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convert_super.py
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convert_super.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import inspect
import decorator
import logging
import numbers
import paddle
from ...common import get_logger
from .utils.utils import get_paddle_version
pd_ver = get_paddle_version()
from .layers import *
from . import layers
from .layers_base import Block
_logger = get_logger(__name__, level=logging.INFO)
__all__ = ['supernet', 'Convert']
WEIGHT_LAYER = ['conv', 'linear', 'embedding']
class Convert:
"""
Convert network to the supernet according to the search space.
Parameters:
context(paddleslim.nas.ofa.supernet): search space defined by the user.
Examples:
.. code-block:: python
from paddleslim.nas.ofa import supernet, Convert
sp_net_config = supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4])
convert = Convert(sp_net_config)
"""
def __init__(self, context):
self.context = context
def _change_name(self,
layer,
pd_ver,
has_bias=True,
conv=False,
use_bn_old=False):
if conv:
w_attr = layer._param_attr
else:
w_attr = layer._param_attr if pd_ver == 185 or use_bn_old else layer._weight_attr
if isinstance(w_attr, paddle.ParamAttr):
if w_attr != None and not isinstance(w_attr,
bool) and w_attr.name != None:
w_attr.name = 'super_' + w_attr.name
if has_bias:
if isinstance(layer._bias_attr, paddle.ParamAttr):
if layer._bias_attr != None and not isinstance(
layer._bias_attr,
bool) and layer._bias_attr.name != None:
layer._bias_attr.name = 'super_' + layer._bias_attr.name
def convert(self, network):
"""
The function to convert the network to a supernet.
Parameters:
network(paddle.nn.Layer|list(paddle.nn.Layer)): instance of the model or list of instance of layers.
Examples:
.. code-block:: python
from paddle.vision.models import mobilenet_v1
from paddleslim.nas.ofa import supernet, Convert
sp_net_config = supernet(kernel_size=(3, 5, 7), expand_ratio=[1, 2, 4])
convert = Convert(sp_net_config).convert(mobilenet_v1())
"""
# search the first and last weight layer, don't change out channel of the last weight layer
# don't change in channel of the first weight layer
model = []
if isinstance(network, paddle.nn.Layer):
for name, sublayer in network.named_sublayers():
model.append(sublayer)
else:
model = network
first_weight_layer_idx = -1
last_weight_layer_idx = -1
weight_layer_count = 0
# NOTE: pre_channel store for shortcut module
pre_channel = None
cur_channel = None
for idx, layer in enumerate(model):
cls_name = layer.__class__.__name__.lower()
### basic api in paddle
if len(layer.sublayers()) == 0:
if 'conv' in cls_name or 'linear' in cls_name or 'embedding' in cls_name:
weight_layer_count += 1
last_weight_layer_idx = idx
if first_weight_layer_idx == -1:
first_weight_layer_idx = idx
if getattr(self.context, 'channel', None) != None:
assert len(
self.context.channel
) == weight_layer_count, "length of channel must same as weight layer."
for idx, layer in enumerate(model):
if isinstance(layer, paddle.nn.Conv2D):
attr_dict = layer.__dict__
key = attr_dict['_full_name']
new_attr_name = [
'stride', 'padding', 'dilation', 'groups', 'bias_attr'
]
if pd_ver == 185:
new_attr_name += ['param_attr', 'use_cudnn', 'act', 'dtype']
else:
new_attr_name += [
'weight_attr', 'data_format', 'padding_mode'
]
self._change_name(layer, pd_ver, conv=True)
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['candidate_config'] = dict()
if pd_ver == 185:
new_attr_dict['num_channels'] = None
new_attr_dict['num_filters'] = None
new_attr_dict['filter_size'] = None
else:
new_attr_dict['in_channels'] = None
new_attr_dict['out_channels'] = None
new_attr_dict['kernel_size'] = None
self.kernel_size = getattr(self.context, 'kernel_size', None)
# if the kernel_size of conv is 1, don't change it.
fks = '_filter_size' if '_filter_size' in attr_dict.keys(
) else '_kernel_size'
ks = [attr_dict[fks]] if isinstance(
attr_dict[fks], numbers.Integral) else attr_dict[fks]
if self.kernel_size and int(ks[0]) != 1:
new_attr_dict['transform_kernel'] = True
new_attr_dict[fks[1:]] = max(self.kernel_size)
new_attr_dict['candidate_config'].update({
'kernel_size': self.kernel_size
})
else:
new_attr_dict[fks[1:]] = attr_dict[fks]
in_key = '_num_channels' if '_num_channels' in attr_dict.keys(
) else '_in_channels'
out_key = '_num_filters' if '_num_filters' in attr_dict.keys(
) else '_out_channels'
if self.context.expand:
### first super convolution
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
else:
new_attr_dict[in_key[1:]] = int(self.context.expand *
attr_dict[in_key])
### last super convolution
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = attr_dict[out_key]
else:
new_attr_dict[out_key[1:]] = int(self.context.expand *
attr_dict[out_key])
new_attr_dict['candidate_config'].update({
'expand_ratio': self.context.expand_ratio
})
elif self.context.channel:
if attr_dict['_groups'] != None and (
int(attr_dict['_groups']) == int(attr_dict[in_key])
):
### depthwise conv, if conv is depthwise, use pre channel as cur_channel
_logger.warn(
"If convolution is a depthwise conv, output channel change" \
" to the same channel with input, output channel in search is not used."
)
cur_channel = pre_channel
else:
cur_channel = self.context.channel[0]
self.context.channel = self.context.channel[1:]
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
else:
new_attr_dict[in_key[1:]] = max(pre_channel)
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = attr_dict[out_key]
else:
new_attr_dict[out_key[1:]] = max(cur_channel)
new_attr_dict['candidate_config'].update({
'channel': cur_channel
})
pre_channel = cur_channel
else:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
new_attr_dict[out_key[1:]] = attr_dict[out_key]
for attr in new_attr_name:
if attr == 'weight_attr':
new_attr_dict[attr] = attr_dict['_param_attr']
else:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer
if attr_dict['_groups'] == None or int(attr_dict[
'_groups']) == 1:
### standard conv
layer = Block(SuperConv2D(**new_attr_dict), key=key)
elif int(attr_dict['_groups']) == int(attr_dict[in_key]):
# if conv is depthwise conv, groups = in_channel, out_channel = in_channel,
# channel in candidate_config = in_channel_list
if 'channel' in new_attr_dict['candidate_config']:
new_attr_dict[in_key[1:]] = max(cur_channel)
new_attr_dict[out_key[1:]] = new_attr_dict[in_key[1:]]
new_attr_dict['candidate_config'][
'channel'] = cur_channel
new_attr_dict['groups'] = new_attr_dict[in_key[1:]]
layer = Block(
SuperDepthwiseConv2D(**new_attr_dict), key=key)
else:
### group conv
layer = Block(SuperGroupConv2D(**new_attr_dict), key=key)
model[idx] = layer
elif (isinstance(layer, paddle.nn.BatchNorm2D) or
isinstance(layer, paddle.nn.BatchNorm)) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
# num_features in BatchNorm don't change after last weight operators
if idx > last_weight_layer_idx:
continue
use_bn_old = False
if isinstance(layer, paddle.nn.BatchNorm):
use_bn_old = True
attr_dict = layer.__dict__
new_attr_name = ['momentum', 'epsilon', 'bias_attr']
if pd_ver == 185 or use_bn_old:
new_attr_name += [
'param_attr', 'act', 'dtype', 'in_place', 'data_layout',
'is_test', 'use_global_stats', 'trainable_statistics'
]
else:
new_attr_name += ['weight_attr', 'data_format', 'name']
self._change_name(layer, pd_ver, use_bn_old=use_bn_old)
new_attr_dict = dict.fromkeys(new_attr_name, None)
if pd_ver == 185 or use_bn_old:
new_attr_dict['num_channels'] = None
else:
new_attr_dict['num_features'] = None
new_key = 'num_channels' if 'num_channels' in new_attr_dict.keys(
) else 'num_features'
if self.context.expand:
new_attr_dict[new_key] = int(
self.context.expand *
layer._parameters['weight'].shape[0])
elif self.context.channel:
new_attr_dict[new_key] = max(cur_channel)
else:
new_attr_dict[new_key] = attr_dict[
'_num_channels'] if '_num_channels' in attr_dict.keys(
) else attr_dict['_num_features']
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = layers_old.SuperBatchNorm(
**new_attr_dict
) if pd_ver == 185 or use_bn_old else layers.SuperBatchNorm2D(
**new_attr_dict)
model[idx] = layer
elif isinstance(layer, paddle.nn.SyncBatchNorm) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
# num_features in SyncBatchNorm don't change after last weight operators
if idx > last_weight_layer_idx:
continue
attr_dict = layer.__dict__
new_attr_name = ['momentum', 'epsilon', 'bias_attr']
new_attr_name += ['weight_attr', 'data_format', 'name']
self._change_name(layer, pd_ver)
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['num_features'] = None
new_key = 'num_channels' if 'num_channels' in new_attr_dict.keys(
) else 'num_features'
if self.context.expand:
new_attr_dict[new_key] = int(
self.context.expand *
layer._parameters['weight'].shape[0])
elif self.context.channel:
new_attr_dict[new_key] = max(cur_channel)
else:
new_attr_dict[new_key] = attr_dict[
'_num_channels'] if '_num_channels' in attr_dict.keys(
) else attr_dict['_num_features']
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = layers.SuperSyncBatchNorm(**new_attr_dict)
model[idx] = layer
### assume output_size = None, filter_size != None
### NOTE: output_size != None may raise error, solve when it happend.
elif isinstance(layer, paddle.nn.Conv2DTranspose):
attr_dict = layer.__dict__
key = attr_dict['_full_name']
new_attr_name = [
'stride', 'padding', 'dilation', 'groups', 'bias_attr'
]
assert getattr(
attr_dict, '_filter_size', '_kernel_size'
) != None, "Conv2DTranspose only support kernel size != None now"
if pd_ver == 185:
new_attr_name += [
'output_size', 'param_attr', 'use_cudnn', 'act', 'dtype'
]
else:
new_attr_name += [
'output_padding', 'weight_attr', 'data_format'
]
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['candidate_config'] = dict()
if pd_ver == 185:
new_attr_dict['num_channels'] = None
new_attr_dict['num_filters'] = None
new_attr_dict['filter_size'] = None
else:
new_attr_dict['in_channels'] = None
new_attr_dict['out_channels'] = None
new_attr_dict['kernel_size'] = None
self._change_name(layer, pd_ver, conv=True)
self.kernel_size = getattr(self.context, 'kernel_size', None)
# if the kernel_size of conv transpose is 1, don't change it.
fks = '_filter_size' if '_filter_size' in attr_dict.keys(
) else '_kernel_size'
ks = [attr_dict[fks]] if isinstance(
attr_dict[fks], numbers.Integral) else attr_dict[fks]
if self.kernel_size and int(ks[0]) != 1:
new_attr_dict['transform_kernel'] = True
new_attr_dict[fks[1:]] = max(self.kernel_size)
new_attr_dict['candidate_config'].update({
'kernel_size': self.kernel_size
})
else:
new_attr_dict[fks[1:]] = attr_dict[fks]
in_key = '_num_channels' if '_num_channels' in attr_dict.keys(
) else '_in_channels'
out_key = '_num_filters' if '_num_filters' in attr_dict.keys(
) else '_out_channels'
if self.context.expand:
### first super convolution transpose
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
else:
new_attr_dict[in_key[1:]] = int(self.context.expand *
attr_dict[in_key])
### last super convolution transpose
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = attr_dict[out_key]
else:
new_attr_dict[out_key[1:]] = int(self.context.expand *
attr_dict[out_key])
new_attr_dict['candidate_config'].update({
'expand_ratio': self.context.expand_ratio
})
elif self.context.channel:
if attr_dict['_groups'] != None and (
int(attr_dict['_groups']) == int(attr_dict[in_key])
):
### depthwise conv_transpose
_logger.warn(
"If convolution is a depthwise conv_transpose, output channel " \
"change to the same channel with input, output channel in search is not used."
)
cur_channel = pre_channel
else:
cur_channel = self.context.channel[0]
self.context.channel = self.context.channel[1:]
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
else:
new_attr_dict[in_key[1:]] = max(pre_channel)
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = attr_dict[out_key]
else:
new_attr_dict[out_key[1:]] = max(cur_channel)
new_attr_dict['candidate_config'].update({
'channel': cur_channel
})
pre_channel = cur_channel
else:
new_attr_dict[in_key[1:]] = attr_dict[in_key]
new_attr_dict[out_key[1:]] = attr_dict[out_key]
for attr in new_attr_name:
if attr == 'weight_attr':
new_attr_dict[attr] = attr_dict['_param_attr']
elif attr == 'output_padding':
new_attr_dict[attr] = attr_dict[attr]
else:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer
if getattr(new_attr_dict, 'output_size', None) == []:
new_attr_dict['output_size'] = None
if attr_dict['_groups'] == None or int(attr_dict[
'_groups']) == 1:
### standard conv_transpose
layer = Block(
SuperConv2DTranspose(**new_attr_dict), key=key)
elif int(attr_dict['_groups']) == int(attr_dict[in_key]):
# if conv is depthwise conv, groups = in_channel, out_channel = in_channel,
# channel in candidate_config = in_channel_list
if 'channel' in new_attr_dict['candidate_config']:
new_attr_dict[in_key[1:]] = max(cur_channel)
new_attr_dict[out_key[1:]] = new_attr_dict[in_key[1:]]
new_attr_dict['candidate_config'][
'channel'] = cur_channel
new_attr_dict['groups'] = new_attr_dict[in_key[1:]]
layer = Block(
SuperDepthwiseConv2DTranspose(**new_attr_dict), key=key)
else:
### group conv_transpose
layer = Block(
SuperGroupConv2DTranspose(**new_attr_dict), key=key)
model[idx] = layer
elif isinstance(layer, paddle.nn.Linear) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
attr_dict = layer.__dict__
key = attr_dict['_full_name']
if pd_ver == 185:
new_attr_name = ['act', 'dtype']
else:
new_attr_name = ['weight_attr', 'bias_attr']
self._change_name(layer, pd_ver) if pd_ver != 185 else None
in_nc, out_nc = layer._parameters['weight'].shape
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['candidate_config'] = dict()
if pd_ver == 185:
new_attr_dict['input_dim'] = None
new_attr_dict['output_dim'] = None
else:
new_attr_dict['in_features'] = None
new_attr_dict['out_features'] = None
in_key = '_input_dim' if pd_ver == 185 else '_in_features'
out_key = '_output_dim' if pd_ver == 185 else '_out_features'
attr_dict[in_key] = in_nc
attr_dict[out_key] = out_nc
if self.context.expand:
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = int(attr_dict[in_key])
else:
new_attr_dict[in_key[1:]] = int(self.context.expand *
attr_dict[in_key])
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = int(attr_dict[out_key])
else:
new_attr_dict[out_key[1:]] = int(self.context.expand *
attr_dict[out_key])
new_attr_dict['candidate_config'].update({
'expand_ratio': self.context.expand_ratio
})
elif self.context.channel:
cur_channel = self.context.channel[0]
self.context.channel = self.context.channel[1:]
if idx == first_weight_layer_idx:
new_attr_dict[in_key[1:]] = int(attr_dict[in_key])
else:
new_attr_dict[in_key[1:]] = max(pre_channel)
if idx == last_weight_layer_idx:
new_attr_dict[out_key[1:]] = int(attr_dict[out_key])
else:
new_attr_dict[out_key[1:]] = max(cur_channel)
new_attr_dict['candidate_config'].update({
'channel': cur_channel
})
pre_channel = cur_channel
else:
new_attr_dict[in_key[1:]] = int(attr_dict[in_key])
new_attr_dict[out_key[1:]] = int(attr_dict[out_key])
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = Block(SuperLinear(**new_attr_dict), key=key)
model[idx] = layer
elif isinstance(layer, paddle.nn.InstanceNorm2D) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
# num_features in InstanceNorm don't change after last weight operators
if idx > last_weight_layer_idx:
continue
attr_dict = layer.__dict__
if pd_ver == 185:
new_attr_name = [
'bias_attr', 'epsilon', 'param_attr', 'dtype'
]
else:
new_attr_name = ['bias_attr', 'epsilon', 'weight_attr']
self._change_name(layer, pd_ver)
new_attr_dict = dict.fromkeys(new_attr_name, None)
if pd_ver == 185:
new_attr_dict['num_channels'] = None
else:
new_attr_dict['num_features'] = None
new_key = '_num_channels' if '_num_channels' in new_attr_dict.keys(
) else '_num_features'
### 10 is a default channel in the case of weight_attr=False, in this condition, num of channels if useless, so give it arbitrarily.
attr_dict[new_key] = layer._parameters['scale'].shape[0] if len(
layer._parameters) != 0 else 10
if self.context.expand:
new_attr_dict[new_key[1:]] = int(self.context.expand *
attr_dict[new_key])
elif self.context.channel:
new_attr_dict[new_key[1:]] = max(cur_channel)
else:
new_attr_dict[new_key[1:]] = attr_dict[new_key]
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = layers.SuperInstanceNorm(
**new_attr_dict
) if pd_ver == 185 else layers.SuperInstanceNorm2D(
**new_attr_dict)
model[idx] = layer
elif isinstance(layer, paddle.nn.LayerNorm) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
### TODO(ceci3): fix when normalized_shape != last_dim_of_input
if idx > last_weight_layer_idx:
continue
attr_dict = layer.__dict__
new_attr_name = ['epsilon', 'bias_attr']
if pd_ver == 185:
new_attr_name += [
'scale', 'shift', 'param_attr', 'act', 'dtype'
]
else:
new_attr_name += ['weight_attr']
self._change_name(layer, pd_ver)
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['normalized_shape'] = None
if self.context.expand:
new_attr_dict['normalized_shape'] = int(
self.context.expand * attr_dict['_normalized_shape'][0])
elif self.context.channel:
new_attr_dict['normalized_shape'] = max(cur_channel)
else:
new_attr_dict['normalized_shape'] = attr_dict[
'_normalized_shape']
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
del layer, attr_dict
layer = SuperLayerNorm(**new_attr_dict)
model[idx] = layer
elif isinstance(layer, paddle.nn.Embedding) and (
getattr(self.context, 'expand', None) != None or
getattr(self.context, 'channel', None) != None):
attr_dict = layer.__dict__
key = attr_dict['_full_name']
new_attr_name = []
if pd_ver == 185:
new_attr_name += [
'is_sparse', 'is_distributed', 'param_attr', 'dtype'
]
else:
new_attr_name += ['sparse', 'weight_attr', 'name']
self._change_name(layer, pd_ver, has_bias=False)
new_attr_dict = dict.fromkeys(new_attr_name, None)
new_attr_dict['candidate_config'] = dict()
bef_size = attr_dict['_size']
if self.context.expand:
if pd_ver == 185:
new_attr_dict['size'] = [
bef_size[0], int(self.context.expand * bef_size[1])
]
else:
new_attr_dict['num_embeddings'] = attr_dict[
'_num_embeddings']
new_attr_dict['embedding_dim'] = int(
self.context.expand * attr_dict['_embedding_dim'])
new_attr_dict['candidate_config'].update({
'expand_ratio': self.context.expand_ratio
})
elif self.context.channel:
cur_channel = self.context.channel[0]
self.context.channel = self.context.channel[1:]
if pd_ver == 185:
new_attr_dict['size'] = [bef_size[0], max(cur_channel)]
else:
new_attr_dict['num_embeddings'] = attr_dict[
'_num_embeddings']
new_attr_dict['embedding_dim'] = max(cur_channel)
new_attr_dict['candidate_config'].update({
'channel': cur_channel
})
pre_channel = cur_channel
else:
if pf_ver == 185:
new_attr_dict['size'] = bef_size
else:
new_attr_dict['num_embeddings'] = attr_dict[
'_num_embeddings']
new_attr_dict['embedding_dim'] = attr_dict[
'_embedding_dim']
for attr in new_attr_name:
new_attr_dict[attr] = attr_dict['_' + attr]
new_attr_dict['padding_idx'] = None if attr_dict[
'_padding_idx'] == -1 else attr_dict['_padding_idx']
del layer, attr_dict
layer = Block(SuperEmbedding(**new_attr_dict), key=key)
model[idx] = layer
def split_prefix(net, name_list):
if len(name_list) > 1:
net = split_prefix(getattr(net, name_list[0]), name_list[1:])
elif len(name_list) == 1:
net = getattr(net, name_list[0])
else:
raise NotImplementedError("name error")
return net
def get_split_names(layer, name_list):
if name_list:
self.name_list.append(name_list)
for _, (name, sublayer) in enumerate(layer.named_children()):
if sublayer.named_children():
get_split_names(sublayer, name_list + [name])
if isinstance(network, paddle.nn.Layer):
curr_id = 0
self.name_list = []
get_split_names(network, [])
for idx, nl in enumerate(self.name_list):
if len(nl) > 1:
net = split_prefix(network, nl[:-1])
else:
net = network
setattr(net, nl[-1], model[idx])
return network
class supernet:
"""
Search space of the network.
Parameters:
kernel_size(list|tuple, optional): search space for the kernel size of the Conv2D.
expand_ratio(list|tuple, optional): the search space for the expand ratio of the number of channels of Conv2D, the expand ratio of the output dimensions of the Embedding or Linear, which means this parameter get the number of channels of each OP in the converted super network based on the the channels of each OP in the original model, so this parameter The length is 1. Just set one between this parameter and ``channel``.
channel(list|tuple, optional): the search space for the number of channels of Conv2D, the output dimensions of the Embedding or Linear, this parameter directly sets the number of channels of each OP in the super network, so the length of this parameter needs to be the same as the total number that of Conv2D, Embedding, and Linear included in the network. Just set one between this parameter and ``expand_ratio``.
"""
def __init__(self, **kwargs):
for key, value in kwargs.items():
setattr(self, key, value)
assert (
getattr(self, 'expand_ratio', None) == None or
getattr(self, 'channel', None) == None
), "expand_ratio and channel CANNOT be NOT None at the same time."
self.expand = None
if 'expand_ratio' in kwargs.keys():
if isinstance(self.expand_ratio, list) or isinstance(
self.expand_ratio, tuple):
self.expand = max(self.expand_ratio)
elif isinstance(self.expand_ratio, int):
self.expand = self.expand_ratio
if 'channel' not in kwargs.keys():
self.channel = None
def __enter__(self):
return Convert(self)
def __exit__(self, exc_type, exc_val, exc_tb):
self.expand = None
self.channel = None
self.kernel_size = None
#def ofa_supernet(kernel_size, expand_ratio):
# def _ofa_supernet(func):
# @functools.wraps(func)
# def convert(*args, **kwargs):
# supernet_convert(*args, **kwargs)
# return convert
# return _ofa_supernet