[Modelzoo] Add serving for DIEN, DeepFM and WDL.

modelzoo/features/serving/DIEN/generate_data.py

@@ -0,0 +1,125 @@
+from posixpath import join
+import numpy
+from numpy.lib.npyio import save
+from script.data_iterator import DataIterator
+import tensorflow as tf
+import time
+import random
+import sys
+from script.utils import *
+from tensorflow.python.framework import ops
+import os
+import json
+
+EMBEDDING_DIM = 18
+HIDDEN_SIZE = 18 * 2
+ATTENTION_SIZE = 18 * 2
+best_auc = 0.0
+best_case_acc = 0.0
+batch_size=1
+maxlen=100
+
+data_location='../data'
+test_file = os.path.join(data_location, "local_test_splitByUser")
+uid_voc = os.path.join(data_location, "uid_voc.pkl")
+mid_voc = os.path.join(data_location, "mid_voc.pkl")
+cat_voc = os.path.join(data_location, "cat_voc.pkl")
+
+def prepare_data(input, target, maxlen=None, return_neg=False):
+    # x: a list of sentences
+    lengths_x = [len(s[4]) for s in input]
+    seqs_mid = [inp[3] for inp in input]
+    seqs_cat = [inp[4] for inp in input]
+    noclk_seqs_mid = [inp[5] for inp in input]
+    noclk_seqs_cat = [inp[6] for inp in input]
+
+    if maxlen is not None:
+        new_seqs_mid = []
+        new_seqs_cat = []
+        new_noclk_seqs_mid = []
+        new_noclk_seqs_cat = []
+        new_lengths_x = []
+        for l_x, inp in zip(lengths_x, input):
+            if l_x > maxlen:
+                new_seqs_mid.append(inp[3][l_x - maxlen:])
+                new_seqs_cat.append(inp[4][l_x - maxlen:])
+                new_noclk_seqs_mid.append(inp[5][l_x - maxlen:])
+                new_noclk_seqs_cat.append(inp[6][l_x - maxlen:])
+                new_lengths_x.append(maxlen)
+            else:
+                new_seqs_mid.append(inp[3])
+                new_seqs_cat.append(inp[4])
+                new_noclk_seqs_mid.append(inp[5])
+                new_noclk_seqs_cat.append(inp[6])
+                new_lengths_x.append(l_x)
+        lengths_x = new_lengths_x
+        seqs_mid = new_seqs_mid
+        seqs_cat = new_seqs_cat
+        noclk_seqs_mid = new_noclk_seqs_mid
+        noclk_seqs_cat = new_noclk_seqs_cat
+
+        if len(lengths_x) < 1:
+            return None, None, None, None
+
+    n_samples = len(seqs_mid)
+    maxlen_x = numpy.max(lengths_x)
+    neg_samples = len(noclk_seqs_mid[0][0])
+
+    mid_his = numpy.zeros((n_samples, maxlen_x)).astype('int64')
+    cat_his = numpy.zeros((n_samples, maxlen_x)).astype('int64')
+    noclk_mid_his = numpy.zeros(
+        (n_samples, maxlen_x, neg_samples)).astype('int64')
+    noclk_cat_his = numpy.zeros(
+        (n_samples, maxlen_x, neg_samples)).astype('int64')
+    mid_mask = numpy.zeros((n_samples, maxlen_x)).astype('float32')
+    for idx, [s_x, s_y, no_sx, no_sy] in enumerate(
+            zip(seqs_mid, seqs_cat, noclk_seqs_mid, noclk_seqs_cat)):
+        mid_mask[idx, :lengths_x[idx]] = 1.
+        mid_his[idx, :lengths_x[idx]] = s_x
+        cat_his[idx, :lengths_x[idx]] = s_y
+        noclk_mid_his[idx, :lengths_x[idx], :] = no_sx
+        noclk_cat_his[idx, :lengths_x[idx], :] = no_sy
+
+    uids = numpy.array([inp[0] for inp in input])
+    mids = numpy.array([inp[1] for inp in input])
+    cats = numpy.array([inp[2] for inp in input])
+
+    if return_neg:
+        return uids, mids, cats, mid_his, cat_his, mid_mask, numpy.array(
+            target), numpy.array(lengths_x), noclk_mid_his, noclk_cat_his
+
+    else:
+        return uids, mids, cats, mid_his, cat_his, mid_mask, numpy.array(
+            target), numpy.array(lengths_x)
+
+
+test_data = DataIterator(test_file,
+                        uid_voc,
+                        mid_voc,
+                        cat_voc,
+                        batch_size,
+                        maxlen,
+                        data_location=data_location)
+
+f = open("./test_data.csv","w")
+counter = 0
+
+for src, tgt in test_data:
+        uids, mids, cats, mid_his, cat_his, mid_mask, target, sl = prepare_data(src, tgt)
+        all_data = [uids, mids, cats, mid_his, cat_his, mid_mask, target, sl]
+        for cur_data in all_data:
+            cur_data = numpy.squeeze(cur_data).reshape(-1)
+            for col in range(cur_data.shape[0]):
+                uid = cur_data[col]
+                # print(uid)
+                if col == cur_data.shape[0]-1:
+                    f.write(str(uid)+",k,")
+                    break
+                f.write(str(uid)+",")
+
+        f.write("\n");
+        if counter >= 1:
+            break
+        counter += 1
+
+f.close()

modelzoo/features/serving/DIEN/prepare_savedmodel.py

@@ -0,0 +1,100 @@
+from posixpath import join
+import numpy
+from numpy.lib.npyio import save
+from script.data_iterator import DataIterator
+import tensorflow as tf
+from script.model import *
+import time
+import random
+import sys
+from script.utils import *
+from tensorflow.python.framework import ops
+from tensorflow.python.client import timeline
+import argparse
+import os
+import json
+import pickle as pkl
+
+EMBEDDING_DIM = 18
+HIDDEN_SIZE = 18 * 2
+ATTENTION_SIZE = 18 * 2
+best_auc = 0.0
+best_case_acc = 0.0
+batch_size = 128
+
+def unicode_to_utf8(d):
+    return dict((key.encode("UTF-8"), value) for (key, value) in d.items())
+
+
+def load_dict(filename):
+    try:
+        with open(filename, 'rb') as f:
+            return unicode_to_utf8(json.load(f))
+    except:
+        with open(filename, 'rb') as f:
+            return pkl.load(f)
+
+
+def main(n_uid,n_mid,n_cat):
+
+    with tf.Session() as sess1:
+
+        model = Model_DIN_V2_Gru_Vec_attGru_Neg(n_uid, n_mid, n_cat,
+                                                EMBEDDING_DIM, HIDDEN_SIZE,
+                                                ATTENTION_SIZE)
+
+        # Initialize saver
+        folder_dir = args.checkpoint
+        saver = tf.train.Saver()
+
+        sess1.run(tf.global_variables_initializer())
+        sess1.run(tf.local_variables_initializer())
+        # Restore from checkpoint
+        saver.restore(sess1,tf.train.latest_checkpoint(folder_dir))
+
+        # Get save directory
+        dir = "./savedmodels"
+        os.makedirs(dir,exist_ok=True)
+        cc_time = int(time.time())
+        saved_path = os.path.join(dir,str(cc_time))
+        os.mkdir(saved_path)
+
+
+        tf.saved_model.simple_save(
+            sess1,
+            saved_path,
+            inputs = {"Inputs/mid_his_batch_ph:0":model.mid_his_batch_ph,"Inputs/cat_his_batch_ph:0":model.cat_his_batch_ph,
+                    "Inputs/uid_batch_ph:0":model.uid_batch_ph,"Inputs/mid_batch_ph:0":model.mid_batch_ph,"Inputs/cat_batch_ph:0":model.cat_batch_ph,
+                    "Inputs/mask:0":model.mask,"Inputs/seq_len_ph:0":model.seq_len_ph,"Inputs/target_ph:0":model.target_ph},
+            outputs = {"top_full_connect/add_2:0":model.y_hat}
+        )
+
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--checkpoint',
+                        help='ckpt path',
+                        required=False,
+                        default='../data')
+    parser.add_argument('--bf16',
+                        help='enable DeepRec BF16 in deep model. Default FP32',
+                        action='store_true')
+    parser.add_argument('--data_location',
+                        help='Full path of train data',
+                        required=False,
+                        default='./data')
+    args = parser.parse_args()
+
+    uid_voc = os.path.join(args.data_location, "uid_voc.pkl")
+    mid_voc = os.path.join(args.data_location, "mid_voc.pkl")
+    cat_voc = os.path.join(args.data_location, "cat_voc.pkl")
+
+    uid_d = load_dict(uid_voc)
+    mid_d = load_dict(mid_voc)
+    cat_d = load_dict(cat_voc)
+
+    main(len(uid_d),len(mid_d),len(cat_d))
+
+