第P5周-Pytorch实现运动鞋品牌识别

🍨 本文为🔗365天深度学习训练营中的学习记录博客
🍖 原作者：K同学啊

具体实现

（一）环境

语言环境：Python 3.10
编译器: PyCharm
框架: Pytorch

（二）具体步骤

时间关系，代码很差…

1. utils.py

针对数据文件的目录情况进行了优化

import torch  
import pathlib  
import matplotlib.pyplot as plt  
from torchvision.transforms import transforms  # 第一步：设置GPU  
def USE_GPU():  if torch.cuda.is_available():  print('CUDA is available, will use GPU')  device = torch.device("cuda")  else:  print('CUDA is not available. Will use CPU')  device = torch.device("cpu")  return device  temp_dict = dict()  
def recursive_iterate(path):  """  根据所提供的路径遍历该路径下的所有子目录，列出所有子目录下的文件  :param path: 路径  :return: 返回最后一级目录的数据  """    path = pathlib.Path(path)  for file in path.iterdir():  if file.is_file():  temp_key = str(file).split('\\')[-2]  if temp_key in temp_dict:  temp_dict.update({temp_key: temp_dict[temp_key] + 1})  else:  temp_dict.update({temp_key: 1})  # print(file)  elif file.is_dir():  recursive_iterate(file)  return temp_dict  def data_from_directory(directory, train_dir=None, test_dir=None, show=False):  """  提供是的数据集是文件形式的，提供目录方式导入数据，简单分析数据并返回数据分类  :param test_dir: 是否设置了测试集目录  :param train_dir: 是否设置了训练集目录  :param directory: 数据集所在目录  :param show: 是否需要以柱状图形式显示数据分类情况,默认显示  :return: 数据分类列表,类型: list  """    global total_image  print("数据目录：{}".format(directory))  data_dir = pathlib.Path(directory)  # for d in data_dir.glob('**/*'): # **/*通配符可以遍历所有子目录  #     if d.is_dir():  #         print(d)    class_name = []  total_image = 0  temp_sum = 0  if train_dir is None or test_dir is None:  data_path = list(data_dir.glob('*'))  class_name = [str(path).split('\\')[-1] for path in data_path]  print("数据分类: {}, 类别数量：{}".format(class_name, len(list(data_dir.glob('*')))))  total_image = len(list(data_dir.glob('*/*')))  print("图片数据总数: {}".format(total_image))  else:  temp_dict.clear()  train_data_path = directory + '/' + train_dir  train_data_info = recursive_iterate(train_data_path)  print("{}目录:{},{}".format(train_dir, train_data_path, train_data_info))  temp_dict.clear()  test_data_path = directory + '/' + test_dir  print("{}目录:{},{}".format(test_dir,  test_data_path, recursive_iterate(test_data_path)))  class_name = temp_dict.keys()  if show:  # 隐藏警告  import warnings  warnings.filterwarnings("ignore")  # 忽略警告信息  plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签  plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号  plt.rcParams['figure.dpi'] = 100  # 分辨率  for i in class_name:  data = len(list(pathlib.Path((directory + '\\' + i + '\\')).glob('*')))  plt.title('数据分类情况')  plt.grid(ls='--', alpha=0.5)  plt.bar(i, data)  plt.text(i, data, str(data), ha='center', va='bottom')  print("类别-{}:{}".format(i, data))  temp_sum += data  plt.show()  if temp_sum == total_image:  print("图片数据总数检查一致")  else:  print("数据数据总数检查不一致，请检查数据集是否正确！")  return class_name  def get_transforms_setting(size):  """  获取transforms的初始设置  :param size: 图片大小  :return: transforms.compose设置  """    transform_setting = {  'train': transforms.Compose([  transforms.Resize(size),  transforms.ToTensor(),  transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  ]),  'test': transforms.Compose([  transforms.Resize(size),  transforms.ToTensor(),  transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])  ])  }  return transform_setting

2.model.py

将CNN网络模板写到一个单独文件里，方便调用。

import torch.nn as nn  
import torch
import torch.nn.functional as Fclass Model_Shoes(nn.Module):  def __init__(self, classNames):  super(Model_Shoes, self).__init__()  self.conv1 = nn.Sequential(  nn.Conv2d(3, 12, kernel_size=5, padding=0),  # 12*220*220  nn.BatchNorm2d(12),  nn.ReLU())  self.conv2 = nn.Sequential(  nn.Conv2d(12, 12, kernel_size=5, padding=0),  # 12*216*216  nn.BatchNorm2d(12),  nn.ReLU())  self.pool3 = nn.Sequential(  nn.MaxPool2d(2))  # 12*108*108  self.conv4 = nn.Sequential(  nn.Conv2d(12, 24, kernel_size=5, padding=0),  # 24*104*104  nn.BatchNorm2d(24),  nn.ReLU())  self.conv5 = nn.Sequential(  nn.Conv2d(24, 24, kernel_size=5, padding=0),  # 24*100*100  nn.BatchNorm2d(24),  nn.ReLU())  self.pool6 = nn.Sequential(  nn.MaxPool2d(2))  # 24*50*50  self.dropout = nn.Sequential(  nn.Dropout(0.2))  self.fc = nn.Sequential(  nn.Linear(24 * 50 * 50, len(classNames)))  def forward(self, x):  batch_size = x.size(0)  x = self.conv1(x)  # 卷积-BN-激活  x = self.conv2(x)  # 卷积-BN-激活  x = self.pool3(x)  # 池化  x = self.conv4(x)  # 卷积-BN-激活  x = self.conv5(x)  # 卷积-BN-激活  x = self.pool6(x)  # 池化  x = self.dropout(x)  x = x.view(batch_size, -1)  # flatten 变成全连接网络需要的输入 (batch, 24*50*50) ==> (batch, -1), -1 此处自动算出的是24*50*50  x = self.fc(x)  return x

3. config.py

将训练的相关参数写到config.py中

import argparse  def get_options(parser=argparse.ArgumentParser()):  parser.add_argument('--workers', type=int, default=0, help='Number of parallel workers')  parser.add_argument('--batch-size', type=int, default=32, help='input batch size, default=32')  parser.add_argument('--lr', type=float, default=1e-4, help='learning rate, default=0.0001')  parser.add_argument('--epochs', type=int, default=50, help='number of epochs')  parser.add_argument('--seed', type=int, default=112, help='random seed')  parser.add_argument('--save-path', type=str, default='./models/', help='path to save checkpoints')  opt = parser.parse_args()  if opt:  print(f'num_workers:{opt.workers}')  print(f'batch_size:{opt.batch_size}')  print(f'learn rate:{opt.lr}')  print(f'epochs:{opt.epochs}')  print(f'random seed:{opt.seed}')  print(f'save_path:{opt.save_path}')  return opt  if __name__ == '__main__':  opt = get_options()

**4. main.py

from torch import nn  
from torchvision import datasets  from Utils import USE_GPU, data_from_directory, get_transforms_setting  
import torch  
import os, PIL, pathlib  
from model import Model_Shoes  import config  opt = config.get_options()  
print(opt)  device = USE_GPU()  DATA_DIR = "./data/shoes"  
classNames = data_from_directory(DATA_DIR, train_dir="train", test_dir="test")  
print(list(classNames))  transforms_setting = get_transforms_setting([224, 224])  
train_dataset = datasets.ImageFolder(DATA_DIR + "/train", transforms_setting['train'])  
test_dataset = datasets.ImageFolder(DATA_DIR + "/test", transforms_setting['test'])  
print(train_dataset.class_to_idx)  batch_size = opt.batch_size  
train_dl = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)  
test_dl = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=True)  for X, y in test_dl:  print("Shape of X[N, C, H, W]：", X.shape)  print("Shape of y", y.shape, y.dtype)  break  model = Model_Shoes(classNames).to(device)  
print(model)  # 训练循环  
def train(dataloader, model, loss_fn, optimizer):  size = len(dataloader.dataset)  # 训练集的大小  num_batches = len(dataloader)  # 批次数目, (size/batch_size，向上取整)  train_loss, train_acc = 0, 0  # 初始化训练损失和正确率  for X, y in dataloader:  # 获取图片及其标签  X, y = X.to(device), y.to(device)  # 计算预测误差  pred = model(X)  # 网络输出  loss = loss_fn(pred, y)  # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失  # 反向传播  optimizer.zero_grad()  # grad属性归零  loss.backward()  # 反向传播  optimizer.step()  # 每一步自动更新  # 记录acc与loss  train_acc += (pred.argmax(1) == y).type(torch.float).sum().item()  train_loss += loss.item()  train_acc /= size  train_loss /= num_batches  return train_acc, train_loss  def test(dataloader, model, loss_fn):  size = len(dataloader.dataset)  # 测试集的大小  num_batches = len(dataloader)  # 批次数目, (size/batch_size，向上取整)  test_loss, test_acc = 0, 0  # 当不进行训练时，停止梯度更新，节省计算内存消耗  with torch.no_grad():  for imgs, target in dataloader:  imgs, target = imgs.to(device), target.to(device)  # 计算loss  target_pred = model(imgs)  loss = loss_fn(target_pred, target)  test_loss += loss.item()  test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item()  test_acc /= size  test_loss /= num_batches  return test_acc, test_loss  def adjust_learning_rate(optimizer, epoch, start_lr):  # 每 2 个epoch衰减到原来的 0.92    lr = start_lr * (0.92 ** (epoch // 2))  for param_group in optimizer.param_groups:  param_group['lr'] = lr  learn_rate = opt.lr # 初始学习率  
optimizer  = torch.optim.SGD(model.parameters(), lr=learn_rate)  loss_fn = nn.CrossEntropyLoss()  # 创建损失函数  
epochs = opt.epochs  train_loss = []  
train_acc = []  
test_loss = []  
test_acc = []  for epoch in range(epochs):  # 更新学习率（使用自定义学习率时使用）  adjust_learning_rate(optimizer, epoch, learn_rate)  model.train()  epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)  # scheduler.step() # 更新学习率（调用官方动态学习率接口时使用）  model.eval()  epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)  train_acc.append(epoch_train_acc)  train_loss.append(epoch_train_loss)  test_acc.append(epoch_test_acc)  test_loss.append(epoch_test_loss)  # 获取当前的学习率  lr = optimizer.state_dict()['param_groups'][0]['lr']  template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:.2E}')  print(template.format(epoch + 1, epoch_train_acc * 100, epoch_train_loss,  epoch_test_acc * 100, epoch_test_loss, lr))  
print('Done')  import matplotlib.pyplot as plt  
#隐藏警告  
import warnings  
warnings.filterwarnings("ignore")               #忽略警告信息  
plt.rcParams['font.sans-serif']    = ['SimHei'] # 用来正常显示中文标签  
plt.rcParams['axes.unicode_minus'] = False      # 用来正常显示负号  
plt.rcParams['figure.dpi']         = 100        #分辨率  epochs_range = range(epochs)  plt.figure(figsize=(12, 3))  
plt.subplot(1, 2, 1)  plt.plot(epochs_range, train_acc, label='Training Accuracy')  
plt.plot(epochs_range, test_acc, label='Test Accuracy')  
plt.legend(loc='lower right')  
plt.title('Training and Validation Accuracy')  plt.subplot(1, 2, 2)  
plt.plot(epochs_range, train_loss, label='Training Loss')  
plt.plot(epochs_range, test_loss, label='Test Loss')  
plt.legend(loc='upper right')  
plt.title('Training and Validation Loss')  
plt.show()  # 模型保存  
MODEL_SAVE_NAME = "cnn-shoes.pth"  
torch.save(model.state_dict(), opt.save_path + MODEL_SAVE_NAME)

**5. 预测指定图片

import torch  from model import Model_Shoes  
from Utils import USE_GPU, get_transforms_setting  
from PIL import Image  from PIL import Image  device = USE_GPU()  
transform_setting = get_transforms_setting([224, 224])  classes = ['adidas', 'nike']  
model = Model_Shoes(classes)  
model.load_state_dict(torch.load('./models/cnn-shoes.pth', map_location=device))  
model.to(device)  def predict_one_image(image_path, model, transform, classes):  test_img = Image.open(image_path).convert('RGB')  # plt.imshow(test_img)  # 展示预测的图片  test_img = transform(test_img)  img = test_img.to(device).unsqueeze(0)  model.eval()  output = model(img)  _, pred = torch.max(output, 1)  pred_class = classes[pred]  print(f'预测结果是：{pred_class}')  # 预测训练集中的某张照片  
predict_one_image(image_path='./mydata/shoes/1.png',  model=model,  transform=transform_setting['train'],  classes=classes)