def _np_replication_pad2d_grad(src, dest):
(dx_height, dx_width) = (input.shape[h_idx], input.shape[w_idx])
(dy_height, dy_width) = (output.shape[h_idx], output.shape[w_idx])
numpy_src = np.ones(src.shape, np.int32)
numpy_dest = np.zeros(dest.shape, np.int32)
array_src = FlattenArray(numpy_src)
array_dest = FlattenArray(numpy_dest)
src_num = src.shape[c_idx] * src.shape[h_idx] * src.shape[w_idx]
dest_num = dest.shape[c_idx] * dest.shape[h_idx] * dest.shape[w_idx]
elements_num = src.shape[0] * src_num
for iter_n in range(elements_num):
coords = Index2Coordinate(iter_n, src.shape)
(n, c, i, j) = (coords[0], coords[c_idx], coords[h_idx],
coords[w_idx])
ip_x = ip_y = 0
if j < pad_left:
ip_x = pad_left
elif j >= pad_left and j < dx_width + pad_left:
ip_x = j
else:
ip_x = dx_width + pad_left - 1
if i < pad_top:
ip_y = pad_top
elif i >= pad_top and i < dx_height + pad_top:
ip_y = i
else:
ip_y = dx_height + pad_top - 1
ip_x = ip_x - pad_left
ip_y = ip_y - pad_top
src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j
dest_index = (n * dest_num + c * dx_width * dx_height +
ip_y * dx_width + ip_x)
array_dest[dest_index] += array_src[src_index]
numpy_dest = Array2Numpy(array_dest, dest.shape)
return numpy_dest
def _np_zero_pad2d_grad(src, dest, padding):
(c_idx, h_idx, w_idx) = (1, 2, 3)
pad_left = padding[0]
pad_right = padding[1]
pad_top = padding[2]
pad_bottom = padding[3]
(dx_height, dx_width) = (dest.shape[h_idx], dest.shape[w_idx])
(dy_height, dy_width) = (src.shape[h_idx], src.shape[w_idx])
numpy_src = np.ones(src.shape, np.int32)
numpy_dest = np.zeros(dest.shape, np.int32)
array_src = FlattenArray(numpy_src)
array_dest = FlattenArray(numpy_dest)
src_num = src.shape[c_idx] * src.shape[h_idx] * src.shape[w_idx]
dest_num = dest.shape[c_idx] * dest.shape[h_idx] * dest.shape[w_idx]
elements_num = src.shape[0] * src_num
for iter_n in range(elements_num):
coords = Index2Coordinate(iter_n, src.shape)
(n, c, i, j) = (coords[0], coords[c_idx], coords[h_idx], coords[w_idx])
ip_x = ip_y = 0
if (j >= pad_left and j < dx_width + pad_left and (i >= pad_top)
and (i < dx_height + pad_top)):
ip_x = j - pad_left
ip_y = i - pad_top
src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j
dest_index = (n * dest_num + c * dx_width * dx_height +
ip_y * dx_width + ip_x)
array_dest[dest_index] += array_src[src_index]
numpy_dest = Array2Numpy(array_dest, dest.shape)
return numpy_dest
def _np_constant_pad2d_grad(src, dest):
dx_height, dx_width = input.shape[h_idx], input.shape[w_idx]
dy_height, dy_width = output.shape[h_idx], output.shape[w_idx]
numpy_src = np.ones(src.shape, np.int32)
numpy_dest = np.zeros(dest.shape, np.int32)
array_src = FlattenArray(numpy_src)
array_dest = FlattenArray(numpy_dest)
src_num = src.shape[c_idx] * src.shape[h_idx] * src.shape[w_idx]
dest_num = dest.shape[c_idx] * dest.shape[h_idx] * dest.shape[w_idx]
elements_num = src.shape[0] * src_num
for iter_n in range(elements_num):
coords = Index2Coordinate(iter_n, src.shape)
n, c, i, j = coords[0], coords[c_idx], coords[h_idx], coords[w_idx]
ip_x = ip_y = 0
if (j >= pad_left and j < (dx_width + pad_left) and i >= pad_top
and i < (dx_height + pad_top)):
ip_x = j - pad_left
ip_y = i - pad_top
src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j
dest_index = (n * dest_num + c * dx_width * dx_height +
ip_y * dx_width + ip_x)
array_dest[dest_index] += array_src[src_index]
numpy_dest = Array2Numpy(array_dest, dest.shape)
return numpy_dest
def _np_replication_pad2d_grad(src, dest, padding):
c_idx, h_idx, w_idx = 1, 2, 3
pad_left = padding[0]
pad_right = padding[1]
pad_top = padding[2]
pad_bottom = padding[3]
dx_height, dx_width = dest.shape[h_idx], dest.shape[w_idx]
dy_height, dy_width = src.shape[h_idx], src.shape[w_idx]
numpy_src = np.ones(src.shape, np.int32)
numpy_dest = np.zeros(dest.shape, np.int32)
array_src = FlattenArray(numpy_src)
array_dest = FlattenArray(numpy_dest)
src_num = src.shape[c_idx] * src.shape[h_idx] * src.shape[w_idx]
dest_num = dest.shape[c_idx] * dest.shape[h_idx] * dest.shape[w_idx]
elements_num = src.shape[0] * src_num
for iter_n in range(elements_num):
coords = Index2Coordinate(iter_n, src.shape)
n, c, i, j = coords[0], coords[c_idx], coords[h_idx], coords[w_idx]
ip_x = ip_y = 0
if j < pad_left:
ip_x = pad_left
elif j >= pad_left and j < (dx_width + pad_left):
ip_x = j
else:
ip_x = dx_width + pad_left - 1
if i < pad_top:
ip_y = pad_top
elif i >= pad_top and i < (dx_height + pad_top):
ip_y = i
else:
ip_y = dx_height + pad_top - 1
ip_x = ip_x - pad_left
ip_y = ip_y - pad_top
src_index = n * src_num + c * dy_width * dy_height + i * dy_width + j
dest_index = n * dest_num + c * dx_width * dx_height + ip_y * dx_width + ip_x
array_dest[dest_index] += array_src[src_index]
numpy_dest = Array2Numpy(array_dest, dest.shape)
return numpy_dest