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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 .model_vpt import build_model | |
| from .simple_tokenizer import SimpleTokenizer as _Tokenizer | |
| __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", | |
| "RN50x64": "https://openaipublic.azureedge.net/clip/models/be1cfb55d75a9666199fb2206c106743da0f6468c9d327f3e0d0a543a9919d9c/RN50x64.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", | |
| "ViT-L/14": "https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt", | |
| "ViT-L/14@336px": "https://openaipublic.azureedge.net/clip/models/3035c92b350959924f9f00213499208652fc7ea050643e8b385c2dac08641f02/ViT-L-14-336px.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) 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 available_models(): | |
| return list(_MODELS.keys()) | |
| def load(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit=True, prompt_depth=0, prompt_length=0): | |
| if name not in _MODELS: | |
| raise RuntimeError(f"Model {name} not found; available models = {available_models()}") | |
| model_path = _download(_MODELS[name]) | |
| model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() | |
| n_px = model.input_resolution.item() | |
| transform = Compose([ | |
| Resize(n_px, interpolation=Image.BICUBIC), | |
| CenterCrop(n_px), | |
| lambda image: image.convert("RGB"), | |
| ToTensor(), | |
| Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), | |
| ]) | |
| if not jit: | |
| model = build_model(model.state_dict(), prompt_depth, prompt_length).to(device) | |
| return model, transform | |
| # 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): | |
| graphs = [module.graph] if hasattr(module, "graph") else [] | |
| 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 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): | |
| graphs = [module.graph] if hasattr(module, "graph") else [] | |
| 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 | |
| def load_custom(name: str, device: Union[str, torch.device] = "cuda" if torch.cuda.is_available() else "cpu", jit=True, n_px=224): | |
| if name not in _MODELS: | |
| raise RuntimeError(f"Model {name} not found; available models = {available_models()}") | |
| model_path = _download(_MODELS[name]) | |
| model = torch.jit.load(model_path, map_location=device if jit else "cpu").eval() | |
| # n_px = model.input_resolution.item() | |
| transform = Compose([ | |
| Resize(n_px, interpolation=Image.BICUBIC), | |
| CenterCrop(n_px), | |
| lambda image: image.convert("RGB"), | |
| ToTensor(), | |
| Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), | |
| ]) | |
| if not jit: | |
| model = build_model(model.state_dict()).to(device) | |
| return model, transform | |
| # 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): | |
| graphs = [module.graph] if hasattr(module, "graph") else [] | |
| 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 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): | |
| graphs = [module.graph] if hasattr(module, "graph") else [] | |
| 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 | |
| def tokenize(texts: Union[str, List[str]], context_length: int = 77): | |
| 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: | |
| raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}") | |
| result[i, :len(tokens)] = torch.tensor(tokens) | |
| return result | |