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add formula and result

This commit is contained in:
caozhou 2020-10-29 16:53:00 +08:00
parent fad40466c6
commit 131b3e3933
1 changed files with 28 additions and 4 deletions
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mindspore/nn/layer/conv.py
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@ -481,6 +481,19 @@ class Conv2dTranspose(_Conv):
Input is typically of shape :math:`(N, C, H, W)`, where :math:`N` is batch size and :math:`C` is channel number. Input is typically of shape :math:`(N, C, H, W)`, where :math:`N` is batch size and :math:`C` is channel number.
If the 'pad_mode' is set to be "pad", the height and width of output are defined as:
.. math::
H_{out} = (H_{in} - 1) \times \text{stride} - 2 \times \text{padding} + \text{dilation} \times
(\text{ks_h} - 1) + 1
W_{out} = (W_{in} - 1) \times \text{stride} - 2 \times \text{padding} + \text{dilation} \times
(\text{ks_w} - 1) + 1
where :math:`\text{ks_h}` is the height of the convolution kernel and :math:`\text{ks_w}` is the width
of the convolution kernel.
Args: Args:
in_channels (int): The number of channels in the input space. in_channels (int): The number of channels in the input space.
out_channels (int): The number of channels in the output space. out_channels (int): The number of channels in the output space.
@ -529,9 +542,10 @@ class Conv2dTranspose(_Conv):
Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`. Tensor of shape :math:`(N, C_{out}, H_{out}, W_{out})`.
Examples: Examples:
>>> net = nn.Conv2dTranspose(3, 64, 4, has_bias=False, weight_init='normal') >>> net = nn.Conv2dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad')
>>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32) >>> input = Tensor(np.ones([1, 3, 16, 50]), mindspore.float32)
>>> net(input) >>> net(input).shape
(1, 64, 19, 53)
""" """
def __init__(self, def __init__(self,
@ -654,6 +668,15 @@ class Conv1dTranspose(_Conv):
Input is typically of shape :math:`(N, C, W)`, where :math:`N` is batch size and :math:`C` is channel number. Input is typically of shape :math:`(N, C, W)`, where :math:`N` is batch size and :math:`C` is channel number.
If the 'pad_mode' is set to be "pad", the width of output is defined as:
.. math::
W_{out} = (W_{in} - 1) \times \text{stride} - 2 \times \text{padding} + \text{dilation} \times
(\text{ks_w} - 1) + 1
where :math:`\text{ks_w}` is the width of the convolution kernel.
Args: Args:
in_channels (int): The number of channels in the input space. in_channels (int): The number of channels in the input space.
out_channels (int): The number of channels in the output space. out_channels (int): The number of channels in the output space.
@ -694,9 +717,10 @@ class Conv1dTranspose(_Conv):
Tensor of shape :math:`(N, C_{out}, W_{out})`. Tensor of shape :math:`(N, C_{out}, W_{out})`.
Examples: Examples:
>>> net = nn.Conv1dTranspose(3, 64, 4, has_bias=False, weight_init='normal') >>> net = nn.Conv1dTranspose(3, 64, 4, has_bias=False, weight_init='normal', pad_mode='pad')
>>> input = Tensor(np.ones([1, 3, 50]), mindspore.float32) >>> input = Tensor(np.ones([1, 3, 50]), mindspore.float32)
>>> net(input) >>> net(input).shape
(1, 64, 53)
""" """
def __init__(self, def __init__(self,