def re_organize_middle_weights(self, expand_ratio_stage=0):
# conv3 -> conv2
importance = torch.sum(torch.abs(self.conv3.conv.conv.weight.data), dim=(0, 2, 3))
if isinstance(self.conv2.bn, DynamicGroupNorm):
channel_per_group = self.conv2.bn.channel_per_group
importance_chunks = torch.split(importance, channel_per_group)
for chunk in importance_chunks:
chunk.data.fill_(torch.mean(chunk))
importance = torch.cat(importance_chunks, dim=0)
if expand_ratio_stage > 0:
sorted_expand_list = copy.deepcopy(self.expand_ratio_list)
sorted_expand_list.sort(reverse=True)
target_width_list = [
make_divisible(round(max(self.out_channel_list) * expand), MyNetwork.CHANNEL_DIVISIBLE)
for expand in sorted_expand_list
]
right = len(importance)
base = - len(target_width_list) * 1e5
for i in range(expand_ratio_stage + 1):
left = target_width_list[i]
importance[left:right] += base
base += 1e5
right = left
sorted_importance, sorted_idx = torch.sort(importance, dim=0, descending=True)
self.conv3.conv.conv.weight.data = torch.index_select(self.conv3.conv.conv.weight.data, 1, sorted_idx)
adjust_bn_according_to_idx(self.conv2.bn.bn, sorted_idx)
self.conv2.conv.conv.weight.data = torch.index_select(self.conv2.conv.conv.weight.data, 0, sorted_idx)
# conv2 -> conv1
importance = torch.sum(torch.abs(self.conv2.conv.conv.weight.data), dim=(0, 2, 3))
if isinstance(self.conv1.bn, DynamicGroupNorm):
channel_per_group = self.conv1.bn.channel_per_group
importance_chunks = torch.split(importance, channel_per_group)
for chunk in importance_chunks:
chunk.data.fill_(torch.mean(chunk))
importance = torch.cat(importance_chunks, dim=0)
if expand_ratio_stage > 0:
sorted_expand_list = copy.deepcopy(self.expand_ratio_list)
sorted_expand_list.sort(reverse=True)
target_width_list = [
make_divisible(round(max(self.out_channel_list) * expand), MyNetwork.CHANNEL_DIVISIBLE)
for expand in sorted_expand_list
]
right = len(importance)
base = - len(target_width_list) * 1e5
for i in range(expand_ratio_stage + 1):
left = target_width_list[i]
importance[left:right] += base
base += 1e5
right = left
sorted_importance, sorted_idx = torch.sort(importance, dim=0, descending=True)
self.conv2.conv.conv.weight.data = torch.index_select(self.conv2.conv.conv.weight.data, 1, sorted_idx)
adjust_bn_according_to_idx(self.conv1.bn.bn, sorted_idx)
self.conv1.conv.conv.weight.data = torch.index_select(self.conv1.conv.conv.weight.data, 0, sorted_idx)
return None
def __init__(self, n_classes=1000, width_mult=1.0, bn_param=(0.1, 1e-5), dropout_rate=0.2,
ks=None, expand_ratio=None, depth_param=None, stage_width_list=None):
input_channel = 16
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
cfg = {
# k, exp, c, se, nl, s, e,
'0': [
[3, 16, 16, False, 'relu', 1, 1],
],
'1': [
[3, 64, 24, False, 'relu', 2, None], # 4
[3, 72, 24, False, 'relu', 1, None], # 3
],
'2': [
[5, 72, 40, True, 'relu', 2, None], # 3
[5, 120, 40, True, 'relu', 1, None], # 3
[5, 120, 40, True, 'relu', 1, None], # 3
],
'3': [
[3, 240, 80, False, 'h_swish', 2, None], # 6
[3, 200, 80, False, 'h_swish', 1, None], # 2.5
[3, 184, 80, False, 'h_swish', 1, None], # 2.3
[3, 184, 80, False, 'h_swish', 1, None], # 2.3
],
'4': [
[3, 480, 112, True, 'h_swish', 1, None], # 6
[3, 672, 112, True, 'h_swish', 1, None], # 6
],
'5': [
[5, 672, 160, True, 'h_swish', 2, None], # 6
[5, 960, 160, True, 'h_swish', 1, None], # 6
[5, 960, 160, True, 'h_swish', 1, None], # 6
]
}
cfg = self.adjust_cfg(cfg, ks, expand_ratio, depth_param, stage_width_list)
# width multiplier on mobile setting, change `exp: 1` and `c: 2`
for stage_id, block_config_list in cfg.items():
for block_config in block_config_list:
if block_config[1] is not None:
block_config[1] = make_divisible(block_config[1] * width_mult, MyNetwork.CHANNEL_DIVISIBLE)
block_config[2] = make_divisible(block_config[2] * width_mult, MyNetwork.CHANNEL_DIVISIBLE)
first_conv, blocks, final_expand_layer, feature_mix_layer, classifier = self.build_net_via_cfg(
cfg, input_channel, last_channel, n_classes, dropout_rate
)
super(MobileNetV3Large, self).__init__(first_conv, blocks, final_expand_layer, feature_mix_layer, classifier)
# set bn param
self.set_bn_param(*bn_param)
def re_organize_middle_weights(self, expand_ratio_stage=0):
importance = torch.sum(torch.abs(self.point_linear.conv.conv.weight.data), dim=(0, 2, 3))
if isinstance(self.depth_conv.bn, DynamicGroupNorm):
channel_per_group = self.depth_conv.bn.channel_per_group
importance_chunks = torch.split(importance, channel_per_group)
for chunk in importance_chunks:
chunk.data.fill_(torch.mean(chunk))
importance = torch.cat(importance_chunks, dim=0)
if expand_ratio_stage > 0:
sorted_expand_list = copy.deepcopy(self.expand_ratio_list)
sorted_expand_list.sort(reverse=True)
target_width_list = [
make_divisible(round(max(self.in_channel_list) * expand), MyNetwork.CHANNEL_DIVISIBLE)
for expand in sorted_expand_list
]
right = len(importance)
base = - len(target_width_list) * 1e5
for i in range(expand_ratio_stage + 1):
left = target_width_list[i]
importance[left:right] += base
base += 1e5
right = left
sorted_importance, sorted_idx = torch.sort(importance, dim=0, descending=True)
self.point_linear.conv.conv.weight.data = torch.index_select(
self.point_linear.conv.conv.weight.data, 1, sorted_idx
)
adjust_bn_according_to_idx(self.depth_conv.bn.bn, sorted_idx)
self.depth_conv.conv.conv.weight.data = torch.index_select(
self.depth_conv.conv.conv.weight.data, 0, sorted_idx
)
if self.use_se:
# se expand: output dim 0 reorganize
se_expand = self.depth_conv.se.fc.expand
se_expand.weight.data = torch.index_select(se_expand.weight.data, 0, sorted_idx)
se_expand.bias.data = torch.index_select(se_expand.bias.data, 0, sorted_idx)
# se reduce: input dim 1 reorganize
se_reduce = self.depth_conv.se.fc.reduce
se_reduce.weight.data = torch.index_select(se_reduce.weight.data, 1, sorted_idx)
# middle weight reorganize
se_importance = torch.sum(torch.abs(se_expand.weight.data), dim=(0, 2, 3))
se_importance, se_idx = torch.sort(se_importance, dim=0, descending=True)
se_expand.weight.data = torch.index_select(se_expand.weight.data, 1, se_idx)
se_reduce.weight.data = torch.index_select(se_reduce.weight.data, 0, se_idx)
se_reduce.bias.data = torch.index_select(se_reduce.bias.data, 0, se_idx)
if self.inverted_bottleneck is not None:
adjust_bn_according_to_idx(self.inverted_bottleneck.bn.bn, sorted_idx)
self.inverted_bottleneck.conv.conv.weight.data = torch.index_select(
self.inverted_bottleneck.conv.conv.weight.data, 0, sorted_idx
)
return None
else:
return sorted_idx
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 forward(self, x):
in_channel = x.size(1)
if self.inverted_bottleneck is not None:
self.inverted_bottleneck.conv.active_out_channel = \
make_divisible(round(in_channel * self.active_expand_ratio), MyNetwork.CHANNEL_DIVISIBLE)
self.depth_conv.conv.active_kernel_size = self.active_kernel_size
self.point_linear.conv.active_out_channel = self.active_out_channel
if self.inverted_bottleneck is not None:
x = self.inverted_bottleneck(x)
x = self.depth_conv(x)
x = self.point_linear(x)
return x
def get_active_subnet(self, in_channel, preserve_weight=True):
# build the new layer
sub_layer = set_layer_from_config(self.get_active_subnet_config(in_channel))
sub_layer = sub_layer.to(get_net_device(self))
if not preserve_weight:
return sub_layer
middle_channel = self.active_middle_channel(in_channel)
# copy weight from current layer
if sub_layer.inverted_bottleneck is not None:
sub_layer.inverted_bottleneck.conv.weight.data.copy_(
self.inverted_bottleneck.conv.get_active_filter(middle_channel, in_channel).data,
)
copy_bn(sub_layer.inverted_bottleneck.bn, self.inverted_bottleneck.bn.bn)
sub_layer.depth_conv.conv.weight.data.copy_(
self.depth_conv.conv.get_active_filter(middle_channel, self.active_kernel_size).data
)
copy_bn(sub_layer.depth_conv.bn, self.depth_conv.bn.bn)
if self.use_se:
se_mid = make_divisible(middle_channel // SEModule.REDUCTION, divisor=MyNetwork.CHANNEL_DIVISIBLE)
sub_layer.depth_conv.se.fc.reduce.weight.data.copy_(
self.depth_conv.se.get_active_reduce_weight(se_mid, middle_channel).data
)
sub_layer.depth_conv.se.fc.reduce.bias.data.copy_(
self.depth_conv.se.get_active_reduce_bias(se_mid).data
)
sub_layer.depth_conv.se.fc.expand.weight.data.copy_(
self.depth_conv.se.get_active_expand_weight(se_mid, middle_channel).data
)
sub_layer.depth_conv.se.fc.expand.bias.data.copy_(
self.depth_conv.se.get_active_expand_bias(middle_channel).data
)
sub_layer.point_linear.conv.weight.data.copy_(
self.point_linear.conv.get_active_filter(self.active_out_channel, middle_channel).data
)
copy_bn(sub_layer.point_linear.bn, self.point_linear.bn.bn)
return sub_layer
def __init__(self, in_channel_list, out_channel_list,
kernel_size_list=3, expand_ratio_list=6, stride=1, act_func='relu6', use_se=False):
super(DynamicMBConvLayer, self).__init__()
self.in_channel_list = in_channel_list
self.out_channel_list = out_channel_list
self.kernel_size_list = val2list(kernel_size_list)
self.expand_ratio_list = val2list(expand_ratio_list)
self.stride = stride
self.act_func = act_func
self.use_se = use_se
# build modules
max_middle_channel = make_divisible(
round(max(self.in_channel_list) * max(self.expand_ratio_list)), MyNetwork.CHANNEL_DIVISIBLE)
if max(self.expand_ratio_list) == 1:
self.inverted_bottleneck = None
else:
self.inverted_bottleneck = nn.Sequential(OrderedDict([
('conv', DynamicConv2d(max(self.in_channel_list), max_middle_channel)),
('bn', DynamicBatchNorm2d(max_middle_channel)),
('act', build_activation(self.act_func)),
]))
self.depth_conv = nn.Sequential(OrderedDict([
('conv', DynamicSeparableConv2d(max_middle_channel, self.kernel_size_list, self.stride)),
('bn', DynamicBatchNorm2d(max_middle_channel)),
('act', build_activation(self.act_func))
]))
if self.use_se:
self.depth_conv.add_module('se', DynamicSE(max_middle_channel))
self.point_linear = nn.Sequential(OrderedDict([
('conv', DynamicConv2d(max_middle_channel, max(self.out_channel_list))),
('bn', DynamicBatchNorm2d(max(self.out_channel_list))),
]))
self.active_kernel_size = max(self.kernel_size_list)
self.active_expand_ratio = max(self.expand_ratio_list)
self.active_out_channel = max(self.out_channel_list)
def forward(self, x, groups=None):
in_channel = x.size(1)
num_mid = make_divisible(in_channel // self.reduction,
divisor=MyNetwork.CHANNEL_DIVISIBLE)
y = x.mean(3, keepdim=True).mean(2, keepdim=True)
# reduce
reduce_filter = self.get_active_reduce_weight(
num_mid, in_channel, groups=groups).contiguous()
reduce_bias = self.get_active_reduce_bias(num_mid)
y = F.conv2d(y, reduce_filter, reduce_bias, 1, 0, 1, 1)
# relu
y = self.fc.relu(y)
# expand
expand_filter = self.get_active_expand_weight(
num_mid, in_channel, groups=groups).contiguous()
expand_bias = self.get_active_expand_bias(in_channel, groups=groups)
y = F.conv2d(y, expand_filter, expand_bias, 1, 0, 1, 1)
# hard sigmoid
y = self.fc.h_sigmoid(y)
return x * y
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-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)
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 active_middle_channels(self):
feature_dim = round(self.active_out_channel * self.active_expand_ratio)
feature_dim = make_divisible(feature_dim, MyNetwork.CHANNEL_DIVISIBLE)
return feature_dim
def active_middle_channel(self, in_channel):
return make_divisible(round(in_channel * self.active_expand_ratio), MyNetwork.CHANNEL_DIVISIBLE)
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-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)
