对transforms操作,使数据增强更灵活 transforms.RandomChoice(transforms), 从给定的一系列transforms中选一个进行操作 transforms.RandomApply(transforms, p=0.5),给一个transform加上概率,依概率进行操作 transforms.RandomOrder,将transforms中的操作随机打乱 一、 裁剪——Crop 1.随机裁剪:transforms.RandomCrop class to...
奥义都在代码中,自己体会 :) fromfunctoolsimportpartialimportnumpyasnpimportosimporttorchimporttorch.nnasnnimporttorch.nn.functionalasFimporttorch.optimasoptimfromtorch.utils.dataimportrandom_splitimporttorchvisionimporttorchvision.transformsastransformsfromrayimporttunefromray.tuneimportCLIReporterfromray.tune.scheduler...
transforms.RandomAffine(degrees=0, scale=(0.4, 0.8))(raw_img), "scale=(0.4, 0.8)" ), ( transforms.RandomAffine(degrees=0, shear=60)(raw_img), "shear=60" ), ( transforms.RandomAffine(degrees=0, shear=(30, 80))(raw_img), "shear=(30, 80)" ), ( transforms.RandomAffine(degrees=...
(args.data_path) img_size = 224 data_transform = { "train": transforms.Compose([transforms.RandomResizedCrop(img_size), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]), "val": transforms.Compose([transforms....
请注意,对于验证和测试数据,我们不进行RandomResizedCrop, RandomRotation 和RandomHorizontalFlip转换。我们只需将验证图像和测试图像调整到256×256,并裁剪出中心224×224部分,以便能够使模型正确使用它们。然后将图像转换为张量,并使用ImageNet中所有图像的均值和标准差来进行正则化。 3、数据加载 接下来,让我们看看如何...
torchvision.transforms.RandomHorizontalFlip(), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225))]) val_transform = torchvision.transforms.Compose([ torchvision.transforms.Resize(256), torchvision.transforms.CenterCrop(224), torchvisio...
Random Dense runsin938.743microseconds 使用torch.compile进行 SDPA 随着PyTorch 2.0 的发布,引入了一个名为torch.compile()的新功能,可以在 eager 模式下提供显著的性能改进。缩放点积注意力与torch.compile()完全兼容。为了演示这一点,让我们使用torch.compile()编译CausalSelfAttention模块,并观察结果性能的提升。
Our other network, called the generator, will take random noise as input and transform it using a neural network to produce images. The goal of the generator is to fool the discriminator into thinking the images it produced are real. 在生成网络中,我们建立了两个神经网络。第一个网络是典型的...
In PyTorch 1.6, a regression was introduced that caused the bias flag of nn.MultiheadAttention only to apply to the input projection layer. This caused the output projection layer to always include a bias parameter, even with bias=False specified. The regression is now fixed in PyTorch 1.9, ...
apply(lambda x: ">50K" in x)).astype(int) df.drop("income", axis=1, inplace=True) df_train, df_test = train_test_split(df, test_size=0.2, stratify=df.income_label) # Define the 'column set up' wide_cols = [ "education", "relationship", "workclass", "occupation", "native...