def _graph_dict():
# This function creates a graph that has no real meaning other than
# providing something to traverse.
d = {}
d['i1'] = sequence.input(shape=(2, 3), sequence_axis=Axis('ia'), name='i1')
d['c1'] = constant(shape=(2, 3), value=6, name='c1')
d['p1'] = parameter(shape=(3, 2), init=7, name='p1')
d['op1'] = plus(d['i1'], d['c1'], name='op1')
d['op2'] = times(d['op1'], d['p1'], name='op2')
#d['slice'] = slice(d['c1'], Axis.default_dynamic_axis(), 0, 3)
#label_sentence_start = sequence.first(raw_labels)
# no name
d['p2'] = parameter(shape=(2, 2))
# duplicate names
d['op3a'] = plus(d['op2'], d['p2'], name='op3')
d['op3b'] = plus(d['op3a'], d['p2'], name='op3')
d['first'] = sequence.first(d['op3b'], name='past')
d['root'] = d['first']
return d
unittest_helper(input_op,
forward_input,
expected_forward,
expected_backward,
device_id=device_id,
precision=precision)
RESHAPE_SUBSHAPE_TEST_CASES = [
#(input_shape, replacement_shape, begin_axis, end_axis, expected_output_shape)
((2, 3), (3, 2), 0, Axis.new_leading_axis(), (3, 2)),
((2, 3), (1), 0, 0, (1, 2, 3)),
((2, 3), (1, 1), Axis.new_leading_axis(), Axis.new_leading_axis(), (2, 3,
1, 1)),
((2, 3, 5), (C.InferredDimension), 0, Axis(2), (6, 5)),
((2, 3, 5), (C.InferredDimension), Axis(-3), -1, (6, 5)),
((6, 5), (2, C.InferredDimension), 0, 1, (2, 3, 5)),
]
@pytest.mark.parametrize(
"input_shape, replacement_shape, begin_axis, end_axis, expected_output_shape",
RESHAPE_SUBSHAPE_TEST_CASES)
def test_op_reshape_subshape(input_shape, replacement_shape, begin_axis,
end_axis, expected_output_shape, device_id,
precision):
# Reshaping is just moving the input values to different indexes of the result tensor.
# If we compute the gradients on the unmodified tensor, reshape would get 1 for all inputs
# For testing the gradients we want to have different gradients for each input index otherwise we can't
# test if they get wrongly permuted during test. To this end we multiply
