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import shutil
import sys
import time
import logging
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.nn as nn
from torchvision.transforms import v2 as T
from torchvision.datasets import ImageFolder
import utils
from modeling.unic import build_student_from_args
from teachers import build_teachers, get_teacher_output
from modeling.losses import unic_loss
from dinov2.logging import setup_logging, ExternalLogger, MetricLogger
from dinov2.distributed import get_global_rank
logger = logging.getLogger()
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--arch",
type=str,
default="vit_base",
help="Architecture of the student model. "
"See dinov2/models/vision_transformer.py for options.",
)
parser.add_argument(
"--patch_size",
type=int,
default=16,
help="Patch size for the student model.",
)
parser.add_argument(
"--drop_path_rate",
type=float,
default=0.1,
help="Drop path rate for the student model.",
)
parser.add_argument(
"--lp_args",
type=str,
default="{'std': 0.02}",
help="Dictionary of keyword arguments for the ladder of projector.",
)
parser.add_argument(
"--teachers",
type=str,
default="dino_vitbase_16,deit3_vitbase_16,ibot_vitbase_16,dbotft_vitbase_16",
help="Comma-separated list of teacher names.",
)
parser.add_argument(
"--tnorm_ema_momentum_start",
type=float,
default=1.0,
help="Starting value for the EMA momentum for teacher feature statistics.",
)
parser.add_argument(
"--tnorm_ema_momentum_end",
type=float,
default=0.001,
help="Final value for the EMA momentum for teacher feature statistics.",
)
parser.add_argument(
"--t_drop_prob",
type=float,
default=0.5,
help="Probability of dropping a teacher in loss.",
)
parser.add_argument(
"--lam_lcos",
default=0.5,
type=float,
help="Coefficient for the cosine loss.",
)
parser.add_argument(
"--lam_lsl1",
default=0.5,
type=float,
help="Coefficient for the smooth L1 loss.",
)
parser.add_argument(
"--data_dir",
type=str,
help="Path for the ImageNet-1K data directory",
)
parser.add_argument(
"--image_size",
type=int,
default=224,
help="Image size (for both training and validation). "
"We assume the input images are square.",