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engine_finetune.py
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engine_finetune.py
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# --------------------------------------------------------
# References:
# MAE: https://github.com/facebookresearch/mae
# 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 torch
import wandb
import torch.nn.functional as F
from timm.data import Mixup
from timm.utils import accuracy
import util.misc as misc
import util.lr_sched as lr_sched
from sklearn.metrics import average_precision_score, precision_score
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
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):
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 = 100
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)
# print(targets.shape)
samples = samples.to(device, non_blocking=True)
targets = targets.to(device, non_blocking=True)
#
if mixup_fn is not None and args.dataset_type != 'bigearthnet_finetune':
samples, targets = mixup_fn(samples, targets)
# print(targets)
# print(targets.shape)
# print(targets.dtype)
# if args.smoothing > 0.:
# targets = torch.topk(targets, k=1, dim=1).indices.squeeze(1)
with torch.cuda.amp.autocast():
outputs = model(samples)
# outputs = model(optical_images=samples)
# print(outputs)
# print(outputs.shape)
loss = criterion(outputs, targets)
# print('targets')
loss_value = loss.item()
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
raise ValueError(f"Loss is {loss_value}, stopping training")
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)
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)
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('loss', loss_value_reduce, epoch_1000x)
log_writer.add_scalar('lr', max_lr, epoch_1000x)
if args.local_rank == 0 and args.wandb is not None:
try:
wandb.log({'train_loss_step': loss_value_reduce,
'train_lr_step': max_lr, 'epoch_1000x': epoch_1000x})
except ValueError:
pass
# 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,args):
# criterion = torch.nn.CrossEntropyLoss()
if args.smoothing > 0.:
# criterion = LabelSmoothingCrossEntropy(smoothing=args.smoothing)
criterion = torch.nn.CrossEntropyLoss()
else:
# criterion = SoftTargetCrossEntropy()
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]
# print('images and targets')
images = images.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
# print("before pass model")
# compute output
with torch.cuda.amp.autocast():
output = model(images)
# output = model(optical_images=images)
loss = criterion(output, target)
# if args.dataset_type == 'bigearthnet_finetune':
# output_cpu = output.cpu()
# target_cpu = target.cpu()
#
# output_true = torch.gt(output_cpu, 0.5)
# output_score = torch.where(output_true, torch.tensor(1), torch.tensor(0))
# map = precision_score(output_score, target_cpu, average='macro')
# batch_size = images.shape[0]
# metric_logger.update(loss=loss.item())
# metric_logger.meters['mAP'].update(map.item(), n=batch_size)
# metric_logger.synchronize_between_processes()
# print(' Map {map.global_avg:.3f} loss {losses.global_avg:.3f}'
# .format(map=metric_logger.mAP, losses=metric_logger.loss))
# if args.dataset_type != 'bigearthnet_finetune':
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()}