ó àÆ÷Xc@`spddlmZmZmZddlZddlmZddlmZm Z m Z ddl m Z ddlm Z ddljjZd„Zdefd „ƒYZeeƒZd efd „ƒYZeeƒZddd d „Zd„Zejreedgƒeeddgƒnejjeegƒd„ƒZejjj de ede j!ƒdddƒdS(i(tabsolute_importtprint_functiontdivisionN(tDisconnectedType(tOptApplyt TopoOptimizer(tcopy_stack_trace(ttensorcC`sÓt|ƒ}t|ƒ}|j||j|krAtdƒ‚ntdƒg|j}t|j|dddƒ||<|jt|ƒƒ}t|j ƒ}|j|dkrÏ||c||8<||_ n|S(s— Helper function for DiagonalSubtensor and IncDiagonalSubtensor. Notes ----- It returns a partial view of x, not a partial copy. sis this allowed?iN( tinttshapetNotImplementedErrortslicetNonetndimt __getitem__ttupletlisttstrides(txti0ti1tidxtxviewR((s;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pytget_diagonal_subtensor_view s  ! tDiagonalSubtensorcB`sMeZdZdZd„Zed„Zd„Zd„Zd„Z d„Z RS( s_ Return a form a nd diagonal subtensor. Parameters ---------- x n-d tensor i0 Axis index in x i1 Axis index in x Notes ----- Work on the GPU. Extended summary ---------------- ``x`` is some n-dimensional tensor, but this Op only deals with a matrix-shaped slice, using axes i0 and i1. Without loss of generality, suppose that ``i0`` picks out our ``row`` dimension, and i1 the ``column`` dimension. So the relevant part of ``x`` is some matrix ``u``. Suppose it has 7 rows and 4 columns:: [ 0 0 0 0 ] [ 0 0 0 0 ] [ 0 0 0 0 ] [ 0 0 0 0 ] [ 0 0 0 0 ] [ 0 0 0 0 ] The view returned by this function is also a matrix. It's a thick, diagonal ``stripe`` across u that discards the lower left triangle and the upper right triangle: [ x 0 0 0 ] [ x x 0 0 ] [ x x x 0 ] [ 0 x x x ] [ 0 0 x x ] [ 0 0 0 x ] In this case the return value would be this view of shape 3x4. The returned view has the same number of dimensions as the input ``x``, and the only difference is that the shape along dimension ``i0`` has been reduced by ``shape[i1] - 1`` because of the triangles that got chopped out. The NotImplementedError is meant to catch the case where shape[i0] is too small for the stripe to reach across the matrix, in which case it's not clear what this function should do. Maybe always raise an error. I'd look back to the call site in the Conv3D to see what's necessary at that point. tinplacecC`s%|jrd|jjSd|jjS(Ns %s{inplace}s%s(Rt __class__t__name__(tself((s;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pyt__str__`s cC`s)||_|r%idgd6|_ndS(Ni(Rtview_map(RR((s;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pyt__init__es cC`s@tj|ƒ}tj|ƒ}t||||g|jƒgƒS(N(Rtas_tensor_variableRttype(RRRRt_i0t_i1((s;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pyt make_nodejscC`s>t|Œ}|jr&||dd`. See Also -------- Someone made a script that shows how to swap the axes between both 3d convolution implementations in Theano. See the last `attachment `_ iis'height and width bordermodes must matcht input_shapet filter_shapet border_modeR=tfullthalftsamesinvalid border modeiitaxistdtypeR N(t isinstancetstrR R R Rtnnettconv2dtreshapet ValueErrorR;tsumtzerosREt set_subtensor(tsignalstfilterst signals_shapet filters_shapeR@t_signals_shape_5dt_filters_shape_5dtNstTstCtHstWstNftTftHftWft_signals_shape_4dt_filters_shape_4dtconv2d_signal_shapetconv2d_filter_shapetout_4dtHouttWouttout_tmptout_5dtTpadtout_tmp_padded((s;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pytconv3d­sv'            ! !     !#A c`sTtjjˆtjgƒ‡‡fd†ƒ}dˆj|_tjjƒ|ƒdS(s“ This function create optimizer that move some inputs to the GPU for op that work on both CPU and GPU. The op object is created by calling op(), so good default value are needed. We suppose the same op work with CPU and GPU inputs. Parameters ---------- op The op that support GPU inputs. to_gpu A list of op inputs that are moved to the GPU. c`s\tˆjˆƒrµt‡fd†ˆDƒƒrµtˆjƒ}x%ˆD]}tj||ƒ|| host_from_gpu(op) gpu_from_host(op) -> op(gpu_from_host) c3`s>|]4}ˆj|jo5tˆj|jjtjƒVqdS(N(R(townerRFtoptcudat HostFromGpu(t.0R(R'(s;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pys Gsi( RFRktanyRR(Rlt gpu_from_hostRtoutputst host_from_gpuRjR2(R'tnew_inpRt result_nodet transfer_nodet host_inputtop_nodetnew_node(Rktto_gpu(R's;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pyt local_to_gpu<s.         t local_to_gpu_N(ttheanotgoftlocal_optimizerRlRpRtoptt register_opt(RkRyRz((RkRys;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pytmake_gpu_optimizer*s-#icC`sht|jttfƒrd|jj rd|jjdtƒ}||jŒ}t|j d|ƒ|gSt S(s#Also work for IncDiagonalSubtensor.Ri( RFRkRR8RRR1R(RRqR2(R'tnew_opRx((s;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pytlocal_inplace_DiagonalSubtensorgs Rƒtfailure_callbacki<tfast_runR("t __future__RRRR|ttheano.gradientRt theano.gofRRRttheano.gof.optRRttheano.sandbox.cudatsandboxRlRRR2R;R8R,R RiRtcuda_availableR}R~Rƒtcompiletoptdbtregistert warn_inplace(((s;/tmp/pip-build-X4mzal/theano/theano/tensor/nnet/conv3d2d.pyts0  \ (  { 8 !