「MobileNet V3」70 个犬种的图片分类

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本文目录

- 数据集与 Notebook
- 环境准备
- 数据集
- 可视化
- 模型
- 预测
- Loss 与评价指标

数据集与 Notebook

数据集：70 Dog Breeds-Image Data Set
Notebook：「MobileNet V3」70 Dog Breeds-Image Classification

环境准备

import warnings
warnings.filterwarnings('ignore')

禁用警告，防止干扰。

!pip install lightning --quiet

安装 PyTorch Lightning。

import random
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as snssns.set_theme(style="darkgrid", font_scale=1.5, font="SimHei", rc={"axes.unicode_minus":False})

导入常用的库，设置绘图风格。

import torch
import torchmetrics
from torch import nn, optim
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision import transforms, datasets, models

导入 PyTorch 相关的库。

import lightning.pytorch as pl
from lightning.pytorch.loggers import CSVLogger
from lightning.pytorch.callbacks.early_stopping import EarlyStopping

导入 PyTorch Lightning 相关的库。

seed = 1
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
pl.seed_everything(seed, workers=True)

设置随机种子。

数据集

batch_size = 64

设置批次大小。

train_transform = transforms.Compose([transforms.Resize((224, 224)),transforms.RandomHorizontalFlip(),transforms.ToTensor(),
])test_transform = transforms.Compose([transforms.Resize((224, 224)),transforms.ToTensor(),
])

设置数据集的预处理。

train_dataset = datasets.ImageFolder(root="/kaggle/input/70-dog-breedsimage-data-set/train", transform=train_transform)
val_dataset = datasets.ImageFolder(root="/kaggle/input/70-dog-breedsimage-data-set/valid", transform=test_transform)
test_dataset = datasets.ImageFolder(root="/kaggle/input/70-dog-breedsimage-data-set/test", transform=test_transform)

读取数据集。

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

加载数据集。

可视化

class_names = train_dataset.classes
class_count = [train_dataset.targets.count(i) for i in range(len(class_names))]
df = pd.DataFrame({"Class": class_names, "Count": class_count})plt.figure(figsize=(12, 20), dpi=100)
sns.barplot(x="Count", y="Class", data=df)
plt.tight_layout()
plt.show()

绘制训练集的类别分布。

训练集的类别分布

plt.figure(figsize=(12, 20), dpi=100)
images, labels = next(iter(val_loader))
for i in range(8):ax = plt.subplot(8, 4, i + 1)plt.imshow(images[i].permute(1, 2, 0).numpy())plt.title(class_names[labels[i]])plt.axis("off")
plt.tight_layout()
plt.show()

绘制训练集的样本。

训练集的样本

模型

class LitModel(pl.LightningModule):def __init__(self, num_classes=1000):super().__init__()self.model = models.mobilenet_v3_large(weights="IMAGENET1K_V2")# for param in self.model.parameters():#     param.requires_grad = Falseself.model.classifier[3] = nn.Linear(self.model.classifier[3].in_features, num_classes)self.accuracy = torchmetrics.Accuracy(task="multiclass", num_classes=num_classes)self.precision = torchmetrics.Precision(task="multiclass", average="macro", num_classes=num_classes)self.recall = torchmetrics.Recall(task="multiclass", average="macro", num_classes=num_classes)self.f1score = torchmetrics.F1Score(task="multiclass", num_classes=num_classes)def forward(self, x):x = self.model(x)return xdef configure_optimizers(self):optimizer = optim.Adam(self.parameters(), lr=0.001, betas=(0.9, 0.99), eps=1e-08, weight_decay=1e-5)return optimizerdef training_step(self, batch, batch_idx):x, y = batchy_hat = self(x)loss = F.cross_entropy(y_hat, y)self.log("train_loss", loss, on_step=True, on_epoch=False, prog_bar=True, logger=True)self.log_dict({"train_acc": self.accuracy(y_hat, y),"train_prec": self.precision(y_hat, y),"train_recall": self.recall(y_hat, y),"train_f1score": self.f1score(y_hat, y),},on_step=True,on_epoch=False,logger=True,)return lossdef validation_step(self, batch, batch_idx):x, y = batchy_hat = self(x)loss = F.cross_entropy(y_hat, y)self.log("val_loss", loss, on_step=False, on_epoch=True, logger=True)self.log_dict({"val_acc": self.accuracy(y_hat, y),"val_prec": self.precision(y_hat, y),"val_recall": self.recall(y_hat, y),"val_f1score": self.f1score(y_hat, y),},on_step=False,on_epoch=True,logger=True,)def test_step(self, batch, batch_idx):x, y = batchy_hat = self(x)self.log_dict({"test_acc": self.accuracy(y_hat, y),"test_prec": self.precision(y_hat, y),"test_recall": self.recall(y_hat, y),"test_f1score": self.f1score(y_hat, y),})def predict_step(self, batch, batch_idx, dataloader_idx=None):x, y = batchy_hat = self(x)preds = torch.argmax(y_hat, dim=1)return preds

定义模型。

num_classes = len(class_names)
model = LitModel(num_classes=num_classes)
logger = CSVLogger("./")
early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=0.00, patience=5, verbose=False, mode="min"
)
trainer = pl.Trainer(max_epochs=20,enable_progress_bar=True,logger=logger,callbacks=[early_stop_callback],deterministic=True,
)
trainer.fit(model, train_loader, val_loader)

训练模型。

trainer.test(model, val_loader)

测试模型。

预测

pred = trainer.predict(model, test_loader)
pred = torch.cat(pred, dim=0)
pred = pd.DataFrame(pred.numpy(), columns=["Class"])
pred["Class"] = pred["Class"].apply(lambda x: class_names[x])plt.figure(figsize=(12, 20), dpi=100)
sns.countplot(y="Class", data=pred)
plt.tight_layout()
plt.show()

绘制预测结果的类别分布。

预测结果的类别分布

Loss 与评价指标

log_path = logger.log_dir + "/metrics.csv"
metrics = pd.read_csv(log_path)
x_name = "epoch"plt.figure(figsize=(8, 6), dpi=100)
sns.lineplot(x=x_name, y="train_loss", data=metrics, label="Train Loss", linewidth=2, marker="o", markersize=10)
sns.lineplot(x=x_name, y="val_loss", data=metrics, label="Valid Loss", linewidth=2, marker="X", markersize=12)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.tight_layout()
plt.show()plt.figure(figsize=(14, 12), dpi=100)plt.subplot(2,2,1)
sns.lineplot(x=x_name, y="train_acc", data=metrics, label="Train Accuracy", linewidth=2, marker="o", markersize=10)
sns.lineplot(x=x_name, y="val_acc", data=metrics, label="Valid Accuracy", linewidth=2, marker="X", markersize=12)
plt.xlabel("Epoch")
plt.ylabel("Accuracy")plt.subplot(2,2,2)
sns.lineplot(x=x_name, y="train_prec", data=metrics, label="Train Precision", linewidth=2, marker="o", markersize=10)
sns.lineplot(x=x_name, y="val_prec", data=metrics, label="Valid Precision", linewidth=2, marker="X", markersize=12)
plt.xlabel("Epoch")
plt.ylabel("Precision")plt.subplot(2,2,3)
sns.lineplot(x=x_name, y="train_recall", data=metrics, label="Train Recall", linewidth=2, marker="o", markersize=10)
sns.lineplot(x=x_name, y="val_recall", data=metrics, label="Valid Recall", linewidth=2, marker="X", markersize=12)
plt.xlabel("Epoch")
plt.ylabel("Recall")plt.subplot(2,2,4)
sns.lineplot(x=x_name, y="train_f1score", data=metrics, label="Train F1-Score", linewidth=2, marker="o", markersize=10)
sns.lineplot(x=x_name, y="val_f1score", data=metrics, label="Valid F1-Score", linewidth=2, marker="X", markersize=12)
plt.xlabel("Epoch")
plt.ylabel("F1-Score")plt.tight_layout()
plt.show()