def common_backpropdata_extender(op: Node):
for attr in ['strides', 'output_padding', 'pads_begin', 'pads_end', 'dilations']:
Extender.attr_to_list(op, attr)
if op.has_valid('output_padding'):
op.output_padding = int64_array([0, 0] + op.output_padding)
dim = len(op.strides)
if op.has_valid('pads_begin') and op.has_valid('pads_end'):
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 not op.has_valid('dilations'):
op['dilations'] = [1 for _ in range(dim)]
if not op.has_valid('strides'):
op['strides'] = [1 for _ in range(dim)]
op['dilation'] = int64_array([1, 1] + op.dilations)
op['stride'] = int64_array([1, 1] + op.strides)
op['infer'] = backpropdata_infer
def extend(op: Node):
if not op.has_valid('activations'):
op['activations'] = None
mark_input_bins(op, start_port=2)
op['need_copy_input_blobs'] = True
def extend(op: Node):
for attr in [
'strides', 'dilations', 'pads_begin', 'pads_end',
'output_padding'
]:
Extender.attr_to_list(op, attr)
op['stride'] = int64_array([1, 1] + op.strides)
op['dilation'] = int64_array([1, 1] + op.dilations)
op['batch_dims'] = int64_array([0])
op['channel_dims'] = int64_array([1])
if op.has_valid('output_padding'):
op.output_padding = int64_array([0, 0] + op.output_padding)
# Be VERY careful with these attributes!
op['input_feature_channel'] = 1
op['output_feature_channel'] = 0
dim = len(op.pads_begin)
assert dim in (1, 2, 3), '{}D Convolution not supported!'.format(dim)
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)]
def attr_restore(node: Node, attribute: str, value=None):
# Function to restore some specific attr for PriorBox & PriorBoxClustered layers
if not node.has_valid(attribute):
node[attribute] = [] if value is None else [value]
if isinstance(node[attribute], str):
node[attribute] = []
else:
Extender.attr_to_list(node, attribute)
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 attr_to_list(node: Node, attribute: str):
if not node.has_valid(attribute):
log.warning('Attribute {} missed in node {} with type {}!'.format(
attribute, node.soft_get('name'), node.soft_get('type')))
elif not isinstance(node[attribute], list):
node[attribute] = [node[attribute]]
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)
def extend(op: Node):
if not op.has_valid('activations'):
op['activations'] = None
def extend(op: Node):
if not op.has_valid('activations'):
op['activations'] = None
op['infer'] = Extender.use_shapes_from_ir