diff --git a/tools/train_utils/optimization/__init__.py b/tools/train_utils/optimization/__init__.py
new file mode 100644
index 0000000..888cfcf
--- /dev/null
+++ b/tools/train_utils/optimization/__init__.py
@@ -0,0 +1,68 @@
+from functools import partial
+
+import torch.nn as nn
+import torch.optim as optim
+import torch.optim.lr_scheduler as lr_sched
+
+from .fastai_optim import OptimWrapper
+from .learning_schedules_fastai import CosineWarmupLR, OneCycle, CosineAnnealing
+
+
+def build_optimizer(model, optim_cfg):
+    if optim_cfg.OPTIMIZER == 'adam':
+        optimizer = optim.Adam(model.parameters(), lr=optim_cfg.LR, weight_decay=optim_cfg.WEIGHT_DECAY)
+    elif optim_cfg.OPTIMIZER == 'sgd':
+        optimizer = optim.SGD(
+            model.parameters(), lr=optim_cfg.LR, weight_decay=optim_cfg.WEIGHT_DECAY,
+            momentum=optim_cfg.MOMENTUM
+        )
+    elif optim_cfg.OPTIMIZER in ['adam_onecycle','adam_cosineanneal']:
+        def children(m: nn.Module):
+            return list(m.children())
+
+        def num_children(m: nn.Module) -> int:
+            return len(children(m))
+
+        flatten_model = lambda m: sum(map(flatten_model, m.children()), []) if num_children(m) else [m]
+        get_layer_groups = lambda m: [nn.Sequential(*flatten_model(m))]
+        betas = optim_cfg.get('BETAS', (0.9, 0.99))
+        betas = tuple(betas)
+        optimizer_func = partial(optim.Adam, betas=betas)
+        optimizer = OptimWrapper.create(
+            optimizer_func, 3e-3, get_layer_groups(model), wd=optim_cfg.WEIGHT_DECAY, true_wd=True, bn_wd=True
+        )
+    else:
+        raise NotImplementedError
+
+    return optimizer
+
+
+def build_scheduler(optimizer, total_iters_each_epoch, total_epochs, last_epoch, optim_cfg):
+    decay_steps = [x * total_iters_each_epoch for x in optim_cfg.DECAY_STEP_LIST]
+    def lr_lbmd(cur_epoch):
+        cur_decay = 1
+        for decay_step in decay_steps:
+            if cur_epoch >= decay_step:
+                cur_decay = cur_decay * optim_cfg.LR_DECAY
+        return max(cur_decay, optim_cfg.LR_CLIP / optim_cfg.LR)
+
+    lr_warmup_scheduler = None
+    total_steps = total_iters_each_epoch * total_epochs
+    if optim_cfg.OPTIMIZER == 'adam_onecycle':
+        lr_scheduler = OneCycle(
+            optimizer, total_steps, optim_cfg.LR, list(optim_cfg.MOMS), optim_cfg.DIV_FACTOR, optim_cfg.PCT_START
+        )
+    elif optim_cfg.OPTIMIZER == 'adam_cosineanneal':
+        lr_scheduler = CosineAnnealing(
+            optimizer, total_steps, total_epochs, optim_cfg.LR, list(optim_cfg.MOMS), optim_cfg.PCT_START, optim_cfg.WARMUP_ITER
+        )
+    else:
+        lr_scheduler = lr_sched.LambdaLR(optimizer, lr_lbmd, last_epoch=last_epoch)
+
+        if optim_cfg.LR_WARMUP:
+            lr_warmup_scheduler = CosineWarmupLR(
+                optimizer, T_max=optim_cfg.WARMUP_EPOCH * len(total_iters_each_epoch),
+                eta_min=optim_cfg.LR / optim_cfg.DIV_FACTOR
+            )
+
+    return lr_scheduler, lr_warmup_scheduler