Initial commit

eval.sh

+#!/bin/sh
+
+config=$1
+gpus=$2
+output=$3
+
+if [ -z $config ]
+then
+    echo "No config file found! Run with "sh eval.sh [CONFIG_FILE] [NUM_GPUS] [OUTPUT_DIR] [OPTS]""
+    exit 0
+fi
+
+if [ -z $gpus ]
+then
+    echo "Number of gpus not specified! Run with "sh eval.sh [CONFIG_FILE] [NUM_GPUS] [OUTPUT_DIR] [OPTS]""
+    exit 0
+fi
+
+if [ -z $output ]
+then
+    echo "No output directory found! Run with "sh eval.sh [CONFIG_FILE] [NUM_GPUS] [OUTPUT_DIR] [OPTS]""
+    exit 0
+fi
+
+shift 3
+opts=${@}
+
+#ADE20k-150
+python train_net.py --config $config \
+ --num-gpus $gpus \
+ --dist-url "auto" \
+ --eval-only \
+ OUTPUT_DIR $output/eval-ade150 \
+ MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON "datasets/ade150.json" \
+ DATASETS.TEST \(\"ade20k_150_test_sem_seg\"\,\) \
+ MODEL.SEM_SEG_HEAD.POOLING_SIZES "[1,1]" \
+ MODEL.WEIGHTS $output/model_final.pth \
+ $opts
+
+#ADE20k-847
+python train_net.py --config $config \
+ --num-gpus $gpus \
+ --dist-url "auto" \
+ --eval-only \
+ OUTPUT_DIR $output/eval-ade847 \
+ MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON "datasets/ade847.json" \
+ DATASETS.TEST \(\"ade20k_full_sem_seg_freq_val_all\"\,\) \
+ MODEL.SEM_SEG_HEAD.POOLING_SIZES "[1,1]" \
+ MODEL.WEIGHTS $output/model_final.pth \
+ $opts
+
+#Pascal VOC
+python train_net.py --config $config \
+ --num-gpus $gpus \
+ --dist-url "auto" \
+ --eval-only \
+ OUTPUT_DIR $output/eval-voc20 \
+ MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON "datasets/voc20.json" \
+ DATASETS.TEST \(\"voc_2012_test_sem_seg\"\,\) \
+ MODEL.SEM_SEG_HEAD.POOLING_SIZES "[1,1]" \
+ MODEL.WEIGHTS $output/model_final.pth \
+ $opts
+
+#Pascal VOC-b
+python train_net.py --config $config \
+ --num-gpus $gpus \
+ --dist-url "auto" \
+ --eval-only \
+ OUTPUT_DIR $output/eval-voc20b \
+ MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON "datasets/voc20b.json" \
+ DATASETS.TEST \(\"voc_2012_test_background_sem_seg\"\,\) \
+ MODEL.SEM_SEG_HEAD.POOLING_SIZES "[1,1]" \
+ MODEL.WEIGHTS $output/model_final.pth \
+ $opts
+
+#Pascal Context 59
+python train_net.py --config $config \
+ --num-gpus $gpus \
+ --dist-url "auto" \
+ --eval-only \
+ OUTPUT_DIR $output/eval-pc59 \
+ MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON  "datasets/pc59.json" \
+ DATASETS.TEST \(\"context_59_test_sem_seg\"\,\) \
+ MODEL.SEM_SEG_HEAD.POOLING_SIZES "[1,1]" \
+ MODEL.WEIGHTS $output/model_final.pth \
+ $opts
+
+#Pascal Context 459
+python train_net.py --config $config \
+ --num-gpus $gpus \
+ --dist-url "auto" \
+ --eval-only \
+ OUTPUT_DIR $output/eval-pc459 \
+ MODEL.SEM_SEG_HEAD.TEST_CLASS_JSON "datasets/pc459.json" \
+ DATASETS.TEST \(\"context_459_test_sem_seg\"\,\) \
+ MODEL.SEM_SEG_HEAD.POOLING_SIZES "[1,1]" \
+ MODEL.WEIGHTS $output/model_final.pth \
+ $opts
+
+cat $output/eval-ade150/log.txt | grep copypaste
+cat $output/eval-ade847/log.txt | grep copypaste
+cat $output/eval-voc20/log.txt | grep copypaste
+cat $output/eval-voc20b/log.txt | grep copypaste
+cat $output/eval-pc59/log.txt | grep copypaste
+cat $output/eval-pc459/log.txt | grep copypaste
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=683
+# 模型名称
+modelName=sed_pytorch
+# 模型描述
+modelDescription=用于开放词汇语义分割的简单编码器-解码器-SED模型的推理和训练
+# 应用场景
+appScenario=训练,推理,科研,制造,医疗,家居,教育
+# 框架类型
+frameType=Pytorch

open_clip/.gitignore

+logs/
+wandb/
+models/
+features/
+results/
+
+tests/data/
+*.pt
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+sync.sh
+gpu1sync.sh
+.idea
+*.pdf
+**/._*
+**/*DS_*
+**.jsonl
+src/sbatch
+src/misc
+.vscode
+src/debug
+core.*
+
+# Allow
+!src/evaluation/misc/results_dbs/*
\ No newline at end of file

open_clip/CITATION.cff

+cff-version: 1.1.0
+message: If you use this software, please cite it as below.
+authors:
+  - family-names: Ilharco
+    given-names: Gabriel
+  - family-names: Wortsman
+    given-names: Mitchell
+  - family-names: Wightman
+    given-names: Ross
+  - family-names: Gordon
+    given-names: Cade   
+  - family-names: Carlini
+    given-names: Nicholas
+  - family-names: Taori
+    given-names: Rohan
+  - family-names: Dave
+    given-names: Achal
+  - family-names: Shankar
+    given-names: Vaishaal
+  - family-names: Namkoong
+    given-names: Hongseok
+  - family-names: Miller
+    given-names: John
+  - family-names: Hajishirzi
+    given-names: Hannaneh
+  - family-names: Farhadi
+    given-names: Ali
+  - family-names: Schmidt
+    given-names: Ludwig
+title: OpenCLIP
+version: v0.1
+doi: 10.5281/zenodo.5143773
+date-released: 2021-07-28

open_clip/HISTORY.md

+## 2.10.1
+
+* `hf-hub:org/model_id` support for loading models w/ config and weights in Hugging Face Hub
+
+## 2.10.0
+
+* Added a ViT-bigG-14 model.
+* Added an up-to-date example slurm script for large training jobs.
+* Added a option to sync logs and checkpoints to S3 during training.
+* New options for LR schedulers, constant and constant with cooldown
+* Fix wandb autoresuming when resume is not set
+* ConvNeXt `base` & `base_w` pretrained models added
+* `timm-` model prefix removed from configs
+* `timm` augmentation + regularization (dropout / drop-path) supported
+
+## 2.9.3
+
+* Fix wandb collapsing multiple parallel runs into a single one
+
+## 2.9.2
+
+* Fix braceexpand memory explosion for complex webdataset urls
+
+## 2.9.1
+
+* Fix release
+
+## 2.9.0
+
+* Add training feature to auto-resume from the latest checkpoint on restart via `--resume latest`
+* Allow webp in webdataset
+* Fix logging for number of samples when using gradient accumulation
+* Add model configs for convnext xxlarge
+
+## 2.8.2
+
+* wrapped patchdropout in a torch.nn.Module
+
+## 2.8.1
+
+* relax protobuf dependency
+* override the default patch dropout value in 'vision_cfg'
+
+## 2.8.0
+
+* better support for HF models
+* add support for gradient accumulation
+* CI fixes
+* add support for patch dropout
+* add convnext configs
+
+
+## 2.7.0
+
+* add multilingual H/14 xlm roberta large
+
+## 2.6.1
+
+* fix setup.py _read_reqs
+
+## 2.6.0
+
+* Make openclip training usable from pypi.
+* Add xlm roberta large vit h 14 config.
+
+## 2.5.0
+
+* pretrained B/32 xlm roberta base: first multilingual clip trained on laion5B
+* pretrained B/32 roberta base: first clip trained using an HF text encoder
+
+## 2.4.1
+
+* Add missing hf_tokenizer_name in CLIPTextCfg.
+
+## 2.4.0
+
+* Fix #211, missing RN50x64 config. Fix type of dropout param for ResNet models
+* Bring back LayerNorm impl that casts to input for non bf16/fp16 
+* zero_shot.py: set correct tokenizer based on args
+* training/params.py: remove hf params and get them from model config
+
+## 2.3.1
+
+* Implement grad checkpointing for hf model.
+* custom_text: True if hf_model_name is set
+* Disable hf tokenizer parallelism 
+
+## 2.3.0
+
+* Generalizable Text Transformer with HuggingFace Models (@iejMac)
+
+## 2.2.0
+
+* Support for custom text tower
+* Add checksum verification for pretrained model weights 
+
+## 2.1.0
+
+* lot including sota models, bfloat16 option, better loading, better metrics
+
+## 1.2.0
+
+* ViT-B/32 trained on Laion2B-en
+* add missing openai RN50x64 model
+
+## 1.1.1
+
+* ViT-B/16+
+* Add grad checkpointing support
+* more robust data loader

open_clip/LICENSE

+Copyright (c) 2012-2021 Gabriel Ilharco, Mitchell Wortsman, 
+Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, 
+John Miller, Hongseok Namkoong, Hannaneh Hajishirzi, Ali Farhadi, 
+Ludwig Schmidt
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
+WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

open_clip/MANIFEST.in

+include src/open_clip/bpe_simple_vocab_16e6.txt.gz
+include src/open_clip/model_configs/*.json
+

open_clip/Makefile

+install: ## [Local development] Upgrade pip, install requirements, install package.
+	python -m pip install -U pip
+	python -m pip install -e .
+
+install-training:
+	python -m pip install -r requirements-training.txt
+
+install-test: ## [Local development] Install test requirements
+	python -m pip install -r requirements-test.txt
+
+test: ## [Local development] Run unit tests
+	python -m pytest -x -s -v tests

open_clip/pytest.ini

+[pytest]
+markers =
+    regression_test

open_clip/requirements-test.txt

+pytest-split==0.8.0
+pytest==7.2.0
+transformers
+timm==0.6.11

open_clip/requirements-training.txt

+torch>=1.9.0
+torchvision
+webdataset>=0.2.5
+regex
+ftfy
+tqdm
+pandas
+braceexpand
+huggingface_hub
+transformers
+timm
+fsspec

open_clip/requirements.txt

+torch>=1.9.0
+torchvision
+regex
+ftfy
+tqdm
+huggingface_hub
+sentencepiece
+protobuf==3.20.*
+timm

open_clip/setup.py

+""" Setup
+"""
+from setuptools import setup, find_packages
+from codecs import open
+from os import path
+
+here = path.abspath(path.dirname(__file__))
+
+# Get the long description from the README file
+with open(path.join(here, 'README.md'), encoding='utf-8') as f:
+    long_description = f.read()
+
+def _read_reqs(relpath):
+    fullpath = path.join(path.dirname(__file__), relpath)
+    with open(fullpath) as f:
+        return [s.strip() for s in f.readlines() if (s.strip() and not s.startswith("#"))]
+
+REQUIREMENTS = _read_reqs("requirements.txt")
+TRAINING_REQUIREMENTS = _read_reqs("requirements-training.txt")
+
+exec(open('src/open_clip/version.py').read())
+setup(
+    name='open_clip_torch',
+    version=__version__,
+    description='OpenCLIP',
+    long_description=long_description,
+    long_description_content_type='text/markdown',
+    url='https://github.com/mlfoundations/open_clip',
+    author='',
+    author_email='',
+    classifiers=[
+        # How mature is this project? Common values are
+        #   3 - Alpha
+        #   4 - Beta
+        #   5 - Production/Stable
+        'Development Status :: 3 - Alpha',
+        'Intended Audience :: Education',
+        'Intended Audience :: Science/Research',
+        'License :: OSI Approved :: Apache Software License',
+        'Programming Language :: Python :: 3.7',
+        'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
+        'Topic :: Scientific/Engineering',
+        'Topic :: Scientific/Engineering :: Artificial Intelligence',
+        'Topic :: Software Development',
+        'Topic :: Software Development :: Libraries',
+        'Topic :: Software Development :: Libraries :: Python Modules',
+    ],
+
+    # Note that this is a string of words separated by whitespace, not a list.
+    keywords='CLIP pretrained',
+    package_dir={'': 'src'},
+    packages=find_packages(where='src'),
+    include_package_data=True,
+    install_requires=REQUIREMENTS,
+    extras_require={
+        "training": TRAINING_REQUIREMENTS,
+    },
+    python_requires='>=3.7',
+)

open_clip/src/open_clip/__init__.py

+from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
+from .factory import create_model, create_model_and_transforms, create_model_from_pretrained, get_tokenizer
+from .factory import list_models, add_model_config, get_model_config, load_checkpoint
+from .loss import ClipLoss
+from .model import CLIP, CustomTextCLIP, CLIPTextCfg, CLIPVisionCfg,\
+    convert_weights_to_lp, convert_weights_to_fp16, trace_model, get_cast_dtype
+from .openai import load_openai_model, list_openai_models
+from .pretrained import list_pretrained, list_pretrained_models_by_tag, list_pretrained_tags_by_model,\
+    get_pretrained_url, download_pretrained_from_url, is_pretrained_cfg, get_pretrained_cfg, download_pretrained
+from .tokenizer import SimpleTokenizer, tokenize
+from .transform import image_transform, AugmentationCfg

open_clip/src/open_clip/constants.py

+OPENAI_DATASET_MEAN = (0.48145466, 0.4578275, 0.40821073)
+OPENAI_DATASET_STD = (0.26862954, 0.26130258, 0.27577711)

open_clip/src/open_clip/factory.py

+import json
+import logging
+import os
+import pathlib
+import re
+from copy import deepcopy
+from pathlib import Path
+from typing import Any, Dict, 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, download_pretrained_from_hf
+from .transform import image_transform, AugmentationCfg
+from .tokenizer import HFTokenizer, tokenize
+
+
+HF_HUB_PREFIX = 'hf-hub:'
+_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):
+    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)
+    # 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,
+        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
+        pretrained_image: bool = False,
+        pretrained_hf: bool = True,
+        cache_dir: Optional[str] = None,
+):
+    has_hf_hub_prefix = model_name.startswith(HF_HUB_PREFIX)
+    if has_hf_hub_prefix:
+        model_id = model_name[len(HF_HUB_PREFIX):]
+        checkpoint_path = download_pretrained_from_hf(model_id, cache_dir=cache_dir)
+        config_path = download_pretrained_from_hf(model_id, filename='open_clip_config.json', cache_dir=cache_dir)
+
+        with open(config_path, 'r', encoding='utf-8') as f:
+            config = json.load(f)
+        pretrained_cfg = config['preprocess_cfg']
+        model_cfg = config['model_cfg']
+    else:
+        model_name = model_name.replace('/', '-')  # for callers using old naming with / in ViT names
+        checkpoint_path = None
+        pretrained_cfg = {}
+        model_cfg = None
+
+    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 = model_cfg or 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 force_image_size is not None:
+            # override model config's image size
+            model_cfg["vision_cfg"]["image_size"] = force_image_size
+
+        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)
+        is_hf_model = 'hf_model_name' in model_cfg.get('text_cfg', {})
+        custom_text = model_cfg.pop('custom_text', False) or force_custom_text or is_hf_model
+
+        if custom_text:
+            if is_hf_model:
+                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)
+
+        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)
+        elif has_hf_hub_prefix:
+            logging.info(f'Loading pretrained {model_name} weights ({pretrained}).')
+            load_checkpoint(model, checkpoint_path)
+
+        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,
+        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
+        pretrained_image: bool = False,
+        pretrained_hf: bool = True,
+        image_mean: Optional[Tuple[float, ...]] = None,
+        image_std: Optional[Tuple[float, ...]] = None,
+        aug_cfg: Optional[Union[Dict[str, Any], AugmentationCfg]] = 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,
+        force_image_size=force_image_size,
+        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,
+        aug_cfg=aug_cfg,
+    )
+    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,
+        force_image_size: Optional[Union[int, Tuple[int, int]]] = None,
+        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,
+        force_image_size=force_image_size,
+        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