[UTILS] Move target to tvm; rename convolution as conv2d (#492)

* Move target to tvm; rename convolution as conv2d

* Fix

* Fix

python/tvm/__init__.py

@@ -14,6 +14,7 @@ from . import schedule
 from . import module
 from . import node
 from . import ir_builder
+from . import target
 
 from . import ndarray as nd
 from .ndarray import context, cpu, gpu, opencl, cl, metal, mtl, vpi, rocm

topi/python/topi/target.py → python/tvm/target.py

-"""Target management API of topi"""
+"""Target management API of tvm"""
 
 from __future__ import absolute_import
 

topi/python/topi/__init__.py

@@ -16,6 +16,5 @@ from .broadcast import *
 from . import nn
 from . import cuda
 from . import rasp
-from . import target
 from . import testing
 from . import util

topi/python/topi/nn/__init__.py

@@ -3,7 +3,7 @@
 from __future__ import absolute_import as _abs
 
 from .batch_norm import *
-from .convolution import *
+from .conv2d import *
 from .depthwise_convolution import *
 from .elemwise import *
 from .dilate import *

topi/python/topi/nn/convolution.py → topi/python/topi/nn/conv2d.py

 # pylint: disable=invalid-name, unused-variable, too-many-locals
-"""Convolution operators"""
+"""Conv2D operators"""
 from __future__ import absolute_import as _abs
 from collections import namedtuple
 import tvm
+from tvm import target as _target
 from .pad import pad
 from .util import get_pad_tuple
 from ..util import simplify
-from .. import target as _target
 
-# workload description of convolution
+# workload description of conv2d
 Workload = namedtuple('Workload',
                       ['height', 'width', 'in_filter', 'out_filter',
                        'hkernel', 'wkernel', 'hpad', 'wpad', 'hstride', 'wstride'])
@@ -43,8 +43,8 @@ _CONV_SCHEDULE = {}
 # platform specific declaration
 _CONV_DECLARATION = {}
 
-def convolution(data, kernel, stride, padding, layout='NCHW'):
-    """Convolution operator.
+def conv2d(data, kernel, stride, padding, layout='NCHW'):
+    """Conv2D operator.
 
     Parameters
     ----------
@@ -75,9 +75,9 @@ def convolution(data, kernel, stride, padding, layout='NCHW'):
 
     # default declaration
     if layout == 'NCHW':
-        conv2d_nchw(data, kernel, stride, padding)
+        return conv2d_nchw(data, kernel, stride, padding)
     elif layout == 'HWCN':
-        conv2d_hwcn(data, kernel, stride, padding)
+        return conv2d_hwcn(data, kernel, stride, padding)
     else:
         raise ValueError("not support this layout {} yet".format(layout))
 

topi/python/topi/rasp/__init__.py

@@ -2,4 +2,4 @@
 """Raspberry pi specific declaration and schedules."""
 from __future__ import absolute_import as _abs
 
-from .convolution import *
+from .conv2d import *

topi/python/topi/rasp/convolution.py → topi/python/topi/rasp/conv2d.py

 # pylint: disable=invalid-name,unused-variable,invalid-name
-"""Convolution schedule on raspberry pi"""
+"""Conv2D schedule on raspberry pi"""
 from __future__ import absolute_import as _abs
 import tvm
-from .. import target as _target
+from tvm import target as _target
 from .. import tag
-from ..nn.convolution import SpatialPack, Im2ColPack
-from ..nn.convolution import _CONV_DECLARATION, _CONV_SCHEDULE
-from ..nn.convolution import _WORKLOADS, _SCH_TO_DECL_FUNC
-from ..nn.convolution import _get_workload, _get_schedule
+from ..nn.conv2d import SpatialPack, Im2ColPack
+from ..nn.conv2d import _CONV_DECLARATION, _CONV_SCHEDULE
+from ..nn.conv2d import _WORKLOADS, _SCH_TO_DECL_FUNC
+from ..nn.conv2d import _get_workload, _get_schedule
 from ..nn.util import infer_pad, infer_stride
 
 _SCHEDULES = [
@@ -264,7 +264,7 @@ def _schedule_im2col_conv2d(s, data, data_pad, data_col, data_vec,
 
     return s
 
-def schedule_convolution(outs):
+def schedule_conv2d(outs):
     """Create schedule for tensors"""
     s = tvm.create_schedule([x.op for x in outs])
 

topi/tests/python/test_topi_convolution.py → topi/tests/python/test_topi_conv2d.py

-"""Example code to do convolution."""
+"""Example code to do conv2d."""
 import os
 import numpy as np
 import tvm
@@ -7,20 +7,20 @@ from tvm.contrib.pickle_memoize import memoize
 from topi.util import get_const_tuple
 
 
-def verify_convolution(batch, in_size, in_channel, num_filter, kernel, stride, padding):
+def verify_conv2d(batch, in_size, in_channel, num_filter, kernel, stride, padding):
     in_height = in_width = in_size
 
-    with topi.target.rasp():
+    with tvm.target.rasp():
         A = tvm.placeholder((batch, in_channel, in_height, in_width), name='A')
         W = tvm.placeholder((num_filter, in_channel, kernel, kernel), name='W')
-        B = topi.nn.convolution(A, W, stride, padding)
+        B = topi.nn.conv2d(A, W, stride, padding)
 
-    s = topi.rasp.schedule_convolution([B])
+    s = topi.rasp.schedule_conv2d([B])
     a_shape = get_const_tuple(A.shape)
     w_shape = get_const_tuple(W.shape)
     dtype = A.dtype
 
-    @memoize("topi.tests.test_topi_convolution.verify_convolution")
+    @memoize("topi.tests.test_topi_conv2d.verify_conv2d")
     def get_ref_data():
         a_np = np.random.uniform(size=a_shape).astype(dtype)
         w_np = np.random.uniform(size=w_shape).astype(dtype)
@@ -37,8 +37,8 @@ def verify_convolution(batch, in_size, in_channel, num_filter, kernel, stride, p
     func(a, w, b)
     np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
 
-def test_convolution():
-    verify_convolution(1, 56,  64, 64,  3, 1, 1)
+def test_conv2d():
+    verify_conv2d(1, 56,  64, 64,  3, 1, 1)
 
 if __name__ == "__main__":
-    test_convolution()
+    test_conv2d()