You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
91 lines
4.2 KiB
91 lines
4.2 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
"""
|
|
#################train lstm example on aclImdb########################
|
|
"""
|
|
import argparse
|
|
import os
|
|
|
|
import numpy as np
|
|
|
|
from src.config import lstm_cfg, lstm_cfg_ascend
|
|
from src.dataset import lstm_create_dataset, convert_to_mindrecord
|
|
from src.lstm import SentimentNet
|
|
from mindspore import Tensor, nn, Model, context
|
|
from mindspore.nn import Accuracy, Recall, F1
|
|
from mindspore.train.serialization import load_checkpoint, load_param_into_net
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='MindSpore LSTM Example')
|
|
parser.add_argument('--preprocess', type=str, default='false', choices=['true', 'false'],
|
|
help='whether to preprocess data.')
|
|
parser.add_argument('--aclimdb_path', type=str, default="./aclImdb",
|
|
help='path where the dataset is stored.')
|
|
parser.add_argument('--glove_path', type=str, default="./glove",
|
|
help='path where the GloVe is stored.')
|
|
parser.add_argument('--preprocess_path', type=str, default="./preprocess",
|
|
help='path where the pre-process data is stored.')
|
|
parser.add_argument('--ckpt_path', type=str, default=None,
|
|
help='the checkpoint file path used to evaluate model.')
|
|
parser.add_argument('--device_target', type=str, default="Ascend", choices=['GPU', 'CPU', 'Ascend'],
|
|
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,
|
|
bidirectional=cfg.bidirectional,
|
|
num_classes=cfg.num_classes,
|
|
weight=Tensor(embedding_table),
|
|
batch_size=cfg.batch_size)
|
|
|
|
loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
|
|
ds_eval = lstm_create_dataset(args.preprocess_path, cfg.batch_size, training=False)
|
|
|
|
model = Model(network, loss, metrics={'acc': Accuracy(), 'recall': Recall(), 'f1': F1()})
|
|
|
|
print("============== Starting Testing ==============")
|
|
param_dict = load_checkpoint(args.ckpt_path)
|
|
load_param_into_net(network, param_dict)
|
|
if args.device_target == "CPU":
|
|
acc = model.eval(ds_eval, dataset_sink_mode=False)
|
|
else:
|
|
acc = model.eval(ds_eval)
|
|
print("============== {} ==============".format(acc))
|