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train_simple_model.py
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from models import MLP
from utils import *
from args import parse_train_args, dump_args_dict
from datasets import make_selected_dataset
from viz.eval_simple_model_viz import evaluate_model_visually
from metrics import update_accuracy
import logging
from torchsummary import summary
def assess_loss(args, model, criterion, outputs, targets, is_binary, num_classes=-1):
if args.loss in [CROSS_ENTROPY_TAG, LABEL_SMOOTHING_TAG, LABEL_RELAXATION_TAG]:
if is_binary:
loss = criterion(torch.squeeze(outputs[0]), torch.squeeze(targets).float())
else:
loss = criterion(outputs[0], targets)
elif args.loss == MSE_TAG:
loss = criterion(outputs[0],
nn.functional.one_hot(targets, num_classes=num_classes).type(torch.FloatTensor).to(
args.device))
else:
raise NotImplementedError("No routine for loss {} implemented.".format(args.loss))
# Now decide whether to add weight decay on last weights and last features
if args.sep_decay:
# Find features and weights
features = outputs[1]
w = model.fc.weight
b = model.fc.bias
lamb = args.weight_decay / 2
lamb_feature = args.feature_decay_rate / 2
loss += lamb * (torch.sum(w ** 2) + torch.sum(b ** 2)) + lamb_feature * torch.sum(features ** 2)
return loss
def evaluate_dataloader(dataloader, model, args, criterion, logfile, is_binary, prefix="val", print_stats=True,
step_id=None, num_classes=-1, return_top1=False):
losses_am = AverageMeter()
top1_am = AverageMeter()
# Calculate metrics
for batch_idx, (inputs, targets) in enumerate(dataloader):
inputs, targets = inputs.to(args.device), targets.to(args.device)
model.eval()
outputs = model(inputs)
val_loss = assess_loss(args, model, criterion, outputs, targets, is_binary, num_classes=num_classes)
update_accuracy(top1_am, inputs, outputs, targets, is_binary)
losses_am.update(val_loss.item(), inputs.size(0))
if print_stats:
if step_id is not None:
stat_str = '[epoch: %d] {}_loss: %.4f | {}_top1: %.4f '.format(prefix, prefix) % (
step_id + 1, losses_am.avg, top1_am.avg)
else:
stat_str = '{}_loss: %.4f | {}_top1: %.4f '.format(prefix, prefix) % (
losses_am.avg, top1_am.avg)
print_and_save(stat_str, logfile)
if return_top1:
return top1_am.avg
def trainer(args, model, trainloader, valloader, epoch_id, criterion, optimizer, scheduler, logfile, is_binary,
num_classes):
losses = AverageMeter()
top1 = AverageMeter()
print_and_save('\nTraining Epoch: [%d | %d] LR: %f' % (epoch_id + 1, args.epochs, scheduler.get_last_lr()[-1]),
logfile)
for batch_idx, (inputs, targets) in enumerate(trainloader):
inputs, targets = inputs.to(args.device), targets.to(args.device)
model.train()
outputs = model(inputs)
loss = assess_loss(args, model, criterion, outputs, targets, is_binary, num_classes=num_classes)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# measure accuracy and record loss
model.eval()
outputs = model(inputs)
update_accuracy(top1, inputs, outputs, targets, is_binary)
losses.update(loss.item(), inputs.size(0))
if batch_idx % 10 == 0:
print_and_save('[epoch: %d] (%d/%d) | Loss: %.4f | top1: %.4f ' %
(epoch_id + 1, batch_idx + 1, len(trainloader), losses.avg, top1.avg), logfile)
scheduler.step()
if valloader is not None:
evaluate_dataloader(valloader, model, args, criterion, logfile, is_binary, prefix="val", step_id=epoch_id,
num_classes=num_classes)
def train(args, model, trainloader, valloader, testloader, num_classes, val_prefix="test", force_retrain=False):
is_binary = False
criterion = make_criterion(args, num_classes, is_binary=is_binary)
optimizer = make_optimizer(args, model)
scheduler = make_scheduler(args, optimizer)
logfile = open('%s/train_log.txt' % (args.save_path), 'w')
if os.path.exists(
os.path.join(args.save_path, "epoch_" + str(args.epochs).zfill(3) + ".pth")) and not args.force_retrain \
and not force_retrain:
logging.info("Model already exists, loading this model...")
model.load_state_dict(torch.load(os.path.join(args.save_path, "epoch_" + str(args.epochs).zfill(3) + ".pth")))
else:
print_and_save('# of model parameters: ' + str(count_network_parameters(model)), logfile)
print_and_save('--------------------- Training -------------------------------', logfile)
for epoch_id in range(args.epochs):
trainer(args, model, trainloader, valloader, epoch_id, criterion, optimizer, scheduler, logfile, is_binary,
num_classes)
# Save last model
if (epoch_id + 1) % args.epochs == 0:
torch.save(model.state_dict(), args.save_path + "/epoch_" + str(epoch_id + 1).zfill(3) + ".pth")
test_val_acc = evaluate_dataloader(testloader, model, args, criterion, logfile, is_binary, prefix=val_prefix,
num_classes=num_classes, return_top1=True)
logfile.close()
return test_val_acc
def main():
args = parse_train_args()
if args.val_split_prop == 0.0:
args.val_split_prop = None
set_seed(seed=args.seed)
device = torch.device("cuda:" + str(args.gpu_id) if torch.cuda.is_available() else "cpu")
args.device = device
if args.classes == 2:
selected_labels = [0, 1]
else:
selected_labels = [i for i in range(args.classes)]
trainloader, valloader, testloader, num_classes = make_selected_dataset(args, args.dataset, args.data_dir,
args.batch_size, args.sample_size,
val_split_prop=args.val_split_prop,
label_noise=args.label_noise,
selected_labels=selected_labels,
four_class_problem=args.fourclass_problem)
logging.debug("Training data #: {}".format(len(trainloader)))
if valloader is not None:
logging.debug("Validation data #: {}".format(len(valloader)))
logging.debug("Test data #: {}".format(len(testloader)))
if args.model == "MLP":
if args.fourclass_twofeatures:
num_penultimate_features = 2
else:
num_penultimate_features = num_classes
model = MLP(hidden=args.width, depth=args.depth, fc_bias=args.bias, num_classes=num_classes,
penultimate_layer_features=num_penultimate_features, final_activation=args.act_fn,
use_bn=args.use_bn, use_layer_norm=args.use_layer_norm).to(device)
else:
raise ValueError("Non supported model {}.".format(args.model))
summary(model, input_size=(3, 32, 32), batch_size=1)
train(args, model, trainloader, valloader, testloader, num_classes)
# if not args.fourclass_problem:
if not args.fourclass_problem or args.fourclass_twofeatures:
evaluate_model_visually(args, model, base_path=args.save_path)
dump_args_dict(args)
if __name__ == "__main__":
main()