def common_pool_extender(op: Node):
for attr in ['strides', 'pads_begin', 'pads_end', 'kernel', 'dilations']:
Extender.attr_to_list(op, attr)
op['stride'] = int64_array([1, 1] + op.strides)
op['window'] = int64_array([1, 1] + op.kernel)
op['kernel_spatial'] = op.kernel
op['output_spatial_shape'] = None
if op.has_valid('dilations'):
op['dilation'] = int64_array([1, 1] + op.dilations)
if op.has_valid('index_element_type'):
op['index_element_type'] = destination_type_to_np_data_type(
op.index_element_type)
op['batch_dims'] = int64_array([0]),
op['channel_dims'] = int64_array([1]),
op['pool_method'] = 'max' if op.type is 'MaxPool' else 'avg'
dim = len(op.pads_begin)
assert dim in (1, 2, 3), '{}D {} not supported! Node name: {}'.format(
dim, op.soft_get('type'), op.soft_get('name', op.id))
pad = [[0, 0], [0, 0]]
pad.extend([[op.pads_begin[i], op.pads_end[i]] for i in range(dim)])
op['pad'] = int64_array(pad)
op['spatial_dims'] = [i + 2 for i in range(dim)]
if op.has_valid('rounding_type') and op.rounding_type == 'ceil':
op['pooling_convention'] = 'full'
def extend(op: Node):
assert op.has_valid(
'element_type'
), 'Parameter node {} has missed element_type attr!'.format(op.name)
op['data_type'] = destination_type_to_np_data_type(op.element_type)
if op.shape == '':
op.shape = int64_array([])
else:
Extender.attr_to_list(op, 'shape')
if -1 in op.shape:
op.shape = shape_array([
d if d != -1 else dynamic_dimension_value for d in op.shape
])
def extend(op: Node):
assert op.has_valid(
'element_type'
), 'Parameter node {} has missed element_type attr!'.format(op.name)
op['data_type'] = destination_type_to_np_data_type(op.element_type)
if op.shape == '':
op.shape = int64_array([])
else:
Extender.attr_to_list(op, 'shape')
for i, dim in enumerate(op.shape):
if dim == -1 or (isinstance(dim, str) and ".." in dim):
op.shape[i] = -1
op.shape = shape_array(
[d if d != -1 else dynamic_dimension_value for d in op.shape])
def extend(op: Node):
assert op.has_valid(
'element_type'
), 'Parameter node {} has missed element_type attr!'.format(op.name)
op['data_type'] = destination_type_to_np_data_type(op.element_type)
if op.shape == '':
op.shape = int64_array([])
else:
Extender.attr_to_list(op, 'shape')
shape = op.shape.copy()
has_shapes_with_boundaries = False
for i, dim in enumerate(op.shape):
if dim == -1 or (isinstance(dim, str) and ".." in dim):
shape[i] = -1
if ".." in dim:
has_shapes_with_boundaries = True
shape = shape_array([
d if d not in [-1, '?'] else dynamic_dimension_value
for d in shape
])
if has_shapes_with_boundaries:
shape_list = []
for i, dim in enumerate(op.shape):
if not isinstance(dim, str):
shape_list.append(dim)
else:
shape_list.append(parse_dimension(dim))
# This value is used only for serialization of partial shapes with boundaries
# for Parameter node.
# 'user_shape' is not used in shape inference, as propagation of partial shapes with boundaries
# is not implemented in MO.
op['user_shape'] = tuple(shape_list)
# If 'user_shape' is not set, 'shape' attribute is used for serialization.
# 'shape' is also used for shape inference.
op.shape = shape
def extend(op: Node):
if op.has_valid('output_type'):
op['output_type'] = destination_type_to_np_data_type(
op.output_type)
def extend(op: Node):
op['output_type'] = destination_type_to_np_data_type(op.output_type)
def extend(op: Node):
if op.get_opset() != "extension":
op['output_type'] = destination_type_to_np_data_type(
op.output_type)
def __read_old_api_map_element_type(attr, layer_type):
version = int(attr.attrib['version'])
element_type = destination_type_to_np_data_type(attr.attrib['value'])
old_api_map = OldAPIMapElementType(version=version)
old_api_map.set_legacy_type(element_type)
return {('old_api_map_element_type', version): old_api_map}
def extend(op: Node):
op['dst_type'] = destination_type_to_np_data_type(op.destination_type)
# CompressQuantizeWeights generates IR with constant sub-graph, that should not be ConstFolded:
# Const(u8) -> Convert(to fp) -> (some eltwise operations) -> FakeQuantize
if op.in_node().in_node().soft_get('type') == 'Const':
op['stop_value_propagation'] = True
def extend(op: Node):
if op.out_port(0).disconnected():
op['remove_values_output'] = True
if op.has_valid('index_element_type'):
op['index_element_type'] = destination_type_to_np_data_type(
op.index_element_type)
def extend(op: Node):
if op.has_valid('classes_index_type'):
op['classes_index_type'] = destination_type_to_np_data_type(op.classes_index_type)
if op.has_valid('sequence_length_type'):
op['sequence_length_type'] = destination_type_to_np_data_type(op.sequence_length_type)
