提交 65744362 编写于 作者: T tangwei

Merge branch 'develop' of ssh://gitlab.baidu.com:8022/tangwei12/paddlerec into rec_develop

......@@ -247,6 +247,9 @@ class TranspileTrainer(Trainer):
model_list = [(0, envs.get_global_env(
'evaluate_model_path', "", namespace='evaluate'))]
is_return_numpy = envs.get_global_env(
'is_return_numpy', True, namespace='evaluate')
for (epoch, model_dir) in model_list:
print("Begin to infer No.{} model, model_dir: {}".format(
epoch, model_dir))
......@@ -258,7 +261,8 @@ class TranspileTrainer(Trainer):
while True:
metrics_rets = self._exe.run(
program=program,
fetch_list=metrics_varnames)
fetch_list=metrics_varnames,
return_numpy=is_return_numpy)
metrics = [epoch, batch_id]
metrics.extend(metrics_rets)
......
......@@ -12,6 +12,14 @@
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate:
reader:
batch_size: 1
class: "{workspace}/rsc15_infer_reader.py"
test_data_path: "{workspace}/data/train"
is_return_numpy: False
train:
trainer:
# for cluster training
......@@ -19,8 +27,8 @@ train:
epochs: 3
workspace: "paddlerec.models.recall.gru4rec"
device: cpu
reader:
batch_size: 5
class: "{workspace}/rsc15_reader.py"
......
......@@ -23,7 +23,7 @@ class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def all_vocab_network(self):
def all_vocab_network(self, is_infer=False):
""" network definition """
recall_k = envs.get_global_env("hyper_parameters.recall_k", None, self._namespace)
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
......@@ -39,10 +39,16 @@ class Model(ModelBase):
dst_wordseq = fluid.data(
name="dst_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
if is_infer:
self._infer_data_var = [src_wordseq, dst_wordseq]
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var, capacity=64, use_double_buffer=False, iterable=False)
emb = fluid.embedding(
input=src_wordseq,
size=[vocab_size, hid_size],
param_attr=fluid.ParamAttr(
name="emb",
initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound),
learning_rate=emb_lr_x),
......@@ -70,6 +76,9 @@ class Model(ModelBase):
learning_rate=fc_lr_x))
cost = fluid.layers.cross_entropy(input=fc, label=dst_wordseq)
acc = fluid.layers.accuracy(input=fc, label=dst_wordseq, k=recall_k)
if is_infer:
self._infer_results['recall20'] = acc
return
avg_cost = fluid.layers.mean(x=cost)
self._data_var.append(src_wordseq)
......@@ -84,4 +93,4 @@ class Model(ModelBase):
def infer_net(self):
pass
self.all_vocab_network(is_infer=True)
# 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
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
class EvaluateReader(Reader):
def init(self):
pass
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
l = line.strip().split()
l = [w for w in l]
src_seq = l[:len(l) - 1]
src_seq = [int(e) for e in src_seq]
trg_seq = l[1:]
trg_seq = [int(e) for e in trg_seq]
feature_name = ["src_wordseq", "dst_wordseq"]
yield zip(feature_name, [src_seq] + [trg_seq])
return reader
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