train_engine.py

import math
from typing import Iterable

import torch
import torch.nn as nn
import utils.utils as utils


def train_one_epoch(model: torch.nn.Module, tokenizer: torch.nn.Module,
                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
                    device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
                    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

    if epoch >= 100:  # dynamic masking
        data_loader.dataset.transform.sampling_by_weights = True 
    
    for step, (batch, target) 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]

        # (N, 3, 224, 224); (N, 3, 112, 112); (N, 14, 14)
        if epoch >= 100:
            images, token_images, bool_masked_pos, mask_weights = batch
            mask_weights = mask_weights.to(device, non_blocking=True)
        else:
            images, token_images, bool_masked_pos = batch
        token_images = token_images.to(device, non_blocking=True)
        images = images.to(device, non_blocking=True)
        bool_masked_pos = bool_masked_pos.to(device, non_blocking=True)

        with torch.no_grad():
            # get visual tokens, (N, 196)
            input_ids = tokenizer.get_codebook_indices(token_images).flatten(1)
            bool_masked_pos = bool_masked_pos.to(torch.bool)
            labels = input_ids[bool_masked_pos]

        with torch.cuda.amp.autocast():
            # outputs: (mask_N, 8192); labels: (mask_N)
            outputs = model(images, bool_masked_pos=bool_masked_pos, return_all_tokens=False)
            uncertainty = nn.CrossEntropyLoss(reduction='none')(input=outputs, target=labels)
            if epoch >= 100:
                loss = uncertainty * mask_weights
            else:
                loss = uncertainty
            loss = torch.mean(loss)

            # updating sampling weights
            class_name = data_loader.dataset.transform.target_to_class_name[target.item()]
            weights = data_loader.dataset.transform.sampling_weights[class_name]
            counts = data_loader.dataset.transform.sampling_counts[class_name]
            uncertainty = uncertainty.detach().cpu()
            weights[bool_masked_pos[0]] = (weights[bool_masked_pos[0]] * counts[bool_masked_pos[0]] + uncertainty) / (counts[bool_masked_pos[0]] + 1)
            counts[bool_masked_pos[0]] += 1
            data_loader.dataset.transform.sampling_weights[class_name] = weights
            data_loader.dataset.transform.sampling_counts[class_name] = counts

        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()

        mlm_acc = (outputs.max(-1)[1] == labels).float().mean().item()

        metric_logger.update(mlm_acc=mlm_acc)
        if log_writer is not None:
            log_writer.update(mlm_acc=mlm_acc, head="loss")

        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)
    
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}