increase data diversity
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41
moondream/siglip.py
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41
moondream/siglip.py
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import transformers
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import torch
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import datasets
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import sklearn
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device = torch.device('cuda' if torch.cuda.is_available() else "cpu")
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model = transformers.AutoModel.from_pretrained("google/siglip-base-patch16-224").to(device)
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processor = transformers.AutoProcessor.from_pretrained("google/siglip-base-patch16-224")
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nn = sklearn.neighbors.NearestNeighbors(metric="euclidean", radius=1.0)
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ds = datasets.load_dataset("ehristoforu/midjourney-images", split="train", trust_remote_code=True, streaming=True)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Describe this image.",
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"answer": "This is an AI image."
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}
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})\
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.take(500)
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with torch.no_grad():
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inputs = processor(images=[row["image"] for row in ds], return_tensors="pt").to(device)
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image_features = model.get_image_features(**inputs).cpu()
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nn.fit(image_features)
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used_indices = set()
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unique_indices = []
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for i, row in enumerate(ds):
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if i in used_indices:
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continue
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feature = image_features[i]
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neighbors = nn.radius_neighbors([feature], radius=1.0, return_distance=False)[0]
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unique_indices.append(i)
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used_indices.update(neighbors)
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print(len(unique_indices))
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@@ -18,45 +18,9 @@ moondream = transformers.AutoModelForCausalLM.from_pretrained(
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device_map={"": DEVICE},
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)
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diffusion_db_dataset = datasets.load_dataset("poloclub/diffusiondb", "2m_random_5k", trust_remote_code=True, split="train")\
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.shuffle()\
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.take(100)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Describe this image.",
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"answer": "This is an AI image."
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}
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})
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flickr_dataset = datasets.load_dataset("nlphuji/flickr30k", split="test")\
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.shuffle()\
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.take(100)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Describe this image.",
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"answer": "This is a real image."
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}
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})
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midjourney_dataset = datasets.load_dataset("ehristoforu/midjourney-images", split="train", streaming=True)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Describe this image.",
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"answer": "This is an AI image."
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}
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})
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dataset = datasets.concatenate_datasets([diffusion_db_dataset, flickr_dataset]).shuffle()
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pathlib.Path("./samples").mkdir(parents=True, exist_ok=True)
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img = Image.open("samples/frames_3.jpg")
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img = Image.open("samples/Untitled.jpg")
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md_answer = moondream.answer_question(
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moondream.encode_image(img),
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"Describe this image.",
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@@ -68,28 +32,28 @@ md_answer = moondream.answer_question(
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print(md_answer)
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correct_predictions = 0
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for i, sample in enumerate(midjourney_dataset):
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if i > 4:
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break
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# correct_predictions = 0
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# for i, sample in enumerate(flickr_dataset):
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# if i > 4:
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# break
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sample["image"].save(f"samples/{i}.png", "PNG")
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# sample["image"].save(f"samples/{i}.png", "PNG")
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md_answer = moondream.answer_question(
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moondream.encode_image(sample['image']),
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sample['qa']['question'],
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tokenizer=tokenizer,
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num_beams=4,
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no_repeat_ngram_size=5,
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early_stopping=True
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)
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# md_answer = moondream.answer_question(
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# moondream.encode_image(sample['image']),
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# sample['qa']['question'],
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# tokenizer=tokenizer,
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# num_beams=4,
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# no_repeat_ngram_size=5,
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# early_stopping=True
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# )
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print(f"Question: {sample['qa']['question']}")
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print(f"Ground truth: {sample['qa']['answer']}")
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print(f"Moondream: {md_answer}")
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print()
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# print(f"Question: {sample['qa']['question']}")
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# print(f"Ground truth: {sample['qa']['answer']}")
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# print(f"Moondream: {md_answer}")
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# print()
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if md_answer.lower() == sample['qa']['answer'].lower():
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correct_predictions += 1
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# if md_answer.lower() == sample['qa']['answer'].lower():
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# correct_predictions += 1
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print(f"Accuracy: {correct_predictions * 100 / 10}%")
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# print(f"Accuracy: {correct_predictions * 100 / 10}%")
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@@ -4,58 +4,114 @@ import datasets
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import transformers
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import bitsandbytes
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import pathlib
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import io
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import PIL
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import utils.datasets
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from tqdm import tqdm
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from .hyperparams import TEST_SIZE, ANSWER_EOS, IMG_TOKENS, LR, BATCH_SIZE, EPOCHS, GRAD_ACCUM_STEPS
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DEVICE = "cuda"
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DTYPE = torch.float32 if DEVICE == "cpu" else torch.float16 # CPU doesn't support float16
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MD_REVISION = "2024-07-23"
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TOTAL_DATA_SIZE = 8000
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diffusion_db_dataset = datasets.load_dataset("poloclub/diffusiondb", "2m_random_5k", trust_remote_code=True, split="train")\
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diffusion_db_dataset = datasets.load_dataset("poloclub/diffusiondb", "2m_random_5k", split="train", trust_remote_code=True, streaming=True)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Describe this image.",
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"answer": "This is an AI image."
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"question": "Is this image AI generated?",
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"answer": "Yes."
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}
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})\
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.train_test_split(test_size=TEST_SIZE)
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})
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diffusion_db_dataset = utils.datasets.split_streaming_dataset(diffusion_db_dataset, total_size=2000, test_size=TEST_SIZE)
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flickr_dataset = datasets.load_dataset("nlphuji/flickr30k", split="test")\
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.take(2500)\
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midjourney_dataset = datasets.load_dataset("brivangl/midjourney-v6-llava", split="train", streaming=True)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Describe this image.",
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"answer": "This is a real image."
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"question": "Is this image AI generated?",
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"answer": "Yes."
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}
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})\
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.train_test_split(test_size=TEST_SIZE)
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})
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midjourney_dataset = utils.datasets.split_streaming_dataset(midjourney_dataset, total_size=2000, test_size=TEST_SIZE)
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wiki_art_dataset = datasets.load_dataset("huggan/wikiart", split="train")\
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.take(2500)\
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flickr_dataset = datasets.load_dataset("nlphuji/flickr30k", split="test", streaming=True)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Describe thie image.",
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"answer": "This is a real image."
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"question": "Is this image AI generated?",
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"answer": "No."
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}
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})\
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.train_test_split(test_size=TEST_SIZE)
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})
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flickr_dataset = utils.datasets.split_streaming_dataset(flickr_dataset, total_size=800, test_size=TEST_SIZE)
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training_dataset = datasets.concatenate_datasets([
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wiki_art_dataset = datasets.load_dataset("huggan/wikiart", split="train", streaming=True)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Is this image AI generated?",
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"answer": "No."
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}
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})
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wiki_art_dataset = utils.datasets.split_streaming_dataset(wiki_art_dataset, total_size=800, test_size=TEST_SIZE)
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anime_dataset = datasets.load_dataset("animelover/danbooru2022", "1-full", trust_remote_code=True, split="train", streaming=True)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Is this image AI generated?",
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"answer": "No."
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}
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})
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anime_dataset = utils.datasets.split_streaming_dataset(anime_dataset, total_size=800, test_size=TEST_SIZE)
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coco_dataset = datasets.load_dataset("detection-datasets/coco", split="train", streaming=True)\
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.select_columns(["image"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Is this image AI generated?",
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"answer": "No."
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}
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})
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coco_dataset = utils.datasets.split_streaming_dataset(coco_dataset, total_size=800, test_size=TEST_SIZE)
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movie_poster_dataset = datasets.load_dataset("skvarre/movie_posters-100k", split="train", streaming=True)\
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.select_columns(["age"])\
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.map(lambda row: {
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**row,
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"qa": {
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"question": "Is this image AI generated?",
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"answer": "No."
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}
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})
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movie_poster_dataset = utils.datasets.split_streaming_dataset(movie_poster_dataset, total_size=800, test_size=TEST_SIZE)
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training_dataset = datasets.interleave_datasets([
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diffusion_db_dataset["train"],
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midjourney_dataset["train"],
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flickr_dataset["train"],
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wiki_art_dataset["train"],
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]).shuffle()
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test_dataset = datasets.concatenate_datasets([
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anime_dataset["train"],
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coco_dataset["train"],
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movie_poster_dataset["train"],
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], stopping_strategy="all_exhausted").cast_column("image", datasets.Image(decode=True))
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test_dataset = datasets.interleave_datasets([
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diffusion_db_dataset["test"],
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midjourney_dataset["test"],
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flickr_dataset["test"],
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wiki_art_dataset["test"],
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]).shuffle()
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anime_dataset["test"],
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coco_dataset["test"],
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movie_poster_dataset["test"],
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], stopping_strategy="all_exhausted").cast_column("image", datasets.Image(decode=True))
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print("Training and test dataset prepared.")
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tokenizer = transformers.AutoTokenizer.from_pretrained("vikhyatk/moondream2")
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moondream = transformers.AutoModelForCausalLM.from_pretrained(
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@@ -150,7 +206,6 @@ dataloaders = {
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"train": torch.utils.data.DataLoader(
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training_dataset,
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batch_size=BATCH_SIZE,
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shuffle=True,
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collate_fn=collate,
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),
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}
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@@ -158,7 +213,7 @@ dataloaders = {
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moondream.text_model.train()
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moondream.text_model.transformer.gradient_checkpointing_enable()
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total_steps = EPOCHS * len(dataloaders["train"]) // GRAD_ACCUM_STEPS
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total_steps = EPOCHS * (TOTAL_DATA_SIZE * (1 - TEST_SIZE)) // GRAD_ACCUM_STEPS
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optimizer = bitsandbytes.optim.Adam8bit(
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[{"params": moondream.text_model.parameters()}],
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lr=LR*0.1,
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@@ -184,6 +239,7 @@ for epoch in range(EPOCHS):
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moondream.save_pretrained("checkpoints/moondream-mai")
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moondream.eval()
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pathlib.Path("./samples").mkdir(parents=True, exist_ok=True)
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correct_predictions = 0
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@@ -201,9 +257,6 @@ for sample in tqdm(test_dataset, desc="Validation"):
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if md_answer == ground_truth:
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correct_predictions += 1
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if i % 10 == 0:
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print(f"Question: f{sample["qa"]["answer"]")
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accuracy = correct_predictions * 100 / len(test_dataset)
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accuracy = correct_predictions * 100 / (TOTAL_DATA_SIZE * TEST_SIZE)
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print(f"Model accuracy: f{accuracy}%")
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8
utils/datasets.py
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8
utils/datasets.py
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import datasets
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def split_streaming_dataset(ds: datasets.IterableDataset, total_size: int, test_size: float) -> dict[str, datasets.IterableDataset]:
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size = round(total_size * (1 - test_size))
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return {
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"train": ds.take(size),
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"test": ds.skip(size).take(total_size - size),
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}
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