def do_test_expected(self):
tf_op = test_data[1]
output_name = test_data[2]
inputs = test_data[3]
attrs = test_data[4]
# Now construct input feed dict
# keyed by input name
onnx_feed_dict = {}
# keyed by placeholder op
tf_feed_dict = {}
tf_param_list = []
for idx, input_tensor in enumerate(inputs):
if type(input_tensor) is np.ndarray:
placeholder = tf.placeholder(input_tensor.dtype,
shape=input_tensor.shape,
name="in_" + str(idx))
onnx_feed_dict["in_" + str(idx)] = input_tensor
tf_feed_dict[placeholder] = input_tensor
tf_param_list.append(placeholder)
else:
tf_param_list.append(input_tensor)
test_op = tf_op(*tf_param_list, **attrs)
tf_graph = test_op.graph.as_graph_def(add_shapes=True)
# Construct onnx graph, run with backend.
output_node = get_node_by_name(tf_graph.node, output_name)
onnx_graph = convert_graph(tf_graph, output_node)
backend_rep = prepare(helper.make_model(onnx_graph))
backend_output = backend_rep.run(onnx_feed_dict)[output_name]
with tf.Session() as sess:
tf_output = sess.run(test_op, tf_feed_dict)
tf.reset_default_graph()
np.testing.assert_allclose(backend_output, tf_output)
def do_test_expected(self):
tf_op = test_data[1]
output_name = test_data[2]
inputs = test_data[3]
attrs = test_data[4]
device = test_data[5]
channel_last = test_data[6]
# Now construct input feed dict
# keyed by input name
onnx_feed_dict = {}
# keyed by placeholder op
tf_feed_dict = {}
tf_param_list = []
for idx, input_tensor in enumerate(inputs):
if type(input_tensor) is np.ndarray:
placeholder = tf.placeholder(input_tensor.dtype,
shape=input_tensor.shape,
name="in_" + str(idx))
onnx_feed_dict["in_" + str(idx)] = input_tensor
# TF have to get input in format : NHWC
tf_feed_dict[placeholder] = input_tensor
tf_param_list.append(placeholder)
else:
tf_param_list.append(input_tensor)
test_op = tf_op(*tf_param_list, **attrs)
tf_graph = tf.get_default_graph().as_graph_def(add_shapes=True)
# Construct onnx graph, run with backend.
output_node = get_node_by_name(tf_graph.node, output_name)
onnx_graph = convert_graph(tf_graph,
output_node,
device=device,
channel_last=channel_last)
onnx_model = helper.make_model(onnx_graph)
backend_rep = prepare(onnx_model,
device=device,
channel_last=channel_last)
backend_output = backend_rep.run(onnx_feed_dict)[output_name]
with tf.Session() as sess:
tf_output = sess.run(test_op, tf_feed_dict)
tf.reset_default_graph()
# skip comparison if test_option specifies that
# the test is call only.
if (test_option.get("call_only", False)):
return
np.testing.assert_allclose(backend_output, tf_output)
def do_test_expected(self):
tf.reset_default_graph()
tf_op = test_data[1]
output_name = test_data[2]
inputs = test_data[3]
attrs = test_data[4]
# Now construct input feed dict
# keyed by input name
onnx_feed_dict = {}
# keyed by placeholder op
tf_feed_dict = {}
tf_param_list = []
for idx, input_tensor in enumerate(inputs):
if type(input_tensor) is np.ndarray:
placeholder = tf.placeholder(input_tensor.dtype,
shape=input_tensor.shape,
name="in_" + str(idx))
onnx_feed_dict["in_" + str(idx)] = input_tensor
tf_feed_dict[placeholder] = input_tensor
tf_param_list.append(placeholder)
else:
tf_param_list.append(input_tensor)
test_op = tf_op(*tf_param_list, **attrs)
tf_graph = tf.get_default_graph().as_graph_def(add_shapes=True)
# Construct onnx graph, run with backend.
output_node = get_node_by_name(tf_graph.node, output_name)
onnx_graph = convert_graph(tf_graph, output_node)
onnx_model = helper.make_model(onnx_graph)
backend_rep = prepare(onnx_model)
backend_output = []
backend_rep_outputs = backend_rep.run(onnx_feed_dict)
for ext_output in backend_rep.predict_net.external_output:
backend_output.append(backend_rep_outputs[ext_output])
backend_output = np.asarray(backend_output)
backend_output = np.squeeze(
backend_output,
0) if backend_output.shape[0] == 1 else backend_output
with tf.Session() as sess:
tf_output = sess.run(test_op, tf_feed_dict)
# skip comparison if test_option specifies that
# the test is call only.
if (test_option.get("call_only", False)):
return
for backend_o, tf_o in zip(backend_output, tf_output):
np.testing.assert_allclose(backend_o, tf_o)