<reponame>peterjc123/tvm<gh_stars>10-100
# 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,unused-variable,unused-argument,no-member
"""Conv2D Transpose schedule on x86"""

import tvm
from tvm import autotvm
from .. import generic, tag
from ..nn.conv2d_transpose import conv2d_transpose_nchw, declaration_conv2d_transpose_impl

@autotvm.register_topi_compute(conv2d_transpose_nchw, 'cpu', ['direct'])
def _declaration_conv2d_transpose(cfg, data, kernel, strides, padding, out_dtype):
    # TODO cfg is not used for now
    return declaration_conv2d_transpose_impl(data, kernel, strides, padding, out_dtype)

@autotvm.register_topi_schedule(generic.schedule_conv2d_transpose_nchw, 'cpu', ['direct'])
def schedule_conv2d_transpose(cfg, outs):
    """Create schedule for tensors"""
    outs = [outs] if isinstance(outs, tvm.tensor.Tensor) else outs
    s = tvm.create_schedule([x.op for x in outs])
    scheduled_ops = []

    def traverse(op):
        """Traverse operators from computation graph"""
        # inline all one-to-one-mapping operators except the last stage (output)
        if tag.is_injective(op.tag):
            if op not in s.outputs:
                s[op].compute_inline()
            for tensor in op.input_tensors:
                if tensor.op.input_tensors and tensor.op not in scheduled_ops:
                    traverse(tensor.op)

        if 'conv2d_transpose_nchw' in op.tag:
            C = op.output(0)

            N, OC, OH, OW = C.op.axis
            rc, ry, rx = C.op.reduce_axis

            OH, oh = s[C].split(OH, factor=2)
            OC, oc = s[C].split(OC, factor=32)
            IC, ic = s[C].split(rc, factor=32)

            s[C].reorder(N, OC, OH, OW, oc, IC, ry, rx, ic)
            N = s[C].fuse(N, OC)
            s[C].vectorize(oc)
            s[C].parallel(N)

        scheduled_ops.append(op)

    traverse(outs[0].op)
    return s
