def apply(self, input_):
"""Perform the convolution.
Parameters
----------
input_ : :class:`~tensor.TensorVariable`
A 5D tensor with the axes representing batch size, number of
channels, height, width and time.
Returns
-------
output : :class:`~tensor.TensorVariable`
A 5D tensor of filtered images (feature maps) with dimensions
representing batch size, number of filters, feature map height,
feature map width and feature map time.
"""
if self.use_bias:
W, b = self.parameters
else:
W, = self.parameters
if self.cudnn_impl:
output = dnn_conv3d(input_,
W,
subsample=tuple(self.kernel_stride),
border_mode=self.padding)
else:
output = GpuCorr3dMM(subsample=tuple(self.step),
pad=self.padding)(input_, W)
if self.use_bias:
if self.shared_bias:
output += b.dimshuffle('x', 0, 'x', 'x', 'x')
else:
output += b.dimshuffle('x', 0, 1, 2, 3)
return output
def run_conv_valid(self,
inputs_shape,
filters_shape,
border_mode='valid',
filter_dilation=(1, 1, 1),
subsample=(1, 1, 1),
verify_grad=False):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs = shared(inputs_val)
filters = shared(filters_val)
conv_ref = Corr3dMM(border_mode=border_mode,
filter_dilation=filter_dilation,
subsample=subsample)(inputs.dimshuffle(
0, 4, 1, 2,
3), filters.dimshuffle(0, 4, 1, 2, 3))
conv_ref = conv_ref.dimshuffle(0, 2, 3, 4, 1)
f_ref = theano.function([], conv_ref, mode='FAST_RUN')
conv = GpuCorr3dMM(border_mode=border_mode,
filter_dilation=filter_dilation,
subsample=subsample)(inputs.dimshuffle(
0, 4, 1, 2,
3), filters.dimshuffle(0, 4, 1, 2, 3))
conv = conv.dimshuffle(0, 2, 3, 4, 1)
f = theano.function([], conv, mode=mode_with_gpu)
res_ref = f_ref()
res = f()
utt.assert_allclose(res_ref, res)
if verify_grad:
utt.verify_grad(GpuCorr3dMM(border_mode=border_mode,
filter_dilation=filter_dilation,
subsample=subsample),
[
inputs_val.transpose(0, 4, 1, 2, 3),
filters_val.transpose(0, 4, 1, 2, 3)
],
mode=mode_with_gpu)
def fprop_conv(self, state_below, stride=1):
"""fprop theano state_below using conv method
:param state_below: batch, chl, x, y, z"""
import theano
import theano.tensor as T
from theano.sandbox.cuda.blas import GpuCorr3dMM
assert state_below.ndim == 5
stride = int(stride)
corr = GpuCorr3dMM(subsample=(stride, stride, stride))
conv_rst = corr(state_below, sharedX(self.get_conv_coeff()))
conv_rst += sharedX(self.bias).dimshuffle('x', 0, 'x', 'x', 'x')
sqr = T.square(conv_rst)
vsum = T.tensordot(sharedX(self.outhid_conn), sqr, axes=[1, 1])
vsum = vsum.dimshuffle(1, 0, 2, 3, 4)
return T.sqrt(vsum)
def run_conv_valid(self, inputs_shape, filters_shape,
subsample=(1, 1, 1)):
inputs_val = numpy.random.random(inputs_shape).astype('float32')
filters_val = numpy.random.random(filters_shape).astype('float32')
inputs = shared(inputs_val)
filters = shared(filters_val)
bias = shared(numpy.zeros(filters_shape[0]).astype('float32'))
conv_ref = theano.tensor.nnet.conv3D(V=inputs, W=filters,
b=bias, d=subsample)
conv = GpuCorr3dMM(border_mode = "valid",
subsample=subsample)(inputs.dimshuffle(0, 4, 1, 2, 3),
filters.dimshuffle(0, 4, 1, 2, 3))
conv = conv.dimshuffle(0, 2, 3, 4, 1)
f_ref = theano.function([], conv_ref)
f = theano.function([], conv, mode=mode_with_gpu)
res_ref = f_ref()
res = f()
utt.assert_allclose(res_ref, res)
def __init__(
self,
input_layer,
num_filters,
filter_size,
strides=(1, 1, 1),
border_mode=None,
W=lasagne.init.Normal(std=0.001), # usually 0.01
b=lasagne.init.Constant(0.),
nonlinearity=lasagne.nonlinearities.rectify,
pad=None,
flip_filters=True,
**kwargs):
"""
input_shape: (frames, height, width)
W_shape: (out, in, kern_frames, kern_height, kern_width)
"""
super(Conv3dMMLayer, self).__init__(input_layer, **kwargs)
# TODO note that lasagne allows 'untied' biases, the same shape
# as the input filters.
self.num_filters = num_filters
self.filter_size = filter_size
if strides is None:
self.strides = (1, 1, 1)
else:
self.strides = tuple(strides)
self.flip_filters = flip_filters
if nonlinearity is None:
self.nonlinearity = lasagne.nonlinearities.identity
else:
self.nonlinearity = nonlinearity
if border_mode is not None and pad is not None:
raise RuntimeError(
"You cannot specify both 'border_mode' and 'pad'. To avoid ambiguity, please specify only one of them."
)
elif border_mode is None and pad is None:
# no option specified, default to valid mode
self.pad = (0, 0, 0)
elif border_mode is not None:
if border_mode == 'valid':
self.pad = (0, 0, 0)
elif border_mode == 'full':
self.pad = (self.filter_size[0] - 1, self.filter_size[1] - 1,
self.filter_size[2] - 1)
elif border_mode == 'same':
# only works for odd filter size, but the even filter size case is probably not worth supporting.
self.pad = ((self.filter_size[0] - 1) // 2,
(self.filter_size[1] - 1) // 2,
(self.filter_size[2] - 1) // 2)
else:
raise RuntimeError(
"Unsupported border_mode for Conv3dLayer: %s" %
border_mode)
else:
self.pad = tuple(pad)
self.W = self.add_param(W, self.get_W_shape(), name='W')
if b is None:
self.b = None
else:
self.b = self.add_param(b, (num_filters, ),
name='b',
regularizable=False)
self.corr_mm_op = GpuCorr3dMM(subsample=self.strides, pad=self.pad)
print(sys.version)
from voxnet import isovox
from theano.sandbox.cuda.basic_ops import gpu_contiguous
from theano.sandbox.cuda.blas import GpuCorr3dMM
import numpy as np
vis_mat = np.load('../../voxnet/scripts/weights.npz')
W1 = vis_mat['conv1.W'] # size(32,1,5,5,5)
W2 = vis_mat['conv2.W'] # size(32,32,3,3,3)
contiguous_W1 = gpu_contiguous(W1)
contiguous_W2 = gpu_contiguous(W2)
strides = (1, 1, 1)
pad = (2, 2, 2)
corr_mm_op = GpuCorr3dMM(subsample=strides, pad=pad)(contiguous_W2,
contiguous_W1)
print(corr_mm_op)
size = 32
wviz = W1[:, 0, :, :, :]
for i in range(wviz.shape[0]):
w = wviz[i, :, :, :]
# centerize the plot
fz = len(w)
xd = np.zeros((size, size, size))
pad = (size - fz) / 2
xd[pad:pad + fz, pad:pad + fz, pad:pad + fz] = w
# only visualize the largest value
t = 0.2
xd[xd < t] = 0
# store as png
def conv_and_add_bias(self, x):
x = gpu_contiguous(x)
rval = GpuCorr3dMM(subsample=tuple(self.kernel_stride))(x,
self.filters)
rval = rval + self.bias.dimshuffle('x', 0, 'x', 'x', 'x')
return rval
def conv3d(x,
kernel,
strides=(1, 1, 1),
border_mode='valid',
dim_ordering=_IMAGE_DIM_ORDERING,
volume_shape=None,
filter_shape=None,
conv_algo="GpuCorr3dMM"):
'''
Run on cuDNN if available.
border_mode: string, "same" or "valid".
conv_algo: string,
"conv3d2d": internally reshapes the data and performs 2d convs
"dnn_conv3d": uses CuDNNs 3d convolution
"GpuCorr3dM": performs a correlation, not a conolution
(filter not flipped), uses the "Toeplitz"-
matrix (which means it needs a little more memory)
'''
if dim_ordering not in {'th', 'tf'}:
raise Exception('Unknown dim_ordering ' + str(dim_ordering))
if border_mode not in {'same', 'valid'}:
raise Exception('Invalid border mode: ' + str(border_mode))
if dim_ordering == 'tf':
# TF uses the last dimension as channel dimension,
# instead of the 2nd one.
# TH input shape: (samples, input_depth, conv_dim1, conv_dim2, conv_dim3)
# TF input shape: (samples, conv_dim1, conv_dim2, conv_dim3, input_depth)
# TH kernel shape: (out_depth, input_depth, kernel_dim1, kernel_dim2, kernel_dim3)
# TF kernel shape: (kernel_dim1, kernel_dim2, kernel_dim3, input_depth, out_depth)
x = x.dimshuffle((0, 4, 1, 2, 3))
kernel = kernel.dimshuffle((4, 3, 0, 1, 2))
if volume_shape:
volume_shape = (volume_shape[0], volume_shape[4], volume_shape[1],
volume_shape[2], volume_shape[3])
if filter_shape:
filter_shape = (filter_shape[4], filter_shape[3], filter_shape[0],
filter_shape[1], filter_shape[2])
if border_mode == 'same':
assert (strides == (1, 1, 1))
pad_dim1 = (kernel.shape[2] - 1)
pad_dim2 = (kernel.shape[3] - 1)
pad_dim3 = (kernel.shape[4] - 1)
output_shape = (x.shape[0], x.shape[1], x.shape[2] + pad_dim1,
x.shape[3] + pad_dim2, x.shape[4] + pad_dim3)
output = T.zeros(output_shape)
indices = (slice(None), slice(None),
slice(pad_dim1 // 2, x.shape[2] + pad_dim1 // 2),
slice(pad_dim2 // 2, x.shape[3] + pad_dim2 // 2),
slice(pad_dim3 // 2, x.shape[4] + pad_dim3 // 2))
x = T.set_subtensor(output[indices], x)
border_mode = 'valid'
border_mode_3d = (border_mode, border_mode, border_mode)
if conv_algo == "conv3d2d":
conv_out = conv3d2d.conv3d(signals=x.dimshuffle(0, 2, 1, 3, 4),
filters=kernel.dimshuffle(0, 2, 1, 3, 4),
border_mode=border_mode_3d)
conv_out = conv_out.dimshuffle(0, 2, 1, 3, 4)
elif conv_algo == "dnn_conv3d":
conv_out = dnn_conv3d(img=x, kerns=kernel, border_mode=border_mode)
elif conv_algo == "GpuCorr3dMM":
bias = np.zeros((volume_shape[1]))
conv_out = GpuCorr3dMM()(x, kernel)
else:
raise ("Unknown algorithm to perform 3d convolution")
# support strides by manually slicing the output
if strides != (1, 1, 1):
conv_out = conv_out[:, :, ::strides[0], ::strides[1], ::strides[2]]
if dim_ordering == 'tf':
conv_out = conv_out.dimshuffle((0, 2, 3, 4, 1))
return conv_out