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import json
import logging
import os
import pathlib
import re
from copy import deepcopy
from pathlib import Path
import torch
from .model import CLAP, convert_weights_to_fp16
from .openai import load_openai_model
from .pretrained import get_pretrained_url, download_pretrained
from .transform import image_transform
_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:
if os.path.basename(cf)[0] == ".":
continue # Ignore hidden files
with open(cf, "r") as f:
model_cfg = json.load(f)
if all(a in model_cfg for a in ("embed_dim", "audio_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 load_state_dict(checkpoint_path: str, map_location="cpu", skip_params=True):
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 skip_params:
if next(iter(state_dict.items()))[0].startswith("module"):
state_dict = {k[7:]: v for k, v in state_dict.items()}
# for k in state_dict:
# if k.startswith('transformer'):
# v = state_dict.pop(k)
# state_dict['text_branch.' + k[12:]] = v
return state_dict
def create_model(
amodel_name: str,
tmodel_name: str,
pretrained: str = "",
precision: str = "fp32",
device: torch.device = torch.device("cpu"),
jit: bool = False,
force_quick_gelu: bool = False,
openai_model_cache_dir: str = os.path.expanduser("~/.cache/clip"),
skip_params=True,
pretrained_audio: str = "",
pretrained_text: str = "",
enable_fusion: bool = False,
fusion_type: str = "None"
# pretrained_image: bool = False,
):
amodel_name = amodel_name.replace(
"/", "-"
) # for callers using old naming with / in ViT names
pretrained_orig = pretrained
pretrained = pretrained.lower()
if pretrained == "openai":
if amodel_name in _MODEL_CONFIGS:
logging.info(f"Loading {amodel_name} model config.")
model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name])
else:
logging.error(
f"Model config for {amodel_name} not found; available models {list_models()}."
)
raise RuntimeError(f"Model config for {amodel_name} not found.")
logging.info(f"Loading pretrained ViT-B-16 text encoder from OpenAI.")
# Hard Code in model name
model_cfg["text_cfg"]["model_type"] = tmodel_name
model = load_openai_model(
"ViT-B-16",
model_cfg,
device=device,
jit=jit,
cache_dir=openai_model_cache_dir,
enable_fusion=enable_fusion,
fusion_type=fusion_type,
)
# See https://discuss.pytorch.org/t/valueerror-attemting-to-unscale-fp16-gradients/81372
if precision == "amp" or precision == "fp32":
model = model.float()
else:
if amodel_name in _MODEL_CONFIGS:
logging.info(f"Loading {amodel_name} model config.")
model_cfg = deepcopy(_MODEL_CONFIGS[amodel_name])
else:
logging.error(
f"Model config for {amodel_name} not found; available models {list_models()}."
)
raise RuntimeError(f"Model config for {amodel_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_amodel_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'
model_cfg["text_cfg"]["model_type"] = tmodel_name
model_cfg["enable_fusion"] = enable_fusion
model_cfg["fusion_type"] = fusion_type
model = CLAP(**model_cfg)
if pretrained:
checkpoint_path = ""
url = get_pretrained_url(amodel_name, pretrained)
if url:
checkpoint_path = download_pretrained(url, root=openai_model_cache_dir)
elif os.path.exists(pretrained_orig):
checkpoint_path = pretrained_orig
if checkpoint_path:
logging.info(
f"Loading pretrained {amodel_name}-{tmodel_name} weights ({pretrained})."
)
# import pdb
# pdb.set_trace()
ckpt = load_state_dict(checkpoint_path, skip_params=True)
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in ckpt.items():
if k in model.state_dict():
new_state_dict[k] = v
model.load_state_dict(new_state_dict)
param_names = [n for n, p in model.named_parameters()]
# for n in param_names:
# print(n, "\t", "Loaded" if n in ckpt else "Unloaded")
else:
logging.warning(
f"Pretrained weights ({pretrained}) not found for model {amodel_name}."
)
raise RuntimeError(
f"Pretrained weights ({pretrained}) not found for model {amodel_name}."
)
if pretrained_audio:
if amodel_name.startswith("PANN"):
if "Cnn14_mAP" in pretrained_audio: # official checkpoint
audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
audio_ckpt = audio_ckpt["model"]
keys = list(audio_ckpt.keys())
for key in keys:
if (
"spectrogram_extractor" not in key
and "logmel_extractor" not in key
):
v = audio_ckpt.pop(key)
audio_ckpt["audio_branch." + key] = v
elif os.path.basename(pretrained_audio).startswith(
"PANN"
): # checkpoint trained via HTSAT codebase
audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
audio_ckpt = audio_ckpt["state_dict"]
keys = list(audio_ckpt.keys())
for key in keys:
if key.startswith("sed_model"):
v = audio_ckpt.pop(key)
audio_ckpt["audio_branch." + key[10:]] = v
elif os.path.basename(pretrained_audio).startswith(
"finetuned"
): # checkpoint trained via linear probe codebase
audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
else:
raise ValueError("Unknown audio checkpoint")
elif amodel_name.startswith("HTSAT"):
if "HTSAT_AudioSet_Saved" in pretrained_audio: # official checkpoint
audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
audio_ckpt = audio_ckpt["state_dict"]
keys = list(audio_ckpt.keys())
for key in keys:
if key.startswith("sed_model") and (
"spectrogram_extractor" not in key
and "logmel_extractor" not in key
):
v = audio_ckpt.pop(key)
audio_ckpt["audio_branch." + key[10:]] = v
elif os.path.basename(pretrained_audio).startswith(
"HTSAT"
): # checkpoint trained via HTSAT codebase
audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
audio_ckpt = audio_ckpt["state_dict"]
keys = list(audio_ckpt.keys())
for key in keys:
if key.startswith("sed_model"):
v = audio_ckpt.pop(key)
audio_ckpt["audio_branch." + key[10:]] = v
elif os.path.basename(pretrained_audio).startswith(
"finetuned"
): # checkpoint trained via linear probe codebase
audio_ckpt = torch.load(pretrained_audio, map_location="cpu")
else:
raise ValueError("Unknown audio checkpoint")
else:
raise f"this audio encoder pretrained checkpoint is not support"
model.load_state_dict(audio_ckpt, strict=False)
logging.info(
f"Loading pretrained {amodel_name} weights ({pretrained_audio})."
)
param_names = [n for n, p in model.named_parameters()]
for n in param_names:
print(n, "\t", "Loaded" if n in audio_ckpt else "Unloaded")
model.to(device=device)
if precision == "fp16":
assert device.type != "cpu"
convert_weights_to_fp16(model)
if jit:
model = torch.jit.script(model)
return model, model_cfg
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,
):
model = create_model(
model_name,
pretrained,
precision,
device,
jit,
force_quick_gelu=force_quick_gelu,
# pretrained_image=pretrained_image
)
preprocess_train = image_transform(model.visual.image_size, is_train=True)
preprocess_val = image_transform(model.visual.image_size, is_train=False)
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()
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