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import math
class Config:
def __init__(self) -> None:
# PATH settings
# Make up your file system as: SYS_HOME_DIR/codes/dis/BiRefNet, SYS_HOME_DIR/datasets/dis/xx, SYS_HOME_DIR/weights/xx
self.sys_home_dir = [os.path.expanduser("~"), "/mnt/data"][0] # Default, custom
self.data_root_dir = os.path.join(self.sys_home_dir, "datasets/dis")
# TASK settings
self.task = ["DIS5K", "COD", "HRSOD", "General", "General-2K", "Matting"][0]
self.testsets = {
# Benchmarks
"DIS5K": ",".join(
["DIS-VD", "DIS-TE1", "DIS-TE2", "DIS-TE3", "DIS-TE4"][:1]
),
"COD": ",".join(["CHAMELEON", "NC4K", "TE-CAMO", "TE-COD10K"]),
"HRSOD": ",".join(
["DAVIS-S", "TE-HRSOD", "TE-UHRSD", "DUT-OMRON", "TE-DUTS"]
),
# Practical use
"General": ",".join(["DIS-VD", "TE-P3M-500-NP"]),
"General-2K": ",".join(["DIS-VD", "TE-P3M-500-NP"]),
"Matting": ",".join(["TE-P3M-500-NP", "TE-AM-2k"]),
}[self.task]
datasets_all = "+".join(
[
ds
for ds in (
os.listdir(os.path.join(self.data_root_dir, self.task))
if os.path.isdir(os.path.join(self.data_root_dir, self.task))
else []
)
if ds not in self.testsets.split(",")
]
)
self.training_set = {
"DIS5K": ["DIS-TR", "DIS-TR+DIS-TE1+DIS-TE2+DIS-TE3+DIS-TE4"][0],
"COD": "TR-COD10K+TR-CAMO",
"HRSOD": [
"TR-DUTS",
"TR-HRSOD",
"TR-UHRSD",
"TR-DUTS+TR-HRSOD",
"TR-DUTS+TR-UHRSD",
"TR-HRSOD+TR-UHRSD",
"TR-DUTS+TR-HRSOD+TR-UHRSD",
][5],
"General": datasets_all,
"General-2K": datasets_all,
"Matting": datasets_all,
}[self.task]
self.prompt4loc = ["dense", "sparse"][0]
# Faster-Training settings
self.load_all = False # Turn it on/off by your case. It may consume a lot of CPU memory. And for multi-GPU (N), it would cost N times the CPU memory to load the data.
self.compile = True # 1. Trigger CPU memory leak in some extend, which is an inherent problem of PyTorch.
# Machines with > 70GB CPU memory can run the whole training on DIS5K with default setting.
# 2. Higher PyTorch version may fix it: https://github.com/pytorch/pytorch/issues/119607.
# 3. But compile in Pytorch > 2.0.1 seems to bring no acceleration for training.
self.precisionHigh = True
# MODEL settings
self.ms_supervision = True
self.out_ref = self.ms_supervision and True
self.dec_ipt = True
self.dec_ipt_split = True
self.cxt_num = [0, 3][1] # multi-scale skip connections from encoder
self.mul_scl_ipt = ["", "add", "cat"][2]
self.dec_att = ["", "ASPP", "ASPPDeformable"][2]
self.squeeze_block = [
"",
"BasicDecBlk_x1",
"ResBlk_x4",
"ASPP_x3",
"ASPPDeformable_x3",
][1]
self.dec_blk = ["BasicDecBlk", "ResBlk"][0]
# TRAINING settings
self.batch_size = 4
self.finetune_last_epochs = [
0,
{
"DIS5K": -40,
"COD": -20,
"HRSOD": -20,
"General": -40,
"General-2K": -20,
"Matting": -20,
}[self.task],
][
1
] # choose 0 to skip
self.lr = (1e-4 if "DIS5K" in self.task else 1e-5) * math.sqrt(
self.batch_size / 4
) # DIS needs high lr to converge faster. Adapt the lr linearly
self.size = (
(1024, 1024) if self.task not in ["General-2K"] else (2560, 1440)
) # wid, hei
self.num_workers = max(
4, self.batch_size
) # will be decrease to min(it, batch_size) at the initialization of the data_loader
# Backbone settings
self.bb = [
"vgg16",
"vgg16bn",
"resnet50", # 0, 1, 2
"swin_v1_t",
"swin_v1_s", # 3, 4
"swin_v1_b",
"swin_v1_l", # 5-bs9, 6-bs4
"pvt_v2_b0",
"pvt_v2_b1", # 7, 8
"pvt_v2_b2",
"pvt_v2_b5", # 9-bs10, 10-bs5
][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],
"swin_v1_t": [768, 384, 192, 96],
"swin_v1_s": [768, 384, 192, 96],
"pvt_v2_b0": [256, 160, 64, 32],
"pvt_v2_b1": [512, 320, 128, 64],
}[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 []
)
# MODEL settings - inactive
self.lat_blk = ["BasicLatBlk"][0]
self.dec_channels_inter = ["fixed", "adap"][0]
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.auxiliary_classification = (
False # Only for DIS5K, where class labels are saved in `dataset.py`.
)
self.refine_iteration = 1
self.freeze_bb = False
self.model = [
"BiRefNet",
"BiRefNetC2F",
][0]
# TRAINING settings - inactive
self.preproc_methods = ["flip", "enhance", "rotate", "pepper", "crop"][:4]
self.optimizer = ["Adam", "AdamW"][1]
self.lr_decay_epochs = [
1e5
] # Set to negative N to decay the lr in the last N-th epoch.
self.lr_decay_rate = 0.5
# Loss
if self.task in ["Matting"]:
self.lambdas_pix_last = {
"bce": 30 * 1,
"iou": 0.5 * 0,
"iou_patch": 0.5 * 0,
"mae": 100 * 1,
"mse": 30 * 0,
"triplet": 3 * 0,
"reg": 100 * 0,
"ssim": 10 * 1,
"cnt": 5 * 0,
"structure": 5 * 0,
}
elif self.task in ["General", "General-2K"]:
self.lambdas_pix_last = {
"bce": 30 * 1,
"iou": 0.5 * 1,
"iou_patch": 0.5 * 0,
"mae": 100 * 1,
"mse": 30 * 0,
"triplet": 3 * 0,
"reg": 100 * 0,
"ssim": 10 * 1,
"cnt": 5 * 0,
"structure": 5 * 0,
}
else:
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)
"mae": 30 * 0,
"mse": 30 * 0, # can smooth the saliency map
"triplet": 3 * 0,
"reg": 100 * 0,
"ssim": 10 * 1, # help contours,
"cnt": 5 * 0, # help contours
"structure": 5
* 0, # structure loss from codes of MVANet. A little improvement on DIS-TE[1,2,3], a bit more decrease on DIS-TE4.
}
self.lambdas_cls = {"ce": 5.0}
# PATH settings - inactive
self.weights_root_dir = os.path.join(self.sys_home_dir, "weights/cv")
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],
),
"swin_v1_t": os.path.join(
self.weights_root_dir,
["swin_tiny_patch4_window7_224_22kto1k_finetune.pth"][0],
),
"swin_v1_s": os.path.join(
self.weights_root_dir,
["swin_small_patch4_window7_224_22kto1k_finetune.pth"][0],
),
"pvt_v2_b0": os.path.join(self.weights_root_dir, ["pvt_v2_b0.pth"][0]),
"pvt_v2_b1": os.path.join(self.weights_root_dir, ["pvt_v2_b1.pth"][0]),
}
# Callbacks - inactive
self.verbose_eval = True
self.only_S_MAE = False
self.SDPA_enabled = False # Bugs. Slower and errors occur in multi-GPUs
# others
self.device = [0, "cpu"][0] # .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
]
if run_sh_file:
with open(run_sh_file[0], "r") as f:
lines = f.readlines()
self.save_last = int(
[
l.strip()
for l in lines
if "'{}')".format(self.task) in l and "val_last=" in l
][0]
.split("val_last=")[-1]
.split()[0]
)
self.save_step = int(
[
l.strip()
for l in lines
if "'{}')".format(self.task) in l and "step=" in l
][0]
.split("step=")[-1]
.split()[0]
)
# Return task for choosing settings in shell scripts.
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(
description="Only choose one argument to activate."
)
parser.add_argument("--print_task", action="store_true", help="print task name")
parser.add_argument(
"--print_testsets", action="store_true", help="print validation set"
)
args = parser.parse_args()
config = Config()
for arg_name, arg_value in args._get_kwargs():
if arg_value:
print(config.__getattribute__(arg_name[len("print_") :]))
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