diff --git a/ppcls/configs/ssl/CCSSL/FixMatchCCSSL_cifar100_10000_4gpu.yaml b/ppcls/configs/ssl/CCSSL/FixMatchCCSSL_cifar100_10000_4gpu.yaml
index 72e39816e2b2402b0047dab76562124e3c48accd..a2382817d742cfadd35426e444c73054b4b3be1f 100644
--- a/ppcls/configs/ssl/CCSSL/FixMatchCCSSL_cifar100_10000_4gpu.yaml
+++ b/ppcls/configs/ssl/CCSSL/FixMatchCCSSL_cifar100_10000_4gpu.yaml
@@ -162,7 +162,7 @@ DataLoader:
                   contrast: 0.4
                   saturation: 0.4
                   hue: 0.1
-            p: 1.0
+            p: 1.0  # refer to official settings
         - RandomGrayscale:
             p: 0.2
         - NormalizeImage:
diff --git a/ppcls/configs/ssl/CCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml b/ppcls/configs/ssl/CCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml
index d7f640bbc03e483523177b52abc9215b66bae305..79667edc68df7b90a94cb8c7f6e33c02511470c7 100644
--- a/ppcls/configs/ssl/CCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml
+++ b/ppcls/configs/ssl/CCSSL/FixMatchCCSSL_cifar10_4000_4gpu.yaml
@@ -162,7 +162,7 @@ DataLoader:
                   contrast: 0.4
                   saturation: 0.4
                   hue: 0.1
-            p: 1.0
+            p: 1.0  # refer to official settings
         - RandomGrayscale:
             p: 0.2
         - NormalizeImage:
diff --git a/ppcls/engine/evaluation/retrieval.py b/ppcls/engine/evaluation/retrieval.py
index 4d823809762aaad741497bc7ff3ed486084ee631..875a01c3bffb1b9fe81e621ccdea6ca7162e0e05 100644
--- a/ppcls/engine/evaluation/retrieval.py
+++ b/ppcls/engine/evaluation/retrieval.py
@@ -15,7 +15,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-import platform
 from collections import defaultdict
 
 import numpy as np
@@ -257,7 +256,7 @@ def compute_re_ranking_dist(query_feat: paddle.Tensor,
 
     original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
     V = np.zeros_like(original_dist).astype(np.float16)
-    initial_rank = np.argpartition(original_dist, range(1, k1 + 1))  # 22.2s
+    initial_rank = np.argpartition(original_dist, range(1, k1 + 1))
     logger.info("Start re-ranking...")
 
     for p in range(num_all):