Merge pull request #1798 from jacquesqiao/fix-ctr-reader

fix ctr test reader

fluid/PaddleRec/ctr/infer.py

@@ -61,14 +61,14 @@ def infer():
     startup_program = fluid.framework.Program()
     test_program = fluid.framework.Program()
     with fluid.framework.program_guard(test_program, startup_program):
-        loss, data_list, auc_var, batch_auc_var = ctr_dnn_model(args.embedding_size, args.sparse_feature_dim)
+        loss, auc_var, batch_auc_var, _, data_list = ctr_dnn_model(args.embedding_size, args.sparse_feature_dim, False)
         
-    exe = fluid.Executor(place)
-    
-    feeder = fluid.DataFeeder(feed_list=data_list, place=place)
-    
-    with fluid.scope_guard(inference_scope):
-        [inference_program, _, fetch_targets] = fluid.io.load_inference_model(args.model_path, exe)
+        exe = fluid.Executor(place)
+
+        feeder = fluid.DataFeeder(feed_list=data_list, place=place)
+
+        fluid.io.load_persistables(executor=exe, dirname=args.model_path,
+                                   main_program=fluid.default_main_program())
 
         def set_zero(var_name):
             param = inference_scope.var(var_name).get_tensor()
@@ -80,9 +80,9 @@ def infer():
             set_zero(name)
 
         for batch_id, data in enumerate(test_reader()):
-            loss_val, auc_val = exe.run(inference_program,
+            loss_val, auc_val = exe.run(test_program,
                 feed=feeder.feed(data),
-                fetch_list=fetch_targets)
+                fetch_list=[loss, auc_var])
             if batch_id % 100 == 0:
                 logger.info("TEST --> batch: {} loss: {} auc: {}".format(batch_id, loss_val/args.batch_size, auc_val))
 

fluid/PaddleRec/ctr/network_conf.py

@@ -104,7 +104,7 @@ def ctr_deepfm_model(factor_size, sparse_feature_dim, dense_feature_dim, sparse_
     return avg_cost, auc_var, batch_auc_var, py_reader
 
 
-def ctr_dnn_model(embedding_size, sparse_feature_dim):
+def ctr_dnn_model(embedding_size, sparse_feature_dim, use_py_reader=True):
 
     def embedding_layer(input):
         return fluid.layers.embedding(
@@ -126,13 +126,15 @@ def ctr_dnn_model(embedding_size, sparse_feature_dim):
 
     label = fluid.layers.data(name='label', shape=[1], dtype='int64')
 
-    datas = [dense_input] + sparse_input_ids + [label]
+    words = [dense_input] + sparse_input_ids + [label]
 
-    py_reader = fluid.layers.create_py_reader_by_data(capacity=64,
-                                                      feed_list=datas,
-                                                      name='py_reader',
-                                                      use_double_buffer=True)
-    words = fluid.layers.read_file(py_reader)
+    py_reader = None
+    if use_py_reader:
+        py_reader = fluid.layers.create_py_reader_by_data(capacity=64,
+                                                          feed_list=words,
+                                                          name='py_reader',
+                                                          use_double_buffer=True)
+        words = fluid.layers.read_file(py_reader)
 
     sparse_embed_seq = list(map(embedding_layer, words[1:-1]))
     concated = fluid.layers.concat(sparse_embed_seq + words[0:1], axis=1)
@@ -156,4 +158,4 @@ def ctr_dnn_model(embedding_size, sparse_feature_dim):
     auc_var, batch_auc_var, auc_states = \
         fluid.layers.auc(input=predict, label=words[-1], num_thresholds=2 ** 12, slide_steps=20)
 
-    return avg_cost, auc_var, batch_auc_var, py_reader
+    return avg_cost, auc_var, batch_auc_var, py_reader, words

fluid/PaddleRec/ctr/reader.py

@@ -46,7 +46,7 @@ class CriteoDataset(Dataset):
         return self._reader_creator(file_list, True, trainer_num, trainer_id)
 
     def test(self, file_list):
-        return self._reader_creator(file_list, False, -1)
+        return self._reader_creator(file_list, False, 1, 0)
 
     def infer(self, file_list):
-        return self._reader_creator(file_list, False, -1)
+        return self._reader_creator(file_list, False, 1, 0)

fluid/PaddleRec/ctr/train.py

@@ -174,7 +174,8 @@ def train_loop(args, train_program, py_reader, loss, auc_var, batch_auc_var,
                 if batch_id % 1000 == 0 and batch_id != 0:
                     model_dir = args.model_output_dir + '/batch-' + str(batch_id)
                     if args.trainer_id == 0:
-                        fluid.io.save_inference_model(model_dir, data_name_list, [loss, auc_var], exe)
+                        fluid.io.save_persistables(executor=exe, dirname=model_dir,
+                                                   main_program=fluid.default_main_program())
                 batch_id += 1
         except fluid.core.EOFException:
             py_reader.reset()
@@ -184,7 +185,8 @@ def train_loop(args, train_program, py_reader, loss, auc_var, batch_auc_var,
 
         model_dir = args.model_output_dir + '/pass-' + str(pass_id)
         if args.trainer_id == 0:
-            fluid.io.save_inference_model(model_dir, data_name_list, [loss, auc_var], exe)
+            fluid.io.save_persistables(executor=exe, dirname=model_dir,
+                                       main_program=fluid.default_main_program())
 
     # only for ce
     if args.enable_ce:
@@ -206,7 +208,7 @@ def train():
     if not os.path.isdir(args.model_output_dir):
         os.mkdir(args.model_output_dir)
 
-    loss, auc_var, batch_auc_var, py_reader = ctr_dnn_model(args.embedding_size, args.sparse_feature_dim)
+    loss, auc_var, batch_auc_var, py_reader, _ = ctr_dnn_model(args.embedding_size, args.sparse_feature_dim)
     optimizer = fluid.optimizer.Adam(learning_rate=1e-4)
     optimizer.minimize(loss)
     if args.cloud_train: