)
return parser
if __name__ == "__main__":
args = default_argument_parser().parse_args()
np.random.seed(args.seed)
num_classes = 2
transforms = []
if args.max_points > 0:
transforms.append(T.FixedPoints(args.max_points))
if args.augment:
transforms.append(T.RandomRotate((-180, 180),
axis=2)) # Rotate around z axis
transforms.append(T.RandomFlip(0)) # Flp about x axis
transforms.append(T.RandomFlip(1)) # Flip about y axis
transforms.append(T.RandomTranslate(0.0001)) # Random jitter
if args.norm:
transforms.append(T.NormalizeScale())
transform = T.Compose(transforms=transforms) if transforms else None
train_dataset = EventDataset(args.dataset,
"trainval",
include_proton=True,
task="separation",
cleanliness=args.clean,
pre_transform=None,
transform=transform,
balanced_classes=True,
fraction=0.001)
test_dataset = EventDataset(args.dataset,
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
train_batch_logger = Logger(os.path.join('./Results', 'train_batch.log'), ['epoch', 'batch', 'loss', 'acc'])
test_batch_logger = Logger(os.path.join('./Results', 'test_batch.log'), ['batch', 'loss', 'acc'])
acc_logger = Logger(os.path.join('./Results', 'acc.log'), ['epoch', 'acc'])
#shutil.rmtree(osp.join('..', 'data/Traingraph/processed'))
#shutil.rmtree(osp.join('..', 'data/Testgraph/processed'))
for epoch in range(1, 180):
train_path = osp.join('..', 'data/Traingraph')
test_path = osp.join('..', 'data/Testgraph')
train_data_aug = T.Compose([T.Cartesian(cat=False), T.RandomFlip(axis=0, p=0.3), T.RandomScale([0.95,0.999]) ])
test_data_aug = T.Compose([T.Cartesian(cat=False), T.RandomFlip(axis=0, p=0.3), T.RandomScale([0.95,0.999])])
train_dataset = MyOwnDataset(train_path, transform=train_data_aug) #### transform=T.Cartesian()
test_dataset = MyOwnDataset(test_path, transform=test_data_aug)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64)
train(epoch, train_batch_logger, train_loader)
test_acc = test(test_batch_logger, test_loader)
print('Epoch: {:02d}, Test: {:.4f}'.format(epoch, test_acc))
torch.save(model, './runs_model/model.pkl')
['epoch', 'batch', 'loss', 'acc'])
test_batch_logger = Logger(os.path.join('./Results', 'test_batch.log'),
['batch', 'loss', 'acc'])
acc_logger = Logger(os.path.join('./Results', 'acc.log'), ['epoch', 'acc'])
#shutil.rmtree(osp.join('..', 'data/Traingraph/processed'))
#shutil.rmtree(osp.join('..', 'data/Testgraph/processed'))
for epoch in range(1, 180):
train_path = osp.join('..', 'data/Traingraph')
test_path = osp.join('..', 'data/Testgraph')
#train_data_aug = T.Compose([T.Cartesian(cat=False), T.RandomFlip(axis=0)])
train_data_aug = T.Compose([
T.Cartesian(cat=False),
T.RandomFlip(axis=0, p=0.3),
T.RandomScale([0.96, 0.999])
])
test_data_aug = T.Compose([
T.Cartesian(cat=False),
T.RandomFlip(axis=0, p=0.3),
T.RandomScale([0.96, 0.999])
])
train_dataset = MyOwnDataset(
train_path, transform=train_data_aug) #### transform=T.Cartesian()
test_dataset = MyOwnDataset(test_path, transform=test_data_aug)
train_loader = DataLoader(train_dataset, batch_size=3, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=3)
train_batch_logger = Logger(os.path.join('./Results', 'train_batch.log'),
['epoch', 'batch', 'loss', 'acc'])
test_batch_logger = Logger(os.path.join('./Results', 'test_batch.log'),
['batch', 'loss', 'acc'])
#shutil.rmtree(osp.join('..', 'data/Traingraph/processed'))
#shutil.rmtree(osp.join('..', 'data/Testgraph/processed'))
for epoch in range(1, 150):
train_path = osp.join('..', 'data/Traingraph')
test_path = osp.join('..', 'data/Testgraph')
train_data_aug = T.Compose([
T.Cartesian(cat=False),
T.RandomFlip(axis=0, p=0.3),
T.RandomScale([0.95, 0.999]),
T.RandomFlip(axis=1, p=0.2)
])
test_data_aug = T.Compose([
T.Cartesian(cat=False),
T.RandomFlip(axis=0, p=0.3),
T.RandomScale([0.95, 0.999]),
T.RandomFlip(axis=1, p=0.2)
])
train_dataset = MyOwnDataset(
train_path, transform=train_data_aug) #### transform=T.Cartesian()
test_dataset = MyOwnDataset(test_path, transform=test_data_aug)
train_loader = DataLoader(train_dataset, batch_size=3, shuffle=True)
train_batch_logger = Logger(os.path.join('./Results', 'train_batch.log'),
['epoch', 'batch', 'loss', 'acc'])
test_batch_logger = Logger(os.path.join('./Results', 'test_batch.log'),
['batch', 'loss', 'acc'])
acc_logger = Logger(os.path.join('./Results', 'acc.log'), ['epoch', 'acc'])
#shutil.rmtree(osp.join('..', 'data/Traingraph/processed'))
#shutil.rmtree(osp.join('..', 'data/Testgraph/processed'))
for epoch in range(1, 150):
train_path = osp.join('..', 'data/Traingraph')
test_path = osp.join('..', 'data/Testgraph')
train_data_aug = T.Compose([
T.Cartesian(cat=False),
T.RandomFlip(axis=0, p=0.5),
T.RandomScale([0.96, 0.999]),
T.RandomFlip(axis=1, p=0.5)
])
test_data_aug = T.Compose([
T.Cartesian(cat=False),
T.RandomFlip(axis=0, p=0.5),
T.RandomScale([0.96, 0.999]),
T.RandomFlip(axis=1, p=0.5)
])
train_dataset = MyOwnDataset(
train_path, transform=train_data_aug) #### transform=T.Cartesian()
test_dataset = MyOwnDataset(test_path, transform=test_data_aug)
train_loader = DataLoader(train_dataset, batch_size=3, shuffle=True)
logging.basicConfig(filename=os.path.join(log_dir, 'n_mnist.log'),
level=logging.DEBUG)
model_file = 'n_mnist.pkl'
device = torch.device("cuda:" +
args.cuda if torch.cuda.is_available() else "cpu")
model = model.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
pre_transform = T.Compose([T.Cartesian(cat=False)])
# train_data_aug = T.Compose([T.RandomScale([0.95, 1])])
train_data_aug = T.Compose([
T.RandomScale([0.95, 1]),
T.RandomRotate((0, 10), axis=0),
T.RandomFlip(axis=0, p=0.5)
])
train_dataset = Graph_2D_Memory_Dataset(train_dir,
transform=train_data_aug,
pre_transform=pre_transform)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers)
# train
print("train")
for epoch in range(1, args.epoch):
model.train()
correct = 0