From 726dbef3261cdb3a22d51237b7a7afff1ebda42b Mon Sep 17 00:00:00 2001
From: D-X-Y <280835372@qq.com>
Date: Thu, 27 May 2021 21:51:42 +0800
Subject: [PATCH] Update ablation for GeMOSA

---
 xautodl/utils/__init__.py  |  1 +
 xautodl/utils/str_utils.py | 13 +++++++++++++
 xautodl/utils/temp_sync.py |  6 ++----
 3 files changed, 16 insertions(+), 4 deletions(-)

diff --git a/xautodl/utils/__init__.py b/xautodl/utils/__init__.py
index 3b6052f..4c39ad1 100644
--- a/xautodl/utils/__init__.py
+++ b/xautodl/utils/__init__.py
@@ -11,3 +11,4 @@ from .affine_utils import normalize_points, denormalize_points
 from .affine_utils import identity2affine, solve2theta, affine2image
 from .hash_utils import get_md5_file
 from .str_utils import split_str2indexes
+from .str_utils import show_mean_var
diff --git a/xautodl/utils/str_utils.py b/xautodl/utils/str_utils.py
index 06bda3d..829379e 100644
--- a/xautodl/utils/str_utils.py
+++ b/xautodl/utils/str_utils.py
@@ -1,3 +1,6 @@
+import numpy as np
+
+
 def split_str2indexes(string: str, max_check: int, length_limit=5):
     if not isinstance(string, str):
         raise ValueError("Invalid scheme for {:}".format(string))
@@ -19,3 +22,13 @@ def split_str2indexes(string: str, max_check: int, length_limit=5):
         for i in range(srange[0], srange[1] + 1):
             indexes.add(i)
     return indexes
+
+
+def show_mean_var(xlist):
+    values = np.array(xlist)
+    print(
+        "{:.3f}".format(values.mean())
+        + "$_{{\pm}{"
+        + "{:.3f}".format(values.std())
+        + "}}$"
+    )
diff --git a/xautodl/utils/temp_sync.py b/xautodl/utils/temp_sync.py
index 8dd89a6..fe8526d 100644
--- a/xautodl/utils/temp_sync.py
+++ b/xautodl/utils/temp_sync.py
@@ -20,9 +20,7 @@ def optimize_fn(xs, ys, device="cpu", max_iter=2000, max_lr=0.1):
         SuperLinear(100, 1),
     ).to(device)
     model.train()
-    optimizer = torch.optim.Adam(
-        model.parameters(), lr=max_lr, amsgrad=True
-    )
+    optimizer = torch.optim.Adam(model.parameters(), lr=max_lr, amsgrad=True)
     loss_func = torch.nn.MSELoss()
     lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
         optimizer,
@@ -47,7 +45,7 @@ def optimize_fn(xs, ys, device="cpu", max_iter=2000, max_lr=0.1):
         if best_loss is None or best_loss > loss.item():
             best_loss = loss.item()
             best_param = copy.deepcopy(model.state_dict())
-        
+
         # print('loss={:}, best-loss={:}'.format(loss.item(), best_loss))
     model.load_state_dict(best_param)
     return model, loss_func, best_loss