Don't need to set data loader singleton; formatting tweaks

data/every_dream.py

@@ -16,15 +16,12 @@ limitations under the License.
 import logging
 import torch
 from torch.utils.data import Dataset
-from data.data_loader import DataLoaderMultiAspect as dlma
-import math
-import data.dl_singleton as dls
+from data.data_loader import DataLoaderMultiAspect
 from data.image_train_item import ImageTrainItem
 import random
 from torchvision import transforms
 from transformers import CLIPTokenizer
 import torch.nn.functional as F
-import numpy
 
 class EveryDreamBatch(Dataset):
     """
@@ -38,7 +35,7 @@ class EveryDreamBatch(Dataset):
     jitter: number of pixels to jitter the crop by, only for non-square images
     """
     def __init__(self,
-                 data_loader: dlma,
+                 data_loader: DataLoaderMultiAspect,
                  debug_level=0,
                  conditional_dropout=0.02,
                  crop_jitter=20,
@@ -59,7 +56,6 @@ class EveryDreamBatch(Dataset):
         self.unloaded_to_idx = 0
         self.tokenizer = tokenizer
         self.log_folder = log_folder
-        #print(f"tokenizer: {tokenizer}")
         self.max_token_length = self.tokenizer.model_max_length
         self.retain_contrast = retain_contrast
         self.write_schedule = write_schedule
@@ -67,8 +63,9 @@ class EveryDreamBatch(Dataset):
         self.seed = seed
         self.rated_dataset = rated_dataset
         self.rated_dataset_dropout_target = rated_dataset_dropout_target
-        self.image_train_items = self.data_loader.get_shuffled_image_buckets(1.0) # First epoch always trains on all images
-
+        # First epoch always trains on all images
+        self.image_train_items = self.data_loader.get_shuffled_image_buckets(1.0)
+            
         num_images = len(self.image_train_items)
 
         logging.info(f" ** Trainer Set: {num_images / self.batch_size:.0f}, num_images: {num_images}, batch_size: {self.batch_size}")
@@ -83,20 +80,15 @@ class EveryDreamBatch(Dataset):
                 except Exception as e:
                     logging.error(f" * Error writing to batch schedule for file path: {self.image_train_items[i].pathname}")
 
-    def get_runts():
-        return dls.shared_dataloader.runts
-
     def shuffle(self, epoch_n: int, max_epochs: int):
         self.seed += 1
-        if dls.shared_dataloader:
-            if self.rated_dataset:
-                dropout_fraction = (max_epochs - (epoch_n * self.rated_dataset_dropout_target)) / max_epochs
-            else:
-                dropout_fraction = 1.0
-
-            self.image_train_items = dls.shared_dataloader.get_shuffled_image_buckets(dropout_fraction)
+        
+        if self.rated_dataset:
+            dropout_fraction = (max_epochs - (epoch_n * self.rated_dataset_dropout_target)) / max_epochs
         else:
-            raise Exception("No dataloader singleton to shuffle")
+            dropout_fraction = 1.0
+
+        self.image_train_items = self.data_loader.get_shuffled_image_buckets(dropout_fraction)
 
         if self.write_schedule:
             self.__write_batch_schedule(epoch_n + 1)