torch笔记合集

Torch笔记

import torch
import numpy as np
import torch.nn as nn

a_np = np.random.rand(10,100)

numpy知识回顾

a_np.dtype  # 数据类型
a_np.ndim #维度个数
a_np.shape # 形状   整数元祖
a_np.dtype=np.int32  # 修改数据类型

获取tensor基本信息

tensor_a = torch.from_numpy(a_np)
tensor_a.type() # 获取tensor类型
tensor_a.size() # 获取维度特征 并返回数组
tensor_a.size(0) #获取第一个维度信息
tensor_a.dim() # 获取维度个数
tensor_a.device # 返回设备类型

tensor数据类型转换

tensor_b = tensor_a.float()  
tensor_b = tensor_a.to(torch.float) 

设备类型转换

tensor_b = tensor_b.cuda()
tensor_b = tensor_b.cpu()
tensor_b = tensor_a.to(torch.device(0)) 

tensor 转换到 numpy数据

tensor_b = tensor_b.numpy()
# 注意*gpu上的tensor不能直接转为numpy
ndarray = tensor_b.cpu().numpy()
# numpy 转 torch.Tensor
tensor = torch.from_numpy(ndarray) 

tensor的维度变换

• squeeze_

tensor_a.t_().size() # 对tensor进行转置
tensor_a.t_().size()
tensor_a.unsqueeze_(2) #
unsqu_tensor = tensor_a.squeeze_() #  将tensor中维度元素数为1 的全部压缩掉

• reshape()

tensor = torch.reshape(tensor_a, [50,20])
tensor.size()

从只包含一个元素的张量中提取值

tensor_a = tensor_a.cuda()
tensor_a[1][1].item()
tensor_a.device

拼接张量

• 例如当参数是3个10×5的张量，torch.cat的结果是30×5的张量，而torch.stack的结果是3×10×5的张量。

temp1 = torch.from_numpy(np.random.rand(5,4))
temp2 = torch.from_numpy(np.random.rand(5,4))

temp1.size()
temp2.size()
temp3 = torch.stack([temp1,temp2],dim=0)
temp4 = torch.cat([temp1,temp2],dim=0)

temp3.size()
temp4.size()

矩阵乘法

# Matrix multiplication: (m*n) * (n*p) -> (m*p).
result = torch.mm(tensor1, tensor2)
# Batch matrix multiplication: (b*m*n) * (b*n*p) -> (b*m*p).
result = torch.bmm(tensor1, tensor2)

打印模型信息

• 参数量
• 模型结构 使用torch summary

class myNet(nn.Module):
    def __init__(self,*other_para):
        super(myNet,self).__init__()
        self.embedding_layer = nn.Embedding(10,3)

net = myNet()
num_parameters = sum(torch.numel(parameter) for parameter in net.parameters())
from torchsummary import summary
summary(net,input_size=(2,2))

模型初始化

# Common practise for initialization.
for layer in model.modules():
    if isinstance(layer, torch.nn.Conv2d):
        torch.nn.init.kaiming_normal_(layer.weight, mode='fan_out',
                                      nonlinearity='relu')
        if layer.bias is not None:
            torch.nn.init.constant_(layer.bias, val=0.0)
    elif isinstance(layer, torch.nn.BatchNorm2d):
        torch.nn.init.constant_(layer.weight, val=1.0)
        torch.nn.init.constant_(layer.bias, val=0.0)
    elif isinstance(layer, torch.nn.Linear):
        torch.nn.init.xavier_normal_(layer.weight)
        if layer.bias is not None:
            torch.nn.init.constant_(layer.bias, val=0.0)

# Initialization with given tensor.
layer.weight = torch.nn.Parameter(tensor)

计算Softmax输出的准确率

score = model(images)
prediction = torch.argmax(score, dim=1)
num_correct = torch.sum(prediction == labels).item()
accuruacy = num_correct / labels.size(0)

保存模型

• torch.save(my_model.state_dict(), "params.pkl")

加载模型

• 先初始化model网络结构
• model.load_state_dict(torch.load("params.pkl"))

一些奇怪的注意事项

• torch.nn.CrossEntropyLoss的输入不需要经过Softmax。torch.nn.CrossEntropyLoss等价于torch.nn.functional.log_softmax + torch.nn.NLLLoss。
• torch的y不用转成onehot 它会自动帮你转 这点比较怪异，相比之下keras在多分类时 y必须是onehot的才能计算损失
• model.train() ：启用 BatchNormalization 和 Dropout
• model.eval() ：不启用 BatchNormalization 和 Dropout

tricks

• 用del及时删除不用的中间变量，节约GPU存储。
• 使用inplace操作可节约GPU存储，如