def test_local_sigm_times_exp(self):
# Test the `local_sigm_times_exp` optimization.
# exp(x) * sigm(-x) -> sigm(x)
# exp(-x) * sigm(x) -> sigm(-x)
def match(func, ops):
# print [node.op.scalar_op for node in func.maker.fgraph.toposort()]
assert [node.op for node in func.maker.fgraph.toposort()] == ops
m = self.get_mode(excluding=["local_elemwise_fusion", "inplace"])
x, y = tt.vectors("x", "y")
f = theano.function([x], sigmoid(-x) * tt.exp(x), mode=m)
match(f, [sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], sigmoid(x) * tt.exp(-x), mode=m)
match(f, [tt.neg, sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], -(-(-(sigmoid(x)))) * tt.exp(-x), mode=m)
match(f, [tt.neg, sigmoid, tt.neg])
# assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function(
[x, y],
(sigmoid(x) * sigmoid(-y) * -tt.exp(-x) * tt.exp(x * y) * tt.exp(y)),
mode=m,
)
topo = f.maker.fgraph.toposort()
for op, nb in [(sigmoid, 2), (tt.mul, 2), (tt.neg, 1), (tt.exp, 1)]:
assert sum([n.op == op for n in topo]) == nb
def test_local_sigm_times_exp(self):
"""
Test the `local_sigm_times_exp` optimization.
exp(x) * sigm(-x) -> sigm(x)
exp(-x) * sigm(x) -> sigm(-x)
"""
def match(func, ops):
# print [node.op.scalar_op for node in func.maker.fgraph.toposort()]
assert [node.op for node in func.maker.fgraph.toposort()] == ops
m = self.get_mode(excluding=['local_elemwise_fusion', 'inplace'])
x, y = tensor.vectors('x', 'y')
f = theano.function([x], sigmoid(-x) * tensor.exp(x), mode=m)
match(f, [sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], sigmoid(x) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], -(-(-(sigmoid(x)))) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid, tensor.neg])
# assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x, y],
(sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *
tensor.exp(x * y) * tensor.exp(y)),
mode=m)
match(
f,
[sigmoid, tensor.mul, tensor.neg, tensor.exp, sigmoid, tensor.mul])
def test_local_sigm_times_exp(self):
"""
Test the `local_sigm_times_exp` optimization.
exp(x) * sigm(-x) -> sigm(x)
exp(-x) * sigm(x) -> sigm(-x)
"""
def match(func, ops):
# print [node.op.scalar_op for node in func.maker.fgraph.toposort()]
assert [node.op for node in func.maker.fgraph.toposort()] == ops
m = self.get_mode(excluding=['local_elemwise_fusion', 'inplace'])
x, y = tensor.vectors('x', 'y')
f = theano.function([x], sigmoid(-x) * tensor.exp(x), mode=m)
match(f, [sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], sigmoid(x) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid])
assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function([x], -(-(-(sigmoid(x)))) * tensor.exp(-x), mode=m)
match(f, [tensor.neg, sigmoid, tensor.neg])
# assert check_stack_trace(f, ops_to_check=sigmoid)
f = theano.function(
[x, y],
(sigmoid(x) * sigmoid(-y) * -tensor.exp(-x) *
tensor.exp(x * y) * tensor.exp(y)), mode=m)
topo = f.maker.fgraph.toposort()
for op, nb in [(sigmoid, 2), (tensor.mul, 2),
(tensor.neg, 1), (tensor.exp, 1)]:
assert sum([n.op == op for n in topo]) == nb
def test_blocksparse_inplace_gemv_opt():
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.lmatrix()
oIdx = tensor.lmatrix()
o = sparse_block_dot(W, h, iIdx, b, oIdx)
f = theano.function([W, h, iIdx, b, oIdx], o)
if theano.config.mode == "FAST_COMPILE":
assert not f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=[sparse_block_gemv])
else:
assert f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=[sparse_block_gemv_inplace])
def test_blocksparse_inplace_gemv_opt():
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.lmatrix()
oIdx = tensor.lmatrix()
o = sparse_block_dot(W, h, iIdx, b, oIdx)
f = theano.function([W, h, iIdx, b, oIdx], o)
if theano.config.mode == "FAST_COMPILE":
assert not f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=[sparse_block_gemv])
else:
assert f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=[sparse_block_gemv_inplace])
def test_blocksparse_inplace_outer_opt():
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.lmatrix()
oIdx = tensor.lmatrix()
o = sparse_block_dot(W, h, iIdx, b, oIdx)
f = theano.function([W, h, iIdx, b, oIdx], [o, tensor.grad(o.sum(), wrt=W)])
if theano.config.mode == "FAST_COMPILE":
assert not f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=sparse_block_outer)
else:
assert f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=sparse_block_outer_inplace)
def test_blocksparse_inplace_outer_opt():
b = tensor.fmatrix()
W = tensor.ftensor4()
h = tensor.ftensor3()
iIdx = tensor.lmatrix()
oIdx = tensor.lmatrix()
o = sparse_block_dot(W, h, iIdx, b, oIdx)
f = theano.function([W, h, iIdx, b, oIdx], [o, tensor.grad(o.sum(), wrt=W)])
if theano.config.mode == "FAST_COMPILE":
assert not f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=sparse_block_outer)
else:
assert f.maker.fgraph.toposort()[-1].op.inplace
assert check_stack_trace(f, ops_to_check=sparse_block_outer_inplace)
def run_gradinput(self, inputs_shape, filters_shape, output_shape,
gradInputs_fn, ref,
subsample=None, filter_flip=True,
verify_grad=True, mode=None, border_mode='valid',
provide_shape=False, target_op=None,
check_trace=False, filter_dilation=None):
if subsample is None:
subsample = (1,) * (len(inputs_shape) - 2)
if filter_dilation is None:
filter_dilation = (1,) * (len(inputs_shape) - 2)
output_val = numpy.random.random(output_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
output = self.shared(output_val)
filters = self.shared(filters_val)
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c = gradInputs_fn(border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
imshp=imshp, kshp=kshp,
filter_dilation=filter_dilation)
c = c(filters, output, inputs_shape[2:])
c_ref = ref(filters, output, inputs_shape,
border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode, filter_dilation=filter_dilation)
f = theano.function([], c, mode=mode)
f_ref = theano.function([], c_ref, mode='FAST_RUN')
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
assert_true(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
def abstract_conv_gradinputs(filters_val, output_val):
conv_op = gradInputs_fn(border_mode=border_mode,
subsample=subsample,
filter_dilation=filter_dilation)
return conv_op(filters_val, output_val, inputs_shape[2:])
if verify_grad:
utt.verify_grad(abstract_conv_gradinputs,
[filters_val, output_val],
mode=mode, eps=1)
def run_gradinput(self, inputs_shape, filters_shape, output_shape,
gradInputs_fn, ref,
subsample=None, filter_flip=True,
verify_grad=True, mode=None, border_mode='valid',
provide_shape=False, target_op=None,
check_trace=False, filter_dilation=None):
if subsample is None:
subsample = (1,) * (len(inputs_shape) - 2)
if filter_dilation is None:
filter_dilation = (1,) * (len(inputs_shape) - 2)
output_val = numpy.random.random(output_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
output = self.shared(output_val)
filters = self.shared(filters_val)
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c = gradInputs_fn(border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
imshp=imshp, kshp=kshp,
filter_dilation=filter_dilation)
c = c(filters, output, inputs_shape[2:])
c_ref = ref(filters, output, inputs_shape,
border_mode=border_mode, subsample=subsample,
conv_mode=conv_mode, filter_dilation=filter_dilation)
f = theano.function([], c, mode=mode)
f_ref = theano.function([], c_ref, mode='FAST_RUN')
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
assert_true(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
def abstract_conv_gradinputs(filters_val, output_val):
conv_op = gradInputs_fn(border_mode=border_mode,
subsample=subsample,
filter_dilation=filter_dilation)
return conv_op(filters_val, output_val, inputs_shape[2:])
if verify_grad:
utt.verify_grad(abstract_conv_gradinputs,
[filters_val, output_val],
mode=mode, eps=1)
def run_gradweight(self, inputs_shape, filters_shape, output_shape,
ref=conv_corr_gw, subsample=(1, 1),
filter_flip=True, verify_grad=True, mode=None,
border_mode='valid', provide_shape=False,
target_op=None, check_trace=False,
filter_dilation=(1, 1)):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
output_val = numpy.random.random(output_shape).astype('float32')
inputs = self.shared(inputs_val)
output = self.shared(output_val)
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c = conv.AbstractConv2d_gradWeights(border_mode=border_mode,
filter_flip=filter_flip,
subsample=subsample,
imshp=imshp, kshp=kshp,
filter_dilation=filter_dilation)
c = c(inputs, output, filters_shape[-2:])
c_ref = ref(inputs, output,
filters_shape,
border_mode=border_mode,
subsample=subsample,
conv_mode=conv_mode,
filter_dilation=filter_dilation)
f = theano.function([], c, mode=mode)
f_ref = theano.function([], c_ref, mode='FAST_RUN')
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
self.assertTrue(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
def abstract_conv2d_gradweight(inputs_val, output_val):
conv_op = conv.AbstractConv2d_gradWeights(border_mode=border_mode,
subsample=subsample,
filter_dilation=filter_dilation)
return conv_op(inputs_val, output_val, filters_shape[-2:])
if verify_grad:
utt.verify_grad(abstract_conv2d_gradweight,
[inputs_val, output_val],
mode=mode, eps=1)
def test_1msigmoid(self):
if not register_local_1msigmoid:
return
m = self.get_mode()
x = T.fmatrix()
# tests exp_over_1_plus_exp
f = theano.function([x], 1 - T.exp(x) / (1 + T.exp(x)), mode=m)
assert check_stack_trace(f, ops_to_check=[tensor.neg, sigmoid_inplace])
assert [node.op for node in f.maker.fgraph.toposort()] == [
tensor.neg, sigmoid_inplace]
# tests inv_1_plus_exp
f = theano.function([x], 1 - T.fill(x, 1.0) / (1 + T.exp(-x)), mode=m)
assert check_stack_trace(f, ops_to_check=[tensor.neg, sigmoid_inplace])
assert ([node.op for node in f.maker.fgraph.toposort()] == [tensor.neg,
sigmoid_inplace])
def test_1msigmoid(self):
if not register_local_1msigmoid:
return
m = self.get_mode()
x = T.fmatrix()
# tests exp_over_1_plus_exp
f = theano.function([x], 1 - T.exp(x) / (1 + T.exp(x)), mode=m)
assert check_stack_trace(f, ops_to_check=[tensor.neg, sigmoid_inplace])
assert [node.op for node in f.maker.fgraph.toposort()
] == [tensor.neg, sigmoid_inplace]
# tests inv_1_plus_exp
f = theano.function([x], 1 - T.fill(x, 1.0) / (1 + T.exp(-x)), mode=m)
assert check_stack_trace(f, ops_to_check=[tensor.neg, sigmoid_inplace])
assert ([node.op for node in f.maker.fgraph.toposort()
] == [tensor.neg, sigmoid_inplace])
def test_local_ultra_fast_sigmoid(self):
x = tt.matrix("x")
s = sigmoid(x)
mode = self.get_mode("local_ultra_fast_sigmoid")
f = theano.function([x], s, mode=mode)
assert check_stack_trace(f, ops_to_check=sigmoid)
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert topo[0].op == sigmoid
mode = self.get_mode().including("local_ultra_fast_sigmoid")
f = theano.function([x], s, mode=mode)
assert check_stack_trace(f, ops_to_check=ultra_fast_sigmoid)
topo = f.maker.fgraph.toposort()
assert topo[0].op == ultra_fast_sigmoid
assert len(topo) == 1
f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
def test_local_ultra_fast_sigmoid(self):
x = tensor.matrix('x')
s = sigmoid(x)
mode = self.get_mode('local_ultra_fast_sigmoid')
f = theano.function([x], s, mode=mode)
assert check_stack_trace(f, ops_to_check=sigmoid)
topo = f.maker.fgraph.toposort()
assert len(topo) == 1
assert topo[0].op == sigmoid
mode = self.get_mode().including('local_ultra_fast_sigmoid')
f = theano.function([x], s, mode=mode)
assert check_stack_trace(f, ops_to_check=ultra_fast_sigmoid)
topo = f.maker.fgraph.toposort()
assert topo[0].op == ultra_fast_sigmoid
assert len(topo) == 1
f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
def test_local_hard_sigmoid(self):
x = tt.matrix("x")
s = sigmoid(x)
mode = self.get_mode("local_hard_sigmoid")
f = theano.function([x], s, mode=mode)
assert check_stack_trace(f, ops_to_check=sigmoid)
topo = f.maker.fgraph.toposort()
assert topo[0].op == sigmoid
assert len(topo) == 1
mode = self.get_mode().including("local_hard_sigmoid")
f = theano.function([x], s, mode=mode)
topo = f.maker.fgraph.toposort()
assert not any([n.op == sigmoid for n in topo])
f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
mode2 = mode.excluding("fusion").excluding("inplace")
f2 = theano.function([x], s, mode=mode2)
assert check_stack_trace(f2, ops_to_check=tt.clip)
def run_fwd(self, inputs_shape, filters_shape, ref=conv_corr,
subsample=(1, 1), verify_grad=True, mode=None,
border_mode='valid', filter_flip=True,
provide_shape=False, target_op=None,
check_trace=False, filter_dilation=(1, 1)):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs = self.shared(inputs_val)
filters = self.shared(filters_val)
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c_ref = ref(inputs, filters,
border_mode=border_mode,
subsample=subsample,
conv_mode=conv_mode,
filter_dilation=filter_dilation)
c = conv.conv2d(inputs, filters,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
input_shape=imshp,
filter_shape=kshp,
filter_dilation=filter_dilation)
f_ref = theano.function([], c_ref, mode='FAST_RUN')
f = theano.function([], c, mode=mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
assert_true(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
if verify_grad:
utt.verify_grad(conv.AbstractConv2d(border_mode=border_mode,
imshp=imshp, kshp=kshp,
subsample=subsample,
filter_dilation=filter_dilation),
[inputs_val, filters_val],
mode=mode)
def test_local_hard_sigmoid(self):
x = tensor.matrix('x')
s = sigmoid(x)
mode = self.get_mode('local_hard_sigmoid')
f = theano.function([x], s, mode=mode)
assert check_stack_trace(f, ops_to_check=sigmoid)
topo = f.maker.fgraph.toposort()
assert topo[0].op == sigmoid
assert len(topo) == 1
mode = self.get_mode().including('local_hard_sigmoid')
f = theano.function([x], s, mode=mode)
topo = f.maker.fgraph.toposort()
assert not any([n.op == sigmoid for n in topo])
f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
mode2 = mode.excluding('fusion').excluding('inplace')
f2 = theano.function([x], s, mode=mode2)
self.assertTrue(check_stack_trace(f2, ops_to_check=theano.tensor.clip))
def test_local_hard_sigmoid(self):
x = tensor.matrix('x')
s = sigmoid(x)
mode = self.get_mode('local_hard_sigmoid')
f = theano.function([x], s, mode=mode)
assert check_stack_trace(f, ops_to_check=sigmoid)
topo = f.maker.fgraph.toposort()
assert topo[0].op == sigmoid
assert len(topo) == 1
mode = self.get_mode().including('local_hard_sigmoid')
f = theano.function([x], s, mode=mode)
topo = f.maker.fgraph.toposort()
assert not any([n.op == sigmoid for n in topo])
f([[-50, -10, -4, -1, 0, 1, 4, 10, 50]])
mode2 = mode.excluding('fusion').excluding('inplace')
f2 = theano.function([x], s, mode=mode2)
self.assertTrue(check_stack_trace(f2, ops_to_check=theano.tensor.clip))
def run_gradweight(self, inputs_shape, filters_shape, output_shape,
ref=conv_corr_gw, subsample=(1, 1), filter_flip=True,
verify_grad=True, mode=None, border_mode='valid',
provide_shape=False, target_op=None, check_trace=False):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
output_val = numpy.random.random(output_shape).astype('float32')
inputs = self.shared(inputs_val)
output = self.shared(output_val)
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c = conv.AbstractConv2d_gradWeights(border_mode=border_mode,
filter_flip=filter_flip,
subsample=subsample,
imshp=imshp, kshp=kshp)
c = c(inputs, output, filters_shape[-2:])
c_ref = ref(inputs, output,
filters_shape,
border_mode=border_mode,
subsample=subsample,
conv_mode=conv_mode)
f = theano.function([], c, mode=mode)
f_ref = theano.function([], c_ref, mode='FAST_RUN')
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
self.assertTrue(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
def abstract_conv2d_gradweight(inputs_val, output_val):
conv_op = conv.AbstractConv2d_gradWeights(border_mode=border_mode,
subsample=subsample)
return conv_op(inputs_val, output_val, filters_shape[-2:])
if verify_grad:
utt.verify_grad(abstract_conv2d_gradweight,
[inputs_val, output_val],
mode=mode, eps=1)
def run_fwd(self, inputs_shape, filters_shape, ref=conv_corr,
subsample=(1, 1), verify_grad=True, mode=None,
border_mode='valid', filter_flip=True, provide_shape=False,
target_op=None, check_trace=False):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs = self.shared(inputs_val)
filters = self.shared(filters_val)
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c_ref = ref(inputs, filters,
border_mode=border_mode,
subsample=subsample,
conv_mode=conv_mode)
c = conv.conv2d(inputs, filters,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
input_shape=imshp,
filter_shape=kshp)
f_ref = theano.function([], c_ref, mode='FAST_RUN')
f = theano.function([], c, mode=mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
self.assertTrue(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
if verify_grad:
utt.verify_grad(conv.AbstractConv2d(border_mode=border_mode,
imshp=imshp, kshp=kshp,
subsample=subsample),
[inputs_val, filters_val],
mode=mode)
def check_diagonal_subtensor_view_traces(fn):
assert check_stack_trace(fn,
ops_to_check=(DiagonalSubtensor,
IncDiagonalSubtensor))
def run_fwd(self, inputs_shape, filters_shape,
conv_fn, conv_op, ref,
subsample=None, verify_grad=True, mode=None,
border_mode='valid', filter_flip=True,
provide_shape=False, target_op=None,
check_trace=False, filter_dilation=None):
if subsample is None:
subsample = (1,) * (len(inputs_shape) - 2)
if filter_dilation is None:
filter_dilation = (1,) * (len(inputs_shape) - 2)
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
# scale down values to prevent rounding errors
inputs_val /= 10
filters_val /= 10
inputs = self.shared(inputs_val)
filters = self.shared(filters_val)
if provide_shape:
imshp = inputs_shape
kshp = filters_shape
else:
imshp = None
kshp = None
if filter_flip:
conv_mode = 'conv'
else:
conv_mode = 'cross'
c_ref = ref(inputs, filters,
border_mode=border_mode,
subsample=subsample,
conv_mode=conv_mode,
filter_dilation=filter_dilation)
c = conv_fn(inputs, filters,
border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
input_shape=imshp,
filter_shape=kshp,
filter_dilation=filter_dilation)
f_ref = theano.function([], c_ref, mode='FAST_RUN')
f = theano.function([], c, mode=mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
if check_trace:
assert_true(check_stack_trace(f, ops_to_check=target_op))
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
if verify_grad:
utt.verify_grad(conv_op(border_mode=border_mode,
imshp=imshp, kshp=kshp,
subsample=subsample,
filter_dilation=filter_dilation),
[inputs_val, filters_val],
mode=mode)
def check_diagonal_subtensor_view_traces(fn):
assert check_stack_trace(fn, ops_to_check=(DiagonalSubtensor, IncDiagonalSubtensor))
