add VIT

 .DS_Store
 .DS_Store
 logs/
 logs/
+
+__pycache__
+
+*.log
+
+dataset/

+seed: 3407
+
+dataset:
+  name: 'CoordinatesData'
+  args:
+    data_root: '/Users/zhouweiqi/Downloads/gcfp/data/dataset'
+    train_anno_file: '/Users/zhouweiqi/Downloads/gcfp/data/dataset/train.csv'
+    val_anno_file: '/Users/zhouweiqi/Downloads/gcfp/data/dataset/valid.csv'
+
+dataloader:
+  batch_size: 32
+  num_workers: 4
+  pin_memory: true
+  shuffle: true
+
+model:
+  name: 'VisionTransformer'
+  args:
+    img_size: 224
+    patch_size: 16
+    in_c: 3
+    num_classes: 5
+    embed_dim: 8
+    depth: 12
+    num_heads: 12
+    mlp_ratio: 4.0
+    qkv_bias: true
+    qk_scale: none
+    representation_size: none
+    distilled: false 
+    drop_ratio: 0.
+    attn_drop_ratio: 0.
+    drop_path_ratio: 0.
+    norm_layer: none
+    act_layer: none
+
+solver:
+  name: 'VITSolver'
+  args:
+    epoch: 100
+
+  optimizer:
+    name: 'Adam'
+    args:
+      lr: !!float 1e-4
+      weight_decay: !!float 5e-5
+
+  lr_scheduler:
+    name: 'StepLR'
+    args:
+      step_size: 15
+      gamma: 0.1
+
+  loss:
+    name: 'SigmoidFocalLoss'
+    # name: 'CrossEntropyLoss'
+    args:
+      reduction: "mean"
+
+  logger:
+    log_root: '/Users/zhouweiqi/Downloads/test/logs'
+    suffix: 'vit'
\ No newline at end of file

+import os
+import copy
+import torch
+
+from model import build_model
+from data import build_dataloader
+from optimizer import build_optimizer, build_lr_scheduler
+from loss import build_loss
+from utils import SOLVER_REGISTRY, get_logger_and_log_dir
+
+
+@SOLVER_REGISTRY.register()
+class VITSolver(object):
+
+    def __init__(self, cfg):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        self.cfg = copy.deepcopy(cfg)
+
+        self.train_loader, self.val_loader = build_dataloader(cfg)
+        self.train_loader_size, self.val_loader_size = len(self.train_loader), len(self.val_loader)
+        self.train_dataset_size, self.val_dataset_size = len(self.train_loader.dataset), len(self.val_loader.dataset)
+
+        # BatchNorm ?
+        self.model = build_model(cfg).to(self.device)
+
+        self.loss_fn = build_loss(cfg)
+
+        self.optimizer = build_optimizer(cfg)(self.model.parameters(), **cfg['solver']['optimizer']['args'])
+
+        self.hyper_params = cfg['solver']['args']
+        try:
+            self.epoch = self.hyper_params['epoch']
+        except Exception:
+            raise 'should contain epoch in {solver.args}'
+
+        self.logger, self.log_dir = get_logger_and_log_dir(**cfg['solver']['logger'])
+
+    @staticmethod
+    def evaluate(y_pred, y_true, thresholds=0.5):
+        y_pred_idx = torch.argmax(y_pred, dim=1) + 1
+        y_pred_is_other = (torch.amax(y_pred, dim=1) > 0.5).int()
+        y_pred_rebuild = torch.multiply(y_pred_idx, y_pred_is_other)
+
+        y_true_idx = torch.argmax(y_true, dim=1) + 1
+        y_true_is_other = torch.sum(y_true, dim=1)
+        y_true_rebuild = torch.multiply(y_true_idx, y_true_is_other)
+
+        return torch.sum((y_pred_rebuild == y_true_rebuild).int()).item()
+
+    def train_loop(self):
+        self.model.train()
+
+        train_loss = torch.zeros(1).to(self.device)
+        correct = torch.zeros(1).to(self.device)
+        for batch, (X, y) in enumerate(self.train_loader):
+            X, y = X.to(self.device), y.to(self.device)
+
+            pred = self.model(X)
+
+            correct += self.evaluate(pred, y)
+
+            # loss = self.loss_fn(pred, y, reduction="mean")
+            loss = self.loss_fn(pred, y)
+            train_loss += loss.item()
+
+            if batch % 100 == 0:
+                loss_value, current = loss.item(), batch
+                self.logger.info(f'train iteration: {current}/{self.train_loader_size}, train loss: {loss_value :.4f}')
+            
+            self.optimizer.zero_grad()
+            loss.backward()
+            self.optimizer.step()
+
+        correct /= self.train_dataset_size
+        train_loss /= self.train_loader_size
+        self.logger.info(f'train accuracy: {correct.item() :.4f}, train mean loss: {train_loss.item() :.4f}')
+
+    @torch.no_grad()
+    def val_loop(self, t):
+        self.model.eval()
+
+        val_loss = torch.zeros(1).to(self.device)
+        correct = torch.zeros(1).to(self.device)
+        for X, y in self.val_loader:
+            X, y = X.to(self.device), y.to(self.device)
+
+            pred = self.model(X)
+
+            correct += self.evaluate(pred, y)
+
+            loss = self.loss_fn(pred, y)
+            val_loss += loss.item()
+
+        correct /= self.val_dataset_size
+        val_loss /= self.val_loader_size
+            
+        self.logger.info(f"val accuracy: {correct.item() :.4f}, val mean loss: {val_loss.item() :.4f}")
+
+    def save_checkpoint(self, epoch_id):
+        self.model.eval()
+        torch.save(self.model.state_dict(), os.path.join(self.log_dir, f'ckpt_epoch_{epoch_id}.pt'))
+
+    def run(self):
+        self.logger.info('==> Start Training')
+        print(self.model)
+
+        # lr_scheduler = build_lr_scheduler(self.cfg)(self.optimizer, **self.cfg['solver']['lr_scheduler']['args'])
+
+        for t in range(self.epoch):
+            self.logger.info(f'==> epoch {t + 1}')
+
+            self.train_loop()
+            self.val_loop(t + 1)
+            self.save_checkpoint(t + 1)
+
+            # lr_scheduler.step()
+
+        self.logger.info('==> End Training')