【Pytorch】transforms的使用(2 CV领域)

目录

一、tensorboard的使用

writer.add_scalar()

writer.add_image()

二、transforms的使用

三、常见的transforms

3.1   __call__()的作用

3.2 ToTensor() / Normalize() / Resize()

四、使用torchvision中自带的图像数据集

ps

取消pycharm大小写匹配

---

1. 关注输入和输出类型
2. 多看官方文档
3.  关注方法需要什么参数
4. 不知道返回值的时候 1)print   2)print(type())   3) debug

一、tensorboard的使用

from torch.utils.tensorboard import SummaryWriter


# ImportError: TensorBoard logging requires TensorBoard with Python summary writer installed. This should be available in 1.14 or above.
# 要安装tensorboard


     def add_scalar(self, tag, scalar_value, global_step=None, walltime=None):
        """Add scalar data to summary.

        Args:
            tag (string): Data identifier
            scalar_value (float or string/blobname): Value to save
            global_step (int): Global step value to record
            walltime (float): Optional override default walltime (time.time())
              with seconds after epoch of event

writer.add_scalar()

from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Image

writer = SummaryWriter("logs")
image_path = "data/train/ants_image/6240329_72c01e663e.jpg"
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)
print(type(img_array))
print(img_array.shape)

writer.add_image("train", img_array, 1, dataformats='HWC')
# y = 2x
for i in range(100):
    writer.add_scalar("y=2x", 3*i, i)

writer.close()

打开该图，

指定端口打开，

writer.add_image()

    def add_image(self, tag, img_tensor, global_step=None, walltime=None, dataformats='CHW'):
        """Add image data to summary.

        Note that this requires the ``pillow`` package.

        Args:
            ## title
            tag (string): Data identifier
            ## 图像的数据类型
            img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data
            global_step (int): Global step value to record
            walltime (float): Optional override default walltime (time.time())
              seconds after epoch of event
        Shape:
            img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to
            convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.
            Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitible as long as
            corresponding ``dataformats`` argument is passed. e.g. CHW, HWC, HW.

可以直观看出训练过程中，给model提供了哪些数据。 

二、transforms的使用

transforms主要是对图片进行一些变换。

from torchvision import transforms
from PIL import Image

# python的用法-》 tensor数据类型
img_path = ""
img = Image.open(img_path)

# 1. transform如何被使用
tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)
print(tensor_img)

三、常见的transforms

3.1   __call__()的作用

- 使用__call__()的话，实例化变量后不需要.方法；

class Person():
    def __call__(self, name):
        print("__call__"+"hello "+name)

    def hello(self, name):
        print("hello "+name)

person = Person()
person("zhangsan") # __call__hello zhangsan
person.hello("lisi") # hello lisi

3.2 ToTensor() / Normalize() / Resize()

四、使用torchvision中自带的图像数据集

https://pytorch.org/vision/stable/ 官方文档

使用torchvision中自带的图像数据集

import torchvision
from torch.utils.tensorboard import SummaryWriter

dataset_transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])

# 训练数据True
train_set = torchvision.datasets.CIFAR10(root="./dataset", train=True, transform=dataset_transform, download=True)
# 测试数据False
test_set = torchvision.datasets.CIFAR10(root="./dataset", train=False, transform=dataset_transform, download=True)

# print(test_set[0])
# print(test_set.classes)
#
# img, target = test_set[0]
# print(img)
# print(target)
# print(test_set.classes[target])
# img.show()
#
# print(test_set[0])

writer = SummaryWriter("p10")
for i in range(10):
    img, target = test_set[i]
    writer.add_image("test_set", img, i)

writer.close()

ps

取消pycharm大小写匹配