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| import os | |
| import math | |
| import torch | |
| class Config(): | |
| def __init__(self) -> None: | |
| self.ms_supervision = True | |
| self.out_ref = self.ms_supervision and True | |
| self.dec_ipt = True | |
| self.dec_ipt_split = True | |
| self.locate_head = False | |
| self.cxt_num = [0, 3][1] # multi-scale skip connections from encoder | |
| self.mul_scl_ipt = ['', 'add', 'cat'][2] | |
| self.refine = ['', 'itself', 'RefUNet', 'Refiner', 'RefinerPVTInChannels4'][0] | |
| self.progressive_ref = self.refine and True | |
| self.ender = self.progressive_ref and False | |
| self.scale = self.progressive_ref and 2 | |
| self.dec_att = ['', 'ASPP', 'ASPPDeformable'][2] | |
| self.squeeze_block = ['', 'BasicDecBlk_x1', 'ResBlk_x4', 'ASPP_x3', 'ASPPDeformable_x3'][1] | |
| self.dec_blk = ['BasicDecBlk', 'ResBlk', 'HierarAttDecBlk'][0] | |
| self.auxiliary_classification = False | |
| self.refine_iteration = 1 | |
| self.freeze_bb = False | |
| self.precisionHigh = True | |
| self.compile = True | |
| self.load_all = True | |
| self.verbose_eval = True | |
| self.size = 1024 | |
| self.batch_size = 2 | |
| self.IoU_finetune_last_epochs = [0, -40][1] # choose 0 to skip | |
| if self.dec_blk == 'HierarAttDecBlk': | |
| self.batch_size = 2 ** [0, 1, 2, 3, 4][2] | |
| self.model = [ | |
| 'BSL', | |
| ][0] | |
| # Components | |
| self.lat_blk = ['BasicLatBlk'][0] | |
| self.dec_channels_inter = ['fixed', 'adap'][0] | |
| # Backbone | |
| self.bb = [ | |
| 'vgg16', 'vgg16bn', 'resnet50', # 0, 1, 2 | |
| 'pvt_v2_b2', 'pvt_v2_b5', # 3-bs10, 4-bs5 | |
| 'swin_v1_b', 'swin_v1_l' # 5-bs9, 6-bs6 | |
| ][6] | |
| self.lateral_channels_in_collection = { | |
| 'vgg16': [512, 256, 128, 64], 'vgg16bn': [512, 256, 128, 64], 'resnet50': [1024, 512, 256, 64], | |
| 'pvt_v2_b2': [512, 320, 128, 64], 'pvt_v2_b5': [512, 320, 128, 64], | |
| 'swin_v1_b': [1024, 512, 256, 128], 'swin_v1_l': [1536, 768, 384, 192], | |
| }[self.bb] | |
| if self.mul_scl_ipt == 'cat': | |
| self.lateral_channels_in_collection = [channel * 2 for channel in self.lateral_channels_in_collection] | |
| self.cxt = self.lateral_channels_in_collection[1:][::-1][-self.cxt_num:] if self.cxt_num else [] | |
| self.sys_home_dir = '/root/autodl-tmp' | |
| self.weights_root_dir = os.path.join(self.sys_home_dir, 'weights') | |
| self.weights = { | |
| 'pvt_v2_b2': os.path.join(self.weights_root_dir, 'pvt_v2_b2.pth'), | |
| 'pvt_v2_b5': os.path.join(self.weights_root_dir, ['pvt_v2_b5.pth', 'pvt_v2_b5_22k.pth'][0]), | |
| 'swin_v1_b': os.path.join(self.weights_root_dir, ['swin_base_patch4_window12_384_22kto1k.pth', 'swin_base_patch4_window12_384_22k.pth'][0]), | |
| 'swin_v1_l': os.path.join(self.weights_root_dir, ['swin_large_patch4_window12_384_22kto1k.pth', 'swin_large_patch4_window12_384_22k.pth'][0]), | |
| } | |
| # Training | |
| self.num_workers = 5 # will be decrease to min(it, batch_size) at the initialization of the data_loader | |
| self.optimizer = ['Adam', 'AdamW'][0] | |
| self.lr = 1e-5 * math.sqrt(self.batch_size / 5) # adapt the lr linearly | |
| self.lr_decay_epochs = [1e4] # Set to negative N to decay the lr in the last N-th epoch. | |
| self.lr_decay_rate = 0.5 | |
| self.only_S_MAE = False | |
| self.SDPA_enabled = False # Bug. Slower and errors occur in multi-GPUs | |
| # Data | |
| self.data_root_dir = os.path.join(self.sys_home_dir, 'datasets/dis') | |
| self.dataset = ['DIS5K', 'COD', 'SOD'][0] | |
| self.preproc_methods = ['flip', 'enhance', 'rotate', 'pepper', 'crop'][:4] | |
| # Loss | |
| self.lambdas_pix_last = { | |
| # not 0 means opening this loss | |
| # original rate -- 1 : 30 : 1.5 : 0.2, bce x 30 | |
| 'bce': 30 * 1, # high performance | |
| 'iou': 0.5 * 1, # 0 / 255 | |
| 'iou_patch': 0.5 * 0, # 0 / 255, win_size = (64, 64) | |
| 'mse': 150 * 0, # can smooth the saliency map | |
| 'triplet': 3 * 0, | |
| 'reg': 100 * 0, | |
| 'ssim': 10 * 1, # help contours, | |
| 'cnt': 5 * 0, # help contours | |
| } | |
| self.lambdas_cls = { | |
| 'ce': 5.0 | |
| } | |
| # Adv | |
| self.lambda_adv_g = 10. * 0 # turn to 0 to avoid adv training | |
| self.lambda_adv_d = 3. * (self.lambda_adv_g > 0) | |
| # others | |
| self.device = "cuda" if torch.cuda.is_available() else "cpu" | |
| self.batch_size_valid = 1 | |
| self.rand_seed = 7 | |
| run_sh_file = [f for f in os.listdir('.') if 'train.sh' == f] + [os.path.join('..', f) for f in os.listdir('..') if 'train.sh' == f] | |
| # with open(run_sh_file[0], 'r') as f: | |
| # lines = f.readlines() | |
| # self.save_last = int([l.strip() for l in lines if 'val_last=' in l][0].split('=')[-1]) | |
| # self.save_step = int([l.strip() for l in lines if 'step=' in l][0].split('=')[-1]) | |
| # self.val_step = [0, self.save_step][0] | |