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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()
parser.add_argument(
"--method-type",
choices=['region_clip', "clipself", "densevlm"],
default="densevlm",
help="Which type of method to process."
)
parser.add_argument(
"--max-boxes",
type=int,
default=36,
)
parser.add_argument(
"--max-masks",
type=int,
default=36)
parser.add_argument(
"--downsample-factor",
type=int,
default=16)
parser.add_argument(
"--alpha",
type=float,
default=2.0, # not used when alpha >=1.0
)
parser.add_argument(
"--grid-noise",
action="store_true",
default=False
)
parser.add_argument(
"--shift-range",
type=float,
default=0.0
)
parser.add_argument(
"--scale-range",
type=float,
default=0.0
)
parser.add_argument(
"--crop-scale",
type=float,
default=1.0,
)
parser.add_argument(
"--box-scale",
type=float,
default=1.5,
)
parser.add_argument(
"--multiscale",
action="store_true",
default=False,
)
parser.add_argument(
"--pre-transforms",
action="store_true",
default=False,
)
parser.add_argument(
"--max-size",
type=int,
default=1024,
)
parser.add_argument(
"--embed-dim",
type=int,
default=768,
)
parser.add_argument(
"--fix-logit-scale",
action="store_true",
default=False,
)
parser.add_argument(
"--min-size",
type=int,
default=8,
)
parser.add_argument(
"--max-split",
type=int,
default=6,
)
parser.add_argument(
"--extract-type",
type=str,
choices=['v1', 'v2'],
default="v2",
)
parser.add_argument(
"--cache-dir",
type=str,
default="checkpoints",
)
parser.add_argument(
"--kl-weight",
type=float,
default=1.0,
)
parser.add_argument(
"--contrast-weight",
type=float,
default=1.0,
)
parser.add_argument(
"--train-ratio",
type=float,
default=1.0,
)
parser.add_argument(
"--l1-weight",
type=float,
default=0.10,
)
parser.add_argument(
"--smooth-weight",
type=float,
default=0.0,
)
parser.add_argument(
"--cosine-weight",
type=float,
default=1.0,
)
parser.add_argument(
"--det-image-size",
type=int,
default=1024,
)
parser.add_argument(
"--train-image-size",
type=int,
default=1024,
)
parser.add_argument(
"--image-ave-pool",
action="store_true",
default=False,
)
parser.add_argument(
"--roi-teacher",
action="store_true",
default=False,
)
parser.add_argument(
"--mask-thr",
type=float,
default=0.7,
)
parser.add_argument(
"--train-image-root",
type=str,
default="data/coco/val2017",
)
parser.add_argument(
"--train-ceph-root",
type=str,
default="",
)
parser.add_argument(
"--val-image-root",
type=str,
default="data/coco/val2017",
)
parser.add_argument(
"--val-segm-root",
type=str,
default="data/coco/annotations/panoptic_val2017",
)
parser.add_argument(
"--train-segm-root",
type=str,
default="data/coco/annotations/panoptic_val2017",
)
parser.add_argument(
"--embed-path",
type=str,
default="metadata/coco_clip_hand_craft_RN50.npy",
)
parser.add_argument(
"--uvlm-embed-path",
type=str,
default="metadata/COCO_STUFF_ADE20k_Thing204_STUFF112_clip_hand_craft_EVACLIP_ViTB16.npy",
)
parser.add_argument(
"--pvlm-embed-path",
type=str,
default="metadata/COCO_STUFF_ADE20k_Thing204_STUFF112_clip_hand_craft_EVACLIP_ViTL14x336.npy",
)
parser.add_argument(
"--train-embed-path",
type=str,
default="",
)
parser.add_argument(
"--del-dist-model",
action="store_true",
default=False,
)
parser.add_argument(
"--train-data",
type=str,
default="",
help="Path to file(s) with training data. When using webdataset, "
"multiple datasources can be combined using the `::` separator.",
)
parser.add_argument(
"--val-data",
type=str,
default="data/coco/annotations/instances_val2017_100.json"
)
parser.add_argument(
"--dataset-type",
choices=['proposals_distill', "region_clip", "grid_distill"],
default="grid_distill",
help="Which type of dataset to process."
)
parser.add_argument(
"--test-type",
choices=['coco_panoptic', 'ade_panoptic'],
default="coco_panoptic",
help="Which type of dataset to process."
)
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=1, 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("--lr", type=float, default=1e-5, 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(
"--warmup", type=int, default=10000, help="Number of steps to warmup for."
)
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(
"--zeroshot-frequency", type=int, default=2, help="How often to run zero shot."
)
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", "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=3, # freeze at 2
help="Leave last n image tower layer groups unlocked.",
)
parser.add_argument(
"--lock-image-freeze-bn-stats",
default=True,
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('--aug-cfg', nargs='*', default={}, action=ParseKwargs)
parser.add_argument(
"--grad-checkpointing",
default=False,
action='store_true',
help="Enable gradient checkpointing.",
)
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(
"--accum-freq", type=int, default=1, help="Update the model every --acum-freq steps."
)
# arguments for distributed training
parser.add_argument(
"--dist-url",
default="env://",
type=str,
help="url used to set up distributed training",
)
parser.add_argument(
"--dist-backend", default="nccl", type=str, help="distributed backend"
)
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(
"--log-every-n-steps",
type=int,
default=100,
)
parser.add_argument(
"--delete-previous-checkpoint",
default=False,
action="store_true",
help="If true, delete previous checkpoint after storing a new one."
)
args = parser.parse_args(args)
# If some params are not passed, we use the default values based on model name.
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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