def test(cfg, dataset_name):
cfg.DATASETS.TEST = (dataset_name, )
predictor = DefaultPredictor(cfg)
evaluator_FLIR = FLIREvaluator(dataset_name, cfg, False, output_dir=out_folder, out_pr_name='pr_val.png')
#DefaultTrainer.test(cfg, trainer.model, evaluators=evaluator_FLIR)
val_loader = build_detection_test_loader(cfg, dataset_name)
inference_on_dataset(predictor.model, val_loader, evaluator_FLIR)
def test(cfg, dataset_name, file_name='FLIR_thermal_only_result.out'):
cfg.DATASETS.TEST = (dataset_name, )
predictor = DefaultPredictor(cfg)
out_name = out_folder + file_name
evaluator_FLIR = FLIREvaluator(dataset_name, cfg, False, output_dir=out_folder, save_eval=True, out_eval_path=out_name)
val_loader = build_detection_test_loader(cfg, dataset_name)
inference_on_dataset(predictor.model, val_loader, evaluator_FLIR)
def test_during_train(trainer, dataset_name):
cfg.DATASETS.TEST = (dataset_name, )
evaluator_FLIR = FLIREvaluator(dataset_name,
cfg,
False,
output_dir=out_folder,
out_pr_name='pr_val.png')
val_loader = build_detection_test_loader(cfg, dataset_name)
inference_on_dataset(trainer.model, val_loader, evaluator_FLIR)
def test_during_train(cfg, dataset_name, save_eval_name, save_folder):
cfg.DATASETS.TEST = (dataset_name, )
trainer = DefaultTrainer(cfg)
#predictor = DefaultPredictor(cfg)
#evaluator_FLIR = FLIREvaluator(dataset_name, cfg, False, output_dir=out_folder, out_pr_name='pr_val.png')
evaluator_FLIR = FLIREvaluator(dataset_name,
cfg,
False,
output_dir=save_folder,
save_eval=True,
out_eval_path=(save_folder +
save_eval_name))
#DefaultTrainer.test(cfg, trainer.model, evaluators=evaluator_FLIR)
val_loader = build_detection_test_loader(cfg, dataset_name)
inference_on_dataset(trainer.model, val_loader, evaluator_FLIR)
def test(cfg, dataset_name, file_name='FLIR_thermal_only_result.out'):
cfg.DATASETS.TEST = (dataset_name, )
predictor = DefaultPredictor(cfg)
#evaluator_FLIR = FLIREvaluator(dataset_name, cfg, False, output_dir=out_folder, out_pr_name='pr_val.png')
out_name = out_folder + file_name
#pdb.set_trace()
evaluator_FLIR = FLIREvaluator(dataset_name,
cfg,
False,
output_dir=out_folder,
save_eval=True,
out_eval_path=out_name)
#DefaultTrainer.test(cfg, trainer.model, evaluators=evaluator_FLIR)
val_loader = build_detection_test_loader(cfg, dataset_name)
inference_on_dataset(predictor.model, val_loader, evaluator_FLIR)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 3
cfg.DATASETS.TEST = (dataset, )
cfg.INPUT.FORMAT = 'BGR'
cfg.INPUT.NUM_IN_CHANNELS = 3
cfg.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
cfg.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
# Read detection results
det_1 = json.load(open(det_file_1, 'r'))
det_2 = json.load(open(det_file_2, 'r'))
det_3 = json.load(open(det_file_3, 'r'))
evaluator = FLIREvaluator(dataset,
cfg,
False,
output_dir=out_folder,
save_eval=True,
out_eval_path='out/mAP/FLIR_' + data_set +
'_var_box_fusion_gnll.out')
"""
Method lists: 'bayesian_prior_wt_score_box': This is for tuning different background prior
'bayesian_wt_score_box'
'sumLogits'
'sumLogits_softmax'
'avgLogits_softmax'
'baysian_avg_bbox'
'avg_score'
'avg_score_wt_score_box'
'avg_score_var_score_box'
'avg_score_bbox': same as top-k voting
'avg_score_avg_box'
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 3
cfg.DATASETS.TEST = (dataset, )
cfg.INPUT.FORMAT = 'BGR'
cfg.INPUT.NUM_IN_CHANNELS = 3
cfg.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
cfg.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
# Read detection results
det_1 = json.load(open(det_file_1, 'r'))
det_2 = json.load(open(det_file_2, 'r'))
det_3 = json.load(open(det_file_3, 'r'))
evaluator = FLIREvaluator(dataset,
cfg,
False,
output_dir=out_folder,
save_eval=True,
out_eval_path='out/mAP/FLIR_Baysian_' +
data_set + '_avg_box_all.out')
"""
Method lists: 'bayesian_prior_wt_score_box': This is for tuning different background prior
'bayesian_wt_score_box'
'sumLogits_softmax'
'avgLogits_softmax'
'baysian_avg_bbox'
'avg_score'
'avg_score_wt_score_box'
'avg_score_bbox': same as top-k voting
'pooling'
'bayesian'
trainer = DefaultTrainer(cfg)
trainer.resume_or_load(resume=False)
out_name = 'out_model_iter_'+ str(idx*eval_every_iter) +'.pth'
out_model_path = os.path.join(out_folder, out_name)
trainer.train()
torch.save(trainer.model.state_dict(), out_model_path)
#pdb.set_trace()
# Evaluation on validation set
test(cfg, dataset_train)
test(cfg, dataset_test)
del trainer
#pdb.set_trace()
# Test on training set
cfg.DATASETS.TEST = (dataset_train, )
predictor = DefaultPredictor(cfg)
evaluator = FLIREvaluator(dataset, cfg, False, output_dir=out_folder, save_eval=True, out_eval_path='FLIR_train_eval.out')
val_loader = build_detection_test_loader(cfg, dataset_train)
inference_on_dataset(predictor.model, val_loader, evaluator)
# Test on evaluation set
cfg.DATASETS.TEST = (dataset_test, )
predictor = DefaultPredictor(cfg)
evaluator = FLIREvaluator(dataset, cfg, False, output_dir=out_folder, save_eval=True, out_eval_path='FLIR_train_eval.out')
val_loader = build_detection_test_loader(cfg, dataset_test)
inference_on_dataset(predictor.model, val_loader, evaluator)
cfg.MODEL.WEIGHTS = "detectron2://COCO-Detection/faster_rcnn_R_101_FPN_3x/137851257/model_final_f6e8b1.pkl"
#cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "good_model/out_model_iter_32000.pth")
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 80
cfg.DATASETS.TEST = (dataset, )
cfg.INPUT.FORMAT = 'BGR'
cfg.INPUT.NUM_IN_CHANNELS = 3
cfg.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
cfg.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
# Read detection results
val_1 = json.load(open(val_file_1, 'r'))
val_2 = json.load(open(val_file_2, 'r'))
#val_3 = json.load(open(det_file_3, 'r'))
evaluator = FLIREvaluator(dataset,
cfg,
False,
output_dir=out_folder,
save_eval=True,
out_eval_path='out/mAP/FLIR_Baysian_Day.out')
save_file_name = 'train_labels_2_model.npz'
"""
print('Perpare training data ... ')
X_train, Y_train = train_late_fusion(det_1, det_2, anno_train_gt)
np.savez(save_file_name, X=X_train, Y=Y_train)
"""
print('Loading saved data ...')
train_data = np.load(save_file_name)
X_train = train_data['X']
Y_train = train_data['Y']
#print('Perpare validation data ... ')
#X_val, Y_val = train_late_fusion(val_1, val_2, anno_val_gt)
trainer.resume_or_load(resume=False)
trainer.train()
cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, "model_final.pth")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set the testing threshold for this model
from detectron2.evaluation import FLIREvaluator, inference_on_dataset
from detectron2.data import build_detection_test_loader
from tools.plain_train_net import do_test
# Test on training set
cfg.DATASETS.TEST = (dataset, )
predictor = DefaultPredictor(cfg)
evaluator = FLIREvaluator(dataset,
cfg,
False,
output_dir=out_folder,
out_pr_name='pr_train.png')
val_loader = build_detection_test_loader(cfg, dataset)
inference_on_dataset(predictor.model, val_loader, evaluator)
# Test on validation set
dataset = 'FLIR_val'
cfg.DATASETS.TEST = (dataset, )
register_coco_instances(dataset, {}, val_json_path, val_folder)
FLIR_metadata = MetadataCatalog.get(dataset)
dataset_dicts = DatasetCatalog.get(dataset)
evaluator = FLIREvaluator(dataset,
cfg,
False,
img_folder = '../../../Datasets/FLIR/val/thermal_8_bit/'
dataset_train = FLIRDataset(X_train=X_train,
Y_train=Y_train,
img_folder=img_folder)
batch_size = len(X_train)
train_loader = DataLoader(
dataset_train,
batch_size=batch_size,
shuffle=to_shuffle,
num_workers=16,
)
cfg = get_cfg_function(out_folder)
evaluator = FLIREvaluator(
dataset,
cfg,
False,
output_dir=out_folder,
save_eval=True,
out_eval_path='out/mAP/FLIR_pytorch_learning_fusion.out')
device = torch.device('cuda')
torch.cuda.set_device(0)
model = learnFusionModel(use_bias=use_bias, random_init=random_init)
model = model.train()
model = model.to(device)
#optimizer = torch.optim.Adam(model.parameters(), lr=lr)
optimizer = torch.optim.SGD(model.parameters(),
lr=lr,
momentum=momentum,
weight_decay=weight_decay)
cfg = get_cfg()
cfg.OUTPUT_DIR = out_folder
cfg.merge_from_file("./configs/COCO-Detection/faster_rcnn_R_101_FPN_3x.yaml")
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set threshold for this model
cfg.MODEL.WEIGHTS = "output_val/model_0009999.pth"
# Train config
cfg.DATALOADER.NUM_WORKERS = 2
cfg.SOLVER.IMS_PER_BATCH = 2
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512 # faster, and good enough for this toy dataset (default: 512)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 17
os.makedirs(cfg.OUTPUT_DIR, exist_ok=True)
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5 # set the testing threshold for this model
cfg.DATASETS.TEST = (dataset, )
predictor = DefaultPredictor(cfg)
from detectron2.evaluation import FLIREvaluator, inference_on_dataset
from detectron2.data import build_detection_test_loader
from tools.plain_train_net import do_test
# Test on validation set
dataset = 'FLIR_val'
cfg.DATASETS.TEST = (dataset, )
register_coco_instances(dataset, {}, val_json_path, val_folder)
FLIR_metadata = MetadataCatalog.get(dataset)
dataset_dicts = DatasetCatalog.get(dataset)
evaluator = FLIREvaluator(dataset, cfg, False, output_dir=out_folder)
val_loader = build_detection_test_loader(cfg, dataset)
inference_on_dataset(predictor.model, val_loader, evaluator)
cfg.INPUT.FORMAT = 'BGR'
cfg.INPUT.NUM_IN_CHANNELS = 3
cfg.MODEL.PIXEL_MEAN = [103.530, 116.280, 123.675]
cfg.MODEL.PIXEL_STD = [1.0, 1.0, 1.0]
# Read detection results
det_1 = json.load(open(det_file_1, 'r'))
det_2 = json.load(open(det_file_2, 'r'))
det_3 = json.load(open(det_file_3, 'r'))
val_1 = json.load(open(val_file_1, 'r'))
val_2 = json.load(open(val_file_2, 'r'))
#val_3 = json.load(open(det_file_3, 'r'))
evaluator = FLIREvaluator(
dataset,
cfg,
False,
output_dir=out_folder,
save_eval=True,
out_eval_path='out/mAP/FLIR_learned_l1_w_o_bias.out')
method = 'avg_score' #'baysian_wt_score_box'#'sumLogits_softmax'#'avgLogits_softmax'#'baysian_avg_bbox'#'avg_score'#'pooling' #'baysian'#'nms'
#result = apply_late_fusion_and_evaluate(cfg, evaluator, det_1, det_2, det_3, method)
save_file_name = 'train_labels_2_model_train2.npz'
"""
# Get training labels
print('Perpare training data ... ')
X_train, Y_train = train_late_fusion(det_1, det_2, anno_train_gt)
np.savez(save_file_name, X=X_train, Y=Y_train)
"""
print('Loading saved data ...')
train_data = np.load(save_file_name)
X_train = train_data['X']
