def all_last_saved(max_elapsed=60):
from time import strftime
from fastai.data.transforms import get_files
print('\n')
lib_path = Path(os.getcwd()).parent
folder = lib_path / 'tsai'
print('Checking folder:', folder)
counter = 0
elapsed = 0
current_time = time.time()
for fp in get_files(folder):
fp = str(fp)
fn = fp.split('/')[-1]
if not fn.endswith(".py") or fn.startswith("_") or fn.startswith(
".") or fn in ['imports.py', 'all.py', 'basics.py'
]: # add files without a notebook
continue
elapsed_time = current_time - os.path.getmtime(fp)
if elapsed_time > max_elapsed:
print(f"{fn:30} saved {elapsed_time:10.0f} s ago ***")
counter += 1
elapsed += elapsed_time
if counter == 0:
print('Correct conversion! 😃')
output = 1
else:
print('Incorrect conversion! 😔')
output = 0
print(f'Total time elapsed {elapsed:.3f} s')
print(strftime("%A %d/%m/%y %T %Z"))
return output
def load_ensemble(self, path=None):
path = path or self.ensemble_dir
models = get_files(path, extensions='.pth', recurse=False)
assert len(models) > 0, f'No models found in {path}'
self.models = {}
for m in models:
model_id = int(m.stem[-1])
self.models[model_id] = m
print(f'Found {len(self.models)} models in folder {path}')
print(self.models)
def tokenize_folder(path,
extensions=None,
folders=None,
output_dir=None,
skip_if_exists=True,
**kwargs):
"Tokenize text files in `path` in parallel using `n_workers`"
path, extensions = Path(path), ifnone(extensions, ['.txt'])
files = get_files(path,
extensions=extensions,
recurse=True,
folders=folders)
def _f(i, output_dir):
return output_dir / files[i].relative_to(path)
return _tokenize_files(_f,
files,
path,
skip_if_exists=skip_if_exists,
**kwargs)
def load_ensemble(self, path=None):
path = path or self.ensemble_dir
models = sorted(get_files(path, extensions='.pth', recurse=False))
self.models = {}
for i, m in enumerate(models, 1):
if i == 0: self.n_classes = int(m.name.split('_')[2][0])
else:
assert self.n_classes == int(
m.name.split('_')[2][0]
), 'Check models. Models are trained on different number of classes.'
self.models[i] = m
if len(self.models) > 0:
self.set_n(len(self.models))
print(f'Found {len(self.models)} models in folder {path}:')
print([m.name for m in self.models.values()])
# Reset stats
print(f'Loading stats from {self.models[1].name}')
_, self.stats = load_smp_model(self.models[1])
def get_audio_files(path, recurse=True, folders=None):
"Get audio files in `path` recursively, only in `folders`, if specified."
return get_files(
path, extensions=audio_extensions, recurse=recurse, folders=folders
)
