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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 numpy as np
import copy
import itertools
import torch
import util.misc as utils
from datasets.hico_eval import HICOEvaluator
from datasets.vcoco_eval import VCOCOEvaluator
from datasets.hake_eval import HAKEEvaluator
from loguru import logger
# from torch.utils.tensorboard import SummaryWriter
# writer = SummaryWriter(comment='logs/coco_resnet_50_HSAM_TA_obj')
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):
model.train()
criterion.train()
metric_logger = utils.MetricLogger(delimiter=" ")
metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
if hasattr(criterion, 'loss_labels'):
metric_logger.add_meter('class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
else:
metric_logger.add_meter('obj_class_error', utils.SmoothedValue(window_size=1, fmt='{value:.2f}'))
header = 'Epoch: [{}]'.format(epoch)
print_freq = 10
for samples, targets in metric_logger.log_every(data_loader, print_freq, header):
# torch.cuda.empty_cache()
samples = samples.to(device)
targets = [{k: v.to(device) for k, v in t.items()} for t in targets]
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)
# 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)
optimizer.zero_grad()
continue
# sys.exit(1)
optimizer.zero_grad()
losses.backward()
if max_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
optimizer.step()
metric_logger.update(loss=loss_value, **loss_dict_reduced_scaled, **loss_dict_reduced_unscaled)
if hasattr(criterion, 'loss_labels'):
metric_logger.update(class_error=loss_dict_reduced['class_error'])
else:
metric_logger.update(obj_class_error=loss_dict_reduced['obj_class_error'])
metric_logger.update(lr=optimizer.param_groups[0]["lr"])
# gather the stats from all processes
metric_logger.synchronize_between_processes()
if utils.get_rank() == 0:
logger.info("\nAveraged stats: {}".format(metric_logger))
return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
@torch.no_grad()
def evaluate_hoi(dataset_file, model, postprocessors, data_loader, subject_category_id, device, out_dir, epoch, args):
model.eval()
metric_logger = utils.MetricLogger(delimiter=" ")
header = 'Test:'
preds = []
gts = []
indices = []
for samples, targets in metric_logger.log_every(data_loader, 10, header):
samples = samples.to(device)
outputs = model(samples)
orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
results = postprocessors['hoi'](outputs, orig_target_sizes)
preds.extend(list(itertools.chain.from_iterable(utils.all_gather(results))))
# For avoiding a runtime error, the copy is used
gts.extend(list(itertools.chain.from_iterable(utils.all_gather(copy.deepcopy(targets)))))
# gather the stats from all processes
metric_logger.synchronize_between_processes()
img_ids = [img_gts['id'] for img_gts in gts]
_, indices = np.unique(img_ids, return_index=True)
preds = [img_preds for i, img_preds in enumerate(preds) if i in indices]
gts = [img_gts for i, img_gts in enumerate(gts) if i in indices]
if dataset_file == 'hico':
evaluator = HICOEvaluator(preds, gts, args.hoi_path, out_dir, epoch, use_nms=args.use_nms, nms_thresh=args.nms_thresh)
rank = utils.get_rank()
stats = evaluator.evaluation_default()
if rank == 0:
logger.info('\n--------------------\ndefault mAP: {}\ndefault mAP rare: {}\ndefault mAP non-rare: {}\n--------------------'.format(stats['mAP_def'], stats['mAP_def_rare'], stats['mAP_def_non_rare']))
stats_ko = evaluator.evaluation_ko()
if rank == 0:
logger.info('\n--------------------\nko mAP: {}\nko mAP rare: {}\nko mAP non-rare: {}\n--------------------'.format(stats_ko['mAP_ko'], stats_ko['mAP_ko_rare'], stats_ko['mAP_ko_non_rare']))
stats.update(stats_ko)
if args.eval_extra:
evaluator.evaluation_extra()
# if utils.is_main_process():
# writer.add_scalar('mAP_def/mAP_def',stats['mAP_def'] , epoch)
# writer.add_scalar('mAP_def/mAP_def_rare', stats['mAP_def_rare'], epoch)
# writer.add_scalar('mAP_def/mAP_def_non_rare',stats['mAP_def_non_rare'] , epoch)
# writer.add_scalar('mAP_ko/mAP_ko', stats_ko['mAP_ko'], epoch)
# writer.add_scalar('mAP_ko/mAP_ko_rare',stats_ko['mAP_ko_rare'] , epoch)
# writer.add_scalar('mAP_ko/mAP_ko_non_rare', stats_ko['mAP_ko_non_rare'], epoch)
elif dataset_file == 'vcoco':
evaluator = VCOCOEvaluator(preds, gts, subject_category_id, data_loader.dataset.correct_mat, use_nms=args.use_nms, nms_thresh=args.nms_thresh)
stats = evaluator.evaluate()
# if utils.is_main_process():
# writer.add_scalar('mAP/mAP_all',stats['mAP_all'] , epoch)
# writer.add_scalar('mAP/mAP_thesis', stats['mAP_thesis'], epoch)
elif dataset_file == 'hake':
evaluator = HAKEEvaluator(args.hake_split_test, preds, gts, args.hake_path, out_dir, epoch, use_nms=args.use_nms, nms_thresh=args.nms_thresh)
rank = utils.get_rank()
stats = evaluator.evaluation_default()
# if utils.is_main_process():
# writer.add_scalar('mAP_def/mAP_def',stats['mAP_def'] , epoch)
# writer.add_scalar('mAP_def/mAP_def_rare', stats['mAP_def_rare'], epoch)
# writer.add_scalar('mAP_def/mAP_def_non_rare',stats['mAP_def_non_rare'] , epoch)
# writer.add_scalar('mAP_ko/mAP_ko', stats_ko['mAP_ko'], epoch)
# writer.add_scalar('mAP_ko/mAP_ko_rare',stats_ko['mAP_ko_rare'] , epoch)
# writer.add_scalar('mAP_ko/mAP_ko_non_rare', stats_ko['mAP_ko_non_rare'], epoch)
return stats