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"""
DEIM: DETR with Improved Matching for Fast Convergence
Copyright (c) 2024 The DEIM Authors. All Rights Reserved.
---------------------------------------------------------------------------------
Modified from DETR (https://github.com/facebookresearch/detr/blob/main/engine.py)
Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
import sys
import math
from typing import Iterable
import torch
import torch.amp
from torch.utils.tensorboard import SummaryWriter
from torch.cuda.amp.grad_scaler import GradScaler
from ..optim import ModelEMA, Warmup
from ..data import CocoEvaluator
from ..misc import MetricLogger, SmoothedValue, dist_utils
def train_one_epoch(self_lr_scheduler, lr_scheduler, model: torch.nn.Module, criterion: torch.nn.Module,
data_loader: Iterable, optimizer: torch.optim.Optimizer,
device: torch.device, epoch: int, max_norm: float = 0, **kwargs):
model.train()
criterion.train()
metric_logger = MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', SmoothedValue(window_size=1, fmt='{value:.6f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = kwargs.get('print_freq', 10)
writer :SummaryWriter = kwargs.get('writer', None)
ema :ModelEMA = kwargs.get('ema', None)
scaler :GradScaler = kwargs.get('scaler', None)
lr_warmup_scheduler :Warmup = kwargs.get('lr_warmup_scheduler', None)
cur_iters = epoch * len(data_loader)
for i, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
global_step = epoch * len(data_loader) + i
metas = dict(epoch=epoch, step=i, global_step=global_step, epoch_step=len(data_loader))
if scaler is not None:
with torch.autocast(device_type=str(device), cache_enabled=True):
outputs = model(samples, targets=targets)
if torch.isnan(outputs['pred_boxes']).any() or torch.isinf(outputs['pred_boxes']).any():
print(outputs['pred_boxes'])
state = model.state_dict()
new_state = {}
for key, value in model.state_dict().items():
# Replace 'module' with 'model' in each key
new_key = key.replace('module.', '')
# Add the updated key-value pair to the state dictionary
state[new_key] = value
new_state['model'] = state
dist_utils.save_on_master(new_state, "./NaN.pth")
with torch.autocast(device_type=str(device), enabled=False):
loss_dict = criterion(outputs, targets, **metas)
loss = sum(loss_dict.values())
scaler.scale(loss).backward()
if max_norm > 0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
scaler.step(optimizer)
scaler.update()
optimizer.zero_grad()
else:
outputs = model(samples, targets=targets)
loss_dict = criterion(outputs, targets, **metas)
loss : torch.Tensor = sum(loss_dict.values())
optimizer.zero_grad()
loss.backward()
if max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
# ema
if ema is not None:
ema.update(model)
if self_lr_scheduler:
optimizer = lr_scheduler.step(cur_iters + i, optimizer)
else:
if lr_warmup_scheduler is not None:
lr_warmup_scheduler.step()
loss_dict_reduced = dist_utils.reduce_dict(loss_dict)
loss_value = sum(loss_dict_reduced.values())
if not math.isfinite(loss_value):
print("Loss is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
metric_logger.update(loss=loss_value, **loss_dict_reduced)
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
if writer and dist_utils.is_main_process() and global_step % 10 == 0:
writer.add_scalar('Loss/total', loss_value.item(), global_step)
for j, pg in enumerate(optimizer.param_groups):
writer.add_scalar(f'Lr/pg_{j}', pg['lr'], global_step)
for k, v in loss_dict_reduced.items():
writer.add_scalar(f'Loss/{k}', v.item(), global_step)
# 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(model: torch.nn.Module, criterion: torch.nn.Module, postprocessor, data_loader, coco_evaluator: CocoEvaluator, device):
model.eval()
criterion.eval()
coco_evaluator.cleanup()
metric_logger = MetricLogger(delimiter=" ")
# metric_logger.add_meter('class_error', SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Test:'
# iou_types = tuple(k for k in ('segm', 'bbox') if k in postprocessor.keys())
iou_types = coco_evaluator.iou_types
# coco_evaluator = CocoEvaluator(base_ds, iou_types)
# coco_evaluator.coco_eval[iou_types[0]].params.iouThrs = [0, 0.1, 0.5, 0.75]
for samples, targets in metric_logger.log_every(data_loader, 10, header):
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
outputs = model(samples)
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
results = postprocessor(outputs, orig_target_sizes)
# if 'segm' in postprocessor.keys():
# target_sizes = torch.stack([t["size"] for t in targets], dim=0)
# results = postprocessor['segm'](results, outputs, orig_target_sizes, target_sizes)
res = {target['image_id'].item(): output for target, output in zip(targets, results)}
if coco_evaluator is not None:
coco_evaluator.update(res)
# gather the stats from all processes
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
if coco_evaluator is not None:
coco_evaluator.synchronize_between_processes()
# accumulate predictions from all images
if coco_evaluator is not None:
coco_evaluator.accumulate()
coco_evaluator.summarize()
stats = {}
# stats = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
if coco_evaluator is not None:
if 'bbox' in iou_types:
stats['coco_eval_bbox'] = coco_evaluator.coco_eval['bbox'].stats.tolist()
if 'segm' in iou_types:
stats['coco_eval_masks'] = coco_evaluator.coco_eval['segm'].stats.tolist()
return stats, coco_evaluator
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