230 lines
8.4 KiB
Python
230 lines
8.4 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 Any, Union, List
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from pkg_resources import packaging
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import torch
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from PIL import Image
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from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
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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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"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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}
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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 hashlib.sha256(open(download_target, "rb").read()).hexdigest() == expected_sha256:
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return download_target
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else:
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warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file")
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with urllib.request.urlopen(url) as source, open(download_target, "wb") as output:
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with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) 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 hashlib.sha256(open(download_target, "rb").read()).hexdigest() != expected_sha256:
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raise RuntimeError(f"Model has been downloaded but the SHA256 checksum does not not match")
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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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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((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
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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(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit: bool = False, download_root: str = None):
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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(_MODELS[name], download_root or os.path.expanduser("~/.cache/clip"))
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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(f"Model {name} not found; available models = {available_models()}")
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try:
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# loading JIT archive
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model = torch.jit.load(model_path, map_location=device if jit else "cpu").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(f"File {model_path} is not a JIT archive. Loading as a state dict instead")
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jit = False
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state_dict = torch.load(model_path, 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(lambda: torch.ones([]).to(torch.device(device)), example_inputs=[])
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device_node = [n for n in device_holder.graph.findAllNodes("prim::Constant") if "Device" in repr(n)][-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("cuda"):
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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(lambda: torch.ones([]).float(), example_inputs=[])
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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 [1, 2]: # 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(texts: Union[str, List[str]], context_length: int = 77, truncate: bool = False) -> 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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"""
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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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result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
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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(f"Input {texts[i]} is too long for context length {context_length}")
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result[i, :len(tokens)] = torch.tensor(tokens)
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return result
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