前言

• ResNet34模型，做圖像分類
• 數(shù)據(jù)使用水果圖片數(shù)據(jù)集，下載見Kaggle Fruits Dataset (Images)
• Kaggle的Notebook示例見 PyTorch——ResNet34模型和Fruits數(shù)據(jù)
• 下面見代碼

導(dǎo)包

from PIL import Image
import os
import random
import matplotlib.pyplot as plt
import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torch.nn import functional as F
from torchvision import transforms as T
from torchvision.datasets import ImageFolder
import tqdm

數(shù)據(jù)探索查看

• 查看圖像

path = "/kaggle/input/fruits-dataset-images/images"
fruit_path = "apple fruit"
apple_files = os.listdir(path + "/" + fruit_path)

Image.open(path + "/"+fruit_path+"/" + apple_files[2])

• 展示多張圖片

def show_images(n_rows, n_cols, x_data):
    assert n_rows * n_cols <= len(x_data)
    
    plt.figure(figsize=(n_cols * 1.5, n_rows * 1.5))
    for row in range(n_rows):
        for col in range(n_cols):
            index = row * n_cols + col
            plt.subplot(n_rows, n_cols, index + 1)
            plt.imshow(x_data[index][0], cmap="binary", interpolation="nearest")  # 圖像
            plt.axis("off")
            plt.title(x_data[index][1])  # 標(biāo)簽
    plt.show()
   
def show_fruit_imgs(fruit, cols, rows):
    files = os.listdir(path + "/" + fruit)
    images = []
    for _ in range(cols * rows):
        file = files[random.randint(0, len(files) -1)]
        image = Image.open(path + "/" + fruit + "/" + file)
        label = file.split(".")[0]
        images.append((image, label))
    show_images(cols, rows, images)

• 蘋果

show_fruit_imgs("apple fruit", 3, 3)

• 櫻桃

show_fruit_imgs("cherry fruit", 3, 3)

數(shù)據(jù)集構(gòu)建

• 直接使用ImageFolder加載數(shù)據(jù)，按目錄解析水果類別

transforms = T.Compose([
    T.Resize(224),
    T.CenterCrop(224),
    T.ToTensor(),
    T.Normalize(mean=[5., 5., 5.], std=[.5, .5, .5])
])

train_dataset = ImageFolder(path, transform=transforms)
classification = os.listdir(path)

train_dataset[2]

• 輸出如下

(tensor([[[-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.],
          ...,
          [-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.]],
 
         [[-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.],
          ...,
          [-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.]],
 
         [[-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.],
          ...,
          [-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.],
          [-8., -8., -8.,  ..., -8., -8., -8.]]]),
 0)

構(gòu)建模型 ResNet34

• ResidualBlock

class ResidualBlock(nn.Module):
    
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=(3, 3), stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=(3, 3), stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.is_shortcut = stride > 1
        self.shortcut = None if not self.is_shortcut else self._shortcut(in_channels, out_channels, stride)
    
    def forward(self, X):
        out = self.conv1(X)
        out = self.bn1(out)
        out = F.relu(out, inplace=True)
        out = self.conv2(out)
        out = self.bn2(out)
        # 當(dāng)X的維度和out不一致時，需要用shortcut處理X
        out += X if not self.shortcut else self.shortcut(X)
        out = F.relu(out)
        return out
    
    def _shortcut(self, in_channels, out_channels, stride):
        return nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 1, stride, bias=False),
            nn.BatchNorm2d(out_channels)
        )

• ResNet34

class ResNet34(nn.Module):
    
    def __init__(self, num_classes=2):
        super().__init__()
        self.pre = nn.Sequential(
            nn.Conv2d(3, 64, 7, 2, 3, bias=False),  # 64 * 112 * 112
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3, 2, 1)  # 64 * 56 * 56
        )
        # layer1 不需要shortcut，因為圖像沒變化(kernel_size=3,stride=1, padding=1)
        self.layer1 = self._make_layer(64, 64, 3, 1)
        self.layer2 = self._make_layer(64, 128, 4, 2)
        self.layer3 = self._make_layer(128, 256, 6, 2)
        self.layer4 = self._make_layer(256, 512, 3, 2)
        self.fc = nn.Linear(512, num_classes)
        
    def _make_layer(self, in_channels, out_channels, block_num, stride):
        layers = [ResidualBlock(in_channels, out_channels, stride)]
        for i in range(1, block_num):
            layers.append(ResidualBlock(out_channels, out_channels))
        return nn.Sequential(*layers)
    
    def forward(self, X):
        # X: 3 * 224 * 224
        out = self.pre(X)                # 64 * 56 * 56
        out = self.layer1(out)           # 64 * 56 * 56
        out = self.layer2(out)           # 128 * 28 * 28
        out = self.layer3(out)           # 256 * 14 * 14
        out = self.layer4(out)           # 512 * 7 * 7
        out = F.avg_pool2d(out, 7)       # 512 * 1 * 1
        out = out.view(out.size(0), -1)  # 512 
        out = self.fc(out)               # len(classification)
        return out

模型訓(xùn)練

• 準(zhǔn)備代碼

def pad(num, target) -> str:
    """
    將num長度與target對齊
    """
    return str(num).zfill(len(str(target)))

# 參數(shù)配置
epoch_num = 50
batch_size = 32
learning_rate = 0.0005

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 數(shù)據(jù)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)

# 構(gòu)建模型
model = ResNet34(len(classification)).to(device)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

print(model)

ResNet34(
  (pre): Sequential(
    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (2): ReLU(inplace=True)
    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  )
  (layer1): Sequential(
    (0): ResidualBlock(
      (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)
      (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): ResidualBlock(
      (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)
      (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)
    )
    (2): ResidualBlock(
      (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)
      (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): ResidualBlock(
      (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)
      (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)
      (right): 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): ResidualBlock(
      (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)
      (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)
    )
    (2): ResidualBlock(
      (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)
      (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)
    )
    (3): ResidualBlock(
      (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)
      (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): ResidualBlock(
      (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)
      (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)
      (right): 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): ResidualBlock(
      (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)
      (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)
    )
    (2): ResidualBlock(
      (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)
      (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)
    )
    (3): ResidualBlock(
      (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)
      (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)
    )
    (4): ResidualBlock(
      (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)
      (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)
    )
    (5): ResidualBlock(
      (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)
      (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): ResidualBlock(
      (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)
      (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)
      (right): 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): ResidualBlock(
      (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)
      (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)
    )
    (2): ResidualBlock(
      (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)
      (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)
    )
  )
  (fc): Linear(in_features=512, out_features=9, bias=True)
)

• 開始訓(xùn)練

# 開始訓(xùn)練
train_loss_list = []
total_step = len(train_loader)
for epoch in range(1, epoch_num + 1):
    model.train
    train_total_loss, train_total, train_correct  = 0, 0, 0
    train_progress = tqdm.tqdm(train_loader, desc="Train...")
    for i, (X, y) in enumerate(train_progress, 1):
        X, y = X.to(device), y.to(device)
        
        out = model(X)
        loss = criterion(out, y)
        
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        
        _, pred = torch.max(out, 1)
        train_total += y.size(0)
        train_correct += (pred == y).sum().item()
        train_total_loss += loss.item()

        train_progress.set_description(f"Train... [epoch {pad(epoch, epoch_num)}/{epoch_num}, loss {(train_total_loss / i):.4f}, accuracy {train_correct / train_total:.4f}]")
    train_loss_list.append(train_total_loss / total_step) 

Train... [epoch 01/50, loss 2.3034, accuracy 0.2006]: 100%|██████████| 12/12 [00:15<00:00,  1.32s/it]
Train... [epoch 02/50, loss 1.9193, accuracy 0.3064]: 100%|██████████| 12/12 [00:16<00:00,  1.36s/it]
Train... [epoch 03/50, loss 1.6338, accuracy 0.3482]: 100%|██████████| 12/12 [00:15<00:00,  1.30s/it]
Train... [epoch 04/50, loss 1.6031, accuracy 0.3649]: 100%|██████████| 12/12 [00:16<00:00,  1.38s/it]
Train... [epoch 05/50, loss 1.5298, accuracy 0.4401]: 100%|██████████| 12/12 [00:15<00:00,  1.31s/it]
Train... [epoch 06/50, loss 1.4189, accuracy 0.4429]: 100%|██████████| 12/12 [00:16<00:00,  1.34s/it]
Train... [epoch 07/50, loss 1.5439, accuracy 0.4708]: 100%|██████████| 12/12 [00:15<00:00,  1.31s/it]
Train... [epoch 08/50, loss 1.4378, accuracy 0.4596]: 100%|██████████| 12/12 [00:16<00:00,  1.36s/it]
Train... [epoch 09/50, loss 1.4005, accuracy 0.5348]: 100%|██████████| 12/12 [00:15<00:00,  1.32s/it]
Train... [epoch 10/50, loss 1.2937, accuracy 0.5599]: 100%|██████████| 12/12 [00:16<00:00,  1.34s/it]
......
Train... [epoch 45/50, loss 0.7966, accuracy 0.7354]: 100%|██████████| 12/12 [00:15<00:00,  1.27s/it]
Train... [epoch 46/50, loss 0.8075, accuracy 0.7660]: 100%|██████████| 12/12 [00:15<00:00,  1.33s/it]
Train... [epoch 47/50, loss 0.8587, accuracy 0.7131]: 100%|██████████| 12/12 [00:15<00:00,  1.27s/it]
Train... [epoch 48/50, loss 0.7171, accuracy 0.7604]: 100%|██████████| 12/12 [00:16<00:00,  1.35s/it]
Train... [epoch 49/50, loss 0.9715, accuracy 0.7047]: 100%|██████████| 12/12 [00:15<00:00,  1.27s/it]
Train... [epoch 50/50, loss 0.7050, accuracy 0.7855]: 100%|██████████| 12/12 [00:15<00:00,  1.33s/it]

繪制訓(xùn)練曲線

plt.plot(range(len(train_loss_list)), train_loss_list)
plt.xlabel("epoch")
plt.ylabel("loss_val")
plt.show()

文章來源地址http://www.zghlxwxcb.cn/news/detail-530182.html

到了這里，關(guān)于PyTorch示例——ResNet34模型和Fruits圖像數(shù)據(jù)的文章就介紹完了。如果您還想了解更多內(nèi)容，請在右上角搜索TOY模板網(wǎng)以前的文章或繼續(xù)瀏覽下面的相關(guān)文章，希望大家以后多多支持TOY模板網(wǎng)！