device: torch.device, epoch: int, loss_scaler, max_norm: float = 0, patch_size: int = 16,

normlize_target: bool = True, log_writer=None, lr_scheduler=None, start_steps=None,

lr_schedule_values=None, wd_schedule_values=None):

model.train()

metric_logger = utils.MetricLogger(delimiter=" ")

metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))

metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))

header = 'Epoch: [{}]'.format(epoch)

print_freq = 10

loss_func = nn.MSELoss()

for step, batch in enumerate(metric_logger.log_every(data_loader, print_freq, header)):

# assign learning rate & weight decay for each step

it = start_steps + step # global training iteration

if lr_schedule_values is not None or wd_schedule_values is not None:

for i, param_group in enumerate(optimizer.param_groups):

if lr_schedule_values is not None:

param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"]

if wd_schedule_values is not None and param_group["weight_decay"] > 0:

param_group["weight_decay"] = wd_schedule_values[it]

videos, bool_masked_pos = batch

videos = videos.to(device, non_blocking=True)

bool_masked_pos = bool_masked_pos.to(device, non_blocking=True).flatten(1).to(torch.bool)

with torch.no_grad():

# calculate the predict label

mean = torch.as_tensor(IMAGENET_DEFAULT_MEAN).to(device)[None, :, None, None, None]

std = torch.as_tensor(IMAGENET_DEFAULT_STD).to(device)[None, :, None, None, None]

unnorm_videos = videos * std + mean # in [0, 1]

if normlize_target:

videos_squeeze = rearrange(unnorm_videos, 'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2) c', p0=2, p1=patch_size, p2=patch_size)

videos_norm = (videos_squeeze - videos_squeeze.mean(dim=-2, keepdim=True)

) / (videos_squeeze.var(dim=-2, unbiased=True, keepdim=True).sqrt() + 1e-6)

# we find that the mean is about 0.48 and standard deviation is about 0.08.

videos_patch = rearrange(videos_norm, 'b n p c -> b n (p c)')

else:

videos_patch = rearrange(unnorm_videos, 'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2 c)', p0=2, p1=patch_size, p2=patch_size)

B, _, C = videos_patch.shape

labels = videos_patch[bool_masked_pos].reshape(B, -1, C)

with torch.cuda.amp.autocast():

outputs = model(videos, bool_masked_pos)

loss = loss_func(input=outputs, target=labels)

loss_value = loss.item()

if not math.isfinite(loss_value):

print("Loss is {}, stopping training".format(loss_value))

sys.exit(1)

optimizer.zero_grad()

# this attribute is added by timm on one optimizer (adahessian)

is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order

grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,

parameters=model.parameters(), create_graph=is_second_order)

loss_scale_value = loss_scaler.state_dict()["scale"]

torch.cuda.synchronize()

metric_logger.update(loss=loss_value)

metric_logger.update(loss_scale=loss_scale_value)

min_lr = 10.

max_lr = 0.

for group in optimizer.param_groups:

min_lr = min(min_lr, group["lr"])

max_lr = max(max_lr, group["lr"])

metric_logger.update(lr=max_lr)

metric_logger.update(min_lr=min_lr)

weight_decay_value = None

for group in optimizer.param_groups:

if group["weight_decay"] > 0:

weight_decay_value = group["weight_decay"]

metric_logger.update(weight_decay=weight_decay_value)

metric_logger.update(grad_norm=grad_norm)

if log_writer is not None:

log_writer.update(loss=loss_value, head="loss")

log_writer.update(loss_scale=loss_scale_value, head="opt")

log_writer.update(lr=max_lr, head="opt")

log_writer.update(min_lr=min_lr, head="opt")

log_writer.update(weight_decay=weight_decay_value, head="opt")

log_writer.update(grad_norm=grad_norm, head="opt")

log_writer.set_step()

if lr_scheduler is not None:

lr_scheduler.step_update(start_steps + step)

# gather the stats from all processes

metric_logger.synchronize_between_processes()

print("Averaged stats:", metric_logger)