add midjourney dataset

moondream/test.py

@@ -2,6 +2,8 @@ import torch
 import datasets
 import transformers
 import pathlib
+from PIL import Image
+from tqdm import tqdm
 
 DEVICE = "cuda"
 DTYPE = torch.float32 if DEVICE == "cpu" else torch.float16 # CPU doesn't support float16
@@ -40,12 +42,38 @@ flickr_dataset = datasets.load_dataset("nlphuji/flickr30k", split="test")\
                              }
                          })
 
+midjourney_dataset = datasets.load_dataset("ehristoforu/midjourney-images", split="train", streaming=True)\
+                             .select_columns(["image"])\
+                             .map(lambda row: {
+                                 **row,
+                                 "qa": {
+                                     "question": "Describe this image.",
+                                     "answer": "This is an AI image."
+                                 }
+                             })
+
 dataset = datasets.concatenate_datasets([diffusion_db_dataset, flickr_dataset]).shuffle()
 
 pathlib.Path("./samples").mkdir(parents=True, exist_ok=True)
 
-for i, sample in enumerate(dataset):
-    sample['image'].save(f"samples/{i}.png", "PNG")
+img = Image.open("samples/frames_3.jpg")
+md_answer = moondream.answer_question(
+    moondream.encode_image(img),
+    "Describe this image.",
+    tokenizer=tokenizer,
+    num_beams=4,
+    no_repeat_ngram_size=5,
+    early_stopping=True,
+)
+
+print(md_answer)
+
+correct_predictions = 0
+for i, sample in enumerate(midjourney_dataset):
+    if i > 4:
+        break
+
+    sample["image"].save(f"samples/{i}.png", "PNG")
 
     md_answer = moondream.answer_question(
         moondream.encode_image(sample['image']),
@@ -56,9 +84,12 @@ for i, sample in enumerate(dataset):
         early_stopping=True
     )
 
-    if i < 3:
-        print('Question:', sample['qa']['question'])
-        print('Ground Truth:', sample['qa']['answer'])
-        print('Moondream:', md_answer)
-    else:
-        break
+    print(f"Question: {sample['qa']['question']}")
+    print(f"Ground truth: {sample['qa']['answer']}")
+    print(f"Moondream: {md_answer}")
+    print()
+
+    if md_answer.lower() == sample['qa']['answer'].lower():
+        correct_predictions += 1
+
+print(f"Accuracy: {correct_predictions * 100 / 10}%")

moondream/train.py

@@ -3,6 +3,7 @@ import torch
 import datasets
 import transformers
 import bitsandbytes
+import pathlib
 from tqdm import tqdm
 from .hyperparams import TEST_SIZE, ANSWER_EOS, IMG_TOKENS, LR, BATCH_SIZE, EPOCHS, GRAD_ACCUM_STEPS
 
@@ -22,7 +23,7 @@ diffusion_db_dataset = datasets.load_dataset("poloclub/diffusiondb", "2m_random_
                                .train_test_split(test_size=TEST_SIZE)
 
 flickr_dataset = datasets.load_dataset("nlphuji/flickr30k", split="test")\
-                         .take(5000)\
+                         .take(2500)\
                          .select_columns(["image"])\
                          .map(lambda row: {
                              **row,
@@ -33,8 +34,28 @@ flickr_dataset = datasets.load_dataset("nlphuji/flickr30k", split="test")\
                          })\
                          .train_test_split(test_size=TEST_SIZE)
 
-training_dataset = datasets.concatenate_datasets([diffusion_db_dataset["train"], flickr_dataset["train"]]).shuffle()
-test_dataset = datasets.concatenate_datasets([diffusion_db_dataset["test"], flickr_dataset["test"]]).shuffle()
+wiki_art_dataset = datasets.load_dataset("huggan/wikiart", split="train")\
+                           .take(2500)\
+                           .select_columns(["image"])\
+                           .map(lambda row: {
+                               **row,
+                               "qa": {
+                                   "question": "Describe thie image.",
+                                   "answer": "This is a real image."
+                               }
+                           })\
+                           .train_test_split(test_size=TEST_SIZE)
+
+training_dataset = datasets.concatenate_datasets([
+    diffusion_db_dataset["train"],
+    flickr_dataset["train"],
+    wiki_art_dataset["train"],
+]).shuffle()
+test_dataset = datasets.concatenate_datasets([
+    diffusion_db_dataset["test"],
+    flickr_dataset["test"],
+    wiki_art_dataset["test"],
+]).shuffle()
 
 tokenizer = transformers.AutoTokenizer.from_pretrained("vikhyatk/moondream2")
 moondream = transformers.AutoModelForCausalLM.from_pretrained(
@@ -131,7 +152,7 @@ dataloaders = {
         batch_size=BATCH_SIZE,
         shuffle=True,
         collate_fn=collate,
-    )
+    ),
 }
 
 moondream.text_model.train()
@@ -162,3 +183,27 @@ for epoch in range(EPOCHS):
                 param_group["lr"] = lr
 
 moondream.save_pretrained("checkpoints/moondream-mai")
+
+pathlib.Path("./samples").mkdir(parents=True, exist_ok=True)
+
+correct_predictions = 0
+for sample in tqdm(test_dataset, desc="Validation"):
+    md_answer = moondream.answer_question(
+        moondream.encode_image(sample['image']),
+        sample['qa']['question'],
+        tokenizer=tokenizer,
+        num_beams=4,
+        no_repeat_ngram_size=5,
+        early_stopping=True
+    )
+
+    ground_truth = sample["qa"]["answer"]
+    if md_answer == ground_truth:
+        correct_predictions += 1
+
+    if i % 10 == 0:
+        print(f"Question: f{sample["qa"]["answer"]")
+
+accuracy = correct_predictions * 100 / len(test_dataset)
+
+print(f"Model accuracy: f{accuracy}%")