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main.py
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from tqdm import tqdm
import torch
import torchvision
from torchvision import transforms
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader, random_split
from torch.utils.tensorboard import SummaryWriter
from model import Backbone
writer = SummaryWriter('/home/jhj/Desktop/JHJ/git/yolov4_backbone_pre-training/runs/1')
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
# hyper-parameters #####
trainset_rate = 0.9 # 0 ~ 1
epochs = 120
learning_rate = 0.001
batch_size = 32
pretrained_weights_path = "/home/jhj/Desktop/JHJ/git/yolov4_backbone_pre-training/yolov4.conv.137.pth"
classes = ['blossom_end_rot',
'graymold',
'powdery_mildew',
'spider_mite',
'spotting_disease',
'snails_and_slugs'
]
########################
trans = transforms.Compose([transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
dataset = torchvision.datasets.ImageFolder(root='/home/jhj/Desktop/JHJ/projects/data/paprika_6classes/for_imagefolder',
transform=trans)
train_dataset, val_dataset = random_split(dataset, [int(len(dataset) * trainset_rate),
len(dataset) - int(len(dataset) * trainset_rate)],
generator=torch.Generator().manual_seed(42))
train_loader = DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
)
val_loader = DataLoader(val_dataset,
batch_size=batch_size,
shuffle=True,
)
net = Backbone(yolov4conv137weight='/home/jhj/Desktop/JHJ/projects/data/yolov4.conv.137.pth')
net.to(device)
def one_hot(labels, num_classes):
mold = torch.zeros((len(labels), num_classes))
mold[range(len(labels)), labels] = 1
return mold
class FocalLossV1(nn.Module):
def __init__(self,
alpha=0.25,
gamma=2,
reduction='mean', ):
super(FocalLossV1, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.reduction = reduction
self.crit = nn.BCEWithLogitsLoss(reduction='none')
def forward(self, logits, label):
# compute loss
logits = logits.float() # use fp32 if logits is fp16
with torch.no_grad():
alpha = torch.empty_like(logits).fill_(1 - self.alpha)
alpha[label == 1] = self.alpha
probs = torch.sigmoid(logits)
pt = torch.where(label == 1, probs, 1 - probs)
ce_loss = self.crit(logits, label.float())
loss = (alpha * torch.pow(1 - pt, self.gamma) * ce_loss)
if self.reduction == 'mean':
loss = loss.mean()
if self.reduction == 'sum':
loss = loss.sum()
return loss
class FocalLoss(nn.Module):
def __init__(self, weight=None, size_average=True):
super(FocalLoss, self).__init__()
def forward(self, inputs, targets, alpha=0.8, gamma=2, smooth=1):
# comment out if your model contains a sigmoid or equivalent activation layer
inputs = torch.sigmoid(inputs)
# flatten label and prediction tensors
inputs = inputs.view(-1)
targets = targets.view(-1)
# first compute binary cross-entropy
BCE = F.binary_cross_entropy(inputs, targets, reduction='mean')
BCE_EXP = torch.exp(-BCE)
focal_loss = alpha * (1 - BCE_EXP) ** gamma * BCE
return focal_loss
criterion = FocalLossV1()
optimizer = optim.Adam(net.parameters(), lr=learning_rate)
average_accuracy_list = []
for epoch in range(epochs):
net.train()
batch_pbar = tqdm(enumerate(train_loader, 0), total=len(train_loader), unit="batch")
for i, data in batch_pbar:
inputs, labels = data
labels = one_hot(labels, len(classes))
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
if epoch == 0 and i == 0:
first_loss = loss.item()
if i == 0:
start_loss = loss.item()
# print a status
# first_description = '\n%9s%12s%32s' % ("Epoch", "First Loss", "Epoch Start Loss->Present Loss")
# second_description = f"\n{str(epoch) + '/' + str(epochs - 1):>9}" \
# f" {first_loss:>11.4g}" \
# f" {start_loss:^16.4g}->{loss.item():^12.4g}"
# batch_pbar.set_description(first_description + second_description)
batch_pbar.set_description(
f"Epoch: {epoch+1}/{epochs}, First Loss: {first_loss:.4g}, "
f"Start Loss->Running Loss: {start_loss:.4g}->{loss.item():.4g}")
save_path = f"/home/jhj/Desktop/JHJ/projects/data/yolov4_backbone_pth/backbone_{epoch + 1}.pth"
torch.save(net.state_dict(), save_path)
# pretrained = torch.load(f"/home/jhj/Desktop/JHJ/projects/data/yolov4_backbone_pth/backbone_{epoch + 1}.pth")
# net_dict = net.state_dict()
# net_dict.update(pretrained)
# net.load_state_dict(net_dict)
class_correct = list(0. for i in range(6))
class_total = list(0. for i in range(6))
with torch.no_grad():
for data in val_loader:
net.eval()
images, labels = data
images, labels = images.to(device), labels.to(device)
outputs = net(images)
outputs = torch.sigmoid(outputs)
_, predicted = torch.max(outputs, 1)
c = (predicted == labels).squeeze()
for i in range(len(labels)):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1
accuracy_sum = 0
for i in range(len(classes)):
temp = 100 * class_correct[i] / class_total[i]
print('Accuracy of %5s : %2d %%' % (classes[i], temp))
accuracy_sum += temp
print("correct:", class_correct)
print(" total :", class_total)
print('Accuracy average: ', accuracy_sum / len(classes))
average_accuracy_list.append(accuracy_sum)
print(max(average_accuracy_list), average_accuracy_list.index(max(average_accuracy_list)))