| ```python | |
| import tensorflow as tf | |
| import numpy as np | |
| import pandas as pd | |
| import sys | |
| from sklearn.model_selection import train_test_split | |
| sys.path.insert(0, "bert_experimental") | |
| from bert_experimental.finetuning.text_preprocessing import build_preprocessor | |
| from bert_experimental.finetuning.graph_ops import load_graph | |
| df = pd.read_csv("data/test.tsv", sep='\t') | |
| texts = [] | |
| delimiter = " ||| " | |
| for vis, cap in zip(df.visual.tolist(), df.caption.tolist()): | |
| texts.append(delimiter.join((str(vis), str(cap)))) | |
| texts = np.array(texts) | |
| trX, tsX = train_test_split(texts, shuffle=False, test_size=0.01) | |
| restored_graph = load_graph("frozen_graph.pb") | |
| graph_ops = restored_graph.get_operations() | |
| input_op, output_op = graph_ops[0].name, graph_ops[-1].name | |
| print(input_op, output_op) | |
| x = restored_graph.get_tensor_by_name(input_op + ':0') | |
| y = restored_graph.get_tensor_by_name(output_op + ':0') | |
| preprocessor = build_preprocessor("uncased_L-12_H-768_A-12/vocab.txt", 64) | |
| py_func = tf.numpy_function(preprocessor, [x], [tf.int32, tf.int32, tf.int32], name='preprocessor') | |
| py_func = tf.numpy_function(preprocessor, [x], [tf.int32, tf.int32, tf.int32]) | |
| ##predictions | |
| sess = tf.Session(graph=restored_graph) | |
| print(trX[:4]) | |
| y = tf.print(y, summarize=-1) | |
| #x = tf.print(x, summarize=-1) | |
| y_out = sess.run(y, feed_dict={ | |
| x: trX[:4].reshape((-1,1)) | |
| #x: trX[:90000].reshape((-1,1)) | |
| }) | |
| print(y_out) | |
| ```` |