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jblitzar
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github-python

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from pathlib import Path
from timm.data.mixup import Mixup
from timm.models import create_model
from timm.loss import LabelSmoothingCrossEntropy, SoftTargetCrossEntropy
from timm.utils import ModelEma
from optim_factory import create_optimizer, LayerDecayValueAssigner
from datasets import build_dataset
from engine import train_one_epoch, evaluate
from utils import NativeScalerWithGradNormCount as NativeScaler
import utils
import models.convnext
def str2bool(v):
"""
Converts string to bool type; enables command line
arguments in the format of '--arg1 true --arg2 false'
"""
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def get_args_parser():
parser = argparse.ArgumentParser('ConvNeXt training and evaluation script for image classification', add_help=False)
parser.add_argument('--batch_size', default=64, type=int,
help='Per GPU batch size')
parser.add_argument('--epochs', default=300, type=int)
parser.add_argument('--update_freq', default=1, type=int,
help='gradient accumulation steps')
# Model parameters
parser.add_argument('--model', default='convnext_tiny', type=str, metavar='MODEL',
help='Name of model to train')
parser.add_argument('--variant', default='2d', type=str, help='Name of model to train')
parser.add_argument('--drop_path', type=float, default=None, metavar='PCT',
help='Drop path rate (default: 0.0)')
parser.add_argument('--input_size', default=224, type=int,
help='image input size')
parser.add_argument('--layer_scale_init_value', default=1e-6, type=float,
help="Layer scale initial values")
# EMA related parameters
parser.add_argument('--model_ema', type=str2bool, default=False)
parser.add_argument('--model_ema_decay', type=float, default=0.9999, help='')
parser.add_argument('--model_ema_force_cpu', type=str2bool, default=False, help='')
parser.add_argument('--model_ema_eval', type=str2bool, default=False, help='Using ema to eval during training.')
# Optimization parameters
parser.add_argument('--opt', default='adamw', type=str, metavar='OPTIMIZER',
help='Optimizer (default: "adamw"')
parser.add_argument('--opt_eps', default=1e-8, type=float, metavar='EPSILON',
help='Optimizer Epsilon (default: 1e-8)')
parser.add_argument('--opt_betas', default=None, type=float, nargs='+', metavar='BETA',
help='Optimizer Betas (default: None, use opt default)')
parser.add_argument('--clip_grad', type=float, default=None, metavar='NORM',
help='Clip gradient norm (default: None, no clipping)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--weight_decay', type=float, default=0.05,
help='weight decay (default: 0.05)')
parser.add_argument('--weight_decay_end', type=float, default=None, help="""Final value of the
weight decay. We use a cosine schedule for WD and using a larger decay by
the end of training improves performance for ViTs.""")
parser.add_argument('--lr', type=float, default=4e-3, metavar='LR',
help='learning rate (default: 4e-3), with total batch size 4096')
parser.add_argument('--layer_decay', type=float, default=1.0)
parser.add_argument('--min_lr', type=float, default=1e-6, metavar='LR',
help='lower lr bound for cyclic schedulers that hit 0 (1e-6)')
parser.add_argument('--warmup_epochs', type=int, default=20, metavar='N',
help='epochs to warmup LR, if scheduler supports')
parser.add_argument('--warmup_steps', type=int, default=-1, metavar='N',
help='num of steps to warmup LR, will overload warmup_epochs if set > 0')
# Augmentation parameters
parser.add_argument('--color_jitter', type=float, default=0.4, metavar='PCT',
help='Color jitter factor (default: 0.4)')
parser.add_argument('--aa', type=str, default='rand-m9-mstd0.5-inc1', metavar='NAME',
help='Use AutoAugment policy. "v0" or "original". " + "(default: rand-m9-mstd0.5-inc1)'),
parser.add_argument('--smoothing', type=float, default=0.1,
help='Label smoothing (default: 0.1)')
parser.add_argument('--train_interpolation', type=str, default='bicubic',
help='Training interpolation (random, bilinear, bicubic default: "bicubic")')
# Evaluation parameters
parser.add_argument('--crop_pct', type=float, default=None)
# * Random Erase params
parser.add_argument('--reprob', type=float, default=0.25, metavar='PCT',
help='Random erase prob (default: 0.25)')
parser.add_argument('--remode', type=str, default='pixel',
help='Random erase mode (default: "pixel")')
parser.add_argument('--recount', type=int, default=1,