def run_gradweight(self, inputs_shape, filters_shape, output_shape,
ref=dnn_gradweight, subsample=(1, 1), filter_flip=True,
verify_grad=True, mode=mode_without_gpu, border_mode='valid',
device='cpu', provide_shape=False, target_op=None):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
output_val = numpy.random.random(output_shape).astype('float32')
if device == 'gpu':
inputs = gpu_shared(inputs_val)
output = gpu_shared(output_val)
else:
inputs = theano.tensor.as_tensor_variable(cpu_shared(inputs_val))
output = theano.tensor.as_tensor_variable(cpu_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)
f_ref = theano.function([], c_ref, mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
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=dnn_conv,
subsample=(1, 1), verify_grad=True, mode=mode_without_gpu,
border_mode='valid', filter_flip=True, device='cpu', provide_shape=False,
target_op=None):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
if device == 'gpu':
inputs = gpu_shared(inputs_val)
filters = gpu_shared(filters_val)
else:
inputs = theano.tensor.as_tensor_variable(cpu_shared(inputs_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_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=mode)
f = theano.function([], c, mode)
if target_op is not None:
assert any([isinstance(n.op, target_op) for n
in f.maker.fgraph.toposort()])
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="valid", imshp=imshp, kshp=kshp,
subsample=subsample),
[inputs_val, filters_val],
mode=mode)
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 run_fwd(self,
inputs_shape,
filters_shape,
ref=dnn_conv,
subsample=(1, 1),
verify_grad=True,
mode=mode_without_gpu,
border_mode='valid',
filter_flip=True,
device='cpu',
provide_shape=False,
target_op=None):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
if device == 'gpu':
inputs = gpu_shared(inputs_val)
filters = gpu_shared(filters_val)
else:
inputs = theano.tensor.as_tensor_variable(cpu_shared(inputs_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_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=mode)
f = theano.function([], c, mode)
if target_op is not None:
assert any([
isinstance(n.op, target_op) for n in f.maker.fgraph.toposort()
])
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="valid",
imshp=imshp,
kshp=kshp,
subsample=subsample),
[inputs_val, filters_val],
mode=mode)
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,
target_op=None):
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)
if target_op is not None:
assert any([
isinstance(n.op, target_op) for n in f.maker.fgraph.toposort()
])
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)
