From e83b2ce8531aa950d15d32670dd1cfc1d35519e3 Mon Sep 17 00:00:00 2001
From: XYE <92607131+Itok2000u@users.noreply.github.com>
Date: Wed, 13 Jul 2022 11:33:16 +0800
Subject: [PATCH] [NFC] polish colossalai/nn/layer/vanilla/layers.py code style
 (#1295)

---
 colossalai/nn/layer/vanilla/layers.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/colossalai/nn/layer/vanilla/layers.py b/colossalai/nn/layer/vanilla/layers.py
index dfc37af13..a90871236 100644
--- a/colossalai/nn/layer/vanilla/layers.py
+++ b/colossalai/nn/layer/vanilla/layers.py
@@ -29,9 +29,9 @@ def drop_path(x, drop_prob: float = 0., training: bool = False):
     if drop_prob == 0. or not training:
         return x
     keep_prob = 1 - drop_prob
-    shape = (x.shape[0], ) + (1, ) * (x.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)    # work with diff dim tensors, not just 2D ConvNets
     random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
+    random_tensor.floor_()    # binarize
     output = x.div(keep_prob) * random_tensor
     return output
 
@@ -190,7 +190,7 @@ class VanillaPatchEmbedding(nn.Module):
             f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
         output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size)
         if self.flatten:
-            output = output.flatten(2).transpose(1, 2)  # BCHW -> BNC
+            output = output.flatten(2).transpose(1, 2)    # BCHW -> BNC
 
         cls_token = self.cls_token.expand(output.shape[0], -1, -1)
         output = torch.cat((cls_token, output), dim=1)