1

train_vit.py

@@ -1,5 +1,5 @@
 from datasets import Dataset, DatasetDict, load_dataset, load_from_disk
-from transformers import ViTForImageClassification, ViTImageProcessor, Trainer, TrainingArguments
+from transformers import ViTForImageClassification, ViTImageProcessor, Trainer, TrainingArguments, ViTConfig
 from PIL import Image
 from torch.optim import AdamW
 from torch.optim.lr_scheduler import StepLR
@@ -9,23 +9,35 @@ from sklearn.metrics import f1_score, accuracy_score, recall_score, precision_sc
 import os
 
 
-MODEL_NAME = "/Users/XXXXRT/vit_pretrain/vit-base-patch16-384"
+MODEL_NAME = "google/vit-base-patch16-224"
 SIZE = "base"
 PATCH = 16
-IMAGE_SIZE = 384
-BATCH_SIZE = 8
+IMAGE_SIZE = 224
+BATCH_SIZE = 128
 OPTIMIZER = "AdamW"
 SCHEDULER = "StepLR"
+NAME = 'no-pretrain'
 
-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'
+IMAGE_PATH = '/root/autodl-tmp/ISIC-2019'
+TRAIN_CSV_PATH = '/root/autodl-tmp/ISIC-2019/train_labels.csv'
+TEST_CSV_PATH = '/root/autodl-tmp/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}"
+checkpoint_dir = "/root/autodl-tmp/ISIC-2019/logs/vit-base-patch16-224-bs128-AdamW-StepLR-lables-9"
+
+if os.path.isdir(checkpoint_dir) and any(os.scandir(checkpoint_dir)):
+    checkpoint = max([os.path.join(checkpoint_dir, d) for d in os.listdir(checkpoint_dir) if "checkpoint" in d], key=os.path.getctime)
+else:
+    checkpoint = None
+    
+checkpoint = "/root/autodl-tmp/ISIC-2019/logs/vit-base-patch16-224-bs128-AdamW-StepLR-lables-9-train-linear/checkpoint-895"
+checkpoint = None
+print(f"从检查点 {checkpoint} 恢复训练")
+
+processed_dataset_path = f"/root/autodl-tmp/ISIC-2019/dataset-{IMAGE_SIZE}"
+# processed_dataset_path = f"/root/autodl-tmp/dataset-{IMAGE_SIZE}"
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-device = torch.device("mps")
+# device = torch.device("mps")
 
 processor = ViTImageProcessor.from_pretrained(MODEL_NAME)
 
@@ -48,11 +60,11 @@ def preprocess_image_test(example):
     return example
 
 if os.path.exists(processed_dataset_path):
-    dataset = load_from_disk(processed_dataset_path)
+    dataset = load_from_disk(processed_dataset_path,keep_in_memory=True)
     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 = load_dataset('csv', data_files=TRAIN_CSV_PATH, keep_in_memory=True)["train"]
+    test_dataset = load_dataset('csv', data_files=TEST_CSV_PATH, keep_in_memory=True)["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)
@@ -69,24 +81,35 @@ test_dataset = dataset['test']
 
 num_labels = 9
 
-model = ViTForImageClassification.from_pretrained(
-    MODEL_NAME,
-    num_labels=num_labels,
-    problem_type="multi_label_classification"
-).to(device)
+# model = ViTForImageClassification.from_pretrained(
+#     MODEL_NAME,
+#     num_labels=num_labels,
+#     problem_type="multi_label_classification",
+#     ignore_mismatched_sizes=True
+# ).to(device)
+
+# for param in model.parameters():
+#     param.requires_grad = False
+
+# for param in model.classifier.parameters():
+#     param.requires_grad = True
 
+config = ViTConfig(image_size=IMAGE_SIZE, num_labels=num_labels, problem_type="multi_label_classification", patch_size = PATCH)
+model = ViTForImageClassification(config)
 
 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}",
+    output_dir=f"/root/autodl-tmp/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}-{NAME}",
     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,
+    num_train_epochs=20,
     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"
+    logging_dir=f"/root/autodl-tmp/ISIC-2019/logs/vit-{SIZE}-patch{PATCH}-{IMAGE_SIZE}-bs{BATCH_SIZE}-{OPTIMIZER}-{SCHEDULER}-lables-{num_labels}-{NAME}/logs",
+    logging_steps=10,
+    report_to="tensorboard",
+    fp16=True,   
+    dataloader_num_workers = 8
 )
 
 
@@ -107,10 +130,10 @@ def compute_metrics(pred):
 
 learning_rate = 5e-5
 weight_decay = 0.01
-step_size = 100
+step_size = 1000
 gamma = 0.1
 optimizer = AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
-scheduler = StepLR(optimizer, step_size=step_size, gamma=gamma)
+# scheduler = StepLR(optimizer, step_size=step_size, gamma=gamma)
 
 
 trainer = Trainer(
@@ -119,8 +142,10 @@ trainer = Trainer(
     train_dataset=train_dataset,
     eval_dataset=test_dataset,
     compute_metrics=compute_metrics,
-    optimizers=(optimizer, scheduler)
+    optimizers=(optimizer,None)
+    # optimizers=(optimizer, scheduler)
 )
 
 
-trainer.train()
+# trainer.train()
+trainer.train(resume_from_checkpoint=checkpoint)