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add reduce arg to PoissonNLLLoss #3770

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Merged
soumith merged 7 commits into from
Dec 18, 2017
Merged

add reduce arg to PoissonNLLLoss #3770

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da6fc46
add reduce arg to PoissonNLLLoss
kevinzakka Nov 18, 2017
c24ec45
fixed comments except reference function
kevinzakka Nov 21, 2017
a9a5645
fixed unit test
kevinzakka Nov 21, 2017
ed2137f
small indentation fix
kevinzakka Nov 21, 2017
89e2d6d
fixing last comments by richard
kevinzakka Nov 21, 2017
9c9bafd
lint check
kevinzakka Nov 21, 2017
6ff159e
another linting issue
kevinzakka Nov 21, 2017
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15 changes: 13 additions & 2 deletions test/test_nn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3728,18 +3728,28 @@ def forward(self, *args):
module_name='PoissonNLLLoss',
input_size=(2, 3, 4, 5),
target_fn=lambda: torch.randn(2, 3, 4, 5).floor_().abs_(),
desc='reduced_loss',
desc='no_full_loss', # without sterling approx
),
dict(
module_name='PoissonNLLLoss',
constructor_args=(False, True, True),
input_fn=lambda: torch.randn(2, 3, 4, 5).abs_().add_(0.001),
target_fn=lambda: torch.randn(2, 3, 4, 5).floor_().abs_(),
desc='full_loss',
desc='full_loss', # with sterling approx
),
]


def poissonnllloss_no_reduce_test():
t = Variable(torch.randn(10, 10))
return dict(
fullname='PoissonNLLLLoss_no_reduce',
constructor=wrap_functional(
lambda i: F.poisson_nll_loss(i, t.type_as(i), reduce=False)),
input_fn=lambda: torch.rand(10, 10),
pickle=False)


def kldivloss_no_reduce_test():
t = Variable(torch.randn(10, 10))
return dict(
Expand Down Expand Up @@ -3910,6 +3920,7 @@ def smoothl1loss_no_reduce_test():


new_module_tests = [
poissonnllloss_no_reduce_test(),
kldivloss_no_reduce_test(),
l1loss_no_reduce_test(),
mseloss_no_reduce_test(),
Expand Down
11 changes: 8 additions & 3 deletions torch/nn/functional.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1103,7 +1103,7 @@ def nll_loss(input, target, weight=None, size_average=True, ignore_index=-100, r
raise ValueError('Expected 2 or 4 dimensions (got {})'.format(dim))


def poisson_nll_loss(input, target, log_input=True, full=False, size_average=True, eps=1e-8):
def poisson_nll_loss(input, target, log_input=True, full=False, size_average=True, eps=1e-8, reduce=True):
r"""Poisson negative log likelihood loss.

See :class:`~torch.nn.PoissonNLLLoss` for details.
Expand All @@ -1122,6 +1122,10 @@ def poisson_nll_loss(input, target, log_input=True, full=False, size_average=Tru
the losses are instead summed for each minibatch. Default: ``True``
eps (float, optional): Small value to avoid evaluation of log(0) when
log_input=False. Default: 1e-8
reduce (bool, optional): By default, the losses are averaged
over observations for each minibatch, or summed, depending on
size_average. When reduce is ``False``, returns a loss per batch
element instead and ignores size_average. Default: ``True``
"""
if log_input:
loss = torch.exp(input) - target * input
Expand All @@ -1130,10 +1134,11 @@ def poisson_nll_loss(input, target, log_input=True, full=False, size_average=Tru
if full:
mask = target > 1
loss[mask] += (target * torch.log(target) - target + 0.5 * torch.log(2 * math.pi * target))[mask]
if not reduce:
return loss
if size_average:
return torch.mean(loss)
else:
return torch.sum(loss)
return torch.sum(loss)


kl_div = _add_docstr(torch._C._nn.kl_div, r"""
Expand Down
13 changes: 9 additions & 4 deletions torch/nn/modules/loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -208,6 +208,10 @@ class PoissonNLLLoss(_Loss):
is set to ``False``, the losses are instead summed for each minibatch.
eps (float, optional): Small value to avoid evaluation of log(0) when
log_input==``False``. Default: 1e-8
reduce (bool, optional): By default, the losses are averaged
over observations for each minibatch, or summed, depending on
size_average. When reduce is ``False``, returns a loss per batch
element instead and ignores size_average. Default: ``True``

Examples::

Expand All @@ -217,16 +221,17 @@ class PoissonNLLLoss(_Loss):
>>> output = loss(log_input, target)
>>> output.backward()
"""
def __init__(self, log_input=True, full=False, size_average=True, eps=1e-8):
super(PoissonNLLLoss, self).__init__()
def __init__(self, log_input=True, full=False, size_average=True, eps=1e-8, reduce=True):
super(PoissonNLLLoss, self).__init__(size_average)
self.log_input = log_input
self.full = full
self.size_average = size_average
self.eps = eps
self.reduce = reduce

def forward(self, log_input, target):
_assert_no_grad(target)
return F.poisson_nll_loss(log_input, target, self.log_input, self.full, self.size_average, self.eps)
return F.poisson_nll_loss(log_input, target, self.log_input, self.full,
self.size_average, self.eps, self.reduce)


class KLDivLoss(_Loss):
Expand Down