【PyTorch】Transforms基本使用
文章目录
- 二、Transforms基本使用
-
- 1、Transforms的结构及用法
-
- 1.1 如何使用
- 1.2 TensorBoard查看
- 2、常用的Transforms
-
- 2.1 ToTensor
- 2.2 Normalize
- 2.3 Resize
- 2.4 Compose
- 2.5 RandomCrop
二、Transforms基本使用
Transforms主要是对特定格式的图片进行一些变化。
1、Transforms的结构及用法
Compose：
ToTensor：
1.1 如何使用
PIL Image to Tensor
from torchvision import transforms
from PIL import Image
img_path = "../data/tensorboard_data/train/ants_image/0013035.jpg"
img = Image.open(img_path) # tensor([[[0.3137, 0.3137, 0.3137, ..., 0.3176, 0.3098, 0.2980],
[0.3176, 0.3176, 0.3176, ..., 0.3176, 0.3098, 0.2980],
[0.3216, 0.3216, 0.3216, ..., 0.3137, 0.3098, 0.3020],
...,
...,
[0.9294, 0.9294, 0.9255, ..., 0.5529, 0.9216, 0.8941],
[0.9294, 0.9294, 0.9255, ..., 0.8863, 1.0000, 0.9137],
[0.9294, 0.9294, 0.9255, ..., 0.9490, 0.9804, 0.9137]]])
numpy.ndarry to Tensor
from torchvision import transforms
import cv2
img_path = "../data/tensorboard_data/train/ants_image/0013035.jpg"
img = cv2.imread(img_path) # tensor([[[0.9137, 0.9137, 0.9137, ..., 0.9176, 0.9098, 0.8980],
[0.9176, 0.9176, 0.9176, ..., 0.9176, 0.9098, 0.8980],
[0.9216, 0.9216, 0.9216, ..., 0.9137, 0.9098, 0.9020],
...,
...,
[0.3412, 0.3412, 0.3373, ..., 0.1725, 0.3725, 0.3529],
[0.3412, 0.3412, 0.3373, ..., 0.3294, 0.3529, 0.3294],
[0.3412, 0.3412, 0.3373, ..., 0.3098, 0.3059, 0.3294]]])
1.2 TensorBoard查看
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
img_path = "../data/tensorboard_data/train/ants_image/0013035.jpg"
img = Image.open(img_path)
writer = SummaryWriter("logs")
tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)
writer.add_image("tensor_img", tensor_img)
writer.close()
2、常用的Transforms
2.1 ToTensor
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
writer = SummaryWriter("logs")
img = Image.open("../images/pytorch.webp")
trans_totensor = transforms.ToTensor()
img_totensor = trans_totensor(img)
writer.add_image("ToTensor", img_totensor)
writer.close()
2.2 Normalize
1、计算公式：
output[channel] = (input[channel] - mean[channel]) / std[channel]
2、本实例中即：
(input - 0.5)/0.5 = 2 * input - 1
3、大致范围：
input[0, 1] —> result[-1, 1]
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
writer = SummaryWriter("logs")
img = Image.open("../images/pytorch.webp")
# 1.ToTensor
trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("ToTensor", img_tensor)
# 2.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)
writer.close()
2.3 Resize
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
writer = SummaryWriter("logs")
img = Image.open("../images/pytorch.webp")
# 1.ToTensor
trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("ToTensor", img_tensor)
# 2.Resize
print(img.size) # (889, 500)
# img PIL -> resize -> img_resize PIL
trans_resize = transforms.Resize((450, 450))
# img_resize PIL -> totensor -> img_resize tensor
img_resize = trans_resize(img)
print(img_resize) # 
2.4 Compose
还可以直接使用Compose！
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
writer = SummaryWriter("logs")
img = Image.open("../images/pytorch.webp")
# 1.ToTensor
trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("ToTensor", img_tensor)
# 2.Compose - Resize - 2
trans_resize_2 = transforms.Resize(100)
# PIL -> PIL -> tensor
trans_compose = transforms.Compose([trans_resize_2, trans_totensor])
img_resize_2 = trans_compose(img)
writer.add_image("Compose - Resize", img_resize_2, 0)
writer.close()
2.5 RandomCrop
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
from PIL import Image
writer = SummaryWriter("logs")
img = Image.open("../images/pytorch.webp")
# 1.ToTensor
trans_totensor = transforms.ToTensor()
img_tensor = trans_totensor(img)
writer.add_image("ToTensor", img_tensor)
# 2.RandomCrop
trans_random = transforms.RandomCrop((250, 444))
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()