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