def test_slice_infer(self, inp_value, inp_shape, starts, ends, axes, steps,
expected_value, expected_shape):
if inp_value is None:
input_node = shaped_data('data_1', int64_array(inp_shape))
else:
input_node = valued_data('data_1', int64_array(inp_value))
if inp_value is not None and inp_shape is not None:
assert np.array_equal(np.array(inp_value).shape, inp_shape)
def convert_args(val, name=''):
if val is not None:
return valued_const_with_data(name, int64_array(val))
else:
return shaped_const_with_data(name, [0]) #fake shape
starts = convert_args(starts, 'starts')
ends = convert_args(ends, 'ends')
axes = convert_args(axes, 'axes')
steps = convert_args(steps, 'steps')
if expected_shape is not None:
expected_shape = shape_array(expected_shape)
nodes = {
**input_node,
**regular_op_with_empty_data('slice', {'op': 'Slice'}),
**starts,
**ends,
**axes,
**steps,
}
graph = build_graph(
nodes,
[('data_1', 'slice'), *connect('starts', '1:slice'),
*connect('ends', '2:slice'), *connect('axes', '3:slice'),
*connect('steps', '4:slice'), *connect('slice', 'slice_d')])
graph.stage = 'middle'
slice_node = Node(graph, 'slice')
Slice.infer(slice_node)
if expected_value is not None:
self.assertTrue(
strict_compare_tensors(slice_node.out_node().value,
expected_value))
self.assertTrue(
strict_compare_tensors(slice_node.out_node().shape,
expected_shape))
def test_slice_infer_negative(self,
inp_value,
starts,
ends,
axes,
steps,
expected,
inp_shape=None):
if inp_value is None:
input_node = shaped_data('data_1', int64_array(inp_shape))
else:
input_node = valued_data('data_1', int64_array(inp_value))
def convert_args(val, name=''):
if val is not None:
return valued_const_with_data(name, int64_array(val))
else:
return shaped_const_with_data(name, [0]) #fake shape
starts = convert_args(starts, 'starts')
ends = convert_args(ends, 'ends')
axes = convert_args(axes, 'axes')
steps = convert_args(steps, 'steps')
nodes = {
**input_node,
**regular_op_with_empty_data('slice', {'op': 'Slice'}),
**starts,
**ends,
**axes,
**steps
}
graph = build_graph(
nodes,
[('data_1', 'slice'), *connect('starts', '1:slice'),
*connect('ends', '2:slice'), *connect('axes', '3:slice'),
*connect('steps', '4:slice'), *connect('slice', 'slice_d')])
graph.stage = 'middle'
slice_node = Node(graph, 'slice')
self.assertRaises(Error, Slice.infer, slice_node)
def test_slice_infer(self,
inp_value,
starts,
ends,
axes,
steps,
expected,
inp_shape=None):
if inp_value is None:
input_node = shaped_data('data_1', int64_array(inp_shape))
else:
input_node = valued_data('data_1', int64_array(inp_value))
nodes = {
**input_node,
**regular_op_with_empty_data('slice', {'op': 'Slice'}),
**valued_const_with_data('starts', int64_array(starts)),
**valued_const_with_data('ends', int64_array(ends)),
**valued_const_with_data('axes', int64_array(axes)),
**valued_const_with_data('steps', int64_array(steps)),
}
graph = build_graph(
nodes,
[('data_1', 'slice'), *connect('starts', '1:slice'),
*connect('ends', '2:slice'), *connect('axes', '3:slice'),
*connect('steps', '4:slice'), *connect('slice', 'slice_d')])
graph.stage = 'middle'
slice_node = Node(graph, 'slice')
Slice.infer(slice_node)
if inp_value is not None:
self.assertTrue(
np.array_equal(slice_node.out_node().value, expected))
else:
self.assertTrue(
np.array_equal(slice_node.out_node().shape, expected))
}),
**valued_const_with_data('split_1_axis', int64_array(1), {
'type': 'Const'
}),
**regular_op('split_1', {
'type': 'Split',
'can_be_fused': True
}),
**shaped_data('split_1_data1', [1, 4, 10, 10]),
**shaped_data('split_1_data2', [1, 4, 10, 10]),
**shaped_data('split_1_data3', [1, 4, 10, 10]),
**shaped_data('split_1_data4', [1, 4, 10, 10]),
**shaped_const_with_data('split_2_in_const_weights',
int64_array([3, 3, 4, 16]), {'type': 'Const'}),
**regular_op('split_2', {'type': 'Split'}),
**valued_data('split_2_data1', np.zeros([3, 3, 4, 4])),
**valued_data('split_2_data2', np.zeros([3, 3, 4, 4])),
**valued_data('split_2_data3', np.zeros([3, 3, 4, 4])),
**valued_data('split_2_data4', np.zeros([3, 3, 4, 4])),
**regular_op_with_shaped_data(
'conv2d_1', [1, 4, 8, 8], {
'type':
'Convolution',
'channel_dims':
np.array([1]),
'pad':
np.array([2, 2]),
'stride':
np.array([2, 2]),
'get_weights_permute':
PermuteAttrs.Permutation(perm=int64_array([3, 2, 0, 1]),