!10486 add export file for fasttext
From: @zhaojichen Reviewed-by: @liangchenghui,@c_34 Signed-off-by: @c_34
This commit is contained in:
Gitee
commit
92f576287d
|
|
@ -0,0 +1,100 @@
|
|||
# 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.
|
||||
# ============================================================================
|
||||
"""export checkpoint file into models"""
|
||||
|
||||
import argparse
|
||||
import numpy as np
|
||||
import mindspore.nn as nn
|
||||
from mindspore.common.tensor import Tensor
|
||||
import mindspore.ops.operations as P
|
||||
from mindspore import context
|
||||
from mindspore.train.serialization import load_checkpoint, export, load_param_into_net
|
||||
from src.fasttext_model import FastText
|
||||
|
||||
parser = argparse.ArgumentParser(description='fasttexts')
|
||||
parser.add_argument('--device_target', type=str, choices=["Ascend", "GPU", "CPU"],
|
||||
default='Ascend', help='Device target')
|
||||
parser.add_argument('--device_id', type=int, default=0, help='Device id')
|
||||
parser.add_argument('--ckpt_file', type=str, required=True, help='Checkpoint file path')
|
||||
parser.add_argument('--file_name', type=str, default='fasttexts', help='Output file name')
|
||||
parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR',
|
||||
help='Output file format')
|
||||
parser.add_argument('--data_name', type=str, required=True, default='ag',
|
||||
help='Dataset name. eg. ag, dbpedia, yelp_p')
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.data_name == "ag":
|
||||
from src.config import config_ag as config
|
||||
target_label1 = ['0', '1', '2', '3']
|
||||
elif args.data_name == 'dbpedia':
|
||||
from src.config import config_db as config
|
||||
target_label1 = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13']
|
||||
elif args.data_name == 'yelp_p':
|
||||
from src.config import config_yelpp as config
|
||||
target_label1 = ['0', '1']
|
||||
|
||||
context.set_context(
|
||||
mode=context.GRAPH_MODE,
|
||||
save_graphs=False,
|
||||
device_target="Ascend")
|
||||
|
||||
class FastTextInferExportCell(nn.Cell):
|
||||
"""
|
||||
Encapsulation class of FastText network infer.
|
||||
|
||||
Args:
|
||||
network (nn.Cell): FastText model.
|
||||
|
||||
Returns:
|
||||
Tuple[Tensor, Tensor], predicted_ids
|
||||
"""
|
||||
def __init__(self, network):
|
||||
super(FastTextInferExportCell, self).__init__(auto_prefix=False)
|
||||
self.network = network
|
||||
self.argmax = P.ArgMaxWithValue(axis=1, keep_dims=True)
|
||||
self.log_softmax = nn.LogSoftmax(axis=1)
|
||||
|
||||
def construct(self, src_tokens, src_tokens_lengths):
|
||||
"""construct fasttext infer cell"""
|
||||
prediction = self.network(src_tokens, src_tokens_lengths)
|
||||
predicted_idx = self.log_softmax(prediction)
|
||||
predicted_idx, _ = self.argmax(predicted_idx)
|
||||
|
||||
return predicted_idx
|
||||
|
||||
def run_fasttext_export():
|
||||
"""export function"""
|
||||
fasttext_model = FastText(config.vocab_size, config.embedding_dims, config.num_class)
|
||||
parameter_dict = load_checkpoint(args.ckpt_file)
|
||||
load_param_into_net(fasttext_model, parameter_dict)
|
||||
ft_infer = FastTextInferExportCell(fasttext_model)
|
||||
|
||||
if args.data_name == "ag":
|
||||
src_tokens_shape = [config.batch_size, 467]
|
||||
src_tokens_length_shape = [config.batch_size, 1]
|
||||
elif args.data_name == 'dbpedia':
|
||||
src_tokens_shape = [config.batch_size, 1120]
|
||||
src_tokens_length_shape = [config.batch_size, 1]
|
||||
elif args.data_name == 'yelp_p':
|
||||
src_tokens_shape = [config.batch_size, 2955]
|
||||
src_tokens_length_shape = [config.batch_size, 1]
|
||||
|
||||
file_name = args.file_name + '_' + args.data_name
|
||||
src_tokens = Tensor(np.ones((src_tokens_shape)).astype(np.int32))
|
||||
src_tokens_length = Tensor(np.ones((src_tokens_length_shape)).astype(np.int32))
|
||||
export(ft_infer, src_tokens, src_tokens_length, file_name=file_name, file_format=args.file_format)
|
||||
|
||||
if __name__ == '__main__':
|
||||
run_fasttext_export()
|
||||
|
|
@ -53,14 +53,14 @@ class FastTextDataPreProcess():
|
|||
self.ngram = ngram
|
||||
self.text_greater = '>'
|
||||
self.text_less = '<'
|
||||
self.ngram2idx = dict()
|
||||
self.idx2gram = dict()
|
||||
self.word2vec = dict()
|
||||
self.vec2words = dict()
|
||||
self.non_str = '\\'
|
||||
self.end_string = ['.', '?', '!']
|
||||
self.ngram2idx['PAD'] = 0
|
||||
self.idx2gram[0] = 'PAD'
|
||||
self.ngram2idx['UNK'] = 1
|
||||
self.idx2gram[1] = 'UNK'
|
||||
self.word2vec['PAD'] = 0
|
||||
self.vec2words[0] = 'PAD'
|
||||
self.word2vec['UNK'] = 1
|
||||
self.vec2words[1] = 'UNK'
|
||||
self.str_html = re.compile(r'<[^>]+>')
|
||||
|
||||
def load(self):
|
||||
|
|
@ -144,13 +144,13 @@ class FastTextDataPreProcess():
|
|||
for l in range(train_dataset_list_length):
|
||||
bucket_length = self._get_bucket_length(train_dataset_list[l][0], self.buckets)
|
||||
while len(train_dataset_list[l][0]) < bucket_length:
|
||||
train_dataset_list[l][0].append(self.ngram2idx['PAD'])
|
||||
train_dataset_list[l][0].append(self.word2vec['PAD'])
|
||||
train_dataset_list[l][1] = len(train_dataset_list[l][0])
|
||||
# pad test dataset
|
||||
for j in range(test_dataset_list_length):
|
||||
test_bucket_length = self._get_bucket_length(test_dataset_list[j][0], self.test_bucket)
|
||||
while len(test_dataset_list[j][0]) < test_bucket_length:
|
||||
test_dataset_list[j][0].append(self.ngram2idx['PAD'])
|
||||
test_dataset_list[j][0].append(self.word2vec['PAD'])
|
||||
test_dataset_list[j][1] = len(test_dataset_list[j][0])
|
||||
|
||||
train_example_data = []
|
||||
|
|
@ -173,7 +173,7 @@ class FastTextDataPreProcess():
|
|||
for key in self.test_feature_dict:
|
||||
if key == test_example_data[h]['src_tokens_length']:
|
||||
self.test_feature_dict[key].append(test_example_data[h])
|
||||
print("train vocab size is ", len(self.ngram2idx))
|
||||
print("train vocab size is ", len(self.word2vec))
|
||||
|
||||
return self.train_feature_dict, self.test_feature_dict
|
||||
|
||||
|
|
@ -210,13 +210,13 @@ class FastTextDataPreProcess():
|
|||
|
||||
if train_mode is True:
|
||||
for ngms in bo_ngrams:
|
||||
idx = self.ngram2idx.get(ngms)
|
||||
idx = self.word2vec.get(ngms)
|
||||
if idx is None:
|
||||
idx = len(self.ngram2idx)
|
||||
self.ngram2idx[ngms] = idx
|
||||
self.idx2gram[idx] = ngms
|
||||
idx = len(self.word2vec)
|
||||
self.word2vec[ngms] = idx
|
||||
self.vec2words[idx] = ngms
|
||||
|
||||
processed_out = [self.ngram2idx[ng] if ng in self.ngram2idx else self.ngram2idx['UNK'] for ng in bo_ngrams]
|
||||
processed_out = [self.word2vec[ng] if ng in self.word2vec else self.word2vec['UNK'] for ng in bo_ngrams]
|
||||
|
||||
return processed_out
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue