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| """ OpenAI pretrained model functions | |
| Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI. | |
| """ | |
| import os | |
| import warnings | |
| from typing import List, Optional, Union | |
| import torch | |
| from .model import build_model_from_openai_state_dict, convert_weights_to_lp, get_cast_dtype | |
| from .pretrained import get_pretrained_url, list_pretrained_models_by_tag, download_pretrained_from_url | |
| __all__ = ["list_openai_models", "load_openai_model"] | |
| def list_openai_models() -> List[str]: | |
| """Returns the names of available CLIP models""" | |
| return list_pretrained_models_by_tag('openai') | |
| def load_openai_model( | |
| name: str, | |
| precision: Optional[str] = None, | |
| device: Optional[Union[str, torch.device]] = None, | |
| jit: bool = True, | |
| cache_dir: Optional[str] = None, | |
| ): | |
| """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 | |
| precision: str | |
| Model precision, if None defaults to 'fp32' if device == 'cpu' else 'fp16'. | |
| device : Union[str, torch.device] | |
| The device to put the loaded model | |
| jit : bool | |
| Whether to load the optimized JIT model (default) or more hackable non-JIT model. | |
| cache_dir : Optional[str] | |
| The directory to cache the downloaded model weights | |
| 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 device is None: | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| if precision is None: | |
| precision = 'fp32' if device == 'cpu' else 'fp16' | |
| if get_pretrained_url(name, 'openai'): | |
| model_path = download_pretrained_from_url(get_pretrained_url(name, 'openai'), cache_dir=cache_dir) | |
| elif os.path.isfile(name): | |
| model_path = name | |
| else: | |
| raise RuntimeError(f"Model {name} not found; available models = {list_openai_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: | |
| # Build a non-jit model from the OpenAI jitted model state dict | |
| cast_dtype = get_cast_dtype(precision) | |
| try: | |
| model = build_model_from_openai_state_dict(state_dict or model.state_dict(), cast_dtype=cast_dtype) | |
| except KeyError: | |
| sd = {k[7:]: v for k, v in state_dict["state_dict"].items()} | |
| model = build_model_from_openai_state_dict(sd, cast_dtype=cast_dtype) | |
| # model from OpenAI state dict is in manually cast fp16 mode, must be converted for AMP/fp32/bf16 use | |
| model = model.to(device) | |
| if precision.startswith('amp') or precision == 'fp32': | |
| model.float() | |
| elif precision == 'bf16': | |
| convert_weights_to_lp(model, dtype=torch.bfloat16) | |
| return model | |
| # 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 (typically for CPU) | |
| if precision == 'fp32': | |
| 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() | |
| # ensure image_size attr available at consistent location for both jit and non-jit | |
| model.visual.image_size = model.input_resolution.item() | |
| return model | |