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engine_finetune.py
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engine_finetune.py
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# DeiT: https://github.com/facebookresearch/deit
# BEiT: https://github.com/microsoft/unilm/tree/master/beit
# --------------------------------------------------------
import math
import sys
from typing import Iterable, Optional
import mae_st.util.lr_sched as lr_sched
import mae_st.util.misc as misc
import torch
from mae_st.util.logging import master_print as print
from timm.data import Mixup
from timm.utils import accuracy
def train_one_epoch(
model: torch.nn.Module,
criterion: torch.nn.Module,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device,
epoch: int,
loss_scaler,
max_norm: float = 0,
mixup_fn: Optional[Mixup] = None,
log_writer=None,
args=None,
fp32=False,
):
model.train(True)
metric_logger = misc.MetricLogger(delimiter=" ")
metric_logger.add_meter("lr", misc.SmoothedValue(window_size=1, fmt="{value:.6f}"))
metric_logger.add_meter(
"cpu_mem", misc.SmoothedValue(window_size=1, fmt="{value:.6f}")
)
metric_logger.add_meter(
"cpu_mem_all", misc.SmoothedValue(window_size=1, fmt="{value:.6f}")
)
metric_logger.add_meter(
"gpu_mem", 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, targets) 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
)
if len(samples.shape) == 6:
b, r, c, t, h, w = samples.shape
samples = samples.view(b * r, c, t, h, w)
targets = targets.view(b * r)
if args.cpu_mix:
if mixup_fn is not None:
samples, targets = mixup_fn(samples, targets)
samples = samples.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
else:
samples = samples.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
if mixup_fn is not None:
samples, targets = mixup_fn(samples, targets)
with torch.cuda.amp.autocast(enabled=not fp32):
outputs = model(samples)
loss = criterion(outputs, targets)
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,
clip_grad=max_norm,
parameters=model.parameters(),
create_graph=False,
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)
metric_logger.update(cpu_mem=misc.cpu_mem_usage()[0])
metric_logger.update(cpu_mem_all=misc.cpu_mem_usage()[1])
metric_logger.update(gpu_mem=misc.gpu_mem_usage())
min_lr = 10.0
max_lr = 0.0
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)
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 * args.repeat_aug
)
log_writer.add_scalar("loss", loss_value_reduce, epoch_1000x)
log_writer.add_scalar("lr", max_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(data_loader, model, device):
criterion = torch.nn.CrossEntropyLoss()
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)
if len(images.shape) == 6:
b, r, c, t, h, w = images.shape
images = images.view(b * r, c, t, h, w)
target = target.view(b * r)
# compute output
with torch.cuda.amp.autocast():
output = model(images)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
batch_size = images.shape[0]
metric_logger.update(loss=loss.item())
metric_logger.meters["acc1"].update(acc1.item(), n=batch_size)
metric_logger.meters["acc5"].update(acc5.item(), n=batch_size)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print(
"* Acc@1 {top1.global_avg:.3f} Acc@5 {top5.global_avg:.3f} loss {losses.global_avg:.3f}".format(
top1=metric_logger.acc1, top5=metric_logger.acc5, losses=metric_logger.loss
)
)
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}