def validate(data_loader, model, best_score, global_step, cfg):
"""
Loads the model weights from the state dictionary. Function will only load
the weights which have matching key names and dimensions in the state
dictionary.
:param state_dict: Pytorch model state dictionary
:param verbose: bool, If True, the function will print the
weight keys of parameters that can and cannot be loaded from the
checkpoint state dictionary.
:return:
"""
model.eval()
gts, predictions = [], []
log.info("Validation started...")
for data in data_loader:
imgs, labels = data
imgs = util.to_device(imgs, gpu=cfg.gpu)
with torch.no_grad():
logits = model(imgs)
probs = model.module.probability(logits)
preds = torch.argmax(probs, dim=1).cpu().numpy()
labels = labels.cpu().detach().numpy()
predictions.extend(preds)
gts.extend(labels)
predictions = np.array(predictions, dtype=np.int32)
gts = np.array(gts, dtype=np.int32)
acc, f1, prec, rec = util.clf_metrics(predictions=predictions,
targets=gts,
average="macro")
report = classification_report(gts, predictions, output_dict=True)
log.info("VALIDATION | Accuracy {:.4f} | F1 {:.4f} | Precision {:.4f} | "
"Recall {:.4f}".format(acc, f1, prec, rec))
if acc > best_score:
save_config = {
'name': config.name,
'save_dir': config.ckpts_dir,
'global_step': global_step,
'clf_report': report
}
save_model(model=model, config=save_config)
best_score = acc
log.info("Validation end")
model.train()
return best_score
def validate(data_loader, model, best_score, global_step, cfg):
model.eval()
gts, predictions = [], []
log.info("Validation started...")
for data in data_loader:
imgs, labels = data
imgs = util.to_device(imgs, gpu=cfg.gpu)
with torch.no_grad():
logits = model(imgs)
probs = model.module.probability(logits)
preds = torch.argmax(probs, dim=1).cpu().numpy()
labels = labels.cpu().detach().numpy()
predictions.extend(preds)
gts.extend(labels)
predictions = np.array(predictions, dtype=np.int32)
gts = np.array(gts, dtype=np.int32)
acc, f1, prec, rec = util.clf_metrics(predictions=predictions,
targets=gts,
average="macro")
report = classification_report(gts, predictions, output_dict=True)
log.info("VALIDATION | Accuracy {:.4f} | F1 {:.4f} | Precision {:.4f} | "
"Recall {:.4f}".format(acc, f1, prec, rec))
if f1 > best_score:
save_config = {
'name': config.name,
'save_dir': config.ckpts_dir,
'global_step': global_step,
'clf_report': report
}
save_model(model=model, config=save_config)
best_score = f1
log.info("Validation end")
model.train()
return best_score
def main():
if config.gpu and not torch.cuda.is_available():
raise ValueError("GPU not supported or enabled on this system.")
use_gpu = config.gpu
log.info("Loading train dataset")
train_dataset = COVIDxFolder(
config.train_imgs, config.train_labels,
transforms.train_transforms(config.width, config.height))
train_loader = DataLoader(train_dataset,
batch_size=config.batch_size,
shuffle=True,
drop_last=True,
num_workers=config.n_threads,
pin_memory=use_gpu)
log.info("Number of training examples {}".format(len(train_dataset)))
log.info("Loading val dataset")
val_dataset = COVIDxFolder(
config.val_imgs, config.val_labels,
transforms.val_transforms(config.width, config.height))
val_loader = DataLoader(val_dataset,
batch_size=config.batch_size,
shuffle=False,
num_workers=config.n_threads,
pin_memory=use_gpu)
log.info("Number of validation examples {}".format(len(val_dataset)))
if config.weights:
#state = torch.load(config.weights)
state = None
log.info("Loaded model weights from: {}".format(config.weights))
else:
state = None
state_dict = state["state_dict"] if state else None
model = architecture.COVIDNext50(n_classes=config.n_classes)
if state_dict:
model = util.load_model_weights(model=model, state_dict=state_dict)
if use_gpu:
model.cuda()
model = torch.nn.DataParallel(model)
optim_layers = filter(lambda p: p.requires_grad, model.parameters())
# optimizer and lr scheduler
optimizer = Adam(optim_layers,
lr=config.lr,
weight_decay=config.weight_decay)
scheduler = ReduceLROnPlateau(optimizer=optimizer,
factor=config.lr_reduce_factor,
patience=config.lr_reduce_patience,
mode='max',
min_lr=1e-7)
# Load the last global_step from the checkpoint if existing
global_step = 0 if state is None else state['global_step'] + 1
class_weights = util.to_device(torch.FloatTensor(config.loss_weights),
gpu=use_gpu)
loss_fn = CrossEntropyLoss()
# Reset the best metric score
best_score = -1
for epoch in range(config.epochs):
log.info("Started epoch {}/{}".format(epoch + 1, config.epochs))
for data in train_loader:
imgs, labels = data
imgs = util.to_device(imgs, gpu=use_gpu)
labels = util.to_device(labels, gpu=use_gpu)
logits = model(imgs)
loss = loss_fn(logits, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if global_step % config.log_steps == 0 and global_step > 0:
probs = model.module.probability(logits)
preds = torch.argmax(probs, dim=1).detach().cpu().numpy()
labels = labels.cpu().detach().numpy()
acc, f1, _, _ = util.clf_metrics(preds, labels)
lr = util.get_learning_rate(optimizer)
log.info("Step {} | TRAINING batch: Loss {:.4f} | F1 {:.4f} | "
"Accuracy {:.4f} | LR {:.2e}".format(
global_step, loss.item(), f1, acc, lr))
if global_step % config.eval_steps == 0 and global_step > 0:
best_score = validate(val_loader,
model,
best_score=best_score,
global_step=global_step,
cfg=config)
scheduler.step(best_score)
global_step += 1