李宏毅机器学习课程笔记EP9

【HW3】Image_Classification0.1李宏毅2021/2022春机器学习课程笔记EP9(P35-P37)

从今天开始我将学习李宏毅教授的机器学习视频,下面是课程的连接(强推)李宏毅2021/2022春机器学习课程_哔哩哔哩_bilibili。一共有155个视频,争取都学习完成吧。

那么首先这门课程需要有一定的代码基础,简单学习一下Python的基本用法,还有里面的NumPy库等等的基本知识。再就是数学方面的基础啦,微积分、线性代数和概率论的基础都是听懂这门课必须的。


这次的作业我根据B站上的一些解题思路,对助教的代码做了一定的修改,但是遭到了史无前例的滑铁卢,在train的时候,loss越train越大直接给我看沉默了。然后因为这一次作业的资料集比较大,所以train的时间很长,我的这次代码差不多train了快有4个小时了,虽然对于一个正常的深度学习来说这个训练时间不算什么,但是我感觉对于一个作业来说还是太长了,因为时间有限,所以我先在这里放一下这个神奇的模型(第一次修改基础代码越改越差的TT),还是一样,等之后回来看看有没有办法改进这个作业。


获取数据

! wget https://www.dropbox.com/s/6l2vcvxl54b0b6w/food11.zip

–2024-03-19 08:18:39– https://www.dropbox.com/s/6l2vcvxl54b0b6w/food11.zip Resolving www.dropbox.com (www.dropbox.com)… 162.125.80.18, 2620:100:6035:18::a27d:5512 Connecting to www.dropbox.com (www.dropbox.com)|162.125.80.18|:443… connected. HTTP request sent, awaiting response… 302 Found Location: /s/raw/6l2vcvxl54b0b6w/food11.zip [following] –2024-03-19 08:18:40– https://www.dropbox.com/s/raw/6l2vcvxl54b0b6w/food11.zip Reusing existing connection to www.dropbox.com:443. HTTP request sent, awaiting response… 302 Found Location: https://ucb153777487cbb387ce8f130013.dl.dropboxusercontent.com/cd/0/inline/CPX0jxc1zqSB2pUYi8Xnv6y67Sqvb8A2hnZJ_FPLoyWl_zk8igVGJeGRElbFCIY51mcJ55DVqhQoo3yCSp9QLumsnQPO3VT4cTiXlgU55qTKpoxzJj-lHUkgMCcFuBWUw0MQD4hYDcv4UWosioji6eSi/file# [following] –2024-03-19 08:18:40– https://ucb153777487cbb387ce8f130013.dl.dropboxusercontent.com/cd/0/inline/CPX0jxc1zqSB2pUYi8Xnv6y67Sqvb8A2hnZJ_FPLoyWl_zk8igVGJeGRElbFCIY51mcJ55DVqhQoo3yCSp9QLumsnQPO3VT4cTiXlgU55qTKpoxzJj-lHUkgMCcFuBWUw0MQD4hYDcv4UWosioji6eSi/file Resolving ucb153777487cbb387ce8f130013.dl.dropboxusercontent.com (ucb153777487cbb387ce8f130013.dl.dropboxusercontent.com)… 162.125.80.15, 2620:100:6035:15::a27d:550f Connecting to ucb153777487cbb387ce8f130013.dl.dropboxusercontent.com (ucb153777487cbb387ce8f130013.dl.dropboxusercontent.com)|162.125.80.15|:443… connected. HTTP request sent, awaiting response… 302 Found Location: /cd/0/inline2/CPVqTqS-pcgwn15YCjiwiYMQcmFfliImZ-jJQIHNxn9OVK3epftOh0V215JaFr5NvpHj_uCXuyUpYDIFAi0k4_GO92Sy5vhi2l8KUJiFB-pHuZqowkb3-SsK3EHcZ9XJjwQCWTz-Adpe1okuY0bE1mHKaElIKLagIawsenvBbYz9osXqMVZrVoM5aeRXdVbO5IH0YXxBsl8oTKvsBpURZaK5nRSg4i3VVTFCkbPiLH5qLDunV8iaaJoNt507pHwLtl4scbTX7-coHFqI0BUjQ1I3pr3MFQFtNv6YuTe83knaI7VfXe9GKX-oDh7xPyRk6q1NKvhxvzEddBO1Uj4JFM2c_RiUTz2YmKeuENHQkmqrVcP210HAuv_PKlUUh7cqohU/file [following] –2024-03-19 08:18:41– https://ucb153777487cbb387ce8f130013.dl.dropboxusercontent.com/cd/0/inline2/CPVqTqS-pcgwn15YCjiwiYMQcmFfliImZ-jJQIHNxn9OVK3epftOh0V215JaFr5NvpHj_uCXuyUpYDIFAi0k4_GO92Sy5vhi2l8KUJiFB-pHuZqowkb3-SsK3EHcZ9XJjwQCWTz-Adpe1okuY0bE1mHKaElIKLagIawsenvBbYz9osXqMVZrVoM5aeRXdVbO5IH0YXxBsl8oTKvsBpURZaK5nRSg4i3VVTFCkbPiLH5qLDunV8iaaJoNt507pHwLtl4scbTX7-coHFqI0BUjQ1I3pr3MFQFtNv6YuTe83knaI7VfXe9GKX-oDh7xPyRk6q1NKvhxvzEddBO1Uj4JFM2c_RiUTz2YmKeuENHQkmqrVcP210HAuv_PKlUUh7cqohU/file Reusing existing connection to ucb153777487cbb387ce8f130013.dl.dropboxusercontent.com:443. HTTP request sent, awaiting response… 200 OK Length: 1163077597 (1.1G) [application/zip] Saving to: ‘food11.zip.3’ food11.zip.3 100%[===================>] 1.08G 26.2MB/s in 44s 2024-03-19 08:19:25 (25.4 MB/s) – ‘food11.zip.3’ saved [1163077597/1163077597]

! unzip food11.zip.3

流式输出内容被截断,只能显示最后 5000 行内容。 inflating: food11/training/8_805.jpg inflating: food11/training/8_806.jpg inflating: food11/training/8_807.jpg inflating: food11/training/8_808.jpg inflating: food11/training/8_809.jpg inflating: food11/training/8_81.jpg inflating: food11/training/8_810.jpg inflating: food11/training/8_811.jpg inflating: food11/training/8_812.jpg inflating: food11/training/8_813.jpg inflating: food11/training/8_814.jpg inflating: food11/training/8_815.jpg inflating: food11/training/8_816.jpg inflating: food11/training/8_817.jpg inflating: food11/training/8_818.jpg inflating: food11/training/8_819.jpg inflating: food11/training/8_82.jpg inflating: food11/training/8_820.jpg inflating: food11/training/8_821.jpg inflating: food11/training/8_822.jpg inflating: food11/training/8_823.jpg inflating: food11/training/8_824.jpg inflating: food11/training/8_825.jpg inflating: food11/training/8_826.jpg inflating: food11/training/8_827.jpg inflating: food11/training/8_828.jpg inflating: 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训练

_exp_name = "sample"
# Import necessary packages.
import numpy as np
import pandas as pd
import torch
import os
import torch.nn as nn
import torchvision.transforms as transforms
from PIL import Image
# "ConcatDataset" and "Subset" are possibly useful when doing semi-supervised learning.
from torch.utils.data import ConcatDataset, DataLoader, Subset, Dataset
from torchvision.datasets import DatasetFolder, VisionDataset

# This is for the progress bar.
from tqdm.auto import tqdm
import random
myseed = 6666  # set a random seed for reproducibility
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(myseed)
torch.manual_seed(myseed)
if torch.cuda.is_available():
    torch.cuda.manual_seed_all(myseed)

转换

Torchvision为图像预处理、数据包装和数据增强提供了许多有用的实用程序。请参考PyTorch官方网站了解不同转换的详细信息。

# Normally, We don't need augmentations in testing and validation.
# All we need here is to resize the PIL image and transform it into Tensor.
test_tfm = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
])

# However, it is also possible to use augmentation in the testing phase.
# You may use train_tfm to produce a variety of images and then test using ensemble methods
train_tfm = transforms.Compose([
    # Resize the image into a fixed shape (height = width = 128)
    transforms.Resize((128, 128)),
    # You may add some transforms here.
    # ToTensor() should be the last one of the transforms.
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])

数据集

数据按名称进行标记,因此我们在调用’getitem’时加载图像和标签。

class FoodDataset(Dataset):

    def __init__(self,path,tfm=test_tfm,files = None):
        super(FoodDataset).__init__()
        self.path = path
        self.files = sorted([os.path.join(path,x) for x in os.listdir(path) if x.endswith(".jpg")])
        if files != None:
            self.files = files
        print(f"One {path} sample",self.files[0])
        self.transform = tfm

    def __len__(self):
        return len(self.files)

    def __getitem__(self,idx):
        fname = self.files[idx]
        im = Image.open(fname)
        im = self.transform(im)
        #im = self.data[idx]
        try:
            label = int(fname.split("/")[-1].split("_")[0])
        except:
            label = -1 # test has no label
        return im,label

Model

class Classifier(nn.Module):
    def __init__(self):
        super(Classifier, self).__init__()
        # torch.nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        # torch.nn.MaxPool2d(kernel_size, stride, padding)
        # input 維度 [3, 128, 128]
        self.cnn = nn.Sequential(
            nn.Conv2d(3, 64, 3, 1, 1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),

            nn.Conv2d(64, 128, 3, 1, 1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),

            nn.Conv2d(128, 256, 3, 1, 1),
            nn.BatchNorm2d(256),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),

            nn.Conv2d(256, 512, 3, 1, 1),
            nn.BatchNorm2d(512),
            nn.ReLU(),

            nn.Conv2d(512, 512, 3, 1, 1),
            nn.BatchNorm2d(512),
            nn.ReLU(),
            nn.MaxPool2d(2, 2),

            nn.Conv2d(512, 512, 3, 1, 1),
            nn.BatchNorm2d(512),
            nn.ReLU(),

            nn.Conv2d(512, 512, 3, 1, 1),
            nn.BatchNorm2d(512),
            nn.ReLU(),

            nn.AdaptiveAvgPool2d(output_size = 1)
        )
        self.fc = nn.Sequential(
            nn.Linear(512, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.ReLU(),
            nn.Linear(512, 11)
        )

    def forward(self, x):
        out = self.cnn(x)
        out = out.view(out.size()[0], -1)
        return self.fc(out)
batch_size = 64
_dataset_dir = "./food11"
# Construct datasets.
# The argument "loader" tells how torchvision reads the data.
train_set = FoodDataset(os.path.join(_dataset_dir,"training"), tfm=train_tfm)
train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True)
valid_set = FoodDataset(os.path.join(_dataset_dir,"validation"), tfm=test_tfm)
valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True)

One ./food11/training sample ./food11/training/0_0.jpg One ./food11/validation sample ./food11/validation/0_0.jpg

Config

# "cuda" only when GPUs are available.
device = "cuda" if torch.cuda.is_available() else "cpu"

# The number of training epochs and patience.
n_epochs = 4
patience = 300 # If no improvement in 'patience' epochs, early stop

# Initialize a model, and put it on the device specified.
model = Classifier().to(device)

# For the classification task, we use cross-entropy as the measurement of performance.
criterion = nn.CrossEntropyLoss()

# Initialize optimizer, you may fine-tune some hyperparameters such as learning rate on your own.
optimizer = torch.optim.Adam(model.parameters(), lr = 1e-6, weight_decay = 1e-5)

scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, eta_min = 1e-9, T_0 = 15)

# Initialize trackers, these are not parameters and should not be changed
stale = 0
best_acc = 0.84702

Train

for epoch in range(n_epochs):

    # ---------- Training ----------
    # Make sure the model is in train mode before training.
    model.train()

    # These are used to record information in training.
    train_loss = []
    train_accs = []

    for batch in tqdm(train_loader):

        # A batch consists of image data and corresponding labels.
        imgs, labels = batch
        #imgs = imgs.half()
        #print(imgs.shape,labels.shape)

        # Forward the data. (Make sure data and model are on the same device.)
        logits = model(imgs.to(device))

        # Calculate the cross-entropy loss.
        # We don't need to apply softmax before computing cross-entropy as it is done automatically.
        loss = criterion(logits, labels.to(device))

        # Gradients stored in the parameters in the previous step should be cleared out first.
        optimizer.zero_grad()

        # Compute the gradients for parameters.
        loss.backward()

        # Clip the gradient norms for stable training.
        grad_norm = nn.utils.clip_grad_norm_(model.parameters(), max_norm=10)

        # Update the parameters with computed gradients.
        optimizer.step()

        # Compute the accuracy for current batch.
        acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()

        # Record the loss and accuracy.
        train_loss.append(loss.item())
        train_accs.append(acc)

    train_loss = sum(train_loss) / len(train_loss)
    train_acc = sum(train_accs) / len(train_accs)

    # Print the information.
    print(f"[ Train | {epoch + 1:03d}/{n_epochs:03d} ] loss = {train_loss:.5f}, acc = {train_acc:.5f}")

    # ---------- Validation ----------
    # Make sure the model is in eval mode so that some modules like dropout are disabled and work normally.
    model.eval()

    # These are used to record information in validation.
    valid_loss = []
    valid_accs = []

    # Iterate the validation set by batches.
    for batch in tqdm(valid_loader):

        # A batch consists of image data and corresponding labels.
        imgs, labels = batch
        #imgs = imgs.half()

        # We don't need gradient in validation.
        # Using torch.no_grad() accelerates the forward process.
        with torch.no_grad():
            logits = model(imgs.to(device))

        # We can still compute the loss (but not the gradient).
        loss = criterion(logits, labels.to(device))

        # Compute the accuracy for current batch.
        acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()

        # Record the loss and accuracy.
        valid_loss.append(loss.item())
        valid_accs.append(acc)
        #break

    # The average loss and accuracy for entire validation set is the average of the recorded values.
    valid_loss = sum(valid_loss) / len(valid_loss)
    valid_acc = sum(valid_accs) / len(valid_accs)

    # Print the information.
    print(f"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}")


    # update logs
    if valid_acc > best_acc:
        with open(f"./{_exp_name}_log.txt","a"):
            print(f"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f} -> best")
    else:
        with open(f"./{_exp_name}_log.txt","a"):
            print(f"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}")


    # save models
    if valid_acc > best_acc:
        print(f"Best model found at epoch {epoch}, saving model")
        torch.save(model.state_dict(), f"{_exp_name}_best.ckpt") # only save best to prevent output memory exceed error
        best_acc = valid_acc
        stale = 0
    else:
        stale += 1
        if stale > patience:
            print(f"No improvment {patience} consecutive epochs, early stopping")
            break

100%

 155/155 [50:10<00:00, 14.37s/it][ Train | 001/004 ] loss = 2.36545, acc = 0.22069

100%

 54/54 [06:34<00:00,  6.38s/it][ Valid | 001/004 ] loss = 2.36588, acc = 0.14507 [ Valid | 001/004 ] loss = 2.36588, acc = 0.14507

100%

 155/155 [50:04<00:00, 14.96s/it][ Train | 002/004 ] loss = 2.31105, acc = 0.26454

100%

 54/54 [06:33<00:00,  6.43s/it][ Valid | 002/004 ] loss = 2.35643, acc = 0.14626 [ Valid | 002/004 ] loss = 2.35643, acc = 0.14626

100%

 155/155 [49:54<00:00, 14.66s/it][ Train | 003/004 ] loss = 2.25773, acc = 0.27482

100%

 54/54 [06:33<00:00,  6.51s/it][ Valid | 003/004 ] loss = 2.38181, acc = 0.14606 [ Valid | 003/004 ] loss = 2.38181, acc = 0.14606

100%

 155/155 [50:03<00:00, 14.59s/it][ Train | 004/004 ] loss = 2.20798, acc = 0.27958

100%

 54/54 [06:45<00:00,  6.59s/it][ Valid | 004/004 ] loss = 2.42378, acc = 0.14567 [ Valid | 004/004 ] loss = 2.42378, acc = 0.14567

test_set = FoodDataset(os.path.join(_dataset_dir,"test"), tfm=test_tfm)
test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0, pin_memory=True)

One ./food11/test sample ./food11/test/0001.jpg

测试并生成预测CSV(这里可能会有问题,暂时不知道为什么)

model_best = Classifier().to(device)
model_best.load_state_dict(torch.load(f"{_exp_name}_best.ckpt"))
model_best.eval()
prediction = []
with torch.no_grad():
    for data,_ in test_loader:
        test_pred = model_best(data.to(device))
        test_label = np.argmax(test_pred.cpu().data.numpy(), axis=1)
        prediction += test_label.squeeze().tolist()
#create test csv
def pad4(i):
    return "0"*(4-len(str(i)))+str(i)
df = pd.DataFrame()
df["Id"] = [pad4(i) for i in range(1,len(test_set)+1)]
df["Category"] = prediction
df.to_csv("submission.csv",index = False)

Q1

Q1. Augmentation implementation通过使用您选择的图像大小完成代码中的train_tfm来实现增强。 在完成代码后,直接复制下面的块并将其粘贴到GradeScope上。当多次给定相同的图像时,train_tfm必须能够产生5+不同的结果。 报告中的train_tfm可以不同于训练代码中的train_tfm。

train_tfm = transforms.Compose([
    # Resize the image into a fixed shape (height = width = 128)
    transforms.Resize((128, 128)),
    # You need to add some transforms here.
    transforms.ToTensor(),
])

Q2

Q2. 剩余实现直接复制下面的代码块,并在完成代码后将其粘贴到GradeScope上

from torch import nn
class Residual_Network(nn.Module):
    def __init__(self):
        super(Residual_Network, self).__init__()

        self.cnn_layer1 = nn.Sequential(
            nn.Conv2d(3, 64, 3, 1, 1),
            nn.BatchNorm2d(64),
        )

        self.cnn_layer2 = nn.Sequential(
            nn.Conv2d(64, 64, 3, 1, 1),
            nn.BatchNorm2d(64),
        )

        self.cnn_layer3 = nn.Sequential(
            nn.Conv2d(64, 128, 3, 2, 1),
            nn.BatchNorm2d(128),
        )

        self.cnn_layer4 = nn.Sequential(
            nn.Conv2d(128, 128, 3, 1, 1),
            nn.BatchNorm2d(128),
        )
        self.cnn_layer5 = nn.Sequential(
            nn.Conv2d(128, 256, 3, 2, 1),
            nn.BatchNorm2d(256),
        )
        self.cnn_layer6 = nn.Sequential(
            nn.Conv2d(256, 256, 3, 1, 1),
            nn.BatchNorm2d(256),
        )
        self.fc_layer = nn.Sequential(
            nn.Linear(256* 32* 32, 256),
            nn.ReLU(),
            nn.Linear(256, 11)
        )
        self.relu = nn.ReLU()

    def forward(self, x):
        # input (x): [batch_size, 3, 128, 128]
        # output: [batch_size, 11]

        # Extract features by convolutional layers.
        x1 = self.cnn_layer1(x)

        x1 = self.relu(x1)

        x2 = self.cnn_layer2(x1)

        x2 = self.relu(x2)

        x3 = self.cnn_layer3(x2)

        x3 = self.relu(x3)

        x4 = self.cnn_layer4(x3)

        x4 = self.relu(x4)

        x5 = self.cnn_layer5(x4)

        x5 = self.relu(x5)

        x6 = self.cnn_layer6(x5)

        x6 = self.relu(x6)

        # The extracted feature map must be flatten before going to fully-connected layers.
        xout = x6.flatten(1)

        # The features are transformed by fully-connected layers to obtain the final logits.
        xout = self.fc_layer(xout)
        return xout

以上就是这个神奇的model,看看之后会怎么修改吧。

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Source: github.com/k4yt3x/flowerhd
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