def prepare_data(self):
self.train_loader, self.valid_loader = make_train_loader(self.hparams)
model = models.resnet50()
fc_features = model.fc.in_features
model.fc = nn.Linear(fc_features, 3)
valid_size = cfg.DATA.VALIDATION_SIZE
epochs = cfg.MODEL.EPOCH
lr = cfg.MODEL.LR
weight_path = cfg.MODEL.OUTPUT_PATH
use_cuda = cfg.DEVICE.CUDA
gpu_id = cfg.DEVICE.GPU
if use_cuda:
torch.cuda.set_device(gpu_id)
model = model.cuda()
train_loader, valid_loader = make_train_loader(cfg)
optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=0.0001)
for epoch in range(1, epochs + 1):
model.train()
train_loss = 0.
valid_loss = 0.
for data, target in train_loader:
if use_cuda:
data, target = data.cuda(), target.cuda()
optimizer.zero_grad()
output = model(data)
loss = torch.nn.functional.cross_entropy(output, target)