mirror of
https://github.com/brycedrennan/imaginAIry
synced 2024-11-09 13:10:27 +00:00
290 lines
9.8 KiB
Python
290 lines
9.8 KiB
Python
import hashlib
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import os
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import urllib
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import warnings
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from typing import List, Union
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import torch
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from PIL import Image
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from pkg_resources import packaging
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from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
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from tqdm import tqdm
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from .model import build_model
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from .simple_tokenizer import SimpleTokenizer as _Tokenizer
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try:
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from torchvision.transforms import InterpolationMode
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BICUBIC = InterpolationMode.BICUBIC
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except ImportError:
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BICUBIC = Image.BICUBIC
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if packaging.version.parse(torch.__version__) < packaging.version.parse("1.7.1"):
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warnings.warn("PyTorch version 1.7.1 or higher is recommended")
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__all__ = ["available_models", "load", "tokenize"]
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_tokenizer = _Tokenizer()
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_MODELS = {
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"RN50": "https://openaipublic.azureedge.net/clip/models/afeb0e10f9e5a86da6080e35cf09123aca3b358a0c3e3b6c78a7b63bc04b6762/RN50.pt",
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"RN101": "https://openaipublic.azureedge.net/clip/models/8fa8567bab74a42d41c5915025a8e4538c3bdbe8804a470a72f30b0d94fab599/RN101.pt",
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"RN50x4": "https://openaipublic.azureedge.net/clip/models/7e526bd135e493cef0776de27d5f42653e6b4c8bf9e0f653bb11773263205fdd/RN50x4.pt",
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"RN50x16": "https://openaipublic.azureedge.net/clip/models/52378b407f34354e150460fe41077663dd5b39c54cd0bfd2b27167a4a06ec9aa/RN50x16.pt",
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"RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.pt",
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"ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
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"ViT-B/16": "https://openaipublic.azureedge.net/clip/models/5806e77cd80f8b59890b7e101eabd078d9fb84e6937f9e85e4ecb61988df416f/ViT-B-16.pt",
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"ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt",
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"ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.pt",
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}
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def _download(url: str, root: str):
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os.makedirs(root, exist_ok=True)
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filename = os.path.basename(url)
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expected_sha256 = url.split("/")[-2]
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download_target = os.path.join(root, filename)
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if os.path.exists(download_target) and not os.path.isfile(download_target):
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raise RuntimeError(f"{download_target} exists and is not a regular file")
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if os.path.isfile(download_target):
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if (
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hashlib.sha256(open(download_target, "rb").read()).hexdigest()
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== expected_sha256
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):
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return download_target
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else:
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warnings.warn(
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f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"
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)
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with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
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with tqdm(
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total=int(source.info().get("Content-Length")),
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ncols=80,
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unit="iB",
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unit_scale=True,
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unit_divisor=1024,
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) as loop:
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while True:
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buffer = source.read(8192)
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if not buffer:
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break
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output.write(buffer)
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loop.update(len(buffer))
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if (
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hashlib.sha256(open(download_target, "rb").read()).hexdigest()
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!= expected_sha256
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):
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raise RuntimeError(
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"Model has been downloaded but the SHA256 checksum does not not match"
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)
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return download_target
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def _convert_image_to_rgb(image):
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return image.convert("RGB")
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def _transform(n_px):
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return Compose(
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[
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Resize(n_px, interpolation=BICUBIC),
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CenterCrop(n_px),
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_convert_image_to_rgb,
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ToTensor(),
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Normalize(
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(0.48145466, 0.4578275, 0.40821073),
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(0.26862954, 0.26130258, 0.27577711),
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),
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]
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)
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def available_models() -> List[str]:
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"""Returns the names of available CLIP models."""
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return list(_MODELS.keys())
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def load(
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name: str,
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device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu",
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jit: bool = False,
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download_root: str = None,
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):
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"""Load a CLIP model.
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Parameters
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----------
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name : str
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A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict
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device : Union[str, torch.device]
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The device to put the loaded model
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jit : bool
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Whether to load the optimized JIT model or more hackable non-JIT model (default).
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download_root: str
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path to download the model files; by default, it uses "~/.cache/clip"
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Returns
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-------
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model : torch.nn.Module
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The CLIP model
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preprocess : Callable[[PIL.Image], torch.Tensor]
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A torchvision transform that converts a PIL image into a tensor that the returned model can take as its input
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"""
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if name in _MODELS:
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model_path = _download(
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_MODELS[name], download_root or os.path.expanduser("~/.cache/clip")
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)
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elif os.path.isfile(name):
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model_path = name
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else:
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raise RuntimeError(
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f"Model {name} not found; available models = {available_models()}"
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)
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with open(model_path, "rb") as opened_file:
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try:
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# loading JIT archive
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model = torch.jit.load(
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opened_file, map_location=device if jit else "cpu"
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).eval()
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state_dict = None
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except RuntimeError:
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# loading saved state dict
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if jit:
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warnings.warn(
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f"File {model_path} is not a JIT archive. Loading as a state dict instead"
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)
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jit = False
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state_dict = torch.load(opened_file, map_location="cpu")
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if not jit:
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model = build_model(state_dict or model.state_dict()).to(device)
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if str(device) == "cpu":
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model.float()
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return model, _transform(model.visual.input_resolution)
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# patch the device names
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device_holder = torch.jit.trace(
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lambda: torch.ones([]).to(torch.device(device)), example_inputs=[]
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)
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device_node = [
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n
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for n in device_holder.graph.findAllNodes("prim::Constant")
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if "Device" in repr(n)
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][-1]
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def patch_device(module):
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try:
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graphs = [module.graph] if hasattr(module, "graph") else []
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except RuntimeError:
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graphs = []
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if hasattr(module, "forward1"):
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graphs.append(module.forward1.graph)
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for graph in graphs:
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for node in graph.findAllNodes("prim::Constant"):
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if "value" in node.attributeNames() and str(node["value"]).startswith(
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"cuda"
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):
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node.copyAttributes(device_node)
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model.apply(patch_device)
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patch_device(model.encode_image)
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patch_device(model.encode_text)
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# patch dtype to float32 on CPU
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if str(device) == "cpu":
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float_holder = torch.jit.trace(
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lambda: torch.ones([]).float(), example_inputs=[]
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)
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float_input = list(float_holder.graph.findNode("aten::to").inputs())[1]
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float_node = float_input.node()
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def patch_float(module):
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try:
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graphs = [module.graph] if hasattr(module, "graph") else []
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except RuntimeError:
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graphs = []
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if hasattr(module, "forward1"):
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graphs.append(module.forward1.graph)
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for graph in graphs:
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for node in graph.findAllNodes("aten::to"):
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inputs = list(node.inputs())
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for i in [
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1,
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2,
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]: # dtype can be the second or third argument to aten::to()
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if inputs[i].node()["value"] == 5:
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inputs[i].node().copyAttributes(float_node)
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model.apply(patch_float)
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patch_float(model.encode_image)
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patch_float(model.encode_text)
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model.float()
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return model, _transform(model.input_resolution.item())
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def tokenize(
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texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False
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) -> Union[torch.IntTensor, torch.LongTensor]:
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"""
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Returns the tokenized representation of given input string(s).
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Parameters
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----------
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texts : Union[str, List[str]]
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An input string or a list of input strings to tokenize
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context_length : int
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The context length to use; all CLIP models use 77 as the context length
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truncate: bool
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Whether to truncate the text in case its encoding is longer than the context length
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Returns
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-------
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A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length].
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We return LongTensor when torch version is <1.8.0, since older index_select requires indices to be long.
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"""
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if isinstance(texts, str):
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texts = [texts]
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sot_token = _tokenizer.encoder["<|startoftext|>"]
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eot_token = _tokenizer.encoder["<|endoftext|>"]
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all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
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if packaging.version.parse(torch.__version__) < packaging.version.parse("1.8.0"):
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result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
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else:
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result = torch.zeros(len(all_tokens), context_length, dtype=torch.int)
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for i, tokens in enumerate(all_tokens):
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if len(tokens) > context_length:
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if truncate:
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tokens = tokens[:context_length]
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tokens[-1] = eot_token
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else:
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raise RuntimeError(
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f"Input {texts[i]} is too long for context length {context_length}"
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)
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result[i, : len(tokens)] = torch.tensor(tokens)
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return result
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