chore: import upstream snapshot with attribution

python/tvm/topi/testing/conv3d_transpose_ncdhw_python.py

@@ -0,0 +1,145 @@
+# 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, too-many-branches
+# ruff: noqa: F841
+"""Convolution 3D transpose in python"""
+
+import numpy as np
+
+import tvm.topi.testing
+from tvm.topi.nn.utils import get_pad_tuple3d
+
+
+def _conv3d_transpose_ncdhw_python(a_np, w_np, stride, padding, output_padding):
+    """Transposed 3d convolution operator in NCDHW layout.
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        5-D with shape [batch, in_channel, in_depth, in_height, in_width]
+
+    w_np : numpy.ndarray
+        5-D with shape [in_channel, num_filter, filter_depth, filter_height, filter_width]
+
+    stride : int or a list/tuple of two ints
+        Stride size, or [stride_depth, stride_height, stride_width]
+
+    padding : int or str
+        Padding size
+
+    output_padding : int or list/tuple of three ints
+        Used to disambiguate output shape.
+
+    Returns
+    -------
+    b_np : np.ndarray
+        5-D with shape [batch, out_channel, out_depth, out_height, out_width]
+    """
+    batch, in_c, in_d, in_h, in_w = a_np.shape
+    _, out_c, filter_d, filter_h, filter_w = w_np.shape
+    if isinstance(stride, int):
+        stride_d = stride_h = stride_w = stride
+    else:
+        stride_d, stride_h, stride_w = stride
+    if isinstance(output_padding, int):
+        opad_d = opad_h = opad_w = output_padding
+    else:
+        opad_d, opad_h, opad_w = output_padding
+    assert opad_d < stride_d and opad_h < stride_h and opad_w < stride_w
+
+    # dilate stage
+    dilated_a_np = tvm.topi.testing.dilate_python(a_np, [1, 1, stride_d, stride_h, stride_w])
+
+    # padding stage
+    fpad_front, fpad_top, fpad_left, fpad_back, fpad_bottom, fpad_right = get_pad_tuple3d(
+        padding, (filter_d, filter_h, filter_w)
+    )
+
+    bpad_front = filter_d - 1 - fpad_front
+    bpad_back = filter_d - 1 - fpad_back + opad_d
+    bpad_top = filter_h - 1 - fpad_top
+    bpad_bottom = filter_h - 1 - fpad_bottom + opad_h
+    bpad_left = filter_w - 1 - fpad_left
+    bpad_right = filter_w - 1 - fpad_right + opad_w
+
+    padded_a_np = np.zeros(
+        (
+            batch,
+            in_c,
+            dilated_a_np.shape[2] + bpad_front + bpad_back,
+            dilated_a_np.shape[3] + bpad_top + bpad_bottom,
+            dilated_a_np.shape[4] + bpad_left + bpad_right,
+        )
+    )
+
+    padded_a_np[
+        :,
+        :,
+        bpad_front : dilated_a_np.shape[2] + bpad_front,
+        bpad_top : dilated_a_np.shape[3] + bpad_top,
+        bpad_left : dilated_a_np.shape[4] + bpad_left,
+    ] = dilated_a_np
+
+    # convolution stage
+    out_d = (in_d - 1) * stride_d - bpad_front - bpad_back + filter_d
+    out_h = (in_h - 1) * stride_h - fpad_top - fpad_bottom + filter_h
+    out_w = (in_w - 1) * stride_w - fpad_left - fpad_right + filter_w
+
+    w_np = np.flip(w_np, axis=[2, 3, 4]).transpose((1, 0, 2, 3, 4))
+    b_np = tvm.topi.testing.conv3d_ncdhw_python(
+        padded_a_np, w_np, stride=(1, 1, 1), padding=(0, 0, 0)
+    )
+
+    return b_np
+
+
+def conv3d_transpose_ncdhw_python(a_np, w_np, stride, padding, output_padding, groups=1):
+    """Transposed 3d convolution operator in NCDHW layout.
+
+    Parameters
+    ----------
+    a_np : numpy.ndarray
+        5-D with shape [batch, in_channel, in_depth, in_height, in_width]
+
+    w_np : numpy.ndarray
+        5-D with shape [in_channel, num_filter, filter_depth, filter_height, filter_width]
+
+    stride : int or a list/tuple of two ints
+        Stride size, or [stride_depth, stride_height, stride_width]
+
+    padding : int or str
+        Padding size
+
+    output_padding : int or list/tuple of three ints
+        Used to disambiguate output shape.
+
+    groups : int
+        Number of groups
+
+    Returns
+    -------
+    b_np : np.ndarray
+        5-D with shape [batch, out_channel, out_depth, out_height, out_width]
+    """
+    a_slices = np.array_split(a_np, groups, axis=1)
+    w_slices = np.array_split(w_np, groups, axis=0)
+    b_slices = [
+        _conv3d_transpose_ncdhw_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