def run_test_case(self, func, feed_dict, input_names_with_port, output_names_with_port, rtol=1e-07, atol=1e-5,
convert_var_to_const=True, constant_fold=True, check_value=True, check_shape=True,
check_dtype=True, process_args=None, onnx_feed_dict=None, graph_validator=None, as_session=False,
large_model=False, premade_placeholders=False):
# optional - passed to process_tf_graph
if process_args is None:
process_args = {}
# optional - pass distinct feed_dict to onnx runtime
if onnx_feed_dict is None:
onnx_feed_dict = feed_dict
input_names_with_port = list(feed_dict)
tf_reset_default_graph()
graph_def = None
initialized_tables = None
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)
expected = func(*input_list)
if isinstance(expected, (list, tuple)):
# list or tuple
expected = [x.numpy() for x in expected]
else:
# single result
expected = [expected.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=output_names_with_port,
large_model=large_model)
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 output_names_with_port:
output_dict.append(sess.graph.get_tensor_by_name(out_name))
expected = sess.run(output_dict, feed_dict=feed_dict)
graph_def = freeze_session(sess,
input_names=list(feed_dict.keys()),
output_names=output_names_with_port)
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()), output_names_with_port,
graph_def, fold_constant=constant_fold)
tf_reset_default_graph()
with tf_session() as sess:
const_node_values = None
if large_model:
const_node_values = compress_graph_def(graph_def)
tf.import_graph_def(graph_def, name='')
if self.config.is_debug_mode:
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)
g = process_tf_graph(sess.graph, opset=self.config.opset,
input_names=list(feed_dict.keys()),
output_names=output_names_with_port,
target=self.config.target,
const_node_values=const_node_values,
initialized_tables=initialized_tables,
**process_args)
g = optimizer.optimize_graph(g, catch_errors=False)
actual = self.run_backend(g, output_names_with_port, onnx_feed_dict, large_model)
for expected_val, actual_val in zip(expected, actual):
if check_value:
if expected_val.dtype == np.object:
decode = np.vectorize(lambda x: x.decode('UTF-8'))
expected_val_str = decode(expected_val)
self.assertAllEqual(expected_val_str, actual_val)
else:
self.assertAllClose(expected_val, actual_val, rtol=rtol, atol=atol)
if check_dtype:
self.assertEqual(expected_val.dtype, actual_val.dtype)
# why need shape checke: issue when compare [] with scalar
# https://github.com/numpy/numpy/issues/11071
if check_shape:
self.assertEqual(expected_val.shape, actual_val.shape)
if graph_validator:
self.assertTrue(graph_validator(g))
return g
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 freeze_and_run_tf(self, func, feed_dict, outputs, as_session,
premade_placeholders, large_model):
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_info = get_hash_table_info(graph_def)
initialized_tables = {}
for info in table_info:
if info.shared_name is None:
continue
h = lookup_ops.hash_table_v2(info.key_dtype,
info.val_dtype,
shared_name=info.shared_name)
k, v = lookup_ops.lookup_table_export_v2(
h, info.key_dtype, info.val_dtype)
initialized_tables[info.shared_name] = (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)
return result, graph_def, initialized_tables
