data4fun | 小数据不简单

import torch
from torch import nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import os

# 数据源来自： https://www.kaggle.com/datasets/muniryadi/cat-vs-rabbit
train_path = './cat_or_rabbit/train-cat-rabbit'
test_path = './cat_or_rabbit/test-images'
val_path = './cat_or_rabbit/val-cat-rabbit'
# ToTensor 对输入做归一化处理，将资料范围变成[0,1]之间
# 正规化是机器学习常用的资料前处理，将资料范围变成[-1,1]之间

normalize=transforms.Normalize(mean=[.5,.5,.5],std=[.5,.5,.5])
transform=transforms.Compose([
 transforms.RandomCrop(224), # 随即裁剪
 transforms.RandomHorizontalFlip(), # 水平翻转
 transforms.ToTensor(), # tensor 格式
 normalize
])
# 建立資料集
train_dataset = datasets.ImageFolder(train_path, transform = transform)
val_dataset = datasets.ImageFolder(val_path, transform = transform)
test_dataset = datasets.ImageFolder(test_path, transform = transform)

train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)
val_dataloader = DataLoader(val_dataset, batch_size=64, shuffle=True)

device = (
 "cuda"
 if torch.cuda.is_available()
 else "mps"
 if torch.backends.mps.is_available()
 else "cpu"
)
print(f"Using {device} device")
class NeuralNetwork(nn.Module):
 def __init__(self, img_size):
 super().__init__()
 self.flatten = nn.Flatten()
 self.linear_relu_stack = nn.Sequential(
 nn.Linear(img_size, 512),
 nn.ReLU(),
 nn.Linear(512, 512),
 nn.ReLU(),
 nn.Linear(512, 10),
 )

 def forward(self, x):
 x = self.flatten(x)
 logits = self.linear_relu_stack(x)
 return logits

model = NeuralNetwork(3*224*224).to(device)
print(model)
learning_rate = 1e-3
batch_size = 64
epochs = 10
# 初始化loss function
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
def train_loop(dataloader, model, loss_fn, optimizer):
 size = len(dataloader.dataset)
 model.train()
 for batch, (X, y) in enumerate(dataloader):
 # 將資料讀取到GPU中
 X, y = X.to(device), y.to(device)
 # 運算出結果並計算loss
 pred = model(X)
 loss = loss_fn(pred, y)

 # 反向傳播
 loss.backward()
 optimizer.step()
 optimizer.zero_grad()

 if batch % 100 == 0:
 print(batch)
 loss, current = loss.item(), (batch + 1) * len(X)
 print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
def test_loop(dataloader, model, loss_fn):
 model.eval()
 size = len(dataloader.dataset)
 num_batches = len(dataloader)
 test_loss, correct = 0, 0
 # 驗證或測試時記得加入 torch.no_grad() 讓神經網路不要更新
 with torch.no_grad():
 for X, y in dataloader:
 X, y = X.to(device), y.to(device)
 pred = model(X)
 test_loss += loss_fn(pred, y).item()
 correct += (pred.argmax(1) == y).type(torch.float).sum().item()

 test_loss /= num_batches
 correct /= size
 print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
 
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

epochs = 10
for t in range(epochs):
 print(f"Epoch {t+1}\n-------------------------------")
 train_loop(train_dataloader, model, loss_fn, optimizer)
 test_loop(val_dataloader, model, loss_fn)
print("Done!")

# 保存为内部字典
torch.save(model.state_dict(), 'cat_vs_rabbit_cls_v1.pth')

# 重新载入的时候，要先建立和原来一样的模型结构
reload_model = NeuralNetwork(3*224*224)
reload_model.load_state_dict(torch.load('cat_vs_rabbit_cls_v1.pth'))
reload_model.to(device)
reload_model.eval()

# 验证
epochs = 1
for t in range(epochs):
 test_loop(val_dataloader, reload_model)

import torchvision.models as models
import torchvision

# torchvision.models下面有很多已经训练好的模型，我们可以直接加载
mobilenet_v3_model = models.mobilenet_v3_small(pretrained=True)
print(mobilenet_v3_model)

# 针对新模型参数做优化
optimizer = torch.optim.SGD(mobilenet_v3_model.parameters(), lr=learning_rate)

# 修改一下输出参数，因为我们的输出类别是2个
num_classes = 2
mobilenet_v3_model.classifier = nn.Sequential(
 nn.Linear(576, 1024), 
 nn.ReLU(), 
 nn.Linear(1024, num_classes)
)
mobilenet_v3_model.to(device)

input_image = torch.rand(3,224,224)
print(input_image.size())
# torch.Size([3, 224, 224])
flatten = nn.Flatten()
flat_image = flatten(input_image)
print(flat_image.size())
# torch.Size([3, 50176])

layer1 = nn.Linear(in_features=224*224, out_features=1024)
hidden1 = layer1(flat_image)
print(hidden1.size()
#torch.Size([3, 1024])

print(f"Before ReLU: {hidden1}\n\n")
hidden1 = nn.ReLU()(hidden1)
print(f"After ReLU: {hidden1}")
nn.Sequential

softmax = nn.Softmax(dim=1)
pred_probab = softmax(logits)

# 引入对应的模块
import os
import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
# 选择模型的训练资源
device = (
 "cuda"
 if torch.cuda.is_available()
 else "mps"
 if torch.backends.mps.is_available()
 else "cpu"
)
print(f"Using {device} device")
# 初始化神经网络实例，并把它迁移到对应的设备上
model = NeuralNetwork(224*224).to(device)
print(model)
# 验证输出
X = torch.rand(1, 224, 224, device=device)
logits = model(X)
pred_probab = nn.Softmax(dim=1)(logits)
y_pred = pred_probab.argmax(1)
print(f"Predicted class: {y_pred}")