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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from contextlib import contextmanager
from torch.nn import Module
from nemo.core.classes.common import FileIO, Serialization, Typing
__all__ = ['NeuralModule']
class NeuralModule(Module, Typing, Serialization, FileIO):
"""
Abstract class offering interface shared between all PyTorch Neural Modules.
"""
@property
def num_weights(self):
"""
Utility property that returns the total number of parameters of NeuralModule.
"""
num: int = 0
for p in self.parameters():
if p.requires_grad:
num += p.numel()
return num
def input_example(self, max_batch=None, max_dim=None):
"""
Override this method if random inputs won't work
Returns:
A tuple sample of valid input data.
"""
return None
def freeze(self) -> None:
r"""
Freeze all params for inference.
"""
for param in self.parameters():
param.requires_grad = False
self.eval()
def unfreeze(self) -> None:
"""
Unfreeze all parameters for training.
"""
for param in self.parameters():
param.requires_grad = True
self.train()
@contextmanager
def as_frozen(self):
"""
Context manager which temporarily freezes a module, yields control and finally unfreezes the module.
"""
training_mode = self.training
grad_map = {}
for pname, param in self.named_parameters():
grad_map[pname] = param.requires_grad
self.freeze()
try:
yield
finally:
self.unfreeze()
for pname, param in self.named_parameters():
param.requires_grad = grad_map[pname]
if training_mode:
self.train()
else:
self.eval()
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