From c16d5ba5ab1445386efcb87325557d4c784891e6 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sun, 8 Jan 2023 14:42:40 +0100
Subject: Cleanup

---
 data/csv.py | 40 +++++++++++++++++++++++++---------------
 1 file changed, 25 insertions(+), 15 deletions(-)

(limited to 'data')

diff --git a/data/csv.py b/data/csv.py
index 289a64d..eaef5e6 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -40,15 +40,22 @@ def prepare_prompt(prompt: Union[str, dict[str, str]]):
     return {"content": prompt} if isinstance(prompt, str) else prompt
 
 
-def generate_buckets(items: list[str], size: int, num_buckets: int, progressive_buckets: bool):
+def generate_buckets(
+    items: list[str],
+    base_size: int,
+    step_size: int = 64,
+    num_buckets: int = 4,
+    progressive_buckets: bool = False,
+    return_tensor: bool = True
+):
     bucket_items: list[int] = []
     bucket_assignments: list[int] = []
     buckets = [1.0]
 
     for i in range(1, num_buckets + 1):
-        s = size + i * 64
-        buckets.append(s / size)
-        buckets.append(size / s)
+        s = base_size + i * step_size
+        buckets.append(s / base_size)
+        buckets.append(base_size / s)
 
     buckets = torch.tensor(buckets)
     bucket_indices = torch.arange(len(buckets))
@@ -58,9 +65,9 @@ def generate_buckets(items: list[str], size: int, num_buckets: int, progressive_
         ratio = image.width / image.height
 
         if ratio >= 1:
-            mask = torch.bitwise_and(buckets >= 1, buckets <= ratio)
+            mask = torch.logical_and(buckets >= 1, buckets <= ratio)
         else:
-            mask = torch.bitwise_and(buckets <= 1, buckets >= ratio)
+            mask = torch.logical_and(buckets <= 1, buckets >= ratio)
 
         if not progressive_buckets:
             mask = (buckets + (~mask) * math.inf - ratio).abs().argmin()
@@ -73,7 +80,13 @@ def generate_buckets(items: list[str], size: int, num_buckets: int, progressive_
         bucket_items += [i] * len(indices)
         bucket_assignments += indices
 
-    return buckets.tolist(), bucket_items, bucket_assignments
+    if return_tensor:
+        bucket_items = torch.tensor(bucket_items)
+        bucket_assignments = torch.tensor(bucket_assignments)
+    else:
+        buckets = buckets.tolist()
+
+    return buckets, bucket_items, bucket_assignments
 
 
 class VlpnDataItem(NamedTuple):
@@ -256,17 +269,14 @@ class VlpnDataset(IterableDataset):
         self.interpolation = interpolations[interpolation]
         self.generator = generator
 
-        buckets, bucket_items, bucket_assignments = generate_buckets(
+        self.buckets, self.bucket_items, self.bucket_assignments = generate_buckets(
             [item.instance_image_path for item in items],
-            size,
-            num_buckets,
-            progressive_buckets
+            base_size=size,
+            num_buckets=num_buckets,
+            progressive_buckets=progressive_buckets,
         )
 
-        self.buckets = torch.tensor(buckets)
-        self.bucket_items = torch.tensor(bucket_items)
-        self.bucket_assignments = torch.tensor(bucket_assignments)
-        self.bucket_item_range = torch.arange(len(bucket_items))
+        self.bucket_item_range = torch.arange(len(self.bucket_items))
 
         self.length_ = (self.bucket_assignments.bincount() / self.batch_size).ceil().long().sum().item()
 
-- 
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