classes from unique `items` and add `background`."
classes = super().generate_classes([o[1] for o in items])
classes = ['background'] + list(classes)
return classes | python | def generate_classes(self, items):
"Generate classes from unique `items` and add `background`."
classes = super().generate_classes([o[1] for o in items])
classes = ['background'] + list(classes)
return classes | [
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20,881 | fastai/fastai | fastai/utils/mem.py | reduce_mem_usage | def reduce_mem_usage(df):
""" iterate through all the columns of a dataframe and modify the data type
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"""
start_mem = df.memory_usage().sum() / 1024**2
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
#Removed from debugging
columns = df.columns
... | python | def reduce_mem_usage(df):
""" iterate through all the columns of a dataframe and modify the data type
to reduce memory usage.
"""
start_mem = df.memory_usage().sum() / 1024**2
print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))
#Removed from debugging
columns = df.columns
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20,882 | fastai/fastai | fastai/distributed.py | _learner_distributed | def _learner_distributed(learn:Learner, cuda_id:int, cache_dir:PathOrStr='tmp'):
"Put `learn` on distributed training with `cuda_id`."
learn.callbacks.append(DistributedTrainer(learn, cuda_id))
learn.callbacks.append(DistributedRecorder(learn, cuda_id, cache_dir))
return learn | python | def _learner_distributed(learn:Learner, cuda_id:int, cache_dir:PathOrStr='tmp'):
"Put `learn` on distributed training with `cuda_id`."
learn.callbacks.append(DistributedTrainer(learn, cuda_id))
learn.callbacks.append(DistributedRecorder(learn, cuda_id, cache_dir))
return learn | [
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20,883 | fastai/fastai | fastai/vision/models/xresnet2.py | xresnet18 | def xresnet18(pretrained=False, **kwargs):
"""Constructs a XResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet18']))
... | python | def xresnet18(pretrained=False, **kwargs):
"""Constructs a XResNet-18 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet18']))
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20,884 | fastai/fastai | fastai/vision/models/xresnet2.py | xresnet50_2 | def xresnet50_2(pretrained=False, **kwargs):
"""Constructs a XResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet50']))
... | python | def xresnet50_2(pretrained=False, **kwargs):
"""Constructs a XResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = XResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained: model.load_state_dict(model_zoo.load_url(model_urls['xresnet50']))
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20,885 | fastai/fastai | fastai/basic_train.py | loss_batch | def loss_batch(model:nn.Module, xb:Tensor, yb:Tensor, loss_func:OptLossFunc=None, opt:OptOptimizer=None,
cb_handler:Optional[CallbackHandler]=None)->Tuple[Union[Tensor,int,float,str]]:
"Calculate loss and metrics for a batch, call out to callbacks as necessary."
cb_handler = ifnone(cb_handler, Ca... | python | def loss_batch(model:nn.Module, xb:Tensor, yb:Tensor, loss_func:OptLossFunc=None, opt:OptOptimizer=None,
cb_handler:Optional[CallbackHandler]=None)->Tuple[Union[Tensor,int,float,str]]:
"Calculate loss and metrics for a batch, call out to callbacks as necessary."
cb_handler = ifnone(cb_handler, Ca... | [
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20,886 | fastai/fastai | fastai/basic_train.py | validate | def validate(model:nn.Module, dl:DataLoader, loss_func:OptLossFunc=None, cb_handler:Optional[CallbackHandler]=None,
pbar:Optional[PBar]=None, average=True, n_batch:Optional[int]=None)->Iterator[Tuple[Union[Tensor,int],...]]:
"Calculate `loss_func` of `model` on `dl` in evaluation mode."
model.eval(... | python | def validate(model:nn.Module, dl:DataLoader, loss_func:OptLossFunc=None, cb_handler:Optional[CallbackHandler]=None,
pbar:Optional[PBar]=None, average=True, n_batch:Optional[int]=None)->Iterator[Tuple[Union[Tensor,int],...]]:
"Calculate `loss_func` of `model` on `dl` in evaluation mode."
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20,887 | fastai/fastai | fastai/basic_train.py | train_epoch | def train_epoch(model:nn.Module, dl:DataLoader, opt:optim.Optimizer, loss_func:LossFunction)->None:
"Simple training of `model` for 1 epoch of `dl` using optim `opt` and loss function `loss_func`."
model.train()
for xb,yb in dl:
loss = loss_func(model(xb), yb)
loss.backward()
opt.ste... | python | def train_epoch(model:nn.Module, dl:DataLoader, opt:optim.Optimizer, loss_func:LossFunction)->None:
"Simple training of `model` for 1 epoch of `dl` using optim `opt` and loss function `loss_func`."
model.train()
for xb,yb in dl:
loss = loss_func(model(xb), yb)
loss.backward()
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20,888 | fastai/fastai | fastai/basic_train.py | fit | def fit(epochs:int, learn:BasicLearner, callbacks:Optional[CallbackList]=None, metrics:OptMetrics=None)->None:
"Fit the `model` on `data` and learn using `loss_func` and `opt`."
assert len(learn.data.train_dl) != 0, f"""Your training dataloader is empty, can't train a model.
Use a smaller batch size (ba... | python | def fit(epochs:int, learn:BasicLearner, callbacks:Optional[CallbackList]=None, metrics:OptMetrics=None)->None:
"Fit the `model` on `data` and learn using `loss_func` and `opt`."
assert len(learn.data.train_dl) != 0, f"""Your training dataloader is empty, can't train a model.
Use a smaller batch size (ba... | [
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20,889 | fastai/fastai | fastai/basic_train.py | Recorder.on_train_begin | def on_train_begin(self, pbar:PBar, metrics_names:Collection[str], **kwargs:Any)->None:
"Initialize recording status at beginning of training."
self.pbar = pbar
self.names = ['epoch', 'train_loss'] if self.no_val else ['epoch', 'train_loss', 'valid_loss']
self.metrics_names = metrics_nam... | python | def on_train_begin(self, pbar:PBar, metrics_names:Collection[str], **kwargs:Any)->None:
"Initialize recording status at beginning of training."
self.pbar = pbar
self.names = ['epoch', 'train_loss'] if self.no_val else ['epoch', 'train_loss', 'valid_loss']
self.metrics_names = metrics_nam... | [
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20,890 | fastai/fastai | fastai/basic_train.py | Recorder.on_batch_begin | def on_batch_begin(self, train, **kwargs:Any)->None:
"Record learning rate and momentum at beginning of batch."
if train:
self.lrs.append(self.opt.lr)
self.moms.append(self.opt.mom) | python | def on_batch_begin(self, train, **kwargs:Any)->None:
"Record learning rate and momentum at beginning of batch."
if train:
self.lrs.append(self.opt.lr)
self.moms.append(self.opt.mom) | [
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20,891 | fastai/fastai | fastai/basic_train.py | Recorder.on_backward_begin | def on_backward_begin(self, smooth_loss:Tensor, **kwargs:Any)->None:
"Record the loss before any other callback has a chance to modify it."
self.losses.append(smooth_loss)
if self.pbar is not None and hasattr(self.pbar,'child'):
self.pbar.child.comment = f'{smooth_loss:.4f}' | python | def on_backward_begin(self, smooth_loss:Tensor, **kwargs:Any)->None:
"Record the loss before any other callback has a chance to modify it."
self.losses.append(smooth_loss)
if self.pbar is not None and hasattr(self.pbar,'child'):
self.pbar.child.comment = f'{smooth_loss:.4f}' | [
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20,892 | fastai/fastai | fastai/basic_train.py | Recorder.format_stats | def format_stats(self, stats:TensorOrNumList)->None:
"Format stats before printing."
str_stats = []
for name,stat in zip(self.names,stats):
str_stats.append('#na#' if stat is None else str(stat) if isinstance(stat, int) else f'{stat:.6f}')
if self.add_time: str_stats.append(f... | python | def format_stats(self, stats:TensorOrNumList)->None:
"Format stats before printing."
str_stats = []
for name,stat in zip(self.names,stats):
str_stats.append('#na#' if stat is None else str(stat) if isinstance(stat, int) else f'{stat:.6f}')
if self.add_time: str_stats.append(f... | [
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20,893 | fastai/fastai | fastai/basic_train.py | Recorder.add_metric_names | def add_metric_names(self, names):
"Add `names` to the inner metric names."
if hasattr(self, '_added_met_names'): self._added_met_names += names
else: self._added_met_names = names | python | def add_metric_names(self, names):
"Add `names` to the inner metric names."
if hasattr(self, '_added_met_names'): self._added_met_names += names
else: self._added_met_names = names | [
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20,894 | fastai/fastai | fastai/basic_train.py | Recorder.plot_lr | def plot_lr(self, show_moms=False, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot learning rate, `show_moms` to include momentum."
lrs = self._split_list(self.lrs, skip_start, skip_end)
iterations = self._split_list(range_of(self.lrs), skip_start, skip_end)
... | python | def plot_lr(self, show_moms=False, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot learning rate, `show_moms` to include momentum."
lrs = self._split_list(self.lrs, skip_start, skip_end)
iterations = self._split_list(range_of(self.lrs), skip_start, skip_end)
... | [
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20,895 | fastai/fastai | fastai/basic_train.py | Recorder.plot | def plot(self, skip_start:int=10, skip_end:int=5, suggestion:bool=False, return_fig:bool=None,
**kwargs)->Optional[plt.Figure]:
"Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. Optionally plot and return min gradient"
lrs = self._split_list(self.lrs, skip_start, ... | python | def plot(self, skip_start:int=10, skip_end:int=5, suggestion:bool=False, return_fig:bool=None,
**kwargs)->Optional[plt.Figure]:
"Plot learning rate and losses, trimmed between `skip_start` and `skip_end`. Optionally plot and return min gradient"
lrs = self._split_list(self.lrs, skip_start, ... | [
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20,896 | fastai/fastai | fastai/basic_train.py | Recorder.plot_losses | def plot_losses(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot training and validation losses."
fig, ax = plt.subplots(1,1)
losses = self._split_list(self.losses, skip_start, skip_end)
iterations = self._split_list(range_of(self.losses), skip_s... | python | def plot_losses(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot training and validation losses."
fig, ax = plt.subplots(1,1)
losses = self._split_list(self.losses, skip_start, skip_end)
iterations = self._split_list(range_of(self.losses), skip_s... | [
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20,897 | fastai/fastai | fastai/basic_train.py | Recorder.plot_metrics | def plot_metrics(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot metrics collected during training."
assert len(self.metrics) != 0, "There are no metrics to plot."
fig, axes = plt.subplots(len(self.metrics[0]),1,figsize=(6, 4*len(self.metrics[0])))
... | python | def plot_metrics(self, skip_start:int=0, skip_end:int=0, return_fig:bool=None)->Optional[plt.Figure]:
"Plot metrics collected during training."
assert len(self.metrics) != 0, "There are no metrics to plot."
fig, axes = plt.subplots(len(self.metrics[0]),1,figsize=(6, 4*len(self.metrics[0])))
... | [
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20,898 | fastai/fastai | fastai/script.py | call_parse | def call_parse(func):
"Decorator to create a simple CLI from `func` using `anno_parser`"
name = inspect.currentframe().f_back.f_globals['__name__']
if name == "__main__":
args = anno_parser(func).parse_args()
func(**args.__dict__)
else: return func | python | def call_parse(func):
"Decorator to create a simple CLI from `func` using `anno_parser`"
name = inspect.currentframe().f_back.f_globals['__name__']
if name == "__main__":
args = anno_parser(func).parse_args()
func(**args.__dict__)
else: return func | [
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20,899 | fastai/fastai | fastai/script.py | call_plac | def call_plac(f):
"Decorator to create a simple CLI from `func` using `plac`"
name = inspect.currentframe().f_back.f_globals['__name__']
if name == '__main__':
import plac
res = plac.call(f)
if callable(res): res()
else: return f | python | def call_plac(f):
"Decorator to create a simple CLI from `func` using `plac`"
name = inspect.currentframe().f_back.f_globals['__name__']
if name == '__main__':
import plac
res = plac.call(f)
if callable(res): res()
else: return f | [
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