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import logging
import os
import pathlib
import re
from copy import deepcopy
from pathlib import Path
from typing import Optional, Tuple
import torch
from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
from .model import CLIP, convert_weights_to_fp16, resize_pos_embed
from .model import load_pruned_model, prune_model
from .pretrained import get_pretrained_cfg, download_pretrained
# Optional imports - these may not be available in all environments
try:
from .openai import load_openai_model
except ImportError:
load_openai_model = None
try:
from .transform import image_transform
except ImportError:
# Simple placeholder for image_transform
def image_transform(image_size, is_train=True, mean=None, std=None, val_keep_ratio=True):
from torchvision import transforms
if mean is None:
mean = OPENAI_DATASET_MEAN
if std is None:
std = OPENAI_DATASET_STD
if is_train:
return transforms.Compose([
transforms.RandomResizedCrop(image_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
else:
return transforms.Compose([
transforms.Resize(image_size),
transforms.CenterCrop(image_size),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
try:
from .tokenizer import HFTokenizer, tokenize
except ImportError:
# Placeholder tokenizers
class HFTokenizer:
def __init__(self, *args, **kwargs):
raise NotImplementedError("HFTokenizer requires additional dependencies")
def tokenize(*args, **kwargs):
raise NotImplementedError("tokenize requires additional dependencies")
HF_HUB_PREFIX = 'hf-hub:'
_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
# directory (model_name: config) of model architecture configs
_MODEL_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 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):
if model_name.startswith(HF_HUB_PREFIX):
tokenizer = HFTokenizer(model_name[len(HF_HUB_PREFIX):])
else:
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)
resize_pos_embed(state_dict, model)
incompatible_keys = model.load_state_dict(state_dict, strict=strict)
return incompatible_keys
def load_pruned_checkpoint(model, checkpoint_path, strict=True):
state_dict = load_state_dict(checkpoint_path)
resize_pos_embed(state_dict, model)
incompatible_keys = load_pruned_model(model, state_dict, strict=strict)
return incompatible_keys
def create_model(
model_name: str,
pretrained: str = '',
precision: str = 'fp32',
device: torch.device = torch.device('cpu'),
jit: bool = False,
force_quick_gelu: bool = False,
pretrained_image: bool = False,
cache_dir: Optional[str] = None,
args=None,
):
# for callers using old naming with / in ViT names
model_name = model_name.replace('/', '-')
if pretrained.lower() == 'openai':
if load_openai_model is None:
raise RuntimeError("load_openai_model is not available. Please install required dependencies.")
logging.info(f'Loading pretrained {model_name} from OpenAI.')
model = load_openai_model(
model_name, device=device, jit=jit, cache_dir=cache_dir)
# See https://discuss.pytorch.org/t/valueerror-attemting-to-unscale-fp16-gradients/81372
if precision == "amp" or precision == "fp32":
model = model.float()
else:
if model_name in _MODEL_CONFIGS:
logging.info(f'Loading {model_name} model config.')
model_cfg = deepcopy(_MODEL_CONFIGS[model_name])
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 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'
if args is not None:
model_cfg['mask_image'] = getattr(args, 'prune_image', False)
model_cfg['mask_text'] = getattr(args, 'prune_text', False)
model_cfg['sparsity_warmup'] = getattr(
args, 'sparsity_warmup', 1000)
model_cfg['start_sparsity'] = getattr(args, 'start_sparsity', 0.0)
model_cfg['sparsity'] = getattr(args, 'target_sparsity', 0.25)
logging.info(
f'model sparsity varies from {model_cfg["start_sparsity"]} to {model_cfg["sparsity"]}, sparsity warmup steps: {model_cfg["sparsity_warmup"]}')
logging.info(str(model_cfg))
auto_weight_inheritance = model_cfg.get('mask_image', False) or \
model_cfg.get('mask_text', False)
model = CLIP(**model_cfg)
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}).')
if not auto_weight_inheritance:
load_checkpoint(model, checkpoint_path)
else:
load_pruned_checkpoint(model, checkpoint_path)
model = prune_model(model)
else:
logging.warning(
f'Pretrained weights ({pretrained}) not found for model {model_name}.')
raise RuntimeError(
f'Pretrained weights ({pretrained}) not found for model {model_name}.')
model.to(device=device)
if precision == "fp16":
assert device.type != 'cpu'
convert_weights_to_fp16(model)
# set image / mean metadata from pretrained_cfg if available, or use default
if 'davit' in model_name.lower():
pretrained_cfg['mean'] = [0.485, 0.456, 0.406]
pretrained_cfg['std'] = [0.229, 0.224, 0.225]
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: str = '',
precision: str = 'fp32',
device: torch.device = torch.device('cpu'),
jit: bool = False,
force_quick_gelu: bool = False,
pretrained_image: bool = False,
image_mean: Optional[Tuple[float, ...]] = None,
image_std: Optional[Tuple[float, ...]] = None,
cache_dir: Optional[str] = None,
args=None,
):
model = create_model(
model_name, pretrained, precision, device, jit,
force_quick_gelu=force_quick_gelu,
pretrained_image=pretrained_image,
cache_dir=cache_dir,
args=args)
image_mean = image_mean or getattr(model.visual, 'image_mean', None)
image_std = image_std or getattr(model.visual, 'image_std', None)
val_keep_ratio = 'davit' not in model_name.lower()
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, val_keep_ratio=val_keep_ratio)
return model, preprocess_train, preprocess_val
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 load_exp(name, device='cpu'):
assert '@' in name
teacher_model_name, teacher_pretrained = name.split('@')
return create_model_and_transforms(teacher_model_name, pretrained=teacher_pretrained)
def load_model(name, device='cpu'):
return load_exp(name, device)[0]
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