add dataloader plugin hooks for caption and pil image

data/every_dream.py

@@ -42,6 +42,7 @@ class EveryDreamBatch(Dataset):
                  tokenizer=None,
                  shuffle_tags=False,
                  keep_tags=0,
+                 plugin_runner=None,
                  rated_dataset=False,
                  rated_dataset_dropout_target=0.5,
                  name='train'
@@ -56,6 +57,7 @@ class EveryDreamBatch(Dataset):
         self.max_token_length = self.tokenizer.model_max_length
         self.shuffle_tags = shuffle_tags
         self.keep_tags = keep_tags
+        self.plugin_runner = plugin_runner
         self.seed = seed
         self.rated_dataset = rated_dataset
         self.rated_dataset_dropout_target = rated_dataset_dropout_target
@@ -101,6 +103,8 @@ class EveryDreamBatch(Dataset):
             example["caption"] = train_item["caption"].get_caption()
 
         example["image"] = image_transforms(train_item["image"])
+        example["image"] = self.plugin_runner.transform_pil_image(example["image"])
+        example["caption"] = self.plugin_runner.transform_caption(example["caption"])
 
         if random.random() > (train_item.get("cond_dropout", self.conditional_dropout)):
             example["tokens"] = self.tokenizer(example["caption"],

plugins/plugins.py

@@ -3,6 +3,7 @@ import importlib
 import logging
 import time
 import warnings
+from PIL import Image
 
 class BasePlugin:
     def on_epoch_start(self, **kwargs):
@@ -17,8 +18,10 @@ class BasePlugin:
         pass
     def on_step_end(self, **kwargs):
         pass
-
-
+    def transform_caption(self, caption:str):
+        return caption
+    def transform_pil_image(self, img:Image):
+        return img
 
 def load_plugin(plugin_path):
     print(f" - Attempting to load plugin: {plugin_path}")
@@ -89,3 +92,15 @@ class PluginRunner:
         for plugin in self.plugins:
             with Timer(warn_seconds=self.step_warn_seconds, label=f'{plugin.__class__.__name__}'):
                 plugin.on_step_end(**kwargs)
+    
+    def run_transform_caption(self, caption):
+        with Timer(warn_seconds=self.step_warn_seconds, label="plugin.transform_caption"):
+            for plugin in self.plugins:
+                caption = plugin.transform_caption(caption)
+        return caption
+
+    def run_transform_pil_image(self, img):
+        with Timer(warn_seconds=self.step_warn_seconds, label="plugin.transform_pil_image"):
+            for plugin in self.plugins:
+                img = plugin.transform_pil_image(img)
+        return img

train.py

@@ -775,6 +775,16 @@ def main(args):
 
     report_image_train_item_problems(log_folder, image_train_items, batch_size=args.batch_size)
 
+    from plugins.plugins import load_plugin
+    if args.plugins is not None:
+        plugins = [load_plugin(name) for name in args.plugins]
+    else:
+        logging.info("No plugins specified")
+        plugins = []
+
+    from plugins.plugins import PluginRunner
+    plugin_runner = PluginRunner(plugins=plugins)
+
     data_loader = DataLoaderMultiAspect(
         image_train_items=image_train_items,
         seed=seed,
@@ -790,6 +800,7 @@ def main(args):
         seed = seed,
         shuffle_tags=args.shuffle_tags,
         keep_tags=args.keep_tags,
+        plugin_runner=plugin_runner,
         rated_dataset=args.rated_dataset,
         rated_dataset_dropout_target=(1.0 - (args.rated_dataset_target_dropout_percent / 100.0))
     )
@@ -1082,16 +1093,6 @@ def main(args):
         _, batch = next(enumerate(train_dataloader))
         generate_samples(global_step=0, batch=batch)
 
-    from plugins.plugins import load_plugin
-    if args.plugins is not None:
-        plugins = [load_plugin(name) for name in args.plugins]
-    else:
-        logging.info("No plugins specified")
-        plugins = []
-
-    from plugins.plugins import PluginRunner
-    plugin_runner = PluginRunner(plugins=plugins)
-
     def make_current_ed_state() -> EveryDreamTrainingState:
         return EveryDreamTrainingState(optimizer=ed_optimizer,
                                        train_batch=train_batch,