v0.2.0, MinDalleTorch -> MinDalle, breaking change

README.md

@@ -22,12 +22,12 @@ $ pip install min-dalle
 
 ### Python
 
-To load a model once and generate multiple times, first initialize `MinDalleTorch`.
+To load a model once and generate multiple times, first initialize `MinDalle`.
 
 ```python
-from min_dalle import MinDalleTorch
+from min_dalle import MinDalle
 
-model = MinDalleTorch(
+model = MinDalle(
     is_mega=True, 
     is_reusable=True,
     models_root='./pretrained'

image_from_text.py

@@ -2,14 +2,15 @@
 import os
 from PIL import Image
 
-from min_dalle import MinDalleTorch
+from min_dalle import MinDalle
+
 
 parser = argparse.ArgumentParser()
 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('--text', type=str, default='alien life')
-parser.add_argument('--seed', type=int, default=7)
+parser.add_argument('--seed', type=int, default=-1)
 parser.add_argument('--image_path', type=str, default='generated')
 parser.add_argument('--token_count', type=int, default=256) # for debugging
 
@@ -39,7 +40,7 @@ def generate_image(
     image_path: str,
     token_count: int
 ):
-    model = MinDalleTorch(
+    model = MinDalle(
         is_mega=is_mega, 
         models_root='pretrained',
         is_reusable=False,

min_dalle.ipynb

@@ -77,9 +77,9 @@
         }
       ],
       "source": [
-        "from min_dalle import MinDalleTorch\n",
+        "from min_dalle import MinDalle\n",
         "\n",
-        "model = MinDalleTorch(is_mega=True, is_reusable=True)"
+        "model = MinDalle(is_mega=True, is_reusable=True)"
       ]
     },
     {

min_dalle/__init__.py

@@ -1 +1 @@
-from .min_dalle_torch import MinDalleTorch
+from .min_dalle import MinDalle

min_dalle/min_dalle_torch.py → min_dalle/min_dalle.py

@@ -1,6 +1,5 @@
 import os
 from PIL import Image
-from typing import Dict
 import numpy
 from torch import LongTensor
 import torch
@@ -10,16 +9,13 @@
 torch.set_grad_enabled(False)
 torch.set_num_threads(os.cpu_count())
 
+from .text_tokenizer import TextTokenizer
+from .models import DalleBartEncoder, DalleBartDecoder, VQGanDetokenizer
+
 MIN_DALLE_REPO = 'https://huggingface.co/kuprel/min-dalle/resolve/main/'
 
-from .text_tokenizer import TextTokenizer
-from .models import (
-    DalleBartEncoderTorch, 
-    DalleBartDecoderTorch, 
-    VQGanDetokenizer
-)
 
-class MinDalleTorch:
+class MinDalle:
     def __init__(
         self,
         is_mega: bool, 
@@ -104,7 +100,7 @@ def init_encoder(self):
         is_downloaded = os.path.exists(self.encoder_params_path)
         if not is_downloaded: self.download_encoder()
         print("initializing DalleBartEncoderTorch")
-        self.encoder = DalleBartEncoderTorch(
+        self.encoder = DalleBartEncoder(
             attention_head_count = self.attention_head_count,
             embed_count = self.embed_count,
             glu_embed_count = self.glu_embed_count,
@@ -122,7 +118,7 @@ def init_decoder(self):
         is_downloaded = os.path.exists(self.decoder_params_path)
         if not is_downloaded: self.download_decoder()
         print("initializing DalleBartDecoderTorch")
-        self.decoder = DalleBartDecoderTorch(
+        self.decoder = DalleBartDecoder(
             sample_token_count = self.sample_token_count,
             image_token_count = self.image_token_count,
             image_vocab_count = self.image_vocab_count,

min_dalle/models/__init__.py

@@ -1,3 +1,3 @@
-from .dalle_bart_encoder_torch import DalleBartEncoderTorch
-from .dalle_bart_decoder_torch import DalleBartDecoderTorch
+from .dalle_bart_encoder import DalleBartEncoder
+from .dalle_bart_decoder import DalleBartDecoder
 from .vqgan_detokenizer import VQGanDetokenizer

min_dalle/models/dalle_bart_decoder_torch.py → min_dalle/models/dalle_bart_decoder.py

@@ -3,10 +3,10 @@
 from torch import LongTensor, nn, FloatTensor, BoolTensor
 torch.set_grad_enabled(False)
 
-from .dalle_bart_encoder_torch import GLUTorch, AttentionTorch
+from .dalle_bart_encoder import GLU, AttentionBase
 
 
-class DecoderCrossAttentionTorch(AttentionTorch):
+class DecoderCrossAttention(AttentionBase):
     def forward(
         self,
         decoder_state: FloatTensor,
@@ -19,7 +19,7 @@ def forward(
         return super().forward(keys, values, queries, attention_mask)
 
 
-class DecoderSelfAttentionTorch(AttentionTorch):
+class DecoderSelfAttention(AttentionBase):
     def forward(
         self, 
         decoder_state: FloatTensor,
@@ -42,7 +42,7 @@ def forward(
         return decoder_state, attention_state
 
 
-class DecoderLayerTorch(nn.Module):
+class DecoderLayer(nn.Module):
     def __init__(
         self, 
         image_token_count: int,
@@ -53,12 +53,12 @@ def __init__(
         super().__init__()
         self.image_token_count = image_token_count
         self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count)
-        self.self_attn = DecoderSelfAttentionTorch(head_count, embed_count)
+        self.self_attn = DecoderSelfAttention(head_count, embed_count)
         self.self_attn_layer_norm = nn.LayerNorm(embed_count)
         self.pre_encoder_attn_layer_norm = nn.LayerNorm(embed_count)
-        self.encoder_attn = DecoderCrossAttentionTorch(head_count, embed_count)
+        self.encoder_attn = DecoderCrossAttention(head_count, embed_count)
         self.encoder_attn_layer_norm = nn.LayerNorm(embed_count)
-        self.glu = GLUTorch(embed_count, glu_embed_count)
+        self.glu = GLU(embed_count, glu_embed_count)
 
         self.token_indices = torch.arange(self.image_token_count)
         if torch.cuda.is_available():
@@ -106,7 +106,7 @@ def forward(
         return decoder_state, attention_state
 
 
-class DalleBartDecoderTorch(nn.Module):
+class DalleBartDecoder(nn.Module):
     def __init__(
         self,
         image_vocab_count: int,
@@ -126,8 +126,8 @@ def __init__(
         self.image_token_count = image_token_count
         self.embed_tokens = nn.Embedding(image_vocab_count + 1, embed_count)
         self.embed_positions = nn.Embedding(image_token_count, embed_count)
-        self.layers: List[DecoderLayerTorch] = nn.ModuleList([
-            DecoderLayerTorch(
+        self.layers: List[DecoderLayer] = nn.ModuleList([
+            DecoderLayer(
                 image_token_count,
                 attention_head_count,
                 embed_count,

min_dalle/models/dalle_bart_encoder_torch.py → min_dalle/models/dalle_bart_encoder.py

@@ -4,7 +4,7 @@
 torch.set_grad_enabled(False)
 
 
-class GLUTorch(nn.Module):
+class GLU(nn.Module):
     def __init__(self, count_in_out, count_middle):
         super().__init__()
         self.gelu = nn.GELU()
@@ -24,7 +24,7 @@ def forward(self, z: FloatTensor) -> FloatTensor:
         return z
 
 
-class AttentionTorch(nn.Module):
+class AttentionBase(nn.Module):
     def __init__(self, head_count: int, embed_count: int):
         super().__init__()
         self.head_count = head_count
@@ -72,7 +72,7 @@ def forward(
         return attention_output
 
 
-class EncoderSelfAttentionTorch(AttentionTorch):
+class EncoderSelfAttention(AttentionBase):
     def forward(
         self,
         encoder_state: FloatTensor,
@@ -84,13 +84,13 @@ def forward(
         return super().forward(keys, values, queries, attention_mask)
 
 
-class EncoderLayerTorch(nn.Module):
+class EncoderLayer(nn.Module):
     def __init__(self, embed_count: int, head_count: int, glu_embed_count: int):
         super().__init__()
         self.pre_self_attn_layer_norm = nn.LayerNorm(embed_count)
-        self.self_attn = EncoderSelfAttentionTorch(head_count, embed_count)
+        self.self_attn = EncoderSelfAttention(head_count, embed_count)
         self.self_attn_layer_norm = nn.LayerNorm(embed_count)
-        self.glu = GLUTorch(embed_count, glu_embed_count)
+        self.glu = GLU(embed_count, glu_embed_count)
 
     def forward(
         self,
@@ -108,7 +108,7 @@ def forward(
         return encoder_state
 
 
-class DalleBartEncoderTorch(nn.Module):
+class DalleBartEncoder(nn.Module):
     def __init__(
         self,
         layer_count: int,
@@ -121,8 +121,8 @@ def __init__(
         super().__init__()
         self.embed_tokens = nn.Embedding(text_vocab_count, embed_count)
         self.embed_positions = nn.Embedding(text_token_count, embed_count)
-        self.layers: List[EncoderLayerTorch] = nn.ModuleList([
-            EncoderLayerTorch(
+        self.layers: List[EncoderLayer] = nn.ModuleList([
+            EncoderLayer(
                 embed_count = embed_count,
                 head_count = attention_head_count,
                 glu_embed_count = glu_embed_count

replicate/predict.py

@@ -1,11 +1,11 @@
 import tempfile
 from cog import BasePredictor, Path, Input
 
-from min_dalle.min_dalle_torch import MinDalleTorch
+from min_dalle import MinDalle
 
 class Predictor(BasePredictor):
     def setup(self):
-        self.model = MinDalleTorch(is_mega=True)
+        self.model = MinDalle(is_mega=True)
 
     def predict(
         self,

setup.py

@@ -3,9 +3,9 @@
 setuptools.setup(
     name='min-dalle',
     description = 'min(DALL·E)',
-    version='0.1.4',
+    version='0.2.0',
     author='Brett Kuprel',
-    author_email = '[email protected]',
+    author_email='[email protected]',
     packages=[
         'min_dalle', 
         'min_dalle.models'
@@ -18,6 +18,7 @@
     keywords = [
         'artificial intelligence',
         'deep learning',
-        'text to image'
+        'text-to-image',
+        'pytorch'
     ]
 )