chore: import upstream snapshot with attribution

python/tvm/topi/testing/conv1d_transpose_ncw_python.py

@@ -0,0 +1,92 @@
+# 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=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
+
+
+def group_conv1d_transpose_ncw_python(a_np, w_np, stride, padding, output_padding, groups=1):
+    "Grouped version of `conv1d_transpose_ncw_python`, see that for documentation"
+    a_slices = np.array_split(a_np, groups, axis=1)
+    w_slices = np.array_split(w_np, groups, axis=0)
+    b_slices = [
+        conv1d_transpose_ncw_python(a_slice, w_slice, stride, padding, output_padding)
+        for a_slice, w_slice in zip(a_slices, w_slices)
+    ]
+    b_np = np.concatenate(b_slices, axis=1)
+    return b_np
+
+
+def conv1d_transpose_ncw_python(a_np, w_np, stride, padding, output_padding):
+    """Transposed 1D convolution operator in NCW layout.
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        3-D with shape [batch, in_channel, in_width]
+
+    w_np : numpy.ndarray
+        3-D with shape [in_channel, num_filter, filter_width]
+
+    stride : int or a list/tuple of one int
+        Stride size, or [stride_width]
+
+    padding : int, tuple, or str
+        Single int for padding size, or
+        tuple of 2 ints for left and right padding, or
+        ['VALID', 'SAME']
+
+    output_padding : tuple
+        Used to recover the actual output shape in case more than one
+        is possible
+
+    Returns
+    -------
+    b_np : np.ndarray
+        3-D with shape [batch, out_channel, out_width]
+
+    """
+    batch, in_c, in_w = a_np.shape
+    _, out_c, filter_w = w_np.shape
+    opad = output_padding[0]
+    if isinstance(stride, int):
+        stride_w = stride
+    else:
+        stride_w = stride[0]
+    assert opad < stride_w
+    fpad_left, fpad_right = get_pad_tuple1d(padding, filter_w)
+    # dilate stage
+    dilated_a_np = tvm.topi.testing.dilate_python(a_np, [1, 1, stride_w])
+    # padding stage
+    bpad_left = filter_w - 1 - fpad_left
+    bpad_right = filter_w - 1 - fpad_right + opad
+    padded_a_np = np.zeros((batch, in_c, dilated_a_np.shape[2] + bpad_left + bpad_right))
+    padded_a_np[:, :, bpad_left : dilated_a_np.shape[2] + bpad_left] = dilated_a_np
+    # convolution stage
+    out_w = (in_w - 1) * stride_w - fpad_left - fpad_right + filter_w + opad
+    b_np = np.zeros((batch, out_c, out_w))
+    for n in range(batch):
+        for f in range(out_c):
+            for c in range(in_c):
+                out = scipy.signal.convolve(padded_a_np[n, c], w_np[c, f], mode="valid")
+                b_np[n, f] += out
+    return b_np