From 7b898d058cbabd922dc34476cd0ac3c0371c7a8d Mon Sep 17 00:00:00 2001
From: Ramana Radhakrishnan <ramana.radhakrishnan@arm.com>
Date: Fri, 2 Jul 2021 22:28:45 +0100
Subject: [PATCH] Fix np.int and np.float usage in the tree. (#8389)

* Fix np.int and np.float usage in the tree.

Newer versions of numpy give loads of warnings that suggest
that np.int and np.float will be deprecated. CI uses pytest
and these warning logs clog memory for testing and make it
slower.

* Fix formatting
---
 python/tvm/autotvm/task/task.py               |  2 +-
 .../topi/testing/depthwise_conv2d_python.py   | 36 +++++++++----------
 2 files changed, 17 insertions(+), 21 deletions(-)

diff --git a/python/tvm/autotvm/task/task.py b/python/tvm/autotvm/task/task.py
index 1f5827d7e9d0..3097c29c3b00 100644
--- a/python/tvm/autotvm/task/task.py
+++ b/python/tvm/autotvm/task/task.py
@@ -61,7 +61,7 @@ def _encode(x):
             return ("TENSOR", get_const_tuple(x.shape), x.dtype)
         if isinstance(x, (tuple, list, container.Array)):
             return tuple([_encode(a) for a in x])
-        if isinstance(x, (str, int, float, np.int, np.float, expr.Var, expr.Any)):
+        if isinstance(x, (str, int, float, expr.Var, expr.Any)):
             return x
         if isinstance(x, (expr.StringImm, expr.IntImm, expr.FloatImm)):
             return x.value
diff --git a/python/tvm/topi/testing/depthwise_conv2d_python.py b/python/tvm/topi/testing/depthwise_conv2d_python.py
index 2239c56134f5..02964ecfae3b 100644
--- a/python/tvm/topi/testing/depthwise_conv2d_python.py
+++ b/python/tvm/topi/testing/depthwise_conv2d_python.py
@@ -67,17 +67,15 @@ def depthwise_conv2d_python_nchw(input_np, filter_np, stride, padding):
                 ]
     elif padding == "SAME":
         out_channel = in_channel * channel_multiplier
-        out_height = np.int(np.ceil(float(in_height) / float(stride_h)))
-        out_width = np.int(np.ceil(float(in_width) / float(stride_w)))
+        out_height = int(np.ceil(float(in_height) / float(stride_h)))
+        out_width = int(np.ceil(float(in_width) / float(stride_w)))
         output_np = np.zeros((batch, out_channel, out_height, out_width))
-        pad_along_height = np.int(
-            np.max((out_height - 1) * stride_h + filter_height - in_height, 0)
-        )
-        pad_along_width = np.int(np.max((out_width - 1) * stride_w + filter_width - in_width, 0))
-        pad_top_tvm = np.int(np.ceil(float(pad_along_height) / 2))
-        pad_left_tvm = np.int(np.ceil(float(pad_along_width) / 2))
-        pad_top_scipy = np.int(np.ceil(float(filter_height - 1) / 2))
-        pad_left_scipy = np.int(np.ceil(float(filter_width - 1) / 2))
+        pad_along_height = int(np.max((out_height - 1) * stride_h + filter_height - in_height, 0))
+        pad_along_width = int(np.max((out_width - 1) * stride_w + filter_width - in_width, 0))
+        pad_top_tvm = int(np.ceil(float(pad_along_height) / 2))
+        pad_left_tvm = int(np.ceil(float(pad_along_width) / 2))
+        pad_top_scipy = int(np.ceil(float(filter_height - 1) / 2))
+        pad_left_scipy = int(np.ceil(float(filter_width - 1) / 2))
         index_h = pad_top_scipy - pad_top_tvm
         index_w = pad_left_scipy - pad_left_tvm
         for i in range(batch):
@@ -138,17 +136,15 @@ def depthwise_conv2d_python_nhwc(input_np, filter_np, stride, padding):
                 ]
     if padding == "SAME":
         out_channel = in_channel * channel_multiplier
-        out_height = np.int(np.ceil(float(in_height) / float(stride_h)))
-        out_width = np.int(np.ceil(float(in_width) / float(stride_w)))
+        out_height = int(np.ceil(float(in_height) / float(stride_h)))
+        out_width = int(np.ceil(float(in_width) / float(stride_w)))
         output_np = np.zeros((batch, out_height, out_width, out_channel))
-        pad_along_height = np.int(
-            np.max((out_height - 1) * stride_h + filter_height - in_height, 0)
-        )
-        pad_along_width = np.int(np.max((out_width - 1) * stride_w + filter_width - in_width, 0))
-        pad_top_tvm = np.int(np.ceil(float(pad_along_height) / 2))
-        pad_left_tvm = np.int(np.ceil(float(pad_along_width) / 2))
-        pad_top_scipy = np.int(np.ceil(float(filter_height - 1) / 2))
-        pad_left_scipy = np.int(np.ceil(float(filter_width - 1) / 2))
+        pad_along_height = int(np.max((out_height - 1) * stride_h + filter_height - in_height, 0))
+        pad_along_width = int(np.max((out_width - 1) * stride_w + filter_width - in_width, 0))
+        pad_top_tvm = int(np.ceil(float(pad_along_height) / 2))
+        pad_left_tvm = int(np.ceil(float(pad_along_width) / 2))
+        pad_top_scipy = int(np.ceil(float(filter_height - 1) / 2))
+        pad_left_scipy = int(np.ceil(float(filter_width - 1) / 2))
         index_h = pad_top_scipy - pad_top_tvm
         index_w = pad_left_scipy - pad_left_tvm
         for i in range(batch):