Update README.md

README.md

@@ -114,31 +114,95 @@ This is a copy of the dataset uploaded on Hugging Face for easy access. The orig
 ## Usage
 
 ```python
-# usage
-from datasets import load_dataset
-from transformers import CLIPProcessor
+from typing import TypedDict, cast
+
+import pytorch_lightning as pl
+from datasets import Dataset, load_dataset
+from torch import Tensor
 from torch.utils.data import DataLoader
+from transformers import CLIPProcessor
+
+
+class MMSDModelInput(TypedDict):
+    pixel_values: Tensor
+    input_ids: Tensor
+    attention_mask: Tensor
+    label: Tensor
+    id: list[str]
+
+
+class MMSDDatasetModule(pl.LightningDataModule):
+
+    def __init__(
+        self,
+        clip_ckpt_name: str = "openai/clip-vit-base-patch32",
+        dataset_version: str = "mmsd-v2",
+        max_length: int = 77,
+        train_batch_size: int = 32,
+        val_batch_size: int = 32,
+        test_batch_size: int = 32,
+        num_workers: int = 19,
+    ) -> None:
+        super().__init__()
+        self.clip_ckpt_name = clip_ckpt_name
+        self.dataset_version = dataset_version
+        self.train_batch_size = train_batch_size
+        self.val_batch_size = val_batch_size
+        self.test_batch_size = test_batch_size
+        self.num_workers = num_workers
+        self.max_length = max_length
+
+    def setup(self, stage: str) -> None:
+        processor = CLIPProcessor.from_pretrained(self.clip_ckpt_name)
+
+        def preprocess(example):
+            inputs = processor(
+                text=example["text"],
+                images=example["image"],
+                return_tensors="pt",
+                padding="max_length",
+                truncation=True,
+                max_length=self.max_length,
+            )
+
+            return {
+                "pixel_values": inputs["pixel_values"],
+                "input_ids": inputs["input_ids"],
+                "attention_mask": inputs["attention_mask"],
+                "label": example["label"],
+            }
+
+        self.raw_dataset = cast(
+            Dataset,
+            load_dataset("coderchen01/MMSD2.0", name=self.dataset_version),
+        )
+        self.dataset = self.raw_dataset.map(
+            preprocess,
+            batched=True,
+        )
+
+    def train_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.dataset["train"],
+            batch_size=self.train_batch_size,
+            shuffle=True,
+            num_workers=self.num_workers,
+        )
+
+    def val_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.dataset["validation"],
+            batch_size=self.val_batch_size,
+            num_workers=self.num_workers,
+        )
+
+    def test_dataloader(self) -> DataLoader:
+        return DataLoader(
+            self.dataset["test"],
+            batch_size=self.test_batch_size,
+            num_workers=self.num_workers,
+        )
 
-processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
-
-def tokenization(example):
-    inputs = processor(
-        text=example["text"], images=example["image"], return_tensors="pt"
-    )
-    return {
-        "pixel_values": inputs["pixel_values"],
-        "input_ids": inputs["input_ids"],
-        "attention_mask": inputs["attention_mask"],
-        "label": example["label"],
-    }
-
-dataset = load_dataset('coderchen01/MMSD2.0', 'mmsd-v2')
-dataset.set_transform(tokenization)
-
-# get torch dataloader
-train_dl = DataLoader(dataset['train'], batch_size=256, shuffle=True)
-test_dl = DataLoader(dataset['test'], batch_size=256, shuffle=True)
-val_dl = DataLoader(dataset['validation'], batch_size=256, shuffle=True)
 ```
 
 ## References