fix lint

python/tvm/relay/frontend/coreml.py

@@ -313,7 +313,8 @@ def _UpsampleLayerParams(op, inexpr, etab):
         raise tvm.error.OpAttributeUnimplemented(
             'Upsample height and width must be equal.')
     interpolationMode = 'nearest_neighbor' if op.mode == 0 else 'bilinear'
-    return _op.nn.upsampling(inexpr, scaleH=op.scalingFactor[0], scaleW=op.scalingFactor[1], method=interpolationMode)
+    return _op.nn.upsampling(inexpr, scaleH=op.scalingFactor[0],
+                             scaleW=op.scalingFactor[1], method=interpolationMode)
 
 
 def _L2NormalizeLayerParams(op, inexpr, etab):

python/tvm/relay/frontend/onnx.py

@@ -590,7 +590,8 @@ def _impl_v9(cls, inputs, attr, params):
         else:
             raise tvm.error.OpAttributeInvalid(
                 'Value {} in attribute "mode" of operator Upsample is not valid.'.format(mode))
-        attr = {'scaleH':scales[-2], 'scaleW':scales[-1], 'method':method, 'layout':'NCHW', 'align_corners':True}
+        attr = {'scaleH':scales[-2], 'scaleW':scales[-1], 'method':method,
+                'layout':'NCHW', 'align_corners':True}
         return AttrCvt('upsampling')(inputs, attr)
 
 

tests/python/relay/test_op_level2.py

@@ -237,7 +237,8 @@ def test_upsampling_infer_type():
     y = relay.nn.upsampling(x, scaleH=2, scaleW=2, layout="NCHW", method="bilinear")
     "method=\"BINLINEAR\"" in y.astext()
     yy = run_infer_type(y)
-    assert yy.checked_type == relay.TensorType((n, c, tvm.expr.Cast("int32", tvm.round(h*scale)), tvm.expr.Cast("int32", tvm.round(w*scale))), "float32")
+    assert yy.checked_type == relay.TensorType((n, c, tvm.expr.Cast("int32", tvm.round(h*scale)),
+                                                tvm.expr.Cast("int32", tvm.round(w*scale))), "float32")
     n, c = tvm.var("n"), tvm.var("c")
     x = relay.var("x", relay.TensorType((n, c, 100, 200), "float32"))
     y = relay.nn.upsampling(x, scaleH=2, scaleW=2, layout="NCHW", method="bilinear")
@@ -528,7 +529,8 @@ def get_shape():
     if method == "nearest_neighbor":
         ref = topi.testing.upsampling_python(data, (scaleH, scaleW), layout)
     else:
-        ref = topi.testing.bilinear_resize_python(data, (int(round(h*scaleH)), int(round(w*scaleW))), layout)
+        ref = topi.testing.bilinear_resize_python(data, (int(round(h*scaleH)),
+                                                  int(round(w*scaleW))), layout)
     for target, ctx in ctx_list():
         executor = relay.create_executor("graph", ctx=ctx, target=target)
         out = executor.evaluate(func)(data)

topi/python/topi/nn/upsampling.py

@@ -52,9 +52,11 @@ def upsampling(data, scaleH, scaleW, layout="NCHW", method='nearest_neighbor', a
     """
     base_layout = layout[0:4]
     if base_layout == "NCHW":
-        out_shape = (simplify(topi.cast(tvm.round(data.shape[2] * scaleH), data.shape[2].dtype)), simplify(topi.cast(tvm.round(data.shape[3] * scaleW), data.shape[3].dtype)))
+        out_shape = (simplify(topi.cast(tvm.round(data.shape[2] * scaleH), data.shape[2].dtype)),
+                     simplify(topi.cast(tvm.round(data.shape[3] * scaleW), data.shape[3].dtype)))
     elif layout == "NHWC":
-        out_shape = (simplify(topi.cast(tvm.round(data.shape[1] * scaleH), data.shape[1].dtype)), simplify(topi.cast(tvm.round(data.shape[2] * scaleW), data.shape[2].dtype)))
+        out_shape = (simplify(topi.cast(tvm.round(data.shape[1] * scaleH), data.shape[1].dtype)),
+                     simplify(topi.cast(tvm.round(data.shape[2] * scaleW), data.shape[2].dtype)))
 
     else:
         raise ValueError("not support this layout {} yet".format(layout))