佛山企业网站建设公司推荐,网站建设吴江,焦作建设厅网站,中国十大公关公司排名一、Pytorch中如何加载数据 在Pytorch中涉及到如何读取数据#xff0c;主要是两个类一个类是Dataset、Dataloader Dataset 提供一种方式获取数据#xff0c;及其对应的label。主要包含以下两个功能#xff1a; 如何获取每一个数据以及label 告诉我们总共有多少的数据
Datal…一、Pytorch中如何加载数据 在Pytorch中涉及到如何读取数据主要是两个类一个类是Dataset、Dataloader Dataset 提供一种方式获取数据及其对应的label。主要包含以下两个功能 如何获取每一个数据以及label 告诉我们总共有多少的数据
Dataloader可以对数据进行打包为后面的网络提供不同的数据形式。
二、Tensorboard的使用用来观察训练结果
from torch.utils.tensorboard import SummaryWriterwriter SummaryWriter(log)# writer.add_image()for i in range(100):writer.add_scalar(yx, i, i)writer.close()在Terminal中先切换到conda activate pytorch 使用命令 tensorboard --logdirlogs
TensorBoard的使用 1、使用add_image()方法
from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Image
# 利用openCV中的numpy库可以获得numpy型的图片writer SummaryWriter(log)
img_path ../dataset/bees/26589803_5ba7000313.jpg
img_PIL Image.open(img_path) # 打开图片
img_array np.array(img_PIL) # 图片转换
print(type(img_array)) # 打印图片类型
print(img_array.shape) # 打印图片格式writer.add_images(test, img_array, 2, dataformatsHWC) # 根据img_array.shape来指定如果不指定dataformats就会报错
# y 2x
for i in range(100):writer.add_scalar(y2x, 2*i, i)writer.close()三、Transforms的使用 transform表示对图片进行一些变换 python的用法 - tensor数据类型 通过transform.ToTensor去解决两个问题 transforms该如何使用Python 为什么我们需要Tensor的数据类型
from torchvision import transforms
from PIL import Imageimg_path ../dataset/bees/10870992_eebeeb3a12.jpg
img Image.open(img_path)
tensor_trans transforms.ToTensor()
tensor_img tensor_trans(img)
print(tensor_img)运行后的结果
D:\tools\anaconda\envs\pytorch\python.exe D:/code/captcha_ocr-main/learn/transforms.py
tensor([[[0.5725, 0.5725, 0.5725, ..., 0.5686, 0.5725, 0.5765],[0.5725, 0.5725, 0.5725, ..., 0.5686, 0.5725, 0.5765],[0.5686, 0.5686, 0.5725, ..., 0.5686, 0.5725, 0.5765],...,[0.5490, 0.5647, 0.5725, ..., 0.6314, 0.6235, 0.6118],[0.5608, 0.5765, 0.5843, ..., 0.5961, 0.5843, 0.5765],[0.5725, 0.5843, 0.5922, ..., 0.5647, 0.5529, 0.5490]],[[0.4471, 0.4471, 0.4471, ..., 0.4235, 0.4275, 0.4314],[0.4471, 0.4471, 0.4471, ..., 0.4235, 0.4275, 0.4314],[0.4431, 0.4431, 0.4471, ..., 0.4235, 0.4275, 0.4314],...,[0.4000, 0.4157, 0.4235, ..., 0.4706, 0.4627, 0.4510],[0.4118, 0.4275, 0.4353, ..., 0.4431, 0.4314, 0.4235],[0.4235, 0.4353, 0.4431, ..., 0.4118, 0.4000, 0.3961]],[[0.2471, 0.2471, 0.2471, ..., 0.2588, 0.2627, 0.2667],[0.2471, 0.2471, 0.2471, ..., 0.2588, 0.2627, 0.2667],[0.2431, 0.2431, 0.2471, ..., 0.2588, 0.2627, 0.2667],...,[0.2157, 0.2314, 0.2392, ..., 0.2510, 0.2431, 0.2314],[0.2275, 0.2431, 0.2510, ..., 0.2196, 0.2078, 0.2000],[0.2392, 0.2510, 0.2588, ..., 0.1961, 0.1843, 0.1804]]])Process finished with exit code 0加载tensor类型的图片
from torchvision import transforms
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
img_path ../dataset/bees/10870992_eebeeb3a12.jpg
img Image.open(img_path)
writer SummaryWriter(log)
tensor_trans transforms.ToTensor()
tensor_img tensor_trans(img)
writer.add_image(Tensor_img, tensor_img)
writer.close()四、常见的transforms类的使用
ToTensor类 将PIL图片转换成tensor图片。
from torchvision import transforms
from PIL import Image
from torch.utils.tensorboard import SummaryWriter
# Tensor的使用
writer SummaryWriter(log)
img_path ../dataset/bees/10870992_eebeeb3a12.jpg
img Image.open(img_path)
print(img)
tensor_trans transforms.ToTensor() # 创建ToTensor()对象
tensor_img tensor_trans(img) # 传入图片参数将PIL图片转换成tensor图片
writer.add_image(Tensor_img, tensor_img)
writer.close()2. Normalize类
对tensor类型的图片进行归一化处理。 Normalize的使用归一化处理 公式output[channel] (input[channel] - mean[channel]) / std[channel]
from torchvision import transforms
from PIL import Image
from torch.utils.tensorboard import SummaryWriter# Tensor的使用
writer SummaryWriter(log)
img_path ../dataset/bees/10870992_eebeeb3a12.jpg
img Image.open(img_path)
print(img)tensor_trans transforms.ToTensor()
tensor_img tensor_trans(img)writer.add_image(Tensor_img, tensor_img)# Normalize的使用
print(tensor_img[0][0][0]) # 归一化处理之前的数据
trans_norm transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
img_norm trans_norm(tensor_img)
print(img_norm[0][0][0]) # 归一化处理后的结果
writer.add_image(Normalize, img_norm)writer.close()3. Resize类 重置图片大小。
from torchvision import transforms
from PIL import Image
from torch.utils.tensorboard import SummaryWriter# Tensor的使用
writer SummaryWriter(log)
img_path ../dataset/bees/10870992_eebeeb3a12.jpg
img Image.open(img_path)
print(img)tensor_trans transforms.ToTensor()
tensor_img tensor_trans(img)writer.add_image(Tensor_img, tensor_img)print(tensor_img[0][0][0]) # 归一化处理之前的数据
trans_norm transforms.Normalize([1, 3, 5], [3, 2, 1])
img_norm trans_norm(tensor_img)
print(img_norm[0][0][0]) # 归一化处理后的结果
writer.add_image(Normalize, img_norm)writer.close()# Resize的使用:重置图片大小
print(img.size) # (500, 464)
trans_resize transforms.Resize((512, 512))img_resize trans_resize(img)
print(img_resize) # PIL.Image.Image image modeRGB size512x512 at 0x2A17E774248 img_resize tensor_trans(img_resize)
writer.add_image(Resize, img_resize, 0)
print( img_resize)
writer.close() 4. Compose的使用 等比例缩放。 Compose的使用整体缩放不改变高宽比例 Compose()中的参数需要的是一个列表列表中的数据需要的是transforms类型。 即 Compose([transforms参数1, transforms参数2, …])
from torchvision import transforms
from PIL import Image
from torch.utils.tensorboard import SummaryWriter# Tensor的使用
writer SummaryWriter(log)
img_path ../dataset/bees/10870992_eebeeb3a12.jpg
img Image.open(img_path)
print(img)tensor_trans transforms.ToTensor()
tensor_img tensor_trans(img)writer.add_image(Tensor_img, tensor_img)print(tensor_img[0][0][0]) # 归一化处理之前的数据
trans_norm transforms.Normalize([1, 3, 5], [3, 2, 1])
img_norm trans_norm(tensor_img)
# print(img_norm[0][0][0]) # 归一化处理后的结果
writer.add_image(Normalize, img_norm)# Resize的使用:重置图片大小
print(img.size) # (500, 464)
trans_resize transforms.Resize((512, 512))img_resize trans_resize(img)
print(img_resize) # PIL.Image.Image image modeRGB size512x512 at 0x2A17E774248# Compose的使用
img_resize tensor_trans(img_resize)
writer.add_image(Resize, img_resize, 0)
# print(img_resize)
trans_resize_2 transforms.Resize(1024)
trans_compose transforms.Compose([trans_resize_2, tensor_trans])
img_resize_2 trans_compose(img)
writer.add_image(Resize, img_resize_2, 1)writer.close()5.RandomCrop类的使用
随机裁剪。
from torchvision import transforms
from PIL import Image
from torch.utils.tensorboard import SummaryWriter# Tensor的使用
writer SummaryWriter(log)
img_path ../dataset/bees/10870992_eebeeb3a12.jpg
img Image.open(img_path)
print(img)tensor_trans transforms.ToTensor()
tensor_img tensor_trans(img)writer.add_image(Tensor_img, tensor_img)print(tensor_img[0][0][0]) # 归一化处理之前的数据
trans_norm transforms.Normalize([1, 3, 5], [3, 2, 1])
img_norm trans_norm(tensor_img)
# print(img_norm[0][0][0]) # 归一化处理后的结果
writer.add_image(Normalize, img_norm)# Resize的使用:重置图片大小
print(img.size) # (500, 464)
trans_resize transforms.Resize((512, 512))img_resize trans_resize(img)
print(img_resize) # PIL.Image.Image image modeRGB size512x512 at 0x2A17E774248# Compose的使用
img_resize tensor_trans(img_resize)
writer.add_image(Resize, img_resize, 0)
# print(img_resize)
trans_resize_2 transforms.Resize(1024)
trans_compose transforms.Compose([trans_resize_2, tensor_trans])
img_resize_2 trans_compose(img)
writer.add_image(Resize, img_resize_2, 1)# RendomCrop类的使用随机裁剪
# trans_random transforms.RandomCrop(512)
trans_random transforms.RandomCrop(1000, 500)
trans_compose_2 transforms.Compose([trans_random, tensor_trans])
for i in range(10):img_crop trans_compose_2(img)# writer.add_image(RancomCrop, img_crop, i)writer.add_image(RancomCropHW, img_crop, i)writer.close()
