113 lines
3.3 KiB
Python
113 lines
3.3 KiB
Python
import numpy
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from typing import Dict
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from torch import LongTensor, FloatTensor
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import torch
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torch.no_grad()
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from .models.vqgan_detokenizer import VQGanDetokenizer
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from .models.dalle_bart_encoder_torch import DalleBartEncoderTorch
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from .models.dalle_bart_decoder_torch import DalleBartDecoderTorch
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from .load_params import (
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load_vqgan_torch_params,
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convert_dalle_bart_torch_from_flax_params
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)
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def encode_torch(
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text_tokens: LongTensor,
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config: dict,
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params: dict
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) -> FloatTensor:
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print("loading torch encoder")
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encoder = DalleBartEncoderTorch(
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layer_count = config['encoder_layers'],
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embed_count = config['d_model'],
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attention_head_count = config['encoder_attention_heads'],
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text_vocab_count = config['encoder_vocab_size'],
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text_token_count = config['max_text_length'],
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glu_embed_count = config['encoder_ffn_dim']
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)
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encoder_params = convert_dalle_bart_torch_from_flax_params(
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params.pop('encoder'),
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layer_count=config['encoder_layers'],
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is_encoder=True
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)
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encoder.load_state_dict(encoder_params, strict=False)
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del encoder_params
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print("encoding text tokens")
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encoder_state = encoder(text_tokens)
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del encoder
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return encoder_state
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def decode_torch(
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text_tokens: LongTensor,
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encoder_state: FloatTensor,
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config: dict,
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seed: int,
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params: dict,
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image_token_count: int
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) -> LongTensor:
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print("loading torch decoder")
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decoder = DalleBartDecoderTorch(
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image_vocab_size = config['image_vocab_size'],
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image_token_count = config['image_length'],
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sample_token_count = image_token_count,
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embed_count = config['d_model'],
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attention_head_count = config['decoder_attention_heads'],
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glu_embed_count = config['decoder_ffn_dim'],
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layer_count = config['decoder_layers'],
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batch_count = 2,
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start_token = config['decoder_start_token_id'],
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is_verbose = True
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)
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decoder_params = convert_dalle_bart_torch_from_flax_params(
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params.pop('decoder'),
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layer_count=config['decoder_layers'],
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is_encoder=False
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)
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decoder.load_state_dict(decoder_params, strict=False)
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del decoder_params
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print("sampling image tokens")
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torch.manual_seed(seed)
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image_tokens = decoder.forward(text_tokens, encoder_state)
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return image_tokens
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def generate_image_tokens_torch(
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text_tokens: numpy.ndarray,
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seed: int,
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config: dict,
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params: dict,
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image_token_count: int
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) -> LongTensor:
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text_tokens = torch.tensor(text_tokens).to(torch.long)
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if torch.cuda.is_available(): text_tokens = text_tokens.cuda()
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encoder_state = encode_torch(
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text_tokens,
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config,
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params
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)
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image_tokens = decode_torch(
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text_tokens,
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encoder_state,
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config,
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seed,
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params,
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image_token_count
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)
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return image_tokens
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def detokenize_torch(image_tokens: LongTensor) -> numpy.ndarray:
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print("detokenizing image")
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model_path = './pretrained/vqgan'
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params = load_vqgan_torch_params(model_path)
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detokenizer = VQGanDetokenizer()
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detokenizer.load_state_dict(params)
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image = detokenizer.forward(image_tokens).to(torch.uint8)
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return image.detach().numpy()
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