def test_scalar_shape():
from utensor_cgen.frontend.tensorflow import GraphDefParser
graph = tf.Graph()
with graph.as_default():
tf.constant(1, dtype=tf.float32, name='x')
parser = GraphDefParser({})
ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x'])
# shape of scalar tensor should be empty list
out_tensor = ugraph.ops_info['x'].output_tensors[0]
assert out_tensor.shape == []
assert out_tensor.dtype is np.dtype('float32')
def test_placeholder_shape():
from utensor_cgen.frontend.tensorflow import GraphDefParser
graph = tf.Graph()
with graph.as_default():
tf.placeholder(dtype=tf.float32, name='x')
parser = GraphDefParser({})
ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x'])
# nondeterministic shape, can be any shape
out_tensor = ugraph.ops_info['x'].output_tensors[0]
assert out_tensor.shape is None
assert out_tensor.dtype is np.dtype('float32')
graph = tf.Graph()
with graph.as_default():
tf.placeholder(dtype=tf.float32, name='x', shape=[None, 5])
parser = GraphDefParser({})
ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x'])
# nondeterministic dimension
out_tensor = ugraph.ops_info['x'].output_tensors[0]
assert out_tensor.shape == [None, 5]
assert out_tensor.dtype is np.dtype('float32')
def test_normal_tensor_shape():
from utensor_cgen.frontend.tensorflow import GraphDefParser
shape = np.random.randint(1, 10, size=(10, )).tolist()
graph = tf.Graph()
with graph.as_default():
tf.constant(np.random.rand(*shape), dtype=tf.float32, name='x')
parser = GraphDefParser({})
ugraph = parser.parse(graph.as_graph_def(), output_nodes=['x'])
# deterministic shape
out_tensor = ugraph.ops_info['x'].output_tensors[0]
assert out_tensor.shape == shape, 'expecting {}, get {}'.format(
shape, out_tensor.shape)
assert out_tensor.dtype is np.dtype('float32')