def replace_op(self, graph: Graph, node: Node):
pb = node.parameters
weights_size = read_binary_integer32_token(pb)
weights = read_blob(pb, weights_size, dtype=np.int32) - 1
node_name = node.soft_get('name', node.id)
const_attrs = {
'name': node_name + '/indexes',
'value': np.array(weights),
'shape': [weights_size],
'data_type': np.int32
}
indexes_node = Const(graph).create_node(attrs=const_attrs)
perm_in_1 = Const(graph, {'value': int64_array([1, 0]), 'name': node_name + '/order'}).create_node()
perm1_node = Transpose(graph, {'name': node_name + '/input_permute'}).create_node([node.in_node(0)])
perm1_node.in_port(0).connect(node.in_port(0).get_source())
perm1_node.in_port(1).connect(perm_in_1.out_port(0))
gather_node = create_op_with_const_inputs(graph, Gather, {2: int64_array(0)}, {'name': node_name + '/gather'})
gather_node.in_port(0).connect(perm1_node.out_port(0))
gather_node.in_port(1).connect(indexes_node.out_port(0))
perm2_node = Transpose(graph, {'name': node_name + '/output_permute'}).create_node()
perm2_node.in_port(0).connect(gather_node.out_port(0))
perm2_node.in_port(1).connect(perm_in_1.out_port(0))
return [perm2_node.id]
def read_binary_vector(file_desc: io.BufferedReader,
read_token: bool = True,
dtype=np.float32):
if read_token:
read_placeholder(file_desc)
elements_number = read_binary_integer32_token(file_desc)
return read_blob(file_desc, elements_number, dtype)
def replace_op(self, graph: Graph, node: Node):
pb = node.parameters
weights_size = read_binary_integer32_token(pb)
weights = read_blob(pb, weights_size, dtype=np.int32) - 1
const_attrs = {
'name': 'indexes/{}'.format(node.id),
'value': np.array(weights),
'shape': [weights_size],
'data_type': np.int32
}
indexes_node = Const(graph).create_node(attrs=const_attrs)
perm_in_1 = Const(
graph, {
'value': np.array([1, 0], dtype=np.int64),
'shape': [2],
'data_type': np.int64
}).create_node()
axis_const = Const(graph, {'value': int64_array(0)}).create_node()
perm1_node = Transpose(graph, {
'name': 'input_permute'
}).create_node([node.in_node(0)])
perm1_node.in_port(0).connect(node.in_port(0).get_source())
perm1_node.in_port(1).connect(perm_in_1.out_port(0))
gather_node = Gather(graph, {}).create_node()
gather_node.in_port(0).connect(perm1_node.out_port(0))
gather_node.in_port(1).connect(indexes_node.out_port(0))
gather_node.in_port(2).connect(axis_const.out_port(0))
perm2_node = Transpose(graph, {'name': 'output_permute'}).create_node()
perm2_node.in_port(0).connect(gather_node.out_port(0))
perm2_node.in_port(1).connect(perm_in_1.out_port(0))
return [perm2_node.id]
def read_binary_matrix(file_desc: io.BufferedReader, read_token: bool = True):
if read_token:
read_placeholder(file_desc)
rows_number = read_binary_integer32_token(file_desc)
cols_number = read_binary_integer32_token(file_desc)
# to compare: ((float *)a->buffer())[10]
return read_blob(file_desc,
rows_number * cols_number), (rows_number, cols_number)
def extract(node):
pb = node.parameters
weights_size = read_binary_integer32_token(pb)
weights = read_blob(pb, weights_size, dtype=np.int32) - 1
attrs = {'infer': copy_shape_infer}
embed_input(attrs, 1, 'indexes', weights)
Permute.update_node_stat(node, attrs)
return __class__.enabled
def extract(node):
pb = node.parameters
num_slice_points = read_binary_integer32_token(pb)
mapping_rule = {
'axis': 1,
'slice_point': read_blob(pb, num_slice_points, np.int32),
'batch_dims': 0,
'spatial_dims': 1,
'infer': caffe_slice_infer
}
node.parameters.close()
Slice.update_node_stat(node, mapping_rule)
return __class__.enabled
