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主頁 > 知識庫 > PyTorch 遷移學習實踐(幾分鐘即可訓練好自己的模型)

PyTorch 遷移學習實踐(幾分鐘即可訓練好自己的模型)

前言

如果你認為深度學習非常的吃GPU，或者說非常的耗時間，訓練一個模型要非常久，但是你如果了解了遷移學習那你的模型可能只需要幾分鐘，而且準確率不比你自己訓練的模型準確率低，本節我們將會介紹兩種方法來實現遷移學習

遷移學習方法介紹

微調網絡的方法實現遷移學習，更改最后一層全連接，并且微調訓練網絡
將模型看成特征提取器，如果一個模型的預訓練模型非常的好，那完全就把前面的層看成特征提取器，凍結所有層并且更改最后一層，只訓練最后一層，這樣我們只訓練了最后一層，訓練會非常的快速

遷移基本步驟

數據的準備
選擇數據增廣的方式
選擇合適的模型
更換最后一層全連接
凍結層，開始訓練
選擇預測結果最好的模型保存

需要導入的包

import zipfile # 解壓文件
import torchvision
from torchvision import datasets, transforms, models
import torch
from torch.utils.data import DataLoader, Dataset
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
import copy

數據準備

本次實驗的數據到這里下載
首先按照上一章節講的數據讀取方法來準備數據

# 解壓數據到指定文件
def unzip(filename, dst_dir):
  z = zipfile.ZipFile(filename)
  z.extractall(dst_dir)
unzip('./data/hymenoptera_data.zip', './data/')
# 實現自己的Dataset方法，主要實現兩個方法__len__和__getitem__
class MyDataset(Dataset):
  def __init__(self, dirname, transform=None):
    super(MyDataset, self).__init__()
    self.classes = os.listdir(dirname)
    self.images = []
    self.transform = transform
    for i, classes in enumerate(self.classes):
      classes_path = os.path.join(dirname, classes)
      for image_name in os.listdir(classes_path):
        self.images.append((os.path.join(classes_path, image_name), i))
  def __len__(self):
    return len(self.images)
  def __getitem__(self, idx):
    image_name, classes = self.images[idx]
    image = Image.open(image_name)
    if self.transform:
      image = self.transform(image)
    return image, classes
  def get_claesses(self):
    return self.classes
# 分布實現訓練和預測的transform
train_transform = transforms.Compose([
  transforms.Grayscale(3),
  transforms.RandomResizedCrop(224), #隨機裁剪一個area然后再resize
  transforms.RandomHorizontalFlip(), #隨機水平翻轉
  transforms.Resize(size=(256, 256)),
  transforms.ToTensor(),
  transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
val_transform = transforms.Compose([
  transforms.Grayscale(3),
  transforms.Resize(size=(256, 256)),
  transforms.CenterCrop(224),
  transforms.ToTensor(),
  transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# 分別實現loader
train_dataset = MyDataset('./data/hymenoptera_data/train/', train_transform)
train_loader = DataLoader(train_dataset, shuffle=True, batch_size=32)
val_dataset = MyDataset('./data/hymenoptera_data/val/', val_transform)
val_loader = DataLoader(val_dataset, shuffle=True, batch_size=32)

選擇預訓練的模型

這里我們選擇了resnet18在ImageNet 1000類上進行了預訓練的

model = models.resnet18(pretrained=True) # 使用預訓練

使用model.buffers查看網絡基本結構

bound method Module.buffers of ResNet(
 (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
 (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
 (relu): ReLU(inplace=True)
 (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
 (layer1): Sequential(
  (0): BasicBlock(
   (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (relu): ReLU(inplace=True)
   (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
  (1): BasicBlock(
   (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (relu): ReLU(inplace=True)
   (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
 )
 (layer2): Sequential(
  (0): BasicBlock(
   (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
   (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (relu): ReLU(inplace=True)
   (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (downsample): Sequential(
    (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
    (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   )
  )
  (1): BasicBlock(
   (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (relu): ReLU(inplace=True)
   (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
 )
 (layer3): Sequential(
  (0): BasicBlock(
   (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
   (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (relu): ReLU(inplace=True)
   (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (downsample): Sequential(
    (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
    (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   )
  )
  (1): BasicBlock(
   (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (relu): ReLU(inplace=True)
   (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
 )
 (layer4): Sequential(
  (0): BasicBlock(
   (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
   (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (relu): ReLU(inplace=True)
   (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (downsample): Sequential(
    (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
    (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   )
  )
  (1): BasicBlock(
   (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
   (relu): ReLU(inplace=True)
   (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
   (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  )
 )
 (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
 (fc): Linear(in_features=512, out_features=1000, bias=True)
)>

我們現在需要做的就是將最后一層進行替換

only_train_fc = True
if only_train_fc:
  for param in model.parameters():
    param.requires_grad_(False)
fc_in_features = model.fc.in_features
model.fc = torch.nn.Linear(fc_in_features, 2, bias=True)

注釋:only_train_fc如果我們設置為True那么就只訓練最后的fc層
現在觀察一下可導的參數有那些（在只訓練最后一層的情況下）

for i in model.parameters():
  if i.requires_grad:
    print(i)

Parameter containing:
tensor([[ 0.0342, -0.0336, 0.0279, ..., -0.0428, 0.0421, 0.0366],
    [-0.0162, 0.0286, -0.0379, ..., -0.0203, -0.0016, -0.0440]],
    requires_grad=True)
Parameter containing:
tensor([-0.0120, -0.0086], requires_grad=True)

注釋:由于最后一層使用了bias因此我們會多加兩個參數

訓練主體的實現

epochs = 50
loss_fn = torch.nn.CrossEntropyLoss()
opt = torch.optim.SGD(lr=0.01, params=model.parameters())
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# device = torch.device('cpu')
model.to(device)
opt_step = torch.optim.lr_scheduler.StepLR(opt, step_size=20, gamma=0.1)
max_acc = 0
epoch_acc = []
epoch_loss = []
for epoch in range(epochs):
  for type_id, loader in enumerate([train_loader, val_loader]):
    mean_loss = []
    mean_acc = []
    for images, labels in loader:
      if type_id == 0:
        # opt_step.step()
        model.train()
      else:
        model.eval()
      images = images.to(device)
      labels = labels.to(device).long()
      opt.zero_grad()
      with torch.set_grad_enabled(type_id==0):
        outputs = model(images)
        _, pre_labels = torch.max(outputs, 1)
        loss = loss_fn(outputs, labels)
      if type_id == 0:
        loss.backward()
        opt.step()
      acc = torch.sum(pre_labels==labels) / torch.tensor(labels.shape[0], dtype=torch.float32)    
      mean_loss.append(loss.cpu().detach().numpy())
      mean_acc.append(acc.cpu().detach().numpy())
    if type_id == 1:
      epoch_acc.append(np.mean(mean_acc))
      epoch_loss.append(np.mean(mean_loss))
      if max_acc  np.mean(mean_acc):
        max_acc = np.mean(mean_acc)
    print(type_id, np.mean(mean_loss),np.mean(mean_acc))
print(max_acc)

在使用cpu訓練的情況，也能快速得到較好的結果，這里訓練了50次，其實很快的就已經得到了很好的結果了