def _pooling_op(self, cntk_op, inputs):
"""
Computes the pooling of a tensor.
Arguments:
cntk_op: CNTK function to be imported.
inputs: List of inputs to this node.
Returns:
A ngraph Op.
"""
inputs = self._expand_input_axes(inputs[0])
C, D, H, W, N = inputs.axes
M, P, Q = self._make_out_axes(cntk_op.shape)
if cntk_op.attributes['poolingType'] == 0:
pool_type = 'max'
else:
pool_type = 'avg'
strides = self._make_strides(cntk_op.attributes['strides'])
kernel = self._make_kernel(cntk_op.attributes['poolingWindowShape'])
pad = self._make_padding('pool', cntk_op.attributes['autoPadding'],
(D.length, H.length, W.length, C.length),
kernel,
(M.length, P.length, Q.length, C.length),
strides)
params = dict(op=pool_type,
pad_d=pad[0],
pad_h=pad[1],
pad_w=pad[2],
pad_c=pad[3],
str_d=strides[0],
str_h=strides[1],
str_w=strides[2],
str_c=strides[3],
T=kernel[0],
R=kernel[1],
S=kernel[2],
J=kernel[3])
pool = ng.pooling(params, inputs, [C, M, P, Q, N])
return squeeze_axes([pool])[0]
def _convolution_op(self, cntk_op, inputs):
"""
Computes the convolution of a tensor with operand.
Arguments:
cntk_op: CNTK operation to be imported.
inputs: List of inputs to this node.
Returns:
A ngraph Op.
"""
filters, inputs = inputs
inputs = self._expand_input_axes(inputs)
C, D, H, W, N = inputs.axes
filters = self._expand_filters_axes(filters, C)
_, T, M1, M2, O = filters.axes
M, oH, oW = self._make_out_axes(cntk_op.shape)
out_axes = [O, M, oH, oW, N]
strides = self._make_strides(cntk_op.attributes['strides'])
pad = self._make_padding('conv', cntk_op.attributes['autoPadding'],
(D.length, H.length, W.length, C.length),
(T.length, M1.length, M2.length, C.length),
(M.length, oH.length, oW.length, O.length),
strides)
params = dict(pad_d=pad[0],
pad_h=pad[1],
pad_w=pad[2],
pad_c=pad[3],
str_d=strides[0],
str_h=strides[1],
str_w=strides[2],
str_c=strides[3],
dil_d=1,
dil_h=1,
dil_w=1)
conv = ng.convolution(params, inputs, filters, out_axes)
return squeeze_axes([conv])[0]
def CrossEntropyWithSoftmax(self, cntk_op, inputs):
"""
Computes the softmax cross entropy between the inputs[0] and inputs[1].
Arguments:
cntk_op: CNTK operation to be imported.
inputs: List of inputs to this node.
Returns:
A ngraph Op.
"""
cast_0, cast_1 = squeeze_axes(inputs)
if cast_0.axes.lengths != cast_1.axes.lengths:
cast_0 = ng.Transpose(cast_0)
assert cast_0.axes.lengths == cast_1.axes.lengths
cast_0 = ng.cast_axes(cast_0, axes=cast_1.axes)
loss = ng.cross_entropy_multi(ng.softmax(cast_0), cast_1)
return ng.mean(loss, out_axes=()).named(cntk_op.uid)
def _cast_for_binary_op(self, inputs):
"""
Cast axes for input with more axes by matching
its axes with second input's axes.
Arguments:
inputs: List of inputs to be casted.
Returns:
Casted inputs.
"""
assert len(inputs) == 2
cast_0, cast_1 = squeeze_axes(inputs)
if len(cast_0.axes) >= len(cast_1.axes):
axes = self._match_axes(cast_0.axes, cast_1.axes)
cast_0 = ng.cast_axes(cast_0, axes)
else:
axes = self._match_axes(cast_1.axes, cast_0.axes)
cast_1 = ng.cast_axes(cast_1, axes)
return cast_0, cast_1
