def test_process_visual_observation_bad_shape():
in_array_1 = np.random.rand(128, 64, 3)
proto_obs_1 = generate_compressed_proto_obs(in_array_1)
ap1 = AgentInfoProto()
ap1.observations.extend([proto_obs_1])
ap_list = [ap1]
with pytest.raises(UnityObservationException):
_process_visual_observation(0, (128, 42, 3), ap_list)
def test_process_visual_observation_padded_channels():
in_array_1 = np.random.rand(128, 64, 12)
in_array_1_mapping = [0, 1, 2, 3, -1, -1, 4, 5, 6, 7, -1, -1]
proto_obs_1 = generate_compressed_proto_obs_with_mapping(
in_array_1, in_array_1_mapping)
expected_out_array_1 = np.take(in_array_1, [0, 1, 2, 3, 6, 7, 8, 9],
axis=2)
ap1 = AgentInfoProto()
ap1.observations.extend([proto_obs_1])
ap_list = [ap1]
arr = _process_visual_observation(0, (128, 64, 8), ap_list)
assert list(arr.shape) == [1, 128, 64, 8]
assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
def test_process_visual_observation():
in_array_1 = np.random.rand(128, 64, 3)
proto_obs_1 = generate_compressed_proto_obs(in_array_1)
in_array_2 = np.random.rand(128, 64, 3)
proto_obs_2 = generate_uncompressed_proto_obs(in_array_2)
ap1 = AgentInfoProto()
ap1.observations.extend([proto_obs_1])
ap2 = AgentInfoProto()
ap2.observations.extend([proto_obs_2])
ap_list = [ap1, ap2]
arr = _process_visual_observation(0, (128, 64, 3), ap_list)
assert list(arr.shape) == [2, 128, 64, 3]
assert np.allclose(arr[0, :, :, :], in_array_1, atol=0.01)
assert np.allclose(arr[1, :, :, :], in_array_2, atol=0.01)
def test_process_visual_observation_grayscale():
in_array_1 = np.random.rand(128, 64, 3)
proto_obs_1 = generate_compressed_proto_obs(in_array_1, grayscale=True)
expected_out_array_1 = np.mean(in_array_1, axis=2, keepdims=True)
in_array_2 = np.random.rand(128, 64, 3)
in_array_2_mapping = [0, 0, 0]
proto_obs_2 = generate_compressed_proto_obs_with_mapping(
in_array_2, in_array_2_mapping)
expected_out_array_2 = np.mean(in_array_2, axis=2, keepdims=True)
ap1 = AgentInfoProto()
ap1.observations.extend([proto_obs_1])
ap2 = AgentInfoProto()
ap2.observations.extend([proto_obs_2])
ap_list = [ap1, ap2]
arr = _process_visual_observation(0, (128, 64, 1), ap_list)
assert list(arr.shape) == [2, 128, 64, 1]
assert np.allclose(arr[0, :, :, :], expected_out_array_1, atol=0.01)
assert np.allclose(arr[1, :, :, :], expected_out_array_2, atol=0.01)
