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train.py
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train.py
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import math
import os
import sys
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms
from utils import writer, yolo_utils
from dataset import NumberPlateDataset
from net import Net
class Detect():
def __init__(self,
batch_size, lr0, epochs):
self.batch_size = batch_size
self.lr = lr0
self.epochs = epochs
self.device = ('cuda' if torch.cuda.is_available() else "cpu")
def save_model(self, net, PATH):
torch.save(net.state_dict(), PATH)
def reload_model(self, PATH):
net = Net()
net.load_state_dict(torch.load(PATH))
return net
def train(self, trainloader):
num_samples = len(trainloader.dataset)
num_batches = math.ceil(num_samples/self.batch_size)
### initialize neural net
net = Net()
net.to(self.device)
### clear gradient buffer
net.zero_grad()
### define optimizer
optimizer = optim.Adam(filter(lambda p: p.requires_grad, net.parameters()),
lr = self.lr*(10**(epochs/10)), weight_decay=5e-4)
### start training
print("[#] Training started ...")
epoch_loss = []
for epoch in range(epochs):
print("Epoch : %3d"%(epoch+1))
running_loss = 0
for data in trainloader:
### get inputs and annotations from image and xml file
inputs = data['image'].to(self.device)
true_boxes = data['true_boxes'].to(self.device)
### clear the gradient buffer of optimizer
optimizer.zero_grad()
### forward propagation
outputs = net(inputs)
### calculate loss
loss = yolo_utils.Loss(batch_output = outputs, batch_true=true_boxes)
### backpropagate cost function
loss.backward()
### update the weights
optimizer.step()
running_loss += loss.item()
del outputs
del inputs
del true_boxes
# print(f"Running loss : {round(running_loss, 5)}", end = "\r")
running_loss /= num_batches
epoch_loss.append(running_loss)
# writer.log(epoch+1, running_loss, 0)
# print(100*"=")
# print("Epoch : %3d\tLoss : %.5f"%(epoch+1, running_loss))
### plot the loss during training
plt.plot([i for i in range(1, self.epochs+1)], epoch_loss)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.title(f"Training Loss : Learning Rate = {self.lr}")
plt.show()
print("Training Complete :)")
return net
def dataloader(images_path, labels_path, batch_size):
transform = transforms.Compose([transforms.Grayscale(num_output_channels=1),
transforms.ToTensor()])
anchors = [(0.65, 0.31), (0.12,0.38), (0.56, 0.39), (0.7,0.3),(0.6,0.42)]
train = NumberPlateDataset(images_path = images_path,
labels_path = labels_path,
anchors = anchors,
transform = transform)
train_loader = DataLoader(train,
batch_size = batch_size,
shuffle = True,
num_workers = 1)
return train_loader
if __name__ == "__main__":
### parameters
images_path = "../traffic_management/data/samples/images/"
labels_path = "../traffic_management/data/samples/labels/"
batch_size = 4
lr0 = 0.00001
epochs = 10
train_loader = dataloader(images_path = images_path,
labels_path = labels_path,
batch_size = batch_size)
d = Detect(lr0 = lr0,
epochs = epochs,
batch_size = batch_size)
net = d.train(train_loader)
os.makedirs("./checkpoints/", exist_ok = True)
d.save_model(net, PATH = './checkpoints/saved_model.pth')