def test_space_to_depth_operator():
runtime = get_runtime()
data_shape = [1, 2, 4, 4]
data_value = np.arange(start=0, stop=32, step=1.0,
dtype=np.float32).reshape(data_shape)
mode = "blocks_first"
block_size = 2
parameter_data = ng.parameter(data_shape, name="Data", dtype=np.float32)
model = ng.space_to_depth(parameter_data, mode, block_size)
computation = runtime.computation(model, parameter_data)
result = computation(data_value)
expected = np.array(
[
0,
2,
8,
10,
16,
18,
24,
26,
1,
3,
9,
11,
17,
19,
25,
27,
4,
6,
12,
14,
20,
22,
28,
30,
5,
7,
13,
15,
21,
23,
29,
31,
],
dtype=np.float32,
).reshape(1, 8, 2, 2)
assert np.allclose(result, expected)
def test_space_to_depth_operator():
runtime = get_runtime()
data_shape = [1, 2, 4, 4]
data_value = np.arange(start=0, stop=32, step=1.0,
dtype=np.float32).reshape(data_shape)
mode = "blocks_first"
block_size = 2
parameter_data = ng.parameter(data_shape, name="Data", dtype=np.float32)
model = ng.space_to_depth(parameter_data, mode, block_size)
computation = runtime.computation(model, parameter_data)
result = computation(data_value)
expected = np.array(
[
0,
2,
8,
10,
16,
18,
24,
26,
1,
3,
9,
11,
17,
19,
25,
27,
4,
6,
12,
14,
20,
22,
28,
30,
5,
7,
13,
15,
21,
23,
29,
31,
],
dtype=np.float32,
).reshape(1, 8, 2, 2)
assert np.allclose(result, expected)
runtime = get_runtime()
batch_size = 2
input_size = 3
hidden_size = 3
X_shape = [batch_size, input_size]
H_t_shape = [batch_size, hidden_size]
W_shape = [hidden_size, input_size]
R_shape = [hidden_size, hidden_size]
B_shape = [hidden_size]
parameter_X = ng.parameter(X_shape, name="X", dtype=np.float32)
parameter_H_t = ng.parameter(H_t_shape, name="H_t", dtype=np.float32)
parameter_W = ng.parameter(W_shape, name="W", dtype=np.float32)
parameter_R = ng.parameter(R_shape, name="R", dtype=np.float32)
parameter_B = ng.parameter(B_shape, name="B", dtype=np.float32)
X_value = np.array(
[0.3432185, 0.612268, 0.20272376, 0.9513413, 0.30585995, 0.7265472],
dtype=np.float32).reshape(X_shape)
H_t_value = np.array(
[0.12444675, 0.52055854, 0.46489045, 0.4983964, 0.7730452, 0.28439692],
dtype=np.float32).reshape(H_t_shape)
W_value = np.array(
[
0.41930267,
0.7872176,
0.89940447,
0.23659843,
0.24676207,
0.17101714,
0.3147149,
0.6555601,
0.4559603,
],
dtype=np.float32,
).reshape(W_shape)
R_value = np.array(
[
0.8374871,
0.86660194,
0.82114047,
0.71549815,
0.18775631,
0.3182116,
0.25392973,
0.38301638,
0.85531586,
],
dtype=np.float32,
).reshape(R_shape)
B_value = np.array([1.0289404, 1.6362579, 0.4370661],
dtype=np.float32).reshape(B_shape)
activations = ["sigmoid"]
activation_alpha = []
activation_beta = []
clip = 2.88
model = ng.rnn_cell(
parameter_X,
parameter_H_t,
parameter_W,
parameter_R,
parameter_B,
hidden_size,
activations,
activation_alpha,
activation_beta,
clip,
)
computation = runtime.computation(model, parameter_X, parameter_H_t,
parameter_W, parameter_R, parameter_B)
result = computation(X_value, H_t_value, W_value, R_value, B_value)
expected = np.array(
[0.94126844, 0.9036043, 0.841243, 0.9468489, 0.934215, 0.873708],
dtype=np.float32).reshape(batch_size, hidden_size)
assert np.allclose(result, expected)