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.gitattributes

@@ -56,3 +56,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+README.md filter=lfs diff=lfs merge=lfs -text
+processed_dataset filter=lfs diff=lfs merge=lfs -text
+test_labels.csv filter=lfs diff=lfs merge=lfs -text
+train_labels.csv filter=lfs diff=lfs merge=lfs -text
+

.gitignore

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+test
+train
+logs
+.DS_Store
+test/*
+train/*
+dataset-224
+dataset-384
+dataset-224/*
+dataset-384/*
+train_lable.zip
+test_lable.zip

README.md

@@ -1,3 +1,3 @@
----
-license: mit
----
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+size 24

dataset-224.zip

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+size 3110989729

dataset-384.zip

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+oid sha256:932c66d2e656d85e1799b49bbf9f49511988a7a091a81650796380ff498caf32
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test_labels.csv

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train_labels.csv

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train_vit.py

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+from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
+from transformers import ViTForImageClassification, ViTImageProcessor, Trainer, TrainingArguments
+from PIL import Image
+from torch.optim import AdamW
+from torch.optim.lr_scheduler import StepLR
+import torch
+import numpy as np
+from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_score
+import os
+
+
+MODEL_NAME = "/Users/XXXXRT/vit_pretrain/vit-base-patch16-384"
+SIZE = "base"
+PATCH = 16
+IMAGE_SIZE = 384
+BATCH_SIZE = 8
+OPTIMIZER = "AdamW"
+SCHEDULER = "StepLR"
+
+IMAGE_PATH = '/Users/XXXXRT/ISIC-2019'
+TRAIN_CSV_PATH = '/Users/XXXXRT/ISIC-2019/train_labels.csv'
+TEST_CSV_PATH = '/Users/XXXXRT/ISIC-2019/test_labels.csv'
+
+processed_dataset_path = f"/Users/XXXXRT/ISIC-2019/dataset-{IMAGE_SIZE}"
+processed_dataset_path = f"/Volumes/T9 APFS/ML Dataset/dataset-{IMAGE_SIZE}"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("mps")
+
+processor = ViTImageProcessor.from_pretrained(MODEL_NAME)
+
+def preprocess_image_train(example):
+    image = Image.open(os.path.join(IMAGE_PATH,'train', example["image"])).convert("RGB")
+    example["pixel_values"] = processor(images=image, return_tensors="pt")["pixel_values"].squeeze(0).numpy()
+    
+    labels = example.copy()
+    example["labels"] = np.array([labels["MEL"], labels["NV"], labels["BCC"], labels["AK"], labels["BKL"], labels["DF"], labels["VASC"], labels["SCC"], labels["UNK"]], dtype=np.float32)
+    
+    return example
+
+def preprocess_image_test(example):
+    image = Image.open(os.path.join(IMAGE_PATH,'test', example["image"])).convert("RGB")
+    example["pixel_values"] = processor(images=image, return_tensors="pt")["pixel_values"].squeeze(0).numpy()
+    
+    labels = example.copy()
+    example["labels"] = np.array([labels["MEL"], labels["NV"], labels["BCC"], labels["AK"], labels["BKL"], labels["DF"], labels["VASC"], labels["SCC"], labels["UNK"]], dtype=np.float32)
+    
+    return example
+
+if os.path.exists(processed_dataset_path):
+    dataset = load_from_disk(processed_dataset_path)
+    print("LOADED")
+else:
+    train_dataset = load_dataset('csv', data_files=TRAIN_CSV_PATH)["train"]
+    test_dataset = load_dataset('csv', data_files=TEST_CSV_PATH)["train"]
+
+    train_dataset = train_dataset.map(preprocess_image_train, batched=False, num_proc=2)
+    test_dataset = test_dataset.map(preprocess_image_test, batched=False, num_proc=2)
+
+    dataset = DatasetDict({
+        'train': train_dataset,
+        'test': test_dataset
+    })
+    dataset.save_to_disk(processed_dataset_path,num_proc=2)
+    print(f"SAVED TO {processed_dataset_path}")
+
+train_dataset = dataset['train']
+test_dataset = dataset['test']
+
+num_labels = 9
+
+model = ViTForImageClassification.from_pretrained(
+    MODEL_NAME,
+    num_labels=num_labels,
+    problem_type="multi_label_classification"
+).to(device)
+
+
+training_args = TrainingArguments(
+    output_dir=f"/Users/XXXXRT/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}",
+    evaluation_strategy="epoch",
+    learning_rate=5e-5,
+    per_device_train_batch_size=BATCH_SIZE,
+    per_device_eval_batch_size=BATCH_SIZE,
+    num_train_epochs=5,
+    save_strategy="epoch",
+    logging_dir=f"/Users/XXXXRT/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}/logs",
+    logging_steps=50,
+    report_to="tensorboard"
+)
+
+
+def compute_metrics(pred):
+    logits, labels = pred
+    predictions = (logits >= 0.5).astype(int)
+    f1 = f1_score(labels, predictions, average="macro")
+    accuracy = accuracy_score(labels, predictions)
+    recall = recall_score(labels, predictions, average="macro")
+    precision = precision_score(labels, predictions, average="macro")
+    return {
+        "accuracy": accuracy,
+        "f1": f1,
+        "recall": recall,
+        "precision": precision,
+    }
+
+
+learning_rate = 5e-5
+weight_decay = 0.01
+step_size = 100
+gamma = 0.1
+optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
+scheduler = StepLR(optimizer, step_size=step_size, gamma=gamma)
+
+
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=test_dataset,
+    compute_metrics=compute_metrics,
+    optimizers=(optimizer, scheduler)
+)
+
+
+trainer.train()