make training splits into its own pydantic base model, validate it sums to 1, make decoder script cleaner

dalle2_pytorch/train_configs.py

@@ -1,5 +1,5 @@
 from torchvision import transforms as T
-from pydantic import BaseModel, validator
+from pydantic import BaseModel, validator, root_validator
 from typing import List, Iterable, Optional, Union, Tuple, Dict, Any
 
 def exists(val):
@@ -38,6 +38,17 @@ class DecoderConfig(BaseModel):
     class Config:
         extra = "allow"
 
+class TrainSplitConfig(BaseModel):
+    train: float = 0.75
+    val: float = 0.15
+    test: float = 0.1
+
+    @root_validator
+    def validate_all(cls, fields):
+        if sum([*fields.values()]) != 1.:
+            raise ValueError(f'{fields.keys()} must sum to 1.0')
+        return fields
+
 class DecoderDataConfig(BaseModel):
     webdataset_base_url: str     # path to a webdataset with jpg images
     embeddings_url: str          # path to .npy files with embeddings
@@ -47,11 +58,7 @@ class DecoderDataConfig(BaseModel):
     end_shard: int = 9999999
     shard_width: int = 6
     index_width: int = 4
-    splits: Dict[str, float] = {
-        'train': 0.75,
-        'val': 0.15,
-        'test': 0.1
-    }
+    splits: TrainSplitConfig
     shuffle_train: bool = True
     resample_train: bool = False
     preprocessing: Dict[str, Any] = {'ToTensor': True}

setup.py

@@ -10,7 +10,7 @@ setup(
       'dream = dalle2_pytorch.cli:dream'
     ],
   },
-  version = '0.4.0',
+  version = '0.4.1',
   license='MIT',
   description = 'DALL-E 2',
   author = 'Phil Wang',

train_decoder.py

@@ -422,9 +422,9 @@ def initialize_training(config):
     dataloaders = create_dataloaders (
         available_shards=all_shards,
         img_preproc = config.img_preproc,
-        train_prop = config.data["splits"]["train"],
-        val_prop = config.data["splits"]["val"],
-        test_prop = config.data["splits"]["test"],
+        train_prop = config.data.splits.train,
+        val_prop = config.data.splits.val,
+        test_prop = config.data.splits.test,
         n_sample_images=config.train.n_sample_images,
         **config.data.dict()
     )