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import torch |
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from torch import nn |
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import torch.nn.functional as F |
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from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize |
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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 PIL import Image |
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from tqdm import tqdm |
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from pathlib import Path |
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import html |
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import os |
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from functools import lru_cache |
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from collections import OrderedDict |
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import ftfy |
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import regex as re |
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MODEL_PATH = "https://openaipublic.azureedge.net/clip/models/40d365715913c9da98579312b702a82c18be219cc2a73407c4526f58eba950af/ViT-B-32.pt" |
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@lru_cache() |
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def default_bpe(): |
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return os.path.join(os.path.dirname(os.path.abspath(__file__)), "data/bpe_simple_vocab_16e6.txt") |
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@lru_cache() |
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def bytes_to_unicode(): |
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bs = list(range(ord("!"), ord("~")+1))+list(range(ord("¡"), ord("¬")+1))+list(range(ord("®"), ord("ÿ")+1)) |
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cs = bs[:] |
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n = 0 |
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for b in range(2**8): |
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if b not in bs: |
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bs.append(b) |
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cs.append(2**8+n) |
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n += 1 |
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cs = [chr(n) for n in cs] |
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return dict(zip(bs, cs)) |
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def get_pairs(word): |
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pairs = set() |
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prev_char = word[0] |
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for char in word[1:]: |
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pairs.add((prev_char, char)) |
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prev_char = char |
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return pairs |
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def basic_clean(text): |
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text = ftfy.fix_text(text) |
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text = html.unescape(html.unescape(text)) |
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return text.strip() |
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def whitespace_clean(text): |
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text = re.sub(r'\s+', ' ', text) |
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text = text.strip() |
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return text |
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class SimpleTokenizer(object): |
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def __init__(self, bpe_path: str = default_bpe()): |
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self.byte_encoder = bytes_to_unicode() |
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self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} |
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merges = Path(bpe_path).read_text().split('\n') |
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merges = merges[1:49152-256-2+1] |
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merges = [tuple(merge.split()) for merge in merges] |
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vocab = list(bytes_to_unicode().values()) |
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vocab = vocab + [v+'</w>' for v in vocab] |
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for merge in merges: |
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vocab.append(''.join(merge)) |
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vocab.extend(['<|startoftext|>', '<|endoftext|>']) |
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self.encoder = dict(zip(vocab, range(len(vocab)))) |
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self.decoder = {v: k for k, v in self.encoder.items()} |
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self.bpe_ranks = dict(zip(merges, range(len(merges)))) |
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self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'} |
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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) |
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def bpe(self, token): |
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if token in self.cache: |
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return self.cache[token] |
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word = tuple(token[:-1]) + ( token[-1] + '</w>',) |
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pairs = get_pairs(word) |
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if not pairs: |
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return token+'</w>' |
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while True: |
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bigram = min(pairs, key = lambda pair: self.bpe_ranks.get(pair, float('inf'))) |
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if bigram not in self.bpe_ranks: |
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break |
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first, second = bigram |
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new_word = [] |
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i = 0 |
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while i < len(word): |
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try: |
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j = word.index(first, i) |
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new_word.extend(word[i:j]) |
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i = j |
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except: |
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new_word.extend(word[i:]) |
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break |
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if word[i] == first and i < len(word)-1 and word[i+1] == second: |
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new_word.append(first+second) |
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i += 2 |
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else: |
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new_word.append(word[i]) |
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i += 1 |
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new_word = tuple(new_word) |
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word = new_word |
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if len(word) == 1: |
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break |
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else: |
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pairs = get_pairs(word) |
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word = ' '.join(word) |
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self.cache[token] = word |
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return word |
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def encode(self, text): |
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bpe_tokens = [] |
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text = whitespace_clean(basic_clean(text)).lower() |
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for token in re.findall(self.pat, text): |
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token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) |
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bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' ')) |
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return bpe_tokens |
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def decode(self, tokens): |
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text = ''.join([self.decoder[token] for token in tokens]) |
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text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors="replace").replace('</w>', ' ') |
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return text |
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def _download(url, root = 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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normalize_image = Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)) |
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def load(device = ("cuda" if torch.cuda.is_available() else "cpu")): |
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model_path = _download(MODEL_PATH) |
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model = torch.jit.load(model_path, map_location = device).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_image, |
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]) |
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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 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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tokenizer = SimpleTokenizer() |
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def tokenize(texts, 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 |