conv1d.py 文件源码

def convolve1d_2D_numpy(a, b, mode='full'):
  nwords, ndim = a.shape
  filter_width, ndim = b.shape
  b = np.flipud(b)  # flip the kernel
  if mode == 'full':
    pad = np.zeros((filter_width-1, ndim))
    a = np.vstack([pad, a, pad])
    shape = (nwords+filter_width-1, filter_width, ndim)
  elif mode == 'valid':
    shape = (nwords-filter_width+1, filter_width, ndim)

  strides = (a.strides[0],) + a.strides
  view = np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)

  conv_out = np.einsum('kij,ij->kj', view, b)
  return conv_out