def grad(self, inp, grads):
softmaxes, y_idxes, y_lengths, y_startidxes = inp
g_costs, = grads
return [masked_loss_dx(softmaxes, y_idxes, y_lengths, y_startidxes, g_costs),
grad_not_implemented(self, 1, y_idxes),
grad_not_implemented(self, 1, y_lengths),
grad_not_implemented(self, 1, y_startidxes)]
def grad(self, inp, grads):
softmaxes, y_idxes, y_lengths, y_startidxes = inp
g_costs, = grads
return [masked_loss_dx(softmaxes, y_idxes, y_lengths, y_startidxes, g_costs),
grad_not_implemented(self, 1, y_idxes),
grad_not_implemented(self, 1, y_lengths),
grad_not_implemented(self, 1, y_startidxes)]
def grad(self, inp, grads):
y, y_starts, y_lengths, = inp
g_costs, = grads
return [
masked_sum_dx(y, y_starts, y_lengths, g_costs),
grad_not_implemented(self, 1, y_starts),
grad_not_implemented(self, 1, y_lengths)
]
def grad(self, inputs, gradients):
x, new_length, nonconstants = inputs
d_out = gradients[0]
swap = range(self.ndim)
swap.remove(self.axis)
swap.insert(0, self.axis)
return [d_out.dimshuffle(swap)[nonconstants].dimshuffle(swap),
grad_not_implemented(self, 1, new_length),
grad_not_implemented(self, 2, nonconstants)]
def grad(self, inputs, gradients):
x, new_length, nonconstants = inputs
d_out = gradients[0]
swap = range(self.ndim)
swap.remove(self.axis)
swap.insert(0, self.axis)
return [
d_out.dimshuffle(swap)[nonconstants].dimshuffle(swap),
grad_not_implemented(self, 1, new_length),
grad_not_implemented(self, 2, nonconstants),
]
def grad(self, inp, grads):
kerns, top, output, desc, alpha, beta = inp
img, = grads
img = gpu_contiguous(img)
d_kerns = GpuDnnConvGradW()(img, top, empty_like(kerns), desc)
d_top = GpuDnnConv()(img, kerns, empty_like(top), desc)
d_alpha = grad_not_implemented(self, 4, alpha)
d_beta = grad_not_implemented(self, 5, beta)
return (d_kerns * alpha, d_top * alpha, img * beta, DisconnectedType()(), d_alpha, d_beta)
def grad(self, inp, grads):
kerns, top, output, desc, alpha, beta = inp
img, = grads
img = gpu_contiguous(img)
d_kerns = GpuDnn3dConvGradW()(img, top, gpu_alloc_empty(*kerns.shape), desc)
d_top = GpuDnn3dConv()(img, kerns, gpu_alloc_empty(*top.shape), desc)
d_alpha = grad_not_implemented(self, 4, alpha)
d_beta = grad_not_implemented(self, 5, beta)
return (d_kerns * alpha, d_top * alpha, img * beta,
DisconnectedType()(), d_alpha, d_beta)
def grad(self, inp, grads):
img, kerns, output, desc, alpha, beta = inp
top, = grads
top = gpu_contiguous(top)
d_img = GpuDnnConvGradI()(kerns, top, empty_like(img), desc)
d_kerns = GpuDnnConvGradW()(img, top, empty_like(kerns), desc)
d_alpha = grad_not_implemented(self, 4, alpha)
d_beta = grad_not_implemented(self, 5, beta)
return [d_img * alpha, d_kerns * alpha, top * beta, DisconnectedType()(), d_alpha, d_beta]
def grad(self, inp, grads):
img, top, output, desc, alpha, beta = inp
kerns, = grads
kerns = gpu_contiguous(kerns)
d_img = GpuDnn3dConvGradI()(kerns, top, gpu_alloc_empty(*img.shape), desc)
d_top = GpuDnn3dConv()(img, kerns, gpu_alloc_empty(*top.shape), desc)
d_alpha = grad_not_implemented(self, 4, alpha)
d_beta = grad_not_implemented(self, 5, beta)
return (d_img * alpha, d_top * alpha, kerns * beta,
DisconnectedType()(), d_alpha, d_beta)
def grad(self, inp, grads):
kerns, top, output, desc, alpha, beta = inp
img, = grads
img = gpu_contiguous(img)
d_kerns = GpuDnnConvGradW()(img, top, empty_like(kerns), desc)
d_top = GpuDnnConv()(img, kerns, empty_like(top), desc)
d_alpha = grad_not_implemented(self, 4, alpha)
d_beta = grad_not_implemented(self, 5, beta)
return (d_kerns * alpha, d_top * alpha, img * beta,
DisconnectedType()(), d_alpha, d_beta)
def grad(self, inp, grads):
img, kerns, output, desc, alpha, beta = inp
top, = grads
top = gpu_contiguous(top)
d_img = GpuDnnConvGradI()(kerns, top, empty_like(img), desc)
d_kerns = GpuDnnConvGradW()(img, top, empty_like(kerns), desc)
d_alpha = grad_not_implemented(self, 4, alpha)
d_beta = grad_not_implemented(self, 5, beta)
return [d_img * alpha, d_kerns * alpha, top * beta,
DisconnectedType()(), d_alpha, d_beta]
def grad(self, inputs, grads):
v, x = inputs
(gz, ) = grads
return [
grad_not_implemented(self, 0, v),
gz * (jv(v - 1, x) - jv(v + 1, x)) / 2.0,
]
def grad(self, inp, grads):
x, neib_shape, neib_step = inp
gz, = grads
if self.mode in ['valid', 'ignore_borders']:
if (neib_shape is neib_step or neib_shape == neib_step or
# Theano Constant == do not compare the data
# the equals function do that.
(hasattr(neib_shape, "equals") and neib_shape.equals(neib_step)
)):
return [
neibs2images(gz, neib_shape, x.shape, mode=self.mode),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)
]
if self.mode in ['valid']:
# Iterate over neighborhood positions, summing contributions.
def pos2map(pidx, pgz, prior_result, neib_shape, neib_step):
'''
Helper function that adds gradient contribution from a single
neighborhood position i,j.
pidx = Index of position within neighborhood.
pgz = Gradient of shape (batch_size*num_channels*neibs)
prior_result = Shape (batch_size, num_channnels, rows, cols)
neib_shape = Number of rows, cols in a neighborhood.
neib_step = Step sizes from image2neibs.
'''
nrows, ncols = neib_shape
rstep, cstep = neib_step
batch_size, num_channels, rows, cols = prior_result.shape
i = pidx // ncols
j = pidx - (i * ncols)
# This position does not touch some img pixels in valid mode.
result_indices = prior_result[:, :,
i:(rows - nrows + i + 1):rstep,
j:(cols - ncols + j + 1):cstep]
newshape = (batch_size, num_channels) + \
((rows - nrows) // rstep + 1,) + \
((cols - ncols) // cstep + 1,)
return T.inc_subtensor(result_indices, pgz.reshape(newshape))
indices = T.arange(neib_shape[0] * neib_shape[1])
pgzs = gz.dimshuffle((1, 0))
result, _ = theano.scan(fn=pos2map,
sequences=[indices, pgzs],
outputs_info=T.zeros(x.shape),
non_sequences=[neib_shape, neib_step])
grad_input = result[-1]
return [
grad_input,
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)
]
return [
grad_not_implemented(self, 0, x),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)
]
def grad(self, inputs, grads):
v, x = inputs
gz, = grads
return [
grad_not_implemented(self, 0, v),
gz * (iv(v - 1, x) + iv(v + 1, x)) / 2.
]
def grad(self, inp, grads):
coding, one_of_n = inp
g_y, = grads
return [
hierarchical_crossentropy_categorical_1hot_grad(
g_y, coding, one_of_n, self._hierarchy, self._inv_hierarchy,
self._level_list),
grad_not_implemented(self, 1, one_of_n)
]
def grad(self, inp, grads):
x, neib_shape, neib_step = inp
gz, = grads
if self.mode in ['valid', 'ignore_borders']:
if (neib_shape is neib_step or
neib_shape == neib_step or
# Theano Constant == do not compare the data
# the equals function do that.
(hasattr(neib_shape, "equals") and
neib_shape.equals(neib_step))):
return [neibs2images(gz, neib_shape, x.shape, mode=self.mode),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)]
if self.mode in ['valid']:
# Iterate over neighborhood positions, summing contributions.
def pos2map(pidx, pgz, prior_result, neib_shape, neib_step):
'''
Helper function that adds gradient contribution from a single
neighborhood position i,j.
pidx = Index of position within neighborhood.
pgz = Gradient of shape (batch_size*num_channels*neibs)
prior_result = Shape (batch_size, num_channnels, rows, cols)
neib_shape = Number of rows, cols in a neighborhood.
neib_step = Step sizes from image2neibs.
'''
nrows, ncols = neib_shape
rstep, cstep = neib_step
batch_size, num_channels, rows, cols = prior_result.shape
i = pidx // ncols
j = pidx - (i * ncols)
# This position does not touch some img pixels in valid mode.
result_indices = prior_result[:, :,
i:(rows - nrows + i + 1):rstep,
j:(cols - ncols + j + 1):cstep]
newshape = (batch_size, num_channels) + \
((rows - nrows) // rstep + 1,) + \
((cols - ncols) // cstep + 1,)
return T.inc_subtensor(result_indices, pgz.reshape(newshape))
indices = T.arange(neib_shape[0] * neib_shape[1])
pgzs = gz.dimshuffle((1, 0))
result, _ = theano.scan(fn=pos2map,
sequences=[indices, pgzs],
outputs_info=T.zeros(x.shape),
non_sequences=[neib_shape, neib_step])
grad_input = result[-1]
return [grad_input,
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)]
return [grad_not_implemented(self, 0, x),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)]
def grad(self, inp, grads):
x, neib_shape, neib_step = inp
gz, = grads
if self.mode in ['valid', 'ignore_borders']:
if (neib_shape is neib_step or
neib_shape == neib_step or
# Theano Constant == do not compare the data
# the equals function do that.
(hasattr(neib_shape, "equals") and
neib_shape.equals(neib_step))):
return [neibs2images(gz, neib_shape, x.shape, mode=self.mode),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)]
return [grad_not_implemented(self, 0, x),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)]
def grad(self, inp, grads):
x, neib_shape, neib_step = inp
gz, = grads
if self.mode in ['valid', 'ignore_borders']:
if (neib_shape is neib_step or
neib_shape == neib_step or
# Theano Constant == do not compare the data
# the equals function do that.
(hasattr(neib_shape, "equals") and
neib_shape.equals(neib_step))):
return [neibs2images(gz, neib_shape, x.shape, mode=self.mode),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)]
return [grad_not_implemented(self, 0, x),
grad_undefined(self, 1, neib_shape),
grad_undefined(self, 2, neib_step)]
def grad(self, inp, grads):
return [grad_not_implemented(self, i, inp[i]) for i in range(2)]
def grad(self, inputs, gout):
(input_x,) = inputs
return [grad_not_implemented(self, 0, input_x)]
def grad(self, inputs, ograds):
return [
grad_not_implemented(self, i, inputs[i])
for i in xrange(len(inputs))
]
def grad(self, inputs, ograds):
return [grad_not_implemented(self, i, inputs[i]) for i in xrange(len(inputs))]
def grad(self, inp, grads):
y, y_starts, y_lengths, = inp
g_costs, = grads
return [masked_sum_dx(y, y_starts, y_lengths, g_costs),
grad_not_implemented(self, 1, y_starts),
grad_not_implemented(self, 1, y_lengths)]
def grad(self, inputs, grads):
y_true, y_score = inputs
g_y, = grads
roc_grad = rocGrad()
return [roc_grad(g_y, y_true, y_score),
grad_not_implemented(self, 1, y_score)]
def grad(self, inputs, grads):
v, x = inputs
gz, = grads
return [grad_not_implemented(self, 0, v),
gz * (iv(v - 1, x) + iv(v + 1, x)) / 2.]
def grad(self, inp, grads):
return [GpuExtractDiag2D(keepdims=(inp[0].ndim==2))(grads[0], inp[1])] + [grad_not_implemented(self, i, inp[i]) for i in range(1,4)]
def grad(self, inputs, gout):
(input_x,) = inputs
return [grad_not_implemented(self, 0, input_x)]
def grad(self, inp, grads):
return [GpuAllocDiag2D()(grads[0], inp[1], *(inp[0].shape)), grad_not_implemented(self, 1, inp[1])]
def grad(self, inp, grads):
coding, one_of_n = inp
g_y, = grads
crossentropy_categorical_1hot_grad = rocGrad()
return [crossentropy_categorical_1hot_grad(g_y, coding, one_of_n),
grad_not_implemented(self, 1, one_of_n)]
