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