def test_infer_inputs_and_outputs(self):
x_shape = (3, 4, 5)
@make_tf_graph([x_shape])
def build_model(x):
return tf.nn.relu(x)
model, inputs, outputs = build_model
mlmodel = converter.convert(model)
assert mlmodel is not None
input_values = [random_gen(x_shape, -10.0, 10.0)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(model, input_dict, outputs)
def test_scalar_placeholder_shape(self):
x_shape = () # Scalar Placeholder Shape
@make_tf_graph([x_shape])
def build_model(x):
return tf.nn.relu(x)
model, inputs, outputs = build_model
mlmodel = converter.convert(model, source=frontend)
assert mlmodel is not None
input_values = [random_gen(x_shape, -10.0, 10.0)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(model, input_dict, outputs)
def test_infer_outputs(self):
x_shape = (3, 4, 5)
@make_tf_graph([x_shape])
def build_model(x):
return tf.nn.relu(x)
model, inputs, outputs = build_model
input_name = (
inputs[0] if isinstance(inputs[0], six.string_types) else inputs[0].op.name
)
mlmodel = converter.convert(model, inputs=[TensorType(input_name, (3, 4, 5))])
assert mlmodel is not None
input_values = [random_gen(x_shape, -10.0, 10.0)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(model, input_dict, outputs)
def test_selu(self, use_cpu_only, backend, rank):
input_shape = np.random.randint(low=1, high=6, size=rank)
@make_tf_graph([input_shape])
def build_model(x):
return tf.keras.activations.selu(x)
model, inputs, outputs = build_model
input_values = [random_gen(input_shape, -10.0, 10.0)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model,
input_dict,
outputs,
use_cpu_only=use_cpu_only,
frontend_only=False,
backend=backend,
)
def test_infer_inputs(self):
x_shape = (3, 4, 5)
@make_tf_graph([x_shape])
def build_model(x):
return tf.nn.relu(x)
model, inputs, outputs = build_model
if not isinstance(outputs, (tuple, list)):
outputs = [outputs]
output_names = [
j if isinstance(j, six.string_types) else j.op.name for j in outputs
]
mlmodel = converter.convert(model, outputs=output_names)
assert mlmodel is not None
input_values = [random_gen(x_shape, -10.0, 10.0)]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(model, input_dict, outputs)
def test_masked_input(self, use_cpu_only, backend):
input_shape = [4, 10, 8]
val = np.random.rand(*input_shape).astype(np.float32)
@make_tf_graph([input_shape])
def build_model(input):
sliced_input = input[..., 4]
mask = tf.where_v2(sliced_input > 0)
masked_input = tf.gather_nd(input, mask)
return masked_input
model, inputs, outputs = build_model
input_values = [val]
input_dict = dict(zip(inputs, input_values))
run_compare_tf(
model,
input_dict,
outputs,
use_cpu_only=use_cpu_only,
frontend_only=False,
backend=backend,
)
