""" % dict(img=img,
kerns=kern,
out=out,
bmode=bmode,
fail=sub['fail'],
id=sub['struct_id'],
name=name)
def c_code_cache_version(self):
return (4, )
from theano.sandbox.cuda.opt import (local_optimizer, gpu_contiguous,
gpu_optimizer)
@local_optimizer([GpuConv])
def local_conv_dnn(node):
if isinstance(node.op, GpuConv):
if (node.op.subsample != (1, 1)
or node.op.border_mode not in ['full', 'valid']):
return
img, kern = node.inputs
border_mode = node.op.border_mode
return [
GpuDnnConv(border_mode)(gpu_contiguous(img), gpu_contiguous(kern))
]
gpu_optimizer.register("conv_cudnn", local_conv_dnn, 'cudnn')
return codeSource
gpu_conv_transpd = GpuConvTransp3D()
@local_optimizer([ConvTransp3D])
def local_gpu_conv_transp3d(node):
if isinstance(node.op, ConvTransp3D):
if numpy.any([i.owner and isinstance(i.owner.op, HostFromGpu)
for i in node.inputs]):
if numpy.all([o.type.dtype == 'float32' for o in node.outputs]):
W, b, d, H, RShape = node.inputs
return [host_from_gpu(gpu_conv_transpd(W, b, d, H, RShape))]
# Not enabled by default as we don't want people to use it.
gpu_optimizer.register("local_gpu_conv_transp3d", local_gpu_conv_transp3d)
#If the input size wasn't a multiple of D we may need to cause some automatic padding to get the right size of reconstruction
def computeR(W, b, d, H, Rshape=None):
assert len(W.shape) == 5
assert len(H.shape) == 5
assert len(b.shape) == 1
assert len(d) == 3
outputChannels, inputChannels, filterHeight, filterWidth, filterDur = W.shape
batchSize, outputChannelsAgain, outputHeight, outputWidth, outputDur = H.shape
assert outputChannelsAgain == outputChannels
assert b.shape[0] == inputChannels
output%(id)d, CudaNdarray_DEV_DATA(%(out)s),
CUDNN_RESULT_NO_ACCUMULATE
);
if (err%(name)s != CUDNN_STATUS_SUCCESS) {
PyErr_Format(PyExc_RuntimeError, "error doing operation: %%s",
cudnnGetErrorString(err%(name)s));
%(fail)s
}
""" % dict(img=img, kerns=kern, out=out, bmode=bmode,
fail=sub['fail'], id=sub['struct_id'], name=name)
def c_code_cache_version(self):
return (4,)
from theano.sandbox.cuda.opt import (local_optimizer, gpu_contiguous,
gpu_optimizer)
@local_optimizer([GpuConv])
def local_conv_dnn(node):
if isinstance(node.op, GpuConv):
if (node.op.subsample != (1, 1) or
node.op.border_mode not in ['full', 'valid']):
return
img, kern = node.inputs
border_mode = node.op.border_mode
return [GpuDnnConv(border_mode)(gpu_contiguous(img),
gpu_contiguous(kern))]
gpu_optimizer.register("conv_cudnn", local_conv_dnn, 'cudnn')
return codeSource
gpu_conv_transpd = GpuConvTransp3D()
@local_optimizer([ConvTransp3D])
def local_gpu_conv_transp3d(node):
if isinstance(node.op, ConvTransp3D):
if numpy.any([i.owner and isinstance(i.owner.op, HostFromGpu)
for i in node.inputs]):
if numpy.all([o.type.dtype == 'float32' for o in node.outputs]):
W, b, d, H, RShape = node.inputs
return [host_from_gpu(gpu_conv_transpd(W, b, d, H, RShape))]
# Not enabled by default as we don't want people to use it.
gpu_optimizer.register("local_gpu_conv_transp3d", local_gpu_conv_transp3d)
# If the input size wasn't a multiple of D we may need to cause some automatic padding to get the right size of reconstruction
def computeR(W, b, d, H, Rshape=None):
assert len(W.shape) == 5
assert len(H.shape) == 5
assert len(b.shape) == 1
assert len(d) == 3
outputChannels, inputChannels, filterHeight, filterWidth, filterDur = W.shape
batchSize, outputChannelsAgain, outputHeight, outputWidth, outputDur = H.shape
assert outputChannelsAgain == outputChannels
assert b.shape[0] == inputChannels
dr, dc, dt = d
}
"""
return codeSource
gpu_convd = GpuConv3D()
@local_optimizer([Conv3D])
def local_gpu_conv3d(node):
if isinstance(node.op, Conv3D):
if numpy.any([
i.owner and isinstance(i.owner.op, HostFromGpu)
for i in node.inputs
]):
if numpy.all([o.type.dtype == 'float32' for o in node.outputs]):
V, W, b, d = node.inputs
return [
host_from_gpu(
gpu_convd(as_cuda_ndarray_variable(V),
as_cuda_ndarray_variable(W),
as_cuda_ndarray_variable(b), d))
]
# Not enabled by default as we don't want people to use it.
gpu_optimizer.register("local_gpu_conv3d", local_gpu_conv3d)
from theano.sandbox.cuda.opt import local_optimizer, gpu_optimizer
@local_optimizer([GpuConv])
def local_conv_dnn(node):
raise_no_dnn()
if isinstance(node.op, GpuConv):
if node.op.border_mode not in ['full', 'valid']:
return
img, kern = node.inputs
border_mode = node.op.border_mode
subsample = node.op.subsample
return [dnn_conv(gpu_contiguous(img), gpu_contiguous(kern),
border_mode=border_mode, subsample=subsample)]
gpu_optimizer.register("conv_cudnn", local_conv_dnn, 'cudnn')
@local_optimizer([GpuDownsampleFactorMax])
def local_pool_dnn(node):
if isinstance(node.op, GpuDownsampleFactorMax):
if node.op.ignore_border:
return
img, = node.inputs
ds = node.op.ds
return [dnn_pool(gpu_contiguous(img), ds, ds)]
gpu_optimizer.register("pool_cudnn", local_pool_dnn, 'cudnn')
@local_optimizer([GpuDownsampleFactorMaxGrad])
out[batch_id*nkern*out_len*out_wid*out_dur+//the good batch
out_frame*nkern+//the output frame
out_row*out_wid*out_dur*nkern+//the output row
out_col*out_dur*nkern + //the output_col
kern_id //the output image (channel)
] = sum + bias[kern_id];
}
}
"""
return codeSource
gpu_convd = GpuConv3D()
@local_optimizer([Conv3D])
def local_gpu_conv3d(node):
if isinstance(node.op, Conv3D):
if numpy.any([i.owner and isinstance(i.owner.op, HostFromGpu)
for i in node.inputs]):
if numpy.all([o.type.dtype == 'float32' for o in node.outputs]):
V, W, b, d = node.inputs
return [host_from_gpu(gpu_convd(as_cuda_ndarray_variable(V),
as_cuda_ndarray_variable(W),
as_cuda_ndarray_variable(b),
d))]
# Not enabled by default as we don't want people to use it.
gpu_optimizer.register("local_gpu_conv3d", local_gpu_conv3d)