text stringlengths 1 93.6k |
|---|
if grad_norms is not None:
|
metric_dict.update(
|
{
|
"grad_norm/mean": grad_norms.mean().item(),
|
"grad_norm/std": grad_norms.std().item(),
|
"grad_norm/max": grad_norms.max().item(),
|
"grad_norm/min": grad_norms.min().item(),
|
}
|
)
|
metric_logger.synchronize_between_processes()
|
metric_dict = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
|
logger.info("Averaged train stats:")
|
for k, v in metric_dict.items():
|
logger.info("{}: {}".format(k, v))
|
ext_logger.log(
|
metric_dict,
|
epoch + 1,
|
prefix="train/epoch/",
|
save_path=os.path.join(args.output_dir, "log_train.txt"),
|
)
|
return metric_dict
|
@torch.no_grad()
|
def evaluate(
|
model,
|
teachers,
|
teacher_ft_stats,
|
data_loader,
|
epoch,
|
ext_logger,
|
args,
|
):
|
metric_logger = MetricLogger(delimiter=" ")
|
header = "Test - Epoch: [{}/{}]".format(epoch, args.epochs)
|
model.eval()
|
for it, (image, target) in enumerate(
|
metric_logger.log_every(data_loader, 10, header)
|
):
|
image = image.cuda(non_blocking=True)
|
target = target.cuda(non_blocking=True)
|
student_output = model(image)
|
teacher_output = get_teacher_output(image, teachers, teacher_ft_stats, 0.0)
|
metric_dict = {}
|
unic_loss(
|
student_output,
|
teacher_output,
|
args.lam_lcos,
|
args.lam_lsl1,
|
args.t_drop_prob,
|
metric_dict=metric_dict,
|
)
|
metric_logger.update(**metric_dict)
|
metric_logger.synchronize_between_processes()
|
metric_dict = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
|
logger.info("Averaged test stats:")
|
for k, v in metric_dict.items():
|
logger.info("{}: {}".format(k, v))
|
ext_logger.log(
|
metric_dict,
|
epoch + 1,
|
prefix="test/epoch/",
|
save_path=os.path.join(args.output_dir, "log_test.txt"),
|
)
|
return metric_dict
|
def get_dataloaders(args):
|
normalize = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
|
train_dataset = ImageFolder(
|
os.path.join(args.data_dir, "train"),
|
# fmt:off
|
transform=T.Compose(
|
[
|
T.ToImage(),
|
T.RandomResizedCrop(args.image_size, interpolation=T.InterpolationMode.BICUBIC, antialias=True),
|
T.RandomHorizontalFlip(p=0.5),
|
T.RandomApply([T.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.2, hue=0.1)], p=0.8),
|
T.RandomApply([T.Grayscale(num_output_channels=3)], p=0.2),
|
T.ToDtype(torch.float32, scale=True),
|
T.RandomApply([T.GaussianBlur(kernel_size=9, sigma=(0.1, 5.0))], p=0.2),
|
T.RandomSolarize(threshold=0.5, p=0.2),
|
normalize,
|
]
|
),
|
# fmt:on
|
)
|
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