def eval(cfg):
# Setup seeds
torch.manual_seed(cfg.get("seed", 1337))
torch.cuda.manual_seed(cfg.get("seed", 1337))
np.random.seed(cfg.get("seed", 1337))
random.seed(cfg.get("seed", 1337))
# Setup device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Setup evaluation data
loader_source = get_loader(cfg, "train")
# data_eval_labels = utils.recursive_glob(os.path.join(cfg["data"]["path"], 'labels'))
# Setup model
model = Unet(cfg).to(device)
checkpoint = torch.load(cfg["training"]["checkpoint"])
model.load_state_dict(checkpoint["model_state"])
stats = None
model.eval()
for images, labels in tqdm.tqdm(loader_source):
model.set_input(images, labels)
model.forward()
if stats is None:
stats = [StatsRecorder() for i in range(len(model.hooks))]
for i, hook in enumerate(model.hooks):
activation = hook.output
b, c, h, w = activation.shape
activation = activation.transpose(0,
1).reshape(c,
-1).transpose(0, 1)
stats[i].update(activation.cpu().data.numpy())
print([s.mean for s in stats])
print([s.std for s in stats])
if initial_checkpoint is not None:
model, optimizer, epochnum, valid_loss_min = load_ckp(checkpoint_path+initial_checkpoint, model, optimizer)
print(f'initial ckp: {epochnum}')
i = i + epochnum
for epoch in range(epochs):
running_train_loss = []
running_train_score = []
model.train()
for step,(image, mask) in enumerate(train_loader):
image = image.cuda()
mask = mask.cuda()
pred_mask = model.forward(image) # forward propogation
loss = criterion(pred_mask, mask)
score = iou_batch(pred_mask, mask)
optimizer.zero_grad() # setting gradient to zero
loss.backward()
optimizer.step()
running_train_loss.append(loss.item())
running_train_score.append(score)
print(f'batch DiceBCELoss: {loss.item()}')
print(f'batch iou: {score}')
else:
### do valid after epoch ###
running_val_loss = []
running_val_score = []
with torch.no_grad():
