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| # -------------------------------------------------------- | |
| # BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers (https://arxiv.org/abs/2208.06366) | |
| # Github source: https://github.com/microsoft/unilm/tree/master/beitv2 | |
| # Copyright (c) 2022 Microsoft | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # By Zhiliang Peng | |
| # Based on BEiT, timm, DeiT and DINO code bases | |
| # https://github.com/microsoft/unilm/tree/master/beit | |
| # https://github.com/rwightman/pytorch-image-models/tree/master/timm | |
| # https://github.com/facebookresearch/deit/ | |
| # https://github.com/facebookresearch/dino | |
| # --------------------------------------------------------' | |
| from cgitb import enable | |
| import math | |
| import sys | |
| from typing import Iterable | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import utils | |
| def train_one_epoch(model: torch.nn.Module, vqkd: 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, args=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_fn = nn.CrossEntropyLoss() | |
| for step, (batch, extra_info) 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] | |
| samples, images, bool_masked_pos = batch | |
| images = images.to(device, non_blocking=True) | |
| samples = samples.to(device, non_blocking=True) | |
| bool_masked_pos = bool_masked_pos.to(device, non_blocking=True) | |
| with torch.no_grad(): | |
| with torch.cuda.amp.autocast(): | |
| input_ids = vqkd.get_codebook_indices(images) | |
| bool_masked_pos = bool_masked_pos.flatten(1).to(torch.bool) | |
| labels = input_ids[bool_masked_pos] | |
| with torch.cuda.amp.autocast(): # enabled=False | |
| outputs = model(samples, bool_masked_pos=bool_masked_pos) | |
| if isinstance(outputs, list): | |
| loss_1 = loss_fn(input=outputs[0], target=labels) | |
| loss_2 = loss_fn(input=outputs[1], target=labels) | |
| loss = loss_1 + loss_2 | |
| else: | |
| loss = loss_fn(input=outputs, target=labels) | |
| loss_value = loss.item() | |
| if not math.isfinite(loss_value): | |
| print(f"Loss is {loss_value}, stopping training at rank {utils.get_rank()}", force=True) | |
| 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() | |
| if isinstance(outputs, list): | |
| mlm_acc_1 = (outputs[0].max(-1)[1] == labels).float().mean().item() | |
| mlm_acc_2 = (outputs[1].max(-1)[1] == labels).float().mean().item() | |
| metric_logger.update(mlm_acc_1=mlm_acc_1) | |
| metric_logger.update(mlm_acc_2=mlm_acc_2) | |
| metric_logger.update(loss_1=loss_1.item()) | |
| metric_logger.update(loss_2=loss_2.item()) | |
| if log_writer is not None: | |
| log_writer.update(mlm_acc_1=mlm_acc_1, head="loss") | |
| log_writer.update(mlm_acc_2=mlm_acc_2, head="loss") | |
| log_writer.update(loss_1=loss_1.item(), head="loss") | |
| log_writer.update(loss_2=loss_2.item(), head="loss") | |
| else: | |
| 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()} | |