def get_active_subnet(self, preserve_weight=True):
input_stem = [self.input_stem[0].get_active_subnet(3, preserve_weight)]
if self.input_stem_skipping <= 0:
input_stem.append(
ResidualBlock(
self.input_stem[1].conv.get_active_subnet(
self.input_stem[0].active_out_channel,
preserve_weight),
IdentityLayer(self.input_stem[0].active_out_channel,
self.input_stem[0].active_out_channel)))
input_stem.append(self.input_stem[2].get_active_subnet(
self.input_stem[0].active_out_channel, preserve_weight))
input_channel = self.input_stem[2].active_out_channel
blocks = []
for stage_id, block_idx in enumerate(self.grouped_block_index):
depth_param = self.runtime_depth[stage_id]
active_idx = block_idx[:len(block_idx) - depth_param]
for idx in active_idx:
blocks.append(self.blocks[idx].get_active_subnet(
input_channel, preserve_weight))
input_channel = self.blocks[idx].active_out_channel
classifier = self.classifier.get_active_subnet(input_channel,
preserve_weight)
subnet = ResNets(input_stem, blocks, classifier)
subnet.set_bn_param(**self.get_bn_param())
return subnet
def get_active_net_config(self):
input_stem_config = [self.input_stem[0].get_active_subnet_config(3)]
if self.input_stem_skipping <= 0:
input_stem_config.append({
'name':
ResidualBlock.__name__,
'conv':
self.input_stem[1].conv.get_active_subnet_config(
self.input_stem[0].active_out_channel),
'shortcut':
IdentityLayer(self.input_stem[0].active_out_channel,
self.input_stem[0].active_out_channel),
})
input_stem_config.append(self.input_stem[2].get_active_subnet_config(
self.input_stem[0].active_out_channel))
input_channel = self.input_stem[2].active_out_channel
blocks_config = []
for stage_id, block_idx in enumerate(self.grouped_block_index):
depth_param = self.runtime_depth[stage_id]
active_idx = block_idx[:len(block_idx) - depth_param]
for idx in active_idx:
blocks_config.append(
self.blocks[idx].get_active_subnet_config(input_channel))
input_channel = self.blocks[idx].active_out_channel
classifier_config = self.classifier.get_active_subnet_config(
input_channel)
return {
'name': ResNets.__name__,
'bn': self.get_bn_param(),
'input_stem': input_stem_config,
'blocks': blocks_config,
'classifier': classifier_config,
}
def __init__(self, n_classes=1000, bn_param=(0.1, 1e-5), dropout_rate=0, depth_list=2, expand_ratio_list=0.25, width_mult_list=1.0): self.depth_list = val2list(depth_list) self.expand_ratio_list = val2list(expand_ratio_list) self.width_mult_list = val2list(width_mult_list) # sort self.depth_list.sort() self.expand_ratio_list.sort() self.width_mult_list.sort() input_channel = [ make_divisible(64 * width_mult, MyNetwork.CHANNEL_DIVISIBLE) for width_mult in self.width_mult_list ] mid_input_channel = [ make_divisible(channel // 2, MyNetwork.CHANNEL_DIVISIBLE) for channel in input_channel ] stage_width_list = ResNets.STAGE_WIDTH_LIST.copy() for i, width in enumerate(stage_width_list): stage_width_list[i] = [ make_divisible(width * width_mult, MyNetwork.CHANNEL_DIVISIBLE) for width_mult in self.width_mult_list ] n_block_list = [base_depth + max(self.depth_list) for base_depth in ResNets.BASE_DEPTH_LIST] stride_list = [1, 2, 2, 2] # build input stem input_stem = [ DynamicConvLayer(val2list(3), mid_input_channel, 3, stride=2, use_bn=True, act_func='relu'), ResidualBlock( DynamicConvLayer(mid_input_channel, mid_input_channel, 3, stride=1, use_bn=True, act_func='relu'), IdentityLayer(mid_input_channel, mid_input_channel) ), DynamicConvLayer(mid_input_channel, input_channel, 3, stride=1, use_bn=True, act_func='relu') ] # blocks blocks = [] for d, width, s in zip(n_block_list, stage_width_list, stride_list): for i in range(d): stride = s if i == 0 else 1 bottleneck_block = DynamicResNetBottleneckBlock( input_channel, width, expand_ratio_list=self.expand_ratio_list, kernel_size=3, stride=stride, act_func='relu', downsample_mode='avgpool_conv', ) blocks.append(bottleneck_block) input_channel = width # classifier classifier = DynamicLinearLayer(input_channel, n_classes, dropout_rate=dropout_rate) super(OFAResNets, self).__init__(input_stem, blocks, classifier) # set bn param self.set_bn_param(*bn_param) # runtime_depth self.input_stem_skipping = 0 self.runtime_depth = [0] * len(n_block_list)
def __init__(self, in_channel_list, out_channel_list, expand_ratio_list=0.25,
kernel_size=3, stride=1, act_func='relu', downsample_mode='avgpool_conv'):
super(DynamicResNetBottleneckBlock, self).__init__()
self.in_channel_list = in_channel_list
self.out_channel_list = out_channel_list
self.expand_ratio_list = val2list(expand_ratio_list)
self.kernel_size = kernel_size
self.stride = stride
self.act_func = act_func
self.downsample_mode = downsample_mode
# build modules
max_middle_channel = make_divisible(
round(max(self.out_channel_list) * max(self.expand_ratio_list)), MyNetwork.CHANNEL_DIVISIBLE)
self.conv1 = nn.Sequential(OrderedDict([
('conv', DynamicConv2d(max(self.in_channel_list), max_middle_channel)),
('bn', DynamicBatchNorm2d(max_middle_channel)),
('act', build_activation(self.act_func, inplace=True)),
]))
self.conv2 = nn.Sequential(OrderedDict([
('conv', DynamicConv2d(max_middle_channel, max_middle_channel, kernel_size, stride)),
('bn', DynamicBatchNorm2d(max_middle_channel)),
('act', build_activation(self.act_func, inplace=True))
]))
self.conv3 = nn.Sequential(OrderedDict([
('conv', DynamicConv2d(max_middle_channel, max(self.out_channel_list))),
('bn', DynamicBatchNorm2d(max(self.out_channel_list))),
]))
if self.stride == 1 and self.in_channel_list == self.out_channel_list:
self.downsample = IdentityLayer(max(self.in_channel_list), max(self.out_channel_list))
elif self.downsample_mode == 'conv':
self.downsample = nn.Sequential(OrderedDict([
('conv', DynamicConv2d(max(self.in_channel_list), max(self.out_channel_list), stride=stride)),
('bn', DynamicBatchNorm2d(max(self.out_channel_list))),
]))
elif self.downsample_mode == 'avgpool_conv':
self.downsample = nn.Sequential(OrderedDict([
('avg_pool', nn.AvgPool2d(kernel_size=stride, stride=stride, padding=0, ceil_mode=True)),
('conv', DynamicConv2d(max(self.in_channel_list), max(self.out_channel_list))),
('bn', DynamicBatchNorm2d(max(self.out_channel_list))),
]))
else:
raise NotImplementedError
self.final_act = build_activation(self.act_func, inplace=True)
self.active_expand_ratio = max(self.expand_ratio_list)
self.active_out_channel = max(self.out_channel_list)
def build_net_via_cfg(cfg, input_channel, last_channel, n_classes,
dropout_rate):
# first conv layer
first_conv = ConvLayer(3,
input_channel,
kernel_size=3,
stride=2,
use_bn=True,
act_func='h_swish',
ops_order='weight_bn_act')
# build mobile blocks
feature_dim = input_channel
blocks = []
for stage_id, block_config_list in cfg.items():
for k, mid_channel, out_channel, use_se, act_func, stride, expand_ratio in block_config_list:
mb_conv = MBConvLayer(feature_dim, out_channel, k, stride,
expand_ratio, mid_channel, act_func,
use_se)
if stride == 1 and out_channel == feature_dim:
shortcut = IdentityLayer(out_channel, out_channel)
else:
shortcut = None
blocks.append(ResidualBlock(mb_conv, shortcut))
feature_dim = out_channel
# final expand layer
final_expand_layer = ConvLayer(
feature_dim,
feature_dim * 6,
kernel_size=1,
use_bn=True,
act_func='h_swish',
ops_order='weight_bn_act',
)
# feature mix layer
feature_mix_layer = ConvLayer(
feature_dim * 6,
last_channel,
kernel_size=1,
bias=False,
use_bn=False,
act_func='h_swish',
)
# classifier
classifier = LinearLayer(last_channel,
n_classes,
dropout_rate=dropout_rate)
return first_conv, blocks, final_expand_layer, feature_mix_layer, classifier
def __init__(self,
n_classes=1000,
bn_param=(0.1, 1e-5),
dropout_rate=0.1,
base_stage_width=None,
width_mult=1.0,
ks_list=3,
expand_ratio_list=6,
depth_list=4):
self.width_mult = width_mult
self.ks_list = val2list(ks_list, 1)
self.expand_ratio_list = val2list(expand_ratio_list, 1)
self.depth_list = val2list(depth_list, 1)
self.ks_list.sort()
self.expand_ratio_list.sort()
self.depth_list.sort()
base_stage_width = [16, 16, 24, 40, 80, 112, 160, 960, 1280]
final_expand_width = make_divisible(
base_stage_width[-2] * self.width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
last_channel = make_divisible(base_stage_width[-1] * self.width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
stride_stages = [1, 2, 2, 2, 1, 2]
act_stages = ['relu', 'relu', 'relu', 'h_swish', 'h_swish', 'h_swish']
se_stages = [False, False, True, False, True, True]
n_block_list = [1] + [max(self.depth_list)] * 5
width_list = []
for base_width in base_stage_width[:-2]:
width = make_divisible(base_width * self.width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
width_list.append(width)
input_channel, first_block_dim = width_list[0], width_list[1]
# first conv layer
first_conv = ConvLayer(3,
input_channel,
kernel_size=3,
stride=2,
act_func='h_swish')
first_block_conv = MBConvLayer(
in_channels=input_channel,
out_channels=first_block_dim,
kernel_size=3,
stride=stride_stages[0],
expand_ratio=1,
act_func=act_stages[0],
use_se=se_stages[0],
)
first_block = ResidualBlock(
first_block_conv,
IdentityLayer(first_block_dim, first_block_dim)
if input_channel == first_block_dim else None,
)
# inverted residual blocks
self.block_group_info = []
blocks = [first_block]
_block_index = 1
feature_dim = first_block_dim
for width, n_block, s, act_func, use_se in zip(width_list[2:],
n_block_list[1:],
stride_stages[1:],
act_stages[1:],
se_stages[1:]):
self.block_group_info.append(
[_block_index + i for i in range(n_block)])
_block_index += n_block
output_channel = width
for i in range(n_block):
if i == 0:
stride = s
else:
stride = 1
mobile_inverted_conv = DynamicMBConvLayer(
in_channel_list=val2list(feature_dim),
out_channel_list=val2list(output_channel),
kernel_size_list=ks_list,
expand_ratio_list=expand_ratio_list,
stride=stride,
act_func=act_func,
use_se=use_se,
)
if stride == 1 and feature_dim == output_channel:
shortcut = IdentityLayer(feature_dim, feature_dim)
else:
shortcut = None
blocks.append(ResidualBlock(mobile_inverted_conv, shortcut))
feature_dim = output_channel
# final expand layer, feature mix layer & classifier
final_expand_layer = ConvLayer(feature_dim,
final_expand_width,
kernel_size=1,
act_func='h_swish')
feature_mix_layer = ConvLayer(
final_expand_width,
last_channel,
kernel_size=1,
bias=False,
use_bn=False,
act_func='h_swish',
)
classifier = LinearLayer(last_channel,
n_classes,
dropout_rate=dropout_rate)
super(OFAMobileNetV3,
self).__init__(first_conv, blocks, final_expand_layer,
feature_mix_layer, classifier)
# set bn param
self.set_bn_param(momentum=bn_param[0], eps=bn_param[1])
# runtime_depth
self.runtime_depth = [
len(block_idx) for block_idx in self.block_group_info
]
def __init__(self,
n_classes=1000,
width_mult=1.0,
bn_param=(0.1, 1e-3),
dropout_rate=0.2,
ks=None,
expand_ratio=None,
depth_param=None,
stage_width_list=None):
ks = 3 if ks is None else ks
expand_ratio = 6 if expand_ratio is None else expand_ratio
input_channel = 32
last_channel = 1280
input_channel = make_divisible(input_channel * width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
last_channel = make_divisible(last_channel * width_mult, MyNetwork.CHANNEL_DIVISIBLE) \
if width_mult > 1.0 else last_channel
inverted_residual_setting = [
# t, c, n, s
[1, 16, 1, 1],
[expand_ratio, 24, 2, 2],
[expand_ratio, 32, 3, 2],
[expand_ratio, 64, 4, 2],
[expand_ratio, 96, 3, 1],
[expand_ratio, 160, 3, 2],
[expand_ratio, 320, 1, 1],
]
if depth_param is not None:
assert isinstance(depth_param, int)
for i in range(1, len(inverted_residual_setting) - 1):
inverted_residual_setting[i][2] = depth_param
if stage_width_list is not None:
for i in range(len(inverted_residual_setting)):
inverted_residual_setting[i][1] = stage_width_list[i]
ks = val2list(ks,
sum([n for _, _, n, _ in inverted_residual_setting]) - 1)
_pt = 0
# first conv layer
first_conv = ConvLayer(3,
input_channel,
kernel_size=3,
stride=2,
use_bn=True,
act_func='relu6',
ops_order='weight_bn_act')
# inverted residual blocks
blocks = []
for t, c, n, s in inverted_residual_setting:
output_channel = make_divisible(c * width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
for i in range(n):
if i == 0:
stride = s
else:
stride = 1
if t == 1:
kernel_size = 3
else:
kernel_size = ks[_pt]
_pt += 1
mobile_inverted_conv = MBConvLayer(
in_channels=input_channel,
out_channels=output_channel,
kernel_size=kernel_size,
stride=stride,
expand_ratio=t,
)
if stride == 1:
if input_channel == output_channel:
shortcut = IdentityLayer(input_channel, input_channel)
else:
shortcut = None
else:
shortcut = None
blocks.append(ResidualBlock(mobile_inverted_conv, shortcut))
input_channel = output_channel
# 1x1_conv before global average pooling
feature_mix_layer = ConvLayer(
input_channel,
last_channel,
kernel_size=1,
use_bn=True,
act_func='relu6',
ops_order='weight_bn_act',
)
classifier = LinearLayer(last_channel,
n_classes,
dropout_rate=dropout_rate)
super(MobileNetV2, self).__init__(first_conv, blocks,
feature_mix_layer, classifier)
# set bn param
self.set_bn_param(*bn_param)
def __init__(self, n_classes=1000, bn_param=(0.1, 1e-3), dropout_rate=0.1, base_stage_width=None, width_mult=1.0, ks_list=3, expand_ratio_list=6, depth_list=4): self.width_mult = width_mult self.ks_list = val2list(ks_list, 1) self.expand_ratio_list = val2list(expand_ratio_list, 1) self.depth_list = val2list(depth_list, 1) self.ks_list.sort() self.expand_ratio_list.sort() self.depth_list.sort() if base_stage_width == 'google': # MobileNetV2 Stage Width base_stage_width = [32, 16, 24, 32, 64, 96, 160, 320, 1280] else: # ProxylessNAS Stage Width base_stage_width = [32, 16, 24, 40, 80, 96, 192, 320, 1280] input_channel = make_divisible(base_stage_width[0] * self.width_mult, MyNetwork.CHANNEL_DIVISIBLE) first_block_width = make_divisible(base_stage_width[1] * self.width_mult, MyNetwork.CHANNEL_DIVISIBLE) last_channel = make_divisible(base_stage_width[-1] * self.width_mult, MyNetwork.CHANNEL_DIVISIBLE) # first conv layer first_conv = ConvLayer( 3, input_channel, kernel_size=3, stride=2, use_bn=True, act_func='relu6', ops_order='weight_bn_act' ) # first block first_block_conv = MBConvLayer( in_channels=input_channel, out_channels=first_block_width, kernel_size=3, stride=1, expand_ratio=1, act_func='relu6', ) first_block = ResidualBlock(first_block_conv, None) input_channel = first_block_width # inverted residual blocks self.block_group_info = [] blocks = [first_block] _block_index = 1 stride_stages = [2, 2, 2, 1, 2, 1] n_block_list = [max(self.depth_list)] * 5 + [1] width_list = [] for base_width in base_stage_width[2:-1]: width = make_divisible(base_width * self.width_mult, MyNetwork.CHANNEL_DIVISIBLE) width_list.append(width) for width, n_block, s in zip(width_list, n_block_list, stride_stages): self.block_group_info.append([_block_index + i for i in range(n_block)]) _block_index += n_block output_channel = width for i in range(n_block): if i == 0: stride = s else: stride = 1 mobile_inverted_conv = DynamicMBConvLayer( in_channel_list=val2list(input_channel, 1), out_channel_list=val2list(output_channel, 1), kernel_size_list=ks_list, expand_ratio_list=expand_ratio_list, stride=stride, act_func='relu6', ) if stride == 1 and input_channel == output_channel: shortcut = IdentityLayer(input_channel, input_channel) else: shortcut = None mb_inverted_block = ResidualBlock(mobile_inverted_conv, shortcut) blocks.append(mb_inverted_block) input_channel = output_channel # 1x1_conv before global average pooling feature_mix_layer = ConvLayer( input_channel, last_channel, kernel_size=1, use_bn=True, act_func='relu6', ) classifier = LinearLayer(last_channel, n_classes, dropout_rate=dropout_rate) super(OFAProxylessNASNets, self).__init__(first_conv, blocks, feature_mix_layer, classifier) # set bn param self.set_bn_param(momentum=bn_param[0], eps=bn_param[1]) # runtime_depth self.runtime_depth = [len(block_idx) for block_idx in self.block_group_info]
def __init__(self,
n_classes=1000,
width_mult=1.0,
bn_param=(0.1, 1e-5),
dropout_rate=0,
expand_ratio=None,
depth_param=None):
expand_ratio = 0.25 if expand_ratio is None else expand_ratio
input_channel = make_divisible(64 * width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
mid_input_channel = make_divisible(input_channel // 2,
MyNetwork.CHANNEL_DIVISIBLE)
stage_width_list = ResNets.STAGE_WIDTH_LIST.copy()
for i, width in enumerate(stage_width_list):
stage_width_list[i] = make_divisible(width * width_mult,
MyNetwork.CHANNEL_DIVISIBLE)
depth_list = [3, 4, 6, 3]
if depth_param is not None:
for i, depth in enumerate(ResNets.BASE_DEPTH_LIST):
depth_list[i] = depth + depth_param
stride_list = [1, 2, 2, 2]
# build input stem
input_stem = [
ConvLayer(3,
mid_input_channel,
3,
stride=2,
use_bn=True,
act_func='relu'),
ResidualBlock(
ConvLayer(mid_input_channel,
mid_input_channel,
3,
stride=1,
use_bn=True,
act_func='relu'),
IdentityLayer(mid_input_channel, mid_input_channel)),
ConvLayer(mid_input_channel,
input_channel,
3,
stride=1,
use_bn=True,
act_func='relu')
]
# blocks
blocks = []
for d, width, s in zip(depth_list, stage_width_list, stride_list):
for i in range(d):
stride = s if i == 0 else 1
bottleneck_block = ResNetBottleneckBlock(
input_channel,
width,
kernel_size=3,
stride=stride,
expand_ratio=expand_ratio,
act_func='relu',
downsample_mode='avgpool_conv',
)
blocks.append(bottleneck_block)
input_channel = width
# classifier
classifier = LinearLayer(input_channel,
n_classes,
dropout_rate=dropout_rate)
super(ResNet50D, self).__init__(input_stem, blocks, classifier)
# set bn param
self.set_bn_param(*bn_param)