fixed relative imports

image_from_text_flax.py

@@ -3,16 +3,16 @@ from jax import numpy as jnp
 import numpy
 import argparse
 
-from load_params import load_dalle_bart_flax_params
+from min_dalle.load_params import load_dalle_bart_flax_params
-from image_from_text import (
+from min_dalle.image_from_text import (
     load_dalle_bart_metadata, 
     tokenize, 
     detokenize_torch,
     save_image, 
     ascii_from_image
 )
-from models.dalle_bart_encoder_flax import DalleBartEncoderFlax
+from min_dalle.models.dalle_bart_encoder_flax import DalleBartEncoderFlax
-from models.dalle_bart_decoder_flax import DalleBartDecoderFlax
+from min_dalle.models.dalle_bart_decoder_flax import DalleBartDecoderFlax
 
 
 parser = argparse.ArgumentParser()

image_from_text_torch.py

@@ -4,17 +4,17 @@ from torch import Tensor
 import argparse
 from typing import Dict
 
-from image_from_text import (
+from min_dalle.image_from_text import (
     load_dalle_bart_metadata, 
     tokenize,
     detokenize_torch,
     save_image, 
     ascii_from_image
 )
-from models.dalle_bart_encoder_torch import DalleBartEncoderTorch
+from min_dalle.models.dalle_bart_encoder_torch import DalleBartEncoderTorch
-from models.dalle_bart_decoder_torch import DalleBartDecoderTorch
+from min_dalle.models.dalle_bart_decoder_torch import DalleBartDecoderTorch
 
-from load_params import (
+from min_dalle.load_params import (
     load_dalle_bart_flax_params, 
     convert_dalle_bart_torch_from_flax_params
 )

min_dalle/image_from_text.py

@@ -5,9 +5,9 @@ import torch
 from PIL import Image
 from typing import Tuple, List
 
-from text_tokenizer import TextTokenizer
+from .text_tokenizer import TextTokenizer
-from models.vqgan_detokenizer import VQGanDetokenizer
+from .models.vqgan_detokenizer import VQGanDetokenizer
-from load_params import load_vqgan_torch_params
+from .load_params import load_vqgan_torch_params
 
 
 def load_dalle_bart_metadata(path: str) -> Tuple[dict, dict, List[str]]:

min_dalle/image_from_text_flax.py

@@ -0,0 +1,126 @@
+import jax
+from jax import numpy as jnp
+import numpy
+import argparse
+
+from load_params import load_dalle_bart_flax_params
+from image_from_text import (
+    load_dalle_bart_metadata, 
+    tokenize, 
+    detokenize_torch,
+    save_image, 
+    ascii_from_image
+)
+from models.dalle_bart_encoder_flax import DalleBartEncoderFlax
+from models.dalle_bart_decoder_flax import DalleBartDecoderFlax
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument(
+    '--text',
+    help='text to generate image from',
+    type=str
+)
+parser.add_argument(
+    '--seed',
+    help='random seed',
+    type=int,
+    default=0
+)
+parser.add_argument(
+    '--image_path',
+    help='generated image path',
+    type=str,
+    default='generated.png'
+)
+parser.add_argument(
+    '--dalle_bart_path',
+    help='pretraied dalle bart path',
+    type=str,
+    default='./pretrained/dalle_bart_mini'
+)
+parser.add_argument(
+    '--vqgan_path',
+    help='pretraied vqgan path',
+    type=str,
+    default='./pretrained/vqgan'
+)
+
+
+def encode_flax(
+    text_tokens: numpy.ndarray,
+    config: dict, 
+    params: dict
+) -> jnp.ndarray:
+    print("loading flax encoder")
+    encoder: DalleBartEncoderFlax = DalleBartEncoderFlax(
+        attention_head_count = config['encoder_attention_heads'],
+        embed_count = config['d_model'],
+        glu_embed_count = config['encoder_ffn_dim'],
+        text_token_count = config['max_text_length'],
+        text_vocab_count = config['encoder_vocab_size'],
+        layer_count = config['encoder_layers']
+    ).bind({'params': params.pop('encoder')})
+
+    print("encoding text tokens")
+    encoder_state = encoder(text_tokens)
+    del encoder
+    return encoder_state
+
+def decode_flax(
+    text_tokens: jnp.ndarray,
+    encoder_state: jnp.ndarray,
+    config: dict,
+    seed: int,
+    params: dict
+) -> jnp.ndarray:
+    print("loading flax decoder")
+    decoder = DalleBartDecoderFlax(
+        image_token_count = config['image_length'],
+        text_token_count = config['max_text_length'],
+        image_vocab_count = config['image_vocab_size'],
+        attention_head_count = config['decoder_attention_heads'],
+        embed_count = config['d_model'],
+        glu_embed_count = config['decoder_ffn_dim'],
+        layer_count = config['decoder_layers'],
+        start_token = config['decoder_start_token_id']
+    )
+    print("sampling image tokens")
+    image_tokens = decoder.sample_image_tokens(
+        text_tokens,
+        encoder_state,
+        jax.random.PRNGKey(seed),
+        params.pop('decoder')
+    )
+    del decoder
+    return image_tokens
+
+def generate_image_tokens_flax(
+    text: str,
+    seed: int, 
+    dalle_bart_path: str
+) -> numpy.ndarray:
+    config, vocab, merges = load_dalle_bart_metadata(dalle_bart_path)
+    text_tokens = tokenize(text, config, vocab, merges)
+    params_dalle_bart = load_dalle_bart_flax_params(dalle_bart_path)
+    encoder_state = encode_flax(text_tokens, config, params_dalle_bart)
+    image_tokens = decode_flax(
+        text_tokens, 
+        encoder_state, 
+        config, seed, 
+        params_dalle_bart
+    )
+    return numpy.array(image_tokens)
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+
+    image_tokens = generate_image_tokens_flax(
+        args.text, 
+        args.seed, 
+        args.dalle_bart_path
+    )
+    print("image tokens", list(image_tokens))
+    image = detokenize_torch(image_tokens, args.vqgan_path)
+    image = save_image(image, args.image_path)
+    print(ascii_from_image(image, size=128))