fly6516
9266859f0a
- 重构了代码结构，优化了导入顺序和格式 - 改进了模型训练流程，添加了早停机制和学习率调度器- 增加了模型测试和可视化部分的代码 -优化了量子卷积层和模型的实现 - 调整了训练参数和数据预处理方法
508 lines
18 KiB
Python
508 lines
18 KiB
Python
#%%
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# 首先我们导入所有需要的包：
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import os
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import random
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import numpy as np
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import pandas as pd
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torchvision.transforms as transforms
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from tqdm import tqdm
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from sklearn.metrics import roc_auc_score
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from torch.utils.data import DataLoader
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from torchvision.datasets import FashionMNIST
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import deepquantum as dq
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import matplotlib.pyplot as plt
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def seed_torch(seed=1024):
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"""
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Set random seeds for reproducibility.
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Args:
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seed (int): Random seed number to use. Default is 1024.
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"""
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random.seed(seed)
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os.environ['PYTHONHASHSEED'] = str(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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# Seed all GPUs with the same seed if using multi-GPU
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.benchmark = False
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torch.backends.cudnn.deterministic = True
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seed_torch(42) # 使用更常见的随机种子值
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#%%
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def calculate_score(y_true, y_preds):
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# 将模型预测结果转为概率分布
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preds_prob = torch.softmax(y_preds, dim=1)
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# 获得预测的类别（概率最高的一类）
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preds_class = torch.argmax(preds_prob, dim=1)
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# 计算准确率
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correct = (preds_class == y_true).float()
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accuracy = correct.sum() / len(correct)
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return accuracy.cpu().numpy()
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def train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device):
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"""
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训练和验证模型。
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Args:
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model (torch.nn.Module): 要训练的模型。
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criterion (torch.nn.Module): 损失函数。
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optimizer (torch.optim.Optimizer): 优化器。
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train_loader (torch.utils.data.DataLoader): 训练数据加载器。
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valid_loader (torch.utils.data.DataLoader): 验证数据加载器。
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num_epochs (int): 训练的epoch数。
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Returns:
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model (torch.nn.Module): 训练后的模型。
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"""
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model.train()
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train_loss_list = []
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valid_loss_list = []
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train_acc_list = []
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valid_acc_list = []
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best_valid_acc = 0.0
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patience = 10 # 早停耐心值
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counter = 0 # 计数器
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scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', factor=0.5, patience=10)
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with tqdm(total=num_epochs) as pbar:
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for epoch in range(num_epochs):
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# 训练阶段
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train_loss = 0.0
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train_acc = 0.0
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for images, labels in train_loader:
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images = images.to(device)
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labels = labels.to(device)
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optimizer.zero_grad()
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outputs = model(images)
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loss = criterion(outputs, labels)
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loss.backward()
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optimizer.step()
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train_loss += loss.item()
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train_acc += calculate_score(labels, outputs)
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train_loss /= len(train_loader)
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train_acc /= len(train_loader)
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# 验证阶段
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model.eval()
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valid_loss = 0.0
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valid_acc = 0.0
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with torch.no_grad():
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for images, labels in valid_loader:
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images = images.to(device)
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labels = labels.to(device)
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outputs = model(images)
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loss = criterion(outputs, labels)
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valid_loss += loss.item()
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valid_acc += calculate_score(labels, outputs)
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valid_loss /= len(valid_loader)
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valid_acc /= len(valid_loader)
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# 学习率调度器更新
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scheduler.step(valid_acc)
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# 早停机制
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if valid_acc > best_valid_acc:
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best_valid_acc = valid_acc
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torch.save(model.state_dict(), './data/notebook2/best_model.pt')
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counter = 0
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else:
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counter += 1
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if counter >= patience:
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print(f'Early stopping at epoch {epoch+1} due to no improvement in validation accuracy.')
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break
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pbar.set_description(f"Train loss: {train_loss:.3f} Valid Acc: {valid_acc:.3f}")
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pbar.update()
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train_loss_list.append(train_loss)
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valid_loss_list.append(valid_loss)
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train_acc_list.append(train_acc)
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valid_acc_list.append(valid_acc)
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# 加载最佳模型权重
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if os.path.exists('./data/notebook2/best_model.pt'):
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model.load_state_dict(torch.load('./data/notebook2/best_model.pt'))
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# 修改metrics构建方式，确保各数组长度一致
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metrics = {
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'epoch': list(range(1, len(train_loss_list) + 1)),
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'train_acc': train_acc_list,
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'valid_acc': valid_acc_list,
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'train_loss': train_loss_list,
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'valid_loss': valid_loss_list
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}
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return model, metrics
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def test_model(model, test_loader, device):
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model.eval()
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test_acc = 0.0
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with torch.no_grad():
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for images, labels in test_loader:
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images = images.to(device)
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labels = labels.to(device)
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outputs = model(images)
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test_acc += calculate_score(labels, outputs)
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test_acc /= len(test_loader)
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print(f'Test Acc: {test_acc:.3f}')
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return test_acc
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#%%
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# 定义图像变换
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trans1 = transforms.Compose([
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transforms.RandomHorizontalFlip(), # 随机水平翻转
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transforms.RandomRotation(10), # 随机旋转±10度
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transforms.ColorJitter(brightness=0.2, contrast=0.2), # 颜色调整
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transforms.Resize((18, 18)), # 调整大小为18x18
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transforms.ToTensor(), # 转换为张量
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transforms.Normalize((0.5,), (0.5,)) # 归一化到[-1, 1]
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])
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trans2 = transforms.Compose([
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transforms.RandomHorizontalFlip(), # 随机水平翻转
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transforms.RandomRotation(10), # 随机旋转±10度
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transforms.ColorJitter(brightness=0.2, contrast=0.2), # 颜色调整
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transforms.Resize((16, 16)), # 调整大小为16x16
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transforms.ToTensor(), # 转换为张量
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transforms.Normalize((0.5,), (0.5,)) # 归一化到[-1, 1]
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])
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train_dataset = FashionMNIST(root='./data/notebook2', train=False, transform=trans1,download=True)
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test_dataset = FashionMNIST(root='./data/notebook2', train=False, transform=trans1,download=True)
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# 定义训练集和测试集的比例
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train_ratio = 0.8 # 训练集比例为80%，验证集比例为20%
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valid_ratio = 0.2
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total_samples = len(train_dataset)
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train_size = int(train_ratio * total_samples)
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valid_size = int(valid_ratio * total_samples)
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# 分割训练集和测试集
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train_dataset, valid_dataset = torch.utils.data.random_split(train_dataset, [train_size, valid_size])
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# 加载随机抽取的训练数据集
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train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True, drop_last=True)
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valid_loader = DataLoader(valid_dataset, batch_size=64, shuffle=False, drop_last=True)
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test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False, drop_last=True)
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#%%
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singlegate_list = ['rx', 'ry', 'rz', 's', 't', 'p', 'u3']
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doublegate_list = ['rxx', 'ryy', 'rzz', 'swap', 'cnot', 'cp', 'ch', 'cu', 'ct', 'cz']
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#%%
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# 随机量子卷积层
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class RandomQuantumConvolutionalLayer(nn.Module):
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def __init__(self, nqubit, num_circuits, seed:int=1024):
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super(RandomQuantumConvolutionalLayer, self).__init__()
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random.seed(seed)
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self.nqubit = nqubit
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self.cirs = nn.ModuleList([self.circuit(nqubit) for _ in range(num_circuits)])
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def circuit(self, nqubit):
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cir = dq.QubitCircuit(nqubit)
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cir.rxlayer(encode=True) # 对原论文的量子线路结构并无影响，只是做了一个数据编码的操作
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cir.barrier()
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for iter in range(3):
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for i in range(nqubit):
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singlegate = random.choice(singlegate_list)
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getattr(cir, singlegate)(i)
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control_bit, target_bit = random.sample(range(0, nqubit - 1), 2)
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doublegate = random.choice(doublegate_list)
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if doublegate[0] in ['r', 's']:
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getattr(cir, doublegate)([control_bit, target_bit])
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else:
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getattr(cir, doublegate)(control_bit, target_bit)
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cir.barrier()
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cir.observable(0)
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return cir
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def forward(self, x):
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kernel_size, stride = 2, 2
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# [64, 1, 18, 18] -> [64, 1, 9, 18, 2] -> [64, 1, 9, 9, 2, 2]
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x_unflod = x.unfold(2, kernel_size, stride).unfold(3, kernel_size, stride)
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w = int((x.shape[-1] - kernel_size) / stride + 1)
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x_reshape = x_unflod.reshape(-1, self.nqubit)
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exps = []
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for cir in self.cirs: # out_channels
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cir(x_reshape)
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exp = cir.expectation()
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exps.append(exp)
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exps = torch.stack(exps, dim=1)
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exps = exps.reshape(x.shape[0], 3, w, w)
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return exps
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#%%
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net = RandomQuantumConvolutionalLayer(nqubit=4, num_circuits=3, seed=1024)
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net.cirs[0].draw()
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#%%
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# 基于随机量子卷积层的混合模型
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class RandomQCCNN(nn.Module):
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def __init__(self):
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super(RandomQCCNN, self).__init__()
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self.conv = nn.Sequential(
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RandomQuantumConvolutionalLayer(nqubit=4, num_circuits=3, seed=1024), # num_circuits=3代表我们在quanv1层只用了3个量子卷积核
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nn.BatchNorm2d(3), # 添加批量归一化
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=1),
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nn.Conv2d(3, 6, kernel_size=2, stride=1),
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nn.BatchNorm2d(6), # 添加批量归一化
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=1)
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)
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self.fc = nn.Sequential(
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nn.Linear(6 * 6 * 6, 1024),
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nn.BatchNorm1d(1024), # 添加批量归一化
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nn.Dropout(0.5), # 增加dropout比例
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nn.ReLU(),
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nn.Linear(1024, 10)
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)
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def forward(self, x):
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x = self.conv(x)
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x = x.reshape(x.size(0), -1)
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x = self.fc(x)
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return x
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#%%
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# 修改RandomQCCNN模型的训练参数
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num_epochs = 300
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(device)
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seed_torch(42) # 使用相同的随机种子值
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model = RandomQCCNN()
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model.to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-5) # 使用AdamW优化器和适当的权重衰减
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optim_model, metrics = train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device)
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torch.save(optim_model.state_dict(), './data/notebook2/random_qccnn_weights.pt') # 保存训练好的模型参数，用于后续的推理或测试
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pd.DataFrame(metrics).to_csv('./data/notebook2/random_qccnn_metrics.csv', index='None') # 保存模型训练过程，用于后续图标展示
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#%%
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state_dict = torch.load('./data/notebook2/random_qccnn_weights.pt', map_location=device)
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random_qccnn_model = RandomQCCNN()
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random_qccnn_model.load_state_dict(state_dict)
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random_qccnn_model.to(device)
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test_acc = test_model(random_qccnn_model, test_loader, device)
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#%%
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data = pd.read_csv('./data/notebook2/random_qccnn_metrics.csv')
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epoch = data['epoch']
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train_loss = data['train_loss']
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valid_loss = data['valid_loss']
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train_acc = data['train_acc']
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valid_acc = data['valid_acc']
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# 创建图和Axes对象
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
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# 绘制训练损失曲线
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ax1.plot(epoch, train_loss, label='Train Loss')
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ax1.plot(epoch, valid_loss, label='Valid Loss')
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ax1.set_title('Training Loss Curve')
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ax1.set_xlabel('Epoch')
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ax1.set_ylabel('Loss')
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ax1.legend()
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# 绘制训练准确率曲线
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ax2.plot(epoch, train_acc, label='Train Accuracy')
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ax2.plot(epoch, valid_acc, label='Valid Accuracy')
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ax2.set_title('Training Accuracy Curve')
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ax2.set_xlabel('Epoch')
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ax2.set_ylabel('Accuracy')
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ax2.legend()
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plt.show()
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#%%
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class ParameterizedQuantumConvolutionalLayer(nn.Module):
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def __init__(self, nqubit, num_circuits):
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super().__init__()
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self.nqubit = nqubit
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self.cirs = nn.ModuleList([self.circuit(nqubit) for _ in range(num_circuits)])
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def circuit(self, nqubit):
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cir = dq.QubitCircuit(nqubit)
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cir.rxlayer(encode=True) #对原论文的量子线路结构并无影响，只是做了一个数据编码的操作
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cir.barrier()
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for iter in range(4): #对应原论文中一个量子卷积线路上的深度为4，可控参数一共16个
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cir.rylayer()
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cir.cnot_ring()
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cir.barrier()
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cir.observable(0)
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return cir
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def forward(self, x):
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kernel_size, stride = 2, 2
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# [64, 1, 18, 18] -> [64, 1, 9, 18, 2] -> [64, 1, 9, 9, 2, 2]
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x_unflod = x.unfold(2, kernel_size, stride).unfold(3, kernel_size, stride)
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w = int((x.shape[-1] - kernel_size) / stride + 1)
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x_reshape = x_unflod.reshape(-1, self.nqubit)
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exps = []
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for cir in self.cirs: # out_channels
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cir(x_reshape)
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exp = cir.expectation()
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exps.append(exp)
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exps = torch.stack(exps, dim=1)
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exps = exps.reshape(x.shape[0], 3, w, w)
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return exps
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#%%
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# 此处我们可视化其中一个量子卷积核的线路结构：
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net = ParameterizedQuantumConvolutionalLayer(nqubit=4, num_circuits=3)
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net.cirs[0].draw()
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#%%
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# QCCNN整体网络架构：
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class QCCNN(nn.Module):
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def __init__(self):
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super(QCCNN, self).__init__()
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self.conv = nn.Sequential(
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ParameterizedQuantumConvolutionalLayer(nqubit=4, num_circuits=3),
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nn.BatchNorm2d(3), # 添加批量归一化
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nn.ReLU(),
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nn.MaxPool2d(kernel_size=2, stride=1)
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)
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self.fc = nn.Sequential(
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nn.Linear(8 * 8 * 3, 128),
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nn.BatchNorm1d(128), # 添加批量归一化
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nn.Dropout(0.5), # 增加dropout比例
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nn.ReLU(),
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nn.Linear(128, 10)
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)
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def forward(self, x):
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x = self.conv(x)
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x = x.reshape(x.size(0), -1)
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x = self.fc(x)
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return x
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#%%
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# 修改QCCNN模型的训练参数
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num_epochs = 300
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = QCCNN()
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model.to(device)
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criterion = nn.CrossEntropyLoss()
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optimizer = optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-5) # 使用AdamW优化器和适当的权重衰减
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optim_model, metrics = train_model(model, criterion, optimizer, train_loader, valid_loader, num_epochs, device)
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torch.save(optim_model.state_dict(), './data/notebook2/qccnn_weights.pt') # 保存训练好的模型参数，用于后续的推理或测试
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pd.DataFrame(metrics).to_csv('./data/notebook2/qccnn_metrics.csv', index='None') # 保存模型训练过程，用于后续图标展示
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#%%
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state_dict = torch.load('./data/notebook2/qccnn_weights.pt', map_location=device)
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qccnn_model = QCCNN()
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qccnn_model.load_state_dict(state_dict)
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qccnn_model.to(device)
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test_acc = test_model(qccnn_model, test_loader, device)
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#%%
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def vgg_block(in_channel,out_channel,num_convs):
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layers = nn.ModuleList()
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assert num_convs >= 1
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layers.append(nn.Conv2d(in_channel,out_channel,kernel_size=3,padding=1))
|
||
layers.append(nn.ReLU())
|
||
for _ in range(num_convs-1):
|
||
layers.append(nn.Conv2d(out_channel,out_channel,kernel_size=3,padding=1))
|
||
layers.append(nn.ReLU())
|
||
layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
|
||
return nn.Sequential(*layers)
|
||
|
||
VGG = nn.Sequential(
|
||
vgg_block(1, 32, 2), # 增加通道数和调整卷积层数量
|
||
vgg_block(32, 64, 2),
|
||
nn.Flatten(),
|
||
nn.Linear(64 * 4 * 4, 256), # 调整全连接层大小
|
||
nn.BatchNorm1d(256), # 添加批量归一化
|
||
nn.ReLU(),
|
||
nn.Dropout(0.5), # 增加dropout比例
|
||
nn.Linear(256, 128),
|
||
nn.BatchNorm1d(128), # 添加批量归一化
|
||
nn.ReLU(),
|
||
nn.Dropout(0.5),
|
||
nn.Linear(128, 10),
|
||
nn.Softmax(dim=-1)
|
||
)
|
||
#%%
|
||
# 修改VGG模型的训练参数
|
||
num_epochs = 300
|
||
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||
|
||
vgg_model = VGG
|
||
vgg_model.to(device)
|
||
criterion = nn.CrossEntropyLoss()
|
||
optimizer = optim.AdamW(vgg_model.parameters(), lr=3e-4, weight_decay=1e-5) # 使用AdamW优化器和适当的权重衰减
|
||
vgg_model, metrics = train_model(vgg_model, criterion, optimizer, train_loader, valid_loader, num_epochs, device)
|
||
torch.save(vgg_model.state_dict(), './data/notebook2/vgg_weights.pt') # 保存训练好的模型参数，用于后续的推理或测试
|
||
pd.DataFrame(metrics).to_csv('./data/notebook2/vgg_metrics.csv', index='None') # 保存模型训练过程，用于后续图标展示
|
||
#%%
|
||
state_dict = torch.load('./data/notebook2/vgg_weights.pt', map_location=device)
|
||
vgg_model = VGG
|
||
vgg_model.load_state_dict(state_dict)
|
||
vgg_model.to(device)
|
||
|
||
vgg_test_acc = test_model(vgg_model, test_loader, device)
|
||
#%%
|
||
vgg_data = pd.read_csv('./data/notebook2/vgg_metrics.csv')
|
||
qccnn_data = pd.read_csv('./data/notebook2/qccnn_metrics.csv')
|
||
vgg_epoch = vgg_data['epoch']
|
||
vgg_train_loss = vgg_data['train_loss']
|
||
vgg_valid_loss = vgg_data['valid_loss']
|
||
vgg_train_acc = vgg_data['train_acc']
|
||
vgg_valid_acc = vgg_data['valid_acc']
|
||
|
||
qccnn_epoch = qccnn_data['epoch']
|
||
qccnn_train_loss = qccnn_data['train_loss']
|
||
qccnn_valid_loss = qccnn_data['valid_loss']
|
||
qccnn_train_acc = qccnn_data['train_acc']
|
||
qccnn_valid_acc = qccnn_data['valid_acc']
|
||
|
||
# 创建图和Axes对象
|
||
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
|
||
|
||
# 绘制训练损失曲线
|
||
ax1.plot(vgg_epoch, vgg_train_loss, label='VGG Train Loss')
|
||
ax1.plot(vgg_epoch, vgg_valid_loss, label='VGG Valid Loss')
|
||
ax1.plot(qccnn_epoch, qccnn_train_loss, label='QCCNN Valid Loss')
|
||
ax1.plot(qccnn_epoch, qccnn_valid_loss, label='QCCNN Valid Loss')
|
||
ax1.set_title('Training Loss Curve')
|
||
ax1.set_xlabel('Epoch')
|
||
ax1.set_ylabel('Loss')
|
||
ax1.legend()
|
||
|
||
# 绘制训练准确率曲线
|
||
ax2.plot(vgg_epoch, vgg_train_acc, label='VGG Train Accuracy')
|
||
ax2.plot(vgg_epoch, vgg_valid_acc, label='VGG Valid Accuracy')
|
||
ax2.plot(qccnn_epoch, qccnn_train_acc, label='QCCNN Train Accuracy')
|
||
ax2.plot(qccnn_epoch, qccnn_valid_acc, label='QCCNN Valid Accuracy')
|
||
ax2.set_title('Training Accuracy Curve')
|
||
ax2.set_xlabel('Epoch')
|
||
ax2.set_ylabel('Accuracy')
|
||
ax2.legend()
|
||
|
||
plt.show()
|
||
#%%
|
||
# 这里我们对比不同模型之间可训练参数量的区别
|
||
|
||
def count_parameters(model):
|
||
"""
|
||
计算模型的参数数量
|
||
"""
|
||
return sum(p.numel() for p in model.parameters() if p.requires_grad)
|
||
|
||
number_params_VGG = count_parameters(VGG)
|
||
number_params_QCCNN = count_parameters(QCCNN())
|
||
print(f'VGG 模型可训练参数量：{number_params_VGG}\t QCCNN模型可训练参数量：{number_params_QCCNN}') |