class SequentialMeanPoolingBlock(object):
# TODO(sergii): change sequentially_tile to add_sequentially_tile, because can erase gradients
def __init__(self, matrices, device_id=None):
self.context = Context(device_id)
device_id = self.context.device_id
self.output = Matrix.empty_like(matrices[0], device_id)
learning = matrices[0].bpropagable
self.output = Connector(self.output, device_id if learning else None)
if learning:
self.matrices, self.dL_dmatrices = izip(
*matrices.register_usage(device_id, device_id))
else:
self.matrices = matrices.register_usage(device_id)
self.length = matrices.length
def fprop(self):
self.output.assign_sequential_mean_pooling(self.context,
self.matrices[:self.length])
self.output.fprop()
def bprop(self):
dL_doutput = self.output.backward_matrix
dL_doutput.scale(self.context, ct.c_float(1.0 / self.length))
Matrix.sequentially_tile(self.context, dL_doutput,
self.dL_dmatrices[:self.length])
class SequentialMeanPoolingBlock(object):
# TODO(sergii): change sequentially_tile to add_sequentially_tile, because can erase gradients
def __init__(self, matrices, device_id=None):
self.context = Context(device_id)
device_id = self.context.device_id
self.output = Matrix.empty_like(matrices[0], device_id)
learning = matrices[0].bpropagable
self.output = Connector(self.output, device_id if learning else None)
if learning:
self.matrices, self.dL_dmatrices = izip(*matrices.register_usage(device_id, device_id))
else:
self.matrices = matrices.register_usage(device_id)
self.length = matrices.length
def fprop(self):
self.output.assign_sequential_mean_pooling(self.context, self.matrices[:self.length])
self.output.fprop()
def bprop(self):
dL_doutput = self.output.backward_matrix
dL_doutput.scale(self.context, ct.c_float(1.0 / self.length))
Matrix.sequentially_tile(self.context, dL_doutput, self.dL_dmatrices[:self.length])
