def test_constant_input(self):
# Check the AbstractConv Ops for constant inputs
input = self.input
filters = self.filters
topgrad = self.topgrad
constant_tensor = self.constant_tensor
out_shape = tensor.lvector()
# Check the forward Op
output = conv.conv2d(constant_tensor, filters)
grad_filters = theano.grad(output.sum(), wrt=filters)
assert grad_filters.type == filters.type, (grad_filters,
grad_filters.type, filters,
filters.type)
output = conv.conv2d(input, constant_tensor)
grad_input = theano.grad(output.sum(), wrt=input)
assert grad_input.type == input.type, (grad_input, grad_input.type,
input, input.type)
# Check grad wrt weights
grad_filters = conv.AbstractConv2d_gradWeights()(constant_tensor,
topgrad, out_shape)
grad_topgrad = theano.grad(grad_filters.sum(), wrt=topgrad)
assert grad_topgrad.type == topgrad.type, (grad_topgrad,
grad_topgrad.type, topgrad,
topgrad.type)
grad_filters = conv.AbstractConv2d_gradWeights()(input,
constant_tensor,
out_shape)
grad_input = theano.grad(grad_filters.sum(), wrt=input)
assert grad_input.type == input.type, (grad_input, grad_input.type,
input, input.type)
# Check grad wrt inputs
grad_input = conv.AbstractConv2d_gradInputs()(constant_tensor, topgrad,
out_shape)
grad_topgrad = theano.grad(grad_input.sum(), wrt=topgrad)
assert grad_topgrad.type == topgrad.type, (grad_topgrad,
grad_topgrad.type, topgrad,
topgrad.type)
grad_input = conv.AbstractConv2d_gradInputs()(filters, constant_tensor,
out_shape)
grad_filters = theano.grad(grad_input.sum(), wrt=filters)
assert grad_filters.type == filters.type, (grad_filters,
grad_filters.type, filters,
filters.type)
def run_gradinput(self,
inputs_shape,
filters_shape,
output_shape,
ref=dnn_gradinput,
subsample=(1, 1),
filter_flip=True,
verify_grad=True,
mode=mode_without_gpu,
border_mode='valid',
device='cpu',
provide_shape=False):
output_val = numpy.random.random(output_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
if device == 'gpu':
output = gpu_shared(output_val)
filters = gpu_shared(filters_val)
else:
output = theano.tensor.as_tensor_variable(cpu_shared(output_val))
filters = theano.tensor.as_tensor_variable(cpu_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 = conv.AbstractConv2d_gradInputs(border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
imshp=imshp,
kshp=kshp)
c = c(filters, output, inputs_shape[-2:])
c_ref = ref(filters,
output,
inputs_shape,
border_mode=border_mode,
subsample=subsample,
conv_mode=conv_mode)
f = theano.function([], c, mode)
f_ref = theano.function([], c_ref, mode)
res_ref = numpy.array(f_ref())
res = numpy.array(f())
utt.assert_allclose(res_ref, res)
def abstract_conv2d_gradinputs(filters_val, output_val):
conv_op = conv.AbstractConv2d_gradInputs(border_mode=border_mode,
subsample=subsample)
return conv_op(filters_val, output_val, inputs_shape[-2:])
if verify_grad:
utt.verify_grad(abstract_conv2d_gradinputs,
[filters_val, output_val],
mode=mode,
eps=1)
def test_grad_types(self):
# This function simply tests the behaviour of the AbstractConv
# Ops, not their optimizations
cpu_input = tensor.ftensor4()
cpu_filters = tensor.ftensor4()
cpu_topgrad = tensor.ftensor4()
gpu_input = gpu_ftensor4()
gpu_filters = gpu_ftensor4()
gpu_topgrad = gpu_ftensor4()
out_shape = tensor.lvector()
# Check the gradient of the forward conv2d
for input, filters in itertools.product((cpu_input, gpu_input),
(cpu_filters, gpu_filters)):
output = conv.conv2d(input, filters)
grad_input, grad_filters = theano.grad(output.sum(),
wrt=(input, filters))
assert grad_input.type == input.type, (grad_input, grad_input.type,
input, input.type)
assert grad_filters.type == filters.type, (grad_filters,
grad_filters.type,
filters, filters.type)
# Check the gradient of gradweight
for input, topgrad in itertools.product((cpu_input, gpu_input),
(cpu_topgrad, gpu_topgrad)):
grad_filters = conv.AbstractConv2d_gradWeights()(input, topgrad,
out_shape)
grad_input, grad_topgrad = theano.grad(grad_filters.sum(),
wrt=(input, topgrad))
assert grad_input.type == input.type, (grad_input, grad_input.type,
input, input.type)
assert grad_topgrad.type == topgrad.type, (grad_topgrad,
grad_topgrad.type,
topgrad, topgrad.type)
# Check the gradient of gradinputs
for filters, topgrad in itertools.product((cpu_filters, gpu_filters),
(cpu_topgrad, gpu_topgrad)):
grad_input = conv.AbstractConv2d_gradInputs()(filters, topgrad,
out_shape)
grad_filters, grad_topgrad = theano.grad(grad_input.sum(),
wrt=(filters, topgrad))
assert grad_filters.type == filters.type, (grad_filters,
grad_filters.type,
filters, filters.type)
assert grad_topgrad.type == topgrad.type, (grad_topgrad,
grad_topgrad.type,
topgrad, topgrad.type)
def run_gradinput(self, inputs_shape, filters_shape, output_shape,
ref=conv_corr_gi, subsample=(1, 1), filter_flip=True,
verify_grad=True, mode=None, border_mode='valid',
provide_shape=False, target_op=None, check_trace=False):
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 = conv.AbstractConv2d_gradInputs(border_mode=border_mode,
subsample=subsample,
filter_flip=filter_flip,
imshp=imshp, kshp=kshp)
c = c(filters, output, inputs_shape[-2:])
c_ref = ref(filters, output, inputs_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_gradinputs(filters_val, output_val):
conv_op = conv.AbstractConv2d_gradInputs(border_mode=border_mode,
subsample=subsample)
return conv_op(filters_val, output_val, inputs_shape[-2:])
if verify_grad:
utt.verify_grad(abstract_conv2d_gradinputs,
[filters_val, output_val],
mode=mode, eps=1)
def test_grad_types(self):
# This function simply tests the behaviour of the AbstractConv
# Ops, not their optimizations
input = self.input
filters = self.filters
topgrad = self.topgrad
out_shape = tensor.lvector()
output = conv.conv2d(input, filters)
grad_input, grad_filters = theano.grad(output.sum(),
wrt=(input, filters))
assert grad_input.type == input.type, (grad_input, grad_input.type,
input, input.type)
assert grad_filters.type == filters.type, (grad_filters,
grad_filters.type, filters,
filters.type)
grad_filters = conv.AbstractConv2d_gradWeights()(input, topgrad,
out_shape)
grad_input, grad_topgrad = theano.grad(grad_filters.sum(),
wrt=(input, topgrad))
assert grad_input.type == input.type, (grad_input, grad_input.type,
input, input.type)
assert grad_topgrad.type == topgrad.type, (grad_topgrad,
grad_topgrad.type, topgrad,
topgrad.type)
grad_input = conv.AbstractConv2d_gradInputs()(filters, topgrad,
out_shape)
grad_filters, grad_topgrad = theano.grad(grad_input.sum(),
wrt=(filters, topgrad))
assert grad_filters.type == filters.type, (grad_filters,
grad_filters.type, filters,
filters.type)
assert grad_topgrad.type == topgrad.type, (grad_topgrad,
grad_topgrad.type, topgrad,
topgrad.type)
def abstract_conv2d_gradinputs(filters_val, output_val):
conv_op = conv.AbstractConv2d_gradInputs(border_mode=border_mode,
subsample=subsample)
return conv_op(filters_val, output_val, inputs_shape[-2:])
