def test_unsqueeze():
runtime = get_runtime()
data_shape = [3, 4, 5]
parameter_data = ng.parameter(data_shape, name="Data", dtype=np.float32)
data_value = np.arange(60.0, dtype=np.float32).reshape(3, 4, 5)
axes = [0, 4]
model = ng.unsqueeze(parameter_data, axes)
computation = runtime.computation(model, parameter_data)
result = computation(data_value)
expected = np.arange(60.0, dtype=np.float32).reshape(1, 3, 4, 5, 1)
assert np.allclose(result, expected)
def test_tensor_iterator():
from ngraph.utils.tensor_iterator_types import (
GraphBody,
TensorIteratorSliceInputDesc,
TensorIteratorMergedInputDesc,
TensorIteratorInvariantInputDesc,
TensorIteratorBodyOutputDesc,
TensorIteratorConcatOutputDesc,
)
# Body parameters
body_timestep = ng.parameter([], np.int32, "timestep")
body_data_in = ng.parameter([1, 2, 2], np.float32, "body_in")
body_prev_cma = ng.parameter([2, 2], np.float32, "body_prev_cma")
body_const_one = ng.parameter([], np.int32, "body_const_one")
# CMA = cumulative moving average
prev_cum_sum = ng.multiply(ng.convert(body_timestep, "f32"), body_prev_cma)
curr_cum_sum = ng.add(prev_cum_sum, ng.squeeze(body_data_in, [0]))
elem_cnt = ng.add(body_const_one, body_timestep)
curr_cma = ng.divide(curr_cum_sum, ng.convert(elem_cnt, "f32"))
cma_hist = ng.unsqueeze(curr_cma, [0])
# TI inputs
data = ng.parameter([16, 2, 2], np.float32, "data")
# Iterations count
zero = ng.constant(0, dtype=np.int32)
one = ng.constant(1, dtype=np.int32)
initial_cma = ng.constant(np.zeros([2, 2], dtype=np.float32),
dtype=np.float32)
iter_cnt = ng.range(zero, np.int32(16), np.int32(1))
ti_inputs = [iter_cnt, data, initial_cma, one]
graph_body = GraphBody(
[body_timestep, body_data_in, body_prev_cma, body_const_one],
[curr_cma, cma_hist])
ti_slice_input_desc = [
# timestep
# input_idx, body_param_idx, start, stride, part_size, end, axis
TensorIteratorSliceInputDesc(0, 0, 0, 1, 1, -1, 0),
# data
TensorIteratorSliceInputDesc(1, 1, 0, 1, 1, -1, 0),
]
ti_merged_input_desc = [
# body prev/curr_cma
TensorIteratorMergedInputDesc(2, 2, 0),
]
ti_invariant_input_desc = [
# body const one
TensorIteratorInvariantInputDesc(3, 3),
]
# TI outputs
ti_body_output_desc = [
# final average
TensorIteratorBodyOutputDesc(0, 0, -1),
]
ti_concat_output_desc = [
# history of cma
TensorIteratorConcatOutputDesc(1, 1, 0, 1, 1, -1, 0),
]
node = ng.tensor_iterator(
ti_inputs,
graph_body,
ti_slice_input_desc,
ti_merged_input_desc,
ti_invariant_input_desc,
ti_body_output_desc,
ti_concat_output_desc,
)
assert node.get_type_name() == "TensorIterator"
assert node.get_output_size() == 2
# final average
assert list(node.get_output_shape(0)) == [2, 2]
# cma history
assert list(node.get_output_shape(1)) == [16, 2, 2]
