diff --git a/topi/python/topi/mali/conv2d.py b/topi/python/topi/mali/conv2d.py
index 4be4161540f8..8f59040b23a6 100644
--- a/topi/python/topi/mali/conv2d.py
+++ b/topi/python/topi/mali/conv2d.py
@@ -122,8 +122,8 @@ def decl_conv2d(data, kernel, stride, padding, layout='NCHW', out_dtype='float32
 
     gemm_factor = 4
 
-    if (kernel_shape[2:4] == (3, 3) and (HPAD, WPAD) == (1, 1) and kernel_shape[0] >= 64
-       and data_shape[2] * data_shape[3] // 4 % gemm_factor == 0 and (HSTR, WSTR) == (1, 1)):
+    if (kernel_shape[2:4] == (3, 3) and (HPAD, WPAD) == (1, 1) and kernel_shape[0] >= 64 and
+            data_shape[2] * data_shape[3] // 4 % gemm_factor == 0 and (HSTR, WSTR) == (1, 1)):
         return _decl_winograd(data, kernel, stride, padding, layout, out_dtype)
     elif kernel_shape[2:4] == (1, 1):
         return _decl_im2col(data, kernel, stride, padding, layout, out_dtype)
@@ -559,7 +559,8 @@ def _decl_winograd(data, kernel, stride, padding, layout, out_dtype):
     # pack input tile
     input_tile = tvm.compute((C, P // bnb, alpha, alpha, bnb),
                              lambda c, b, eps, nu, bb:
-                             data_pad[(b*bnb+bb) // (nH*nW)][c][(b*bnb+bb) // nW % nH * m + eps][(b*bnb+bb) % nW * m + nu],
+                             data_pad[(b*bnb+bb) // (nH*nW)][c][(b*bnb+bb) // nW % nH * m + eps]
+                             [(b*bnb+bb) % nW * m + nu],
                              name='d')
 
     # transform kernel
@@ -567,34 +568,34 @@ def _decl_winograd(data, kernel, stride, padding, layout, out_dtype):
     r_kh = tvm.reduce_axis((0, KH), 'r_kh')
     r_kw = tvm.reduce_axis((0, KW), 'r_kw')
     U = tvm.compute((alpha, alpha, K // bna, C, bna), lambda eps, nu, k, c, kk:
-    tvm.sum(kernel[k * bna + kk][c][r_kh][r_kw] * G[eps][r_kh] * G[nu][r_kw], axis=[r_kh, r_kw]),
-                    name='U')
+                    tvm.sum(kernel[k * bna + kk][c][r_kh][r_kw] * G[eps][r_kh] * G[nu][r_kw],
+                            axis=[r_kh, r_kw]), name='U')
 
     # transform image
     B = const_array(B_data, 'B')
     r_eps = tvm.reduce_axis((0, alpha), 'r_eps')
     r_nu = tvm.reduce_axis((0, alpha), 'r_nu')
     V = tvm.compute((alpha, alpha, P // bnb, C, bnb), lambda eps, nu, b, c, bb:
-    tvm.sum(input_tile[c][b][r_eps][r_nu][bb] * B[r_eps][eps] * B[r_nu][nu], axis=[r_eps, r_nu]),
-                    name='V')
+                    tvm.sum(input_tile[c][b][r_eps][r_nu][bb] * B[r_eps][eps] * B[r_nu][nu],
+                            axis=[r_eps, r_nu]), name='V')
 
     # batch gemm
     c = tvm.reduce_axis((0, C), name='c')
     M = tvm.compute((alpha, alpha, K, P), lambda eps, nu, k, b:
-    tvm.sum(U[eps][nu][k // bna][c][k % bna] *
-            V[eps][nu][b // bnb][c][b % bnb], axis=c), name='M')
+                    tvm.sum(U[eps][nu][k // bna][c][k % bna] *
+                            V[eps][nu][b // bnb][c][b % bnb], axis=c), name='M')
 
     # inverse transform
     A = const_array(A_data, 'A')
     r_eps = tvm.reduce_axis((0, alpha), 'r_eps')
     r_nu = tvm.reduce_axis((0, alpha), 'r_nu')
     Y = tvm.compute((K, P, m, m), lambda k, b, vh, vw:
-    tvm.sum(M[r_eps][r_nu][k][b] * A[r_eps][vh] * A[r_nu][vw], axis=[r_eps, r_nu]),
-                    name='Y')
+                    tvm.sum(M[r_eps][r_nu][k][b] * A[r_eps][vh] * A[r_nu][vw],
+                            axis=[r_eps, r_nu]), name='Y')
 
     # unpack output
     output = tvm.compute((N, K, H, W), lambda n, k, h, w:
-    Y[k][n * nH * nW + (h//m) * nW + w//m][h % m][w % m],
+                         Y[k][n * nH * nW + (h//m) * nW + w//m][h % m][w % m],
                          name='output', tag='winograd_conv_output')
 
     return output
@@ -628,7 +629,7 @@ def _schedule_winograd(s, op):
     eps, nu, k, c, kk, = s[U].op.axis
     r_kh, r_kw = s[U].op.reduce_axis
     s[U].reorder(k, c, kk, eps, nu, r_kh, r_kw)
-    [s[U].unroll(x) for x in [eps, nu, r_kh, r_kw]]
+    _ = [s[U].unroll(x) for x in [eps, nu, r_kh, r_kw]]
     s[U].vectorize(kk)
     tile_and_bind(s, U, k, c, 1, 256)
 
@@ -637,7 +638,7 @@ def _schedule_winograd(s, op):
     eps, nu, b, c, bb = s[V].op.axis
     r_eps, r_nu = s[V].op.reduce_axis
     s[V].reorder(b, c, bb, eps, nu, r_nu, r_eps)
-    [s[V].unroll(x) for x in [eps, nu, r_eps, r_nu]]
+    _ = [s[V].unroll(x) for x in [eps, nu, r_eps, r_nu]]
     s[V].vectorize(bb)
     tile_and_bind(s, V, b, c, 2, 1)
 
@@ -661,7 +662,7 @@ def _schedule_winograd(s, op):
     s[A].compute_inline()
     k, b, vh, vw = s[Y].op.axis
     r_eps, r_nu = s[Y].op.reduce_axis
-    [s[Y].unroll(x) for x in [vh, vw, r_eps, r_nu]]
+    _ = [s[Y].unroll(x) for x in [vh, vw, r_eps, r_nu]]
     tile_and_bind(s, Y, k, b, 4, 1)
 
     # schedule output