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modified network of yolov3_darknet53 and add shell of preprocess dataset in deepfm

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wsq3 2021-05-20 17:05:20 +08:00
parent 2a6bf68809
commit 97945f5ef6
4 changed files with 319 additions and 1 deletions
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2
model_zoo/official/cv/yolov3_darknet53/src/convert_weight.py → model_zoo/official/cv/yolov3_darknet53/convert_weight.py
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@ -1,4 +1,4 @@
# Copyright 2020 Huawei Technologies Co., Ltd
# Copyright 2020-2021 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.

13
model_zoo/official/recommend/deepfm/README.md
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@ -49,6 +49,19 @@ The FM and deep component share the same input raw feature vector, which enables
After installing MindSpore via the official website, you can start training and evaluation as follows:
- preprocess dataset
'''bash
#download dataset
#Please refer to [1] to obtain the download link
mkdir -p data/origin_data && cd data/origin_data
wget DATA_LINK
tar -zxvf dac.tar.gz
#preprocess dataset
python src/preprocess_data.py --data_path=./data/ --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0
'''
- running on Ascend
```shell

12
model_zoo/official/recommend/deepfm/README_CN.md
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@ -54,6 +54,18 @@ FM和深度学习部分拥有相同的输入原样特征向量，让DeepFM能从
通过官方网站安装MindSpore后，您可以按照如下步骤进行训练和评估：
- 数据集预处理
'''bash
#下载数据集
#请参考[1]获得下载链接
mkdir -p data/origin_data && cd data/origin_data
wget DATA_LINK
tar -zxvf dac.tar.gz
#数据集预处理脚步执行
python src/preprocess_data.py --data_path=./data/ --dense_dim=13 --slot_dim=26 --threshold=100 --train_line_count=45840617 --skip_id_convert=0
'''
- Ascend处理器环境运行
```训练示例

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model_zoo/official/recommend/deepfm/src/preprocess_data.py Normal file
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@ -0,0 +1,293 @@
# Copyright 2021 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.
# ============================================================================
"""Download raw data and preprocessed data."""
import os
import pickle
import collections
import argparse
import numpy as np
from mindspore.mindrecord import FileWriter
class StatsDict():
"""preprocessed data"""
def __init__(self, field_size, dense_dim, slot_dim, skip_id_convert):
self.field_size = field_size
self.dense_dim = dense_dim
self.slot_dim = slot_dim
self.skip_id_convert = bool(skip_id_convert)
self.val_cols = ["val_{}".format(i + 1) for i in range(self.dense_dim)]
self.cat_cols = ["cat_{}".format(i + 1) for i in range(self.slot_dim)]
self.val_min_dict = {col: 0 for col in self.val_cols}
self.val_max_dict = {col: 0 for col in self.val_cols}
self.cat_count_dict = {col: collections.defaultdict(int) for col in self.cat_cols}
self.oov_prefix = "OOV"
self.cat2id_dict = {}
self.cat2id_dict.update({col: i for i, col in enumerate(self.val_cols)})
self.cat2id_dict.update(
{self.oov_prefix + col: i + len(self.val_cols) for i, col in enumerate(self.cat_cols)})
def stats_vals(self, val_list):
"""Handling weights column"""
assert len(val_list) == len(self.val_cols)
def map_max_min(i, val):
key = self.val_cols[i]
if val != "":
if float(val) > self.val_max_dict[key]:
self.val_max_dict[key] = float(val)
if float(val) < self.val_min_dict[key]:
self.val_min_dict[key] = float(val)
for i, val in enumerate(val_list):
map_max_min(i, val)
def stats_cats(self, cat_list):
"""Handling cats column"""
assert len(cat_list) == len(self.cat_cols)
def map_cat_count(i, cat):
key = self.cat_cols[i]
self.cat_count_dict[key][cat] += 1
for i, cat in enumerate(cat_list):
map_cat_count(i, cat)
def save_dict(self, dict_path, prefix=""):
with open(os.path.join(dict_path, "{}val_max_dict.pkl".format(prefix)), "wb") as file_wrt:
pickle.dump(self.val_max_dict, file_wrt)
with open(os.path.join(dict_path, "{}val_min_dict.pkl".format(prefix)), "wb") as file_wrt:
pickle.dump(self.val_min_dict, file_wrt)
with open(os.path.join(dict_path, "{}cat_count_dict.pkl".format(prefix)), "wb") as file_wrt:
pickle.dump(self.cat_count_dict, file_wrt)
def load_dict(self, dict_path, prefix=""):
with open(os.path.join(dict_path, "{}val_max_dict.pkl".format(prefix)), "rb") as file_wrt:
self.val_max_dict = pickle.load(file_wrt)
with open(os.path.join(dict_path, "{}val_min_dict.pkl".format(prefix)), "rb") as file_wrt:
self.val_min_dict = pickle.load(file_wrt)
with open(os.path.join(dict_path, "{}cat_count_dict.pkl".format(prefix)), "rb") as file_wrt:
self.cat_count_dict = pickle.load(file_wrt)
print("val_max_dict.items()[:50]:{}".format(list(self.val_max_dict.items())))
print("val_min_dict.items()[:50]:{}".format(list(self.val_min_dict.items())))
def get_cat2id(self, threshold=100):
for key, cat_count_d in self.cat_count_dict.items():
new_cat_count_d = dict(filter(lambda x: x[1] > threshold, cat_count_d.items()))
for cat_str, _ in new_cat_count_d.items():
self.cat2id_dict[key + "_" + cat_str] = len(self.cat2id_dict)
print("cat2id_dict.size:{}".format(len(self.cat2id_dict)))
print("cat2id.dict.items()[:50]:{}".format(list(self.cat2id_dict.items())[:50]))
def map_cat2id(self, values, cats):
"""Cat to id"""
def minmax_scale_value(i, val):
max_v = float(self.val_max_dict["val_{}".format(i + 1)])
return float(val) * 1.0 / max_v
id_list = []
weight_list = []
for i, val in enumerate(values):
if val == "":
id_list.append(i)
weight_list.append(0)
else:
key = "val_{}".format(i + 1)
id_list.append(self.cat2id_dict[key])
weight_list.append(minmax_scale_value(i, float(val)))
for i, cat_str in enumerate(cats):
key = "cat_{}".format(i + 1) + "_" + cat_str
if key in self.cat2id_dict:
if self.skip_id_convert is True:
# For the synthetic data, if the generated id is between [0, max_vcoab], but the num examples is l
# ess than vocab_size/ slot_nums the id will still be converted to [0, real_vocab], where real_vocab
# the actually the vocab size, rather than the max_vocab. So a simple way to alleviate this
# problem is skip the id convert, regarding the synthetic data id as the final id.
id_list.append(cat_str)
else:
id_list.append(self.cat2id_dict[key])
else:
id_list.append(self.cat2id_dict[self.oov_prefix + "cat_{}".format(i + 1)])
weight_list.append(1.0)
return id_list, weight_list
def mkdir_path(file_path):
if not os.path.exists(file_path):
os.makedirs(file_path)
def statsdata(file_path, dict_output_path, recommendation_dataset_stats_dict, dense_dim=13, slot_dim=26):
"""Preprocess data and save data"""
with open(file_path, encoding="utf-8") as file_in:
errorline_list = []
count = 0
for line in file_in:
count += 1
line = line.strip("\n")
items = line.split("\t")
if len(items) != (dense_dim + slot_dim + 1):
errorline_list.append(count)
print("Found line length: {}, suppose to be {}, the line is {}".format(len(items),
dense_dim + slot_dim + 1, line))
continue
if count % 1000000 == 0:
print("Have handled {}w lines.".format(count // 10000))
values = items[1: dense_dim + 1]
cats = items[dense_dim + 1:]
assert len(values) == dense_dim, "values.size: {}".format(len(values))
assert len(cats) == slot_dim, "cats.size: {}".format(len(cats))
recommendation_dataset_stats_dict.stats_vals(values)
recommendation_dataset_stats_dict.stats_cats(cats)
recommendation_dataset_stats_dict.save_dict(dict_output_path)
def random_split_trans2mindrecord(input_file_path, output_file_path, recommendation_dataset_stats_dict,
part_rows=2000000, line_per_sample=1000, train_line_count=None,
test_size=0.1, seed=2020, dense_dim=13, slot_dim=26):
"""Random split data and save mindrecord"""
if train_line_count is None:
raise ValueError("Please provide training file line count")
test_size = int(train_line_count * test_size)
all_indices = [i for i in range(train_line_count)]
np.random.seed(seed)
np.random.shuffle(all_indices)
print("all_indices.size:{}".format(len(all_indices)))
test_indices_set = set(all_indices[:test_size])
print("test_indices_set.size:{}".format(len(test_indices_set)))
print("-----------------------" * 10 + "\n" * 2)
train_data_list = []
test_data_list = []
ids_list = []
wts_list = []
label_list = []
writer_train = FileWriter(os.path.join(output_file_path, "train_input_part.mindrecord"), 21)
writer_test = FileWriter(os.path.join(output_file_path, "test_input_part.mindrecord"), 3)
schema = {"label": {"type": "float32", "shape": [-1]}, "feat_vals": {"type": "float32", "shape": [-1]},
"feat_ids": {"type": "int32", "shape": [-1]}}
writer_train.add_schema(schema, "CRITEO_TRAIN")
writer_test.add_schema(schema, "CRITEO_TEST")
with open(input_file_path, encoding="utf-8") as file_in:
items_error_size_lineCount = []
count = 0
train_part_number = 0
test_part_number = 0
for i, line in enumerate(file_in):
count += 1
if count % 1000000 == 0:
print("Have handle {}w lines.".format(count // 10000))
line = line.strip("\n")
items = line.split("\t")
if len(items) != (1 + dense_dim + slot_dim):
items_error_size_lineCount.append(i)
continue
label = float(items[0])
values = items[1:1 + dense_dim]
cats = items[1 + dense_dim:]
assert len(values) == dense_dim, "values.size: {}".format(len(values))
assert len(cats) == slot_dim, "cats.size: {}".format(len(cats))
ids, wts = recommendation_dataset_stats_dict.map_cat2id(values, cats)
ids_list.extend(ids)
wts_list.extend(wts)
label_list.append(label)
if count % line_per_sample == 0:
if i not in test_indices_set:
train_data_list.append({"feat_ids": np.array(ids_list, dtype=np.int32),
"feat_vals": np.array(wts_list, dtype=np.float32),
"label": np.array(label_list, dtype=np.float32)
})
else:
test_data_list.append({"feat_ids": np.array(ids_list, dtype=np.int32),
"feat_vals": np.array(wts_list, dtype=np.float32),
"label": np.array(label_list, dtype=np.float32)
})
if train_data_list and len(train_data_list) % part_rows == 0:
writer_train.write_raw_data(train_data_list)
train_data_list.clear()
train_part_number += 1
if test_data_list and len(test_data_list) % part_rows == 0:
writer_test.write_raw_data(test_data_list)
test_data_list.clear()
test_part_number += 1
ids_list.clear()
wts_list.clear()
label_list.clear()
if train_data_list:
writer_train.write_raw_data(train_data_list)
if test_data_list:
writer_test.write_raw_data(test_data_list)
writer_train.commit()
writer_test.commit()
print("-------------" * 10)
print("items_error_size_lineCount.size(): {}.".format(len(items_error_size_lineCount)))
print("-------------" * 10)
np.save("items_error_size_lineCount.npy", items_error_size_lineCount)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Recommendation dataset")
parser.add_argument("--data_path", type=str, default="./recommendation_dataset/", help='The path of the data file')
parser.add_argument("--dense_dim", type=int, default=13, help='The number of your continues fields')
parser.add_argument("--slot_dim", type=int, default=26,
help='The number of your sparse fields, it can also be called catelogy features.')
parser.add_argument("--threshold", type=int, default=100,
help='Word frequency below this will be regarded as OOV. It aims to reduce the vocab size')
parser.add_argument("--train_line_count", type=int, default=45840617,
help='The number of examples in your dataset')
parser.add_argument("--skip_id_convert", type=int, default=0, choices=[0, 1],
help='Skip the id convert, regarding the original id as the final id.')
args, _ = parser.parse_known_args()
data_path = args.data_path
target_field_size = args.dense_dim + args.slot_dim
stats = StatsDict(field_size=target_field_size, dense_dim=args.dense_dim, slot_dim=args.slot_dim,
skip_id_convert=args.skip_id_convert)
data_file_path = data_path + "origin_data/train.txt"
stats_output_path = data_path + "stats_dict/"
mkdir_path(stats_output_path)
statsdata(data_file_path, stats_output_path, stats, dense_dim=args.dense_dim, slot_dim=args.slot_dim)
stats.load_dict(dict_path=stats_output_path, prefix="")
stats.get_cat2id(threshold=args.threshold)
in_file_path = data_path + "origin_data/train.txt"
output_path = data_path + "mindrecord/"
mkdir_path(output_path)
random_split_trans2mindrecord(in_file_path, output_path, stats, part_rows=2000000,
train_line_count=args.train_line_count, line_per_sample=1000,
test_size=0.1, seed=2020, dense_dim=args.dense_dim, slot_dim=args.slot_dim)