# 次元の計算について, 公式サイト通りとする.
    # ex. 
    # kernel_size = (2, 3) ならば, kernel_size[0] = 2, kernel_size[1] = 3
    # kernel_size = 5      ならば, kernel_size[0] = 5, kernel_size[1] = 5
    # padding = (3, 5)     ならば, padding[0] = 3, padding[1] = 5
    # padding = 2          ならば, padding[0] = 2, padding[1] = 2
    # stride = (4, 5)      ならば, stride[0] = 4, stride[1] = 5
    # stride = 3           ならば, stride[0] = 3, stride[1] = 3
    # dilation は, ここでは, 初期値 (1, 1) を 使用するため, dilation[0] = 1, dilation[1] = 1
    # output_padding は, ここでは, 初期値 0 を 使用するため, output_padding[0] = 0, output_padding[1] = 0
    
    # Conv2d
    # Input:  (N, Cin, Hin, Win)
    # Output: (N, Cout, Hout, Wout)
    # Hout = (Hin + 2 × padding[0] − dilation[0] × (kernel_size[0] − 1) − 1) / stride[0] + 1
    # Wout = (Win + 2 × padding[1] − dilation[1] × (kernel_size[1] − 1) − 1) / stride[1] + 1
 
    # ConvTranspose2d
    # Input:  (N, Cin, Hin, Win)
    # Output: (N, Cout, Hout, Wout)
    # Hout = (Hin − 1) × stride[0] − 2 × padding[0] + kernel_size[0] + output_padding[0]
    # Wout = (Win − 1) × stride[1] − 2 × padding[1] + kernel_size[1] + output_padding[1]
    
    # 1. Conv2d
    # Input:  (1, 3, 128, 128)
    # Hout =  (128 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 64
    # Wout =  (128 + 2 × 1 − 1 × (4 − 1) − 1) / 2 + 1 = 64
    # Output: (1, 256, 64, 64)

参考

https://start0x00url.net/2018/10/30/pytorch-%E3%81%AE%E5%8B%95%E4%BD%9C%E7%A2%BA%E8%AA%8D%E3%82%92%E3%81%97%E3%81%A6%E3%81%BF%E3%81%9F%EF%BC%88%EF%BC%91%EF%BC%91%EF%BC%89/