def forward(self, x: tensor, h: tensor=None, c: tensor=None):
max_batch_size = x.shape[0] if self.batch_first else x.shape[1]
self.y = self.register_output_shape([self.num_layers * self.num_directions, max_batch_size, self.hidden_siz])
if h is None:
h = zeros([self.num_layers * self.num_directions, max_batch_size, self.hidden_size])
if c is None and self.mode == 'LSTM':
c = zeros([self.num_layers * self.num_directions, max_batch_size, self.hidden_size])
return super(rnnbase, self).forward(x, h, c)
def forward_cpu(self, x: tensor) -> tensor:
if self._col == [] or self._vol == []:
self._col = [1 for _ in range(self.dims)]
self._vol = [1 for _ in range(self.dims)]
for i in range(self.dims - 1, -1, -1):
self._col[i] = int(
(x.shape[i + 2] + 2 * self.padding[i] - self.dilation[i] * (self.kernel_size[i] - 1) - 1) //
self.stride[i]) + 1
self._vol[i] = x.shape[i + 2]
self.batch_size = x.shape[0]
self.kernel = vol2col(self.batch_size, self.in_channels, self._vol, self._col, self.kernel_size,
self.stride, self.padding, self.dilation)
if self._use_bias and self.bias is not None:
self.bias = Parameter(zeros, [self.out_channels, *self._col])
y = zeros([self.batch_size, self.out_channels, *self._col])
self.col = self.kernel.forward_cpu(x)
self.weight.param.reshape([self.weight.param.shape[0], -1])
y.host_data = np.matmul(self.weight.param.host_data, self.col.host_data)
y.reshape([self.out_channels, self.batch_size, self._col[0], self._col[1], self._col[2]])
y.transpose([1, 0, 2, 3, 4])
if self._use_bias and self.bias is not None:
y.host_data += self.bias.param.host_data
self.cache = [x, y]
return y
def forward_cpu(self, x: tensor) -> tensor:
assert len(x.shape) == 2
y = zeros([x.shape[0], self.out_features])
y.host_data = np.matmul(x.host_data, self.weight.param.host_data)
if self.bias is not None:
y.host_data += self.bias.param.host_data
self.cache = [x, y]
return y
def backward_cpu(self):
x = self.cache[0]
tmp = x.gradient.host_data.ravel()
col = self.col.gradient.host_data.ravel()
_col2vol(tmp, col, self.batch_size, self.in_channels,
self.n_output_plane, self.index_length, nbt.List(self._vol),
nbt.List(self._col), nbt.List(self.kernel_size),
nbt.List(self.stride), nbt.List(self.padding),
nbt.List(self.dilation))
x.gradient.host_data = tmp.reshape(x.shape)
self.col = zeros([self.n_output_plane, self.output_length])
return x
def forward_cpu(self, x: tensor) -> tensor:
assert (len(x.shape) == len(self.axes))
if self.old_shape == [] or self.new_shape == []:
self.old_shape = [s for s in x.shape]
self.new_shape = [
self.old_shape[self.axes[i]] for i in range(len(self.axes))
]
self.prepare_stride(self.old_shape, self.new_shape)
y = zeros(self.new_shape)
for i in range(x.size):
old_pos = 0
new_pos = i
for j in range(len(x.shape)):
order = self.stride[j]
old_pos += (new_pos / self.stride[len(x.shape) + j] *
self.stride[len(x.shape) * 2 + order])
new_pos %= self.stride[len(x.shape) + j]
y.host_data[i] = x.host_data[old_pos]
self.cache.append(x)
self.cache.append(y)
return y
def __init__(self, batch_size: int, in_channels: int, _vol: List,
_col: List, kernel_size: List, stride: List, padding: List,
dilation: List):
super(vol2col, self).__init__()
self.batch_size = batch_size
self.in_channels = in_channels
self._vol = _vol
self._col = _col
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.dilation = dilation
self.n_output_plane = self.in_channels
self.output_length = self.batch_size
self.index_length = self.in_channels
self._c = 1
for k in self.kernel_size:
self.n_output_plane *= k
for c in self._col:
self.output_length *= c
self.index_length *= c
self._c *= c
self.col = zeros([self.n_output_plane, self.output_length])
def forward_cpu(self, x: tensor) -> tensor:
if self._col == [] or self._vol == []:
self._col = [1 for _ in range(self.dims)]
self._vol = [1 for _ in range(self.dims)]
for i in range(self.dims - 1, 0, -1):
self._col[i] = int(
(x.shape[i + 2] + 2 * self.padding[i] - self.dilation[i] *
(self.kernel_size[i] - 1) - 1) // self.stride[i]) + 1
self._vol[i] = x.shape[i + 2]
self.channel_offset *= self.kernel_size[i]
self.batch_size = x.shape[0]
self.in_channels = x.shape[1]
self.kernel = vol2col(self.batch_size, self.in_channels, self._vol,
self._col, self.kernel_size, self.stride,
self.padding, self.dilation)
y = zeros([x.shape[0], x.shape[1], *self._col])
y.reshape([self.in_channels * self.batch_size, -1])
self.col = self.kernel.forward_cpu(x)
self.col.reshape(
[self.in_channels * self.batch_size, self.channel_offset, -1])
max_idx = []
for i in range(self.in_channels * self.batch_size):
tmp = self.col.host_data[i]
m_idx = np.argmax(tmp, axis=0)
max_idx.append(m_idx)
y.host_data[i] = self.col.host_data[i][m_idx, range(m_idx.size)]
y.reshape([
self.batch_size, self.in_channels, self._col[0], self._col[1],
self._col[2]
])
x.reset_shape()
self.cache = [x, y, max_idx]
return y
def register_output_shape(self, shape: list, name: str='y') -> tensor:
self.outputs[name] = zeros(shape)
return self.outputs[name]
