37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
import os
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import json
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import numpy
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from .text_tokenizer import TextTokenizer
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from .models.vqgan_detokenizer import VQGanDetokenizer
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class MinDalleBase:
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def __init__(self, is_mega: bool):
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self.is_mega = is_mega
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model_name = 'dalle_bart_{}'.format('mega' if is_mega else 'mini')
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self.model_path = os.path.join('pretrained', model_name)
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print("reading files from {}".format(self.model_path))
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config_path = os.path.join(self.model_path, 'config.json')
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vocab_path = os.path.join(self.model_path, 'vocab.json')
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merges_path = os.path.join(self.model_path, 'merges.txt')
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with open(config_path, 'r', encoding='utf8') as f:
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self.config = json.load(f)
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with open(vocab_path, 'r', encoding='utf8') as f:
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vocab = json.load(f)
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with open(merges_path, 'r', encoding='utf8') as f:
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merges = f.read().split("\n")[1:-1]
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self.tokenizer = TextTokenizer(vocab, merges)
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def tokenize_text(self, text: str) -> numpy.ndarray:
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print("tokenizing text")
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tokens = self.tokenizer.tokenize(text)
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print("text tokens", tokens)
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text_token_count = self.config['max_text_length']
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text_tokens = numpy.ones((2, text_token_count), dtype=numpy.int32)
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text_tokens[0, :2] = [tokens[0], tokens[-1]]
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text_tokens[1, :len(tokens)] = tokens
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return text_tokens |