Spaces:
Sleeping
Sleeping
File size: 5,754 Bytes
85ab89d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import logging
import torch
from omegaconf import OmegaConf
from minigpt4.common.registry import registry
from minigpt4.models.base_model import BaseModel
from minigpt4.models.blip2 import Blip2Base
from minigpt4.models.mini_gpt4 import MiniGPT4
from minigpt4.processors.base_processor import BaseProcessor
__all__ = [
"load_model",
"BaseModel",
"Blip2Base",
"MiniGPT4",
]
def load_model(name, model_type, is_eval=False, device="cpu", checkpoint=None):
"""
Load supported models.
To list all available models and types in registry:
>>> from minigpt4.models import model_zoo
>>> print(model_zoo)
Args:
name (str): name of the model.
model_type (str): type of the model.
is_eval (bool): whether the model is in eval mode. Default: False.
device (str): device to use. Default: "cpu".
checkpoint (str): path or to checkpoint. Default: None.
Note that expecting the checkpoint to have the same keys in state_dict as the model.
Returns:
model (torch.nn.Module): model.
"""
model = registry.get_model_class(name).from_pretrained(model_type=model_type)
if checkpoint is not None:
model.load_checkpoint(checkpoint)
if is_eval:
model.eval()
if device == "cpu":
model = model.float()
return model.to(device)
def load_preprocess(config):
"""
Load preprocessor configs and construct preprocessors.
If no preprocessor is specified, return BaseProcessor, which does not do any preprocessing.
Args:
config (dict): preprocessor configs.
Returns:
vis_processors (dict): preprocessors for visual inputs.
txt_processors (dict): preprocessors for text inputs.
Key is "train" or "eval" for processors used in training and evaluation respectively.
"""
def _build_proc_from_cfg(cfg):
return (
registry.get_processor_class(cfg.name).from_config(cfg)
if cfg is not None
else BaseProcessor()
)
vis_processors = dict()
txt_processors = dict()
vis_proc_cfg = config.get("vis_processor")
txt_proc_cfg = config.get("text_processor")
if vis_proc_cfg is not None:
vis_train_cfg = vis_proc_cfg.get("train")
vis_eval_cfg = vis_proc_cfg.get("eval")
else:
vis_train_cfg = None
vis_eval_cfg = None
vis_processors["train"] = _build_proc_from_cfg(vis_train_cfg)
vis_processors["eval"] = _build_proc_from_cfg(vis_eval_cfg)
if txt_proc_cfg is not None:
txt_train_cfg = txt_proc_cfg.get("train")
txt_eval_cfg = txt_proc_cfg.get("eval")
else:
txt_train_cfg = None
txt_eval_cfg = None
txt_processors["train"] = _build_proc_from_cfg(txt_train_cfg)
txt_processors["eval"] = _build_proc_from_cfg(txt_eval_cfg)
return vis_processors, txt_processors
def load_model_and_preprocess(name, model_type, is_eval=False, device="cpu"):
"""
Load model and its related preprocessors.
List all available models and types in registry:
>>> from minigpt4.models import model_zoo
>>> print(model_zoo)
Args:
name (str): name of the model.
model_type (str): type of the model.
is_eval (bool): whether the model is in eval mode. Default: False.
device (str): device to use. Default: "cpu".
Returns:
model (torch.nn.Module): model.
vis_processors (dict): preprocessors for visual inputs.
txt_processors (dict): preprocessors for text inputs.
"""
model_cls = registry.get_model_class(name)
# load model
model = model_cls.from_pretrained(model_type=model_type)
if is_eval:
model.eval()
# load preprocess
cfg = OmegaConf.load(model_cls.default_config_path(model_type))
if cfg is not None:
preprocess_cfg = cfg.preprocess
vis_processors, txt_processors = load_preprocess(preprocess_cfg)
else:
vis_processors, txt_processors = None, None
logging.info(
f"""No default preprocess for model {name} ({model_type}).
This can happen if the model is not finetuned on downstream datasets,
or it is not intended for direct use without finetuning.
"""
)
if device == "cpu" or device == torch.device("cpu"):
model = model.float()
return model.to(device), vis_processors, txt_processors
class ModelZoo:
"""
A utility class to create string representation of available model architectures and types.
>>> from minigpt4.models import model_zoo
>>> # list all available models
>>> print(model_zoo)
>>> # show total number of models
>>> print(len(model_zoo))
"""
def __init__(self) -> None:
self.model_zoo = {
k: list(v.PRETRAINED_MODEL_CONFIG_DICT.keys())
for k, v in registry.mapping["model_name_mapping"].items()
}
def __str__(self) -> str:
return (
"=" * 50
+ "\n"
+ f"{'Architectures':<30} {'Types'}\n"
+ "=" * 50
+ "\n"
+ "\n".join(
[
f"{name:<30} {', '.join(types)}"
for name, types in self.model_zoo.items()
]
)
)
def __iter__(self):
return iter(self.model_zoo.items())
def __len__(self):
return sum([len(v) for v in self.model_zoo.values()])
model_zoo = ModelZoo()
|