获取tensor中一共包含多少个元素
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import torchx = torch.randn(3,3)print("number elements of x is ",x.numel())y = torch.randn(3,10,5)print("number elements of y is ",y.numel()) |
输出：
number elements of x is 9
number elements of y is 150
27和150分别位x和y中各有多少个元素或变量
补充：pytorch获取张量元素个数numel()的用法
numel就是"number of elements"的简写。
numel()可以直接返回int类型的元素个数
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import torch a = torch.randn(1, 2, 3, 4)b = a.numel()print(type(b)) # intprint(b) # 24 |
通过numel()函数，我们可以迅速查看一个张量到底又多少元素。
补充：pytorch 卷积结构和numel()函数
看代码吧~
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from torch import nn class CNN(nn.Module): def __init__(self, num_channels=1, d=56, s=12, m=4): super(CNN, self).__init__() self.first_part = nn.Sequential( nn.Conv2d(num_channels, d, kernel_size=3, padding=5//2), nn.Conv2d(num_channels, d, kernel_size=(1,3), padding=5//2), nn.Conv2d(num_channels, d, kernel_size=(3,1), padding=5//2), nn.PReLU(d) ) def forward(self, x): x = self.first_part(x) return x model = CNN()for m in model.first_part: if isinstance(m, nn.Conv2d): # print('m:',m.weight.data) print('m:',m.weight.data[0]) print('m:',m.weight.data[0][0]) print('m:',m.weight.data.numel()) #numel() 计算矩阵中元素的个数 结果：m: tensor([[[-0.2822, 0.0128, -0.0244], [-0.2329, 0.1037, 0.2262], [ 0.2845, -0.3094, 0.1443]]]) #卷积核大小为3x3m: tensor([[-0.2822, 0.0128, -0.0244], [-0.2329, 0.1037, 0.2262], [ 0.2845, -0.3094, 0.1443]]) #卷积核大小为3x3m: 504 # = 56 x (3 x 3) 输出通道数为56，卷积核大小为3x3m: tensor([-0.0335, 0.2945, 0.2512, 0.2770, 0.2071, 0.1133, -0.1883, 0.2738, 0.0805, 0.1339, -0.3000, -0.1911, -0.1760, 0.2855, -0.0234, -0.0843, 0.1815, 0.2357, 0.2758, 0.2689, -0.2477, -0.2528, -0.1447, -0.0903, 0.1870, 0.0945, -0.2786, -0.0419, 0.1577, -0.3100, -0.1335, -0.3162, -0.1570, 0.3080, 0.0951, 0.1953, 0.1814, -0.1936, 0.1466, -0.2911, -0.1286, 0.3024, 0.1143, -0.0726, -0.2694, -0.3230, 0.2031, -0.2963, 0.2965, 0.2525, -0.2674, 0.0564, -0.3277, 0.2185, -0.0476, 0.0558]) bias偏置的值m: tensor([[[ 0.5747, -0.3421, 0.2847]]]) 卷积核大小为1x3m: tensor([[ 0.5747, -0.3421, 0.2847]]) 卷积核大小为1x3m: 168 # = 56 x (1 x 3) 输出通道数为56，卷积核大小为1x3m: tensor([ 0.5328, -0.5711, -0.1945, 0.2844, 0.2012, -0.0084, 0.4834, -0.2020, -0.0941, 0.4683, -0.2386, 0.2781, -0.1812, -0.2990, -0.4652, 0.1228, -0.0627, 0.3112, -0.2700, 0.0825, 0.4345, -0.0373, -0.3220, -0.5038, -0.3166, -0.3823, 0.3947, -0.3232, 0.1028, 0.2378, 0.4589, 0.1675, -0.3112, -0.0905, -0.0705, 0.2763, 0.5433, 0.2768, -0.3804, 0.4855, -0.4880, -0.4555, 0.4143, 0.5474, 0.3305, -0.0381, 0.2483, 0.5133, -0.3978, 0.0407, 0.2351, 0.1910, -0.5385, 0.1340, 0.1811, -0.3008]) bias偏置的值m: tensor([[[0.0184], [0.0981], [0.1894]]]) 卷积核大小为3x1m: tensor([[0.0184], [0.0981], [0.1894]]) 卷积核大小为3x1m: 168 # = 56 x (3 x 1) 输出通道数为56，卷积核大小为3x1m: tensor([-0.2951, -0.4475, 0.1301, 0.4747, -0.0512, 0.2190, 0.3533, -0.1158, 0.2237, -0.1407, -0.4756, 0.1637, -0.4555, -0.2157, 0.0577, -0.3366, -0.3252, 0.2807, 0.1660, 0.2949, -0.2886, -0.5216, 0.1665, 0.2193, 0.2038, -0.1357, 0.2626, 0.2036, 0.3255, 0.2756, 0.1283, -0.4909, 0.5737, -0.4322, -0.4930, -0.0846, 0.2158, 0.5565, 0.3751, -0.3775, -0.5096, -0.4520, 0.2246, -0.5367, 0.5531, 0.3372, -0.5593, -0.2780, -0.5453, -0.2863, 0.5712, -0.2882, 0.4788, 0.3222, -0.4846, 0.2170]) bias偏置的值 '''初始化后'''class CNN(nn.Module): def __init__(self, num_channels=1, d=56, s=12, m=4): super(CNN, self).__init__() self.first_part = nn.Sequential( nn.Conv2d(num_channels, d, kernel_size=3, padding=5//2), nn.Conv2d(num_channels, d, kernel_size=(1,3), padding=5//2), nn.Conv2d(num_channels, d, kernel_size=(3,1), padding=5//2), nn.PReLU(d) ) self._initialize_weights() def _initialize_weights(self): for m in self.first_part: if isinstance(m, nn.Conv2d): nn.init.normal_(m.weight.data, mean=0.0, std=math.sqrt(2/(m.out_channels*m.weight.data[0][0].numel()))) nn.init.zeros_(m.bias.data) def forward(self, x): x = self.first_part(x) return x model = CNN()for m in model.first_part: if isinstance(m, nn.Conv2d): # print('m:',m.weight.data) print('m:',m.weight.data[0]) print('m:',m.weight.data[0][0]) print('m:',m.weight.data.numel()) #numel() 计算矩阵中元素的个数 结果：m: tensor([[[-0.0284, -0.0585, 0.0271], [ 0.0125, 0.0554, 0.0511], [-0.0106, 0.0574, -0.0053]]])m: tensor([[-0.0284, -0.0585, 0.0271], [ 0.0125, 0.0554, 0.0511], [-0.0106, 0.0574, -0.0053]])m: 504m: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])m: tensor([[[ 0.0059, 0.0465, -0.0725]]])m: tensor([[ 0.0059, 0.0465, -0.0725]])m: 168m: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])m: tensor([[[ 0.0599], [-0.1330], [ 0.2456]]])m: tensor([[ 0.0599], [-0.1330], [ 0.2456]])m: 168m: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]) |
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原文链接：https://blog.csdn.net/schmiloo/article/details/107020922
