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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,165 @@ | ||
| from easydict import EasyDict as edict | ||
| import os | ||
| import numpy as np | ||
|
|
||
| cfg = edict() | ||
| cfg.obj_types = ['Car', 'Pedestrian', 'Cyclist'] | ||
| cfg.anchor_prior = False | ||
| ## trainer | ||
| trainer = edict( | ||
| gpu = 0, | ||
| max_epochs = 200, | ||
| disp_iter = 50, | ||
| save_iter = 20, | ||
| test_iter = 20, | ||
| cudnn = True, | ||
| training_func = "train_rtm3d", | ||
| test_func = "test_mono_detection", | ||
| evaluate_func = "evaluate_kitti_obj", | ||
| ) | ||
|
|
||
| cfg.trainer = trainer | ||
|
|
||
| ## path | ||
| path = edict() | ||
| path.data_path = "/home/kitti_obj/training" | ||
| path.test_path = "/home/kitti_obj/testing" | ||
| path.visualDet3D_path = "/home/stereo_kitti/visualDet3D" | ||
| path.project_path = "/home/stereo_kitti/workdirs" | ||
|
|
||
| if not os.path.isdir(path.project_path): | ||
| os.mkdir(path.project_path) | ||
| path.project_path = os.path.join(path.project_path, 'RTM3D') | ||
| if not os.path.isdir(path.project_path): | ||
| os.mkdir(path.project_path) | ||
|
|
||
| path.log_path = os.path.join(path.project_path, "log") | ||
| if not os.path.isdir(path.log_path): | ||
| os.mkdir(path.log_path) | ||
|
|
||
| path.checkpoint_path = os.path.join(path.project_path, "checkpoint") | ||
| if not os.path.isdir(path.checkpoint_path): | ||
| os.mkdir(path.checkpoint_path) | ||
|
|
||
| path.preprocessed_path = os.path.join(path.project_path, "output") | ||
| if not os.path.isdir(path.preprocessed_path): | ||
| os.mkdir(path.preprocessed_path) | ||
|
|
||
| path.train_imdb_path = os.path.join(path.preprocessed_path, "training") | ||
| if not os.path.isdir(path.train_imdb_path): | ||
| os.mkdir(path.train_imdb_path) | ||
|
|
||
| path.val_imdb_path = os.path.join(path.preprocessed_path, "validation") | ||
| if not os.path.isdir(path.val_imdb_path): | ||
| os.mkdir(path.val_imdb_path) | ||
|
|
||
| cfg.path = path | ||
|
|
||
| ## optimizer | ||
| optimizer = edict( | ||
| type_name = 'adam', | ||
| keywords = edict( | ||
| lr = 1.25e-4, | ||
| weight_decay = 0, | ||
| ), | ||
| clipped_gradient_norm = 35.0 | ||
| ) | ||
| cfg.optimizer = optimizer | ||
| ## scheduler | ||
| scheduler = edict( | ||
| type_name = 'MultiStepLR', | ||
| keywords = edict( | ||
| milestones = [90, 120] | ||
| ) | ||
| ) | ||
| cfg.scheduler = scheduler | ||
|
|
||
| ## data | ||
| data = edict( | ||
| batch_size = 32, | ||
| num_workers = 4, | ||
| rgb_shape = (384, 1280, 3), | ||
| train_dataset = "KittiRTM3DDataset", | ||
| val_dataset = "KittiMonoDataset", | ||
| test_dataset = "KittiMonoTestDataset", | ||
| train_split_file = os.path.join(cfg.path.visualDet3D_path, 'data', 'kitti', 'chen_split', 'train.txt'), | ||
| val_split_file = os.path.join(cfg.path.visualDet3D_path, 'data', 'kitti', 'chen_split', 'val.txt'), | ||
| max_occlusion = 4, | ||
| min_z = 3, | ||
| ) | ||
|
|
||
| data.augmentation = edict( | ||
| rgb_mean = np.array([0.485, 0.456, 0.406]), | ||
| rgb_std = np.array([0.229, 0.224, 0.225]), | ||
| cropSize = (data.rgb_shape[0], data.rgb_shape[1]), | ||
| ) | ||
| data.train_augmentation = [ | ||
| edict(type_name='ConvertToFloat'), | ||
| edict(type_name='RandomWarpAffine', keywords=edict(output_w=data.augmentation.cropSize[1], output_h=data.augmentation.cropSize[0])), | ||
| #edict(type_name='Resize', keywords=edict(size=data.augmentation.cropSize)), | ||
| edict(type_name="Shuffle", keywords=edict( | ||
| aug_list=[ | ||
| edict(type_name="RandomBrightness", keywords=edict(distort_prob=1.0)), | ||
| edict(type_name="RandomContrast", keywords=edict(distort_prob=1.0, lower=0.6, upper=1.4)), | ||
| edict(type_name="Compose", keywords=edict( | ||
| aug_list=[ | ||
| edict(type_name="ConvertColor", keywords=edict(transform='HSV')), | ||
| edict(type_name="RandomSaturation", keywords=edict(distort_prob=1.0, lower=0.6, upper=1.4)), | ||
| edict(type_name="ConvertColor", keywords=edict(current='HSV', transform='RGB')), | ||
| ] | ||
| )) | ||
| ] | ||
| ) | ||
| ), | ||
| edict(type_name='RandomEigenvalueNoise', keywords=edict(alphastd=0.1)), | ||
| edict(type_name='RandomMirror', keywords=edict(mirror_prob=0.5)), | ||
| edict(type_name="FilterObject"), | ||
| edict(type_name='Normalize', keywords=edict(mean=data.augmentation.rgb_mean, stds=data.augmentation.rgb_std)) | ||
| ] | ||
| data.test_augmentation = [ | ||
| edict(type_name='ConvertToFloat'), | ||
| #edict(type_name='CropTop', keywords=edict(crop_top_index=data.augmentation.crop_top)), | ||
| edict(type_name='Resize', keywords=edict(size=data.augmentation.cropSize)), | ||
| edict(type_name='Normalize', keywords=edict(mean=data.augmentation.rgb_mean, stds=data.augmentation.rgb_std)) | ||
| ] | ||
| cfg.data = data | ||
|
|
||
| ## networks | ||
| detector = edict() | ||
| detector.obj_types = cfg.obj_types | ||
| detector.name = 'KM3D' | ||
| detector.backbone = edict( | ||
| depth=18, | ||
| pretrained=True, | ||
| frozen_stages=-1, | ||
| num_stages=4, | ||
| out_indices=(3, ), | ||
| norm_eval=False, | ||
| dilations=(1, 1, 1, 1), | ||
| ) | ||
| head_loss = edict( | ||
| gamma=2.0, | ||
| rampup_length = 100, | ||
| output_w = data.rgb_shape[1] // 4 | ||
| ) | ||
| head_test = edict( | ||
| score_thr=0.3, | ||
| ) | ||
|
|
||
| head_layer = edict( | ||
| input_features=256, | ||
| head_features=64, | ||
| head_dict={'hm': len(cfg.obj_types), 'wh': 2, 'hps': 18, | ||
| 'rot': 8, 'dim': 3, 'prob': 1, | ||
| 'reg': 2, 'hm_hp': 9, 'hp_offset': 2} | ||
| ) | ||
| detector.head = edict( | ||
| num_classes = len(cfg.obj_types), | ||
| num_joints = 9, | ||
| max_objects = 32, | ||
| layer_cfg = head_layer, | ||
| loss_cfg = head_loss, | ||
| test_cfg = head_test | ||
| ) | ||
| detector.loss = head_loss | ||
| cfg.detector = detector |
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