Python pyopencl.enqueue_copy方法代碼示例

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def memset(self, buffer, value, size):
        """set the memory in allocation to the value in value

        :param allocation: An OpenCL Buffer to fill
        :type allocation: pyopencl.Buffer

        :param value: The value to set the memory to
        :type value: a single 32-bit int

        :param size: The size of to the allocation unit in bytes
        :type size: int

        """
        if isinstance(buffer, cl.Buffer):
            try:
                cl.enqueue_fill_buffer(self.queue, buffer, numpy.uint32(value), 0, size)
            except AttributeError:
                src=numpy.zeros(size, dtype='uint8')+numpy.uint8(value)
                cl.enqueue_copy(self.queue, buffer, src) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def anneal(self, prog, queue, rand, solutions, costs):
        prog.ocl_sa_populate_solutions(queue,
                                       (self.num_of_solutions,),
                                       (1,),
                                       self.__np_iterations,
                                       self.__np_temperature,
                                       self.__np_terminate_temperature,
                                       self.__np_alpha,
                                       solutions,
                                       rand,
                                       self.__dev_cityxy,
                                       costs).wait()

        cl.enqueue_copy(queue, self.__np_solution, solutions).wait()
        cl.enqueue_copy(queue, self.__np_costs, costs).wait()
        self.plot_best_solution() 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def __update_fitness_index_pair(self):
        cl.enqueue_copy(self.__queue, self.__fitnesses, self.__dev_fitnesses)

        ori = []
        fitness_sum = 0.0
        for idx, fitness in enumerate(self.__fitnesses):
            ori.append((idx, fitness))
            fitness_sum += fitness
        self.__avg = fitness_sum / len(self.__fitnesses)

        assert len(self.__best_indices) == len(self.__best_fitnesses)
        assert len(self.__worst_indices) == len(self.__worst_fitnesses)
        size_of_indices = len(self.__best_indices)

        ori.sort(key=lambda item : item[1], reverse=self.__opt_for_max=='max')
        tops = ori[:size_of_indices]
        bottoms = ori[len(ori)-size_of_indices:]
        for idx in range(size_of_indices):
            self.__best_indices[idx] = tops[idx][0]
            self.__best_fitnesses[idx] = tops[idx][1]
            self.__worst_indices[idx] = bottoms[idx][0]
            self.__worst_fitnesses[idx] = bottoms[idx][1]

        cl.enqueue_copy(self.__queue, self.__dev_best_indices, self.__best_indices)
        cl.enqueue_copy(self.__queue, self.__dev_worst_indices, self.__worst_indices) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def memcpy_dtoh(self, dest, src):
        """perform a device to host memory copy

        :param dest: A numpy array in host memory to store the data
        :type dest: numpy.ndarray

        :param src: An OpenCL Buffer to copy data from
        :type src: pyopencl.Buffer
        """
        if isinstance(src, cl.Buffer):
            cl.enqueue_copy(self.queue, dest, src) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def memcpy_htod(self, dest, src):
        """perform a host to device memory copy

        :param dest: An OpenCL Buffer to copy data from
        :type dest: pyopencl.Buffer

        :param src: A numpy array in host memory to store the data
        :type src: numpy.ndarray
        """
        if isinstance(dest, cl.Buffer):
            cl.enqueue_copy(self.queue, dest, src) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def reset_fifo_index(self):
        self.output_forward[:] = 0
        event = pyopencl.enqueue_copy(self.queue,  self.output_backward_cl, self.output_backward)
        event.wait()
        
        self.zi1[:] = 0
        event = pyopencl.enqueue_copy(self.queue,  self.zi1_cl, self.zi1)
        event.wait()

        self.zi2[:] = 0
        event = pyopencl.enqueue_copy(self.queue,  self.zi2_cl, self.zi2)
        event.wait() 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def detect_local_peaks_before_peeling_loop(self):
        if self._plot_debug:
            print('detect_local_peaks_before_peeling_loop')

        #~ self.global_size =  (self.max_wg_size * n, )
        #~ self.local_size = (self.max_wg_size,)
        
        # reset mask_already_tested
        #~ print('yep', self.mask_already_tested.size, self.mask_already_tested.shape)
        #~ pyopencl.enqueue_fill_buffer(self.queue, self.mask_already_tested_cl, np.zeros(1, dtype='uint8'), 0, self.mask_already_tested.size)
        #~ print('yop')

        self.mask_already_tested[:] = 0
        event = pyopencl.enqueue_copy(self.queue,  self.mask_already_tested_cl, self.mask_already_tested)
        #~ print('yop')
        
        gsize = self.fifo_size - (2 * self.n_span)
        if gsize > self.max_wg_size:
            n = int(np.ceil(gsize / self.max_wg_size))
            global_size =  (self.max_wg_size * n, )
            local_size = (self.max_wg_size,)
        else:
            global_size = (gsize, )
            local_size = (gsize, )
        #~ print('global_size', global_size, 'local_size', local_size)
        #~ exit()
        
        event = pyopencl.enqueue_nd_range_kernel(self.queue,  self.kern_detect_local_peaks, global_size, local_size,)

        #~ if self._plot_debug:
            #~ pyopencl.enqueue_copy(self.queue,  self.fifo_residuals, self.fifo_residuals_cl)
            #~ pyopencl.enqueue_copy(self.queue,  self.nb_pending_peaks, self.nb_pending_peaks_cl)
            #~ pyopencl.enqueue_copy(self.queue,  self.pending_peaks, self.pending_peaks_cl)
            #~ fig, ax = plt.subplots()
            #~ ax.plot(self.fifo_residuals)
            #~ plt.show() 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def _plot_before_peeling_loop(self):
        pyopencl.enqueue_copy(self.queue,  self.fifo_residuals, self.fifo_residuals_cl)
        self._plot_sigs_before = self.fifo_residuals.copy()

        pyopencl.enqueue_copy(self.queue,  self.nb_pending_peaks, self.nb_pending_peaks_cl)
        pyopencl.enqueue_copy(self.queue,  self.pending_peaks, self.pending_peaks_cl)
        pending_peaks =  self.pending_peaks[:self.nb_pending_peaks[0]]
        peak_inds = pending_peaks['peak_index']
        chan_inds = pending_peaks['peak_chan']
        print(pending_peaks)

        fig, ax = plt.subplots()
        
        plot_sigs = self.fifo_residuals.copy()
        self._plot_sigs_before = plot_sigs
        
        for c in range(self.nb_channel):
            plot_sigs[:, c] += c*30
        
        ax.plot(plot_sigs, color='k')

        ax.axvline(self.fifo_size - self.n_right, color='r')
        ax.axvline(-self.n_left, color='r')

        ax.scatter(peak_inds, plot_sigs[peak_inds, chan_inds], color='r')
        
        #~ plt.show() 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def _plot_after_inner_peeling_loop(self):
        return
        #~ pyopencl.enqueue_copy(self.queue,  self.nb_pending_peaks, self.nb_pending_peaks_cl)
        #~ pyopencl.enqueue_copy(self.queue,  self.pending_peaks, self.pending_peaks_cl)
        pyopencl.enqueue_copy(self.queue,  self.fifo_residuals, self.fifo_residuals_cl)



        
        #~ self._plot_sigs_before = plot_sigs
        #~ chan_order = np.argsort(self.channel_distances[0, :])
        
        fig, ax = plt.subplots()
        
        plot_sigs = self._plot_sigs_before.copy()
        
        for c in range(self.nb_channel):
            plot_sigs[:, c] += c*30
        ax.plot(plot_sigs, color='b')
        
        plot_sigs = self.fifo_residuals.copy()
        
        
        for c in range(self.nb_channel):
            plot_sigs[:, c] += c*30
        
        ax.plot(plot_sigs, color='k')

        ax.axvline(self.fifo_size - self.n_right, color='r')
        ax.axvline(-self.n_left, color='r')

        #~ mask = self.peakdetector.get_mask_peaks_in_chunk(self.fifo_residuals)
        #~ peak_inds, chan_inds= np.nonzero(mask)
        #~ peak_inds += self.n_span
        
        #~ ax.scatter(peak_inds, plot_sigs[peak_inds, chan_inds], color='r')
        #~ ax.plot(self.fifo_residuals)
        plt.show()
        
        self._plot_sigs_before = self.fifo_residuals.copy() 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def process_data(self, pos, newbuf):
        if newbuf.shape[0] <self.chunksize:
            newbuf2 = np.zeros((self.chunksize, self.nb_channel), dtype=self.dtype)
            newbuf2[-newbuf.shape[0]:, :] = newbuf
            newbuf = newbuf2

        if not newbuf.flags['C_CONTIGUOUS']:
            newbuf = newbuf.copy()

        pyopencl.enqueue_copy(self.queue,  self.sigs_cl, newbuf)
        event = self.kern_detect_peaks(self.queue,  self.global_size, self.local_size,
                                self.sigs_cl, self.ring_sum_cl, self.peak_mask_cl)
        event.wait()
        
        #~ if pos-(newbuf.shape[0]+2*self.n_span)<0:
            # the very first buffer is sacrified because of peak span
            #~ return None, None
        
        pyopencl.enqueue_copy(self.queue,  self.peak_mask, self.peak_mask_cl)
        time_ind_peaks,  = np.nonzero(self.peak_mask)
        
        if time_ind_peaks.size>0:
            time_ind_peaks += pos - self.chunksize - self.n_span
            chan_ind_peaks = None# not in this method
            return time_ind_peaks, chan_ind_peaks, None
        
        return None, None, None 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def get_mask_peaks_in_chunk(self, fifo_residuals):

        pyopencl.enqueue_copy(self.queue,  self.fifo_sigs_cl, fifo_residuals)
        #~ print(self.chunksize, self.max_wg_size)
        event = self.kern_get_mask_spatiotemporal_peaks(self.queue,  self.global_size, self.local_size,
                                self.fifo_sigs_cl, self.neighbours_cl, self.mask_peaks_cl)
        event.wait()
        pyopencl.enqueue_copy(self.queue,  self.mask_peaks, self.mask_peaks_cl)
        
        return self.mask_peaks 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def update_elites(self, elites):
        assert self.__is_elitism_mode, 'Elitism Mode is {}'.format(self.__is_elitism_mode)
        assert len(elites) == self.__elitism_top
        if not self.state_machine.is_running():
            return
        with self.__elite_lock:
            elites_dna_data = []
            elites_fitnesses = []
            # Concatenate all elites' dna / fitness into a single continuous memory
            # layout.
            for idx, elite_info in enumerate(elites):
                fitness, elite_dna, worker_id = elite_info
                if idx == 0:
                    print('updating {}/{} elites ... fitness = {} from worker {}'.format(idx+1, len(elites), fitness, worker_id))
                elites_dna_data.extend(elite_dna)
                elites_fitnesses.append(fitness)

            # Convert the continuous memory to a device compatible memory layout.
            self.__updated_elites = numpy.asarray(elites_dna_data, dtype=numpy.int32)
            self.__updated_elite_fitnesses = numpy.asarray(elites_fitnesses, dtype=numpy.float32)

            # Transfer it into device meory.
            cl.enqueue_copy(self.__queue, self.__dev_updated_elites, self.__updated_elites)
            cl.enqueue_copy(self.__queue, self.__dev_updated_elite_fitnesses, self.__updated_elite_fitnesses)

            self.__elites_updated = True 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def restore(self, data, ctx, queue, population):
        other_chromosomes = data['other_chromosomes']
        ratios = data['ratios']
        # prepare CL memory
        mf = cl.mem_flags
        self.__dev_ratios = cl.Buffer(ctx, mf.WRITE_ONLY, ratios.nbytes)
        self.__dev_other_chromosomes = cl.Buffer(ctx, mf.READ_WRITE | mf.COPY_HOST_PTR,
                                                 hostbuf=other_chromosomes)
        # Copy data from main memory to GPU memory
        cl.enqueue_copy(queue, self.__dev_ratios, ratios)
        cl.enqueue_copy(queue, self.__dev_other_chromosomes, other_chromosomes) 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def process_data(self, pos, data):
        
        assert data.shape[0]==self.chunksize
                
        if not data.flags['C_CONTIGUOUS'] or data.dtype!=self.output_dtype:
            chunk = np.ascontiguousarray(data, dtype=self.output_dtype)
        else:
            chunk = data
        
        
        #Online filtfilt
        
        event = pyopencl.enqueue_copy(self.queue,  self.input_cl, chunk)
        event = self.kern_forward_backward_filter(self.queue,  (self.nb_channel,), (self.nb_channel,),
                            self.input_cl, self.coefficients_cl, self.zi1_cl, self.zi2_cl,
                            self.fifo_input_backward_cl, self.signals_medians_cl, self.signals_mads_cl,  self.output_backward_cl)
        event.wait()
        
         
        
        
        #~ event.wait()
        
        start = pos-self.backward_chunksize
        if start<-self.lostfront_chunksize:
            return None, None
        
        pos2 = pos-self.lostfront_chunksize
        

        event = pyopencl.enqueue_copy(self.queue,  self.output_backward, self.output_backward_cl)
        if start>0:
            data2 = self.output_backward[:self.chunksize, :]
        else:
            data2 = self.output_backward[self.lostfront_chunksize:self.chunksize, :]
        data2 = data2.copy()
        
        if self.common_ref_removal:
            #TODO make OpenCL for this
            # removal ref
            if self.common_ref_removal:
                data2 -= np.median(data2, axis=1)[:, None]
            
            
            #normalize
            if self.normalize:
                # OpenCL for this when no common_ref_removal
                data2 -= self.signals_medians
                data2 /= self.signals_mads
            
        return pos2, data2 

# 需要導入模塊: import pyopencl [as 別名]
# 或者: from pyopencl import enqueue_copy [as 別名]
def apply_processor(self, pos, sigs_chunk):
        if self._plot_debug:
            print('apply_processor')
        assert sigs_chunk.shape[0]==self.chunksize
        
        if not sigs_chunk.flags['C_CONTIGUOUS'] or sigs_chunk.dtype!=self.internal_dtype:
            sigs_chunk = np.ascontiguousarray(sigs_chunk, dtype=self.internal_dtype)
        
        if self.already_processed:
            abs_head_index, preprocessed_chunk =  pos, sigs_chunk
            pyopencl.enqueue_copy(self.queue,  self.sigs_chunk_cl, sigs_chunk)
        else:
            if self.signalpreprocessor.common_ref_removal:
                # because not done in kernel yet
                raise NotImplemenentedError
            
            abs_head_index, preprocessed_chunk = self.signalpreprocessor.process_data(pos, sigs_chunk)
        
        # roll fifo
        gsize = self.fifo_roll_size * self.nb_channel
        global_size = (self.fifo_roll_size, self.nb_channel)
        if gsize > self.max_wg_size:
            local_size = (1, self.nb_channel)
        else:
            local_size = global_size
        event = pyopencl.enqueue_nd_range_kernel(self.queue,  self.kern_roll_fifo, global_size, local_size,)
        
        # add new buffer to fifo residuals
        gsize = self.chunksize * self.nb_channel
        global_size = (self.chunksize, self.nb_channel)
        if gsize > self.max_wg_size:
            local_size = (1, self.nb_channel)
        else:
            local_size = global_size
        event = pyopencl.enqueue_nd_range_kernel(self.queue,  self.kern_add_fifo_residuals, global_size, local_size,)
            
            
            
            # TODO call preprocessor kernel and add_fifo_residuals
            
            #~ abs_head_index, preprocessed_chunk = self.signalpreprocessor.process_data(pos, sigs_chunk)
        
        #~ if self._debug_cl:
            #~ pyopencl.enqueue_copy(self.queue,  self.fifo_residuals, self.fifo_residuals_cl)
            #~ fig, ax = plt.subplots()
            #~ ax.plot(self.fifo_residuals)
            #~ plt.show()

        return abs_head_index, preprocessed_chunk