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Add Module.requires_grad_ #22576

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dfd9600
Add Module.requires_grad_
ssnl Jul 7, 2019
ae34471
address comment
ssnl Jul 8, 2019
08d29c4
update docstring
ssnl Jul 8, 2019
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13 changes: 13 additions & 0 deletions test/test_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -570,6 +570,19 @@ def __init__(self):

return l, n, s

def test_requires_grad_(self):
m = self._create_basic_net()[-1]
assert len(list(m.buffers())) > 0, 'invalid test'
assert all(not b.requires_grad for b in m.buffers()) > 0, 'invalid test'
assert len(list(m.parameters())) > 0, 'invalid test'
assert all(p.requires_grad for p in m.parameters()) > 0, 'invalid test'
for requires_grad in (False, True):
self.assertIs(m.requires_grad_(requires_grad), m)
for p in m.parameters():
self.assertEqual(p.requires_grad, requires_grad)
for b in m.buffers():
self.assertFalse(b.requires_grad)

def test_module_backcompat(self):
from torch.serialization import SourceChangeWarning
path = download_file('https://download.pytorch.org/test_data/linear.pt')
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30 changes: 30 additions & 0 deletions torch/nn/modules/module.py
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Expand Up @@ -1050,6 +1050,10 @@ def train(self, mode=True):
mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
etc.

Args:
mode (bool): whether to set training mode (``True``) or evaluation
mode (``False``). Default: ``True``.

Returns:
Module: self
"""
Expand All @@ -1065,9 +1069,35 @@ def eval(self):
particular modules for details of their behaviors in training/evaluation
mode, if they are affected, e.g. :class:`Dropout`, :class:`BatchNorm`,
etc.

This is equivalent with :meth:`self.train(False) <torch.nn.Module.train>`.

Returns:
Module: self
"""
return self.train(False)

def requires_grad_(self, requires_grad=True):
r"""Change if autograd should record operations on parameters in this
module.

This method sets the parameters' :attr:`requires_grad` attributes
in-place.

This method is helpful for freezing part of the module for finetuning
or training parts of a model individually (e.g., GAN training).

Args:
requires_grad (bool): whether autograd should record operations on
parameters in this module. Default: ``True``.

Returns:
Module: self
"""
for p in self.parameters():
p.requires_grad_(requires_grad)
return self

def zero_grad(self):
r"""Sets gradients of all model parameters to zero."""
for p in self.parameters():
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