PyTorch 普通卷积和空洞卷积实例_Python

PyTorch 普通卷积和空洞卷积实例

2020-05-05 11:19hiudawn Python

今天小编就为大家分享一篇PyTorch 普通卷积和空洞卷积实例，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧

如下所示：

									import numpy as np

									from torchvision.transforms import Compose, ToTensor

									from torch import nn

									import torch.nn.init as init

									def transform():

									  return Compose([

									    ToTensor(),

									    # Normalize((12,12,12),std = (1,1,1)),

									  ])

									arr = range(1,26)

									arr = np.reshape(arr,[5,5])

									arr = np.expand_dims(arr,2)

									arr = arr.astype(np.float32)

									# arr = arr.repeat(3,2)

									print(arr.shape)

									arr = transform()(arr)

									arr = arr.unsqueeze(0)

									print(arr)

									conv1 = nn.Conv2d(1, 1, 3, stride=1, bias=False, dilation=1) # 普通卷积

									conv2 = nn.Conv2d(1, 1, 3, stride=1, bias=False, dilation=2) # dilation就是空洞率，即间隔

									init.constant_(conv1.weight, 1)

									init.constant_(conv2.weight, 1)

									out1 = conv1(arr)

									out2 = conv2(arr)

									print('standare conv:\n', out1.detach().numpy())

									print('dilated conv:\n', out2.detach().numpy())

输出：

									(5, 5, 1)

									tensor([[[[ 1., 2., 3., 4., 5.],

									[ 6., 7., 8., 9., 10.],

									[11., 12., 13., 14., 15.],

									[16., 17., 18., 19., 20.],

									[21., 22., 23., 24., 25.]]]])

									standare conv:

									[[[[ 63. 72. 81.]

									[108. 117. 126.]

									[153. 162. 171.]]]]

									dilated conv:

									[[[[117.]]]]