pytorch從csv加載自定義數據模板的操作

from PIL import Image
import pandas as pd
import numpy as np
import torchvision.transforms as transforms
from torch.utils.data import Dataset, DataLoader
import os
#放文件的路徑
dir_path= './97/train/'
csv_path='./97/train.csv'
class Mydataset(Dataset):
 #傳遞數據路徑，csv路徑 ，數據增強方法
 def __init__(self, dir_path,csv, transform=None, target_transform=None):
  super(Mydataset, self).__init__()
  #一個個往列表裡面加絕對路徑
  self.path = []
  #讀取csv
  self.data = pd.read_csv(csv)
  #對標簽進行硬編碼，例如0 1 2 3 4，把字母變成這個
  colorMap = {elem: index + 1 for index, elem in enumerate(set(self.data["label"]))}
  self.data['label'] = self.data['label'].map(colorMap)
  #創造空的label準備存放標簽
  self.num = int(self.data.shape[0]) # 一共多少照片
  self.label = np.zeros(self.num, dtype=np.int32)
  #迭代得到數據路徑和標簽一一對應
  for index, row in self.data.iterrows():
   self.path.append(os.path.join(dir_path,row['filename']))
   self.label[index] = row['label'] # 將數據全部讀取出來
  #訓練數據增強
  self.transform = transform
  #驗證數據增強在這裡沒用
  self.target_transform = target_transform
 #最關鍵的部分，在這裡使用前面的方法
 def __getitem__(self, index):
  img =Image.open(self.path[index]).convert('RGB')
  labels = self.label[index]
  #在這裡做數據增強
  if self.transform is not None:
   img = self.transform(img) # 轉化tensor類型
  return img, labels
 def __len__(self):
  return len(self.data)
#數據增強的具體內容
transform = transforms.Compose(
 [transforms.ToTensor(),
  transforms.Resize(150),
  transforms.CenterCrop(150),
  transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)
#加載數據
train_data = Mydataset(dir_path=dir_path,csv=csv_path, transform=transform)
trainloader = DataLoader(train_data, batch_size=16, shuffle=True, num_workers=0)
#迭代訓練
for i_batch,batch_data in enumerate(trainloader):
 image,label=batch_data

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple some-package

[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple

import os
import sys
import numpy as np
import cv2
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
import time
import random
import csv
from PIL import Image
def createImgIndex(dataPath, ratio):
 '''
 讀取目錄下面的圖片制作包含圖片信息、圖片label的train.txt和val.txt
 dataPath: 圖片目錄路徑
 ratio: val占比
 return：label列表
 '''
 fileList = os.listdir(dataPath)
 random.shuffle(fileList)
 classList = [] # label列表
 # val 數據集制作
 with open('data/val_section1015.csv', 'w') as f:
  writer = csv.writer(f)
  for i in range(int(len(fileList)*ratio)):
   row = []
   if '.jpg' in fileList[i]:
    fileInfo = fileList[i].split('_')
    sectionName = fileInfo[0] + '_' + fileInfo[1] # 切面名+標準與否
    row.append(os.path.join(dataPath, fileList[i])) # 圖片路徑
    if sectionName not in classList:
     classList.append(sectionName)
    row.append(classList.index(sectionName))
    writer.writerow(row)
  f.close()
 # train 數據集制作
 with open('data/train_section1015.csv', 'w') as f:
  writer = csv.writer(f)
  for i in range(int(len(fileList) * ratio)+1, len(fileList)):
   row = []
   if '.jpg' in fileList[i]:
    fileInfo = fileList[i].split('_')
    sectionName = fileInfo[0] + '_' + fileInfo[1] # 切面名+標準與否
    row.append(os.path.join(dataPath, fileList[i])) # 圖片路徑
    if sectionName not in classList:
     classList.append(sectionName)
    row.append(classList.index(sectionName))
    writer.writerow(row)
  f.close()
 print(classList, len(classList))
 return classList
def default_loader(path):
 '''定義讀取文件的格式'''
 return Image.open(path).resize((128, 128),Image.ANTIALIAS).convert('RGB')
class MyDataset(Dataset):
 '''Dataset類是讀入數據集數據並且對讀入的數據進行索引'''
 def __init__(self, txt, transform=None, target_transform=None, loader=default_loader):
  super(MyDataset, self).__init__() #對繼承自父類的屬性進行初始化
  fh = open(txt, 'r') #按照傳入的路徑和txt文本參數，以隻讀的方式打開這個文本
  reader = csv.reader(fh)
  imgs = []
  for row in reader:
   imgs.append((row[0], int(row[1]))) # (圖片信息，lable)
  self.imgs = imgs
  self.transform = transform
  self.target_transform = target_transform
  self.loader = loader
 
 def __getitem__(self, index):
  '''用於按照索引讀取每個元素的具體內容'''
  # fn是圖片path #fn和label分別獲得imgs[index]也即是剛才每行中row[0]和row[1]的信息
  fn, label = self.imgs[index]
  img = self.loader(fn)
  if self.transform is not None:
   img = self.transform(img) #數據標簽轉換為Tensor
  return img, label
 
 def __len__(self):
  '''返回數據集的長度'''
  return len(self.imgs)
class Model(nn.Module):
 def __init__(self, classNum=31):
  super(Model, self).__init__()
  # torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
  # torch.nn.MaxPool2d(kernel_size, stride, padding)
  # input 維度 [3, 128, 128]
  self.cnn = nn.Sequential(
   nn.Conv2d(3, 64, 3, 1, 1), # [64, 128, 128]
   nn.BatchNorm2d(64),
   nn.ReLU(),
   nn.MaxPool2d(2, 2, 0), # [64, 64, 64]
   nn.Conv2d(64, 128, 3, 1, 1), # [128, 64, 64]
   nn.BatchNorm2d(128),
   nn.ReLU(),
   nn.MaxPool2d(2, 2, 0), # [128, 32, 32]
   nn.Conv2d(128, 256, 3, 1, 1), # [256, 32, 32]
   nn.BatchNorm2d(256),
   nn.ReLU(),
   nn.MaxPool2d(2, 2, 0), # [256, 16, 16]
   nn.Conv2d(256, 512, 3, 1, 1), # [512, 16, 16]
   nn.BatchNorm2d(512),
   nn.ReLU(),
   nn.MaxPool2d(2, 2, 0), # [512, 8, 8]
   nn.Conv2d(512, 512, 3, 1, 1), # [512, 8, 8]
   nn.BatchNorm2d(512),
   nn.ReLU(),
   nn.MaxPool2d(2, 2, 0), # [512, 4, 4]
  )
  self.fc = nn.Sequential(
   nn.Linear(512 * 4 * 4, 1024),
   nn.ReLU(),
   nn.Linear(1024, 512),
   nn.ReLU(),
   nn.Linear(512, classNum)
  )
 def forward(self, x):
  out = self.cnn(x)
  out = out.view(out.size()[0], -1)
  return self.fc(out)
def train(train_set, train_loader, val_set, val_loader):
 model = Model()
 loss = nn.CrossEntropyLoss() # 因為是分類任務，所以loss function使用 CrossEntropyLoss
 optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # optimizer 使用 Adam
 num_epoch = 10
 # 開始訓練
 for epoch in range(num_epoch):
  epoch_start_time = time.time()
  train_acc = 0.0
  train_loss = 0.0
  val_acc = 0.0
  val_loss = 0.0
  model.train() # train model會開放Dropout和BN
  for i, data in enumerate(train_loader):
   optimizer.zero_grad() # 用 optimizer 將 model 參數的 gradient 歸零
   train_pred = model(data[0]) # 利用 model 的 forward 函數返回預測結果
   batch_loss = loss(train_pred, data[1]) # 計算 loss
   batch_loss.backward() # tensor(item, grad_fn=<NllLossBackward>)
   optimizer.step() # 以 optimizer 用 gradient 更新參數
   train_acc += np.sum(np.argmax(train_pred.data.numpy(), axis=1) == data[1].numpy())
   train_loss += batch_loss.item()
  model.eval()
  with torch.no_grad(): # 不跟蹤梯度
   for i, data in enumerate(val_loader):
    # data = [imgData, labelList]
    val_pred = model(data[0])
    batch_loss = loss(val_pred, data[1])
    val_acc += np.sum(np.argmax(val_pred.data.numpy(), axis=1) == data[1].numpy())
    val_loss += batch_loss.item()
   # 打印結果
   print('[%03d/%03d] %2.2f sec(s) Train Acc: %3.6f Loss: %3.6f | Val Acc: %3.6f loss: %3.6f' % \
     (epoch + 1, num_epoch, time.time() - epoch_start_time, \
     train_acc / train_set.__len__(), train_loss / train_set.__len__(), val_acc / val_set.__len__(),
     val_loss / val_set.__len__()))
if __name__ == '__main__':
 dirPath = '/data/Matt/QC_images/test0916' # 圖片文件目錄
 createImgIndex(dirPath, 0.2)    # 創建train.txt, val.txt
 root = os.getcwd() + '/data/'
 train_data = MyDataset(txt=root+'train_section1015.csv', transform=transforms.ToTensor())
 val_data = MyDataset(txt=root+'val_section1015.csv', transform=transforms.ToTensor())
 train_loader = DataLoader(dataset=train_data, batch_size=6, shuffle=True, num_workers = 4)
 val_loader = DataLoader(dataset=val_data, batch_size=6, shuffle=False, num_workers = 4)
 # 開始訓練模型
 train(train_data, train_loader, val_data, val_loader)