clip-caption-reward
copied
wxywb
2 years ago
12 changed files with 66 additions and 776 deletions
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from .clip import * |
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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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"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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"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, unit='iB', unit_scale=True) 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 _transform(n_px): |
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return 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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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=True): |
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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 (default) or more hackable non-JIT model. |
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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]) |
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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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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(model.input_resolution.item()) |
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def tokenize(texts: Union[str, List[str]], context_length: int = 77) -> 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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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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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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from collections import OrderedDict |
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from typing import Tuple, Union |
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import torch |
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import torch.nn.functional as F |
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from torch import nn |
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class Bottleneck(nn.Module): |
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expansion = 4 |
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def __init__(self, inplanes, planes, stride=1): |
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super().__init__() |
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# all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1 |
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self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False) |
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self.bn1 = nn.BatchNorm2d(planes) |
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self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False) |
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self.bn2 = nn.BatchNorm2d(planes) |
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self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity() |
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self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False) |
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self.bn3 = nn.BatchNorm2d(planes * self.expansion) |
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self.relu = nn.ReLU(inplace=True) |
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self.downsample = None |
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self.stride = stride |
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if stride > 1 or inplanes != planes * Bottleneck.expansion: |
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# downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1 |
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self.downsample = nn.Sequential(OrderedDict([ |
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("-1", nn.AvgPool2d(stride)), |
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("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)), |
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("1", nn.BatchNorm2d(planes * self.expansion)) |
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])) |
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def forward(self, x: torch.Tensor): |
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identity = x |
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out = self.relu(self.bn1(self.conv1(x))) |
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out = self.relu(self.bn2(self.conv2(out))) |
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out = self.avgpool(out) |
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out = self.bn3(self.conv3(out)) |
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if self.downsample is not None: |
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identity = self.downsample(x) |
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out += identity |
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out = self.relu(out) |
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return out |
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class AttentionPool2d(nn.Module): |
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def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): |
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super().__init__() |
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self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5) |
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self.k_proj = nn.Linear(embed_dim, embed_dim) |
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self.q_proj = nn.Linear(embed_dim, embed_dim) |
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self.v_proj = nn.Linear(embed_dim, embed_dim) |
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self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) |
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self.num_heads = num_heads |
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def forward(self, x): |
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x = x.reshape(x.shape[0], x.shape[1], x.shape[2] * x.shape[3]).permute(2, 0, 1) # NCHW -> (HW)NC |
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# print(x.shape, self.positional_embedding.shape) |
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x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (HW+1)NC |
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x = x + self.positional_embedding[0, :, None, :].to(x.dtype) # (HW+1)NC |
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x, _ = F.multi_head_attention_forward( |
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query=x, key=x, value=x, |
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embed_dim_to_check=x.shape[-1], |
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num_heads=self.num_heads, |
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q_proj_weight=self.q_proj.weight, |
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k_proj_weight=self.k_proj.weight, |
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v_proj_weight=self.v_proj.weight, |
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in_proj_weight=None, |
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in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), |
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bias_k=None, |
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bias_v=None, |
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add_zero_attn=False, |
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dropout_p=0, |
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out_proj_weight=torch.ones_like(self.q_proj.weight), |
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out_proj_bias=torch.zeros_like(self.q_proj.bias), |
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# out_proj_weight=self.c_proj.weight, |
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# out_proj_bias=self.c_proj.bias, |
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use_separate_proj_weight=True, |
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training=self.training, |
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need_weights=False |
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) |
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return x[0] |
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class ModifiedResNet(nn.Module): |
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""" |
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A ResNet class that is similar to torchvision's but contains the following changes: |
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- There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool. |
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- Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1 |
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- The final pooling layer is a QKV attention instead of an average pool |
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""" |
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def __init__(self, layers, output_dim, heads, input_resolution=224, width=64): |
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super().__init__() |
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self.output_dim = output_dim |
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self.input_resolution = input_resolution |
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# the 3-layer stem |
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self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False) |
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self.bn1 = nn.BatchNorm2d(width // 2) |
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self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False) |
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self.bn2 = nn.BatchNorm2d(width // 2) |
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self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False) |
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self.bn3 = nn.BatchNorm2d(width) |
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self.avgpool = nn.AvgPool2d(2) |
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self.relu = nn.ReLU(inplace=True) |
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# residual layers |
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self._inplanes = width # this is a *mutable* variable used during construction |
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self.layer1 = self._make_layer(width, layers[0]) |
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self.layer2 = self._make_layer(width * 2, layers[1], stride=2) |
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self.layer3 = self._make_layer(width * 4, layers[2], stride=2) |
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self.layer4 = self._make_layer(width * 8, layers[3], stride=2) |
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embed_dim = width * 32 # the ResNet feature dimension |
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self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim) |
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def _make_layer(self, planes, blocks, stride=1): |
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layers = [Bottleneck(self._inplanes, planes, stride)] |
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self._inplanes = planes * Bottleneck.expansion |
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for _ in range(1, blocks): |
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layers.append(Bottleneck(self._inplanes, planes)) |
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return nn.Sequential(*layers) |
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def forward(self, x): |
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def stem(x): |
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for conv, bn in [(self.conv1, self.bn1), (self.conv2, self.bn2), (self.conv3, self.bn3)]: |
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x = self.relu(bn(conv(x))) |
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x = self.avgpool(x) |
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return x |
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x = x.type(self.conv1.weight.dtype) |
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x = stem(x) |
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x = self.layer1(x) |
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x = self.layer2(x) |
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x = self.layer3(x) |
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x = self.layer4(x) |
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# print(x.shape) |
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# x = self.attnpool(x) |
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attnpool = self.attnpool(x) |
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return (x, attnpool) |
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class LayerNorm(nn.LayerNorm): |
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"""Subclass torch's LayerNorm to handle fp16.""" |
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def forward(self, x: torch.Tensor): |
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orig_type = x.dtype |
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ret = super().forward(x.type(torch.float32)) |
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return ret.type(orig_type) |
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class QuickGELU(nn.Module): |
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def forward(self, x: torch.Tensor): |
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return x * torch.sigmoid(1.702 * x) |
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class ResidualAttentionBlock(nn.Module): |
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def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): |
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super().__init__() |
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self.attn = nn.MultiheadAttention(d_model, n_head) |
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self.ln_1 = LayerNorm(d_model) |
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self.mlp = nn.Sequential(OrderedDict([ |
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("c_fc", nn.Linear(d_model, d_model * 4)), |
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("gelu", QuickGELU()), |
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("c_proj", nn.Linear(d_model * 4, d_model)) |
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])) |
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self.ln_2 = LayerNorm(d_model) |
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self.attn_mask = attn_mask |
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def attention(self, x: torch.Tensor): |
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self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None |
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return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] |
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def forward(self, x: torch.Tensor): |
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x = x + self.attention(self.ln_1(x)) |
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x = x + self.mlp(self.ln_2(x)) |
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return x |
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class Transformer(nn.Module): |
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def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None): |
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super().__init__() |
|
||||
self.width = width |
|
||||
self.layers = layers |
|
||||
self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)]) |
|
||||
|
|
||||
def forward(self, x: torch.Tensor): |
|
||||
return self.resblocks(x) |
|
||||
|
|
||||
|
|
||||
class VisualTransformer(nn.Module): |
|
||||
def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int): |
|
||||
super().__init__() |
|
||||
self.input_resolution = input_resolution |
|
||||
self.output_dim = output_dim |
|
||||
self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False) |
|
||||
|
|
||||
scale = width ** -0.5 |
|
||||
self.class_embedding = nn.Parameter(scale * torch.randn(width)) |
|
||||
self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width)) |
|
||||
self.ln_pre = LayerNorm(width) |
|
||||
|
|
||||
self.transformer = Transformer(width, layers, heads) |
|
||||
|
|
||||
self.ln_post = LayerNorm(width) |
|
||||
self.proj = nn.Parameter(scale * torch.randn(width, output_dim)) |
|
||||
|
|
||||
def forward(self, x: torch.Tensor): |
|
||||
x = self.conv1(x) # shape = [*, width, grid, grid] |
|
||||
x = x.reshape(x.shape[0], x.shape[1], -1) # shape = [*, width, grid ** 2] |
|
||||
x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width] |
|
||||
x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1) # shape = [*, grid ** 2 + 1, width] |
|
||||
x = x + self.positional_embedding.to(x.dtype) |
|
||||
x = self.ln_pre(x) |
|
||||
|
|
||||
x = x.permute(1, 0, 2) # NLD -> LND |
|
||||
x = self.transformer(x) |
|
||||
x = x.permute(1, 0, 2) # LND -> NLD |
|
||||
|
|
||||
# x = self.ln_post(x[:, 0, :]) |
|
||||
|
|
||||
x = self.ln_post(x) |
|
||||
# if self.proj is not None: |
|
||||
# x = x @ self.proj |
|
||||
|
|
||||
return x |
|
||||
|
|
||||
|
|
||||
class CLIP(nn.Module): |
|
||||
def __init__(self, |
|
||||
embed_dim: int, |
|
||||
# vision |
|
||||
image_resolution: int, |
|
||||
vision_layers: Union[Tuple[int, int, int, int], int], |
|
||||
vision_width: int, |
|
||||
vision_patch_size: int, |
|
||||
# text |
|
||||
context_length: int, |
|
||||
vocab_size: int, |
|
||||
transformer_width: int, |
|
||||
transformer_heads: int, |
|
||||
transformer_layers: int |
|
||||
): |
|
||||
super().__init__() |
|
||||
|
|
||||
self.context_length = context_length |
|
||||
|
|
||||
if isinstance(vision_layers, (tuple, list)): |
|
||||
vision_heads = vision_width * 32 // 64 |
|
||||
self.visual = ModifiedResNet( |
|
||||
layers=vision_layers, |
|
||||
output_dim=embed_dim, |
|
||||
heads=vision_heads, |
|
||||
input_resolution=image_resolution, |
|
||||
width=vision_width |
|
||||
) |
|
||||
else: |
|
||||
vision_heads = vision_width // 64 |
|
||||
self.visual = VisualTransformer( |
|
||||
input_resolution=image_resolution, |
|
||||
patch_size=vision_patch_size, |
|
||||
width=vision_width, |
|
||||
layers=vision_layers, |
|
||||
heads=vision_heads, |
|
||||
output_dim=embed_dim |
|
||||
) |
|
||||
|
|
||||
self.transformer = Transformer( |
|
||||
width=transformer_width, |
|
||||
layers=transformer_layers, |
|
||||
heads=transformer_heads, |
|
||||
attn_mask=self.build_attention_mask() |
|
||||
) |
|
||||
|
|
||||
self.vocab_size = vocab_size |
|
||||
self.token_embedding = nn.Embedding(vocab_size, transformer_width) |
|
||||
self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width)) |
|
||||
self.ln_final = LayerNorm(transformer_width) |
|
||||
|
|
||||
self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) |
|
||||
self.logit_scale = nn.Parameter(torch.ones([])) |
|
||||
|
|
||||
self.initialize_parameters() |
|
||||
|
|
||||
def initialize_parameters(self): |
|
||||
nn.init.normal_(self.token_embedding.weight, std=0.02) |
|
||||
nn.init.normal_(self.positional_embedding, std=0.01) |
|
||||
|
|
||||
if isinstance(self.visual, ModifiedResNet): |
|
||||
if self.visual.attnpool is not None: |
|
||||
std = self.visual.attnpool.c_proj.in_features ** -0.5 |
|
||||
nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std) |
|
||||
nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std) |
|
||||
nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std) |
|
||||
nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std) |
|
||||
|
|
||||
for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]: |
|
||||
for name, param in resnet_block.named_parameters(): |
|
||||
if name.endswith("bn3.weight"): |
|
||||
nn.init.zeros_(param) |
|
||||
|
|
||||
proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) |
|
||||
attn_std = self.transformer.width ** -0.5 |
|
||||
fc_std = (2 * self.transformer.width) ** -0.5 |
|
||||
for block in self.transformer.resblocks: |
|
||||
nn.init.normal_(block.attn.in_proj_weight, std=attn_std) |
|
||||
nn.init.normal_(block.attn.out_proj.weight, std=proj_std) |
|
||||
nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) |
|
||||
nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) |
|
||||
|
|
||||
if self.text_projection is not None: |
|
||||
nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) |
|
||||
|
|
||||
def build_attention_mask(self): |
|
||||
# lazily create causal attention mask, with full attention between the vision tokens |
|
||||
# pytorch uses additive attention mask; fill with -inf |
|
||||
mask = torch.empty(self.context_length, self.context_length) |
|
||||
mask.fill_(float("-inf")) |
|
||||
mask.triu_(1) # zero out the lower diagonal |
|
||||
return mask |
|
||||
|
|
||||
@property |
|
||||
def dtype(self): |
|
||||
return self.visual.conv1.weight.dtype |
|
||||
|
|
||||
def encode_image(self, image): |
|
||||
return self.visual(image.type(self.dtype)) |
|
||||
|
|
||||
def encode_text(self, text): |
|
||||
x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] |
|
||||
|
|
||||
x = x + self.positional_embedding.type(self.dtype) |
|
||||
x = x.permute(1, 0, 2) # NLD -> LND |
|
||||
x = self.transformer(x) |
|
||||
x = x.permute(1, 0, 2) # LND -> NLD |
|
||||
x = self.ln_final(x).type(self.dtype) |
|
||||
|
|
||||
# x.shape = [batch_size, n_ctx, transformer.width] |
|
||||
# take features from the eot embedding (eot_token is the highest number in each sequence) |
|
||||
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection |
|
||||
|
|
||||
return x |
|
||||
|
|
||||
def forward(self, image, text): |
|
||||
image_features = self.encode_image(image) |
|
||||
text_features = self.encode_text(text) |
|
||||
|
|
||||
# normalized features |
|
||||
image_features = image_features / image_features.norm(dim=-1, keepdim=True) |
|
||||
text_features = text_features / text_features.norm(dim=-1, keepdim=True) |
|
||||
|
|
||||
# cosine similarity as logits |
|
||||
logit_scale = self.logit_scale.exp() |
|
||||
logits_per_image = logit_scale * image_features @ text_features.t() |
|
||||
logits_per_text = logit_scale * text_features @ image_features.t() |
|
||||
|
|
||||
# shape = [global_batch_size, global_batch_size] |
|
||||
return logits_per_image, logits_per_text |
|
||||
|
|
||||
|
|
||||
def convert_weights(model: nn.Module): |
|
||||
"""Convert applicable model parameters to fp16""" |
|
||||
|
|
||||
def _convert_weights_to_fp16(l): |
|
||||
if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)): |
|
||||
l.weight.data = l.weight.data.half() |
|
||||
if l.bias is not None: |
|
||||
l.bias.data = l.bias.data.half() |
|
||||
|
|
||||
if isinstance(l, nn.MultiheadAttention): |
|
||||
for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]: |
|
||||
tensor = getattr(l, attr) |
|
||||
if tensor is not None: |
|
||||
tensor.data = tensor.data.half() |
|
||||
|
|
||||
for name in ["text_projection", "proj"]: |
|
||||
if hasattr(l, name): |
|
||||
attr = getattr(l, name) |
|
||||
if attr is not None: |
|
||||
attr.data = attr.data.half() |
|
||||
|
|
||||
model.apply(_convert_weights_to_fp16) |
|
||||
|
|
||||
|
|
||||
def build_model(state_dict: dict): |
|
||||
vit = "visual.proj" in state_dict |
|
||||
|
|
||||
if vit: |
|
||||
vision_width = state_dict["visual.conv1.weight"].shape[0] |
|
||||
vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")]) |
|
||||
vision_patch_size = state_dict["visual.conv1.weight"].shape[-1] |
|
||||
grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5) |
|
||||
image_resolution = vision_patch_size * grid_size |
|
||||
else: |
|
||||
counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]] |
|
||||
vision_layers = tuple(counts) |
|
||||
vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0] |
|
||||
output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5) |
|
||||
vision_patch_size = None |
|
||||
assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0] |
|
||||
image_resolution = output_width * 32 |
|
||||
|
|
||||
embed_dim = state_dict["text_projection"].shape[1] |
|
||||
context_length = state_dict["positional_embedding"].shape[0] |
|
||||
vocab_size = state_dict["token_embedding.weight"].shape[0] |
|
||||
transformer_width = state_dict["ln_final.weight"].shape[0] |
|
||||
transformer_heads = transformer_width // 64 |
|
||||
transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks"))) |
|
||||
|
|
||||
model = CLIP( |
|
||||
embed_dim, |
|
||||
image_resolution, vision_layers, vision_width, vision_patch_size, |
|
||||
context_length, vocab_size, transformer_width, transformer_heads, transformer_layers |
|
||||
) |
|
||||
|
|
||||
for key in ["input_resolution", "context_length", "vocab_size"]: |
|
||||
if key in state_dict: |
|
||||
del state_dict[key] |
|
||||
|
|
||||
convert_weights(model) |
|
||||
model.load_state_dict(state_dict) |
|
||||
return model.eval() |
|
||||
@ -1,132 +0,0 @@ |
|||||
import gzip |
|
||||
import html |
|
||||
import os |
|
||||
from functools import lru_cache |
|
||||
|
|
||||
import ftfy |
|
||||
import regex as re |
|
||||
|
|
||||
|
|
||||
@lru_cache() |
|
||||
def default_bpe(): |
|
||||
return os.path.join(os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz") |
|
||||
|
|
||||
|
|
||||
@lru_cache() |
|
||||
def bytes_to_unicode(): |
|
||||
""" |
|
||||
Returns list of utf-8 byte and a corresponding list of unicode strings. |
|
||||
The reversible bpe codes work on unicode strings. |
|
||||
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. |
|
||||
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. |
|
||||
This is a signficant percentage of your normal, say, 32K bpe vocab. |
|
||||
To avoid that, we want lookup tables between utf-8 bytes and unicode strings. |
|
||||
And avoids mapping to whitespace/control characters the bpe code barfs on. |
|
||||
""" |
|
||||
bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) |
|
||||
cs = bs[:] |
|
||||
n = 0 |
|
||||
for b in range(2**8): |
|
||||
if b not in bs: |
|
||||
bs.append(b) |
|
||||
cs.append(2**8+n) |
|
||||
n += 1 |
|
||||
cs = [chr(n) for n in cs] |
|
||||
return dict(zip(bs, cs)) |
|
||||
|
|
||||
|
|
||||
def get_pairs(word): |
|
||||
"""Return set of symbol pairs in a word. |
|
||||
Word is represented as tuple of symbols (symbols being variable-length strings). |
|
||||
""" |
|
||||
pairs = set() |
|
||||
prev_char = word[0] |
|
||||
for char in word[1:]: |
|
||||
pairs.add((prev_char, char)) |
|
||||
prev_char = char |
|
||||
return pairs |
|
||||
|
|
||||
|
|
||||
def basic_clean(text): |
|
||||
text = ftfy.fix_text(text) |
|
||||
text = html.unescape(html.unescape(text)) |
|
||||
return text.strip() |
|
||||
|
|
||||
|
|
||||
def whitespace_clean(text): |
|
||||
text = re.sub(r'\s+', ' ', text) |
|
||||
text = text.strip() |
|
||||
return text |
|
||||
|
|
||||
|
|
||||
class SimpleTokenizer(object): |
|
||||
def __init__(self, bpe_path: str = default_bpe()): |
|
||||
self.byte_encoder = bytes_to_unicode() |
|
||||
self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} |
|
||||
merges = gzip.open(bpe_path).read().decode("utf-8").split('\n') |
|
||||
merges = merges[1:49152-256-2+1] |
|
||||
merges = [tuple(merge.split()) for merge in merges] |
|
||||
vocab = list(bytes_to_unicode().values()) |
|
||||
vocab = vocab + [v+'</w>' for v in vocab] |
|
||||
for merge in merges: |
|
||||
vocab.append(''.join(merge)) |
|
||||
vocab.extend(['<|startoftext|>', '<|endoftext|>']) |
|
||||
self.encoder = dict(zip(vocab, range(len(vocab)))) |
|
||||
self.decoder = {v: k for k, v in self.encoder.items()} |
|
||||
self.bpe_ranks = dict(zip(merges, range(len(merges)))) |
|
||||
self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} |
|
||||
self.pat = re.compile(r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""", re.IGNORECASE) |
|
||||
|
|
||||
def bpe(self, token): |
|
||||
if token in self.cache: |
|
||||
return self.cache[token] |
|
||||
word = tuple(token[:-1]) + ( token[-1] + '</w>',) |
|
||||
pairs = get_pairs(word) |
|
||||
|
|
||||
if not pairs: |
|
||||
return token+'</w>' |
|
||||
|
|
||||
while True: |
|
||||
bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) |
|
||||
if bigram not in self.bpe_ranks: |
|
||||
break |
|
||||
first, second = bigram |
|
||||
new_word = [] |
|
||||
i = 0 |
|
||||
while i < len(word): |
|
||||
try: |
|
||||
j = word.index(first, i) |
|
||||
new_word.extend(word[i:j]) |
|
||||
i = j |
|
||||
except: |
|
||||
new_word.extend(word[i:]) |
|
||||
break |
|
||||
|
|
||||
if word[i] == first and i < len(word)-1 and word[i+1] == second: |
|
||||
new_word.append(first+second) |
|
||||
i += 2 |
|
||||
else: |
|
||||
new_word.append(word[i]) |
|
||||
i += 1 |
|
||||
new_word = tuple(new_word) |
|
||||
word = new_word |
|
||||
if len(word) == 1: |
|
||||
break |
|
||||
else: |
|
||||
pairs = get_pairs(word) |
|
||||
word = ' '.join(word) |
|
||||
self.cache[token] = word |
|
||||
return word |
|
||||
|
|
||||
def encode(self, text): |
|
||||
bpe_tokens = [] |
|
||||
text = whitespace_clean(basic_clean(text)).lower() |
|
||||
for token in re.findall(self.pat, text): |
|
||||
token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) |
|
||||
bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) |
|
||||
return bpe_tokens |
|
||||
|
|
||||
def decode(self, tokens): |
|
||||
text = ''.join([self.decoder[token] for token in tokens]) |
|
||||
text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ') |
|
||||
return text |
|
||||
|
After Width: | Height: | Size: 94 KiB |
Loading…
Reference in new issue