min-dalle-test/min_dalle/min_dalle_torch.py
2022-06-28 22:26:51 -04:00

119 lines
3.6 KiB
Python

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