我们从Python开源项目中，提取了以下14个代码示例，用于说明如何使用google.protobuf.text_format.PrintMessage()。
def testPrintMessageSetByFieldNumber(self): out = text_format.TextWriter(False) message = unittest_mset_pb2.TestMessageSetContainer() ext1 = unittest_mset_pb2.TestMessageSetExtension1.message_set_extension ext2 = unittest_mset_pb2.TestMessageSetExtension2.message_set_extension message.message_set.Extensions[ext1].i = 23 message.message_set.Extensions[ext2].str = 'foo' text_format.PrintMessage(message, out, use_field_number=True) self.CompareToGoldenText(out.getvalue(), '1 {\n' ' 1545008 {\n' ' 15: 23\n' ' }\n' ' 1547769 {\n' ' 25: \"foo\"\n' ' }\n' '}\n') out.close()
def change_model(proto, layers=None): model = util.ReadModel(proto) if layers is None: layers = ['image_hidden1', 'image_hidden2', 'image_hidden3', 'text_hidden1', 'text_hidden2', 'text_hidden3', 'image_layer', 'text_layer', 'joint_layer', 'image_tied_hidden', 'text_tied_hidden', 'image_hidden2_recon', 'text_hidden2_recon', 'cross_image_hidden2_recon', 'cross_text_hidden2_recon'] for layer in layers: try: layer_proto = next(lay for lay in model.layer if lay.name == layer) layer_proto.dimensions = dimensions except StopIteration: pass with open(proto, 'w') as f: text_format.PrintMessage(model, f)
def change_data(proto, datas=None): proto_cont = util.ReadData(proto) if datas is None: datas = [] for m in ['image', 'text']: for i in [1,2,3]: for t in ['train', 'validation', 'test']: datas += [m+'_'+'hidden'+str(i)+'_'+t] datas += ['bae_'+m+'_'+'hidden'+str(i)+'_'+t] datas += ['bae_'+m+'_'+'hidden'+str(i)+'_'+t+'_all'] datas += ['corr_'+m+'_hidden'+str(i)+'_'+t] for data in datas: try: data_proto = next(lay for lay in proto_cont.data if lay.name == data) data_proto.dimensions[0] = dimensions except StopIteration: pass with open(proto, 'w') as f: text_format.PrintMessage(proto_cont, f)
def start(self, rank): self.rank = rank if len(self.gpus) > 0: self.device = self.gpus[rank] if debug: s = 'solver gpu %d' % self.gpus[self.rank] + \ ' pid %d' % os.getpid() + ' size %d' % self.size + \ ' rank %d' % self.rank print(s, file = sys.stderr) caffe.set_mode_gpu() caffe.set_device(self.device) caffe.set_solver_count(self.size) caffe.set_solver_rank(self.rank) caffe.set_multiprocess(True) else: print('solver cpu', file = sys.stderr) caffe.set_mode_cpu() if self.cmd.graph.endswith('.json'): with open(self.cmd.graph, mode = 'r') as f: graph = caffe_pb2.SolverParameter() text_format.Merge(f.read(), graph) self.graph = graph else: self.graph = self.solver_graph() import tempfile with tempfile.NamedTemporaryFile(mode = 'w+', delete = False) as f: text_format.PrintMessage(self.graph, f) tmp = f.name self.caffe = caffe.AdamSolver(tmp) if self.uid: self.nccl = caffe.NCCL(self.caffe, self.uid) self.nccl.bcast() self.caffe.add_callback(self.nccl) if self.caffe.param.layer_wise_reduce: self.caffe.net.after_backward(self.nccl)
def EditTrainers(data_dir, model_dir, rep_dir, numsplits): tnames = ['train_CD_image_layer1.pbtxt', 'train_CD_image_layer2.pbtxt', 'train_CD_text_layer1.pbtxt', 'train_CD_text_layer2.pbtxt', 'train_CD_joint_layer.pbtxt'] for tname in tnames: t_op_file = os.path.join('trainers', 'dbn', tname) t_op = util.ReadOperation(t_op_file) if 'layer1' in tname: t_op.data_proto_prefix = data_dir else: t_op.data_proto_prefix = rep_dir t_op.checkpoint_directory = model_dir with open(t_op_file, 'w') as f: text_format.PrintMessage(t_op, f) t_op_file = os.path.join('trainers', 'classifiers', 'baseclassifier.pbtxt') t_op = util.ReadOperation(t_op_file) for i in range(1, numsplits+1): t_op_file = os.path.join('trainers', 'classifiers', 'split_%d.pbtxt' % i) t_op.data_proto_prefix = rep_dir t_op.data_proto = os.path.join('split_%d' % i, 'data.pbtxt') t_op.checkpoint_prefix = model_dir t_op.checkpoint_directory = os.path.join('classifiers','split_%d' % i) with open(t_op_file, 'w') as f: text_format.PrintMessage(t_op, f) # Change prefix in multimodal dbn model mnames = ['multimodal_dbn.pbtxt'] for mname in mnames: model_file = os.path.join('models', mname) model = util.ReadModel(model_file) model.prefix = model_dir with open(model_file, 'w') as f: text_format.PrintMessage(model, f)
def main(): data_dir = sys.argv[1] model_dir = sys.argv[2] rep_dir = sys.argv[3] gpu_mem = sys.argv[4] main_mem = sys.argv[5] numsplits = int(sys.argv[6]) data_pbtxt_file = os.path.join(data_dir, 'flickr.pbtxt') data_pb = util.ReadData(data_pbtxt_file) EditPaths(data_pb, data_dir, gpu_mem, main_mem) with open(data_pbtxt_file, 'w') as f: text_format.PrintMessage(data_pb, f) EditTrainers(data_dir, model_dir, rep_dir, numsplits) data_pbtxt_file_z = os.path.join(data_dir, 'flickr_z.pbtxt') data_pbtxt_file_nnz = os.path.join(data_dir, 'flickr_nnz.pbtxt') if not os.path.exists(data_pbtxt_file_z): CreateMissingTextData(data_pb, data_pbtxt_file_z, data_pbtxt_file_nnz) data_pb = util.ReadData(data_pbtxt_file_z) EditPaths(data_pb, data_dir, gpu_mem, main_mem) with open(data_pbtxt_file_z, 'w') as f: text_format.PrintMessage(data_pb, f) data_pb = util.ReadData(data_pbtxt_file_nnz) EditPaths(data_pb, data_dir, gpu_mem, main_mem) with open(data_pbtxt_file_nnz, 'w') as f: text_format.PrintMessage(data_pb, f)
def WritePbtxt(output_file, pb): with open(output_file, 'w') as f: text_format.PrintMessage(pb, f)
def main(): data_pbtxt = sys.argv[1] output_dir = sys.argv[2] prefix = sys.argv[3] r = int(sys.argv[4]) gpu_mem = sys.argv[5] main_mem = sys.argv[6] if not os.path.isdir(output_dir): os.makedirs(output_dir) rep_dict, stats_files = MakeDict(data_pbtxt) reps = rep_dict.keys() indices_file = os.path.join(prefix, 'splits', 'train_indices_%d.npy' % r) if os.path.exists(indices_file): train = np.load(indices_file) valid = np.load(os.path.join(prefix, 'splits', 'valid_indices_%d.npy' % r)) test = np.load(os.path.join(prefix, 'splits', 'test_indices_%d.npy' % r)) else: print 'Creating new split.' indices = np.arange(25000) np.random.shuffle(indices) train = indices[:10000] valid = indices[10000:15000] test = indices[15000:] np.save(os.path.join(prefix, 'splits', 'train_indices_%d.npy' % r), train) np.save(os.path.join(prefix, 'splits', 'valid_indices_%d.npy' % r), valid) np.save(os.path.join(prefix, 'splits', 'test_indices_%d.npy' % r), test) print 'Splitting data' dataset_pb = deepnet_pb2.Dataset() dataset_pb.name = 'flickr_split_%d' % r dataset_pb.gpu_memory = gpu_mem dataset_pb.main_memory = main_mem for rep in reps: data = rep_dict[rep] stats_file = stats_files[rep] DumpDataSplit(data[train], output_dir, 'train_%s' % rep, dataset_pb, stats_file) DumpDataSplit(data[valid], output_dir, 'valid_%s' % rep, dataset_pb, stats_file) DumpDataSplit(data[test], output_dir, 'test_%s' % rep, dataset_pb, stats_file) print 'Splitting labels' labels = np.load(os.path.join(prefix, 'labels.npy')).astype('float32') DumpLabelSplit(labels[train,], output_dir, 'train_labels', dataset_pb) DumpLabelSplit(labels[valid,], output_dir, 'valid_labels', dataset_pb) DumpLabelSplit(labels[test,], output_dir, 'test_labels', dataset_pb) #d = 'indices' #np.save(os.path.join(output_dir, 'train_%s.npy' % d), train) #np.save(os.path.join(output_dir, 'valid_%s.npy' % d), valid) #np.save(os.path.join(output_dir, 'test_%s.npy' % d), test) with open(os.path.join(output_dir, 'data.pbtxt'), 'w') as f: text_format.PrintMessage(dataset_pb, f) print 'Output written in directory %s' % output_dir
