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engine.py
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engine.py
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# ------------------------------------------------------------------------
# DAC-DETR
# Copyright (c) 2023 University of Technology Sydney & Baidu Inc & Zhejiang University. All Rights Reserved.
# Licensed under the MIT-style license found in the LICENSE file in the root directory
# ------------------------------------------------------------------------
# H-DETR
# Copyright (c) 2022 Peking University & Microsoft Research Asia. All Rights Reserved.
# Licensed under the MIT-style license found in the LICENSE file in the root directory
# ------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified 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 os
import sys
from typing import Iterable
import copy
import wandb
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from datasets.panoptic_eval import PanopticEvaluator
from datasets.data_prefetcher import data_prefetcher
scaler = torch.cuda.amp.GradScaler()
def train_one_epoch(
model: torch.nn.Module,
criterion: torch.nn.Module,
data_loader: Iterable,
optimizer: torch.optim.Optimizer,
device: torch.device,
epoch: int,
max_norm: float = 0,
use_wandb=False,
use_fp16=False,
):
model.train()
criterion.train()
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}")
)
metric_logger.add_meter(
"grad_norm", utils.SmoothedValue(window_size=1, fmt="{value:.2f}")
)
header = "Epoch: [{}]".format(epoch)
print_freq = 10
prefetcher = data_prefetcher(data_loader, device, prefetch=True)
samples, targets = prefetcher.next()
pdb_num = 0
# for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
for _ in metric_logger.log_every(range(len(data_loader)), print_freq, header):
with torch.cuda.amp.autocast() if use_fp16 else torch.cuda.amp.autocast(
enabled=False
):
if use_fp16:
optimizer.zero_grad()
outputs = model(samples, targets)
balance_targets = copy.deepcopy(targets)
balance_num = 3
# repeat the targets
for target in balance_targets:
target["boxes"] = target["boxes"].repeat(balance_num, 1)
target["labels"] = target["labels"].repeat(balance_num)
loss_dict = criterion(outputs, targets, balance_targets)
weight_dict = criterion.weight_dict
losses = sum(
loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict
)
# 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 is {}, stopping training".format(loss_value))
print(loss_dict_reduced)
sys.exit(1)
if use_fp16:
scaler.scale(losses).backward()
scaler.unscale_(optimizer)
else:
optimizer.zero_grad()
losses.backward()
if max_norm > 0:
grad_total_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), max_norm
)
else:
grad_total_norm = utils.get_total_grad_norm(model.parameters(), max_norm)
if use_fp16:
scaler.step(optimizer)
scaler.update()
else:
optimizer.step()
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"])
metric_logger.update(grad_norm=grad_total_norm)
samples, targets = prefetcher.next()
if use_wandb:
try:
wandb.log(loss_dict)
except:
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(
model,
criterion,
postprocessors,
data_loader,
base_ds,
device,
output_dir,
use_wandb=False,
):
# disable the one-to-many branch queries
# save them frist
save_num_queries = model.module.num_queries
save_two_stage_num_proposals = model.module.transformer.two_stage_num_proposals
model.module.num_queries = model.module.num_queries_one2one
model.module.transformer.two_stage_num_proposals = model.module.num_queries
model.eval()
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)
panoptic_evaluator = None
if "panoptic" in postprocessors.keys():
panoptic_evaluator = PanopticEvaluator(
data_loader.dataset.ann_file,
data_loader.dataset.ann_folder,
output_dir=os.path.join(output_dir, "panoptic_eval"),
)
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]
targets_none = None
outputs = model(samples, targets_none)
balance_targets = copy.deepcopy(targets)
balance_num = 2
# please note: In eval, we do not use the output of c-decoer,
# thus this value does not affect the result and is only used for the completeness of the code. You can set 1,2,3,...
for target in balance_targets:
target["boxes"] = target["boxes"].repeat(balance_num, 1)
target["labels"] = target["labels"].repeat(balance_num)
loss_dict = criterion(outputs, targets, balance_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)
if "segm" in postprocessors.keys():
target_sizes = torch.stack([t["size"] for t in targets], dim=0)
results = postprocessors["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)
if panoptic_evaluator is not None:
res_pano = postprocessors["panoptic"](
outputs, target_sizes, orig_target_sizes
)
for i, target in enumerate(targets):
image_id = target["image_id"].item()
file_name = f"{image_id:012d}.png"
res_pano[i]["image_id"] = image_id
res_pano[i]["file_name"] = file_name
panoptic_evaluator.update(res_pano)
# 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()
if panoptic_evaluator is not None:
panoptic_evaluator.synchronize_between_processes()
# accumulate predictions from all images
if coco_evaluator is not None:
coco_evaluator.accumulate()
coco_evaluator.summarize()
panoptic_res = None
if panoptic_evaluator is not None:
panoptic_res = panoptic_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()
if panoptic_res is not None:
stats["PQ_all"] = panoptic_res["All"]
stats["PQ_th"] = panoptic_res["Things"]
stats["PQ_st"] = panoptic_res["Stuff"]
if use_wandb:
try:
wandb.log({"AP": stats["coco_eval_bbox"][0]})
wandb.log(stats)
except:
pass
# recover the model parameters for next training epoch
model.module.num_queries = save_num_queries
model.module.transformer.two_stage_num_proposals = save_two_stage_num_proposals
return stats, coco_evaluator