fixed an issue with argument parser

README.md

@@ -4,27 +4,26 @@ This is a minimal implementation of [DALL·E Mini](https://github.com/borisdayma
 
 ### Setup
 
-Run `sh setup.sh` to install dependencies and download pretrained models.  The only required dependencies are `flax` and `torch`.  In the bash script, GitHub LFS is used to download the VQGan detokenizer and the Weight & Biases python package is used to download the DALL·E Mini and DALL·E Mega transformer models.  You can also download those files manually by visting the links in the bash script.
+Run `sh setup.sh` to install dependencies and download pretrained models.  The only required dependencies are `flax` and `torch`.  In the bash script, GitHub LFS is used to download the VQGan detokenizer and the Weight & Biases python package is used to download the DALL·E Mini and DALL·E Mega transformer models.  Those files can also be downloaded manually by visting the links in the bash script.
 
 ### Run
 
 Here are some examples
 
 ```
-python3 image_from_text.py --text='alien life' --seed=7
+python image_from_text.py --seed=7 --text='alien life'
 ```
 ![Alien](examples/alien.png)
 
+
 ```
-python3 image_from_text.py --mega --seed=4 \
-  --text='a comfy chair that looks like an avocado'
+python image_from_text.py --mega --seed=4 --text='a comfy chair that looks like an avocado'
 ```
 ![Avocado Armchair](examples/avocado_armchair.png)
 
 
 ```
-python3 image_from_text.py --mega --seed=100 \
-  --text='court sketch of godzilla on trial'
+python image_from_text.py --mega --seed=100 --text='court sketch of godzilla on trial'
 ```
 
 ![Godzilla Trial](examples/godzilla_trial.png)

image_from_text.py

@@ -1,66 +1,21 @@
 import argparse
 import os
-import json
-import numpy
 from PIL import Image
-from typing import Tuple, List
 
-from min_dalle.load_params import load_dalle_bart_flax_params
-from min_dalle.text_tokenizer import TextTokenizer
-from min_dalle.min_dalle_flax import generate_image_tokens_flax
-from min_dalle.min_dalle_torch import (
-    generate_image_tokens_torch,
-    detokenize_torch
-)
+from min_dalle.generate_image import generate_image_from_text
 
 
 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(
-    '--mega',
-    help='use larger dalle mega model',
-    action=argparse.BooleanOptionalAction
-)
-parser.add_argument(
-    '--torch',
-    help='use torch transformers',
-    action=argparse.BooleanOptionalAction
-)
-parser.add_argument(
-    '--image_path',
-    help='path to save generated image',
-    type=str,
-    default='generated.png'
-)
-parser.add_argument(
-    '--image_token_count',
-    help='number of image tokens to generate (for debugging)',
-    type=int,
-    default=256
-)
-
-
-def load_dalle_bart_metadata(path: str) -> Tuple[dict, dict, List[str]]:
-    print("loading model")
-    for f in ['config.json', 'flax_model.msgpack', 'vocab.json', 'merges.txt']:
-        assert(os.path.exists(os.path.join(path, f)))
-    with open(path + '/config.json', 'r') as f: 
-        config = json.load(f)
-    with open(path + '/vocab.json') as f:
-        vocab = json.load(f)
-    with open(path + '/merges.txt') as f:
-        merges = f.read().split("\n")[1:-1]
-    return config, vocab, merges
+parser.add_argument('--mega', action='store_true')
+parser.add_argument('--no-mega', dest='mega', action='store_false')
+parser.set_defaults(mega=False)
+parser.add_argument('--torch', action='store_true')
+parser.add_argument('--no-torch', dest='torch', action='store_false')
+parser.set_defaults(torch=False)
+parser.add_argument('--text', type=str)
+parser.add_argument('--seed', type=int, default=0)
+parser.add_argument('--image_path', type=str, default='generated')
+parser.add_argument('--image_token_count', type=int, default=256) # for debugging
 
 
 def ascii_from_image(image: Image.Image, size: int) -> str:
@@ -71,59 +26,29 @@ def ascii_from_image(image: Image.Image, size: int) -> str:
     return '\n'.join(''.join(row) for row in chars)
 
 
-def save_image(image: numpy.ndarray, path: str) -> Image.Image:
+def save_image(image: Image.Image, path: str):
     if os.path.isdir(path):
         path = os.path.join(path, 'generated.png')
     elif not path.endswith('.png'):
         path += '.png'
     print("saving image to", path)
-    image: Image.Image = Image.fromarray(numpy.asarray(image))
     image.save(path)
     return image
 
 
-def tokenize_text(
-    text: str, 
-    config: dict,
-    vocab: dict,
-    merges: List[str]
-) -> numpy.ndarray:
-    print("tokenizing text")
-    tokens = TextTokenizer(vocab, merges)(text)
-    print("text tokens", tokens)
-    text_tokens = numpy.ones((2, config['max_text_length']), dtype=numpy.int32)
-    text_tokens[0, :len(tokens)] = tokens
-    text_tokens[1, :2] = [tokens[0], tokens[-1]]
-    return text_tokens
-
-
 if __name__ == '__main__':
     args = parser.parse_args()
 
-    model_name = 'mega' if args.mega == True else 'mini'
-    model_path = './pretrained/dalle_bart_{}'.format(model_name)
-    config, vocab, merges = load_dalle_bart_metadata(model_path)
-    text_tokens = tokenize_text(args.text, config, vocab, merges)
-    params_dalle_bart = load_dalle_bart_flax_params(model_path)
-
-    image_tokens = numpy.zeros(config['image_length'])
-    if args.torch == True:
-        image_tokens[:args.image_token_count] = generate_image_tokens_torch(
-            text_tokens = text_tokens,
-            seed = args.seed,
-            config = config,
-            params = params_dalle_bart,
-            image_token_count = args.image_token_count
-        )
-    else:
-        image_tokens[...] = generate_image_tokens_flax(
-            text_tokens = text_tokens, 
-            seed = args.seed,
-            config = config,
-            params = params_dalle_bart,
-        )
-
-    if args.image_token_count == config['image_length']:
-        image = detokenize_torch(image_tokens)
-        image = save_image(image, args.image_path)
+    print(args)
+
+    image = generate_image_from_text(
+        text = args.text,
+        is_mega = args.mega,
+        is_torch = args.torch,
+        seed = args.seed,
+        image_token_count = args.image_token_count
+    )
+    
+    if image != None:
+        save_image(image, args.image_path)
         print(ascii_from_image(image, size=128))

min_dalle/generate_image.py

@@ -0,0 +1,77 @@
+import os
+import json
+import numpy
+from PIL import Image
+from typing import Tuple, List
+
+from min_dalle.load_params import load_dalle_bart_flax_params
+from min_dalle.text_tokenizer import TextTokenizer
+from min_dalle.min_dalle_flax import generate_image_tokens_flax
+from min_dalle.min_dalle_torch import (
+    generate_image_tokens_torch,
+    detokenize_torch
+)
+
+def load_dalle_bart_metadata(path: str) -> Tuple[dict, dict, List[str]]:
+    print("parsing metadata from {}".format(path))
+    for f in ['config.json', 'flax_model.msgpack', 'vocab.json', 'merges.txt']:
+        assert(os.path.exists(os.path.join(path, f)))
+    with open(path + '/config.json', 'r') as f: 
+        config = json.load(f)
+    with open(path + '/vocab.json') as f:
+        vocab = json.load(f)
+    with open(path + '/merges.txt') as f:
+        merges = f.read().split("\n")[1:-1]
+    return config, vocab, merges
+
+
+def tokenize_text(
+    text: str, 
+    config: dict,
+    vocab: dict,
+    merges: List[str]
+) -> numpy.ndarray:
+    print("tokenizing text")
+    tokens = TextTokenizer(vocab, merges)(text)
+    print("text tokens", tokens)
+    text_tokens = numpy.ones((2, config['max_text_length']), dtype=numpy.int32)
+    text_tokens[0, :len(tokens)] = tokens
+    text_tokens[1, :2] = [tokens[0], tokens[-1]]
+    return text_tokens
+
+
+def generate_image_from_text(
+    text: str,
+    is_mega: bool = False,
+    is_torch: bool = False,
+    seed: int = 0,
+    image_token_count: int = 256
+) -> Image.Image:
+    model_name = 'mega' if is_mega else 'mini'
+    model_path = './pretrained/dalle_bart_{}'.format(model_name)
+    config, vocab, merges = load_dalle_bart_metadata(model_path)
+    text_tokens = tokenize_text(text, config, vocab, merges)
+    params_dalle_bart = load_dalle_bart_flax_params(model_path)
+
+    image_tokens = numpy.zeros(config['image_length'])
+    if is_torch:
+        image_tokens[:image_token_count] = generate_image_tokens_torch(
+            text_tokens = text_tokens,
+            seed = seed,
+            config = config,
+            params = params_dalle_bart,
+            image_token_count = image_token_count
+        )
+    else:
+        image_tokens[...] = generate_image_tokens_flax(
+            text_tokens = text_tokens, 
+            seed = seed,
+            config = config,
+            params = params_dalle_bart,
+        )
+
+    if image_token_count == config['image_length']:
+        image = detokenize_torch(image_tokens)
+        return Image.fromarray(image)
+    else:
+        return None