def test_get_local_env_and_origin_3d(self):
res = [0.01, 0.01]
full_h_rows = 5
full_w_cols = 5
full_c_channels = 5
local_h_rows = 3
local_w_cols = 3
local_c_channels = 3
center_point = np.array([[0, 0, 0], [-0.01, 0.01, 0.01]], np.float32)
full_env = np.zeros([2, full_h_rows, full_w_cols, full_c_channels],
dtype=np.float32)
full_env_origin = np.array(
[[full_h_rows / 2, full_w_cols / 2, full_c_channels / 2],
[full_h_rows / 2, full_w_cols / 2, full_c_channels / 2]],
dtype=np.float32)
full_env[:, 1, 2, 1] = 1
full_env[:, 2, 3, 3] = 1
local_env, local_env_origin = get_local_env_and_origin_3d_tf(
center_point, full_env, full_env_origin, res, local_h_rows,
local_w_cols, local_c_channels)
expected_origins = tf.constant([[1.5, 1.5, 1.5], [0.5, 2.5, 0.5]],
dtype=tf.float32)
expected_env_occupied_indicies = tf.constant([
[0, 0, 1, 0],
[0, 1, 2, 2],
],
dtype=tf.float32)
assert_close_tf(local_env_origin, expected_origins)
assert_close_tf(tf.cast(tf.where(local_env > 0.5), tf.float32),
expected_env_occupied_indicies)
def test_get_local_env_and_origin_2d(self):
res = [0.01, 0.01]
full_h_rows = 5
full_w_cols = 5
local_h_rows = 3
local_w_cols = 3
center_point = np.array([[0, 0], [-0.01, 0.01]], np.float32)
full_env = np.zeros([2, full_h_rows, full_w_cols], dtype=np.float32)
full_env_origin = np.array([[full_h_rows / 2, full_w_cols / 2],
[full_h_rows / 2, full_w_cols / 2]],
dtype=np.float32)
full_env[:, 1, 2] = 1
full_env[:, 2, 3] = 1
local_env, local_env_origin = get_local_env_and_origin_2d_tf(
center_point, full_env, full_env_origin, res, local_h_rows,
local_w_cols)
expected_origins = tf.constant([[1.5, 1.5], [0.5, 2.5]],
dtype=tf.float32)
expected_env = tf.constant([[[0, 1, 0], [0, 0, 1], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0], [0, 0, 0]]],
dtype=tf.float32)
assert_close_tf(local_env_origin, expected_origins)
assert_close_tf(local_env, expected_env)
def test_batch_outer_product(self):
a = tf.constant([[1, 2], [0, 2]], dtype=tf.float32)
b = tf.constant([[3, 4, 5], [1, 4, 2]], dtype=tf.float32)
out = batch_outer_product(a, b)
expected = tf.constant(
[[[3, 4, 5], [6, 8, 10]], [[0, 0, 0], [2, 8, 4]]],
dtype=tf.float32)
assert_close_tf(out, expected)
def test_inflate_tf1(self):
env = np.array(
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 0], [0, 1, 1, 0]],
dtype=np.float32)
expected_inflated_env = np.array(
[[0, 0, 0, 0], [1, 1, 1, 0], [1, 1, 1, 1], [1, 1, 1, 1]],
dtype=np.float32)
inflated_env = inflate_tf(env, radius_m=0.01, res=0.01)
assert_close_tf(inflated_env, expected_inflated_env)
def test_to_rope_local_frame(self):
rope_states = tf.constant([[0, 0, 1, 0, 1, 1],
[2, 2, 2, 1, 3, 1],
[-2, -2, -2, -1, -3, -1],
[1, 0, 1, 1, 2, 1]],
dtype=tf.float32)
expected_states = tf.constant([[-1, 1, -1, 0, 0, 0],
[-1, 1, -1, 0, 0, 0],
[-1, 1, -1, 0, 0, 0],
[-1, -1, -1, 0, 0, 0]],
dtype=tf.float32)
out_states = LinkBotScenario.to_rope_local_frame_tf(rope_states)
assert_close_tf(out_states, expected_states, atol=1e-5)
def test_negative_weighted_binary_classification_sequence_loss_function_incorrect(
self):
data = {
'is_close':
tf.constant([[1, 1, 1], [1, 1, 1], [1, 1, 0], [1, 0, 1]],
tf.float32)
}
pred = {
'logits':
tf.constant([[[100], [-100]], [[100], [-100]], [[100], [-100]],
[[-100], [-100]]], tf.float32),
'mask':
tf.constant([[True, True, False], [True, True, True],
[True, True, True], [True, True, True]], tf.bool),
}
expected_loss = tf.constant([9.52381])
out_loss = negative_weighted_binary_classification_sequence_loss_function(
data, pred)
assert_close_tf(expected_loss, out_loss)
def test_to_rope_local_frame_with_reference(self):
rope_states = tf.constant([[0, 0, 1, 0, 1, 1]], dtype=tf.float32)
reference_rope_states = tf.constant([[0, 0, 1, 0, 2, 0]], dtype=tf.float32)
expected_states = tf.constant([[-2, 0, -1, 0, -1, 1]], dtype=tf.float32)
out_states = LinkBotScenario.to_rope_local_frame_tf(rope_states, reference_rope_states)
assert_close_tf(out_states, expected_states, atol=1e-5)