def lmul(self, x):
"""
dot(x, A)
aka, do convolution with input image x
"""
check_cuda(str(type(self)) + ".lmul")
# TODO Why is it CPU??
print "Por que?!?!", type(x)
cpu = "Cuda" not in str(type(x))
if cpu:
x = gpu_from_host(x)
assert x.ndim == 5
x_axes = self.input_axes
assert len(x_axes) == 5
op_axes = ("c", 0, 1, "t", "b")
if tuple(x_axes) != op_axes:
print "ssssssssssssssss"
x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])
_x_4d_shape = (
self.signal_shape[0],
self.signal_shape[1],
self.signal_shape[2],
self.signal_shape[3] * self.signal_shape[4],
)
x = x.reshape(_x_4d_shape)
x = gpu_contiguous(x)
rval = FilterActs(self.pad, self.partial_sum, self.kernel_stride[0])(x, self._filters)
if cpu:
rval = host_from_gpu(rval)
rval = rval.reshape(
(
self.filter_shape[3],
self.filter_shape[4],
rval.shape[1],
rval.shape[2],
self.signal_shape[3],
self.signal_shape[4],
)
)
rval = diagonal_subtensor(rval, 4, 0).sum(axis=0)
# Format the output based on the output space
rval_axes = self.output_axes
assert len(rval_axes) == 5
if tuple(rval_axes) != op_axes:
rval = rval.dimshuffle(*[op_axes.index(axis) for axis in rval_axes])
return rval
def lmul(self, x):
"""
dot(x, A)
aka, do convolution with input image x
"""
check_cuda(str(type(self)) + ".lmul")
cpu = 'Cuda' not in str(type(x))
assert cpu
if cpu:
x = gpu_from_host(x)
assert x.ndim == 5
x_axes = self.input_axes
assert len(x_axes) == 5
#x = shapeprint(x)
op_axes = ('b', 0, 1, 't', 'c')
print x_axes, op_axes
if tuple(x_axes) != op_axes:
x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])
#x = shapeprint(x)
#self._filters = shapeprint(self._filters)
im = x.dimshuffle(0,3,4,1,2)
filt = self._filters.dimshuffle(0,3,4,1,2)
rval = conv3d(im, filt, None, None, (self.kernel_stride[0], self.kernel_stride[1]) )
rval = rval.dimshuffle(0,3,4,1,2)
return rval
def lmul(self, x):
"""
dot(x, A)
aka, do convolution with input image x
"""
check_cuda(str(type(self)) + ".lmul")
cpu = 'Cuda' not in str(type(x))
assert cpu
if cpu:
x = gpu_from_host(x)
assert x.ndim == 5
x_axes = self.input_axes
assert len(x_axes) == 5
#x = shapeprint(x)
op_axes = ('b', 0, 1, 't', 'c')
print x_axes, op_axes
if tuple(x_axes) != op_axes:
x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])
#x = shapeprint(x)
#self._filters = shapeprint(self._filters)
rval = self.conv3d_op(x, self._filters, self.b, self.kernel_stride)
#assert len(rval_axes) == 5
#op_axes = self.output_axes
#if tuple(rval_axes) != op_axes:
# rval = rval.dimshuffle(*[op_axes.index(axis) for axis in rval_axes])
return rval
def convnet_available():
check_cuda(check_enabled=False)
# If already compiled, OK
if convnet_available.compiled:
_logger.debug('already compiled')
return True
# If there was an error, do not try again
if convnet_available.compile_error:
_logger.debug('error last time')
return False
# Else, we need CUDA
if not cuda.cuda_available:
convnet_available.compile_error = True
_logger.debug('cuda unavailable')
return False
# Try to actually compile
success = convnet_compile()
if success:
convnet_available.compiled = True
else:
convnet_available.compile_error = False
_logger.debug('compilation success: %s', success)
return convnet_available.compiled
def lmul_T(self, x):
check_cuda(str(type(self)) + ".lmul_T")
assert x.dtype == self._filters.dtype
op_axes = ('c', 0, 1, 'b')
axes = self.output_axes
if tuple(axes) != op_axes:
x = x.dimshuffle(*[axes.index(ax) for ax in op_axes])
x = gpu_contiguous(x)
rval = ImageActs(pad=self.pad,
partial_sum=self.partial_sum,
stride=self.kernel_stride[0])(x, self._filters)
# Format the output based on the input space
axes = self.input_axes
assert len(axes) == 4
if tuple(axes) != op_axes:
rval = rval.dimshuffle(op_axes.index(axes[0]),
op_axes.index(axes[1]),
op_axes.index(axes[2]),
op_axes.index(axes[3]))
return rval
def lmul(self, x):
"""
dot(x, A)
aka, do convolution with input image x
"""
check_cuda(str(type(self)) + ".lmul")
cpu = 'Cuda' not in str(type(x))
assert cpu
if cpu:
x = gpu_from_host(x)
assert x.ndim == 5
x_axes = self.input_axes
assert len(x_axes) == 5
#x = shapeprint(x)
op_axes = ('b', 0, 1, 't', 'c')
print x_axes, op_axes
if tuple(x_axes) != op_axes:
x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])
#x = shapeprint(x)
#self._filters = shapeprint(self._filters)
im = x.dimshuffle(0, 3, 4, 1, 2)
filt = self._filters.dimshuffle(0, 3, 4, 1, 2)
rval = conv3d(im, filt, None, None,
(self.kernel_stride[0], self.kernel_stride[1]))
rval = rval.dimshuffle(0, 3, 4, 1, 2)
return rval
def lmul_T(self, x):
"""
.. todo::
WRITEME
"""
check_cuda(str(type(self)) + ".lmul_T")
assert x.dtype == self._filters.dtype
op_axes = ("c", 0, 1, "b")
axes = self.output_axes
if tuple(axes) != op_axes:
x = x.dimshuffle(*[axes.index(ax) for ax in op_axes])
x = gpu_contiguous(x)
rval = ImageActs(pad=self.pad, partial_sum=self.partial_sum, stride=self.kernel_stride[0])(x, self._filters)
# Format the output based on the input space
axes = self.input_axes
assert len(axes) == 4
if tuple(axes) != op_axes:
rval = rval.dimshuffle(
op_axes.index(axes[0]), op_axes.index(axes[1]), op_axes.index(axes[2]), op_axes.index(axes[3])
)
return rval
def lmul(self, x):
"""
dot(x, A)
aka, do convolution with input image x
"""
check_cuda(str(type(self)) + ".lmul")
cpu = 'Cuda' not in str(type(x))
#assert cpu
if cpu:
x = gpu_from_host(x)
assert x.ndim == 5
x_axes = self.input_axes
assert len(x_axes) == 5
#x = shapeprint(x)
op_axes = ('b', 'c', 0, 1, 't')
print x_axes, op_axes
#if tuple(x_axes) != op_axes:
# x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])
#x = shapeprint(x)
#self._filters = shapeprint(self._filters)
rval = cuda.blas.GpuCorr3dMM(border_mode= 'valid',
subsample = tuple(self.kernel_stride),
pad=tuple(self.pad))(x, self._filters)
#rval = conv3d(im, filt, None, None, (self.kernel_stride[0], self.kernel_stride[1]) )
#rval = rval.dimshuffle(0,4,1,2,3)
#print "hello"
return rval
def convnet_available():
check_cuda(check_enabled=False)
# If already compiled, OK
if convnet_available.compiled:
_logger.debug('already compiled')
return True
# If there was an error, do not try again
if convnet_available.compile_error:
_logger.debug('error last time')
return False
# Else, we need CUDA
if not cuda.cuda_available:
convnet_available.compile_error = True
_logger.debug('cuda unavailable')
return False
# Try to actually compile
success = convnet_compile()
if success:
convnet_available.compiled = True
else:
convnet_available.compile_error = False
_logger.debug('compilation success: %s', success)
return convnet_available.compiled
def lmul(self, x):
"""
dot(x, A)
aka, do convolution with input image x
"""
check_cuda(str(type(self)) + ".lmul")
# TODO Why is it CPU??
print 'Por que?!?!', type(x)
cpu = 'Cuda' not in str(type(x))
if cpu:
x = gpu_from_host(x)
assert x.ndim == 5
x_axes = self.input_axes
assert len(x_axes) == 5
op_axes = ('c', 0, 1, 't', 'b')
if tuple(x_axes) != op_axes:
print 'ssssssssssssssss'
x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])
_x_4d_shape = (self.signal_shape[0], self.signal_shape[1],
self.signal_shape[2],
self.signal_shape[3] * self.signal_shape[4])
x = x.reshape(_x_4d_shape)
x = gpu_contiguous(x)
rval = FilterActs(self.pad, self.partial_sum,
self.kernel_stride[0])(x, self._filters)
if cpu:
rval = host_from_gpu(rval)
rval = rval.reshape(
(self.filter_shape[3], self.filter_shape[4], rval.shape[1],
rval.shape[2], self.signal_shape[3], self.signal_shape[4]))
rval = diagonal_subtensor(rval, 4, 0).sum(axis=0)
# Format the output based on the output space
rval_axes = self.output_axes
assert len(rval_axes) == 5
if tuple(rval_axes) != op_axes:
rval = rval.dimshuffle(
*[op_axes.index(axis) for axis in rval_axes])
return rval
def lmul(self, x):
"""
.. todo::
WRITEME properly
dot(x, A)
aka, do convolution with input image x
"""
check_cuda(str(type(self)) + ".lmul")
cpu = 'Cuda' not in str(type(x))
if cpu:
x = gpu_from_host(x)
# x must be formatted as channel, topo dim 0, topo dim 1, batch_index
# for use with FilterActs
assert x.ndim == 4
x_axes = self.input_axes
assert len(x_axes) == 4
op_axes = ('c', 0, 1, 'b')
if tuple(x_axes) != op_axes:
x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])
x = gpu_contiguous(x)
# Patch old pickle files.
if not hasattr(self, 'kernel_stride'):
self.kernel_stride = (1, 1)
rval = FilterActs(self.pad, self.partial_sum, self.kernel_stride[0])(
x,
self._filters
)
# Format the output based on the output space
rval_axes = self.output_axes
assert len(rval_axes) == 4
if cpu:
rval = host_from_gpu(rval)
if tuple(rval_axes) != op_axes:
rval = rval.dimshuffle(*[op_axes.index(axis)
for axis in rval_axes])
return rval
def lmul(self, x):
"""
.. todo::
WRITEME properly
dot(x, A)
aka, do convolution with input image x
"""
check_cuda(str(type(self)) + ".lmul")
cpu = 'Cuda' not in str(type(x))
if cpu:
x = gpu_from_host(x)
# x must be formatted as channel, topo dim 0, topo dim 1, batch_index
# for use with FilterActs
assert x.ndim == 4
x_axes = self.input_axes
assert len(x_axes) == 4
op_axes = ('c', 0, 1, 'b')
if tuple(x_axes) != op_axes:
x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])
x = gpu_contiguous(x)
# Patch old pickle files.
if not hasattr(self, 'kernel_stride'):
self.kernel_stride = (1, 1)
rval = FilterActs(self.pad, self.partial_sum, self.kernel_stride[0])(
x,
self._filters
)
# Format the output based on the output space
rval_axes = self.output_axes
assert len(rval_axes) == 4
if cpu:
rval = host_from_gpu(rval)
if tuple(rval_axes) != op_axes:
rval = rval.dimshuffle(*[op_axes.index(axis)
for axis in rval_axes])
return rval
def lmul(self, x, b):
"""
dot(x, A)
aka, do convolution with input image x
"""
check_cuda(str(type(self)) + ".lmul")
cpu = 'Cuda' not in str(type(x))
assert cpu
if cpu:
x = gpu_from_host(x)
assert x.ndim == 5
x_axes = self.input_axes
assert len(x_axes) == 5
#op_axes = ('b', 0, 1, 't', 'c')
#if tuple(x_axes) != op_axes:
# x = x.dimshuffle(*[x_axes.index(axis) for axis in op_axes])
rval = self.conv3d_op(x, self._filters, b, (1, 1, 1))
#rval = conv.Conv3DFFT(self.signal_shape, self.filter_shape)(x, self._filters)
#rval = conv.conv3d_fft(x,
# self._filters,
# image_shape = x.shape,
# filter_shape = self.filter_shape)
#rval = x
rval_axes = self.output_axes
assert len(rval_axes) == 5
#op_axes = ('b', 'c', 't', 0, 1)
#if tuple(rval_axes) != op_axes:
# rval = rval.dimshuffle(*[op_axes.index(axis) for axis in rval_axes])
return rval
def setup_detector_layer_c01b(layer, input_space, rng):
"""
.. todo::
WRITEME properly
Takes steps to set up an object for use as being some kind of convolutional
layer. This function sets up only the detector layer.
Does the following:
* raises a RuntimeError if cuda is not available
* sets layer.input_space to input_space
* sets up addition of dummy channels for compatibility with cuda-convnet:
- layer.dummy_channels: # of dummy channels that need to be added
(You might want to check this and raise an Exception if it's not 0)
- layer.dummy_space: The Conv2DSpace representing the input with dummy
channels added
* sets layer.detector_space to the space for the detector layer
* sets layer.transformer to be a Conv2D instance
* sets layer.b to the right value
Parameters
----------
layer : object
Any python object that allows the modifications described below and
has the following attributes:
* pad : int describing amount of zero padding to add
* kernel_shape : 2-element tuple or list describing spatial shape of
kernel
* fix_kernel_shape : bool, if true, will shrink the kernel shape to
make it feasible, as needed (useful for hyperparameter searchers)
* detector_channels : The number of channels in the detector layer
* init_bias : numeric constant added to a tensor of zeros to
initialize the bias
* tied_b : If true, biases are shared across all spatial locations
input_space : WRITEME
A Conv2DSpace to be used as input to the layer
rng : WRITEME
A numpy RandomState or equivalent
"""
# Use "self" to refer to layer from now on, so we can pretend we're
# just running in the set_input_space method of the layer
self = layer
# Make sure cuda is available
check_cuda(str(type(self)))
# Validate input
if not isinstance(input_space, Conv2DSpace):
raise TypeError("The input to a convolutional layer should be a "
"Conv2DSpace, but layer " + self.layer_name + " got " +
str(type(self.input_space)))
if not hasattr(self, 'detector_channels'):
raise ValueError("layer argument must have a 'detector_channels' "
"attribute specifying how many channels to put in "
"the convolution kernel stack.")
# Store the input space
self.input_space = input_space
# Make sure number of channels is supported by cuda-convnet
# (multiple of 4 or <= 3)
# If not supported, pad the input with dummy channels
ch = self.input_space.num_channels
rem = ch % 4
if ch > 3 and rem != 0:
self.dummy_channels = 4 - rem
else:
self.dummy_channels = 0
self.dummy_space = Conv2DSpace(
shape=input_space.shape,
channels=input_space.num_channels + self.dummy_channels,
axes=('c', 0, 1, 'b')
)
if hasattr(self, 'kernel_stride'):
kernel_stride = self.kernel_stride
else:
kernel_stride = [1, 1]
output_shape = \
[int(np.ceil((i_sh + 2. * self.pad - k_sh) / float(k_st))) + 1
for i_sh, k_sh, k_st in zip(self.input_space.shape,
self.kernel_shape, kernel_stride)]
def handle_kernel_shape(idx):
if self.kernel_shape[idx] < 1:
raise ValueError("kernel must have strictly positive size on all "
"axes but has shape: " + str(self.kernel_shape))
if output_shape[idx] <= 0:
if self.fix_kernel_shape:
self.kernel_shape[idx] = \
self.input_space.shape[idx] + 2 * self.pad
assert self.kernel_shape[idx] != 0
output_shape[idx] = 1
warnings.warn("Had to change the kernel shape to make "
"network feasible")
else:
raise ValueError("kernel too big for input "
"(even with zero padding)")
map(handle_kernel_shape, [0, 1])
if self.detector_channels < 16:
raise ValueError("Cuda-convnet requires the detector layer to have "
"at least 16 channels.")
self.detector_space = Conv2DSpace(shape=output_shape,
num_channels=self.detector_channels,
axes=('c', 0, 1, 'b'))
if hasattr(self, 'partial_sum'):
partial_sum = self.partial_sum
else:
partial_sum = 1
if hasattr(self, 'sparse_init') and self.sparse_init is not None:
self.transformer = \
checked_call(make_sparse_random_conv2D,
OrderedDict([('num_nonzero', self.sparse_init),
('input_space', self.input_space),
('output_space', self.detector_space),
('kernel_shape', self.kernel_shape),
('pad', self.pad),
('partial_sum', partial_sum),
('kernel_stride', kernel_stride),
('rng', rng)]))
else:
self.transformer = make_random_conv2D(
irange=self.irange,
input_axes=self.input_space.axes,
output_axes=self.detector_space.axes,
input_channels=self.dummy_space.num_channels,
output_channels=self.detector_space.num_channels,
kernel_shape=self.kernel_shape,
pad=self.pad,
partial_sum=partial_sum,
kernel_stride=kernel_stride,
rng=rng
)
W, = self.transformer.get_params()
W.name = self.layer_name + '_W'
if self.tied_b:
self.b = sharedX(np.zeros(self.detector_space.num_channels) +
self.init_bias)
else:
self.b = sharedX(self.detector_space.get_origin() + self.init_bias)
self.b.name = self.layer_name + '_b'
logger.info('Input shape: {0}'.format(self.input_space.shape))
logger.info('Detector space: {0}'.format(self.detector_space.shape))
def __init__(self,
num_channels,
num_pieces,
kernel_shape,
pool_shape,
pool_stride,
pool_temporal_shape,
pool_temporal_stride,
layer_name,
irange=None,
init_bias=0.,
W_lr_scale=None,
b_lr_scale=None,
pad=0,
fix_pool_shape=False,
fix_pool_stride=False,
fix_kernel_shape=False,
partial_sum=1,
tied_b=False,
max_kernel_norm=None,
input_normalization=None,
detector_normalization=None,
min_zero=False,
output_normalization=None,
kernel_stride=(1, 1, 1)):
"""
layer_name: A name for this layer that will be prepended to
monitoring channels related to this layer.
num_channels: The number of output channels the layer should have.
Note that it must internally compute num_channels * num_pieces
convolution channels.
num_pieces: The number of linear pieces used to make each maxout unit.
kernel_shape: The shape of the convolution kernel.
pool_shape: The shape of the spatial max pooling. A three-tuple of ints.
pool_stride: The stride of the spatial and temporal max pooling.
irange: if specified, initializes each weight randomly in
U(-irange, irange)
init_bias: All biases are initialized to this number
W_lr_scale: The learning rate on the weights for this layer is
multiplied by this scaling factor
b_lr_scale: The learning rate on the biases for this layer is
multiplied by this scaling factor
pad: The amount of zero-padding to implicitly add to the boundary of the
image when computing the convolution. Useful for making sure pixels
at the edge still get to influence multiple hidden units.
fix_pool_shape: If True, will modify self.pool_shape to avoid having
pool shape bigger than the entire detector layer.
If you have this on, you should probably also have
fix_pool_stride on, since the pool shape might shrink
smaller than the stride, even if the stride was initially
valid.
The "fix" parameters are useful for working with a hyperparameter
optimization package, which might often propose sets of hyperparameters
that are not feasible, but can easily be projected back into the feasible
set.
fix_kernel_shape: if True, will modify self.kernel_shape to avoid
having the kernel shape bigger than the implicitly
zero padded input layer
partial_sum: a parameter that controls whether to prefer runtime savings
or memory savings when computing the gradient with respect to
the kernels. See pylearn2.sandbox.cuda_convnet.weight_acts.py
for details. The default is to prefer high speed.
Note that changing this setting may change the value of computed
results slightly due to different rounding error.
tied_b: If true, all biases in the same channel are constrained to be the same
as each other. Otherwise, each bias at each location is learned independently.
max_kernel_norm: If specifed, each kernel is constrained to have at most this norm.
input_normalization, detector_normalization, output_normalization:
if specified, should be a callable object. the state of the network is optionally
replaced with normalization(state) at each of the 3 points in processing:
input: the input the layer receives can be normalized right away
detector: the maxout units can be normalized prior to the spatial pooling
output: the output of the layer, after sptial pooling, can be normalized as well
kernel_stride: vertical, horizontal and time pixel stride between each detector.
"""
check_cuda(str(type(self)))
detector_channels = num_channels * num_pieces
self.__dict__.update(locals())
del self.self
def fprop(self, state_below):
check_cuda(str(type(self)))
self.input_space.validate(state_below)
if not hasattr(self, 'input_normalization'):
self.input_normalization = None
if self.input_normalization:
state_below = self.input_normalization(state_below)
# fft 3d covolution
z = self.transformer.lmul(state_below)
# bias addition
if not hasattr(self, 'tied_b'):
self.tied_b = False
if self.tied_b:
b = self.b.dimshuffle(0, 'x', 'x', 'x', 'x')
else:
b = self.b.dimshuffle('x', 0, 1, 2, 3)
z = z + self.b
if self.layer_name is not None:
z.name = self.layer_name + '_z'
self.detector_space.validate(z)
#assert self.detector_space.num_channels % 16 == 0
#ReLUs
z = T.maximum(z, 0)
if self.output_space.num_channels % 16 != 0:
raise NotImplementedError(
"num channles should always be dvisible by 16")
# alex's max pool op only works when the number of channels
# is divisible by 16. we can only do the cross-channel pooling
# first if the cross-channel pooling preserves that property
# Pooling
# permute axes ['b', 0, 1,'t','c'] -> ['c', 0, 1, 't', 'b'] (axes required for pooling )
z = z.dimshuffle(4, 1, 2, 3, 0)
# spatial pooling x/y
z_shape = z.shape
z = z.reshape(
(z_shape[0], z_shape[1], z_shape[2], z_shape[3] * z_shape[4]))
p = max_pool_c01b(c01b=z,
pool_shape=self.pool_shape,
pool_stride=self.pool_stride)
p_shape = p.shape
p = p.reshape(
(p_shape[0], p_shape[1], p_shape[2], z_shape[3], z_shape[4]))
# temporal pooling with overlap (t)
p_shape = p.shape
#['c', 0, 1, 't', 'b'] -> ['c',0*1,'t','b'] ('c',0, 1,'b') for max_pool_c01b
p = p.reshape(
(p_shape[0], p_shape[1] * p_shape[2], p_shape[3], p_shape[4]))
t = temporal_max_pool_c01b(c01b=p,
pool_shape=self.pool_temporal_shape,
pool_stride=self.pool_temporal_stride,
image_shape=self.temp_pool_input_shape)
t_shape = t.shape
t = t.reshape(
(t_shape[0], p_shape[1], p_shape[2], t_shape[2], t_shape[3]))
# Permute back axes ['c', 0, 1, 't', 'b'] -> ['b', 0, 1, 't', 'c']
t = t.dimshuffle(4, 1, 2, 3, 0)
self.output_space.validate(t)
if not hasattr(self, 'output_normalization'):
self.output_normalization = None
if self.output_normalization:
t = self.output_normalization(t)
return t
def piece_prop(self, state_below):
"""
Note: this only reports pieces in terms of which channel wins, not
which spatial location wins. Depending on the input size, it may report
a piece map for either the pre-spatial pooling or the post-spatial pooling
tensor.
"""
check_cuda(str(type(self)))
self.input_space.validate(state_below)
if not hasattr(self, 'input_normalization'):
self.input_normalization = None
if self.input_normalization:
state_below = self.input_normalization(state_below)
# Alex's code requires # input channels to be <= 3 or a multiple of 4
# so we add dummy channels if necessary
if not hasattr(self, 'dummy_channels'):
self.dummy_channels = 0
if self.dummy_channels > 0:
state_below = T.concatenate(
(state_below,
T.zeros_like(state_below[0:self.dummy_channels, :, :, :])),
axis=0)
z = self.transformer.lmul(state_below)
if not hasattr(self, 'tied_b'):
self.tied_b = False
if self.tied_b:
b = self.b.dimshuffle(0, 'x', 'x', 'x')
else:
b = self.b.dimshuffle(0, 1, 2, 'x')
z = z + b
if self.layer_name is not None:
z.name = self.layer_name + '_z'
self.detector_space.validate(z)
assert self.detector_space.num_channels % 16 == 0
if self.output_space.num_channels % 16 == 0:
# alex's max pool op only works when the number of channels
# is divisible by 16. we can only do the cross-channel pooling
# first if the cross-channel pooling preserves that property
piece = None
if self.num_pieces != 1:
s = None
for i in xrange(self.num_pieces):
t = z[i::self.num_pieces, :, :, :]
if s is None:
s = t
piece = T.zeros_like(t)
else:
s = T.maximum(s, t)
mask = T.eq(s, t)
piece = mask * i + (1 - mask) * piece
z = s
if self.detector_normalization:
z = self.detector_normalization(z)
p = max_pool_c01b(c01b=z,
pool_shape=self.pool_shape,
pool_stride=self.pool_stride,
image_shape=self.detector_space.shape)
else:
if self.detector_normalization is not None:
raise NotImplementedError(
"We can't normalize the detector "
"layer because the detector layer never exists as a "
"stage of processing in this implementation.")
z = max_pool_c01b(c01b=z,
pool_shape=self.pool_shape,
pool_stride=self.pool_stride,
image_shape=self.detector_space.shape)
if self.num_pieces != 1:
s = None
piece = None
for i in xrange(self.num_pieces):
t = z[i::self.num_pieces, :, :, :]
if s is None:
s = t
piece = T.zeros_like(t)
else:
s = T.maximum(s, t)
mask = T.eq(s, t)
piece = mask * i + (1 - mask) * piece
z = s
p = z
self.output_space.validate(p)
if hasattr(self, 'min_zero') and self.min_zero:
p = p * (p > 0.)
if not hasattr(self, 'output_normalization'):
self.output_normalization = None
if self.output_normalization:
p = self.output_normalization(p)
return p, piece
def setup_detector_layer_btc01(layer, input_space, rng, irange):
# Use "self" to refer to layer from now on, so we can pretend we're just running
# in the set_input_space method of the layer
self = layer
# Make sure cuda is available
check_cuda(str(type(self)))
#import pdb; pdb.set_trace()
# Validate input
if not isinstance(input_space, Conv3DSpace):
raise TypeError("The input to a convolutional layer should be a Conv3DSpace, "
" but layer " + self.layer_name + " got "+str(type(self.input_space)))
if not hasattr(self, 'detector_channels'):
raise ValueError('layer argument must have a "detector_channels" attribute specifying how many channels to put in the convolution kernel stack.')
# Store the input space
self.input_space = input_space
#self.dummy_space = Conv3DSpace(shape=input_space.shape,
# channels=input_space.num_channels + self.dummy_channels,
# axes=('b', 'c', 't', 0, 1))
output_shape = [int((i_sh + 2. * p_sh - k_sh) / float(k_st)) +1
for i_sh, p_sh, k_sh, k_st in zip(self.input_space.shape,
self.pad,
self.kernel_shape,
self.kernel_stride)]
def handle_kernel_shape(idx):
if self.kernel_shape[idx] < 1:
raise ValueError("kernel must have strictly positive size on all axes but has shape: "+str(self.kernel_shape))
if output_shape[idx] <= 0:
if self.fix_kernel_shape:
self.kernel_shape[idx] = self.input_space.shape[idx] + 2 * self.pad
assert self.kernel_shape[idx] != 0
output_shape[idx] = 1
warnings.warn("Had to change the kernel shape to make network feasible")
else:
raise ValueError("kernel too big for input (even with zero padding)")
map(handle_kernel_shape, [0, 1, 2])
# space required for 3dconv
self.detector_space = Conv3DSpace(shape=output_shape,
num_channels = self.detector_channels,
axes = ('b', 'c', 0, 1,'t'))
if hasattr(self, 'partial_sum'):
partial_sum = self.partial_sum
else:
partial_sum = 1
# filter shape required for fft3dconv ('c_detector','c','t','0','1')
filter_shape = (self.detector_space.num_channels,
self.input_space.num_channels,
self.kernel_shape[0],
self.kernel_shape[1],
self.kernel_shape[2],
)
# filter shape required for fft-3dconv ('b','c','t','0','1')
signal_shape = (self.mlp.batch_size,
self.input_space.num_channels,
self.input_space.shape[0],
self.input_space.shape[1],
self.input_space.shape[2],
)
self.transformer = make_random_conv3D(
irange = self.irange,
input_axes = ('b', 'c', 0, 1,'t'),
output_axes = self.detector_space.axes,
signal_shape = signal_shape,
filter_shape = filter_shape,
pad = self.pad,
partial_sum = partial_sum,
kernel_stride = self.kernel_stride,
rng = rng)
W, = self.transformer.get_params()
W.name = 'W'
if self.tied_b:
self.b = sharedX(np.zeros((self.detector_space.num_channels)) + self.init_bias)
else:
self.b = sharedX(self.detector_space.get_origin() + self.init_bias)
self.b.name = 'b'
def fprop(self, state_below):
check_cuda(str(type(self)))
self.input_space.validate(state_below)
if not hasattr(self, 'input_normalization'):
self.input_normalization = None
#state_below= Print("state_below")(state_below)
if self.input_normalization:
state_below = self.input_normalization(state_below)
#import pdb; pdb.set_trace()
# GPU 3d correlation
z = self.transformer.lmul(state_below)
# bias addition
if not hasattr(self, 'tied_b'):
self.tied_b = False
if self.tied_b:
b = self.b.dimshuffle('x', 0, 'x', 'x', 'x')
else:
b = self.b.dimshuffle('x', 0, 1, 2, 3)
#z = Print('z')(z)
#b = Print('b')(b)
z = z + b
if self.layer_name is not None:
z.name = self.layer_name + '_z'
self.detector_space.validate(z)
#assert self.detector_space.num_channels % 16 == 0
#ReLUs
#z= Print("z")(z)
z = T.maximum(z, 0)
# Pooling
if tuple(self.pool_shape) != (1, 1, 1):
# Pooling on y, t
z_shape = z.shape
z = z.reshape(
(z_shape[0], z_shape[1] * z_shape[2], z_shape[3], z_shape[4]))
p = dnn_pool(img=z,
ws=tuple(self.pool_shape[1:]),
stride=tuple(self.pool_stride[1:]))
p_shape = p.shape
p = p.reshape(
(p_shape[0], z_shape[1], z_shape[2], p_shape[2], p_shape[3]))
#p = Print("p")(p)
# Pooling on x
p_shape = p.shape
p = p.reshape(
(p_shape[0], p_shape[1], p_shape[2], p_shape[3] * p_shape[4]))
t = dnn_pool(img=p,
ws=tuple([self.pool_shape[0], 1]),
stride=tuple([self.pool_stride[0], 1]))
t_shape = t.shape
#print (t_shape[0], t_shape[1], t_shape[2], p_shape[2] , p_shape[3])
t = t.reshape(
(t_shape[0], t_shape[1], t_shape[2], p_shape[3], p_shape[4]))
else:
t = z
self.output_space.validate(t)
## Gpu contiguous
#t = gpu_contiguous(t)
#t = Print("t")(t)
if not hasattr(self, 'output_normalization'):
self.output_normalization = None
if self.output_normalization:
t = self.output_normalization(t)
return t
def setup_detector_layer_bct01(layer, input_space, rng, irange):
"""
Takes steps to set up an object for use as being some kind of
convolutional layer.
This function sets up only the detector layer.
Parameters
----------
layer: Any python object that allows the modifications described below and has
the following attributes:
pad: int describing amount of zero padding to add
kernel_shape: 2-element tuple or list describing spatial shape of kernel
fix_kernel_shape: bool, if true, will shrink the kernel shape to make it
feasible, as needed (useful for hyperparameter searchers)
detector_channels: The number of channels in the detector layer
init_bias: A numeric constant added to a tensor of zeros to initialize the
bias
tied_b: If true, biases are shared across all spatial locations
input_space: A Conv3DSpace to be used as input to the layer
rng: a numpy RandomState or equivalent
irange: float. kernel elements are initialized randomly from U(-irange, irange)
Does the following:
raises a RuntimeError if cuda is not available
sets layer.input_space to input_space
sets up addition of dummy channels for compatibility with cuda-convnet:
layer.dummy_channels: # of dummy channels that need to be added
(You might want to check this and raise an Exception if it's not 0)
layer.dummy_space: The Conv2DSpace representing the input with dummy channels
added
sets layer.detector_space to the space for the detector layer
sets layer.transformer to be a Conv3DBCT01 instance
sets layer.b to the right value
"""
# Use "self" to refer to layer from now on, so we can pretend we're just running
# in the set_input_space method of the layer
self = layer
# Make sure cuda is available
check_cuda(str(type(self)))
# Validate input
if not isinstance(input_space, Conv3DSpace):
raise TypeError("The input to a convolutional layer should be a Conv3DSpace, "
" but layer " + self.layer_name + " got "+str(type(self.input_space)))
if not hasattr(self, 'detector_channels'):
raise ValueError('layer argument must have a "detector_channels" attribute specifying how many channels to put in the convolution kernel stack.')
# Store the input space
self.input_space = input_space
#self.dummy_space = Conv3DSpace(shape=input_space.shape,
# channels=input_space.num_channels + self.dummy_channels,
# axes=('b', 'c', 't', 0, 1))
if hasattr(self, 'kernel_stride'):
kernel_stride = self.kernel_stride
else:
kernel_stride = [1, 1]
dummy_shape = [self.input_space.shape[0] , self.input_space.shape[1] ]
output_shape = [int(np.ceil((i_sh + 2. * self.pad - k_sh) / float(k_st))) + 1
for i_sh, k_sh, k_st in zip(dummy_shape,
self.kernel_shape,
kernel_stride)]
output_sequence_length = self.input_space.shape[2] - self.kernel_sequence_length + 1
if output_sequence_length < 0:
raise ValueError("Input sequence length ({}) should >= output sequence_length ({})".format(self.input_space.sequence_length, self.kernel_sequence_length))
def handle_kernel_shape(idx):
if self.kernel_shape[idx] < 1:
raise ValueError("kernel must have strictly positive size on all axes but has shape: "+str(self.kernel_shape))
if output_shape[idx] <= 0:
if self.fix_kernel_shape:
self.kernel_shape[idx] = self.input_space.shape[idx] + 2 * self.pad
assert self.kernel_shape[idx] != 0
output_shape[idx] = 1
warnings.warn("Had to change the kernel shape to make network feasible")
else:
raise ValueError("kernel too big for input (even with zero padding)")
map(handle_kernel_shape, [0, 1])
# space required for fft-3dconv
output_shape = [output_shape[0], output_shape[1], output_sequence_length]
self.detector_space = Conv3DSpace(shape=output_shape,
num_channels = self.detector_channels,
axes = ('b', 'c', 't', 0, 1))
if hasattr(self, 'partial_sum'):
partial_sum = self.partial_sum
else:
partial_sum = 1
# filter shape required for fft-3dconv ('c_detector','c','t','0','1')
filter_shape = (self.detector_space.num_channels,
self.input_space.num_channels,
self.kernel_sequence_length,
self.kernel_shape[0],
self.kernel_shape[1]
)
# filter shape required for fft-3dconv ('b','c','t','0','1')
signal_shape = (self.mlp.batch_size,
self.input_space.num_channels,
self.input_space.sequence_length,
self.input_space.shape[0],
self.input_space.shape[1]
)
self.transformer = make_random_conv3D(
irange = self.irange,
input_axes = ('b', 'c', 't', 0, 1),
output_axes = self.detector_space.axes,
signal_shape = signal_shape,
filter_shape = filter_shape,
pad = self.pad,
partial_sum = partial_sum,
kernel_stride = kernel_stride,
rng = rng)
W, = self.transformer.get_params()
W.name = 'W'
if self.tied_b:
self.b = sharedX(np.zeros((self.detector_space.num_channels)) + self.init_bias)
else:
self.b = sharedX(self.detector_space.get_origin() + self.init_bias)
self.b.name = 'b'
def fprop(self, state_below):
check_cuda(str(type(self)))
self.input_space.validate(state_below)
if not hasattr(self, 'input_normalization'):
self.input_normalization = None
#state_below= Print("state_below")(state_below)
if self.input_normalization:
state_below = self.input_normalization(state_below)
#import pdb; pdb.set_trace()
# GPU 3d correlation
z = self.transformer.lmul(state_below)
# bias addition
if not hasattr(self, 'tied_b'):
self.tied_b = False
if self.tied_b:
b = self.b.dimshuffle('x', 0, 'x', 'x', 'x')
else:
b = self.b.dimshuffle('x', 0, 1, 2, 3)
#z = Print('z')(z)
#b = Print('b')(b)
z = z + b
if self.layer_name is not None:
z.name = self.layer_name + '_z'
self.detector_space.validate(z)
#assert self.detector_space.num_channels % 16 == 0
#ReLUs
#z= Print("z")(z)
z = T.maximum(z, 0)
# Pooling
if tuple(self.pool_shape) != (1, 1, 1):
# Pooling on y, t
z_shape = z.shape
z = z.reshape((z_shape[0], z_shape[1] * z_shape[2],
z_shape[3], z_shape[4]))
p = dnn_pool(img=z,
ws=tuple(self.pool_shape[1:]),
stride=tuple(self.pool_stride[1:]))
p_shape = p.shape
p = p.reshape((p_shape[0], z_shape[1],
z_shape[2], p_shape[2],
p_shape[3]))
#p = Print("p")(p)
# Pooling on x
p_shape =p.shape
p = p.reshape((p_shape[0], p_shape[1],
p_shape[2], p_shape[3] * p_shape[4]))
t = dnn_pool(img=p,
ws=tuple([self.pool_shape[0], 1]),
stride=tuple([self.pool_stride[0], 1]))
t_shape = t.shape
#print (t_shape[0], t_shape[1], t_shape[2], p_shape[2] , p_shape[3])
t = t.reshape((t_shape[0], t_shape[1],
t_shape[2], p_shape[3] , p_shape[4]))
else:
t = z
self.output_space.validate(t)
## Gpu contiguous
#t = gpu_contiguous(t)
#t = Print("t")(t)
if not hasattr(self, 'output_normalization'):
self.output_normalization = None
if self.output_normalization:
t = self.output_normalization(t)
return t
def __init__(
self,
num_channels,
num_pieces,
kernel_shape,
pool_shape,
pool_stride,
layer_name,
irange=None,
init_bias=0.0,
W_lr_scale=None,
b_lr_scale=None,
pad=0,
fix_pool_shape=False,
fix_pool_stride=False,
fix_kernel_shape=False,
partial_sum=1,
tied_b=False,
max_kernel_norm=None,
input_normalization=None,
detector_normalization=None,
min_zero=False,
output_normalization=None,
kernel_stride=(1, 1),
):
"""
num_channels: The number of output channels the layer should have.
Note that it must internally compute num_channels * num_pieces
convolution channels.
num_pieces: The number of linear pieces used to make each maxout unit.
kernel_shape: The shape of the convolution kernel.
pool_shape: The shape of the spatial max pooling. A two-tuple of ints.
This is redundant as cuda-convnet requires the pool shape to
be square.
pool_stride: The stride of the spatial max pooling. Also must be square.
layer_name: A name for this layer that will be prepended to
monitoring channels related to this layer.
irange: if specified, initializes each weight randomly in
U(-irange, irange)
init_bias: All biases are initialized to this number
W_lr_scale: The learning rate on the weights for this layer is
multiplied by this scaling factor
b_lr_scale: The learning rate on the biases for this layer is
multiplied by this scaling factor
pad: The amount of zero-padding to implicitly add to the boundary of the
image when computing the convolution. Useful for making sure pixels
at the edge still get to influence multiple hidden units.
fix_pool_shape: If True, will modify self.pool_shape to avoid having
pool shape bigger than the entire detector layer.
If you have this on, you should probably also have
fix_pool_stride on, since the pool shape might shrink
smaller than the stride, even if the stride was initially
valid.
The "fix" parameters are useful for working with a hyperparameter
optimization package, which might often propose sets of hyperparameters
that are not feasible, but can easily be projected back into the feasible
set.
fix_kernel_shape: if True, will modify self.kernel_shape to avoid
having the kernel shape bigger than the implicitly
zero padded input layer
partial_sum: a parameter that controls whether to prefer runtime savings
or memory savings when computing the gradient with respect to
the kernels. See pylearn2.sandbox.cuda_convnet.weight_acts.py
for details. The default is to prefer high speed.
Note that changing this setting may change the value of computed
results slightly due to different rounding error.
tied_b: If true, all biases in the same channel are constrained to be the same
as each other. Otherwise, each bias at each location is learned independently.
max_kernel_norm: If specifed, each kernel is constrained to have at most this norm.
input_normalization, detector_normalization, output_normalization:
if specified, should be a callable object. the state of the network is optionally
replaced with normalization(state) at each of the 3 points in processing:
input: the input the layer receives can be normalized right away
detector: the maxout units can be normalized prior to the spatial pooling
output: the output of the layer, after sptial pooling, can be normalized as well
kernel_stride: vertical and horizontal pixel stride between
each detector.
"""
check_cuda(str(type(self)))
detector_channels = num_channels * num_pieces
self.__dict__.update(locals())
del self.self
def fprop(self, state_below):
check_cuda(str(type(self)))
self.input_space.validate(state_below)
if not hasattr(self, 'input_normalization'):
self.input_normalization = None
if self.input_normalization:
state_below = self.input_normalization(state_below)
# Alex's code requires # input channels to be <= 3 or a multiple of 4
# so we add dummy channels if necessary
if not hasattr(self, 'dummy_channels'):
self.dummy_channels = 0
if self.dummy_channels > 0:
state_below = T.concatenate((state_below,
T.zeros_like(state_below[0:self.dummy_channels, :, :, :, :])),
axis=0)
z = self.transformer.lmul(state_below)
if not hasattr(self, 'tied_b'):
self.tied_b = False
if self.tied_b:
b = self.b.dimshuffle(0, 'x', 'x', 'x', 'x')
else:
b = self.b.dimshuffle(0, 1, 2, 'x', 'x')
z = z + b
if self.layer_name is not None:
z.name = self.layer_name + '_z'
self.detector_space.validate(z)
assert self.detector_space.num_channels % 16 == 0
if self.output_space.num_channels % 16 == 0:
# alex's max pool op only works when the number of channels
# is divisible by 16. we can only do the cross-channel pooling
# first if the cross-channel pooling preserves that property
if self.num_pieces != 1:
s = None
for i in xrange(self.num_pieces):
t = z[i::self.num_pieces,:,:,:]
if s is None:
s = t
else:
s = T.maximum(s, t)
z = s
# pool across sequences
if self.sequence_pool_shape != 1:
s = None
for i in xrange(self.sequence_pool_shape):
t = z[:,:,:,i::self.sequence_pool_shape,:]
if s is None:
s = t
else:
s = T.maximum(s, t)
z = s
if self.detector_normalization:
z = self.detector_normalization(z)
# spatial pooling
z_shape = z.shape
z = z.reshape((z_shape[0], z_shape[1], z_shape[2], z_shape[3] * z_shape[4]))
p = max_pool_c01b(c01b=z, pool_shape=self.pool_shape,
pool_stride=self.pool_stride,
image_shape=self.detector_space.shape)
p_shape = p.shape
p = p.reshape((p_shape[0], p_shape[1], p_shape[2], z_shape[3], z_shape[4]))
else:
raise NotImplementedError("num channles should always be dvisible by 16")
self.output_space.validate(p)
if hasattr(self, 'min_zero') and self.min_zero:
p = p * (p > 0.)
if not hasattr(self, 'output_normalization'):
self.output_normalization = None
if self.output_normalization:
p = self.output_normalization(p)
return p
def setup_detector_layer_b01tc(layer, input_space, rng, irange, stride):
"""
Takes steps to set up an object for use as being some kind of
convolutional layer.
This function sets up only the detector layer.
Parameters
----------
layer: Any python object that allows the modifications described below and has
the following attributes:
pad: int describing amount of zero padding to add
kernel_shape: 3-element tuple or list describing shape of kernel
fix_kernel_shape: bool, if true, will shrink the kernel shape to make it
feasible, as needed (useful for hyperparameter searchers)
detector_channels: The number of channels in the detector layer
init_bias: A numeric constant added to a tensor of zeros to initialize the
bias
tied_b: If true, biases are shared across all spatial locations
input_space: A Conv3DSpace to be used as input to the layer
rng: a numpy RandomState or equivalent
irange: float. kernel elements are initialized randomly from U(-irange, irange)
Does the following:
raises a RuntimeError if cuda is not available
sets layer.input_space to input_space
sets up addition of dummy channels for compatibility with cuda-convnet:
layer.dummy_channels: # of dummy channels that need to be added
(You might want to check this and raise an Exception if it's not 0)
layer.dummy_space: The Conv2DSpace representing the input with dummy channels
added
sets layer.detector_space to the space for the detector layer
sets layer.transformer to be a Conv3DB01TC instance
sets layer.b to the right value
"""
# Use "self" to refer to layer from now on, so we can pretend we're just running
# in the set_input_space method of the layer
self = layer
# Make sure cuda is available
check_cuda(str(type(self)))
# Validate input
if not isinstance(input_space, Conv3DSpace):
raise TypeError(
"The input to a convolutional layer should be a Conv3DSpace, "
" but layer " + self.layer_name + " got " +
str(type(self.input_space)))
if not hasattr(self, 'detector_channels'):
raise ValueError(
'layer argument must have a "detector_channels" attribute specifying how many channels to put in the convolution kernel stack.'
)
# Store the input space
self.input_space = input_space
#self.dummy_space = Conv3DSpace(shape=input_space.shape,
# channels=input_space.num_channels + self.dummy_channels,
# axes=('b', 'c', 't', 0, 1))
if hasattr(self, 'kernel_stride'):
kernel_stride = stride
else:
kernel_stride = stride
#import pdb; pdb.set_trace()
#dummy_shape = [self.input_space.shape[0], self.input_space.shape[1] ]
output_shape = [
int((i_sh + 2. * self.pad - k_sh) / float(k_st)) + 1 for i_sh, k_sh,
k_st in zip(self.input_space.shape, self.kernel_shape, kernel_stride)
]
def handle_kernel_shape(idx):
if self.kernel_shape[idx] < 1:
raise ValueError(
"kernel must have strictly positive size on all axes but has shape: "
+ str(self.kernel_shape))
if output_shape[idx] <= 0:
if self.fix_kernel_shape:
self.kernel_shape[
idx] = self.input_space.shape[idx] + 2 * self.pad
assert self.kernel_shape[idx] != 0
output_shape[idx] = 1
warnings.warn(
"Had to change the kernel shape to make network feasible")
else:
raise ValueError(
"kernel too big for input (even with zero padding)")
map(handle_kernel_shape, [0, 1, 2])
# space required for 3dconv
self.detector_space = Conv3DSpace(shape=output_shape,
num_channels=self.detector_channels,
axes=('b', 0, 1, 't', 'c'))
if hasattr(self, 'partial_sum'):
partial_sum = self.partial_sum
else:
partial_sum = 1
# filter shape required for fft3dconv ('c_detector','c','t','0','1')
filter_shape = (
self.detector_space.num_channels,
self.kernel_shape[0],
self.kernel_shape[1],
self.kernel_shape[2],
self.input_space.num_channels,
)
# filter shape required for fft-3dconv ('b','c','t','0','1')
signal_shape = (
self.mlp.batch_size,
self.input_space.shape[0],
self.input_space.shape[1],
self.input_space.shape[2],
self.input_space.num_channels,
)
self.transformer = make_random_conv3D(irange=self.irange,
input_axes=('b', 0, 1, 't', 'c'),
output_axes=self.detector_space.axes,
signal_shape=signal_shape,
filter_shape=filter_shape,
pad=self.pad,
partial_sum=partial_sum,
kernel_stride=kernel_stride,
rng=rng)
W, = self.transformer.get_params()
W.name = 'W'
if self.tied_b:
self.b = sharedX(
np.zeros((self.detector_space.num_channels)) + self.init_bias)
else:
self.b = sharedX(self.detector_space.get_origin() + self.init_bias)
self.b.name = 'b'
def fprop(self, state_below):
check_cuda(str(type(self)))
self.input_space.validate(state_below)
if not hasattr(self, 'input_normalization'):
self.input_normalization = None
if self.input_normalization:
state_below = self.input_normalization(state_below)
# Alex's code requires # input channels to be <= 3 or a multiple of 4
# so we add dummy channels if necessary
if not hasattr(self, 'dummy_channels'):
self.dummy_channels = 0
if self.dummy_channels > 0:
state_below = T.concatenate(
(state_below,
T.zeros_like(state_below[0:self.dummy_channels, :, :, :, :])),
axis=0)
z = self.transformer.lmul(state_below)
if not hasattr(self, 'tied_b'):
self.tied_b = False
if self.tied_b:
b = self.b.dimshuffle(0, 'x', 'x', 'x', 'x')
else:
b = self.b.dimshuffle(0, 1, 2, 'x', 'x')
z = z + b
if self.layer_name is not None:
z.name = self.layer_name + '_z'
self.detector_space.validate(z)
assert self.detector_space.num_channels % 16 == 0
if self.output_space.num_channels % 16 == 0:
# alex's max pool op only works when the number of channels
# is divisible by 16. we can only do the cross-channel pooling
# first if the cross-channel pooling preserves that property
if self.num_pieces != 1:
s = None
for i in xrange(self.num_pieces):
t = z[i::self.num_pieces, :, :, :]
if s is None:
s = t
else:
s = T.maximum(s, t)
z = s
# pool across sequences
if self.sequence_pool_shape != 1:
s = None
for i in xrange(self.sequence_pool_shape):
t = z[:, :, :, i::self.sequence_pool_shape, :]
if s is None:
s = t
else:
s = T.maximum(s, t)
z = s
if self.detector_normalization:
z = self.detector_normalization(z)
# spatial pooling
z_shape = z.shape
z = z.reshape(
(z_shape[0], z_shape[1], z_shape[2], z_shape[3] * z_shape[4]))
p = max_pool_c01b(c01b=z,
pool_shape=self.pool_shape,
pool_stride=self.pool_stride,
image_shape=self.detector_space.shape)
p_shape = p.shape
p = p.reshape(
(p_shape[0], p_shape[1], p_shape[2], z_shape[3], z_shape[4]))
else:
raise NotImplementedError(
"num channles should always be dvisible by 16")
self.output_space.validate(p)
if hasattr(self, 'min_zero') and self.min_zero:
p = p * (p > 0.)
if not hasattr(self, 'output_normalization'):
self.output_normalization = None
if self.output_normalization:
p = self.output_normalization(p)
return p
def setup_detector_layer_c01b(layer, input_space, rng, irange="not specified"):
"""
.. todo::
WRITEME properly
Takes steps to set up an object for use as being some kind of convolutional
layer. This function sets up only the detector layer.
Does the following:
* raises a RuntimeError if cuda is not available
* sets layer.input_space to input_space
* sets up addition of dummy channels for compatibility with cuda-convnet:
- layer.dummy_channels: # of dummy channels that need to be added
(You might want to check this and raise an Exception if it's not 0)
- layer.dummy_space: The Conv2DSpace representing the input with dummy
channels added
* sets layer.detector_space to the space for the detector layer
* sets layer.transformer to be a Conv2D instance
* sets layer.b to the right value
Parameters
----------
layer : object
Any python object that allows the modifications described below and
has the following attributes:
* pad : int describing amount of zero padding to add
* kernel_shape : 2-element tuple or list describing spatial shape of
kernel
* fix_kernel_shape : bool, if true, will shrink the kernel shape to
make it feasible, as needed (useful for hyperparameter searchers)
* detector_channels : The number of channels in the detector layer
* init_bias : numeric constant added to a tensor of zeros to
initialize the bias
* tied_b : If true, biases are shared across all spatial locations
input_space : WRITEME
A Conv2DSpace to be used as input to the layer
rng : WRITEME
A numpy RandomState or equivalent
"""
if irange != "not specified":
raise AssertionError(
"There was a bug in setup_detector_layer_c01b."
"It uses layer.irange instead of the irange parameter to the "
"function. The irange parameter is now disabled by this "
"AssertionError, so that this error message can alert you that "
"the bug affected your code and explain why the interface is "
"changing. The irange parameter to the function and this "
"error message may be removed after April 21, 2014."
)
# Use "self" to refer to layer from now on, so we can pretend we're
# just running in the set_input_space method of the layer
self = layer
# Make sure cuda is available
check_cuda(str(type(self)))
# Validate input
if not isinstance(input_space, Conv2DSpace):
raise TypeError(
"The input to a convolutional layer should be a "
"Conv2DSpace, but layer " + self.layer_name + " got " + str(type(self.input_space))
)
if not hasattr(self, "detector_channels"):
raise ValueError(
"layer argument must have a 'detector_channels' "
"attribute specifying how many channels to put in "
"the convolution kernel stack."
)
# Store the input space
self.input_space = input_space
# Make sure number of channels is supported by cuda-convnet
# (multiple of 4 or <= 3)
# If not supported, pad the input with dummy channels
ch = self.input_space.num_channels
rem = ch % 4
if ch > 3 and rem != 0:
self.dummy_channels = 4 - rem
else:
self.dummy_channels = 0
self.dummy_space = Conv2DSpace(
shape=input_space.shape, channels=input_space.num_channels + self.dummy_channels, axes=("c", 0, 1, "b")
)
if hasattr(self, "kernel_stride"):
kernel_stride = self.kernel_stride
else:
kernel_stride = [1, 1]
output_shape = [
int(np.ceil((i_sh + 2.0 * self.pad - k_sh) / float(k_st))) + 1
for i_sh, k_sh, k_st in zip(self.input_space.shape, self.kernel_shape, kernel_stride)
]
def handle_kernel_shape(idx):
if self.kernel_shape[idx] < 1:
raise ValueError(
"kernel must have strictly positive size on all " "axes but has shape: " + str(self.kernel_shape)
)
if output_shape[idx] <= 0:
if self.fix_kernel_shape:
self.kernel_shape[idx] = self.input_space.shape[idx] + 2 * self.pad
assert self.kernel_shape[idx] != 0
output_shape[idx] = 1
warnings.warn("Had to change the kernel shape to make " "network feasible")
else:
raise ValueError("kernel too big for input " "(even with zero padding)")
map(handle_kernel_shape, [0, 1])
if self.detector_channels < 16:
raise ValueError("Cuda-convnet requires the detector layer to have " "at least 16 channels.")
self.detector_space = Conv2DSpace(shape=output_shape, num_channels=self.detector_channels, axes=("c", 0, 1, "b"))
if hasattr(self, "partial_sum"):
partial_sum = self.partial_sum
else:
partial_sum = 1
if hasattr(self, "sparse_init") and self.sparse_init is not None:
self.transformer = checked_call(
make_sparse_random_conv2D,
OrderedDict(
[
("num_nonzero", self.sparse_init),
("input_space", self.input_space),
("output_space", self.detector_space),
("kernel_shape", self.kernel_shape),
("pad", self.pad),
("partial_sum", partial_sum),
("kernel_stride", kernel_stride),
("rng", rng),
]
),
)
else:
self.transformer = make_random_conv2D(
irange=self.irange,
input_axes=self.input_space.axes,
output_axes=self.detector_space.axes,
input_channels=self.dummy_space.num_channels,
output_channels=self.detector_space.num_channels,
kernel_shape=self.kernel_shape,
pad=self.pad,
partial_sum=partial_sum,
kernel_stride=kernel_stride,
rng=rng,
)
W, = self.transformer.get_params()
W.name = self.layer_name + "_W"
if self.tied_b:
self.b = sharedX(np.zeros(self.detector_space.num_channels) + self.init_bias)
else:
self.b = sharedX(self.detector_space.get_origin() + self.init_bias)
self.b.name = self.layer_name + "_b"
logger.info("Input shape: {0}".format(self.input_space.shape))
logger.info("Detector space: {0}".format(self.detector_space.shape))
def piece_prop(self, state_below):
"""
Note: this only reports pieces in terms of which channel wins, not
which spatial location wins. Depending on the input size, it may report
a piece map for either the pre-spatial pooling or the post-spatial pooling
tensor.
"""
check_cuda(str(type(self)))
self.input_space.validate(state_below)
if not hasattr(self, 'input_normalization'):
self.input_normalization = None
if self.input_normalization:
state_below = self.input_normalization(state_below)
# Alex's code requires # input channels to be <= 3 or a multiple of 4
# so we add dummy channels if necessary
if not hasattr(self, 'dummy_channels'):
self.dummy_channels = 0
if self.dummy_channels > 0:
state_below = T.concatenate((state_below,
T.zeros_like(state_below[0:self.dummy_channels, :, :, :])),
axis=0)
z = self.transformer.lmul(state_below)
if not hasattr(self, 'tied_b'):
self.tied_b = False
if self.tied_b:
b = self.b.dimshuffle(0, 'x', 'x', 'x')
else:
b = self.b.dimshuffle(0, 1, 2, 'x')
z = z + b
if self.layer_name is not None:
z.name = self.layer_name + '_z'
self.detector_space.validate(z)
assert self.detector_space.num_channels % 16 == 0
if self.output_space.num_channels % 16 == 0:
# alex's max pool op only works when the number of channels
# is divisible by 16. we can only do the cross-channel pooling
# first if the cross-channel pooling preserves that property
piece = None
if self.num_pieces != 1:
s = None
for i in xrange(self.num_pieces):
t = z[i::self.num_pieces,:,:,:]
if s is None:
s = t
piece = T.zeros_like(t)
else:
s = T.maximum(s, t)
mask = T.eq(s, t)
piece = mask * i + (1 - mask) * piece
z = s
if self.detector_normalization:
z = self.detector_normalization(z)
p = max_pool_c01b(c01b=z, pool_shape=self.pool_shape,
pool_stride=self.pool_stride,
image_shape=self.detector_space.shape)
else:
if self.detector_normalization is not None:
raise NotImplementedError("We can't normalize the detector "
"layer because the detector layer never exists as a "
"stage of processing in this implementation.")
z = max_pool_c01b(c01b=z, pool_shape=self.pool_shape,
pool_stride=self.pool_stride,
image_shape=self.detector_space.shape)
if self.num_pieces != 1:
s = None
piece = None
for i in xrange(self.num_pieces):
t = z[i::self.num_pieces,:,:,:]
if s is None:
s = t
piece = T.zeros_like(t)
else:
s = T.maximum(s, t)
mask = T.eq(s, t)
piece = mask * i + (1- mask) * piece
z = s
p = z
self.output_space.validate(p)
if hasattr(self, 'min_zero') and self.min_zero:
p = p * (p > 0.)
if not hasattr(self, 'output_normalization'):
self.output_normalization = None
if self.output_normalization:
p = self.output_normalization(p)
return p, piece
def fprop(self, state_below):
check_cuda(str(type(self)))
self.input_space.validate(state_below)
if not hasattr(self, "input_normalization"):
self.input_normalization = None
if self.input_normalization:
state_below = self.input_normalization(state_below)
# Alex's code requires # input channels to be <= 3 or a multiple of 4
# so we add dummy channels if necessary
if not hasattr(self, "dummy_channels"):
self.dummy_channels = 0
if self.dummy_channels > 0:
state_below = T.concatenate(
(state_below, T.zeros_like(state_below[0 : self.dummy_channels, :, :, :])), axis=0
)
z = self.transformer.lmul(state_below)
if not hasattr(self, "tied_b"):
self.tied_b = False
if self.tied_b:
b = self.b.dimshuffle(0, "x", "x", "x")
else:
b = self.b.dimshuffle(0, 1, 2, "x")
z = z + b
if self.layer_name is not None:
z.name = self.layer_name + "_z"
self.detector_space.validate(z)
assert self.detector_space.num_channels % 16 == 0
if self.output_space.num_channels % 16 == 0:
# alex's max pool op only works when the number of channels
# is divisible by 16. we can only do the cross-channel pooling
# first if the cross-channel pooling preserves that property
if self.num_pieces != 1:
s = None
for i in xrange(self.num_pieces):
t = z[i :: self.num_pieces, :, :, :]
if s is None:
s = t
else:
s = T.maximum(s, t)
z = s
if self.detector_normalization:
z = self.detector_normalization(z)
p = max_pool_c01b(
c01b=z, pool_shape=self.pool_shape, pool_stride=self.pool_stride, image_shape=self.detector_space.shape
)
else:
if self.detector_normalization is not None:
raise NotImplementedError(
"We can't normalize the detector "
"layer because the detector layer never exists as a "
"stage of processing in this implementation."
)
z = max_pool_c01b(
c01b=z, pool_shape=self.pool_shape, pool_stride=self.pool_stride, image_shape=self.detector_space.shape
)
if self.num_pieces != 1:
s = None
for i in xrange(self.num_pieces):
t = z[i :: self.num_pieces, :, :, :]
if s is None:
s = t
else:
s = T.maximum(s, t)
z = s
p = z
self.output_space.validate(p)
if hasattr(self, "min_zero") and self.min_zero:
p = p * (p > 0.0)
if not hasattr(self, "output_normalization"):
self.output_normalization = None
if self.output_normalization:
p = self.output_normalization(p)
return p
def fprop(self, state_below):
check_cuda(str(type(self)))
self.input_space.validate(state_below)
if not hasattr(self, 'input_normalization'):
self.input_normalization = None
if self.input_normalization:
state_below = self.input_normalization(state_below)
# fft 3d covolution
z = self.transformer.lmul(state_below)
# bias addition
if not hasattr(self, 'tied_b'):
self.tied_b = False
if self.tied_b:
b = self.b.dimshuffle(0, 'x', 'x', 'x', 'x')
else:
b = self.b.dimshuffle('x', 0, 1, 2, 3)
z = z + self.b
if self.layer_name is not None:
z.name = self.layer_name + '_z'
self.detector_space.validate(z)
#assert self.detector_space.num_channels % 16 == 0
#ReLUs
z = T.maximum(z, 0)
if self.output_space.num_channels % 16 != 0:
raise NotImplementedError("num channles should always be dvisible by 16")
# alex's max pool op only works when the number of channels
# is divisible by 16. we can only do the cross-channel pooling
# first if the cross-channel pooling preserves that property
# Pooling
# permute axes ['b', 0, 1,'t','c'] -> ['c', 0, 1, 't', 'b'] (axes required for pooling )
z = z.dimshuffle(4, 1, 2, 3, 0)
# spatial pooling x/y
z_shape = z.shape
z = z.reshape((z_shape[0], z_shape[1], z_shape[2], z_shape[3] * z_shape[4]))
p = max_pool_c01b(c01b = z,
pool_shape = self.pool_shape[0:2],
pool_stride = self.pool_stride[0:2])
p = p.reshape((p.shape[0], p.shape[1], p.shape[2], z_shape[3], z_shape[4]))
# temporal pooling with overlap (t)
p_shape = p.shape
#['c', 0, 1, 't', 'b'] -> ['c',0*1,'t','b'] ('c',0, 1,'b') for max_pool_c01b
p = p.reshape((p_shape[0], p_shape[1] * p_shape[2], p_shape[3] , p_shape[4]))
t = temporal_max_pool_c01b(c01b = p,
pool_shape = [1, self.pool_shape[2]],
pool_stride = [1, self.pool_stride[2]],
image_shape = self.temp_pool_input_shape)
t_shape = t.shape
t = t.reshape((t_shape[0], p_shape[1] , p_shape[2], t_shape[2] , t_shape[3]))
# Permute back axes ['c', 0, 1, 't', 'b'] -> ['b', 0, 1, 't', 'c']
t = t.dimshuffle(4, 1, 2, 3, 0)
self.output_space.validate(t)
if not hasattr(self, 'output_normalization'):
self.output_normalization = None
if self.output_normalization:
t = self.output_normalization(t)
return t
