10 2021 档案
摘要：from google.colab import drive drive.mount("/content/drive") import os path="/content/drive/My Drive/untitled" os.chdir(path) os.listdir(path) !pwd 1
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摘要：import torch//引入torch包 print(torch.cuda.is_available())//确定gpu是否可用 from google.colab import drive drive.mount("/content/drive") import os path="/conte
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摘要：import cv2 w=cv2.imread('000000581781.jpg') print(type(w)) print(w.shape) print(w) <class 'numpy.ndarray'>(478, 640, 3)
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摘要：学习人体姿态估计前，要对卷积网络有一定的了解。卷积网络就是提取图像特征的，比如卷积网络就可能提取胳膊等。 keyboard heatmaps是每个关键点的置信度。当卷积网路提取完特征后每个keyboard heatmaps就是各个特征的置信度。
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摘要：Both lists are related to grouping keypoints into person instances. Network predicts two tensors: the first with keypoint heatmaps, to localize all po
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摘要：package zxd0001;import java.util.Scanner;abstract class Figure { //图形抽象类 static public double sum1=0;static public double sum2=0;int a,b;char w;abstra
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摘要：package zxd0001;import java.util.Scanner;abstract class Animal{public static int count=0;Animal(){System.out.printf("Animal\n");count=count+1;}public
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摘要：人体姿态估计是预测出人体各个关键点的位置坐标，然后根据先验知识确定关键点位置关系，构建骨架。Top-down 由高到低先把人选出来再识别骨架。Down-top 由低到高先把特征选取出来然后构成人。CPM在分析时不断考虑整体。
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摘要：w=[] for i in range(14): w1=[] for j in range(14): w1.append(j) w.append(w1) w=np.array(w) w=np.reshape(w,(1,1,14,14)) t=torch.Tensor(w) w1=torchvisio
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摘要：import time import torch from torch import nn,optim import numpy as np import torch.nn.functional as F from torch.optim import lr_scheduler import tor
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摘要：import time import torch from torch import nn,optim import numpy as np import torch.nn.functional as F from torch.optim import lr_scheduler def unpick
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摘要：for i in range(10): lr1=lr scheduler.step() lr2=scheduler.get_last_lr() print(lr1,lr2) lr是最开始的学习率。 输出: 0.0001 [0.0001]0.0001 [0.0001]0.0001 [1e-05]0.0
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摘要：https://blog.csdn.net/nan355655600/article/details/106245563/
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摘要：import time from torch import nn,optim import torch from torch._C import dtype import torchvision import pickle as p import torchvision.transforms as
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摘要：连接是笛卡尔积的选择 （3）M:N当转换为关系模型时，需要将联系转换为实体，然后在该实体上加上另外两个实体的主键，作为联系实体的主键，然后再加上该联系自身带的属性即可。
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摘要：print(y_hat.argmax(dim=1)) print(y_hat.argmax(dim=1) ==lable) print((y_hat.argmax(dim=1) ==lable).sum()) print((y_hat.argmax(dim=1) ==lable).sum().cpu
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摘要：l=loss(y_hat,lable) 损失函数中输出和标签的位置是否写反。
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摘要：数据集下载官网:http://www.cs.toronto.edu/~kriz/cifar.html def unpickle(file): import pickle with open(file, 'rb') as fo: dict = pickle.load(fo, encoding='byt
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摘要：w=np.array([[1,2,3],[4,5,6]]) print(np.max(w,axis=1)) 输出每行的最大值。 w=np.array([[1,2,3],[4,5,6]]) w1=np.array([[1,2,3],[4,5,6]]) print(np.concatenate((w,w
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