diff --git a/python/tvm/topi/adreno/conv2d_nchw_winograd.py b/python/tvm/topi/adreno/conv2d_nchw_winograd.py
index 538fccf9c3e9..16f7cb8b19d9 100644
--- a/python/tvm/topi/adreno/conv2d_nchw_winograd.py
+++ b/python/tvm/topi/adreno/conv2d_nchw_winograd.py
@@ -18,21 +18,7 @@
 """Winograd NCHW template for Adreno backend"""
 
 import logging
-import tvm
-from tvm import te
 from tvm import autotvm
-
-from tvm.topi import nn
-from tvm.topi.utils import get_const_int, get_const_tuple, traverse_inline
-from ..nn.winograd_util import winograd_transform_matrices
-from .utils import (
-    split_to_chunks,
-    pack_input,
-    pack_filter,
-    bind_data_copy,
-    get_texture_storage,
-    infer_tile_size,
-)
 from .conv2d_winograd_common import conv2d_winograd_comp, schedule_conv2d_winograd_impl
 
 
@@ -137,4 +123,6 @@ def conv2d_nchw_winograd_comp(
     output: tvm.te.Tensor
         4-D or 5-D with shape NCHW or NCHW4c
     """
-    return conv2d_winograd_comp(cfg, data, kernel, strides, padding, dilation, out_dtype, args, pre_computed, "NCHW")
+    return conv2d_winograd_comp(
+        cfg, data, kernel, strides, padding, dilation, out_dtype, args, pre_computed, "NCHW"
+    )
diff --git a/python/tvm/topi/adreno/conv2d_nhwc_winograd.py b/python/tvm/topi/adreno/conv2d_nhwc_winograd.py
index f3850fbec171..bfe385f210a4 100644
--- a/python/tvm/topi/adreno/conv2d_nhwc_winograd.py
+++ b/python/tvm/topi/adreno/conv2d_nhwc_winograd.py
@@ -18,21 +18,7 @@
 """Winograd NHWC template for Adreno backend"""
 
 import logging
-import tvm
-from tvm import te
 from tvm import autotvm
-
-from tvm.topi import nn
-from tvm.topi.utils import get_const_int, get_const_tuple, traverse_inline
-from ..nn.winograd_util import winograd_transform_matrices
-from .utils import (
-    split_to_chunks,
-    pack_input,
-    pack_filter,
-    bind_data_copy,
-    get_texture_storage,
-    infer_tile_size,
-)
 from .conv2d_winograd_common import conv2d_winograd_comp, schedule_conv2d_winograd_impl
 
 
@@ -137,4 +123,6 @@ def conv2d_nhwc_winograd_comp(
     output: tvm.te.Tensor
         4-D or 5-D with shape NCHW or NCHW4c
     """
-    return conv2d_winograd_comp(cfg, data, kernel, strides, padding, dilation, out_dtype, args, pre_computed, "NHWC")
+    return conv2d_winograd_comp(
+        cfg, data, kernel, strides, padding, dilation, out_dtype, args, pre_computed, "NHWC"
+    )
diff --git a/python/tvm/topi/adreno/conv2d_winograd_common.py b/python/tvm/topi/adreno/conv2d_winograd_common.py
index 1b10a8cc57e2..494b691a7f07 100644
--- a/python/tvm/topi/adreno/conv2d_winograd_common.py
+++ b/python/tvm/topi/adreno/conv2d_winograd_common.py
@@ -17,7 +17,6 @@
 # pylint: disable=invalid-name,unused-variable,unused-argument
 """Common Winograd implementation for Adreno backend"""
 
-import logging
 import tvm
 from tvm import te
 from tvm import autotvm
@@ -80,7 +79,7 @@ def conv2d_winograd_comp(
     output: tvm.te.Tensor
         4-D or 5-D with shape NCHW or NCHW4c
     """
-    assert layout == "NCHW" or layout == "NHWC"
+    assert layout in ("NCHW", "NHWC")
     tile_size = infer_tile_size(data, layout)
 
     if isinstance(dilation, int):
@@ -301,9 +300,9 @@ def conv2d_winograd_comp(
         else:
             output = te.compute(
                 (N, CO, H, W, COB),
-                lambda n, co, h, w, cob: inverse[co][n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)][
-                    idxmod(h, m)
-                ][idxmod(w, m)][cob].astype(out_dtype),
+                lambda n, co, h, w, cob: inverse[co][
+                    n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)
+                ][idxmod(h, m)][idxmod(w, m)][cob].astype(out_dtype),
                 name="output",
                 tag="cast_from_acc" + args["accumulator"][-2:],
             )
@@ -320,9 +319,9 @@ def conv2d_winograd_comp(
         else:
             output = te.compute(
                 (N, H, W, CO, COB),
-                lambda n, h, w, co, cob: inverse[co][n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)][
-                    idxmod(h, m)
-                ][idxmod(w, m)][cob].astype(out_dtype),
+                lambda n, h, w, co, cob: inverse[co][
+                    n * nH * nW + idxdiv(h, m) * nW + idxdiv(w, m)
+                ][idxmod(h, m)][idxmod(w, m)][cob].astype(out_dtype),
                 name="output",
                 tag="cast_from_acc" + args["accumulator"][-2:],
             )