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engine.py
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engine.py
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import math
import os
import sys
from typing import Iterable
import os.path as osp
from util.utils import to_device
import torch
import util.misc as utils
from datasets.coco_eval import CocoEvaluator
from util import box_ops, keypoint_ops
import json
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
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,
wo_class_error=False, lr_scheduler=None, args=None, logger=None, ema_m=None):
scaler = torch.cuda.amp.GradScaler(enabled=args.amp)
try:
need_tgt_for_training = args.use_dn
except:
need_tgt_for_training = False
model.train()
criterion.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
if not wo_class_error:
metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
long_edge_size = 1280
_cnt = 0
for samples, targets in metric_logger.log_every(data_loader, print_freq, header, logger=logger):
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
# with torch.amp.autocast('cuda', args):
with torch.cuda.amp.autocast(enabled=args.amp):
if need_tgt_for_training:
outputs = model(samples, targets)
else:
outputs = model(samples)
loss_dict = criterion(outputs, 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)
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)
# amp backward function
if args.amp:
optimizer.zero_grad()
scaler.scale(losses).backward()
if max_norm > 0:
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
scaler.step(optimizer)
scaler.update()
else:
optimizer.zero_grad()
losses.backward()
if max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
if args.onecyclelr:
lr_scheduler.step()
if args.use_ema:
if epoch >= args.ema_epoch:
ema_m.update(model)
metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled)
if 'class_error' in loss_dict_reduced:
metric_logger.update(class_error=loss_dict_reduced['class_error'])
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
_cnt += 1
if args.debug: # if debug mode, break the loop test of bath training and testing quickly
if _cnt % 15 == 0:
print("BREAK!"*5)
break
if getattr(criterion, 'loss_weight_decay', False):
criterion.loss_weight_decay(epoch=epoch)
if getattr(criterion, 'tuning_matching', False):
criterion.tuning_matching(epoch)
metric_logger.synchronize_between_processes()
print("Averaged stats:", metric_logger)
resstat = {k: meter.global_avg for k, meter in metric_logger.meters.items() if meter.count > 0}
if getattr(criterion, 'loss_weight_decay', False):
resstat.update({f'weight_{k}': v for k,v in criterion.weight_dict.items()})
return resstat
@torch.no_grad()
def evaluate(model, criterion, postprocessors, data_loader, base_ds, device, output_dir, wo_class_error=False, args=None, logger=None):
try:
need_tgt_for_training = args.use_dn
except:
need_tgt_for_training = False
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
if not wo_class_error:
metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Test:'
iou_types = tuple(k for k in ( 'bbox', 'keypoints'))
try:
useCats = args.useCats
except:
useCats = True
if not useCats:
print("useCats: {} !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!".format(useCats))
if args.dataset_file=="coco":
from datasets.coco_eval import CocoEvaluator
coco_evaluator = CocoEvaluator(base_ds, iou_types, useCats=useCats)
elif args.dataset_file=="crowdpose":
from datasets.crowdpose_eval import CocoEvaluator
coco_evaluator = CocoEvaluator(base_ds, iou_types, useCats=useCats)
elif args.dataset_file=="humanart":
from datasets.humanart_eval import CocoEvaluator
coco_evaluator = CocoEvaluator(base_ds, iou_types, useCats=useCats)
_cnt = 0
for samples, targets in metric_logger.log_every(data_loader, 10, header, logger=logger):
samples = samples.to(device)
targets = [{k: to_device(v, device) for k, v in t.items()} for t in targets]
with torch.cuda.amp.autocast(enabled=args.amp):
if need_tgt_for_training:
outputs = model(samples, targets)
else:
outputs = model(samples)
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
results = postprocessors['bbox'](outputs, orig_target_sizes)
#targets = postprocessors['bbox'](targets, orig_target_sizes)
# import pdb
# pdb.set_trace()
res = {target['image_id'].item(): output for target, output in zip(targets, results)}
if coco_evaluator is not None:
coco_evaluator.update(res)
_cnt += 1
if args.debug:
if _cnt % 15 == 0:
print("BREAK!" * 5)
break
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()
# pdb.set_trace()
stats = {k: meter.global_avg for k, meter in metric_logger.meters.items() if meter.count > 0}
if coco_evaluator is not None:
if 'bbox' in postprocessors.keys():
stats['coco_eval_bbox'] = coco_evaluator.coco_eval['bbox'].stats.tolist()
stats['coco_eval_keypoints_detr'] = coco_evaluator.coco_eval['keypoints'].stats.tolist()
return stats, coco_evaluator
# @torch.no_grad()
# def evaluate(model, criterion, postprocessors, data_loader, base_ds, device, output_dir, wo_class_error=False, args=None, logger=None):
# try:
# need_tgt_for_training = args.use_dn
# except:
# need_tgt_for_training = False
# model.eval()
# criterion.eval()
# metric_logger = utils.MetricLogger(delimiter=" ")
# if not wo_class_error:
# metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
# header = 'Test:'
# iou_types = tuple(k for k in ( 'bbox', 'keypoints'))
# try:
# useCats = args.useCats
# except:
# useCats = True
# if not useCats:
# print("useCats: {} !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!".format(useCats))
# if args.dataset_file=="coco":
# from datasets.coco_eval import CocoEvaluator
# coco_evaluator = CocoEvaluator(base_ds, iou_types, useCats=useCats)
# elif args.dataset_file=="crowdpose":
# from datasets.crowdpose_eval import CocoEvaluator
# coco_evaluator = CocoEvaluator(base_ds, iou_types, useCats=useCats)
# elif args.dataset_file=="humanart":
# from datasets.humanart_eval import CocoEvaluator
# coco_evaluator = CocoEvaluator(base_ds, iou_types, useCats=useCats)
# _cnt = 0
# coco_results = {}
# coco_targets = {}
# coco_results['images'] = []
# coco_results['annotations'] = []
# coco_targets['images'] = []
# coco_targets['annotations'] = []
# import json
# categories = json.load(open(f'/comp_robot/zhangyuhong1/code2/ED-Pose/data/coco_dir/annotations/person_keypoints_val2017_shot1.json'))['categories']
# coco_results['categories'] = categories
# coco_targets['categories'] = categories
# for samples, targets in metric_logger.log_every(data_loader, 10, header, logger=logger):
# samples = samples.to(device)
# targets = [{k: to_device(v, device) for k, v in t.items()} for t in targets]
# with torch.cuda.amp.autocast(enabled=args.amp):
# if need_tgt_for_training:
# outputs = model(samples, targets)
# else:
# outputs = model(samples)
# orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
# results = postprocessors['bbox'](outputs, orig_target_sizes)
# # import pdb
# # pdb.set_trace()
# for target, output in zip(targets, results):
# image_id = target['image_id'].item()
# pred_keypoints = output['keypoints'].cpu().numpy()[0]
# gt_keypoints = target['keypoints'].cpu().numpy()
# # score_list = []
# # for i in range(50):
# # score_list.append(output['scores'][i].item())
# # import pdb
# # pdb.set_trace()
# coco_results["annotations"].append({
# "id": image_id,
# "image_id": image_id,
# "category_id": 1, # Assuming single category for simplicity
# "keypoints": pred_keypoints.flatten().tolist(),
# "score": output['scores'][0].item()
# })
# num_keypoints = target['num_keypoints'].cpu().numpy()
# for keypoint in gt_keypoints:
# coco_targets["annotations"].append({
# "id": image_id,
# "image_id": image_id,
# "category_id": 1, # Assuming single category for simplicity
# "keypoints": keypoint.flatten().tolist(),
# "score": 1.0, # Ground truth keypoints have a score of 1.0
# "num_keypoints": num_keypoints.tolist()[0]
# })
# if coco_evaluator is not None:
# res = {target['image_id'].item(): output for target, output in zip(targets, results)}
# coco_evaluator.update(res)
# _cnt += 1
# if args.debug:
# if _cnt % 15 == 0:
# print("BREAK!" * 5)
# break
# import json
# # Save results and targets to JSON files
# with open(f'{output_dir}/coco_results.json', 'w') as f:
# json.dump(coco_results, f, indent=4)
# with open(f'{output_dir}/coco_targets.json', 'w') as f:
# json.dump(coco_targets, f, indent=4)
# # Load results and targets into COCOeval
# from pycocotools.cocoeval import COCOeval
# from pycocotools.coco import COCO
# coco_dt = COCO(f'{output_dir}/coco_results.json')
# coco_gt = COCO(f'{output_dir}/coco_targets.json')
# coco_eval = COCOeval(coco_gt, coco_dt, 'keypoints')
# coco_eval.evaluate()
# coco_eval.accumulate()
# coco_eval.summarize()
# import pdb
# pdb.set_trace()
# 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()
# # pdb.set_trace()
# stats = {k: meter.global_avg for k, meter in metric_logger.meters.items() if meter.count > 0}
# if coco_evaluator is not None:
# if 'bbox' in postprocessors.keys():
# stats['coco_eval_bbox'] = coco_evaluator.coco_eval['bbox'].stats.tolist()
# stats['coco_eval_keypoints_detr'] = coco_evaluator.coco_eval['keypoints'].stats.tolist()
# return stats, coco_evaluator
@torch.no_grad()
def inference_vis(model, criterion, postprocessors, data_loader, base_ds, device, output_dir, wo_class_error=False, args=None, logger=None):
try:
need_tgt_for_training = args.use_dn
except:
need_tgt_for_training = False
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
if not wo_class_error:
metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Test:'
# for visualize
from util.visualizer import COCOVisualizer
from pycocotools.coco import COCO
COCO_PATH = os.environ.get("EDPOSE_COCO_PATH")
cocodir = COCO_PATH + '/annotations/person_keypoints_val2017.json'
coco = COCO(cocodir)
vslzr = COCOVisualizer(coco)
_cnt = 0
# import pdb
# pdb.set_trace()
for samples, targets in metric_logger.log_every(data_loader, 10, header, logger=logger):
samples = samples.to(device)
targets = [{k: to_device(v, device) for k, v in t.items()} for t in targets]
with torch.cuda.amp.autocast(enabled=args.amp):
if need_tgt_for_training:
outputs = model(samples, targets)
else:
outputs = model(samples)
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
# orig_target_sizes = torch.stack([torch.tensor([1280,1280], device="cuda:0") for t in targets], dim=0)
images = samples.tensors.detach().cpu()
#output_viss = postprocessors['bbox'](outputs, torch.ones_like(orig_target_sizes))
output_viss = postprocessors['bbox'](outputs, torch.ones_like(orig_target_sizes))
# import pdb
# pdb.set_trace()
thersholds = [0.1, 0.13, 0.3, 0.5] # set a thershold
for _idx, (tgt, output_vis) in enumerate(zip(targets, output_viss)):
image_id=tgt["image_id"]
scores = output_vis['scores']
boxes = box_ops.box_xyxy_to_cxcywh(output_vis['boxes'])
keypoints = output_vis['keypoints']
keypoints = keypoint_ops.keypoint_xyzxyz_to_xyxyzz(keypoints)
for thershold in thersholds:
select_mask = scores > thershold
pred_dict = {
'boxes': boxes[select_mask],
'size': tgt['size'],
'image_id': tgt['image_id'],
'keypoints': keypoints[select_mask],
}
vslzr.visualize(images[_idx], pred_dict, caption=f"{int(image_id)}", savedir=os.path.join(args.output_dir, 'vis'))
return