fluid mpi训练好的模型 离线预测时 报错Input Y(0)is not initialized

[maosengshulei]

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预测报错： Traceback (most recent call last): File "dcn_infer.py", line 117, in <module> infer() File "dcn_infer.py", line 107, in infer fetch_list=[v.name for v in fetch_list]) File "/home/work/tools/paddle/paddle_release_home/python/lib/python2.7/site-packages/paddle/fluid/executor.py", line 565, in run use_program_cache=use_program_cache) File "/home/work/tools/paddle/paddle_release_home/python/lib/python2.7/site-packages/paddle/fluid/executor.py", line 642, in _run exe.run(program.desc, scope, 0, True, True, fetch_var_name) paddle.fluid.core.EnforceNotMet: Invoke operator mul error. Python Callstacks: File "/home/work/tools/paddle/paddle_release_home/python/lib/python2.7/site-packages/paddle/fluid/framework.py", line 1654, in append_op attrs=kwargs.get("attrs", None)) File "/home/work/tools/paddle/paddle_release_home/python/lib/python2.7/site-packages/paddle/fluid/layer_helper.py", line 43, in append_op return self.main_program.current_block().append_op(*args, **kwargs) File "/home/work/tools/paddle/paddle_release_home/python/lib/python2.7/site-packages/paddle/fluid/layers/nn.py", line 326, in fc "y_num_col_dims": 1}) File "/home/work/shulei/msd_dcn_v1/fluid_dcn.py", line 112, in DCN ctr_predict = fluid.layers.fc(input=ctr_fc,size=2, act="softmax", param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1 / math.sqrt(ctr_fc.shape[1])))) File "dcn_infer.py", line 96, in infer ctr_predict, dura_predict ,words = DCN(args, feat_list.features) File "dcn_infer.py", line 117, in <module> infer() C++ Callstacks: Input Y(0)is not initialized at [/paddle/paddle/fluid/framework/operator.cc:1142]

预测部分代码： `args = parse_args()

place = fluid.CPUPlace()
inference_scope = fluid.core.Scope()

feat_list = dcn_reader.Msd_Feat_Builder(args.feat_list_path, args.bucket_statistic)
dataset = dcn_reader.MsdDataset(feat_list.features, args.data_path, args.mapping_path, args.seccate_map_path)
dataset.prepare_read()
test_reader = paddle.batch(dataset.infer(), batch_size=args.batch_size)

exe = fluid.Executor(place)
ctr_predict, dura_predict ,words = DCN(args, feat_list.features)
#[inference_program, feed_target_names, fetch_targets] = fluid.io.load_inference_model(executor=exe, dirname=args.model_path)
def if_exist(var):
    return os.path.exists(os.path.join(args.model_path, var.name))
#[inference_program, feed_target_names, fetch_targets] = fluid.io.load_vars(executor=exe, dirname=args.model_path, predicate=if_exist)
fluid.io.load_vars(executor=exe, dirname=args.model_path, predicate=if_exist)
feeder = fluid.DataFeeder(feed_list=words, place=place)
fetch_list = [ctr_predict]
for batch_id, data in enumerate(test_reader()):
    print data
    predict = exe.run(feed=feeder.feed(data),
                    fetch_list=[v.name for v in fetch_list])
    for i in range(len(data)):
        print predict[0][i][1]
exe.close()`

模型部分代码：

`sparse_embed_seq = list(starmap(embedding_layer, zip(words[1 : 1 + sparse_input_length], sparse_attr_names, sparse_attr_size)))

concated = fluid.layers.concat(sparse_embed_seq  + words[0:1] , axis=1)

last_deep_layer = deepnet(concated, args)
last_cross_layer = crossnet(concated, args)

top_fc_input = fluid.layers.concat([last_deep_layer, last_cross_layer], axis=1)

top_fc = fc_block(top_fc_input, np.fromstring(args.top_fc_block,dtype=int, sep="-").tolist())

ctr_fc = fluid.layers.fc(input=top_fc,size=128,act='relu',param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1 / math.sqrt(top_fc.shape[1]))))

#duration_fc = fluid.layers.fc(input=top_fc,size=128,act='relu',param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1 / math.sqrt(top_fc.shape[1]))))

ctr_predict = fluid.layers.fc(input=ctr_fc,size=2, act="softmax", param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1 / math.sqrt(ctr_fc.shape[1]))))

dura_predict = fluid.layers.fc(input=ctr_fc,size=1, act=None, param_attr=fluid.ParamAttr(initializer=fluid.initializer.Normal(scale=1 / math.sqrt(ctr_fc.shape[1]))))`

[kuke]

应该是保存inference model的时候保存了某个输入，但预测的时候没有feed，例如说label，请注意检查下

[maosengshulei]

我check了存model的代码 names = [x.name for x in words[:-1]] print(names) fluid.io.save_inference_model( dirname=infer_model_dir, feeded_var_names=names, target_vars=[ctr_predict,dura_predict], executor=executor) 确实多存了一个label 但是离线infer时feedlist中加入了label也还是报相同的错

[maosengshulei]

而且我之前的另外一个model也是相同的保存方法（也是多存了label），但是离线infer的时候feed_list里不带label这一项，却可以正常infer。

[kuke]

需要执行exe.run(fluid.default_startup_program())