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static_infer.py
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static_infer.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import print_function
import argparse
import time
import os
import warnings
import logging
import paddle
import sys
import numpy as np
import math
__dir__ = os.path.dirname(os.path.abspath(__file__))
# sys.path.append(__dir__)
sys.path.append(
os.path.abspath(os.path.join(__dir__, '..', '..', '..', 'tools')))
from utils.save_load import save_static_model, load_static_model
from utils.utils_single import load_yaml, load_static_model_class, get_abs_model, create_data_loader, reset_auc
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)
logger = logging.getLogger(__name__)
def parse_args():
parser = argparse.ArgumentParser("PaddleRec train static script")
parser.add_argument("-m", "--config_yaml", type=str)
parser.add_argument("-top_n", "--top_n", type=int, default=20)
args = parser.parse_args()
args.abs_dir = os.path.dirname(os.path.abspath(args.config_yaml))
args.config_yaml = get_abs_model(args.config_yaml)
return args
def main(args):
paddle.seed(12345)
# load config
config = load_yaml(args.config_yaml)
config["config_abs_dir"] = args.abs_dir
# load static model class
static_model_class = load_static_model_class(config)
input_data = static_model_class.create_feeds(is_infer=True)
input_data_names = [data.name for data in input_data]
fetch_vars = static_model_class.infer_net(input_data)
logger.info("cpu_num: {}".format(os.getenv("CPU_NUM")))
use_gpu = config.get("runner.use_gpu", True)
use_auc = config.get("runner.use_auc", False)
auc_num = config.get("runner.auc_num", 1)
test_data_dir = config.get("runner.test_data_dir", None)
print_interval = config.get("runner.print_interval", None)
model_load_path = config.get("runner.infer_load_path", "model_output")
start_epoch = config.get("runner.infer_start_epoch", 0)
end_epoch = config.get("runner.infer_end_epoch", 10)
batch_size = config.get("runner.infer_batch_size", None)
os.environ["CPU_NUM"] = str(config.get("runner.thread_num", 1))
logger.info("**************common.configs**********")
logger.info(
"use_gpu: {}, test_data_dir: {}, start_epoch: {}, end_epoch: {}, print_interval: {}, model_load_path: {}".
format(use_gpu, test_data_dir, start_epoch, end_epoch, print_interval,
model_load_path))
logger.info("**************common.configs**********")
place = paddle.set_device('gpu' if use_gpu else 'cpu')
exe = paddle.static.Executor(place)
# initialize
exe.run(paddle.static.default_startup_program())
test_dataloader = create_data_loader(
config=config, place=place, mode="test")
for epoch_id in range(start_epoch, end_epoch):
logger.info("load model epoch {}".format(epoch_id))
model_path = os.path.join(model_load_path, str(epoch_id))
load_static_model(
paddle.static.default_main_program(),
model_path,
prefix='rec_static')
epoch_begin = time.time()
interval_begin = time.time()
b = paddle.static.global_scope().find_var("item_emb").get_tensor()
b = np.array(b)
import faiss
if use_gpu:
res = faiss.StandardGpuResources()
flat_config = faiss.GpuIndexFlatConfig()
flat_config.device = 0
faiss_index = faiss.GpuIndexFlatIP(res, b.shape[-1], flat_config)
faiss_index.add(b)
else:
faiss_index = faiss.IndexFlatIP(b.shape[-1])
faiss_index.add(b)
total = 1
total_recall = 0.0
total_ndcg = 0.0
total_hitrate = 0
for batch_id, batch_data in enumerate(test_dataloader()):
fetch_batch_var = exe.run(
program=paddle.static.default_main_program(),
feed=dict(zip(input_data_names, batch_data[:2])),
fetch_list=[var for _, var in fetch_vars.items()])
user_embs = fetch_batch_var[0]
target_items = np.squeeze(np.array(batch_data[-1]), axis=1)
if len(user_embs.shape) == 2:
D, I = faiss_index.search(user_embs, args.top_n)
for i, iid_list in enumerate(target_items):
recall = 0
dcg = 0.0
item_list = set(I[i])
iid_list = list(filter(lambda x: x != 0, list(iid_list)))
for no, iid in enumerate(iid_list):
if iid in item_list:
recall += 1
dcg += 1.0 / math.log(no + 2, 2)
idcg = 0.0
for no in range(recall):
idcg += 1.0 / math.log(no + 2, 2)
total_recall += recall * 1.0 / len(iid_list)
if recall > 0:
total_ndcg += dcg / idcg
total_hitrate += 1
else:
ni = user_embs.shape[1]
user_embs = np.reshape(user_embs, [-1, user_embs.shape[-1]])
D, I = faiss_index.search(user_embs, args.top_n)
for i, iid_list in enumerate(target_items):
recall = 0
dcg = 0.0
item_list_set = set()
item_list = list(
zip(
np.reshape(I[i * ni:(i + 1) * ni], -1),
np.reshape(D[i * ni:(i + 1) * ni], -1)))
item_list.sort(key=lambda x: x[1], reverse=True)
for j in range(len(item_list)):
if item_list[j][0] not in item_list_set and item_list[
j][0] != 0:
item_list_set.add(item_list[j][0])
if len(item_list_set) >= args.top_n:
break
iid_list = list(filter(lambda x: x != 0, list(iid_list)))
for no, iid in enumerate(iid_list):
if iid == 0:
break
if iid in item_list_set:
recall += 1
dcg += 1.0 / math.log(no + 2, 2)
idcg = 0.0
for no in range(recall):
idcg += 1.0 / math.log(no + 2, 2)
total_recall += recall * 1.0 / len(iid_list)
if recall > 0:
total_ndcg += dcg / idcg
total_hitrate += 1
total += target_items.shape[0]
if batch_id % print_interval == 0:
recall = total_recall / total
ndcg = total_ndcg / total
hitrate = total_hitrate * 1.0 / total
metric_str = ""
metric_str += "recall@%d: %.5f, " % (args.top_n, recall)
metric_str += "ndcg@%d: %.5f, " % (args.top_n, ndcg)
metric_str += "hitrate@%d: %.5f, " % (args.top_n, hitrate)
logger.info("epoch: {}, batch_id: {}, ".format(
epoch_id, batch_id) + metric_str + "speed: {:.2f} ins/s".
format(print_interval * batch_size / (time.time(
) - interval_begin)))
recall = total_recall / total
ndcg = total_ndcg / total
hitrate = total_hitrate * 1.0 / total
metric_str = ""
metric_str += "recall@%d: %.5f, " % (args.top_n, recall)
metric_str += "ndcg@%d: %.5f, " % (args.top_n, ndcg)
metric_str += "hitrate@%d: %.5f, " % (args.top_n, hitrate)
logger.info("epoch: {} done, ".format(epoch_id) + metric_str +
"epoch time: {:.2f} s".format(time.time() - epoch_begin))
if __name__ == "__main__":
paddle.enable_static()
args = parse_args()
main(args)