def processed_files(self):
if touch(lambda: self._processed_files, None):
return self._processed_files
if self.processed_folder.isdir():
temp = self.processed_folder.ls()
while len(temp) == 1 and temp[0].isdir():
temp = temp[0].ls()
self._processed_files = temp
else:
self._processed_files = vector()
return self._processed_files
def _to_byte__default__(data):
try:
import torch
import pyctlib.torchplus as torchplus
except ImportError:
pass
if touch(lambda: isinstance(data, torchplus.Tensor)):
np_array = data.cpu().detach().numpy()
np_array_content, np_array_content_len = file._to_byte(np_array)
assert len(np_array_content) == np_array_content_len
return file.Tensor_plus + np_array_content, np_array_content_len + 1
if touch(lambda: isinstance(data, torch.Tensor)):
np_array = data.cpu().detach().numpy()
np_array_content, np_array_content_len = file._to_byte(np_array)
assert len(np_array_content) == np_array_content_len
return file.torch_Tensor + np_array_content, np_array_content_len + 1
if touch(lambda: isinstance(data, torch.nn.Module)):
module_state_content, module_state_content_len = file._to_byte(
data.state_dict())
return file.torch_Module + module_state_content, module_state_content_len + 1
def extra_repr(self) -> str:
if self.activation is None:
return 'in_features={}, out_features={}, bias={}'.format(self.in_features, self.out_features, self.bias is not None)
elif isinstance(self.activation, vector):
ret = 'in_features={}, out_features={}, bias={}, activation={}\n'.format(self.in_features, self.out_features, self.bias is not None, self.activation.map(lambda x: touch(lambda: x.__name__, str(x))))
ret += "{}".format(self.in_features)
for d, a in zip(self.dims[1:], self.activation):
ret += '->{}->{}'.format(d, touch(lambda: a.__name__, str(a)))
return ret
else:
ret = 'in_features={}, out_features={}, bias={}, activation={}'.format(self.in_features, self.out_features, self.bias is not None, touch(lambda: self.activation.__name__, str(self.activation)))
return ret
def name(self):
if touch(lambda: self._name):
return self._name
return self.__class__.__name__
def raw_files(self):
if touch(lambda: self._raw_files, None):
return self._raw_files
if self.raw_folder.isdir():
self._raw_files = self.raw_folder.ls()
return self._raw_files