torch之多通道与池化

酥酥 发布于 2022-04-17 180 次阅读

多输入通道和多输出通道

多输入通道

				
					import torch
from torch import nn
import sys
sys.path.append("..") 
import d2lzh_pytorch as d2l

print(torch.__version__)
				
			
1.11.0+cu113
				
					def corr2d_multi_in(X, K):
    # 沿着X和K的第0维（通道维）分别计算再相加
    res = d2l.corr2d(X[0, :, :], K[0, :, :])
    for i in range(1, X.shape[0]):
        res += d2l.corr2d(X[i, :, :], K[i, :, :])
    return res
    X = torch.tensor([[[0, 1, 2], [3, 4, 5], [6, 7, 8]],
              [[1, 2, 3], [4, 5, 6], [7, 8, 9]]])
K = torch.tensor([[[0, 1], [2, 3]], [[1, 2], [3, 4]]])

corr2d_multi_in(X, K)
				
			
				
					def corr2d_multi_in_out(X, K):
    # 对K的第0维遍历，每次同输入X做互相关计算。所有结果使用stack函数合并在一起
    return torch.stack([corr2d_multi_in(X, k) for k in K])
K = torch.stack([K, K + 1, K + 2])
K.shape
				
			
torch.Size([3, 2, 2, 2])
				
					corr2d_multi_in_out(X, K)
				
			
				
					def corr2d_multi_in_out_1x1(X, K):
     c_i, h, w = X.shape
     c_o = K.shape[0]
     X = X.view(c_i, h * w)
     K = K.view(c_o, c_i)
     Y = torch.mm(K, X)  # 全连接层的矩阵乘法
     return Y.view(c_o, h, w)
				
			
				
					X = torch.rand(3, 3, 3)
K = torch.rand(2, 3, 1, 1)

Y1 = corr2d_multi_in_out_1x1(X, K)
Y2 = corr2d_multi_in_out(X, K)

(Y1 - Y2).norm().item() < 1e-6
				
			
True

二维最大池化层和平均池化层

				
					def pool2d(X, pool_size, mode='max'):
    X = X.float()
    p_h, p_w = pool_size
    Y = torch.zeros(X.shape[0] - p_h + 1, X.shape[1] - p_w + 1)
    for i in range(Y.shape[0]):
        for j in range(Y.shape[1]):
            if mode == 'max':
                Y[i, j] = X[i: i + p_h, j: j + p_w].max()
            elif mode == 'avg':
                Y[i, j] = X[i: i + p_h, j: j + p_w].mean()       
    return Y
				
			
				
					X = torch.tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
pool2d(X, (2, 2))
				
			
tensor([[4., 5.],
        [7., 8.]])
				
					pool2d(X, (2, 2), 'avg')
				
			
				
					X = torch.arange(16, dtype=torch.float).view((1, 1, 4, 4))
X
				
			
tensor([[[[ 0.,  1.,  2.,  3.],
          [ 4.,  5.,  6.,  7.],
          [ 8.,  9., 10., 11.],
          [12., 13., 14., 15.]]]])
				
					pool2d = nn.MaxPool2d(3)
pool2d(X) 
				
			
tensor([[[[10.]]]])
				
					pool2d = nn.MaxPool2d(3, padding=1, stride=2)
pool2d(X)
				
			
tensor([[[[ 5.,  7.],
          [13., 15.]]]])
				
					pool2d = nn.MaxPool2d((2, 4), padding=(1, 2), stride=(2, 3))
pool2d(X)
				
			
				
					X = torch.cat((X, X + 1), dim=1)
X
				
			
tensor([[[[ 0.,  1.,  2.,  3.],
          [ 4.,  5.,  6.,  7.],
          [ 8.,  9., 10., 11.],
          [12., 13., 14., 15.]],

         [[ 1.,  2.,  3.,  4.],
          [ 5.,  6.,  7.,  8.],
          [ 9., 10., 11., 12.],
          [13., 14., 15., 16.]]]])
				
					pool2d = nn.MaxPool2d(3, padding=1, stride=2)
pool2d(X)
				
			
tensor([[[[ 5.,  7.],
          [13., 15.]],

         [[ 6.,  8.],
          [14., 16.]]]])