Enable batch_size>1 for mixed-sized training

modules/textual_inversion/dataset.py

@@ -3,8 +3,10 @@ import numpy as np
 import PIL
 import torch
 from PIL import Image
-from torch.utils.data import Dataset, DataLoader
+from torch.utils.data import Dataset, DataLoader, Sampler
 from torchvision import transforms
+from collections import defaultdict
+from random import shuffle, choices
 
 import random
 import tqdm
@@ -45,12 +47,12 @@ class PersonalizedBase(Dataset):
         assert data_root, 'dataset directory not specified'
         assert os.path.isdir(data_root), "Dataset directory doesn't exist"
         assert os.listdir(data_root), "Dataset directory is empty"
-        assert batch_size == 1 or not varsize, 'variable img size must have batch size 1'
 
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
 
         self.shuffle_tags = shuffle_tags
         self.tag_drop_out = tag_drop_out
+        groups = defaultdict(list)
 
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
@@ -103,13 +105,14 @@ class PersonalizedBase(Dataset):
             if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):
                 with devices.autocast():
                     entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
-
+            groups[image.size].append(len(self.dataset))
             self.dataset.append(entry)
             del torchdata
             del latent_dist
             del latent_sample
 
         self.length = len(self.dataset)
+        self.groups = list(groups.values())
         assert self.length > 0, "No images have been found in the dataset."
         self.batch_size = min(batch_size, self.length)
         self.gradient_step = min(gradient_step, self.length // self.batch_size)
@@ -137,9 +140,34 @@ class PersonalizedBase(Dataset):
             entry.latent_sample = shared.sd_model.get_first_stage_encoding(entry.latent_dist).to(devices.cpu)
         return entry
 
+class GroupedBatchSampler(Sampler):
+    def __init__(self, data_source: PersonalizedBase, batch_size: int):
+        n = len(data_source)
+        self.groups = data_source.groups
+        self.len = n_batch = n // batch_size
+        expected = [len(g) / n * n_batch * batch_size for g in data_source.groups]
+        self.base = [int(e) // batch_size for e in expected]
+        self.n_rand_batches = nrb = n_batch - sum(self.base)
+        self.probs = [e%batch_size/nrb/batch_size if nrb>0 else 0 for e in expected]
+        self.batch_size = batch_size
+    def __len__(self):
+        return self.len
+    def __iter__(self):
+        b = self.batch_size
+        for g in self.groups:
+            shuffle(g)
+        batches = []
+        for g in self.groups:
+            batches.extend(g[i*b:(i+1)*b] for i in range(len(g) // b))
+        for _ in range(self.n_rand_batches):
+            rand_group = choices(self.groups, self.probs)[0]
+            batches.append(choices(rand_group, k=b))
+        shuffle(batches)
+        yield from batches
+
 class PersonalizedDataLoader(DataLoader):
     def __init__(self, dataset, latent_sampling_method="once", batch_size=1, pin_memory=False):
-        super(PersonalizedDataLoader, self).__init__(dataset, shuffle=True, drop_last=True, batch_size=batch_size, pin_memory=pin_memory)
+        super(PersonalizedDataLoader, self).__init__(dataset, batch_sampler=GroupedBatchSampler(dataset, batch_size), pin_memory=pin_memory)
         if latent_sampling_method == "random":
             self.collate_fn = collate_wrapper_random
         else: