(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda')
return tf.nn.conv2d(x, w, strides=s, padding='VALID', data_format=data_format)
r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.

    Padding is performed only once at the beginning, not between the operations.
    The fused op is considerably more efficient than performing the same calculation
    using standard TensorFlow ops. It supports gradients of arbitrary order.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                      The default is `[1] * factor`, which corresponds to average pooling.
        factor:       Integer downsampling factor (default: 2).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).

    Returns:
        Tensor of the shape `[N, C, H // factor, W // factor]` or
        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.
r Fused tf . nn . conv2d () followed by downsample_2d () .
def conv_downsample_2d(x, w, k=None, factor=2, gain=1, data_format='NCHW', impl='cuda'):
    r"""Fused `tf.nn.conv2d()` followed by `downsample_2d()`.

    Padding is performed only once at the beginning, not between the operations.
    The fused op is considerably more efficient than performing the same calculation
    using standard TensorFlow ops. It supports gradients of arbitrary order.

    Args:
        x:            Input tensor of the shape `[N, C, H, W]` or `[N, H, W, C]`.
        w:            Weight tensor of the shape `[filterH, filterW, inChannels, outChannels]`.
                      Grouped convolution can be performed by `inChannels = x.shape[0] // numGroups`.
        k:            FIR filter of the shape `[firH, firW]` or `[firN]` (separable).
                      The default is `[1] * factor`, which corresponds to average pooling.
        factor:       Integer downsampling factor (default: 2).
        gain:         Scaling factor for signal magnitude (default: 1.0).
        data_format:  `'NCHW'` or `'NHWC'` (default: `'NCHW'`).
        impl:         Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).

    Returns:
        Tensor of the shape `[N, C, H // factor, W // factor]` or
        `[N, H // factor, W // factor, C]`, and same datatype as `x`.
    """

    assert isinstance(factor, int) and factor >= 1
    w = tf.convert_to_tensor(w)
    convH, convW, _inC, _outC = w.shape.as_list()
    assert convW == convH
    if k is None:
        k = [1] * factor
    k = _setup_kernel(k) * gain
    p = (k.shape[0] - factor) + (convW - 1)
    if data_format == 'NCHW':
        s = [1, 1, factor, factor]
    else:
        s = [1, factor, factor, 1]
    x = _simple_upfirdn_2d(x, k, pad0=(p+1)//2, pad1=p//2, data_format=data_format, impl=impl)
    return tf.nn.conv2d(x, w, strides=s, padding='VALID', data_format=data_format)
def conv_downsample_2d ( x , w , k = None , factor = 2 , gain = 1 , data_format = 'NCHW' , impl = 'cuda' ) : assert isinstance ( factor , int ) and factor >= 1 w = tf . convert_to_tensor ( w ) convH , convW , _inC , _outC = w . shape . as_list ( ) assert convW == convH if k is None : k = [ 1 ] * factor k = _setup_kernel ( k ) * gain p = ( k . shape [ 0 ] - factor ) + ( convW - 1 ) if data_format == 'NCHW' : s = [ 1 , 1 , factor , factor ] else : s = [ 1 , factor , factor , 1 ] x = _simple_upfirdn_2d ( x , k , pad0 = ( p + 1 ) // 2 , pad1 = p // 2 , data_format = data_format , impl = impl ) return tf . nn . conv2d ( x , w , strides = s , padding = 'VALID' , data_format = data_format )