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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
# --------------------------------------------------------'
import math
import sys
from typing import Iterable
import torch
import torch.nn as nn
import utils
def train_one_epoch(model: torch.nn.Module,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device,
epoch: int,
loss_scaler,
clip_grad: float = 0,
log_writer=None,
lr_scheduler=None,
start_steps=None,
lr_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
if hasattr(model.module, 'quantize'):
try:
model.module.quantize.reset_cluster_size(device)
print("Reset the codebook statistic info in quantizer before each epoch")
except:
pass
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:
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.get("lr_scale", 1.0)
images = batch.to(device, non_blocking=True)
with torch.cuda.amp.autocast(enabled=True):
loss, log_loss = model(images)
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value), force=True)
utils.save_nan_model(args, model)
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=clip_grad,
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)
new_log_loss = {k.split('/')[-1]:v for k, v in log_loss.items() if k not in ['total_loss']}
metric_logger.update(**new_log_loss)
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(**new_log_loss, head="train/loss")
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.update(loss_scale=loss_scale_value, 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)
# stat the codebook usage information
if hasattr(model.module, 'quantize'):
try:
codebook_cluster_size = model.module.quantize._codebook.cluster_size
except:
codebook_cluster_size = model.module.quantize.cluster_size
zero_cnt = (codebook_cluster_size == 0).sum().item()
train_stat = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
train_stat['Unused_code'] = zero_cnt
print(f"Unused code in codebook: {zero_cnt}")
return train_stat
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate(data_loader, model, device, log_writer=None, epoch=None, args=None):
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Validation:'
# switch to evaluation mode
model.eval()
if hasattr(model.module, 'quantize'):
try:
model.module.quantize.reset_cluster_size(device)
print("Reset the codebook statistic info in quantizer before testing")
except:
pass
for step, (batch, extra_info) in enumerate(metric_logger.log_every(data_loader, 10, header)):
images = batch.to(device, non_blocking=True)
loss, log_loss = model(images)
metric_logger.update(loss=loss.item())
new_log_loss = {k.split('/')[-1]:v for k, v in log_loss.items() if k not in ['total_loss']}
metric_logger.update(**new_log_loss)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
# stat the codebook usage information
if hasattr(model, 'module') and hasattr(model.module, 'quantize'):
try:
codebook_cluster_size = model.module.quantize._codebook.cluster_size
except:
codebook_cluster_size = model.module.quantize.cluster_size
zero_cnt = (codebook_cluster_size == 0).sum().item()
test_stat = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
test_stat['unused_code'] = zero_cnt
print(f"Unused code in codebook: {zero_cnt}")
return test_stat
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def calculate_codebook_usage(data_loader, model, device, log_writer=None, epoch=None, args=None):
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Calculating codebook usage:'
# switch to evaluation mode
model.eval()
codebook_num = args.codebook_n_emd
codebook_cnt = torch.zeros(codebook_num, dtype=torch.float64).to(device)
for step, (images, _) in enumerate(metric_logger.log_every(data_loader, 10, header)):
images = images.to(device, non_blocking=True)
outputs = utils.get_model(model).get_tokens(images)['token'].view(-1)
outputs_gather_list = [torch.zeros_like(outputs) for _ in range(utils.get_world_size())]
torch.distributed.all_gather(outputs_gather_list, outputs)
all_tokens = torch.cat(outputs_gather_list, dim=0).view(-1) # [B * N * Ngpu, ]
codebook_cnt += torch.bincount(all_tokens, minlength=codebook_num)
# statistic
zero_cnt = (codebook_cnt == 0).sum() # 0
print(f"STAT: {zero_cnt} tokens ({(zero_cnt / codebook_num) * 100}%) never are used in this codebook.") |