if __name__ == '__main__': context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target="Ascend") device_id = int(os.getenv('DEVICE_ID')) context.set_context(device_id=device_id) if cfg.preprocess == 'true': print("============== Starting Data Pre-processing ==============") if os.path.exists(cfg.preprocess_path): shutil.rmtree(cfg.preprocess_path) os.mkdir(cfg.preprocess_path) convert_to_mindrecord(cfg.embed_size, cfg.data_path, cfg.preprocess_path, cfg.emb_path) if cfg.cell == "vanilla": print( "============ Precision is lower than expected when using vanilla RNN architecture ===========" ) embedding_table = np.loadtxt( os.path.join(cfg.preprocess_path, "weight.txt")).astype(np.float32) network = textrcnn(weight=Tensor(embedding_table), vocab_size=embedding_table.shape[0], cell=cfg.cell, batch_size=cfg.batch_size) ds_train = create_dataset(cfg.preprocess_path, cfg.batch_size, True)
help= 'the target device to run, support "GPU", "CPU". Default: "Ascend".') args = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, save_graphs=False, device_target=args.device_target) if args.device_target == 'Ascend': cfg = lstm_cfg_ascend else: cfg = lstm_cfg if args.preprocess == "true": print("============== Starting Data Pre-processing ==============") convert_to_mindrecord(cfg.embed_size, args.aclimdb_path, args.preprocess_path, args.glove_path) embedding_table = np.loadtxt( os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32) # DynamicRNN in this network on Ascend platform only support the condition that the shape of input_size # and hiddle_size is multiples of 16, this problem will be solved later. if args.device_target == 'Ascend': pad_num = int(np.ceil(cfg.embed_size / 16) * 16 - cfg.embed_size) if pad_num > 0: embedding_table = np.pad(embedding_table, [(0, 0), (0, pad_num)], 'constant') cfg.embed_size = int(np.ceil(cfg.embed_size / 16) * 16) network = SentimentNet(vocab_size=embedding_table.shape[0], embed_size=cfg.embed_size, num_hiddens=cfg.num_hiddens, num_layers=cfg.num_layers,
mode=context.GRAPH_MODE, save_graphs=False, device_target=args.device_target) # import moxing as mox # mox.file.copy_parallel(src_url='s3://zhengnj-course/lstm/glove', dst_url=args.glove_path) if args.device_target == 'Ascend': cfg = lstm_cfg_ascend else: cfg = lstm_cfg if args.preprocess == "true": print("============== Starting Data Pre-processing ==============") convert_to_mindrecord(cfg.embed_size, args.data_url, args.preprocess_path, args.glove_path) embedding_table = np.loadtxt(os.path.join(args.preprocess_path, "weight.txt")).astype(np.float32) # DynamicRNN in this network on Ascend platform only support the condition that the shape of input_size # and hiddle_size is multiples of 16, this problem will be solved later. if args.device_target == 'Ascend': pad_num = int(np.ceil(cfg.embed_size / 16) * 16 - cfg.embed_size) if pad_num > 0: embedding_table = np.pad(embedding_table, [(0, 0), (0, pad_num)], 'constant') cfg.embed_size = int(np.ceil(cfg.embed_size / 16) * 16) network = SentimentNet(vocab_size=embedding_table.shape[0], embed_size=cfg.embed_size, num_hiddens=cfg.num_hiddens, num_layers=cfg.num_layers, bidirectional=cfg.bidirectional, num_classes=cfg.num_classes,