chore: import upstream snapshot with attribution

python/tvm/topi/testing/conv2d_nhwc_python.py

@@ -0,0 +1,116 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# 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
+
+
+def _conv2d_nhwc_python(a_np, w_np, stride, padding):
+    """Convolution operator in NHWC layout.
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        4-D with shape [batch, in_height, in_width, in_channel]
+
+    w_np : numpy.ndarray
+        4-D with shape [filter_height, filter_width, in_channel, num_filter]
+
+    stride : int or a list/tuple of two ints
+        Stride size, or [stride_height, stride_width]
+
+    padding : int or str or a list/tuple of two ints
+        Padding size, or ['VALID', 'SAME'], or [pad_height, pad_width]
+
+    Returns
+    -------
+    b_np : np.ndarray
+        4-D with shape [batch, out_height, out_width, out_channel]
+    """
+    batch, in_height, in_width, in_channel = a_np.shape
+    kernel_h, kernel_w, _, num_filter = w_np.shape
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
+        stride_h, stride_w = stride
+
+    pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (kernel_h, kernel_w))
+    pad_h = pad_top + pad_bottom
+    pad_w = pad_left + pad_right
+
+    # compute the output shape
+    out_channel = num_filter
+    out_height = (in_height - kernel_h + pad_h) // stride_h + 1
+    out_width = (in_width - kernel_w + pad_w) // stride_w + 1
+    # change the layout from NHWC to NCHW
+    at = a_np.transpose((0, 3, 1, 2))
+    wt = w_np.transpose((3, 2, 0, 1))
+    bt = np.zeros((batch, out_channel, out_height, out_width))
+    # computation
+    for n in range(batch):
+        for f in range(out_channel):
+            for c in range(in_channel):
+                if pad_h > 0 or pad_w > 0:
+                    apad = np.zeros((in_height + pad_h, in_width + pad_w))
+                    apad[pad_top : pad_top + in_height, pad_left : pad_left + in_width] = at[n, c]
+                else:
+                    apad = at[n, c]
+                out = scipy.signal.convolve2d(apad, np.rot90(np.rot90(wt[f, c])), mode="valid")
+                bt[n, f] += out[::stride_h, ::stride_w]
+    return bt.transpose((0, 2, 3, 1))
+
+
+def conv2d_nhwc_python(a_np, w_np, stride, padding, groups=1):
+    """Convolution operator in NHWC layout.
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        4-D with shape [batch, in_height, in_width, in_channel]
+
+    w_np : numpy.ndarray
+        4-D with shape [filter_height, filter_width, in_channel // groups, num_filter]
+
+    stride : int or a list/tuple of two ints
+        Stride size, or [stride_height, stride_width]
+
+    padding : int or str or a list/tuple of 2 or 4 ints
+        Padding size, or ['VALID', 'SAME'], or
+        [pad_height, pad_width] for 2 ints, or
+        [pad_top, pad_left, pad_bottom, pad_right] for 2 ints
+
+    groups : int
+        Number of groups
+
+    Returns
+    -------
+    b_np : np.ndarray
+        4-D with shape [batch, out_height, out_width, out_channel]
+    """
+
+    a_slices = np.array_split(a_np, groups, axis=3)
+    w_slices = np.array_split(w_np, groups, axis=3)
+    b_slices = [
+        _conv2d_nhwc_python(a_slice, w_slice, stride, padding)
+        for a_slice, w_slice in zip(a_slices, w_slices)
+    ]
+    b_np = np.concatenate(b_slices, axis=3)
+    return b_np