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engine_pretrain.py
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# Copyright (c) 2022 Alpha-VL
# --------------------------------------------------------
# References:
# timm: https://github.com/rwightman/pytorch-image-models/tree/master/timm
# DeiT: https://github.com/facebookresearch/deit
# MAE: https://github.com/facebookresearch/mae
# --------------------------------------------------------
import math
import sys
from typing import Iterable
import torch
import util.misc as misc
import util.lr_sched as lr_sched
def train_one_epoch(model: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler,
log_writer=None,
args=None):
model.train(True)
metric_logger = misc.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 20
accum_iter = args.accum_iter
optimizer.zero_grad()
if log_writer is not None:
print('log_dir: {}'.format(log_writer.log_dir))
for data_iter_step, (samples, _) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
# we use a per iteration (instead of per epoch) lr scheduler
if data_iter_step % accum_iter == 0:
lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
samples = samples.to(device, non_blocking=True)
with torch.cuda.amp.autocast():
loss, _, _ = model(samples, mask_ratio=args.mask_ratio)
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
loss /= accum_iter
loss_scaler(loss, optimizer, parameters=model.parameters(),
update_grad=(data_iter_step + 1) % accum_iter == 0)
if (data_iter_step + 1) % accum_iter == 0:
optimizer.zero_grad()
torch.cuda.synchronize()
metric_logger.update(loss=loss_value)
lr = optimizer.param_groups[0]["lr"]
metric_logger.update(lr=lr)
loss_value_reduce = misc.all_reduce_mean(loss_value)
if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
""" We use epoch_1000x as the x-axis in tensorboard.
This calibrates different curves when batch size changes.
"""
epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x)
log_writer.add_scalar('lr', lr, epoch_1000x)
# 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()}
def train_one_epoch_corrupt(model: torch.nn.Module,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, loss_scaler,
log_writer=None,
args=None):
model.train(True)
metric_logger = misc.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 20
accum_iter = args.accum_iter
optimizer.zero_grad()
if log_writer is not None:
print('log_dir: {}'.format(log_writer.log_dir))
for data_iter_step, (samples, samples_orig) in enumerate(metric_logger.log_every_two(
data_loader, print_freq, header)):
# we use a per iteration (instead of per epoch) lr scheduler
if data_iter_step % accum_iter == 0:
lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)
samples = samples.to(device, non_blocking=True)
samples_orig = samples_orig.to(device, non_blocking=True)
#print (samples.shape, samples_orig.shape)
with torch.cuda.amp.autocast():
loss, _, _ = model(samples, samples_orig, mask_ratio=args.mask_ratio)
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
sys.exit(1)
loss /= accum_iter
loss_scaler(loss, optimizer, parameters=model.parameters(),
update_grad=(data_iter_step + 1) % accum_iter == 0)
if (data_iter_step + 1) % accum_iter == 0:
optimizer.zero_grad()
torch.cuda.synchronize()
metric_logger.update(loss=loss_value)
lr = optimizer.param_groups[0]["lr"]
metric_logger.update(lr=lr)
loss_value_reduce = misc.all_reduce_mean(loss_value)
if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
""" We use epoch_1000x as the x-axis in tensorboard.
This calibrates different curves when batch size changes.
"""
epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
log_writer.add_scalar('train_loss', loss_value_reduce, epoch_1000x)
log_writer.add_scalar('lr', lr, epoch_1000x)
# 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()}
@torch.no_grad()
def evaluate_pretrain(data_loader, model, device, args):
metric_logger = misc.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
for batch in metric_logger.log_every(data_loader, 10, header):
images = batch[0]
target = batch[-1]
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
with torch.cuda.amp.autocast():
#print (images.min(), images.max())
loss, _, _ = model(images, mask_ratio=args.mask_ratio)
loss_value = loss.item()
metric_logger.update(loss=loss.item())
# gather the stats from all processes
metric_logger.synchronize_between_processes()
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate_pretrain_corrupt(data_loader, model, device, args):
metric_logger = misc.MetricLogger(delimiter=" ")
header = 'Test:'
# switch to evaluation mode
model.eval()
for [batch, batch_orig ] in metric_logger.log_every_two(data_loader, 10, header):
images = batch
images = images.to(device, non_blocking=True)
images_orig = batch_orig
images_orig = images_orig.to(device, non_blocking=True)
with torch.cuda.amp.autocast():
loss, _, _ = model(images, images_orig, mask_ratio=args.mask_ratio)
loss_value = loss.item()
metric_logger.update(loss=loss.item())
# gather the stats from all processes
metric_logger.synchronize_between_processes()
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}