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change doc for Adaptive Pooling #3746

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apaszke merged 1 commit into from
Nov 24, 2017
+28 −3
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change doc for Adaptive Pooling #3746

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31 changes: 28 additions & 3 deletions torch/nn/modules/pooling.py
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Original file line number Diff line number Diff line change
Expand Up @@ -871,7 +871,9 @@ class AdaptiveMaxPool2d(Module):

Args:
output_size: the target output size of the image of the form H x W.
Can be a tuple (H, W) or a single number H for a square image H x H
Can be a tuple (H, W) or a single H for a square image H x H.
H and W can be either a ``int``, or ``None`` which means the size will
be the same as that of the input.
return_indices: if ``True``, will return the indices along with the outputs.
Useful to pass to nn.MaxUnpool2d. Default: ``False``

Expand All @@ -884,6 +886,10 @@ class AdaptiveMaxPool2d(Module):
>>> m = nn.AdaptiveMaxPool2d(7)
>>> input = autograd.Variable(torch.randn(1, 64, 10, 9))
>>> output = m(input)
>>> # target output size of 10x7
>>> m = nn.AdaptiveMaxPool2d((None, 7))
>>> input = autograd.Variable(torch.randn(1, 64, 10, 9))
>>> output = m(input)

"""

Expand All @@ -908,7 +914,10 @@ class AdaptiveMaxPool3d(Module):

Args:
output_size: the target output size of the image of the form D x H x W.
Can be a tuple (D, H, W) or a single number D for a cube D x D x D
Can be a tuple (D, H, W) or a single D for a cube D x D x D.
D, H and W can be either a ``int``, or ``None`` which means the size will
be the same as that of the input.

return_indices: if ``True``, will return the indices along with the outputs.
Useful to pass to nn.MaxUnpool3d. Default: ``False``

Expand All @@ -921,6 +930,10 @@ class AdaptiveMaxPool3d(Module):
>>> m = nn.AdaptiveMaxPool3d(7)
>>> input = autograd.Variable(torch.randn(1, 64, 10, 9, 8))
>>> output = m(input)
>>> # target output size of 7x9x8
>>> m = nn.AdaptiveMaxPool3d((7, None, None))
>>> input = autograd.Variable(torch.randn(1, 64, 10, 9, 8))
>>> output = m(input)

"""

Expand Down Expand Up @@ -974,7 +987,9 @@ class AdaptiveAvgPool2d(Module):

Args:
output_size: the target output size of the image of the form H x W.
Can be a tuple (H, W) or a single number H for a square image H x H
Can be a tuple (H, W) or a single H for a square image H x H
H and W can be either a ``int``, or ``None`` which means the size will
be the same as that of the input.

Examples:
>>> # target output size of 5x7
Expand All @@ -985,6 +1000,10 @@ class AdaptiveAvgPool2d(Module):
>>> m = nn.AdaptiveAvgPool2d(7)
>>> input = autograd.Variable(torch.randn(1, 64, 10, 9))
>>> output = m(input)
>>> # target output size of 10x7
>>> m = nn.AdaptiveMaxPool2d((None, 7))
>>> input = autograd.Variable(torch.randn(1, 64, 10, 9))
>>> output = m(input)

"""

Expand All @@ -1009,6 +1028,8 @@ class AdaptiveAvgPool3d(Module):
Args:
output_size: the target output size of the form D x H x W.
Can be a tuple (D, H, W) or a single number D for a cube D x D x D
D, H and W can be either a ``int``, or ``None`` which means the size will
be the same as that of the input.

Examples:
>>> # target output size of 5x7x9
Expand All @@ -1019,6 +1040,10 @@ class AdaptiveAvgPool3d(Module):
>>> m = nn.AdaptiveAvgPool3d(7)
>>> input = autograd.Variable(torch.randn(1, 64, 10, 9, 8))
>>> output = m(input)
>>> # target output size of 7x9x8
>>> m = nn.AdaptiveMaxPool3d((7, None, None))
>>> input = autograd.Variable(torch.randn(1, 64, 10, 9, 8))
>>> output = m(input)

"""

Expand Down