Computes a 2-D convolution given input and 4-D filters tensors.

The input tensor may have rank 4 or higher, where shape dimensions [:-3] are considered batch dimensions (batch_shape).

Given an input tensor of shape batch_shape + [in_height, in_width, in_channels] and a filter / kernel tensor of shape [filter_height, filter_width, in_channels, out_channels], this op performs the following:

  1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels].
  2. Extracts image patches from the input tensor to form a virtual tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels].
  3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k] =
    sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
                    filter[di, dj, q, k]

Must have strides[0] = strides[3] = 1. For the most common case of the same horizontal and vertical strides,