torch.utils.data.Dataset torch.utils.data.DataLoader 1. 2. Dataset类的使用: 所有的类都应该是此类的子类(也就是说应该继承该类)。 所有的子类都要重写(override) len(), getitem() 这两个方法。 使用到的python package python package 目的 numpy 矩阵操作,对图像进行转置 skimage 图像处理,图像I/O,图...
open(file_path, 'rb') as f: data = np.frombuffer(f.read(), np.uint8, offset=16) data = data.reshape(-1, img_size) print("Done") return data def _convert_numpy(): dataset = {} dataset['train_img'] = _load_img(key_file['train_img']) dataset['train_label'] = _load_lab...
dataset/mnist/MNIST\raw\train-labels-idx1-ubyte.gz 113.5% Extracting ./dataset/mnist/MNIST\raw\train-labels-idx1-ubyte.gz to ./dataset/mnist/MNIST\raw Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./dataset/mnist/MNIST\raw\t10k-images-idx3-ubyte.gz ...
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True) #测试集加载器 test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11.
batch_size=100mnist=datasets.MNIST('./data/MNIST',download=True,train=True,transform=transform)mnist_loader=DataLoader(dataset=mnist,batch_size=batch_size,shuffle=True)#CPUdefimshow(img,title):img=utils.make_grid(img.cpu().detach())img=(img+1)/2npimg=img.detach().numpy()plt.imshow(np.tr...
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)class Net(torch.nn.Module): # design model using classdef __init__(self):super(Net, self).__init__()self.l1 = torch.nn.Linear(784, 512)self.l2 = torch.nn.Linear(512, 256)self.l3 = torch.nn.Linear(256...
不同数据集就有不同的标准化系数,例如([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])就是 ImageNet dataset 的标准化系数(RGB三个通道对应三组系数),当需要将 Imagenet 预训练的参数迁移到另一神经网络时,被迁移的神经网络就需要使用 Imagenet的系数,否则预训练不仅无法起到应有的作用甚至还会帮倒忙。
torch.utils.data.Dataset torch.utils.data.DataLoader Dataset类的使用: 所有的类都应该是此类的子类(也就是说应该继承该类)。 所有的子类都要重写(override) len(), getitem() 这两个方法。 使用到的python package 导入相关的包 import numpy as np from skimage import io from skimage import transform im...
print('Epoch [{}/{}], Batch [{}/{}] : Total-loss = {:.4f}, BCE-Loss = {:.4f}, KLD-loss = {:.4f}' .format(epoch + 1, args.epochs, batch_index + 1, len(mnist_train.dataset) // args.batch_size, loss.item() / args.batch_size, BCE.item() / args.batch_size, KLD....
root（str）：数据集根文件夹的路径by_class_name（bool）：存储数据集的方式{"input": {"H": 512,"W": 512,"thickness": 0.01 },"output": {"H": 28,"W": 28 },"process": {"volume": 10,"selection": "ROTATE","display_output": true },"storage": {"root": "dataset","...