PyTorch教程【六】Transforms的使用

1、Transforms的使用

from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms

python的用法->tensor数据类型

通过transforms.ToTensor去看两个问题

绝对路径：D:\leran_pytorch\dataset\train\ants\0013035.jpg

相对路径：dataset/train/ants/0013035.jpg

img_path = "dataset/train/ants/0013035.jpg"
img = Image.open(img_path)

writer = SummaryWriter("logs")

1、transforms该如何使用（python）

2、为什么我们需要Tensor数据类型

tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)

writer.add_image("Tensor_img", tensor_img)

writer.close()

2、常见的Transforms

from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms

writer = SummaryWriter("logs")
img = Image.open("images/6a00d8341c630a53ef00e553d0beb18834-800wi.jpg")
print(img)

ToTensor

trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("ToTensor", img_tensor)

Normalize

print(img_tensor[0][0][0])
trans_norm = transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
img_norm = trans_norm(img_tensor)
print(img_norm[0][0][0])
writer.add_image("Normalize", img_norm, 2)

Resize

print(img.size)
trans_resize = transforms.Resize((512, 512))
# img PIL -> resize -> img_resize PIL
img_resize = trans_resize(img)
# img_resize PIL->totensor ->img_resize tensor
img_resize = trans_totensor(img_resize)
writer.add_image("Resize", img_resize, 0)
print(img_resize)

Compose - resize - 2

trans_resize_2 = transforms.Resize(512)
# PIL -> PIL ->tensor
trans_compose = transforms.Compose([trans_resize_2, trans_totensor])
img_resize_2 = trans_compose(img)
writer.add_image("Resize", img_resize_2, 1)

RandomCrop

trans_random = transforms.RandomCrop(512)
trans_compose_2 = transforms.Compose([trans_random, [trans_totensor]])
for i in range(10):
img_crop = trans_compose_2(img)
writer.add_image("RandomCrop", img_crop, i)

writer.close()