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diff --git a/checkpoint_test/0002_acc0.9841_class0383_condition_cifar10_vittiny.pth b/checkpoint_test/0002_acc0.9841_class0383_condition_cifar10_vittiny.pth
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diff --git a/checkpoint_test/0002_acc0.9880_class0128_condition_cifar10_vittiny.pth b/checkpoint_test/0002_acc0.9880_class0128_condition_cifar10_vittiny.pth
new file mode 100644
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--- /dev/null
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diff --git a/dataset.py b/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..60071b28739daa3500278b6e4e9629035b8dafba
--- /dev/null
+++ b/dataset.py
@@ -0,0 +1,41 @@
+import re
+import sys
+from torch.utils.data import Dataset
+from torchvision.datasets import CIFAR10
+import torchvision.transforms as transforms
+
+
+class BinaryClassifierDataset(Dataset):
+    def __init__(self, root, train, optimize_class: list):
+        self.optimize_class = optimize_class
+        self.dataset = CIFAR10(
+            root=root,
+            train=train,
+            download=True,
+            transform=transforms.Compose([
+                transforms.Resize(224),
+                transforms.RandomHorizontalFlip(),
+                transforms.AutoAugment(policy=transforms.AutoAugmentPolicy("cifar10")),
+                transforms.ToTensor(),
+                transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+            ])
+        )
+
+    def __getitem__(self, index):
+        img, origin_target = self.dataset[index]
+        target = 1 if origin_target in self.optimize_class else 0
+        return img, target
+
+    def __len__(self):
+        return self.dataset.__len__()
+
+
+def get_optimize_class():
+    try:  # get string
+        string = sys.argv[1]
+    except IndexError:
+        RuntimeError("sys.argv[1] not found")
+    class_int_string = str(re.findall(r'class(\d+)', string)[-1]).zfill(4)
+    one_hot_string = bin(int(class_int_string))[2:].zfill(10)
+    optimize_class = [index for index, i in enumerate(one_hot_string) if i == "1"]
+    return list(optimize_class), class_int_string
\ No newline at end of file
diff --git a/detail.py b/detail.py
new file mode 100644
index 0000000000000000000000000000000000000000..d925222f2621369e307d818786344263b83de9d0
--- /dev/null
+++ b/detail.py
@@ -0,0 +1,58 @@
+import os
+import sys
+if __name__ == "__main__":
+    from train import *
+else:  # relative import
+    from .train import *
+from torchvision.datasets import CIFAR10
+from torchvision import transforms
+
+
+
+
+try:
+    test_item = sys.argv[1]
+except IndexError:
+    assert __name__ == "__main__"
+    test_item = "./generated"
+test_items = []
+if os.path.isdir(test_item):
+    for item in os.listdir(test_item):
+        item = os.path.join(test_item, item)
+        test_items.append(item)
+elif os.path.isfile(test_item):
+    test_items.append(test_item)
+
+
+
+
+original_dataset = CIFAR10(
+    root=config["dataset_root"],
+    train=False,
+    download=True,
+    transform=transforms.Compose([
+        transforms.Resize(224),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+    ])
+)
+original_targets = [original_dataset[i][1] for i in range(len(original_dataset))]
+original_targets = torch.tensor(original_targets, dtype=torch.long)
+
+
+
+
+for item in test_items:
+    state = torch.load(item, map_location="cpu", weights_only=True)
+    model.load_state_dict({key: value.to(torch.float32).to(device) for key, value in state.items()})
+    loss, acc, all_targets, all_predicts = test(model=model)
+    all_targets, all_predicts = torch.tensor(all_targets), torch.tensor(all_predicts)
+
+    for class_idx in range(10):
+        class_mask = torch.where(original_targets == class_idx, 1, 0)
+        total_number = torch.sum(class_mask)
+        correct = torch.where(all_targets == all_predicts, 1, 0)
+        class_correct = class_mask * correct
+        correct_number = torch.sum(class_correct)
+        class_acc = correct_number.item() / total_number.item()
+        print(f"class{class_idx}:", class_acc)
\ No newline at end of file
diff --git a/download.py b/download.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d9b1901f52dbca3b84e8f3b20d67a287243ec33
--- /dev/null
+++ b/download.py
@@ -0,0 +1,21 @@
+from bypy import ByPy
+
+
+bp = ByPy(
+    configdir="./bypy_config",
+    downloader="aria2",
+    retry=10,
+)
+print("=============== files under /app/ ===============")
+bp.list()
+print("=============== start downloading ===============")
+
+
+bp.download(
+    remotepath="condition_classinput_vittiny/checkpoint_test",
+    localpath="./checkpoint_test",
+)
+bp.download(
+    remotepath="condition_classinput_vittiny/checkpoint_train",
+    localpath="./checkpoint_train",
+)
diff --git a/model.py b/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..6a6737cc25d651f5710ff1e61540fb44b47dc4db
--- /dev/null
+++ b/model.py
@@ -0,0 +1,25 @@
+import torch
+import torch.nn as nn
+import timm
+
+
+def Model():
+    model = timm.create_model("vit_tiny_patch16_224", pretrained=True)
+    model.head = nn.Sequential(
+        nn.Linear(192, 192, bias=True),
+        nn.SiLU(),
+        nn.Linear(192, 2, bias=False),
+    )
+    for param in model.head.parameters():
+        param = nn.Parameter(torch.ones_like(param) / 192)
+        param.requires_grad = True
+    return model, model.head
+
+
+if __name__ == "__main__":
+    model, _ = Model()
+    print(model)
+    num_param = 0
+    for v in model.parameters():
+        num_param += v.numel()
+    print("num_param:", num_param)
\ No newline at end of file
diff --git a/split.sh b/split.sh
new file mode 100644
index 0000000000000000000000000000000000000000..b85c7cfbfe73b0d8f1a496da3566d113ff8c1305
--- /dev/null
+++ b/split.sh
@@ -0,0 +1,28 @@
+mkdir checkpoint_test
+mkdir checkpoint_train
+mkdir generated
+
+mv ./checkpoint/*class0314* ./checkpoint_test
+mv ./checkpoint/*class0482* ./checkpoint_test
+mv ./checkpoint/*class0589* ./checkpoint_test
+mv ./checkpoint/*class0197* ./checkpoint_test
+mv ./checkpoint/*class0462* ./checkpoint_test
+mv ./checkpoint/*class0111* ./checkpoint_test
+mv ./checkpoint/*class0101* ./checkpoint_test
+mv ./checkpoint/*class0278* ./checkpoint_test
+mv ./checkpoint/*class0793* ./checkpoint_test
+mv ./checkpoint/*class0279* ./checkpoint_test
+mv ./checkpoint/*class0653* ./checkpoint_test
+mv ./checkpoint/*class0238* ./checkpoint_test
+mv ./checkpoint/*class1001* ./checkpoint_test
+mv ./checkpoint/*class0141* ./checkpoint_test
+mv ./checkpoint/*class0884* ./checkpoint_test
+mv ./checkpoint/*class0592* ./checkpoint_test
+mv ./checkpoint/*class0502* ./checkpoint_test
+mv ./checkpoint/*class0643* ./checkpoint_test
+mv ./checkpoint/*class0383* ./checkpoint_test
+mv ./checkpoint/*class0128* ./checkpoint_test
+
+mv ./checkpoint/* ./checkpoint_train
+
+rm checkpoint -r
\ No newline at end of file
diff --git a/structure.cache b/structure.cache
new file mode 100644
index 0000000000000000000000000000000000000000..dec49eea75309d0d90c4a5a4885d6c16c8d5a942
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diff --git a/structure.info b/structure.info
new file mode 100644
index 0000000000000000000000000000000000000000..2926ef1ba7eaa2bd456771b7f3d0710628042988
--- /dev/null
+++ b/structure.info
@@ -0,0 +1,153 @@
+['cls_token',
+ 'pos_embed',
+ 'patch_embed.proj.weight',
+ 'patch_embed.proj.bias',
+ 'blocks.0.norm1.weight',
+ 'blocks.0.norm1.bias',
+ 'blocks.0.attn.qkv.weight',
+ 'blocks.0.attn.qkv.bias',
+ 'blocks.0.attn.proj.weight',
+ 'blocks.0.attn.proj.bias',
+ 'blocks.0.norm2.weight',
+ 'blocks.0.norm2.bias',
+ 'blocks.0.mlp.fc1.weight',
+ 'blocks.0.mlp.fc1.bias',
+ 'blocks.0.mlp.fc2.weight',
+ 'blocks.0.mlp.fc2.bias',
+ 'blocks.1.norm1.weight',
+ 'blocks.1.norm1.bias',
+ 'blocks.1.attn.qkv.weight',
+ 'blocks.1.attn.qkv.bias',
+ 'blocks.1.attn.proj.weight',
+ 'blocks.1.attn.proj.bias',
+ 'blocks.1.norm2.weight',
+ 'blocks.1.norm2.bias',
+ 'blocks.1.mlp.fc1.weight',
+ 'blocks.1.mlp.fc1.bias',
+ 'blocks.1.mlp.fc2.weight',
+ 'blocks.1.mlp.fc2.bias',
+ 'blocks.2.norm1.weight',
+ 'blocks.2.norm1.bias',
+ 'blocks.2.attn.qkv.weight',
+ 'blocks.2.attn.qkv.bias',
+ 'blocks.2.attn.proj.weight',
+ 'blocks.2.attn.proj.bias',
+ 'blocks.2.norm2.weight',
+ 'blocks.2.norm2.bias',
+ 'blocks.2.mlp.fc1.weight',
+ 'blocks.2.mlp.fc1.bias',
+ 'blocks.2.mlp.fc2.weight',
+ 'blocks.2.mlp.fc2.bias',
+ 'blocks.3.norm1.weight',
+ 'blocks.3.norm1.bias',
+ 'blocks.3.attn.qkv.weight',
+ 'blocks.3.attn.qkv.bias',
+ 'blocks.3.attn.proj.weight',
+ 'blocks.3.attn.proj.bias',
+ 'blocks.3.norm2.weight',
+ 'blocks.3.norm2.bias',
+ 'blocks.3.mlp.fc1.weight',
+ 'blocks.3.mlp.fc1.bias',
+ 'blocks.3.mlp.fc2.weight',
+ 'blocks.3.mlp.fc2.bias',
+ 'blocks.4.norm1.weight',
+ 'blocks.4.norm1.bias',
+ 'blocks.4.attn.qkv.weight',
+ 'blocks.4.attn.qkv.bias',
+ 'blocks.4.attn.proj.weight',
+ 'blocks.4.attn.proj.bias',
+ 'blocks.4.norm2.weight',
+ 'blocks.4.norm2.bias',
+ 'blocks.4.mlp.fc1.weight',
+ 'blocks.4.mlp.fc1.bias',
+ 'blocks.4.mlp.fc2.weight',
+ 'blocks.4.mlp.fc2.bias',
+ 'blocks.5.norm1.weight',
+ 'blocks.5.norm1.bias',
+ 'blocks.5.attn.qkv.weight',
+ 'blocks.5.attn.qkv.bias',
+ 'blocks.5.attn.proj.weight',
+ 'blocks.5.attn.proj.bias',
+ 'blocks.5.norm2.weight',
+ 'blocks.5.norm2.bias',
+ 'blocks.5.mlp.fc1.weight',
+ 'blocks.5.mlp.fc1.bias',
+ 'blocks.5.mlp.fc2.weight',
+ 'blocks.5.mlp.fc2.bias',
+ 'blocks.6.norm1.weight',
+ 'blocks.6.norm1.bias',
+ 'blocks.6.attn.qkv.weight',
+ 'blocks.6.attn.qkv.bias',
+ 'blocks.6.attn.proj.weight',
+ 'blocks.6.attn.proj.bias',
+ 'blocks.6.norm2.weight',
+ 'blocks.6.norm2.bias',
+ 'blocks.6.mlp.fc1.weight',
+ 'blocks.6.mlp.fc1.bias',
+ 'blocks.6.mlp.fc2.weight',
+ 'blocks.6.mlp.fc2.bias',
+ 'blocks.7.norm1.weight',
+ 'blocks.7.norm1.bias',
+ 'blocks.7.attn.qkv.weight',
+ 'blocks.7.attn.qkv.bias',
+ 'blocks.7.attn.proj.weight',
+ 'blocks.7.attn.proj.bias',
+ 'blocks.7.norm2.weight',
+ 'blocks.7.norm2.bias',
+ 'blocks.7.mlp.fc1.weight',
+ 'blocks.7.mlp.fc1.bias',
+ 'blocks.7.mlp.fc2.weight',
+ 'blocks.7.mlp.fc2.bias',
+ 'blocks.8.norm1.weight',
+ 'blocks.8.norm1.bias',
+ 'blocks.8.attn.qkv.weight',
+ 'blocks.8.attn.qkv.bias',
+ 'blocks.8.attn.proj.weight',
+ 'blocks.8.attn.proj.bias',
+ 'blocks.8.norm2.weight',
+ 'blocks.8.norm2.bias',
+ 'blocks.8.mlp.fc1.weight',
+ 'blocks.8.mlp.fc1.bias',
+ 'blocks.8.mlp.fc2.weight',
+ 'blocks.8.mlp.fc2.bias',
+ 'blocks.9.norm1.weight',
+ 'blocks.9.norm1.bias',
+ 'blocks.9.attn.qkv.weight',
+ 'blocks.9.attn.qkv.bias',
+ 'blocks.9.attn.proj.weight',
+ 'blocks.9.attn.proj.bias',
+ 'blocks.9.norm2.weight',
+ 'blocks.9.norm2.bias',
+ 'blocks.9.mlp.fc1.weight',
+ 'blocks.9.mlp.fc1.bias',
+ 'blocks.9.mlp.fc2.weight',
+ 'blocks.9.mlp.fc2.bias',
+ 'blocks.10.norm1.weight',
+ 'blocks.10.norm1.bias',
+ 'blocks.10.attn.qkv.weight',
+ 'blocks.10.attn.qkv.bias',
+ 'blocks.10.attn.proj.weight',
+ 'blocks.10.attn.proj.bias',
+ 'blocks.10.norm2.weight',
+ 'blocks.10.norm2.bias',
+ 'blocks.10.mlp.fc1.weight',
+ 'blocks.10.mlp.fc1.bias',
+ 'blocks.10.mlp.fc2.weight',
+ 'blocks.10.mlp.fc2.bias',
+ 'blocks.11.norm1.weight',
+ 'blocks.11.norm1.bias',
+ 'blocks.11.attn.qkv.weight',
+ 'blocks.11.attn.qkv.bias',
+ 'blocks.11.attn.proj.weight',
+ 'blocks.11.attn.proj.bias',
+ 'blocks.11.norm2.weight',
+ 'blocks.11.norm2.bias',
+ 'blocks.11.mlp.fc1.weight',
+ 'blocks.11.mlp.fc1.bias',
+ 'blocks.11.mlp.fc2.weight',
+ 'blocks.11.mlp.fc2.bias',
+ 'norm.weight',
+ 'norm.bias',
+ 'head.0.weight',
+ 'head.0.bias',
+ 'head.2.weight']
\ No newline at end of file
diff --git a/test.py b/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e39a653b3c423773a614dc9e8186355c7aef2e1
--- /dev/null
+++ b/test.py
@@ -0,0 +1,30 @@
+import os
+import sys
+if __name__ == "__main__":
+    from train import *
+else:  # relative import
+    from .train import *
+
+
+
+
+try:
+    test_item = sys.argv[1]
+except IndexError:
+    assert __name__ == "__main__"
+    test_item = "./checkpoint_test"
+test_items = []
+if os.path.isdir(test_item):
+    for item in os.listdir(test_item):
+        item = os.path.join(test_item, item)
+        test_items.append(item)
+elif os.path.isfile(test_item):
+    test_items.append(test_item)
+
+
+
+
+for item in test_items:
+    state = torch.load(item, map_location="cpu", weights_only=True)
+    model.load_state_dict({key: value.to(torch.float32).to(device) for key, value in state.items()})
+    loss, acc, all_targets, all_predicts = test(model=model)
\ No newline at end of file
diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..25657bd198104fb00b11434b12061fa5ba640fa1
--- /dev/null
+++ b/train.py
@@ -0,0 +1,209 @@
+# set global seed
+import random
+import numpy as np
+import torch
+seed = SEED = 20
+torch.manual_seed(seed)
+torch.cuda.manual_seed(seed)
+torch.cuda.manual_seed_all(seed)
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = True
+np.random.seed(seed)
+random.seed(seed)
+
+
+try:  # relative import
+    from model import Model
+    from dataset import BinaryClassifierDataset as Dataset
+    from dataset import get_optimize_class
+except ImportError:
+    from .model import Model
+    from .dataset import BinaryClassifierDataset as Dataset
+    from .dataset import get_optimize_class
+
+# import
+import torch.nn as nn
+from torch import optim
+from torch.optim import lr_scheduler
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+import os
+import sys
+import warnings
+warnings.filterwarnings("ignore", category=UserWarning)
+
+# load additional config
+import json
+config_file = os.path.join(os.path.dirname(os.path.dirname(__file__)), "config.json")
+with open(config_file, "r") as f:
+    additional_config = json.load(f)
+
+
+
+
+# config
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+config = {
+    "dataset_root": "from_additional_config",
+    "batch_size": 500 if __name__ == "__main__" else 50,
+    "num_workers": 16,
+    "pre_learning_rate": 0.01,
+    "learning_rate": 1e-4,
+    "pre_epochs": 2,
+    "epochs": 13,
+    "weight_decay": 0.1,
+    "save_learning_rate": 2e-5,
+    "total_save_number": 5,
+    "tag": os.path.basename(os.path.dirname(__file__)),
+    "optimize_class": get_optimize_class()[0],
+    "optimize_class_int": get_optimize_class()[1],
+}
+config.update(additional_config)
+print("Training:", config["optimize_class"])
+
+
+
+
+# Data
+dataset = Dataset(
+    root=config["small_dataset_root"],
+    train=True,
+    optimize_class=config["optimize_class"],
+)
+train_loader = DataLoader(
+    dataset=dataset,
+    batch_size=config["batch_size"],
+    num_workers=config["num_workers"],
+    shuffle=True,
+    drop_last=True,
+    pin_memory=True,
+    persistent_workers=True,
+)
+test_loader = DataLoader(
+    dataset=Dataset(
+        root=config["small_dataset_root"],
+        train=False,
+        optimize_class=config["optimize_class"],
+    ),
+    batch_size=config["batch_size"],
+    num_workers=config["num_workers"],
+    shuffle=False,
+)
+
+# Model
+model, head = Model()
+model = model.to(device)
+class FocalLoss(nn.Module):
+    def __init__(self, weight=None, gamma=2):
+        super(FocalLoss, self).__init__()
+        self.weight = weight
+        self.gamma = gamma
+    def forward(self, input, target):
+        ce_loss = F.cross_entropy(input, target, reduction='none', weight=self.weight)
+        pt = torch.exp(-ce_loss)
+        focal_loss = (1 - pt) ** self.gamma * ce_loss
+        return focal_loss.mean()
+criterion = FocalLoss()
+
+# Optimizer
+head_optimizer = optim.AdamW(
+    head.parameters(),
+    lr=config["pre_learning_rate"],
+    weight_decay=config["weight_decay"],
+)
+optimizer = optim.AdamW(
+    model.parameters(),
+    lr=config["learning_rate"],
+    weight_decay=config["weight_decay"],
+)
+scheduler = lr_scheduler.CosineAnnealingLR(
+    optimizer,
+    T_max=config["epochs"],
+    eta_min=config["save_learning_rate"],
+)
+
+
+
+
+# Training
+def train(model=model, optimizer=optimizer, scheduler=scheduler):
+    model.train()
+    for batch_idx, (inputs, targets) in enumerate(train_loader):
+        inputs, targets = inputs.to(device), targets.to(device)
+        optimizer.zero_grad()
+        with torch.amp.autocast("cuda", enabled=False, dtype=torch.bfloat16):
+            outputs = model(inputs)
+            loss = criterion(outputs, targets)
+        loss.backward()
+        optimizer.step()
+    if scheduler is not None:
+        scheduler.step()
+
+# test
+@torch.no_grad()
+def test(model=model):
+    model.eval()
+    all_targets = []
+    all_predicts = []
+    test_loss = 0
+    correct = 0
+    total = 0
+    for batch_idx, (inputs, targets) in enumerate(test_loader):
+        inputs, targets = inputs.to(device), targets.to(device)
+        with torch.amp.autocast("cuda", enabled=False, dtype=torch.bfloat16):
+            outputs = model(inputs)
+            loss = criterion(outputs, targets)
+        # to logging losses
+        all_targets.extend(targets.flatten().tolist())
+        test_loss += loss.item()
+        _, predicts = outputs.max(1)
+        all_predicts.extend(predicts.flatten().tolist())
+        total += targets.size(0)
+        correct += predicts.eq(targets).sum().item()
+    loss = test_loss / (batch_idx + 1)
+    acc = correct / total
+    print(f"Loss: {loss:.4f} | Acc: {acc:.4f}\n")
+    model.train()
+    return loss, acc, all_targets, all_predicts
+
+# save train
+def save_train(model=model, optimizer=optimizer):
+    data_loader = DataLoader(
+        dataset=dataset,
+        batch_size=min(len(dataset) // config["total_save_number"], config["batch_size"]),
+        num_workers=config["num_workers"],
+        shuffle=True,
+        drop_last=True,
+    )
+    model.train()
+    for batch_idx, (inputs, targets) in enumerate(data_loader):
+        inputs, targets = inputs.to(device), targets.to(device)
+        optimizer.zero_grad()
+        with torch.amp.autocast("cuda", enabled=False, dtype=torch.bfloat16):
+            outputs = model(inputs)
+            loss = criterion(outputs, targets)
+        loss.backward()
+        optimizer.step()
+        # Save checkpoint
+        _, acc, _, _ = test(model=model)
+        if not os.path.isdir('checkpoint'):
+            os.mkdir('checkpoint')
+        save_state = {key: value.cpu().to(torch.float32) for key, value in model.state_dict().items()}
+        torch.save(save_state, f"checkpoint/{str(batch_idx).zfill(4)}_acc{acc:.4f}_class{config['optimize_class_int']}_{config['tag']}.pth")
+        print("save:", f"checkpoint/{str(batch_idx).zfill(4)}_acc{acc:.4f}_class{config['optimize_class_int']}_{config['tag']}.pth")
+        # exit loop
+        if batch_idx+1 == config["total_save_number"]:
+            break
+
+
+
+
+# main
+if __name__ == '__main__':
+    for epoch in range(config["pre_epochs"]):
+        train(model=model, optimizer=head_optimizer, scheduler=None)
+        # test(model=model)
+    for epoch in range(config["epochs"]):
+        train(model=model, optimizer=optimizer, scheduler=scheduler)
+        # test(model=model)
+    save_train(model=model, optimizer=optimizer)
diff --git a/train.sh b/train.sh
new file mode 100644
index 0000000000000000000000000000000000000000..4428136cca889370d4b5356ad5e13a9a8644640c
--- /dev/null
+++ b/train.sh
@@ -0,0 +1,10 @@
+#!/bin/bash
+
+start=1
+end=1022
+
+for i in $(seq $start $end)
+do
+    python train.py class$i
+    sleep 1
+done
\ No newline at end of file