wehub-resource-sync
139 lines
6.1 KiB
Python
139 lines
6.1 KiB
Python
# --------------------------------------------------------
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# BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers (https://arxiv.org/abs/2208.06366)
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# Github source: https://github.com/microsoft/unilm/tree/master/beitv2
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# Copyright (c) 2022 Microsoft
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# Licensed under The MIT License [see LICENSE for details]
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# By Zhiliang Peng
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# Based on BEiT, timm, DeiT and DINO code bases
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# https://github.com/microsoft/unilm/tree/master/beit
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# https://github.com/rwightman/pytorch-image-models/tree/master/timm
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# https://github.com/facebookresearch/deit/
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# https://github.com/facebookresearch/dino
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# --------------------------------------------------------'
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from cgitb import enable
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import math
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import sys
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from typing import Iterable
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import utils
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def train_one_epoch(model: torch.nn.Module, vqkd: torch.nn.Module,
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data_loader: Iterable, optimizer: torch.optim.Optimizer,
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device: torch.device, epoch: int, loss_scaler, max_norm: float = 0,
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log_writer=None, lr_scheduler=None, start_steps=None,
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lr_schedule_values=None, wd_schedule_values=None, args=None):
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model.train()
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metric_logger = utils.MetricLogger(delimiter=" ")
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metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
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metric_logger.add_meter('min_lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
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header = 'Epoch: [{}]'.format(epoch)
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print_freq = 10
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loss_fn = nn.CrossEntropyLoss()
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for step, (batch, extra_info) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
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# assign learning rate & weight decay for each step
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it = start_steps + step # global training iteration
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if lr_schedule_values is not None or wd_schedule_values is not None:
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for i, param_group in enumerate(optimizer.param_groups):
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if lr_schedule_values is not None:
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param_group["lr"] = lr_schedule_values[it] * param_group["lr_scale"]
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if wd_schedule_values is not None and param_group["weight_decay"] > 0:
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param_group["weight_decay"] = wd_schedule_values[it]
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samples, images, bool_masked_pos = batch
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images = images.to(device, non_blocking=True)
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samples = samples.to(device, non_blocking=True)
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bool_masked_pos = bool_masked_pos.to(device, non_blocking=True)
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with torch.no_grad():
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with torch.cuda.amp.autocast():
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input_ids = vqkd.get_codebook_indices(images)
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bool_masked_pos = bool_masked_pos.flatten(1).to(torch.bool)
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labels = input_ids[bool_masked_pos]
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with torch.cuda.amp.autocast(): # enabled=False
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outputs = model(samples, bool_masked_pos=bool_masked_pos)
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if isinstance(outputs, list):
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loss_1 = loss_fn(input=outputs[0], target=labels)
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loss_2 = loss_fn(input=outputs[1], target=labels)
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loss = loss_1 + loss_2
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else:
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loss = loss_fn(input=outputs, target=labels)
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loss_value = loss.item()
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if not math.isfinite(loss_value):
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print(f"Loss is {loss_value}, stopping training at rank {utils.get_rank()}", force=True)
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sys.exit(1)
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optimizer.zero_grad()
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# this attribute is added by timm on one optimizer (adahessian)
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is_second_order = hasattr(optimizer, 'is_second_order') and optimizer.is_second_order
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grad_norm = loss_scaler(loss, optimizer, clip_grad=max_norm,
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parameters=model.parameters(), create_graph=is_second_order)
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loss_scale_value = loss_scaler.state_dict()["scale"]
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torch.cuda.synchronize()
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if isinstance(outputs, list):
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mlm_acc_1 = (outputs[0].max(-1)[1] == labels).float().mean().item()
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mlm_acc_2 = (outputs[1].max(-1)[1] == labels).float().mean().item()
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metric_logger.update(mlm_acc_1=mlm_acc_1)
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metric_logger.update(mlm_acc_2=mlm_acc_2)
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metric_logger.update(loss_1=loss_1.item())
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metric_logger.update(loss_2=loss_2.item())
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if log_writer is not None:
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log_writer.update(mlm_acc_1=mlm_acc_1, head="loss")
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log_writer.update(mlm_acc_2=mlm_acc_2, head="loss")
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log_writer.update(loss_1=loss_1.item(), head="loss")
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log_writer.update(loss_2=loss_2.item(), head="loss")
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else:
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mlm_acc = (outputs.max(-1)[1] == labels).float().mean().item()
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metric_logger.update(mlm_acc=mlm_acc)
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if log_writer is not None:
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log_writer.update(mlm_acc=mlm_acc, head="loss")
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metric_logger.update(loss=loss_value)
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metric_logger.update(loss_scale=loss_scale_value)
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min_lr = 10.
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max_lr = 0.
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for group in optimizer.param_groups:
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min_lr = min(min_lr, group["lr"])
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max_lr = max(max_lr, group["lr"])
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metric_logger.update(lr=max_lr)
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metric_logger.update(min_lr=min_lr)
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weight_decay_value = None
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for group in optimizer.param_groups:
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if group["weight_decay"] > 0:
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weight_decay_value = group["weight_decay"]
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metric_logger.update(weight_decay=weight_decay_value)
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metric_logger.update(grad_norm=grad_norm)
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if log_writer is not None:
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log_writer.update(loss=loss_value, head="loss")
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log_writer.update(loss_scale=loss_scale_value, head="opt")
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log_writer.update(lr=max_lr, head="opt")
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log_writer.update(min_lr=min_lr, head="opt")
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log_writer.update(weight_decay=weight_decay_value, head="opt")
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log_writer.update(grad_norm=grad_norm, head="opt")
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log_writer.set_step()
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if lr_scheduler is not None:
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lr_scheduler.step_update(start_steps + step)
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# gather the stats from all processes
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metric_logger.synchronize_between_processes()
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print("Averaged stats:", metric_logger)
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return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
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