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on
Zero
Running
on
Zero
File size: 5,110 Bytes
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import os
import math
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 = [0, 'cpu'][1] # .to(0) = .to('cuda:0')
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]
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