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tensor_array split test (apache#4619)
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@zhiics
zhiics committed Mar 2, 2020
1 parent b6aeee8 commit 418dbde
Showing 1 changed file with 58 additions and 13 deletions.
71 changes: 58 additions & 13 deletions tests/python/relay/test_adt.py
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Original file line number Diff line number Diff line change
Expand Up @@ -737,7 +737,7 @@ def run(dtype):
expand_dims_func = p.get_var('tensor_expand_dims', dtype)
tensor1 = p.get_var('tensor1', dtype)
mod["main"] = relay.Function([x], expand_dims_func(tensor1(x)))
x_np = np.random.uniform(size=(1,)).astype(dtype)
x_np = np.random.uniform(low=0.0, high=8.0, size=(1,)).astype(dtype)
expected = [np.expand_dims(x_np, axis=0)]
check_tensor_array(mod, expected, x_np)
run('float32')
Expand Down Expand Up @@ -808,7 +808,7 @@ def run(dtype):
tensor_array3 = write(tensor_array2, relay.const(2), tensor1(v))
tensor_array4 = stack(tensor_array3)
mod["main"] = relay.Function([v], tensor_array4)
t = np.random.uniform(size=(1,)).astype(dtype)
t = np.random.uniform(low=0.0, high=8.0, size=(1,)).astype(dtype)
expected = [np.stack([t, t, t])]
check_tensor_array(mod, expected, t, dtype=dtype)
run('float32')
Expand All @@ -822,7 +822,7 @@ def run(dtype):
unstack_tensor1 = p.get_var('tensor_array_unstack_tensor1', dtype)
v = relay.var('v')
mod["main"] = relay.Function([v], unstack_tensor1(v))
t = np.random.uniform(size=(1,)).astype(dtype)
t = np.random.uniform(low=0.0, high=8.0, size=(1,)).astype(dtype)
check_tensor_array(mod, t, t, dtype=dtype)
run('float32')
run('int32')
Expand All @@ -838,7 +838,7 @@ def run(dtype):
lower = relay.var('lower')
upper = relay.var('upper')
mod["main"] = relay.Function([v, lower, upper], take(tensor2(v), lower, upper))
v_data = np.random.uniform(size=(10, 10)).astype(dtype)
v_data = np.random.uniform(low=0.0, high=8.0, size=(10, 10)).astype(dtype)
expected = [np.take(v_data, range(2, 5), axis=0)]
check_tensor_array(mod, expected, *(v_data, 2, 5), dtype=dtype)
expected = [np.take(v_data, range(0, 9), axis=0)]
Expand All @@ -857,8 +857,8 @@ def run(dtype):
v2 = relay.var('v2')
mod["main"] = relay.Function([v1, v2], concat(tensor1(v1),
tensor1(v2)))
v1_data = np.random.uniform(size=(5,)).astype(dtype)
v2_data = np.random.uniform(size=(5,)).astype(dtype)
v1_data = np.random.uniform(low=0.0, high=8.0, size=(5,)).astype(dtype)
v2_data = np.random.uniform(low=0.0, high=8.0, size=(5,)).astype(dtype)
expected = [np.concatenate((v1_data, v2_data))]
check_tensor_array(mod, expected, *(v1_data, v2_data), dtype=dtype)
run('float32')
Expand All @@ -880,8 +880,8 @@ def run(dtype):
tensor_array1 = write_func(tensor_array1, relay.const(1), tensor1(v2))
tensor_array_concat = concat_func(tensor_array1)
mod["main"] = relay.Function([v1, v2], tensor_array_concat)
v1_data = np.random.uniform(size=(2, 3)).astype(dtype)
v2_data = np.random.uniform(size=(1, 3)).astype(dtype)
v1_data = np.random.uniform(low=0.0, high=8.0, size=(2, 3)).astype(dtype)
v2_data = np.random.uniform(low=0.0, high=8.0, size=(1, 3)).astype(dtype)
expected = [np.concatenate((v1_data, v2_data), axis=0)]
check_tensor_array(mod, expected, *(v1_data, v2_data), dtype=dtype)
run('float32')
Expand Down Expand Up @@ -924,12 +924,12 @@ def run(dtype):
tensor_array_scatter)

# initialize and check
v1_data = np.random.uniform(size=(2, 3)).astype(dtype)
v2_data = np.random.uniform(size=(2, 3)).astype(dtype)
v3_data = np.random.uniform(size=(2, 3)).astype(dtype)
v1_data = np.random.uniform(low=0.0, high=8.0, size=(2, 3)).astype(dtype)
v2_data = np.random.uniform(low=0.0, high=8.0, size=(2, 3)).astype(dtype)
v3_data = np.random.uniform(low=0.0, high=8.0, size=(2, 3)).astype(dtype)
index_data = np.array([0, 1], dtype="int32")
val1_data = np.random.uniform(size=(2, 3)).astype(dtype)
val2_data = np.random.uniform(size=(2, 3)).astype(dtype)
val1_data = np.random.uniform(low=0.0, high=8.0, size=(2, 3)).astype(dtype)
val2_data = np.random.uniform(low=0.0, high=8.0, size=(2, 3)).astype(dtype)
expected = [val1_data, val2_data, v3_data]
check_tensor_array(mod, expected, *(v1_data, v2_data, v3_data,
index_data, val1_data,
Expand All @@ -938,6 +938,50 @@ def run(dtype):
run('int32')


def test_tensor_array_split():
def run(dtype):
mod = relay.Module()
p = Prelude(mod)

# tensor array
v1 = relay.var('v1')
v2 = relay.var('v2')
v3 = relay.var('v2')
tensor_array = p.get_var('tensor_array', dtype)
tensor_array1 = tensor_array(relay.const(3))
write_func = p.get_var('tensor_array_write', dtype)
split_func = p.get_var('tensor_array_split', dtype)
tensor2 = p.get_var('tensor2', dtype)
tensor_array1 = write_func(tensor_array1, relay.const(0), tensor2(v1))
tensor_array1 = write_func(tensor_array1, relay.const(1), tensor2(v2))
tensor_array1 = write_func(tensor_array1, relay.const(2), tensor2(v3))

# value tensor
value = relay.var('value')

# lengths tensor
ta_len = relay.var('length')

# create the scatter function
tensor_array_split = split_func(tensor_array1, tensor2(value), ta_len)
mod["main"] = relay.Function([v1, v2, v3, value, ta_len],
tensor_array_split)

# initialize and check
v1_data = np.random.uniform(low=0.0, high=8.0, size=(2, 3)).astype(dtype)
v2_data = np.random.uniform(low=0.0, high=8.0, size=(2, 3)).astype(dtype)
v3_data = np.random.uniform(low=0.0, high=8.0, size=(2, 3)).astype(dtype)
value_data = np.random.uniform(low=0.0, high=8.0, size=(4, 3)).astype(dtype)
length_data = np.array([2, 2], dtype="int32")
expected = np.concatenate([value_data, v3_data])
expected = np.split(expected, indices_or_sections=[2, 4])
check_tensor_array(mod, expected, *(v1_data, v2_data, v3_data,
value_data, length_data),
dtype=dtype)
run('float32')
run('int32')


if __name__ == "__main__":
test_nat_constructor()
test_double()
Expand Down Expand Up @@ -972,3 +1016,4 @@ def run(dtype):
test_tensor_concatenate()
test_tensor_array_concat()
test_tensor_array_scatter()
test_tensor_array_split()

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