You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 

89 lines
3.0 KiB

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddlers.models.ppdet.core.workspace import load_config, merge_config, create
from paddlers.models.ppdet.utils.checkpoint import load_weight, load_pretrain_weight
from paddlers.models.ppdet.utils.logger import setup_logger
from paddlers.models.ppdet.core.workspace import register, serializable
from paddle.utils import try_import
logger = setup_logger(__name__)
@register
@serializable
class OFA(object):
def __init__(self, ofa_config):
super(OFA, self).__init__()
self.ofa_config = ofa_config
def __call__(self, model, param_state_dict):
paddleslim = try_import('paddleslim')
from paddleslim.nas.ofa import OFA, RunConfig, utils
from paddleslim.nas.ofa.convert_super import Convert, supernet
task = self.ofa_config['task']
expand_ratio = self.ofa_config['expand_ratio']
skip_neck = self.ofa_config['skip_neck']
skip_head = self.ofa_config['skip_head']
run_config = self.ofa_config['RunConfig']
if 'skip_layers' in run_config:
skip_layers = run_config['skip_layers']
else:
skip_layers = []
# supernet config
sp_config = supernet(expand_ratio=expand_ratio)
# convert to supernet
model = Convert(sp_config).convert(model)
skip_names = []
if skip_neck:
skip_names.append('neck.')
if skip_head:
skip_names.append('head.')
for name, sublayer in model.named_sublayers():
for n in skip_names:
if n in name:
skip_layers.append(name)
run_config['skip_layers'] = skip_layers
run_config = RunConfig(**run_config)
# build ofa model
ofa_model = OFA(model, run_config=run_config)
ofa_model.set_epoch(0)
ofa_model.set_task(task)
input_spec = [{
"image": paddle.ones(
shape=[1, 3, 640, 640], dtype='float32'),
"im_shape": paddle.full(
[1, 2], 640, dtype='float32'),
"scale_factor": paddle.ones(
shape=[1, 2], dtype='float32')
}]
ofa_model._clear_search_space(input_spec=input_spec)
ofa_model._build_ss = True
check_ss = ofa_model._sample_config('expand_ratio', phase=None)
# tokenize the search space
ofa_model.tokenize()
# check token map, search cands and search space
logger.info('Token map is {}'.format(ofa_model.token_map))
logger.info('Search candidates is {}'.format(ofa_model.search_cands))
logger.info('The length of search_space is {}, search_space is {}'.
format(len(ofa_model._ofa_layers), ofa_model._ofa_layers))
# set model state dict into ofa model
utils.set_state_dict(ofa_model.model, param_state_dict)
return ofa_model