142 lines
5.2 KiB
Python
142 lines
5.2 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 Union, List
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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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__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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"ViT-B/32": "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt",
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}
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def _download(url: str, root: str = os.path.expanduser("~/.cache/clip")):
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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) 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 available_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=True):
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if name not in _MODELS:
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raise RuntimeError(f"Model {name} not found; available models = {available_models()}")
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model_path = _download(_MODELS[name])
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model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval()
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n_px = model.input_resolution.item()
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transform = Compose([
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Resize(n_px, interpolation=Image.BICUBIC),
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CenterCrop(n_px),
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lambda image: image.convert("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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if not jit:
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model = build_model(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
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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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graphs = [module.graph] if hasattr(module, "graph") else []
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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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graphs = [module.graph] if hasattr(module, "graph") else []
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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
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def tokenize(texts: Union[str, List[str]], context_length: int = 77):
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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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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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