pytorch中的卷積和池化計算方式詳解
TensorFlow裡面的padding只有兩個選項也就是valid和same
pytorch裡面的padding麼有這兩個選項，它是數字0,1,2,3等等，預設是0
所以輸出的h和w的計算方式也是稍微有一點點不同的：tf中的輸出大小是和原來的大小成倍數關係，不能任意的輸出大小；而nn輸出大小可以通過padding進行改變
nn裡面的卷積操作或者是池化操作的H和W部分都是一樣的計算公式：H和W的計算
class torch.nn.MaxPool2d(kernel_size,stride=None,padding=0,dilation=1,return_indices=False,ceil_mode=False): """ Parameters: kernel_size – the size of the window to take a max over stride – the stride of the window. 預設值是kernel_size padding – implicit zero padding to be added on both side,預設值是0 dilation – a parameter that controls the stride of elements in the window，預設值是1 return_indices – if True,will return the max indices along with the outputs. Useful when Unpooling later ceil_mode – when True,will use ceil instead of floor to compute the output shape，向上取整和向下取整，預設是向下取整 """
不一樣的地方在於：第一點，步長stride預設值，上面預設和設定的kernel_size一樣，下面預設是1；第二點，輸出通道的不一樣，上面的輸出通道和輸入通道是一樣的也就是沒有改變特徵圖的數目，下面改變特徵圖的數目為out_channels
class torch.nn.Conv2d(in_channels,out_channels,kernel_size,stride=1,groups=1,bias=True):
pass
"""
Parameters:
in_channels (int) – Number of channels in the input image
out_channels (int) – Number of channels produced by the convolution
kernel_size (int or tuple) – Size of the convolving kernel
stride (int or tuple,optional) – Stride of the convolution. Default: 1,預設是1
padding (int or tuple,optional) – Zero-padding added to both sides of the input. Default: 0
dilation (int or tuple,optional) – Spacing between kernel elements. Default: 1
groups (int,optional) – Number of blocked connections from input channels to output channels. Default: 1
bias (bool,optional) – If True,adds a learnable bias to the output. Default: True
"""第三點不一樣是卷積有一個引數groups,將特徵圖分開給不同的卷積進行操作然後再整合到一起，xception就是利用這一個。
""" At groups=1,all inputs are convolved to all outputs. At groups=2,the operation becomes equivalent to having two conv layers side by side,each seeing half the input channels,and producing half the output channels,and both subsequently concatenated. At groups= in_channels,each input channel is convolved with its own set of filters (of size ⌊out_channelsin_channels⌋ ). """
pytorch AvgPool2d函式
class torch.nn.AvgPool2d(kernel_size,ceil_mode=False,count_include_pad=True): pass """ kernel_size: the size of the window stride: the stride of the window. Default value is :attr:`kernel_size` padding: implicit zero padding to be added on both sides ceil_mode: when True,will use `ceil` instead of `floor` to compute the output shape count_include_pad: when True,will include the zero-padding in the averaging calculation """
shape的計算公式，在（h,w)位置處的輸出值的計算。
pytorch中的F.avg_pool1d（）平均池化操作作用於一維，input 的維度是三維比如［２,２,７］。F.avg_pool1d（）中核ｓｉｚｅ是３，步長是２表示每三個數取平均，每隔兩個數取一次．比如[1,3,4,5,6,7]安照3個數取均值，兩步取一次，那麼結果就是[ 2.3333,6 ]，也就是核是一維的，也只作用於一個維度。按照池化操作計算公式input size為[2,7],kernel size為3，步長為2，則輸出維度計算（7-3）/2+1=3所以輸出維度是[2,3]，這與輸出結果是一致的。
pytorch中的F.avg_pool2d（），input 是維度是４維如［２，２，４，４］，表示這裡批量數是２也就是兩張影象，這裡通道數量是２，影象是size 是４＊４的．核size是（２，２），步長是（２，２）表示被核覆蓋的數取平均，橫向縱向的步長都是２．那麼核是二維的，所以取均值時也是覆蓋二維取的。輸出中第一個1.5的計算是：(1+2+1+2)/4=1.5.表示第一張影象左上角的四個畫素點的均值。按照池化操作計算公式input size為[2,4],kernel size為2*2，步長為2，則輸出維度計算（4-2）/2+1=2所以輸出維度是[2,2]，這與輸出結果是一致的。
Conv3d函式
class torch.nn.Conv3d(in_channels,bias=True):
pass
"""
in_channels (int): Number of channels in the input image
out_channels (int): Number of channels produced by the convolution
kernel_size (int or tuple): Size of the convolving kernel
stride (int or tuple,optional): Stride of the convolution. Default: 1
padding (int or tuple,optional): Zero-padding added to all three sides of the input. Default: 0
dilation (int or tuple,optional): Spacing between kernel elements. Default: 1
groups (int,optional): Number of blocked connections from input channels to output channels. Default: 1
bias (bool,optional): If ``True``,adds a learnable bias to the output. Default: ``True``
Shape:
- Input: :math:`(N,C_{in},D_{in},H_{in},W_{in})`
- Output: :math:`(N,C_{out},D_{out},H_{out},W_{out})`
"""
C_out = out_channels
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