def train(args):
print('Dataset of instance(s) and batch size is {}'.format(
args.batch_size))
vgg = models.vgg16(True)
model = YOLO(vgg.features)
if args.use_cuda:
model = torch.nn.DataParallel(model)
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr)
best = 1e+30
for epoch in range(1, args.epochs + 1):
l = train_epoch(epoch, model, optimizer, args)
upperleft, bottomright, classes, confs = test_epoch(
model, jpg='../data/1.jpg')
is_best = l < best
best = min(l, best)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
}, is_best)
checkpoint = torch.load('./model_best.pth.tar')
state_dict = checkpoint['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:]
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
model.cpu()
torch.save(model.state_dict(), 'model_cpu.pth.tar')
from config import device, tc
from model import YOLO
from utils import *
import torch
import numpy as np
# MARK: - load data
cocoDataset = COCODataset(tc.imageDir,
tc.annFile,
fromInternet=False if tc.imageDir else True)
dataLoader = DataLoader(cocoDataset, batch_size=tc.batchSize, shuffle=True)
# MARK: - train
model = YOLO().to(device)
if tc.preTrainedWeight:
model.load_state_dict(torch.load(tc.preTrainedWeight, map_location=device))
model.warmUpBatch = tc.warmUpBatches
optimizer = SGD(model.parameters(), lr=1e-3)
prevBestLoss = np.inf
batches = len(dataLoader)
logger = MetricsLogger()
model.train()
for epoch in range(tc.epochs):
losses = []
for batch, (x, y, z) in enumerate(dataLoader):
x, y, z = x.to(device), y.to(device), z.to(device)
loss = model(x, y, z)
losses.append(loss.cpu().item())
