From baba91864a45939cef4f77f6ca96ade7ae5ef274 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Mon, 24 Oct 2022 23:46:18 +0200
Subject: Advanced datasets

---
 data/csv.py | 64 +++++++++++++++++++++++++++++++++++++------------------------
 1 file changed, 39 insertions(+), 25 deletions(-)

(limited to 'data')

diff --git a/data/csv.py b/data/csv.py
index 5144c0a..f9b5e39 100644
--- a/data/csv.py
+++ b/data/csv.py
@@ -1,16 +1,20 @@
 import math
-import pandas as pd
 import torch
+import json
 from pathlib import Path
 import pytorch_lightning as pl
 from PIL import Image
 from torch.utils.data import Dataset, DataLoader, random_split
 from torchvision import transforms
-from typing import NamedTuple, List, Optional
+from typing import Dict, NamedTuple, List, Optional, Union
 
 from models.clip.prompt import PromptProcessor
 
 
+def prepare_prompt(prompt: Union[str, Dict[str, str]]):
+    return {"content": prompt} if isinstance(prompt, str) else prompt
+
+
 class CSVDataItem(NamedTuple):
     instance_image_path: Path
     class_image_path: Path
@@ -60,24 +64,32 @@ class CSVDataModule(pl.LightningDataModule):
         self.collate_fn = collate_fn
         self.batch_size = batch_size
 
-    def prepare_subdata(self, data, num_class_images=1):
+    def prepare_subdata(self, template, data, num_class_images=1):
+        image = template["image"] if "image" in template else "{}"
+        prompt = template["prompt"] if "prompt" in template else "{content}"
+        nprompt = template["nprompt"] if "nprompt" in template else "{content}"
+
         image_multiplier = max(math.ceil(num_class_images / len(data)), 1)
 
         return [
             CSVDataItem(
-                self.data_root.joinpath(item.image),
-                self.class_root.joinpath(f"{Path(item.image).stem}_{i}{Path(item.image).suffix}"),
-                item.prompt,
-                item.nprompt
+                self.data_root.joinpath(image.format(item["image"])),
+                self.class_root.joinpath(f"{Path(item['image']).stem}_{i}{Path(item['image']).suffix}"),
+                prompt.format(**prepare_prompt(item["prompt"] if "prompt" in item else "")),
+                nprompt.format(**prepare_prompt(item["nprompt"] if "nprompt" in item else ""))
             )
             for item in data
             for i in range(image_multiplier)
         ]
 
     def prepare_data(self):
-        metadata = pd.read_json(self.data_file)
-        metadata = [item for item in metadata.itertuples() if not hasattr(item, "skip") or item.skip != True]
-        num_images = len(metadata)
+        with open(self.data_file, 'rt') as f:
+            metadata = json.load(f)
+        template = metadata["template"] if "template" in metadata else {}
+        items = metadata["items"] if "items" in metadata else []
+
+        items = [item for item in items if not "skip" in item or item["skip"] != True]
+        num_images = len(items)
 
         valid_set_size = int(num_images * 0.2)
         if self.valid_set_size:
@@ -85,10 +97,10 @@ class CSVDataModule(pl.LightningDataModule):
         valid_set_size = max(valid_set_size, 1)
         train_set_size = num_images - valid_set_size
 
-        data_train, data_val = random_split(metadata, [train_set_size, valid_set_size], self.generator)
+        data_train, data_val = random_split(items, [train_set_size, valid_set_size], self.generator)
 
-        self.data_train = self.prepare_subdata(data_train, self.num_class_images)
-        self.data_val = self.prepare_subdata(data_val)
+        self.data_train = self.prepare_subdata(template, data_train, self.num_class_images)
+        self.data_val = self.prepare_subdata(template, data_val)
 
     def setup(self, stage=None):
         train_dataset = CSVDataset(self.data_train, self.prompt_processor, batch_size=self.batch_size,
@@ -133,8 +145,8 @@ class CSVDataset(Dataset):
         self.instance_identifier = instance_identifier
         self.class_identifier = class_identifier
         self.num_class_images = num_class_images
-        self.cache = {}
         self.image_cache = {}
+        self.input_id_cache = {}
 
         self.num_instance_images = len(self.data)
         self._length = self.num_instance_images * repeats
@@ -168,12 +180,19 @@ class CSVDataset(Dataset):
 
         return image
 
+    def get_input_ids(self, prompt, identifier):
+        prompt = prompt.format(identifier)
+
+        if prompt in self.input_id_cache:
+            return self.input_id_cache[prompt]
+
+        input_ids = self.prompt_processor.get_input_ids(prompt)
+        self.input_id_cache[prompt] = input_ids
+
+        return input_ids
+
     def get_example(self, i):
         item = self.data[i % self.num_instance_images]
-        cache_key = f"{item.instance_image_path}_{item.class_image_path}"
-
-        if cache_key in self.cache:
-            return self.cache[cache_key]
 
         example = {}
 
@@ -181,17 +200,12 @@ class CSVDataset(Dataset):
         example["nprompts"] = item.nprompt
 
         example["instance_images"] = self.get_image(item.instance_image_path)
-        example["instance_prompt_ids"] = self.prompt_processor.get_input_ids(
-            item.prompt.format(self.instance_identifier)
-        )
+        example["instance_prompt_ids"] = self.get_input_ids(item.prompt, self.instance_identifier)
 
         if self.num_class_images != 0:
             example["class_images"] = self.get_image(item.class_image_path)
-            example["class_prompt_ids"] = self.prompt_processor.get_input_ids(
-                item.nprompt.format(self.class_identifier)
-            )
+            example["class_prompt_ids"] = self.get_input_ids(item.nprompt, self.class_identifier)
 
-        self.cache[cache_key] = example
         return example
 
     def __getitem__(self, i):
-- 
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