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# ------------------------------------------------------------------------
# RF-DETR
# Copyright (c) 2025 Roboflow. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
# Copyright (c) 2024 Baidu. All Rights Reserved.
# ------------------------------------------------------------------------
# Conditional DETR
# Copyright (c) 2021 Microsoft. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Copied from DETR (https://github.com/facebookresearch/detr)
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# ------------------------------------------------------------------------
"""
Train and eval functions used in main.py
"""
import math
import sys
from typing import Iterable
import torch
import rfdetr.util.misc as utils
from rfdetr.datasets.coco_eval import CocoEvaluator
try:
from torch.amp import autocast, GradScaler
DEPRECATED_AMP = False
except ImportError:
from torch.cuda.amp import autocast, GradScaler
DEPRECATED_AMP = True
from typing import DefaultDict, List, Callable
from rfdetr.util.misc import NestedTensor
def get_autocast_args(args):
if DEPRECATED_AMP:
return {'enabled': args.amp, 'dtype': torch.bfloat16}
else:
return {'device_type': 'cuda', 'enabled': args.amp, 'dtype': torch.bfloat16}
def train_one_epoch(
model: torch.nn.Module,
criterion: torch.nn.Module,
lr_scheduler: torch.optim.lr_scheduler.LRScheduler,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device,
epoch: int,
batch_size: int,
max_norm: float = 0,
ema_m: torch.nn.Module = None,
schedules: dict = {},
num_training_steps_per_epoch=None,
vit_encoder_num_layers=None,
args=None,
callbacks: DefaultDict[str, List[Callable]] = None,
):
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter("lr", utils.SmoothedValue(window_size=1, fmt="{value:.6f}"))
metric_logger.add_meter(
"class_error", utils.SmoothedValue(window_size=1, fmt="{value:.2f}")
)
header = "Epoch: [{}]".format(epoch)
print_freq = 10
start_steps = epoch * num_training_steps_per_epoch
print("Grad accum steps: ", args.grad_accum_steps)
print("Total batch size: ", batch_size * utils.get_world_size())
# Add gradient scaler for AMP
if DEPRECATED_AMP:
scaler = GradScaler(enabled=args.amp)
else:
scaler = GradScaler('cuda', enabled=args.amp)
optimizer.zero_grad()
assert batch_size % args.grad_accum_steps == 0
sub_batch_size = batch_size // args.grad_accum_steps
print("LENGTH OF DATA LOADER:", len(data_loader))
for data_iter_step, (samples, targets) in enumerate(
metric_logger.log_every(data_loader, print_freq, header)
):
it = start_steps + data_iter_step
callback_dict = {
"step": it,
"model": model,
"epoch": epoch,
}
for callback in callbacks["on_train_batch_start"]:
callback(callback_dict)
if "dp" in schedules:
if args.distributed:
model.module.update_drop_path(
schedules["dp"][it], vit_encoder_num_layers
)
else:
model.update_drop_path(schedules["dp"][it], vit_encoder_num_layers)
if "do" in schedules:
if args.distributed:
model.module.update_dropout(schedules["do"][it])
else:
model.update_dropout(schedules["do"][it])
for i in range(args.grad_accum_steps):
start_idx = i * sub_batch_size
final_idx = start_idx + sub_batch_size
new_samples_tensors = samples.tensors[start_idx:final_idx]
new_samples = NestedTensor(new_samples_tensors, samples.mask[start_idx:final_idx])
new_samples = new_samples.to(device)
new_targets = [{k: v.to(device) for k, v in t.items()} for t in targets[start_idx:final_idx]]
with autocast(**get_autocast_args(args)):
outputs = model(new_samples, new_targets)
loss_dict = criterion(outputs, new_targets)
weight_dict = criterion.weight_dict
losses = sum(
(1 / args.grad_accum_steps) * loss_dict[k] * weight_dict[k]
for k in loss_dict.keys()
if k in weight_dict
)
scaler.scale(losses).backward()
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_unscaled = {
f"{k}_unscaled": v for k, v in loss_dict_reduced.items()
}
loss_dict_reduced_scaled = {
k: v * weight_dict[k]
for k, v in loss_dict_reduced.items()
if k in weight_dict
}
losses_reduced_scaled = sum(loss_dict_reduced_scaled.values())
loss_value = losses_reduced_scaled.item()
if not math.isfinite(loss_value):
print(loss_dict_reduced)
raise ValueError("Loss is {}, stopping training".format(loss_value))
if max_norm > 0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
scaler.step(optimizer)
scaler.update()
lr_scheduler.step()
optimizer.zero_grad()
if ema_m is not None:
if epoch >= 0:
ema_m.update(model)
metric_logger.update(
loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled
)
metric_logger.update(class_error=loss_dict_reduced["class_error"])
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# 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 evaluate(model, criterion, postprocessors, data_loader, base_ds, device, args=None):
model.eval()
if args.fp16_eval:
model.half()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter(
"class_error", utils.SmoothedValue(window_size=1, fmt="{value:.2f}")
)
header = "Test:"
iou_types = tuple(k for k in ("segm", "bbox") if k in postprocessors.keys())
coco_evaluator = CocoEvaluator(base_ds, iou_types)
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]
if args.fp16_eval:
samples.tensors = samples.tensors.half()
# Add autocast for evaluation
with autocast(**get_autocast_args(args)):
outputs = model(samples)
if args.fp16_eval:
for key in outputs.keys():
if key == "enc_outputs":
for sub_key in outputs[key].keys():
outputs[key][sub_key] = outputs[key][sub_key].float()
elif key == "aux_outputs":
for idx in range(len(outputs[key])):
for sub_key in outputs[key][idx].keys():
outputs[key][idx][sub_key] = outputs[key][idx][
sub_key
].float()
else:
outputs[key] = outputs[key].float()
loss_dict = criterion(outputs, targets)
weight_dict = criterion.weight_dict
# reduce losses over all GPUs for logging purposes
loss_dict_reduced = utils.reduce_dict(loss_dict)
loss_dict_reduced_scaled = {
k: v * weight_dict[k]
for k, v in loss_dict_reduced.items()
if k in weight_dict
}
loss_dict_reduced_unscaled = {
f"{k}_unscaled": v for k, v in loss_dict_reduced.items()
}
metric_logger.update(
loss=sum(loss_dict_reduced_scaled.values()),
**loss_dict_reduced_scaled,
**loss_dict_reduced_unscaled,
)
metric_logger.update(class_error=loss_dict_reduced["class_error"])
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
results = postprocessors["bbox"](outputs, orig_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 = {k: meter.global_avg for k, meter in metric_logger.meters.items()}
if coco_evaluator is not None:
if "bbox" in postprocessors.keys():
stats["coco_eval_bbox"] = coco_evaluator.coco_eval["bbox"].stats.tolist()
if "segm" in postprocessors.keys():
stats["coco_eval_masks"] = coco_evaluator.coco_eval["segm"].stats.tolist()
return stats, coco_evaluator
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