def test_value_propagation(self, a_shape, a_value, b_shape, b_value, elem_type):
graph = build_graph(
nodes_attrs=graph_nodes_attrs,
edges=graph_edges,
update_attributes={
'A': {'shape': int64_array(a_shape), 'value': a_value.astype(elem_type)},
'A_data': {'shape': int64_array(a_shape), 'value': a_value.astype(elem_type)},
'B': {'shape': int64_array(b_shape), 'value': b_value.astype(elem_type)},
'B_data': {'shape': int64_array(b_shape), 'value': b_value.astype(elem_type)},
}
)
node = Node(graph, 'div')
node['infer'] = Div(graph, node.attrs()).create_node().infer
node.infer(node)
node_data = node.out_port(0).get_destination().data.get_value()
def func_for_ref():
if np.issubdtype(elem_type, np.integer):
return lambda a, b: a // b
else:
return lambda a, b: a / b
ref_data = func_for_ref()(a_value, b_value)
node_data_shape = node_data.shape
ref_data_shape = ref_data.shape
msg = "Value propagation for 'div' node is not correct."
self.assertTrue(node_data_shape == ref_data_shape and np.all(node_data == ref_data), msg)
def test_interpolate4_using_sizes(self, pads_begin, pads_end, input_shape, output_shape, sizes, scales, axes):
graph = build_graph(nodes_attrs=graph_nodes_attrs,
edges=graph_edges,
update_attributes={
'input_data': {'shape': input_shape},
'sizes': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
'sizes_data': {'shape': int64_array(sizes).shape, 'value': int64_array(sizes)},
'scales': {'shape': np.array(scales).shape, 'value': np.array(scales)},
'scales_data': {'shape': np.array(scales).shape, 'value': np.array(scales)},
'axes': {'shape': int64_array(axes).shape, 'value': int64_array(axes)},
'axes_data': {'shape': int64_array(axes).shape, 'value': int64_array(axes)},
'interpolate': {'pads_begin': int64_array(pads_begin),
'pads_end': int64_array(pads_end)}
})
node = Node(graph, 'interpolate')
tested_class = Interpolate(graph=graph, attrs=node.attrs())
tested_class.infer(node)
msg = "Interpolate-4 infer failed for case: sizes={}, scales={}, pads_begin={}, pads_end={}, axes={}," \
" expected_shape={}, actual_shape={}"
self.assertTrue(np.array_equal(graph.node['interpolate_data']['shape'], int64_array(output_shape)),
msg.format(sizes, scales, pads_begin, pads_end, axes, output_shape,
graph.node['interpolate_data']['shape']))
def get_new_cell(multilayer_cell: Node, number: int):
cell_class = Op.get_op_class_by_name(multilayer_cell.op)
new_cell = lambda graph, attrs: cell_class(graph, attrs)
attrs = multilayer_cell.attrs().copy()
new_attrs = {
'num_layers': 1,
'multilayers': False,
'name': multilayer_cell.name + '/LayerSplittedLSTM/{}'.format(number),
}
attrs.update(new_attrs)
return new_cell(multilayer_cell.graph, attrs)
def get_new_cell(bidirectional_cell: Node, direction: str):
assert direction in ['forward', 'reverse']
cell_class = Op.get_op_class_by_name(bidirectional_cell.op)
new_cell = lambda graph, attrs: cell_class(graph, attrs)
attrs = bidirectional_cell.attrs().copy()
new_attrs = {
'direction': direction,
'name': bidirectional_cell.name + '/Split/' + direction,
}
attrs.update(new_attrs)
# split bidirectional activations
assert 'activations' in attrs
if attrs['activations'] is not None and len(attrs['activations']) > 1:
assert len(attrs['activations']) == 2, 'Bidirectional RNN should have 2 activations'
activations = attrs['activations']
attrs['activations'] = [activations[0 if direction == 'forward' else 1]]
return new_cell(bidirectional_cell.graph, attrs)
def is_output_data_in_correct_layout(node: Node, port_ind: int):
assert node.soft_get(
'kind') == 'op', 'The function work with operation nodes only'
return 'correct_out_data_layout' in node.attrs(
) and port_ind in node.attrs()['correct_out_data_layout']
