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 = cuda.ftensor4()
gpu_filters = cuda.ftensor4()
gpu_topgrad = cuda.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 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 = cuda.ftensor4()
gpu_filters = cuda.ftensor4()
gpu_topgrad = cuda.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 setUp(self):
self.input = cuda.ftensor4()
self.filters = cuda.ftensor4()
self.topgrad = cuda.ftensor4()
self.constant_tensor = cuda.CudaNdarray(
numpy.zeros((3, 5, 7, 11), dtype='float32'))
def setUp(self):
self.input = cuda.ftensor4()
self.filters = cuda.ftensor4()
self.topgrad = cuda.ftensor4()
self.constant_tensor = cuda.CudaNdarray(
numpy.zeros((3, 5, 7, 11), dtype='float32'))
def setUp(self):
self.input = cuda.ftensor4()
self.filters = cuda.ftensor4()
self.topgrad = cuda.ftensor4()
def setUp(self):
self.input = cuda.ftensor4()
self.filters = cuda.ftensor4()
self.topgrad = cuda.ftensor4()
