def func(i):
# tf2 works a little different than tf1 - that is why there is a is_tf2() here
if is_tf2():
one = tf.constant(np.array([1], dtype=np.int32))
else:
one = tf.constant(np.array(1, dtype=np.int32))
c = lambda i: tf.less(i, 10)
b = lambda i: tf.add(i, one)
r = tf.while_loop(c, b, [i])
if is_tf2():
r = tf.reshape(r, [-1])
return tf.identity(r, name=_TFOUTPUT)
def func(i):
# tf2 works a little different than tf1 - that is why there is a is_tf2() here
if is_tf2():
one = tf.constant(np.array([1], dtype=np.int32))
else:
one = tf.constant(np.array(1, dtype=np.int32))
c = lambda i: tf.logical_and(tf.less(i, 10), tf.greater_equal(
i, 3))
b = lambda i: tf.add(i, one)
r = tf.while_loop(c, b, [i])
if is_tf2():
r = tf.reshape(r, [-1])
return tf.identity(r, name="output")
def test_simple_while_loop(self):
def func(i):
# tf2 works a little different than tf1 - that is why there is a is_tf2() here
if is_tf2():
one = tf.constant(np.array([1], dtype=np.int32))
else:
one = tf.constant(np.array(1, dtype=np.int32))
c = lambda i: tf.less(i, 10)
b = lambda i: tf.add(i, one)
r = tf.while_loop(c, b, [i])
if is_tf2():
r = tf.reshape(r, [-1])
return tf.identity(r, name=_TFOUTPUT)
if is_tf2():
x_val = np.array([0], dtype=np.int32)
else:
x_val = np.array(0, dtype=np.int32)
self.run_test_case(func, {_INPUT: x_val}, [], [_OUTPUT], rtol=1e-06)
# SPDX-License-Identifier: Apache-2.0
"""Unit Tests for layered lstm"""
import numpy as np
import tensorflow as tf
from tensorflow.python.ops import init_ops
from backend_test_base import Tf2OnnxBackendTestBase
from common import unittest_main, check_lstm_count, check_opset_after_tf_version, skip_tf2
from tf2onnxnightly.tf_loader import is_tf2
# pylint: disable=missing-docstring,invalid-name,unused-argument,using-constant-test,cell-var-from-loop
# pylint: disable=invalid-name
if is_tf2():
LSTMCell = tf.compat.v1.nn.rnn_cell.LSTMCell
MultiRNNCell = tf.compat.v1.nn.rnn_cell.MultiRNNCell
dynamic_rnn = tf.compat.v1.nn.dynamic_rnn
else:
LSTMCell = tf.contrib.rnn.LSTMCell
LSTMBlockCell = tf.contrib.rnn.LSTMBlockCell
MultiRNNCell = tf.contrib.rnn.MultiRNNCell
dynamic_rnn = tf.nn.dynamic_rnn
class LSTMLayeredTests(Tf2OnnxBackendTestBase):
@check_opset_after_tf_version("1.15", 8, "might need Scan")
@skip_tf2()
def test_layered_lstm(self):
units = 5
def freeze_and_run_tf(self, func, feed_dict, outputs, as_session,
premade_placeholders, large_model, constant_fold):
np.random.seed(1) # Make it reproducible.
clean_feed_dict = {utils.node_name(k): v for k, v in feed_dict.items()}
if is_tf2() and not as_session:
#
# use eager to execute the tensorflow func
#
# numpy doesn't work for all ops, make it tf.Tensor()
input_tensors = [
tf.TensorSpec(shape=v.shape,
dtype=tf.as_dtype(v.dtype),
name=utils.node_name(k))
for k, v in feed_dict.items()
]
input_list = [
tf.convert_to_tensor(v,
dtype=tf.as_dtype(v.dtype),
name=utils.node_name(k))
for k, v in feed_dict.items()
]
tf.random.set_seed(1)
result = func(*input_list)
if isinstance(result, (list, tuple)):
# list or tuple
result = [x.numpy() for x in result]
else:
# single result
result = [result.numpy()]
# now make the eager functions a graph
concrete_func = tf.function(func,
input_signature=tuple(input_tensors))
concrete_func = concrete_func.get_concrete_function()
graph_def = from_function(concrete_func,
input_names=list(feed_dict.keys()),
output_names=outputs,
large_model=large_model)
initialized_tables = None
else:
#
# use graph to execute the tensorflow func
#
with tf_session() as sess:
tf_set_random_seed(1)
input_list = []
if not premade_placeholders:
for k, v in clean_feed_dict.items():
input_list.append(
tf_placeholder(name=k,
shape=v.shape,
dtype=tf.as_dtype(v.dtype)))
func(*input_list)
variables_lib.global_variables_initializer().run()
tf_tables_initializer().run()
output_dict = []
for out_name in outputs:
output_dict.append(sess.graph.get_tensor_by_name(out_name))
result = sess.run(output_dict, feed_dict=feed_dict)
graph_def = freeze_session(sess,
input_names=list(feed_dict.keys()),
output_names=outputs)
table_names, key_dtypes, value_dtypes = get_hash_table_info(
graph_def)
initialized_tables = {}
for n, k_dtype, val_dtype in zip(table_names, key_dtypes,
value_dtypes):
h = lookup_ops.hash_table_v2(k_dtype,
val_dtype,
shared_name=n)
k, v = lookup_ops.lookup_table_export_v2(
h, k_dtype, val_dtype)
initialized_tables[n] = (sess.run(k), sess.run(v))
tf_reset_default_graph()
with tf_session() as sess:
tf.import_graph_def(graph_def, name='')
graph_def = tf_optimize(list(feed_dict.keys()),
outputs,
graph_def,
fold_constant=constant_fold)
model_path = os.path.join(
self.test_data_directory,
self._testMethodName + "_after_tf_optimize.pb")
utils.save_protobuf(model_path, graph_def)
self.logger.debug("created file %s", model_path)
return result, graph_def, initialized_tables
def skip_tf2(message=""):
""" Skip if tf_version > max_required_version """
reason = _append_message("test needs to be fixed for tf-2.x", message)
return unittest.skipIf(tf_loader.is_tf2(), reason)