conv2d_transpose.py

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# pylint: disable=invalid-name, unused-variable, unused-argument
"""Transposed 2D convolution operators (sometimes called Deconvolution)."""
from __future__ import absolute_import as _abs
import tvm
from .dilate import dilate
from .pad import pad
from .util import get_pad_tuple
from ..util import simplify


@tvm.target.generic_func
def conv2d_transpose_nchw(Input, Filter, strides, padding, out_dtype):
    """Transposed 2D convolution nchw forward operator.

    Parameters
    ----------
    Input : tvm.Tensor
        4-D with shape [batch, in_channel, in_height, in_width]

    Filter : tvm.Tensor
        4-D with shape [in_channel, num_filter, filter_height, filter_width]

    strides : tuple of two ints
        The spatial stride along height and width

    padding : int or str
        Padding size, or ['VALID', 'SAME']

    out_dtype : str
        The output data type. This is used for mixed precision.

    Returns
    -------
    Output : tvm.Tensor
        4-D with shape [batch, out_channel, out_height, out_width]
    """
    return declaration_conv2d_transpose_impl(Input, Filter, strides, padding, out_dtype)


def conv2d_transpose_nchw_preprocess(data, kernel, strides, padding, out_dtype):
    """Preprocess data and kernel to make the compute pattern
       of conv2d_transpose the same as conv2d"""
    batch, in_c, in_h, in_w = data.shape
    _, out_c, filter_h, filter_w = kernel.shape
    stride_h, stride_w = strides
    # dilate data
    data_dilate = dilate(data, [1, 1, stride_h, stride_w], name='data_dilate')
    # pad data
    fpad_top, fpad_left, fpad_bottom, fpad_right = get_pad_tuple(padding, (filter_h, filter_w))
    bpad_top = filter_h - 1 - fpad_top
    bpad_bottom = filter_h - 1 - fpad_bottom
    bpad_left = filter_w - 1 - fpad_left
    bpad_right = filter_w - 1 - fpad_right
    data_pad = pad(data_dilate, \
                   [0, 0, bpad_top, bpad_left], \
                   [0, 0, bpad_bottom, bpad_right], \
                   name='data_pad')
    # transform kernel layout from IOHW to OIHW, and rotate kernel by 180 degrees
    kernel_transform = tvm.compute((out_c, in_c, filter_h, filter_w), \
                                    lambda o, i, h, w: kernel[i][o][filter_h-1-h][filter_w-1-w], \
                                    name='kernel_transform')
    return data_pad, kernel_transform


def declaration_conv2d_transpose_impl(data, kernel, strides, padding, out_dtype):
    """Implementation of conv2d transpose"""
    data_pad, kernel_transform = \
        conv2d_transpose_nchw_preprocess(data, kernel, strides, padding, out_dtype)
    batch, in_c, in_h, in_w = data_pad.shape
    out_c, _, filter_h, filter_w = kernel_transform.shape
    stride_h, stride_w = strides

    # convolution stage
    out_c = simplify(out_c)
    out_h = simplify(in_h - filter_h + 1)
    out_w = simplify(in_w - filter_w + 1)
    dc = tvm.reduce_axis((0, in_c), name='dc')
    dh = tvm.reduce_axis((0, filter_h), name='dh')
    dw = tvm.reduce_axis((0, filter_w), name='dw')

    Output = tvm.compute(
        (batch, out_c, out_h, out_w),
        lambda b, c, h, w: tvm.sum(
            data_pad[b, dc, h+dh, w+dw].astype(out_dtype) *
            kernel_transform[c, dc, dh, dw].astype(out_dtype),
            axis=[dc, dh, dw]), tag="conv2d_transpose_nchw")

    return Output