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import json
import logging
import os
import pathlib
import re
from copy import deepcopy
from pathlib import Path
from typing import Optional, Tuple, Union
import torch
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from .model import CLIP, CustomTextCLIP, convert_weights_to_lp, convert_to_custom_text_state_dict,\
resize_pos_embed, get_cast_dtype
from .openai import load_openai_model
from .pretrained import is_pretrained_cfg, get_pretrained_cfg, download_pretrained, list_pretrained_tags_by_model
from .transform import image_transform
from .tokenizer import HFTokenizer, tokenize
_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
_MODEL_CONFIGS = {} # directory (model_name: config) of model architecture configs
def _natural_key(string_):
return [int(s) if s.isdigit() else s for s in re.split(r'(\d+)', string_.lower())]
def _rescan_model_configs():
global _MODEL_CONFIGS
config_ext = ('.json',)
config_files = []
for config_path in _MODEL_CONFIG_PATHS:
if config_path.is_file() and config_path.suffix in config_ext:
config_files.append(config_path)
elif config_path.is_dir():
for ext in config_ext:
config_files.extend(config_path.glob(f'*{ext}'))
for cf in config_files:
with open(cf, 'r') as f:
model_cfg = json.load(f)
if all(a in model_cfg for a in ('embed_dim', 'vision_cfg', 'text_cfg')):
_MODEL_CONFIGS[cf.stem] = model_cfg
_MODEL_CONFIGS = {k: v for k, v in sorted(_MODEL_CONFIGS.items(), key=lambda x: _natural_key(x[0]))}
_rescan_model_configs() # initial populate of model config registry
def list_models():
""" enumerate available model architectures based on config files """
return list(_MODEL_CONFIGS.keys())
def add_model_config(path):
""" add model config path or file and update registry """
if not isinstance(path, Path):
path = Path(path)
_MODEL_CONFIG_PATHS.append(path)
_rescan_model_configs()
def get_model_config(model_name):
if model_name in _MODEL_CONFIGS:
return deepcopy(_MODEL_CONFIGS[model_name])
else:
return None
def get_tokenizer(model_name):
config = get_model_config(model_name)
tokenizer = HFTokenizer(config['text_cfg']['hf_tokenizer_name']) if 'hf_tokenizer_name' in config['text_cfg'] else tokenize
return tokenizer
def load_state_dict(checkpoint_path: str, map_location='cpu'):
checkpoint = torch.load(checkpoint_path, map_location=map_location)
if isinstance(checkpoint, dict) and 'state_dict' in checkpoint:
state_dict = checkpoint['state_dict']
else:
state_dict = checkpoint
if next(iter(state_dict.items()))[0].startswith('module'):
state_dict = {k[7:]: v for k, v in state_dict.items()}
return state_dict
def load_checkpoint(model, checkpoint_path, strict=True):
state_dict = load_state_dict(checkpoint_path)
# detect old format and make compatible with new format
if 'positional_embedding' in state_dict and not hasattr(model, 'positional_embedding'):
state_dict = convert_to_custom_text_state_dict(state_dict)
resize_pos_embed(state_dict, model)
incompatible_keys = model.load_state_dict(state_dict, strict=strict)
return incompatible_keys
def create_model(
model_name: str,
pretrained: Optional[str] = None,
precision: str = 'fp32',
device: Union[str, torch.device] = 'cpu',
jit: bool = False,
force_quick_gelu: bool = False,
force_custom_text: bool = False,
force_patch_dropout: Optional[float] = None,
pretrained_image: bool = False,
pretrained_hf: bool = True,
cache_dir: Optional[str] = None,
):
model_name = model_name.replace('/', '-') # for callers using old naming with / in ViT names
if isinstance(device, str):
device = torch.device(device)
if pretrained and pretrained.lower() == 'openai':
logging.info(f'Loading pretrained {model_name} from OpenAI.')
model = load_openai_model(
model_name,
precision=precision,
device=device,
jit=jit,
cache_dir=cache_dir,
)
else:
model_cfg = get_model_config(model_name)
if model_cfg is not None:
logging.info(f'Loaded {model_name} model config.')
else:
logging.error(f'Model config for {model_name} not found; available models {list_models()}.')
raise RuntimeError(f'Model config for {model_name} not found.')
if force_quick_gelu:
# override for use of QuickGELU on non-OpenAI transformer models
model_cfg["quick_gelu"] = True
if force_patch_dropout is not None:
# override the default patch dropout value
model_cfg["vision_cfg"]["patch_dropout"] = force_patch_dropout
if pretrained_image:
if 'timm_model_name' in model_cfg.get('vision_cfg', {}):
# pretrained weight loading for timm models set via vision_cfg
model_cfg['vision_cfg']['timm_model_pretrained'] = True
else:
assert False, 'pretrained image towers currently only supported for timm models'
cast_dtype = get_cast_dtype(precision)
custom_text = model_cfg.pop('custom_text', False) or force_custom_text or ('hf_model_name' in model_cfg.get('text_cfg', {}))
if custom_text:
if 'hf_model_name' in model_cfg.get('text_cfg', {}):
model_cfg['text_cfg']['hf_model_pretrained'] = pretrained_hf
model = CustomTextCLIP(**model_cfg, cast_dtype=cast_dtype)
else:
model = CLIP(**model_cfg, cast_dtype=cast_dtype)
pretrained_cfg = {}
if pretrained:
checkpoint_path = ''
pretrained_cfg = get_pretrained_cfg(model_name, pretrained)
if pretrained_cfg:
checkpoint_path = download_pretrained(pretrained_cfg, cache_dir=cache_dir)
elif os.path.exists(pretrained):
checkpoint_path = pretrained
if checkpoint_path:
logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
load_checkpoint(model, checkpoint_path)
else:
error_str = (
f'Pretrained weights ({pretrained}) not found for model {model_name}.'
f'Available pretrained tags ({list_pretrained_tags_by_model(model_name)}.')
logging.warning(error_str)
raise RuntimeError(error_str)
model.to(device=device)
if precision in ("fp16", "bf16"):
convert_weights_to_lp(model, dtype=torch.bfloat16 if precision == 'bf16' else torch.float16)
# set image / mean metadata from pretrained_cfg if available, or use default
model.visual.image_mean = pretrained_cfg.get('mean', None) or OPENAI_DATASET_MEAN
model.visual.image_std = pretrained_cfg.get('std', None) or OPENAI_DATASET_STD
if jit:
model = torch.jit.script(model)
return model
def create_model_and_transforms(
model_name: str,
pretrained: Optional[str] = None,
precision: str = 'fp32',
device: Union[str, torch.device] = 'cpu',
jit: bool = False,
force_quick_gelu: bool = False,
force_custom_text: bool = False,
force_patch_dropout: Optional[float] = None,
pretrained_image: bool = False,
pretrained_hf: bool = True,
image_mean: Optional[Tuple[float, ...]] = None,
image_std: Optional[Tuple[float, ...]] = None,
cache_dir: Optional[str] = None,
):
model = create_model(
model_name,
pretrained,
precision=precision,
device=device,
jit=jit,
force_quick_gelu=force_quick_gelu,
force_custom_text=force_custom_text,
force_patch_dropout=force_patch_dropout,
pretrained_image=pretrained_image,
pretrained_hf=pretrained_hf,
cache_dir=cache_dir,
)
image_mean = image_mean or getattr(model.visual, 'image_mean', None)
image_std = image_std or getattr(model.visual, 'image_std', None)
preprocess_train = image_transform(
model.visual.image_size,
is_train=True,
mean=image_mean,
std=image_std
)
preprocess_val = image_transform(
model.visual.image_size,
is_train=False,
mean=image_mean,
std=image_std
)
return model, preprocess_train, preprocess_val
def create_model_from_pretrained(
model_name: str,
pretrained: str,
precision: str = 'fp32',
device: Union[str, torch.device] = 'cpu',
jit: bool = False,
force_quick_gelu: bool = False,
force_custom_text: bool = False,
return_transform: bool = True,
image_mean: Optional[Tuple[float, ...]] = None,
image_std: Optional[Tuple[float, ...]] = None,
cache_dir: Optional[str] = None,
):
if not is_pretrained_cfg(model_name, pretrained) and not os.path.exists(pretrained):
raise RuntimeError(
f'{pretrained} is not a valid pretrained cfg or checkpoint for {model_name}.'
f' Use open_clip.list_pretrained() to find one.')
model = create_model(
model_name,
pretrained,
precision=precision,
device=device,
jit=jit,
force_quick_gelu=force_quick_gelu,
force_custom_text=force_custom_text,
cache_dir=cache_dir,
)
if not return_transform:
return model
image_mean = image_mean or getattr(model.visual, 'image_mean', None)
image_std = image_std or getattr(model.visual, 'image_std', None)
preprocess = image_transform(
model.visual.image_size,
is_train=False,
mean=image_mean,
std=image_std
)
return model, preprocess