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import argparse
import ast
def get_default_params(model_name):
# Params from paper (https://arxiv.org/pdf/2103.00020.pdf)
model_name = model_name.lower()
if "vit" in model_name:
return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.98, "eps": 1.0e-6}
else:
return {"lr": 5.0e-4, "beta1": 0.9, "beta2": 0.999, "eps": 1.0e-8}
class ParseKwargs(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
kw = {}
for value in values:
key, value = value.split('=')
try:
kw[key] = ast.literal_eval(value)
except ValueError:
kw[key] = str(value) # fallback to string (avoid need to escape on command line)
setattr(namespace, self.dest, kw)
def parse_args(args):
parser = argparse.ArgumentParser()
# newly added flags
parser.add_argument(
"--datasets-for-testing",
type=str,
nargs='*',
default=None,
help='A list of names of datasets for zero-shot classification testing'
)
parser.add_argument(
"--root-train-img-dir",
type=str,
default=None,
help="Root directory to training images",
)
parser.add_argument(
"--root-val-img-dir",
type=str,
default=None,
help="Root directory to validation images",
)
parser.add_argument(
"--classification-mode",
type=str,
default="multiclass",
help="Choose either binary or multiclass",
)
parser.add_argument(
"--csv-class-key",
type=str,
default="label",
help="For csv-like datasets, the name of the key for image labels (for classification)."
)
parser.add_argument(
"--debugging",
default=False,
action="store_true",
help=""
)
parser.add_argument(
"--test-data-dir",
type=str,
default=None,
help="Root directory to test datasets"
)
# parser.add_argument(
# "--random-rotation",
# action="store_true",
# default=False,
# help="If True, add random rotation into image transform for data augmentation (only for training)."
# )
parser.add_argument(
"--test-data",
type=str,
default=None,
help="Path to file(s) with test data (e.g., for testing zero-shot classification)",
)
parser.add_argument(
"--classnames",
type=str,
default=None,
help="Path to txt file containing class names",
)
parser.add_argument(
"--test-data-name",
type=str,
default=None,
help="The name of the test data (e.g., RSICD, EuroSat)",
)
parser.add_argument(
"--test-result-save-path",
type=str,
default='None',
help="The path to save test results as a pickle file."
)
# parser.add_argument(
# "--csv-actual-label-key",
# type=str,
# default="binary",
# help="If classification_model=binary, then specify the name of the key for actual binary labels (i.e., 0/1)."
# )
# parser.add_argument(
# "--alpha",
# type=float,
# default=None,
# help="The regularization multiplier of logistic regression to try for linear probing. If None, do a search."
# )
parser.add_argument(
"--method",
type=str,
default='vanilla',
choices=['vanilla', 'farslip1', 'farslip2'],
help="alignment method"
)
parser.add_argument(
"--local-method",
type=str,
default='objects',
choices=['randomcrops', 'objects', 'grids'],
)
parser.add_argument(
"--loss-type",
type=str,
nargs='+',
default= ["global_itc", "local_itc", "distill"],
help='A list of loss types'
)
parser.add_argument(
"--max-boxes",
type=int,
default=20,
)
parser.add_argument(
"--max-size",
type=int,
default=None,
)
parser.add_argument(
"--min-size",
type=int,
default=64,
)
parser.add_argument(
"--wandb-tags",
type=str,
nargs='*',
default=[],
)
parser.add_argument(
"--EMA-momentum",
type=float,
default=0.99,
)
parser.add_argument(
"--distill-type",
type=str,
default='ema',
choices=["ema", "active", "frozen"]
)
parser.add_argument(
"--long-clip",
type=str,
default='disable',
choices=["disable", "load_from_clip", "load_from_scratch"]
)
parser.add_argument(
"--train-dataset-name",
type=str,
)
parser.add_argument(
"--local-itc-align",
type=str,
choices=["cls", "pooled", "roi", "combined"]
)
parser.add_argument(
"--distill-align",
type=str,
choices=["roi2cls", "roi2pooled", "combined"]
)
parser.add_argument(
"--last-attn-type",
type=str,
choices=["MaskCLIP", "SegEarth"]
)
parser.add_argument(
"--find-unused-parameters",
action="store_true",
)
parser.add_argument(
"--w-d",
type=float,
default=0.1
)
parser.add_argument(
"--w-l",
type=float,
default=1.0
)
parser.add_argument(
"--w-g",
type=float,
default=1.0
)
parser.add_argument(
"--step",
type=int,
default=1
)
parser.add_argument(
"--use-imagecrop-aug",
action="store_true",
)
parser.add_argument(
"--mpcl-loss",
action="store_true"
)
parser.add_argument(
"--global-type",
type=str,
default='ori',
choices = ['ori', 'pooled_tokens', 'merged', 'merged2']
)
parser.add_argument(
"--frozen-text",
action="store_true"
)
# Original open_clip flags
parser.add_argument(
"--train-data",
type=str,
default=None,
help="Path to file(s) with training data. When using webdataset, multiple datasources can be combined using the `::` separator.",
)
parser.add_argument(
"--train-data-upsampling-factors",
type=str,
default=None,
help=(
"When using multiple data sources with webdataset and sampling with replacement, this can be used to upsample specific data sources. "
"Similar to --train-data, this should be a string with as many numbers as there are data sources, separated by `::` (e.g. 1::2::0.5) "
"By default, datapoints are sampled uniformly regardless of the dataset sizes."
)
)
parser.add_argument(
"--val-data",
type=str,
default=None,
help="Path to file(s) with validation data",
)
parser.add_argument(
"--train-num-samples",
type=int,
default=None,
help="Number of samples in dataset. Required for webdataset if not available in info file.",
)
parser.add_argument(
"--val-num-samples",
type=int,
default=None,
help="Number of samples in dataset. Useful for webdataset if not available in info file.",
)
parser.add_argument(
"--train-dataset-type",
choices=["webdataset", "csv", "synthetic", "auto", "json"],
default="auto",
help="Which type of dataset to process."
)
parser.add_argument(
"--val-dataset-type",
choices=["webdataset", "csv", "synthetic", "auto", "json"],
default="auto",
help="Which type of dataset to process."
)
parser.add_argument(
"--dataset-resampled",
default=False,
action="store_true",
help="Whether to use sampling with replacement for webdataset shard selection."
)
parser.add_argument(
"--csv-separator",
type=str,
default="\t",
help="For csv-like datasets, which separator to use."
)
parser.add_argument(
"--csv-img-key",
type=str,
default="filepath",
help="For csv-like datasets, the name of the key for the image paths."
)
parser.add_argument(
"--csv-caption-key",
type=str,
default="title",
help="For csv-like datasets, the name of the key for the captions."
)
parser.add_argument(
"--imagenet-val",
type=str,
default=None,
help="Path to imagenet val set for conducting zero shot evaluation.",
)
parser.add_argument(
"--imagenet-v2",
type=str,
default=None,
help="Path to imagenet v2 for conducting zero shot evaluation.",
)
parser.add_argument(
"--cache-dir",
type=str,
default=None,
help="Override system default cache path for model & tokenizer file downloads.",
)
parser.add_argument(
"--logs",
type=str,
default="./logs/",
help="Where to store tensorboard logs. Use None to avoid storing logs.",
)
parser.add_argument(
"--log-local",
action="store_true",
default=False,
help="log files on local master, otherwise global master only.",
)
parser.add_argument(
"--name",
type=str,
default=None,
help="Optional identifier for the experiment when storing logs. Otherwise use current time.",
)
parser.add_argument(
"--workers", type=int, default=4, help="Number of dataloader workers per GPU."
)
parser.add_argument(
"--batch-size", type=int, default=64, help="Batch size per GPU."
)
parser.add_argument(
"--epochs", type=int, default=32, help="Number of epochs to train for."
)
parser.add_argument(
"--epochs-cooldown", type=int, default=None,
help="When scheduler w/ cooldown used, perform cooldown from total_epochs - cooldown_epochs onwards."
)
parser.add_argument("--lr", type=float, default=None, help="Learning rate.")
parser.add_argument("--beta1", type=float, default=None, help="Adam beta 1.")
parser.add_argument("--beta2", type=float, default=None, help="Adam beta 2.")
parser.add_argument("--eps", type=float, default=None, help="Adam epsilon.")
parser.add_argument("--wd", type=float, default=0.2, help="Weight decay.")
parser.add_argument("--momentum", type=float, default=None, help="Momentum (for timm optimizers).")
parser.add_argument(
"--warmup", type=int, default=10000, help="Number of steps to warmup for."
)
parser.add_argument(
"--opt", type=str, default='adamw',
help="Which optimizer to use. Choices are ['adamw', or any timm optimizer 'timm/{opt_name}']."
)
parser.add_argument(
"--use-bn-sync",
default=False,
action="store_true",
help="Whether to use batch norm sync.")
parser.add_argument(
"--skip-scheduler",
action="store_true",
default=False,
help="Use this flag to skip the learning rate decay.",
)
parser.add_argument(
"--lr-scheduler",
type=str,
default='cosine',
help="LR scheduler. One of: 'cosine', 'const' (constant), 'const-cooldown' (constant w/ cooldown). Default: cosine",
)
parser.add_argument(
"--lr-cooldown-end", type=float, default=0.0,
help="End learning rate for cooldown schedule. Default: 0"
)
parser.add_argument(
"--lr-cooldown-power", type=float, default=1.0,
help="Power for polynomial cooldown schedule. Default: 1.0 (linear decay)"
)
parser.add_argument(
"--save-frequency", type=int, default=1, help="How often to save checkpoints."
)
parser.add_argument(
"--save-most-recent",
action="store_true",
default=False,
help="Always save the most recent model trained to epoch_latest.pt.",
)
parser.add_argument(
"--val-frequency", type=int, default=1, help="How often to run evaluation with val data."
)
parser.add_argument(
"--resume",
default=None,
type=str,
help="path to latest checkpoint (default: none)",
)
parser.add_argument(
"--precision",
choices=["amp", "amp_bf16", "amp_bfloat16", "bf16", "fp16", "pure_bf16", "pure_fp16", "fp32"],
default="amp",
help="Floating point precision."
)
parser.add_argument(
"--model",
type=str,
default="RN50",
help="Name of the vision backbone to use.",
)
parser.add_argument(
"--pretrained",
default='',
type=str,
help="Use a pretrained CLIP model weights with the specified tag or file path.",
)
parser.add_argument(
"--pretrained-image",
default=False,
action='store_true',
help="Load imagenet pretrained weights for image tower backbone if available.",
)
parser.add_argument(
"--lock-image",
default=False,
action='store_true',
help="Lock full image tower by disabling gradients.",
)
parser.add_argument(
"--lock-image-unlocked-groups",
type=int,
default=0,
help="Leave last n image tower layer groups unlocked.",
)
parser.add_argument(
"--lock-image-freeze-bn-stats",
default=False,
action='store_true',
help="Freeze BatchNorm running stats in image tower for any locked layers.",
)
parser.add_argument(
'--image-mean', type=float, nargs='+', default=None, metavar='MEAN',
help='Override default image mean value of dataset')
parser.add_argument(
'--image-std', type=float, nargs='+', default=None, metavar='STD',
help='Override default image std deviation of of dataset')
parser.add_argument(
'--image-interpolation',
default=None, type=str, choices=['bicubic', 'bilinear', 'random'],
help="Override default image resize interpolation"
)
parser.add_argument(
'--image-resize-mode',
default=None, type=str, choices=['shortest', 'longest', 'squash'],
help="Override default image resize (& crop) mode during inference"
)
parser.add_argument('--aug-cfg', nargs='*', default={}, action=ParseKwargs)
parser.add_argument(
"--grad-checkpointing",
default=False,
action='store_true',
help="Enable gradient checkpointing.",
)
parser.add_argument(
"--local-loss",
default=False,
action="store_true",
help="calculate loss w/ local features @ global (instead of realizing full global @ global matrix)"
)
parser.add_argument(
"--gather-with-grad",
default=False,
action="store_true",
help="enable full distributed gradient for feature gather"
)
parser.add_argument(
'--force-image-size', type=int, nargs='+', default=None,
help='Override default image size'
)
parser.add_argument(
"--force-quick-gelu",
default=False,
action='store_true',
help="Force use of QuickGELU activation for non-OpenAI transformer models.",
)
parser.add_argument(
"--force-patch-dropout",
default=None,
type=float,
help="Override the patch dropout during training, for fine tuning with no dropout near the end as in the paper",
)
parser.add_argument(
"--force-custom-text",
default=False,
action='store_true',
help="Force use of CustomTextCLIP model (separate text-tower).",
)
parser.add_argument(
"--torchscript",
default=False,
action='store_true',
help="torch.jit.script the model, also uses jit version of OpenAI models if pretrained=='openai'",
)
parser.add_argument(
"--torchcompile",
default=False,
action='store_true',
help="torch.compile() the model, requires pytorch 2.0 or later.",
)
parser.add_argument(
"--trace",
default=False,
action='store_true',
help="torch.jit.trace the model for inference / eval only",
)
parser.add_argument(
"--accum-freq", type=int, default=1, help="Update the model every --acum-freq steps."
)
parser.add_argument(
"--device", default="cuda", type=str, help="Accelerator to use."
)
# arguments for distributed training
parser.add_argument(
"--dist-url",
default=None,
type=str,
help="url used to set up distributed training",
)
parser.add_argument(
"--dist-backend",
default=None,
type=str,
help="distributed backend. \"nccl\" for GPU, \"hccl\" for Ascend NPU"
)
parser.add_argument(
"--report-to",
default='',
type=str,
help="Options are ['wandb', 'tensorboard', 'wandb,tensorboard']"
)
parser.add_argument(
"--wandb-notes",
default='',
type=str,
help="Notes if logging with wandb"
)
parser.add_argument(
"--wandb-project-name",
type=str,
default='open-clip',
help="Name of the project if logging with wandb.",
)
parser.add_argument(
"--debug",
default=False,
action="store_true",
help="If true, more information is logged."
)
parser.add_argument(
"--copy-codebase",
default=False,
action="store_true",
help="If true, we copy the entire base on the log directory, and execute from there."
)
parser.add_argument(
"--horovod",
default=False,
action="store_true",
help="Use horovod for distributed training."
)
parser.add_argument(
"--ddp-static-graph",
default=False,
action='store_true',
help="Enable static graph optimization for DDP in PyTorch >= 1.11.",
)
parser.add_argument(
"--no-set-device-rank",
default=False,
action="store_true",
help="Don't set device index from local rank (when CUDA_VISIBLE_DEVICES restricted to one per proc)."
)
parser.add_argument(
"--seed", type=int, default=0, help="Default random seed."
)
parser.add_argument(
"--grad-clip-norm", type=float, default=None, help="Gradient clip."
)
parser.add_argument(
"--lock-text",
default=False,
action='store_true',
help="Lock full text tower by disabling gradients.",
)
parser.add_argument(
"--lock-text-unlocked-layers",
type=int,
default=0,
help="Leave last n text tower layer groups unlocked.",
)
parser.add_argument(
"--lock-text-freeze-layer-norm",
default=False,
action='store_true',
help="Freeze LayerNorm running stats in text tower for any locked layers.",
)
parser.add_argument(
"--log-every-n-steps",
type=int,
default=100,
help="Log every n steps to tensorboard/console/wandb.",
)
parser.add_argument(
"--coca-caption-loss-weight",
type=float,
default=2.0,
help="Weight assigned to caption loss in CoCa."
)
parser.add_argument(
"--coca-contrastive-loss-weight",
type=float,
default=1.0,
help="Weight assigned to contrastive loss when training CoCa."
)
parser.add_argument(
"--remote-sync",
type=str,
default=None,
help="Optinoally sync with a remote path specified by this arg",
)
parser.add_argument(
"--remote-sync-frequency",
type=int,
default=300,
help="How frequently to sync to a remote directly if --remote-sync is not None.",
)
parser.add_argument(
"--remote-sync-protocol",
choices=["s3", "fsspec"],
default="s3",
help="How to do the remote sync backup if --remote-sync is not None.",
)
parser.add_argument(
"--delete-previous-checkpoint",
default=False,
action="store_true",
help="If true, delete previous checkpoint after storing a new one."
)
parser.add_argument(
"--distill-model",
default=None,
help='Which model arch to distill from, if any.'
)
parser.add_argument(
"--distill-pretrained",
default=None,
help='Which pre-trained weights to distill from, if any.'
)
parser.add_argument(
"--use-bnb-linear",
default=None,
help='Replace the network linear layers from the bitsandbytes library. '
'Allows int8 training/inference, etc.'
)
parser.add_argument(
"--siglip",
default=False,
action="store_true",
help='Use SigLip (sigmoid) loss.'
)
parser.add_argument(
"--loss-dist-impl",
default=None,
type=str,
help='A string to specify a specific distributed loss implementation.'
)
args = parser.parse_args(args)
if 'timm' not in args.opt:
# set default opt params based on model name (only if timm optimizer not used)
default_params = get_default_params(args.model)
for name, val in default_params.items():
if getattr(args, name) is None:
setattr(args, name, val)
return args
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