Enable PyPI installation

* Fix or toggle off warnings (mostly TF deprecation warnings)
* Update dependency to TF>=1.10.0
* Upgrade to version v0.2.2

Author: ZhitingHu

CHANGELOG.md

@@ -5,6 +5,16 @@
 
 ### Feature improvements
 
+### Fixes
+
+## [v0.2.2](https://github.com/asyml/texar/releases/tag/v0.2.2) (2019-08-05)
+
+### New features
+
+* Enable installation from [Pypi](https://pypi.org/project/texar/). ([#186](https://github.com/asyml/texar/pull/186))
+
+### Feature improvements
+
 * Use lazy import to be compatible with [texar-pytorch](https://github.com/asyml/texar-pytorch). ([#183](https://github.com/asyml/texar/pull/183)) 
 
 ### Fixes

README.md

@@ -4,6 +4,8 @@
 
 -----------------
 
+
+[![pypi](https://img.shields.io/pypi/v/texar.svg)](https://pypi.python.org/pypi/texar)
 [![Build Status](https://travis-ci.org/asyml/texar.svg?branch=master)](https://travis-ci.org/asyml/texar)
 [![Documentation Status](https://readthedocs.org/projects/texar/badge/?version=latest)](https://texar.readthedocs.io/en/latest/?badge=latest)
 [![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/asyml/texar/blob/master/LICENSE)
@@ -79,6 +81,13 @@ agent = tx.agents.SeqPGAgent(samples=outputs.sample_id,
 Many more examples are available [here](./examples)
 
 ### Installation
+Besides the packages in requirements.txt, Texar additionally requires:
+
+* tensorflow >= 1.10.0 (but <= 2.0). Follow the tensorflow official instructions to install the appropriate version
+
+* tensorflow_probability >= 0.3.0. Follow the tensorflow_probability official instractions to install.
+
+After Tensorflow is installed, please run the following commands to install Texar:
 ```
 git clone https://github.com/asyml/texar.git
 cd texar

examples/bert/bert_classifier_main.py

@@ -107,7 +107,7 @@ def main(_):
     input_ids = batch["input_ids"]
     segment_ids = batch["segment_ids"]
     batch_size = tf.shape(input_ids)[0]
-    input_length = tf.reduce_sum(1 - tf.to_int32(tf.equal(input_ids, 0)),
+    input_length = tf.reduce_sum(1 - tf.cast(tf.equal(input_ids, 0), tf.int32),
                                  axis=1)
     # Builds BERT
     with tf.variable_scope('bert'):
@@ -198,7 +198,7 @@ def _train_epoch(sess):
         periodically.
         """
         iterator.restart_dataset(sess, 'train')
-        
+
         fetches = {
             'train_op': train_op,
             'loss': loss,

examples/bert/bert_classifier_main_v2.py

@@ -85,7 +85,7 @@ def main(_):
     input_ids = batch["input_ids"]
     segment_ids = batch["segment_ids"]
     batch_size = tf.shape(input_ids)[0]
-    input_length = tf.reduce_sum(1 - tf.to_int32(tf.equal(input_ids, 0)),
+    input_length = tf.reduce_sum(1 - tf.cast(tf.equal(input_ids, 0), tf.int32),
                                  axis=1)
     # Builds BERT
     hparams = {

examples/seq2seq_exposure_bias/interpolation_helper.py

@@ -22,6 +22,7 @@
 import tensorflow as tf
 import numpy as np
 
+from tensorflow_probability import distributions as tfpd
 from tensorflow.contrib.seq2seq import SampleEmbeddingHelper
 from texar.evals.bleu import sentence_bleu
 from rouge import Rouge
@@ -95,19 +96,19 @@ def sample(self, time, outputs, state, name=None):
         of 'state'([decoded_ids, rnn_state])
         """
         sample_method_sampler = \
-            tf.distributions.Categorical(probs=self._lambdas)
+            tfpd.Categorical(probs=self._lambdas)
         sample_method_id = sample_method_sampler.sample()
 
         truth_feeding = lambda: tf.cond(
             tf.less(time, tf.shape(self._ground_truth)[1]),
-            lambda: tf.to_int32(self._ground_truth[:, time]),
+            lambda: tf.cast(self._ground_truth[:, time], tf.int32),
             lambda: tf.ones_like(self._ground_truth[:, 0],
                                  dtype=tf.int32) * self._vocab.eos_token_id)
 
-        self_feeding = lambda : SampleEmbeddingHelper.sample(
+        self_feeding = lambda: SampleEmbeddingHelper.sample(
             self, time, outputs, state, name)
 
-        reward_feeding = lambda : self._sample_by_reward(time, state)
+        reward_feeding = lambda: self._sample_by_reward(time, state)
 
         sample_ids = tf.cond(
             tf.logical_or(tf.equal(time, 0), tf.equal(sample_method_id, 1)),
@@ -207,9 +208,9 @@ def _get_rewards(time, prefix_ids, target_ids, ground_truth_length):
 
             return result
 
-        sampler = tf.distributions.Categorical(
+        sampler = tfpd.Categorical(
             logits=tf.py_func(_get_rewards, [
                 time, state[0], self._ground_truth,
                 self._ground_truth_length], tf.float32))
         return tf.reshape(
-            sampler.sample(), (tf.shape(self._ground_truth)[0],))
+            sampler.sample(), (tf.shape(self._ground_truth)[0],))

examples/text_style_transfer/ctrl_gen_model.py

@@ -55,7 +55,7 @@ def _build_model(self, inputs, vocab, gamma, lambda_g):
         label_connector = MLPTransformConnector(self._hparams.dim_c)
 
         # Gets the sentence representation: h = (c, z)
-        labels = tf.to_float(tf.reshape(inputs['labels'], [-1, 1]))
+        labels = tf.cast(tf.reshape(inputs['labels'], [-1, 1]), tf.float32)
         c = label_connector(labels)
         c_ = label_connector(1 - labels)
         h = tf.concat([c, z], 1)
@@ -106,7 +106,7 @@ def _build_model(self, inputs, vocab, gamma, lambda_g):
             inputs=clas_embedder(ids=inputs['text_ids'][:, 1:]),
             sequence_length=inputs['length']-1)
         loss_d_clas = tf.nn.sigmoid_cross_entropy_with_logits(
-            labels=tf.to_float(inputs['labels']), logits=clas_logits)
+            labels=tf.cast(inputs['labels'], tf.float32), logits=clas_logits)
         loss_d_clas = tf.reduce_mean(loss_d_clas)
         accu_d = tx.evals.accuracy(labels=inputs['labels'], preds=clas_preds)
 
@@ -115,7 +115,7 @@ def _build_model(self, inputs, vocab, gamma, lambda_g):
             inputs=clas_embedder(soft_ids=soft_outputs_.sample_id),
             sequence_length=soft_length_)
         loss_g_clas = tf.nn.sigmoid_cross_entropy_with_logits(
-            labels=tf.to_float(1-inputs['labels']), logits=soft_logits)
+            labels=tf.cast(1-inputs['labels'], tf.float32), logits=soft_logits)
         loss_g_clas = tf.reduce_mean(loss_g_clas)
 
         # Accuracy on soft samples, for training progress monitoring

examples/transformer/transformer_main.py

@@ -22,8 +22,8 @@
 import random
 import os
 import importlib
-from torchtext import data
 import tensorflow as tf
+from torchtext import data
 import texar as tx
 from texar.modules import TransformerEncoder, TransformerDecoder
 from texar.utils import transformer_utils
@@ -74,10 +74,10 @@ def main():
     batch_size = tf.shape(encoder_input)[0]
     # (text sequence length excluding padding)
     encoder_input_length = tf.reduce_sum(
-        1 - tf.to_int32(tf.equal(encoder_input, 0)), axis=1)
+        1 - tf.cast(tf.equal(encoder_input, 0), tf.int32), axis=1)
 
     labels = tf.placeholder(tf.int64, shape=(None, None))
-    is_target = tf.to_float(tf.not_equal(labels, 0))
+    is_target = tf.cast(tf.not_equal(labels, 0), tf.float32)
 
     global_step = tf.Variable(0, dtype=tf.int64, trainable=False)
     learning_rate = tf.placeholder(tf.float64, shape=(), name='lr')

requirements.txt

@@ -1,5 +1,6 @@
-tensorflow >= 1.7.0
-tensorflow-gpu >= 1.7.0
+tensorflow >= 1.10.0
+tensorflow-gpu >= 1.10.0
 tensorflow-probability >= 0.3.0
 tensorflow-probability-gpu >= 0.3.0
-funcsigs >= 1.0.2
+funcsigs >= 1.0.2
+packaging

setup.py

@@ -15,7 +15,7 @@
 
 setuptools.setup(
     name="texar",
-    version="0.2.1",
+    version="0.2.2",
     url="https://github.com/asyml/texar",
 
     description="Toolkit for Machine Learning and Text Generation",
@@ -26,19 +26,20 @@
     platforms='any',
 
     install_requires=[
-        'numpy',
+        'numpy<1.17.0',
         'pyyaml',
         'requests',
-        'funcsigs',
+        'funcsigs>=1.0.2',
+        'packaging'
     ],
     extras_require={
         'tensorflow-cpu': [
-            'tensorflow>=1.7.0',
-            'tensorflow-probability >= 0.3.0'
+            'tensorflow>=1.10.0,<2.0',
+            'tensorflow-probability>=0.3.0'
         ],
         'tensorflow-gpu': [
-            'tensorflow-gpu>=1.7.0',
-            'tensorflow-probability-gpu >= 0.3.0'
+            'tensorflow-gpu>=1.10.0,<2.0',
+            'tensorflow-probability-gpu>=0.3.0'
         ]
     },
     package_data={

texar/__init__.py

@@ -19,9 +19,20 @@
 from __future__ import division
 from __future__ import print_function
 
+
 # pylint: disable=wildcard-import
 
 import sys
+from packaging import version
+import tensorflow as tf
+
+
+VERSION_WARNING = "1.13.2"
+
+if version.parse(tf.__version__) <= version.parse(VERSION_WARNING):
+    tf.logging.set_verbosity(tf.logging.ERROR)
+else:
+    tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
 
 if sys.version_info.major < 3:
     # PY 2.x, import as is because Texar-PyTorch cannot be installed.