Python pyopencl.enqueue_write_buffer函数代码示例

 def mineThread(self):
     for data in self.qr:
         for i in range(data.iterations):
             self.kernel.search(
                 self.commandQueue, (data.size, ), (self.WORKSIZE, ),
                 data.state[0], data.state[1], data.state[2], data.state[3],
                 data.state[4], data.state[5], data.state[6], data.state[7],
                 data.state2[1], data.state2[2], data.state2[3],
                 data.state2[5], data.state2[6], data.state2[7],
                 data.base[i],
                 data.f[0],
                 data.f[1],data.f[2],
                 data.f[3],data.f[4],
                 self.output_buf)
             cl.enqueue_read_buffer(
                 self.commandQueue, self.output_buf, self.output)
             self.commandQueue.finish()
             
             # The OpenCL code will flag the last item in the output buffer when
             # it finds a valid nonce. If that's the case, send it to the main
             # thread for postprocessing and clean the buffer for the next pass.
             if self.output[self.OUTPUT_SIZE]:
                 reactor.callFromThread(self.postprocess, self.output.copy(),
                 data.nr)
         
                 self.output.fill(0)
                 cl.enqueue_write_buffer(
                     self.commandQueue, self.output_buf, self.output)

	def compute(self):
		glMatrixMode(GL_MODELVIEW)
		glPushMatrix()
		glLoadIdentity()
		self.applySceneTransforms()
		mat = np.array(glGetFloat(GL_MODELVIEW_MATRIX).transpose(), order='C')
		glPopMatrix()
		inv = np.array(np.linalg.inv(mat), order='C')
		
		e1 = cl.enqueue_write_buffer(queue, self.matrix, mat)
		e2 = cl.enqueue_write_buffer(queue, self.inv_matrix, inv)
		
		e3 = self.program.pdbTracer(queue, self.dst.shape[:2], self.dst_buf,
			self.matrix, self.inv_matrix, 
			np.array(len(self.mol.spheres)), self.spheredata,
			self.envmap, self.phimap, self.sampler)
		e4 = cl.enqueue_read_buffer(queue, self.dst_buf, self.dst)
		queue.finish()
		e4.wait()

		for e in [e3]:
			print (e.profile.END - e.profile.START)*1e-9
		
		glBindTexture(GL_TEXTURE_2D, self.dstTex)
		glTexSubImage2D(GL_TEXTURE_2D, 0, 0, 0, N, N, GL_RGBA, GL_UNSIGNED_BYTE, self.dst)

	def allocations(s):
		s.eh_fieldss = []
		s.ce_fieldss = []
		mf = cl.mem_flags
		for i, nx in enumerate(s.nxs):
			f = np.zeros((nx, s.ny, s.nz), 'f')
			cf = np.ones_like(f) * 0.5

			if i < s.ngpu:
				s.eh_fieldss.append( [cl.Buffer(s.context, mf.READ_WRITE, f.nbytes) for m in range(6)] ) 
				s.ce_fieldss.append( [cl.Buffer(s.context, mf.READ_ONLY, cf.nbytes) for m in range(3)] )
				for eh_field in s.eh_fieldss[-1]:
					cl.enqueue_write_buffer(s.queues[i], eh_field, f) 
				for ce_field in s.ce_fieldss[-1]:
					cl.enqueue_write_buffer(s.queues[i], ce_field, cf) 
			else:
				s.eh_fieldss.append( [f.copy() for i in xrange(6)] )
				s.ce_fieldss.append( [cf.copy() for i in xrange(3)] )

			del f, cf

		s.offsets = []
		s.tmpfs = []
		for nx in s.nxs:
			s.offsets.append( (nx-1) * s.ny * s.nz * np.nbytes['float32'] )
			s.tmpfs.append( [np.zeros((s.ny, s.nz), dtype=np.float32) for m in range(2)] )

	def __init__(s, fdtd, nx, ny, nz):
		super(TestSetFields, s).__init__(nx, ny, nz)
		s.fdtd = fdtd

		for strf in s.strf_list:
			randarr = np.random.rand(nx, ny, nz).astype(s.fdtd.dtype)
			cl.enqueue_write_buffer(s.fdtd.queue, s.fdtd.get_buffer(strf), randarr)

	def exchange_boundary_h(s):
		for queue, eh_fields, tmpf, offset in zip(s.queues, s.eh_fields_gpus, s.tmpfs, s.offsets)[:-1]:
			cl.enqueue_read_buffer(queue, eh_fields[4], tmpf[0], offset)	# hy_gpu
			cl.enqueue_read_buffer(queue, eh_fields[5], tmpf[1], offset)	# hz_gpu
		for queue, eh_fields, tmpf in zip(s.queues[1:], s.eh_fields_gpus[1:], s.tmpfs[:-1]):
			cl.enqueue_write_buffer(queue, eh_fields[4], tmpf[0])
			cl.enqueue_write_buffer(queue, eh_fields[5], tmpf[1])

	def exchange_boundary_e(s):
		for queue, eh_fields, tmpf in zip(s.queues, s.eh_fields_gpus, s.tmpfs)[1:]:
			cl.enqueue_read_buffer(queue, eh_fields[1], tmpf[0])	# ey_gpu
			cl.enqueue_read_buffer(queue, eh_fields[2], tmpf[1])	# ez_gpu
		for queue, eh_fields, tmpf, offset in zip(s.queues[:-1], s.eh_fields_gpus[:-1], s.tmpfs[1:], s.offsets[:-1]):
			cl.enqueue_write_buffer(queue, eh_fields[1], tmpf[0], offset)
			cl.enqueue_write_buffer(queue, eh_fields[2], tmpf[1], offset)

    def update(self, sub_pos, angle, min_dist, max_dist, width, in_weight, out_weight):
        '''
        Perform one update on the probabilities by using the evidence that
        the sub is at position sub_pos, the target is seen at an absolute heading
        of `angle` and is most likely between min_dist and max_dist away.
        in_weight gives the chance that for every point in the region,
        if the buoy is there then we would get this result
        i.e. in_weight = P(this measurement | buoy at point p) for p in our region
        out_weight is the same but for points outside the region
        '''

        n,e = sub_pos
        cl_program.evidence(cl_queue, self.norths.shape, None,
                            self.norths_buf, self.easts_buf, self.prob_buf,
                            float32(n), float32(e),
                            float32(radians(angle)),
                            float32(min_dist**2),
                            float32(max_dist**2),
                            float32(width),
                            float32(in_weight),
                            float32(out_weight))
        #TODO ?
        cl.enqueue_read_buffer(cl_queue, self.prob_buf, self.probabilities).wait()

        #Normalize
        total_prob = numpy.sum( self.probabilities )
        self.probabilities  /= total_prob

        cl.enqueue_write_buffer(cl_queue, self.prob_buf, self.probabilities)

    def mineThread(self):
        for data in self.qr:
            for i in range(data.iterations):
                offset = (unpack('I', data.base[i])[0],) if self.GOFFSET else None
                self.kernel.search(
                    self.commandQueue, (data.size, ), (self.WORKSIZE, ),
                    data.state[0], data.state[1], data.state[2], data.state[3],
                    data.state[4], data.state[5], data.state[6], data.state[7],
                    data.state2[1], data.state2[2], data.state2[3],
                    data.state2[5], data.state2[6], data.state2[7],
                    data.base[i],
                    data.f[0], data.f[1], data.f[2], data.f[3],
                    data.f[4], data.f[5], data.f[6], data.f[7],
                    self.output_buf, global_offset=offset)
                cl.enqueue_read_buffer(self.commandQueue, self.output_buf,
                                       self.output, is_blocking=False)
                self.commandQueue.finish()

                # The OpenCL code will flag the last item in the output buffer
                # when it finds a valid nonce. If that's the case, send it to
                # the main thread for postprocessing and clean the buffer
                # for the next pass.
                if self.output[self.WORKSIZE]:
                    reactor.callFromThread(self.postprocess,
                    self.output.copy(), data.nr)

                    self.output.fill(0)
                    cl.enqueue_write_buffer(self.commandQueue, self.output_buf,
                                            self.output, is_blocking=False)

    def set_target(self, target):
        flags = mf.READ_ONLY | mf.COPY_HOST_PTR
        self.target = np.array(target, np.float32)

        if self.target_buffer is None:
            self.target_buffer = self.buffer(self.target)
        else:
            cl.enqueue_write_buffer(self.queue, self.target_buffer, self.target)

    def randomize_weights( self, context ):
        """
        Initialize weights of layer by random values
        """

        weights = numpy.random.rand( context._weights_buf_size ).astype( numpy.float32 )
        weights -= 0.5
        weights *= 4.0 / numpy.sqrt( numpy.float32( context._weights_buf_size / context._neurons_buf_size ) )

        pyopencl.enqueue_write_buffer( context.opencl.queue, context._weights_buf, weights, is_blocking = True )

 def set_weights( self, weights ):
     """
     Set weights for entire layer.
     
     @param weights
         NumPy.NDArray of float32 values, size equals to inputs_per_neuron * neuron_count
     """
     pyopencl.enqueue_write_buffer( 
         self.opencl.queue, self.context._weights_buf, weights,
         device_offset = int( self._weights_offset * 4 ), is_blocking = True
         )

	def __init__(self, baseTab):
		self.baseTab = baseTab

		self.ctx = cl.create_some_context()
		self.queue = cl.CommandQueue(self.ctx)
		f = open("gutarp.cl", 'r')
		fstr = "".join(f.readlines())
		self.guTarpCL = cl.Program(self.ctx, fstr).build()

		self.baseTabBuffer = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE | cl.mem_flags.COPY_HOST_PTR, hostbuf=self.baseTab)
		cl.enqueue_write_buffer(self.queue, self.baseTabBuffer, self.baseTab)

    def push_particles(self, pos, vel, color):
        nn = pos.shape[0]
        if self.num + nn > self.sph.max_num:
            return

        self.acquire_gl()
        cl.enqueue_write_buffer(self.queue, self.position_u, pos, self.num)
        self.release_gl()

        self.num += nn
        self.update_sphp()
        self.queue.finish()

        def test_process( self ):
            weights = numpy.random.rand( self.nnc._weights_buf_size ).astype( numpy.float32 )
            weights -= 0.5
            weights *= 4.0 / numpy.sqrt( numpy.float32( self.nnc._weights_buf_size / self.nnc._neurons_buf_size ) )

            pyopencl.enqueue_write_buffer( self.ocl.queue, self.nnc._weights_buf, weights, is_blocking = True )

            self.nnc.input_layer.set_inputs( numpy.array( [x * x for x in range( 0, 10 )], numpy.float32 ), is_blocking = True )
            self.nnc.input_layer.process()

            self.assertArrayEqual( self.i.get_outputs()[:3], self.h1.get_inputs() )
            self.assertArrayEqual( self.i.get_outputs()[:5], self.h2.get_inputs() )
            self.assertArrayEqual( self.i.get_outputs()[4:10], self.h3.get_inputs()[:6] )

def sobel(im, cl=None):
    if cl is None:
        cl = setup(im)
    im = im.astype(numpy.float32)
    pyopencl.enqueue_write_buffer(cl['queue'], cl['im_dev'], im)
    cl['prg'].sobel(cl['queue'], im.shape, (3,), \
            cl['im_dev'], cl['m_dev'], cl['x_dev'], cl['y_dev'])
    m = numpy.empty_like(im)
    x = numpy.empty_like(im)
    y = numpy.empty_like(im)
    pyopencl.enqueue_read_buffer(cl['queue'], cl['m_dev'], m).wait()
    pyopencl.enqueue_read_buffer(cl['queue'], cl['x_dev'], x).wait()
    pyopencl.enqueue_read_buffer(cl['queue'], cl['y_dev'], y).wait()
    return m, x, y