Reset debugging

python/tvm/relay/analysis/sparse_dense.py

@@ -22,8 +22,8 @@
 """
 from collections import namedtuple
 import numpy as np
-
-from ... import nd, runtime
+import scipy.sparse as sp
+import tvm
 from . import _ffi_api
 
 
@@ -73,8 +73,6 @@ def process_params(expr, params, block_size, sparsity_threshold):
     ret : Namedtuple[weight_name: Array[String], weight_shape: Array[Array[IntImm]]]
         return names of qualified dense weight and the shape in BSR format
     """
-    import scipy.sparse as sp
-
     memo = SparseAnalysisResult(weight_name=[], weight_shape=[])
     weight_names = _search_dense_op_weight(expr)
     for name in weight_names:
@@ -91,11 +89,11 @@ def process_params(expr, params, block_size, sparsity_threshold):
                 + list(sparse_weight.indices.shape)
                 + list(sparse_weight.indptr.shape)
             )
-            params[name + ".data"] = nd.array(sparse_weight.data)
-            params[name + ".indices"] = nd.array(sparse_weight.indices)
-            params[name + ".indptr"] = nd.array(sparse_weight.indptr)
+            params[name + ".data"] = tvm.nd.array(sparse_weight.data)
+            params[name + ".indices"] = tvm.nd.array(sparse_weight.indices)
+            params[name + ".indptr"] = tvm.nd.array(sparse_weight.indptr)
     ret = SparseAnalysisResult(
-        weight_name=runtime.convert(memo.weight_name),
-        weight_shape=runtime.convert(memo.weight_shape),
+        weight_name=tvm.runtime.convert(memo.weight_name),
+        weight_shape=tvm.runtime.convert(memo.weight_shape),
     )
     return ret

python/tvm/topi/testing/conv1d_transpose_ncw_python.py

@@ -17,6 +17,7 @@
 # pylint: disable=unused-variable
 """Transposed 1D convolution in python"""
 import numpy as np
+import scipy
 import tvm.topi.testing
 from tvm.topi.nn.utils import get_pad_tuple1d
 
@@ -50,8 +51,6 @@ def conv1d_transpose_ncw_python(a_np, w_np, stride, padding, output_padding):
         3-D with shape [batch, out_channel, out_width]
 
     """
-    import scipy
-
     batch, in_c, in_w = a_np.shape
     _, out_c, filter_w = w_np.shape
     opad = output_padding[0]

python/tvm/topi/testing/conv2d_hwcn_python.py

@@ -17,6 +17,7 @@
 # pylint: disable=invalid-name, line-too-long, unused-variable, too-many-locals
 """Convolution in python"""
 import numpy as np
+import scipy.signal
 from tvm.topi.nn.utils import get_pad_tuple
 
 
@@ -44,8 +45,6 @@ def conv2d_hwcn_python(a_np, w_np, stride, padding):
     b_np : np.ndarray
         4-D with shape [out_height, out_width, out_channel, batch]
     """
-    import scipy.signal
-
     in_height, in_width, in_channel, batch = a_np.shape
     kernel_h, kernel_w, _, num_filter = w_np.shape
     if isinstance(stride, int):