Fix np.int and np.float usage in the tree. (apache#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

@@ -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

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):