def local_conv3d_gradweight_cpu(node):
if not isinstance(node.op, AbstractConv3d_gradWeights):
return None
img, topgrad, shape = node.inputs
if ((not isinstance(img.type, TensorType) or
not isinstance(topgrad.type, TensorType))):
return None
if node.op.border_mode not in ['valid', (0, 0, 0)]:
return None
if node.op.filter_dilation != (1, 1, 1):
return None
# conv3D expects shape (batch, row, column, time, channel)
img = img.dimshuffle(0, 2, 3, 4, 1)
topgrad = topgrad.dimshuffle(0, 2, 3, 4, 1)
W_shape = (topgrad.shape[4], shape[0], shape[1], shape[2], img.shape[4])
rval = convGrad3D(img, node.op.subsample, W_shape, topgrad)
copy_stack_trace(node.outputs[0], rval)
rval = rval.dimshuffle(0, 4, 1, 2, 3)
# need to flip the kernel if necessary (conv3D does not flip)
if node.op.filter_flip:
rval = rval[:, :, ::-1, ::-1, ::-1]
rval = theano.tensor.patternbroadcast(rval,
node.outputs[0].broadcastable)
return [rval]
def local_conv3d_gradweight_cpu(node):
if not isinstance(node.op, AbstractConv3d_gradWeights):
return None
img, topgrad, shape = node.inputs
if ((not isinstance(img.type, TensorType)
or not isinstance(topgrad.type, TensorType))):
return None
if node.op.border_mode not in ['valid', (0, 0, 0)]:
return None
if node.op.filter_dilation != (1, 1, 1):
return None
# conv3D expects shape (batch, row, column, time, channel)
img = img.dimshuffle(0, 2, 3, 4, 1)
topgrad = topgrad.dimshuffle(0, 2, 3, 4, 1)
W_shape = (topgrad.shape[4], shape[0], shape[1], shape[2], img.shape[4])
rval = convGrad3D(img, node.op.subsample, W_shape, topgrad)
copy_stack_trace(node.outputs[0], rval)
rval = rval.dimshuffle(0, 4, 1, 2, 3)
# need to flip the kernel if necessary (conv3D does not flip)
if node.op.filter_flip:
rval = rval[:, :, ::-1, ::-1, ::-1]
rval = theano.tensor.patternbroadcast(rval, node.outputs[0].broadcastable)
return [rval]
def grad(self, inputs, output_gradients):
W, b, d, H, RShape = inputs
dCdR, = output_gradients
dCdH = conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
WShape = W.shape
dCdW = convGrad3D(dCdR, d, WShape, H)
dCdb = T.sum(dCdR, axis=(0, 1, 2, 3))
# not differentiable, since d affects the output elements
dCdd = grad_undefined(self, 2, d)
# disconnected, since RShape just determines the output shape
dCdRShape = DisconnectedType()()
if 'name' in dir(dCdR) and dCdR.name is not None:
dCdR_name = dCdR.name
else:
dCdR_name = 'anon_dCdR'
if 'name' in dir(H) and H.name is not None:
H_name = H.name
else:
H_name = 'anon_H'
if 'name' in dir(W) and W.name is not None:
W_name = W.name
else:
W_name = 'anon_W'
if 'name' in dir(b) and b.name is not None:
b_name = b.name
else:
b_name = 'anon_b'
dCdW.name = 'ConvTransp3D_dCdW.H='+H_name+',dCdR='+dCdR_name+',W='+W_name
dCdb.name = 'ConvTransp3D_dCdb.H='+H_name+',dCdR='+dCdR_name+',W='+W_name+',b='+b_name
dCdH.name = 'ConvTransp3D_dCdH.H=' + H_name + ',dCdR=' + dCdR_name
return [dCdW, dCdb, dCdd, dCdH, dCdRShape]
def grad(self, inputs, output_gradients):
W, b, d, H, RShape = inputs
dCdR, = output_gradients
dCdH = conv3D(dCdR, W, T.zeros_like(H[0, 0, 0, 0, :]), d)
WShape = W.shape
dCdW = convGrad3D(dCdR, d, WShape, H)
dCdb = T.sum(dCdR, axis=(0, 1, 2, 3))
# not differentiable, since d affects the output elements
dCdd = grad_undefined(self, 2, d)
# disconnected, since RShape just determines the output shape
dCdRShape = DisconnectedType()()
if 'name' in dir(dCdR) and dCdR.name is not None:
dCdR_name = dCdR.name
else:
dCdR_name = 'anon_dCdR'
if 'name' in dir(H) and H.name is not None:
H_name = H.name
else:
H_name = 'anon_H'
if 'name' in dir(W) and W.name is not None:
W_name = W.name
else:
W_name = 'anon_W'
if 'name' in dir(b) and b.name is not None:
b_name = b.name
else:
b_name = 'anon_b'
dCdW.name = 'ConvTransp3D_dCdW.H='+H_name+',dCdR='+dCdR_name+',W='+W_name
dCdb.name = 'ConvTransp3D_dCdb.H='+H_name+',dCdR='+dCdR_name+',W='+W_name+',b='+b_name
dCdH.name = 'ConvTransp3D_dCdH.H=' + H_name + ',dCdR=' + dCdR_name
return [dCdW, dCdb, dCdd, dCdH, dCdRShape]
def local_conv2d_gradweight_cpu(node):
if not isinstance(node.op, AbstractConv2d_gradWeights):
return None
img, topgrad, shape = node.inputs
if ((not isinstance(img.type, TensorType)
or not isinstance(topgrad.type, TensorType))):
return None
if node.op.border_mode not in ['full', 'valid']:
return None
if not node.op.filter_flip:
# Not tested yet
return
if node.op.border_mode == 'valid' and \
(node.op.subsample != (1, 1)):
# Use the gradient as defined in conv3D, because the implementation
# by Conv is slow (about 3x slower than conv3D, and probably 10x
# slower than it could be), and incorrect when subsample > 2.
# build a "node", that should be equivalent to the one given by
# self.make_node, but using convGrad3D instead.
shuffled_img = img.dimshuffle(0, 2, 3, 'x', 1)
shuffled_topgrad = topgrad.dimshuffle(0, 2, 3, 'x', 1)
rval = convGrad3D(V=shuffled_img,
d=(node.op.subsample[0], node.op.subsample[1], 1),
WShape=(shuffled_topgrad.shape[4], shape[0],
shape[1], 1, shuffled_img.shape[4]),
dCdH=shuffled_topgrad)
copy_stack_trace(node.outputs[0], rval)
rval = theano.tensor.addbroadcast(rval, 3)
rval = rval.dimshuffle(0, 4, 1, 2)
rval = rval[:, :, ::-1, ::-1]
rval = theano.tensor.patternbroadcast(rval,
node.outputs[0].broadcastable)
copy_stack_trace(node.outputs[0], rval)
return [rval]
dx, dy = node.op.subsample
if dx not in (1, 2) or dy not in (1, 2):
# Not implemented in the gradient of ConvOp
return None
if node.op.imshp is None:
op_imshp = (None, None, None, None)
else:
op_imshp = node.op.imshp
if node.op.kshp is None:
op_kshp = (None, None, None, None)
else:
op_kshp = node.op.kshp
if None in op_imshp or None in op_kshp:
if (dx, dy) != (1, 1):
# We cannot infer the shapes
return None
# Determine gradient on kernels
assert len(op_imshp) == 4 and len(op_kshp) == 4
outshp = get_conv_output_shape(op_imshp, op_kshp, node.op.border_mode,
node.op.subsample)[2:]
fulloutshp = get_conv_output_shape(op_imshp, op_kshp, node.op.border_mode,
(1, 1))[2:]
newimg = img.dimshuffle((1, 0, 2, 3))
newtopgrad = topgrad.dimshuffle((1, 0, 2, 3))
if node.op.border_mode == 'valid':
(img, filters) = (newimg, newtopgrad)
kshp_logical = fulloutshp
kshp_logical_top_aligned = False
imshp_logical = None
(bsize, nkern) = (op_imshp[1], op_kshp[0])
imshp = (op_imshp[0], op_imshp[2], op_imshp[3])
kshp = outshp
elif node.op.border_mode == 'full':
(img, filters) = (newtopgrad, newimg)
kshp_logical = None
kshp_logical_top_aligned = True
imshp_logical = (op_imshp[0], fulloutshp[0], fulloutshp[1])
(bsize, nkern) = (op_kshp[0], op_imshp[1])
imshp = (op_imshp[0], outshp[0], outshp[1])
kshp = op_imshp[2:]
else:
raise NotImplementedError(
'Only [full,valid] modes are currently supported.')
# Flip the kernels
filters = filters[:, :, ::-1, ::-1]
dw = ConvOp(imshp,
kshp,
nkern,
bsize,
1,
1,
output_mode='valid',
unroll_batch=None,
unroll_kern=None,
unroll_patch=None,
imshp_logical=imshp_logical,
kshp_logical=kshp_logical,
kshp_logical_top_aligned=kshp_logical_top_aligned,
direction_hint='bprop weights')
res = dw(img, filters)
copy_stack_trace(node.outputs[0], res)
if node.op.border_mode == 'valid':
res = res.dimshuffle((1, 0, 2, 3))
res = res[:, :, ::-1, ::-1]
res = theano.tensor.patternbroadcast(res, node.outputs[0].broadcastable)
copy_stack_trace(node.outputs[0], res)
return [res]
def local_conv2d_gradweight_cpu(node):
if not isinstance(node.op, AbstractConv2d_gradWeights):
return None
img, topgrad, shape = node.inputs
if ((not isinstance(img.type, TensorType) or
not isinstance(topgrad.type, TensorType))):
return None
if node.op.border_mode not in ['full', 'valid']:
return None
if not node.op.filter_flip:
# Not tested yet
return
if node.op.border_mode == 'valid' and \
(node.op.subsample != (1, 1)):
# Use the gradient as defined in conv3D, because the implementation
# by Conv is slow (about 3x slower than conv3D, and probably 10x
# slower than it could be), and incorrect when subsample > 2.
# build a "node", that should be equivalent to the one given by
# self.make_node, but using convGrad3D instead.
shuffled_img = img.dimshuffle(0, 2, 3, 'x', 1)
shuffled_topgrad = topgrad.dimshuffle(0, 2, 3, 'x', 1)
rval = convGrad3D(V=shuffled_img,
d=(node.op.subsample[0], node.op.subsample[1], 1),
WShape=(shuffled_topgrad.shape[4],
shape[0], shape[1], 1,
shuffled_img.shape[4]),
dCdH=shuffled_topgrad)
copy_stack_trace(node.outputs[0], rval)
rval = theano.tensor.addbroadcast(rval, 3)
rval = rval.dimshuffle(0, 4, 1, 2)
rval = rval[:, :, ::-1, ::-1]
rval = theano.tensor.patternbroadcast(rval,
node.outputs[0].broadcastable)
copy_stack_trace(node.outputs[0], rval)
return [rval]
dx, dy = node.op.subsample
if dx not in (1, 2) or dy not in (1, 2):
# Not implemented in the gradient of ConvOp
return None
if node.op.imshp is None:
op_imshp = (None, None, None, None)
else:
op_imshp = node.op.imshp
if node.op.kshp is None:
op_kshp = (None, None, None, None)
else:
op_kshp = node.op.kshp
if None in op_imshp or None in op_kshp:
if (dx, dy) != (1, 1):
# We cannot infer the shapes
return None
# Determine gradient on kernels
assert len(op_imshp) == 4 and len(op_kshp) == 4
outshp = get_conv_output_shape(op_imshp, op_kshp,
node.op.border_mode, node.op.subsample)[2:]
fulloutshp = get_conv_output_shape(op_imshp, op_kshp,
node.op.border_mode, (1, 1))[2:]
newimg = img.dimshuffle((1, 0, 2, 3))
newtopgrad = topgrad.dimshuffle((1, 0, 2, 3))
if node.op.border_mode == 'valid':
(img, filters) = (newimg, newtopgrad)
kshp_logical = fulloutshp
kshp_logical_top_aligned = False
imshp_logical = None
(bsize, nkern) = (op_imshp[1], op_kshp[0])
imshp = (op_imshp[0], op_imshp[2], op_imshp[3])
kshp = outshp
elif node.op.border_mode == 'full':
(img, filters) = (newtopgrad, newimg)
kshp_logical = None
kshp_logical_top_aligned = True
imshp_logical = (op_imshp[0],
fulloutshp[0],
fulloutshp[1])
(bsize, nkern) = (op_kshp[0], op_imshp[1])
imshp = (op_imshp[0], outshp[0], outshp[1])
kshp = op_imshp[2:]
else:
raise NotImplementedError(
'Only [full,valid] modes are currently supported.')
# Flip the kernels
filters = filters[:, :, ::-1, ::-1]
dw = ConvOp(imshp, kshp, nkern, bsize, 1, 1, output_mode='valid',
unroll_batch=None, unroll_kern=None, unroll_patch=None,
imshp_logical=imshp_logical,
kshp_logical=kshp_logical,
kshp_logical_top_aligned=kshp_logical_top_aligned,
direction_hint='bprop weights')
res = dw(img, filters)
copy_stack_trace(node.outputs[0], res)
if node.op.border_mode == 'valid':
res = res.dimshuffle((1, 0, 2, 3))
res = res[:, :, ::-1, ::-1]
res = theano.tensor.patternbroadcast(res, node.outputs[0].broadcastable)
copy_stack_trace(node.outputs[0], res)
return [res]
def __call__(self, t):
output = convGrad3D(self.V + t * self.dV, self.d, self.WShape,
self.dCdH + t * self.ddCdH)
return output
def __call__(self, t):
output = convGrad3D(self.V + t * self.dV, self.d, self.WShape,
self.dCdH + t * self.ddCdH)
return output