>- ** This article is the learning record blog in [365-day deep learning training camp](https://mp.weixin.qq.com/s/Nb93582M_5usednAKp_Jtw)**
>- ** Reference article: [Pytorch Practical Combat | Week P5: Sneaker Identification](https://www.heywhale.com/mw/project/6352467ca42e79f98f6bbf13)**
>- ** Original author: [Classmate K | Tutoring, project customization](https://mtyjkh.blog.csdn.net/)**
>- ** Article source: [Student K’s study circle](https://www.yuque.com/mingtian-fkmxf/zxwb45)**
1. Preparation
1. Set up GPU
import torch
import torchvision
from torchvision import transforms,datasets
import torchvision.transforms as transforms
import torch.nn as nn
import os,PIL,pathlib
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
device
device(type=’cuda’)
2. Import data
#View categories
data_path = r"F:\P5_data\test"
data_path = pathlib.Path(data_path)
class_path = list(data_path.glob("*"))
class_names = [str(i).split("")[-1] for i in class_path]
class_names
['adidas', 'nike']
#View pictures
import matplotlib.pyplot as plt
from PIL import Image
fig,axes = plt.subplots(3, 8, figsize=(16, 6))
image_folder = r"F:\P5_data\train\adidas"
image_files = [f for f in os.listdir(image_folder) if f.endswith((".jpg",".png","jepg"))]
for ax,file in zip(axes.flat,image_files):
img_path = os.path.join(image_folder, file)
img = Image.open(img_path)
ax.imshow(img)
ax.axis('off')
import torchvision.transforms as transforms
train_transforms = transforms.Compose([
transforms.Resize([224,224]),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485,0.456,0.406],
std=[0.229,0.224,0.225])
])
test_transforms = transforms.Compose([
transforms.Resize([224,224]),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.485,0.456,0.406],
std=[0.229,0.224,0.225])
])
train_dataset = datasets.ImageFolder(r"F:\P5_data\train",transform=train_transforms)
test_dataset = datasets.ImageFolder(r"F:\P5_data\test",transform=test_transforms)
train_dataset.class_to_idx test_dataset.class_to_idx
{‘adidas’: 0, ‘nike’: 1}
batch_size = 32
train_dl = torch.utils.data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
test_dl = torch.utils.data.DataLoader(test_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
for X,y in test_dl:
print("shape of X [N,C,H,W]:", X.shape)
print("shape of y:",y.shape,y.dtype)
break
2. Build a simple CNN model
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self):
super(Model,self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(3, 12, kernel_size=5, padding=0),#12*220*220
nn.BatchNorm2d(12),
nn.ReLU())
self.conv2 = nn.Sequential(
nn.Conv2d(12, 12, kernel_size=5, padding=0),#12*216*216
nn.BatchNorm2d(12),
nn.ReLU())
self.pool1 = nn.Sequential(
nn.MaxPool2d(2)) #12*108*108
self.conv4 = nn.Sequential(
nn.Conv2d(12, 24, kernel_size=5, padding=0),#24*104*104
nn.BatchNorm2d(24),
nn.ReLU())
self.conv5 = nn.Sequential(
nn.Conv2d(24, 24, kernel_size=5, padding=0),#24*100*100
nn.BatchNorm2d(24),
nn.ReLU())
self.pool2 = nn.Sequential(
nn.MaxPool2d(2)) #24*50*50
self.conv7 = nn.Sequential(
nn.Conv2d(24, 48, kernel_size=5, padding=0),#48*46*46
nn.BatchNorm2d(48),
nn.ReLU())
self.conv8 = nn.Sequential(
nn.Conv2d(48, 48, kernel_size=5, padding=0),#48*42*42
nn.BatchNorm2d(48),
nn.ReLU())
self.pool3 = nn.Sequential(
nn.MaxPool2d(2)) #48*21*21
self.dropout =nn.Sequential(
nn.Dropout(0.2))
self.fc = nn.Sequential(
nn.Linear(48*21*21,len(class_names)))
def forward(self, x):
batch_size = x.size(0)
x = self.conv1(x) #Convolution-BN-activation
x = self.conv2(x) #Convolution-BN-activation
x = self.pool1(x) #Pooling layer
x = self.conv4(x)
x = self.conv5(x)
x = self.pool2(x)
x = self.conv7(x)
x = self.conv8(x)
x = self.pool3(x)
x = self.dropout(x)
x = x.view(batch_size,-1)
x = self.fc(x)
return x
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print ("Using {} device:".format(device))
model = Model().to(device)
model
Using cuda device:Out[8]:
Model( (conv1): Sequential( (0): Conv2d(3, 12, kernel_size=(5, 5), stride=(1, 1)) (1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) (conv2): Sequential( (0): Conv2d(12, 12, kernel_size=(5, 5), stride=(1, 1)) (1): BatchNorm2d(12, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) (pool1): Sequential( (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (conv4): Sequential( (0): Conv2d(12, 24, kernel_size=(5, 5), stride=(1, 1)) (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) (conv5): Sequential( (0): Conv2d(24, 24, kernel_size=(5, 5), stride=(1, 1)) (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) (pool2): Sequential( (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (conv7): Sequential( (0): Conv2d(24, 48, kernel_size=(5, 5), stride=(1, 1)) (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) (conv8): Sequential( (0): Conv2d(48, 48, kernel_size=(5, 5), stride=(1, 1)) (1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (2): ReLU() ) (pool3): Sequential( (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) ) (dropout): Sequential( (0): Dropout(p=0.2, inplace=False) ) (fc): Sequential( (0): Linear(in_features=21168, out_features=2, bias=True) ) )
3. Training model
##Write training function
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset) #Training set size
num_batches = len(dataloader) #number of batches
train_loss, train_acc =0, 0 #Initialize training loss and accuracy
for X,y in dataloader:
X,y = X.to(device),y.to(device)
#Calculate prediction error
pred = model(X)
loss = loss_fn(pred,y)
#backpropagation
optimizer.zero_grad() #grad attribute zero
loss.backward() #Reverse propagation
optimizer.step() #Automatically update each step
#Record acc and loss
train_acc + =(pred.argmax(1) == y).type(torch.float).sum().item()
train_loss + =loss.item()
train_acc /= size
train_loss /= num_batches
return train_acc,train_loss
##Write test function
def test (dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
test_acc, test_loss =0, 0
with torch.no_grad():
for imgs,target in dataloader:
imgs, target =imgs.to(device),target.to(device)
#Calculate loss and acc
target_pred = model(imgs)
loss = loss_fn(target_pred, target)
test_loss + = loss.item()
test_acc + =(target_pred.argmax(1) == target).type(torch.float).sum().item()
test_acc /= size
test_loss /= num_batches
return test_acc,test_loss
#Set dynamic learning rate
def adjust_learning_rate(optimizer,epoch,start_lr):
# Decay to the original 0.98 every 2 epochs
lr = start_lr * (0.98**(epoch // 2))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
learn_rate = 1e-4 #Initial learning rate
optimizer = torch.optim.SGD(model.parameters(), lr=learn_rate)
#Formal training
loss_fn = nn.CrossEntropyLoss() #Create loss function
epochs = 50
train_loss = []
train_acc = []
test_loss = []
test_acc = []
for epoch in range(epochs):
# Update learning rate (used when using custom learning rate)
adjust_learning_rate(optimizer, epoch, learn_rate)
model.train()
epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, optimizer)
# scheduler.step() # Update learning rate (used when calling the official dynamic learning rate interface)
model.eval()
epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn)
train_acc.append(epoch_train_acc)
train_loss.append(epoch_train_loss)
test_acc.append(epoch_test_acc)
test_loss.append(epoch_test_loss)
# Get the current learning rate
lr = optimizer.state_dict()['param_groups'][0]['lr']
template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%, Test_loss:{:.3f}, Lr:{:. 2E}')
print(template.format(epoch + 1, epoch_train_acc*100, epoch_train_loss,
epoch_test_acc*100, epoch_test_loss, lr))
print('Done')
Epoch: 1, Train_acc:56.4%, Train_loss:0.722, Test_acc:50.0%, Test_loss:0.698, Lr:1.00E-04 Epoch: 2, Train_acc:58.4%, Train_loss:0.678, Test_acc:65.8%, Test_loss:0.657, Lr:1.00E-04 Epoch: 3, Train_acc:61.8%, Train_loss:0.669, Test_acc:73.7%, Test_loss:0.633, Lr:9.80E-05 Epoch: 4, Train_acc:65.3%, Train_loss:0.621, Test_acc:72.4%, Test_loss:0.577, Lr:9.80E-05 Epoch: 5, Train_acc:67.7%, Train_loss:0.607, Test_acc:73.7%, Test_loss:0.566, Lr:9.60E-05 Epoch: 6, Train_acc:67.3%, Train_loss:0.590, Test_acc:76.3%, Test_loss:0.571, Lr:9.60E-05 Epoch: 7, Train_acc:69.1%, Train_loss:0.578, Test_acc:72.4%, Test_loss:0.566, Lr:9.41E-05 Epoch: 8, Train_acc:71.7%, Train_loss:0.561, Test_acc:77.6%, Test_loss:0.552, Lr:9.41E-05 Epoch: 9, Train_acc:69.1%, Train_loss:0.543, Test_acc:82.9%, Test_loss:0.551, Lr:9.22E-05 Epoch:10, Train_acc:71.1%, Train_loss:0.551, Test_acc:81.6%, Test_loss:0.525, Lr:9.22E-05 Epoch:11, Train_acc:74.1%, Train_loss:0.509, Test_acc:81.6%, Test_loss:0.524, Lr:9.04E-05 Epoch:12, Train_acc:73.7%, Train_loss:0.517, Test_acc:81.6%, Test_loss:0.583, Lr:9.04E-05 Epoch:13, Train_acc:73.3%, Train_loss:0.504, Test_acc:78.9%, Test_loss:0.524, Lr:8.86E-05 Epoch:14, Train_acc:78.1%, Train_loss:0.498, Test_acc:84.2%, Test_loss:0.536, Lr:8.86E-05 Epoch:15, Train_acc:76.1%, Train_loss:0.493, Test_acc:84.2%, Test_loss:0.496, Lr:8.68E-05 Epoch:16, Train_acc:80.7%, Train_loss:0.468, Test_acc:84.2%, Test_loss:0.497, Lr:8.68E-05 Epoch:17, Train_acc:77.9%, Train_loss:0.469, Test_acc:85.5%, Test_loss:0.514, Lr:8.51E-05 Epoch:18, Train_acc:80.7%, Train_loss:0.463, Test_acc:85.5%, Test_loss:0.484, Lr:8.51E-05 Epoch:19, Train_acc:80.5%, Train_loss:0.454, Test_acc:85.5%, Test_loss:0.509, Lr:8.34E-05 Epoch:20, Train_acc:80.1%, Train_loss:0.452, Test_acc:76.3%, Test_loss:0.499, Lr:8.34E-05 Epoch:21, Train_acc:82.5%, Train_loss:0.441, Test_acc:84.2%, Test_loss:0.506, Lr:8.17E-05 Epoch:22, Train_acc:84.1%, Train_loss:0.420, Test_acc:81.6%, Test_loss:0.484, Lr:8.17E-05 Epoch:23, Train_acc:84.5%, Train_loss:0.421, Test_acc:85.5%, Test_loss:0.537, Lr:8.01E-05 Epoch:24, Train_acc:85.3%, Train_loss:0.412, Test_acc:88.2%, Test_loss:0.460, Lr:8.01E-05 Epoch:25, Train_acc:85.9%, Train_loss:0.410, Test_acc:86.8%, Test_loss:0.488, Lr:7.85E-05 Epoch:26, Train_acc:86.7%, Train_loss:0.392, Test_acc:80.3%, Test_loss:0.516, Lr:7.85E-05 Epoch:27, Train_acc:86.5%, Train_loss:0.395, Test_acc:82.9%, Test_loss:0.456, Lr:7.69E-05 Epoch:28, Train_acc:87.1%, Train_loss:0.396, Test_acc:88.2%, Test_loss:0.459, Lr:7.69E-05 Epoch:29, Train_acc:88.4%, Train_loss:0.387, Test_acc:86.8%, Test_loss:0.440, Lr:7.54E-05 Epoch:30, Train_acc:88.8%, Train_loss:0.370, Test_acc:84.2%, Test_loss:0.466, Lr:7.54E-05 Epoch:31, Train_acc:87.8%, Train_loss:0.379, Test_acc:88.2%, Test_loss:0.421, Lr:7.39E-05 Epoch:32, Train_acc:88.6%, Train_loss:0.363, Test_acc:86.8%, Test_loss:0.439, Lr:7.39E-05 Epoch:33, Train_acc:89.4%, Train_loss:0.360, Test_acc:73.7%, Test_loss:0.439, Lr:7.24E-05 Epoch:34, Train_acc:88.6%, Train_loss:0.359, Test_acc:85.5%, Test_loss:0.462, Lr:7.24E-05 Epoch:35, Train_acc:90.2%, Train_loss:0.350, Test_acc:86.8%, Test_loss:0.464, Lr:7.09E-05 Epoch:36, Train_acc:87.8%, Train_loss:0.352, Test_acc:88.2%, Test_loss:0.465, Lr:7.09E-05 Epoch:37, Train_acc:91.2%, Train_loss:0.333, Test_acc:86.8%, Test_loss:0.464, Lr:6.95E-05 Epoch:38, Train_acc:88.4%, Train_loss:0.353, Test_acc:86.8%, Test_loss:0.442, Lr:6.95E-05 Epoch:39, Train_acc:91.4%, Train_loss:0.335, Test_acc:88.2%, Test_loss:0.466, Lr:6.81E-05 Epoch:40, Train_acc:89.4%, Train_loss:0.336, Test_acc:88.2%, Test_loss:0.448, Lr:6.81E-05 Epoch:41, Train_acc:90.0%, Train_loss:0.339, Test_acc:89.5%, Test_loss:0.457, Lr:6.68E-05 Epoch:42, Train_acc:91.8%, Train_loss:0.328, Test_acc:88.2%, Test_loss:0.453, Lr:6.68E-05 Epoch:43, Train_acc:91.4%, Train_loss:0.315, Test_acc:89.5%, Test_loss:0.490, Lr:6.54E-05 Epoch:44, Train_acc:90.6%, Train_loss:0.323, Test_acc:86.8%, Test_loss:0.437, Lr:6.54E-05 Epoch:45, Train_acc:92.0%, Train_loss:0.320, Test_acc:88.2%, Test_loss:0.444, Lr:6.41E-05 Epoch:46, Train_acc:93.0%, Train_loss:0.312, Test_acc:88.2%, Test_loss:0.455, Lr:6.41E-05 Epoch:47, Train_acc:92.6%, Train_loss:0.314, Test_acc:89.5%, Test_loss:0.472, Lr:6.28E-05 Epoch:48, Train_acc:92.0%, Train_loss:0.305, Test_acc:86.8%, Test_loss:0.412, Lr:6.28E-05 Epoch:49, Train_acc:92.8%, Train_loss:0.303, Test_acc:90.8%, Test_loss:0.429, Lr:6.16E-05 Epoch:50, Train_acc:92.4%, Train_loss:0.308, Test_acc:90.8%, Test_loss:0.419, Lr:6.16E-05 Done
4. Results visualization
import matplotlib.pyplot as plt
#hidewarning
import warnings
warnings.filterwarnings("ignore") #Ignore warning messages
plt.rcParams['font.sans-serif'] = ['SimHei'] # Used to display Chinese labels normally
plt.rcParams['axes.unicode_minus'] = False # Used to display negative signs normally
plt.rcParams['figure.dpi'] = 100 #Resolution
epochs_range = range(epochs)
plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc, label='Training Accuracy')
plt.plot(epochs_range, test_acc, label='Test Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, test_loss, label='Test Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
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