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config.py
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config.py
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import os
import numpy as np
import torch
import torch.distributed as dist
os.makedirs('results/images', exist_ok=True)
os.makedirs('results/videos', exist_ok=True)
os.makedirs('results/onnx_images', exist_ok=True)
os.makedirs('results/onnx_videos', exist_ok=True)
os.makedirs('results/trt_images', exist_ok=True)
os.makedirs('results/trt_videos', exist_ok=True)
os.makedirs('weights/', exist_ok=True)
os.makedirs('onnx_files/', exist_ok=True)
os.makedirs('trt_files/', exist_ok=True)
os.makedirs('tensorboard_log/', exist_ok=True)
COLORS = np.array([[0, 0, 0], [244, 67, 54], [233, 30, 99], [156, 39, 176], [103, 58, 183], [100, 30, 60],
[63, 81, 181], [33, 150, 243], [3, 169, 244], [0, 188, 212], [20, 55, 200],
[0, 150, 136], [76, 175, 80], [139, 195, 74], [205, 220, 57], [70, 25, 100],
[255, 235, 59], [255, 193, 7], [255, 152, 0], [255, 87, 34], [90, 155, 50],
[121, 85, 72], [158, 158, 158], [96, 125, 139], [15, 67, 34], [98, 55, 20],
[21, 82, 172], [58, 128, 255], [196, 125, 39], [75, 27, 134], [90, 125, 120],
[121, 82, 7], [158, 58, 8], [96, 25, 9], [115, 7, 234], [8, 155, 220],
[221, 25, 72], [188, 58, 158], [56, 175, 19], [215, 67, 64], [198, 75, 20],
[62, 185, 22], [108, 70, 58], [160, 225, 39], [95, 60, 144], [78, 155, 120],
[101, 25, 142], [48, 198, 28], [96, 225, 200], [150, 167, 134], [18, 185, 90],
[21, 145, 172], [98, 68, 78], [196, 105, 19], [215, 67, 84], [130, 115, 170],
[255, 0, 255], [255, 255, 0], [196, 185, 10], [95, 167, 234], [18, 25, 190],
[0, 255, 255], [255, 0, 0], [0, 255, 0], [0, 0, 255], [155, 0, 0],
[0, 155, 0], [0, 0, 155], [46, 22, 130], [255, 0, 155], [155, 0, 255],
[255, 155, 0], [155, 255, 0], [0, 155, 255], [0, 255, 155], [18, 5, 40],
[120, 120, 255], [255, 58, 30], [60, 45, 60], [75, 27, 244], [128, 25, 70]], dtype='uint8')
# 7 classes per row
COCO_CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench',
'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant',
'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
'teddy bear', 'hair drier', 'toothbrush')
PASCAL_CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle',
'bus', 'car', 'cat', 'chair', 'cow',
'diningtable', 'dog', 'horse', 'motorbike', 'person',
'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
CUSTOM_CLASSES = ('dog', 'person', 'bear', 'sheep')
COCO_LABEL_MAP = {1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8,
9: 9, 10: 10, 11: 11, 13: 12, 14: 13, 15: 14, 16: 15, 17: 16,
18: 17, 19: 18, 20: 19, 21: 20, 22: 21, 23: 22, 24: 23, 25: 24,
27: 25, 28: 26, 31: 27, 32: 28, 33: 29, 34: 30, 35: 31, 36: 32,
37: 33, 38: 34, 39: 35, 40: 36, 41: 37, 42: 38, 43: 39, 44: 40,
46: 41, 47: 42, 48: 43, 49: 44, 50: 45, 51: 46, 52: 47, 53: 48,
54: 49, 55: 50, 56: 51, 57: 52, 58: 53, 59: 54, 60: 55, 61: 56,
62: 57, 63: 58, 64: 59, 65: 60, 67: 61, 70: 62, 72: 63, 73: 64,
74: 65, 75: 66, 76: 67, 77: 68, 78: 69, 79: 70, 80: 71, 81: 72,
82: 73, 84: 74, 85: 75, 86: 76, 87: 77, 88: 78, 89: 79, 90: 80}
norm_mean = np.array([103.94, 116.78, 123.68], dtype=np.float32)
norm_std = np.array([57.38, 57.12, 58.40], dtype=np.float32)
class res101_coco:
def __init__(self, args):
self.mode = args.mode
self.cuda = args.cuda
self.gpu_id = args.gpu_id
assert args.img_size % 32 == 0, f'Img_size must be divisible by 32, got {args.img_size}.'
self.img_size = args.img_size
self.class_names = COCO_CLASSES
self.num_classes = len(COCO_CLASSES) + 1
self.continuous_id = COCO_LABEL_MAP
self.scales = [int(self.img_size / 544 * aa) for aa in (24, 48, 96, 192, 384)]
self.aspect_ratios = [1, 1 / 2, 2]
if self.mode == 'train':
self.weight = args.resume if args.resume else 'weights/backbone_res101.pth'
else:
self.weight = args.weight
self.data_root = '/home/feiyu/Data/'
if self.mode == 'train':
self.train_imgs = self.data_root + 'coco2017/train2017/'
self.train_ann = self.data_root + 'coco2017/annotations/instances_train2017.json'
self.train_bs = args.train_bs
self.bs_per_gpu = args.bs_per_gpu
self.val_interval = args.val_interval
self.bs_factor = self.train_bs / 8
self.lr = 0.001 * self.bs_factor
self.warmup_init = self.lr * 0.1
self.warmup_until = 500 # If adapted with bs_factor, inifinte loss may appear.
self.lr_steps = tuple([int(aa / self.bs_factor) for aa in (0, 280000, 560000, 620000, 680000)])
self.pos_iou_thre = 0.5
self.neg_iou_thre = 0.4
self.conf_alpha = 1
self.bbox_alpha = 1.5
self.mask_alpha = 6.125
self.semantic_alpha = 1
# The max number of masks to train for one image.
self.masks_to_train = 100
if self.mode in ('train', 'val'):
self.val_imgs = self.data_root + 'coco2017/val2017/'
self.val_ann = self.data_root + 'coco2017/annotations/instances_val2017.json'
self.val_bs = 1
self.val_num = args.val_num
self.coco_api = args.coco_api
self.traditional_nms = args.traditional_nms
self.nms_score_thre = 0.05
self.nms_iou_thre = 0.5
self.top_k = 200
self.max_detections = 100
if self.mode == 'detect':
for k, v in vars(args).items():
self.__setattr__(k, v)
def print_cfg(self):
print()
print('-' * 30 + self.__class__.__name__ + '-' * 30)
for k, v in vars(self).items():
if k not in ('continuous_id', 'data_root', 'cfg'):
print(f'{k}: {v}')
print()
class res50_coco(res101_coco):
def __init__(self, args):
super().__init__(args)
if self.mode == 'train':
self.weight = args.resume if args.resume else 'weights/backbone_res50.pth'
else:
self.weight = args.weight
class swin_tiny_coco(res101_coco):
def __init__(self, args):
super().__init__(args)
if self.mode == 'train':
self.weight = args.resume if args.resume else 'weights/swin_tiny.pth'
self.lr = 0.00005 * self.bs_factor
else:
self.weight = args.weight
class res50_pascal(res101_coco):
def __init__(self, args):
super().__init__(args)
self.class_names = PASCAL_CLASSES
self.num_classes = len(PASCAL_CLASSES) + 1
self.continuous_id = {(aa + 1): (aa + 1) for aa in range(self.num_classes - 1)}
self.use_square_anchors = False
if self.mode == 'train':
self.weight = args.resume if args.resume else 'weights/backbone_res50.pth'
else:
self.weight = args.weight
if self.mode == 'train':
self.train_imgs = self.data_root + 'pascal_sbd/img'
self.train_ann = self.data_root + 'pascal_sbd/pascal_sbd_train.json'
self.lr_steps = tuple([int(aa / self.bs_factor) for aa in (0, 60000, 100000, 120000)])
self.scales = [int(self.img_size / 544 * aa) for aa in (32, 64, 128, 256, 512)]
if self.mode in ('train', 'val'):
self.val_imgs = self.data_root + 'pascal_sbd/img'
self.val_ann = self.data_root + 'pascal_sbd/pascal_sbd_val.json'
class res101_custom(res101_coco):
def __init__(self, args):
super().__init__(args)
self.class_names = CUSTOM_CLASSES
self.num_classes = len(self.class_names) + 1
self.continuous_id = {(aa + 1): (aa + 1) for aa in range(self.num_classes - 1)}
if self.mode == 'train':
self.train_imgs = 'custom_dataset/'
self.train_ann = 'custom_dataset/custom_ann.json'
self.warmup_until = 100 # just an example
self.lr_steps = (0, 1200, 1600, 2000) # just an example
if self.mode in ('train', 'val'):
self.val_imgs = '' # decide by yourself
self.val_ann = ''
class res50_custom(res101_coco):
def __init__(self, args):
super().__init__(args)
self.class_names = CUSTOM_CLASSES
self.num_classes = len(self.class_names) + 1
self.continuous_id = {(aa + 1): (aa + 1) for aa in range(self.num_classes - 1)}
if self.mode == 'train':
self.weight = args.resume if args.resume else 'weights/backbone_res50.pth'
else:
self.weight = args.weight
if self.mode == 'train':
self.train_imgs = 'custom_dataset/'
self.train_ann = 'custom_dataset/custom_ann.json'
self.warmup_until = 100 # just an example
self.lr_steps = (0, 1200, 1600, 2000) # just an example
if self.mode in ('train', 'val'):
self.val_imgs = '' # decide by yourself
self.val_ann = ''
def get_config(args, mode):
args.cuda = torch.cuda.is_available()
args.mode = mode
if args.cuda:
args.gpu_id = os.environ.get('CUDA_VISIBLE_DEVICES') if os.environ.get('CUDA_VISIBLE_DEVICES') else '0'
if args.mode == 'train':
torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend='nccl', init_method='env://')
# Only launched by torch.distributed.launch, 'WORLD_SIZE' can be add to environment variables.
num_gpus = int(os.environ['WORLD_SIZE'])
assert args.train_bs % num_gpus == 0, 'Total training batch size must be divisible by GPU number.'
args.bs_per_gpu = int(args.train_bs / num_gpus)
else:
assert args.gpu_id.isdigit(), f'Only one GPU can be used in val/detect mode, got {args.gpu_id}.'
else:
args.gpu_id = None
if args.mode == 'train':
args.bs_per_gpu = args.train_bs
print('\n-----No GPU found, training on CPU.-----')
else:
print('\n-----No GPU found, validate on CPU.-----')
cfg = globals()[args.cfg](args)
if not args.cuda or args.mode != 'train':
cfg.print_cfg()
elif dist.get_rank() == 0:
cfg.print_cfg()
return cfg