def __init__(self, in_channels):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, 10, 3)
self.conv2 = nn.LayerChoice([nn.Conv2d(10, 10, 3), nn.MaxPool2d(3)])
self.conv3 = nn.LayerChoice([nn.Identity(), nn.Conv2d(10, 10, 1)])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(10, 1)
def __init__(self, block, layers, num_classes=1000):
super(ResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3,
64,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
torch.nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
torch.nn.init.constant_(m.weight, 1)
torch.nn.init.constant_(m.bias, 0)
def __init__(self,
channels: int,
reduction: int = 4,
activation_layer: Optional[Callable[..., nn.Module]] = None):
super().__init__()
if activation_layer is None:
activation_layer = nn.Sigmoid
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(
nn.Linear(channels, make_divisible(channels // reduction, 8)),
nn.ReLU(inplace=True),
nn.Linear(make_divisible(channels // reduction, 8), channels),
activation_layer())
def __init__(self, num_labels: int = 1000,
base_widths: Tuple[int, ...] = (32, 16, 32, 40, 80, 96, 192, 320, 1280),
dropout_rate: float = 0.,
width_mult: float = 1.0,
bn_eps: float = 1e-3,
bn_momentum: float = 0.1):
super().__init__()
assert len(base_widths) == 9
# include the last stage info widths here
widths = [make_divisible(width * width_mult, 8) for width in base_widths]
downsamples = [True, False, True, True, True, False, True, False]
self.num_labels = num_labels
self.dropout_rate = dropout_rate
self.bn_eps = bn_eps
self.bn_momentum = bn_momentum
self.stem = ConvBNReLU(3, widths[0], stride=2, norm_layer=nn.BatchNorm2d)
blocks: List[nn.Module] = [
# first stage is fixed
DepthwiseSeparableConv(widths[0], widths[1], kernel_size=3, stride=1)
]
# https://github.com/ultmaster/AceNAS/blob/46c8895fd8a05ffbc61a6b44f1e813f64b4f66b7/searchspace/proxylessnas/__init__.py#L21
for stage in range(2, 8):
# Rather than returning a fixed module here,
# we return a builder that dynamically creates module for different `repeat_idx`.
builder = inverted_residual_choice_builder(
[3, 6], [3, 5, 7], downsamples[stage], widths[stage - 1], widths[stage], f's{stage}')
if stage < 7:
blocks.append(nn.Repeat(builder, (1, 4), label=f's{stage}_depth'))
else:
# No mutation for depth in the last stage.
# Directly call builder to initiate one block
blocks.append(builder(0))
self.blocks = nn.Sequential(*blocks)
# final layers
self.feature_mix_layer = ConvBNReLU(widths[7], widths[8], kernel_size=1, norm_layer=nn.BatchNorm2d)
self.global_avg_pooling = nn.AdaptiveAvgPool2d(1)
self.dropout_layer = nn.Dropout(dropout_rate)
self.classifier = nn.Linear(widths[-1], num_labels)
reset_parameters(self, bn_momentum=bn_momentum, bn_eps=bn_eps)
def __init__(self,
input_size,
in_channels,
channels,
n_classes,
n_layers,
n_nodes=4,
stem_multiplier=3,
auxiliary=False):
super().__init__()
self.in_channels = in_channels
self.channels = channels
self.n_classes = n_classes
self.n_layers = n_layers
self.aux_pos = 2 * n_layers // 3 if auxiliary else -1
c_cur = stem_multiplier * self.channels
self.stem = nn.Sequential(
nn.Conv2d(in_channels, c_cur, 3, 1, 1, bias=False),
nn.BatchNorm2d(c_cur))
# for the first cell, stem is used for both s0 and s1
# [!] channels_pp and channels_p is output channel size, but c_cur is input channel size.
channels_pp, channels_p, c_cur = c_cur, c_cur, channels
self.cells = nn.ModuleList()
reduction_p, reduction = False, False
for i in range(n_layers):
reduction_p, reduction = reduction, False
# Reduce featuremap size and double channels in 1/3 and 2/3 layer.
if i in [n_layers // 3, 2 * n_layers // 3]:
c_cur *= 2
reduction = True
cell = Cell(n_nodes, channels_pp, channels_p, c_cur, reduction_p,
reduction)
self.cells.append(cell)
c_cur_out = c_cur * n_nodes
channels_pp, channels_p = channels_p, c_cur_out
#if i == self.aux_pos:
# self.aux_head = AuxiliaryHead(input_size // 4, channels_p, n_classes)
self.gap = nn.AdaptiveAvgPool2d(1)
self.linear = nn.Linear(channels_p, n_classes)
def __init__(self,
op_candidates: List[str],
merge_op: Literal['all', 'loose_end'] = 'all',
num_nodes_per_cell: int = 4,
width: Union[Tuple[int, ...], int] = 16,
num_cells: Union[Tuple[int, ...], int] = 20,
dataset: Literal['cifar', 'imagenet'] = 'imagenet',
auxiliary_loss: bool = False):
super().__init__()
self.dataset = dataset
self.num_labels = 10 if dataset == 'cifar' else 1000
self.auxiliary_loss = auxiliary_loss
# preprocess the specified width and depth
if isinstance(width, Iterable):
C = nn.ValueChoice(list(width), label='width')
else:
C = width
self.num_cells: nn.MaybeChoice[int] = cast(int, num_cells)
if isinstance(num_cells, Iterable):
self.num_cells = nn.ValueChoice(list(num_cells), label='depth')
num_cells_per_stage = [
(i + 1) * self.num_cells // 3 - i * self.num_cells // 3
for i in range(3)
]
# auxiliary head is different for network targetted at different datasets
if dataset == 'imagenet':
self.stem0 = nn.Sequential(
nn.Conv2d(3,
cast(int, C // 2),
kernel_size=3,
stride=2,
padding=1,
bias=False),
nn.BatchNorm2d(cast(int, C // 2)),
nn.ReLU(inplace=True),
nn.Conv2d(cast(int, C // 2),
cast(int, C),
3,
stride=2,
padding=1,
bias=False),
nn.BatchNorm2d(C),
)
self.stem1 = nn.Sequential(
nn.ReLU(inplace=True),
nn.Conv2d(cast(int, C),
cast(int, C),
3,
stride=2,
padding=1,
bias=False),
nn.BatchNorm2d(C),
)
C_pprev = C_prev = C_curr = C
last_cell_reduce = True
elif dataset == 'cifar':
self.stem = nn.Sequential(
nn.Conv2d(3, cast(int, 3 * C), 3, padding=1, bias=False),
nn.BatchNorm2d(cast(int, 3 * C)))
C_pprev = C_prev = 3 * C
C_curr = C
last_cell_reduce = False
else:
raise ValueError(f'Unsupported dataset: {dataset}')
self.stages = nn.ModuleList()
for stage_idx in range(3):
if stage_idx > 0:
C_curr *= 2
# For a stage, we get C_in, C_curr, and C_out.
# C_in is only used in the first cell.
# C_curr is number of channels for each operator in current stage.
# C_out is usually `C * num_nodes_per_cell` because of concat operator.
cell_builder = CellBuilder(op_candidates, C_pprev, C_prev, C_curr,
num_nodes_per_cell, merge_op,
stage_idx > 0, last_cell_reduce)
stage: Union[NDSStage, nn.Sequential] = NDSStage(
cell_builder, num_cells_per_stage[stage_idx])
if isinstance(stage, NDSStage):
stage.estimated_out_channels_prev = cast(int, C_prev)
stage.estimated_out_channels = cast(
int, C_curr * num_nodes_per_cell)
stage.downsampling = stage_idx > 0
self.stages.append(stage)
# NOTE: output_node_indices will be computed on-the-fly in trial code.
# When constructing model space, it's just all the nodes in the cell,
# which happens to be the case of one-shot supernet.
# C_pprev is output channel number of last second cell among all the cells already built.
if len(stage) > 1:
# Contains more than one cell
C_pprev = len(cast(nn.Cell,
stage[-2]).output_node_indices) * C_curr
else:
# Look up in the out channels of last stage.
C_pprev = C_prev
# This was originally,
# C_prev = num_nodes_per_cell * C_curr.
# but due to loose end, it becomes,
C_prev = len(cast(nn.Cell, stage[-1]).output_node_indices) * C_curr
# Useful in aligning the pprev and prev cell.
last_cell_reduce = cell_builder.last_cell_reduce
if stage_idx == 2:
C_to_auxiliary = C_prev
if auxiliary_loss:
assert isinstance(
self.stages[2], nn.Sequential
), 'Auxiliary loss can only be enabled in retrain mode.'
self.stages[2] = SequentialBreakdown(
cast(nn.Sequential, self.stages[2]))
self.auxiliary_head = AuxiliaryHead(
C_to_auxiliary, self.num_labels,
dataset=self.dataset) # type: ignore
self.global_pooling = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = nn.Linear(cast(int, C_prev), self.num_labels)
def __init__(self,
num_labels: int = 1000,
channel_search: bool = False,
affine: bool = False):
super().__init__()
self.num_labels = num_labels
self.channel_search = channel_search
self.affine = affine
# the block number in each stage. 4 stages in total. 20 blocks in total.
self.stage_repeats = [4, 4, 8, 4]
# output channels for all stages, including the very first layer and the very last layer
self.stage_out_channels = [-1, 16, 64, 160, 320, 640, 1024]
# building first layer
out_channels = self.stage_out_channels[1]
self.first_conv = nn.Sequential(
nn.Conv2d(3, out_channels, 3, 2, 1, bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
self.features = []
global_block_idx = 0
for stage_idx, num_repeat in enumerate(self.stage_repeats):
for block_idx in range(num_repeat):
# count global index to give names to choices
global_block_idx += 1
# get ready for input and output
in_channels = out_channels
out_channels = self.stage_out_channels[stage_idx + 2]
stride = 2 if block_idx == 0 else 1
# mid channels can be searched
base_mid_channels = out_channels // 2
if self.channel_search:
k_choice_list = [
int(base_mid_channels * (.2 * k)) for k in range(1, 9)
]
mid_channels = nn.ValueChoice(
k_choice_list, label=f'channel_{global_block_idx}')
else:
mid_channels = int(base_mid_channels)
choice_block = nn.LayerChoice(
[
ShuffleNetBlock(in_channels,
out_channels,
mid_channels=mid_channels,
kernel_size=3,
stride=stride,
affine=affine),
ShuffleNetBlock(in_channels,
out_channels,
mid_channels=mid_channels,
kernel_size=5,
stride=stride,
affine=affine),
ShuffleNetBlock(in_channels,
out_channels,
mid_channels=mid_channels,
kernel_size=7,
stride=stride,
affine=affine),
ShuffleXceptionBlock(in_channels,
out_channels,
mid_channels=mid_channels,
stride=stride,
affine=affine)
],
label=f'layer_{global_block_idx}')
self.features.append(choice_block)
self.features = nn.Sequential(*self.features)
# final layers
last_conv_channels = self.stage_out_channels[-1]
self.conv_last = nn.Sequential(
nn.Conv2d(out_channels, last_conv_channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(last_conv_channels, affine=affine),
nn.ReLU(inplace=True),
)
self.globalpool = nn.AdaptiveAvgPool2d((1, 1))
self.dropout = nn.Dropout(0.1)
self.classifier = nn.Sequential(
nn.Linear(last_conv_channels, num_labels, bias=False), )
self._initialize_weights()
def __init__(self,
num_labels: int = 1000,
base_widths: Tuple[int, ...] = (16, 16, 32, 64, 128, 256, 512,
1024),
width_multipliers: Tuple[float, ...] = (0.5, 0.625, 0.75, 1.0,
1.25, 1.5, 2.0),
expand_ratios: Tuple[int, ...] = (1, 2, 3, 4, 5, 6),
dropout_rate: float = 0.2,
bn_eps: float = 1e-3,
bn_momentum: float = 0.1):
super().__init__()
self.widths = [
nn.ValueChoice([
make_divisible(base_width * mult, 8)
for mult in width_multipliers
],
label=f'width_{i}')
for i, base_width in enumerate(base_widths)
]
self.expand_ratios = expand_ratios
blocks = [
# Stem
ConvBNReLU(3,
self.widths[0],
nn.ValueChoice([3, 5], label='ks_0'),
stride=2,
activation_layer=h_swish),
SeparableConv(self.widths[0],
self.widths[0],
activation_layer=nn.ReLU),
]
# counting for kernel sizes and expand ratios
self.layer_count = 2
blocks += [
# Body
self._make_stage(1, self.widths[0], self.widths[1], False, 2,
nn.ReLU),
self._make_stage(2, self.widths[1], self.widths[2], True, 2,
nn.ReLU),
self._make_stage(1, self.widths[2], self.widths[3], False, 2,
h_swish),
self._make_stage(1, self.widths[3], self.widths[4], True, 1,
h_swish),
self._make_stage(1, self.widths[4], self.widths[5], True, 2,
h_swish),
]
# Head
blocks += [
ConvBNReLU(self.widths[5],
self.widths[6],
1,
1,
activation_layer=h_swish),
nn.AdaptiveAvgPool2d(1),
ConvBNReLU(self.widths[6],
self.widths[7],
1,
1,
norm_layer=nn.Identity,
activation_layer=h_swish),
]
self.blocks = nn.Sequential(*blocks)
self.classifier = nn.Sequential(
nn.Dropout(dropout_rate),
nn.Linear(self.widths[7], num_labels),
)
reset_parameters(self, bn_momentum=bn_momentum, bn_eps=bn_eps)
def __init__(self,
op_candidates: List[str],
merge_op: Literal['all', 'loose_end'] = 'all',
num_nodes_per_cell: int = 4,
width: Union[Tuple[int], int] = 16,
num_cells: Union[Tuple[int], int] = 20,
dataset: Literal['cifar', 'imagenet'] = 'imagenet',
auxiliary_loss: bool = False):
super().__init__()
self.dataset = dataset
self.num_labels = 10 if dataset == 'cifar' else 1000
self.auxiliary_loss = auxiliary_loss
# preprocess the specified width and depth
if isinstance(width, Iterable):
C = nn.ValueChoice(list(width), label='width')
else:
C = width
if isinstance(num_cells, Iterable):
num_cells = nn.ValueChoice(list(num_cells), label='depth')
num_cells_per_stage = [
i * num_cells // 3 - (i - 1) * num_cells // 3 for i in range(3)
]
# auxiliary head is different for network targetted at different datasets
if dataset == 'imagenet':
self.stem0 = nn.Sequential(
nn.Conv2d(3,
C // 2,
kernel_size=3,
stride=2,
padding=1,
bias=False),
nn.BatchNorm2d(C // 2),
nn.ReLU(inplace=True),
nn.Conv2d(C // 2, C, 3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(C),
)
self.stem1 = nn.Sequential(
nn.ReLU(inplace=True),
nn.Conv2d(C, C, 3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(C),
)
C_pprev = C_prev = C_curr = C
last_cell_reduce = True
elif dataset == 'cifar':
self.stem = nn.Sequential(
nn.Conv2d(3, 3 * C, 3, padding=1, bias=False),
nn.BatchNorm2d(3 * C))
C_pprev = C_prev = 3 * C
C_curr = C
last_cell_reduce = False
self.stages = nn.ModuleList()
for stage_idx in range(3):
if stage_idx > 0:
C_curr *= 2
# For a stage, we get C_in, C_curr, and C_out.
# C_in is only used in the first cell.
# C_curr is number of channels for each operator in current stage.
# C_out is usually `C * num_nodes_per_cell` because of concat operator.
cell_builder = CellBuilder(op_candidates, C_pprev, C_prev, C_curr,
num_nodes_per_cell, merge_op,
stage_idx > 0, last_cell_reduce)
stage = nn.Repeat(cell_builder, num_cells_per_stage[stage_idx])
self.stages.append(stage)
# C_pprev is output channel number of last second cell among all the cells already built.
if len(stage) > 1:
# Contains more than one cell
C_pprev = len(stage[-2].output_node_indices) * C_curr
else:
# Look up in the out channels of last stage.
C_pprev = C_prev
# This was originally,
# C_prev = num_nodes_per_cell * C_curr.
# but due to loose end, it becomes,
C_prev = len(stage[-1].output_node_indices) * C_curr
# Useful in aligning the pprev and prev cell.
last_cell_reduce = cell_builder.last_cell_reduce
if stage_idx == 2:
C_to_auxiliary = C_prev
if auxiliary_loss:
assert isinstance(
self.stages[2], nn.Sequential
), 'Auxiliary loss can only be enabled in retrain mode.'
self.stages[2] = SequentialBreakdown(self.stages[2])
self.auxiliary_head = AuxiliaryHead(C_to_auxiliary,
self.num_labels,
dataset=self.dataset)
self.global_pooling = nn.AdaptiveAvgPool2d((1, 1))
self.classifier = nn.Linear(C_prev, self.num_labels)
def __init__(self,
width_stages=[24,40,80,96,192,320],
n_cell_stages=[4,4,4,4,4,1],
stride_stages=[2,2,2,1,2,1],
width_mult=1, n_classes=1000,
dropout_rate=0, bn_param=(0.1, 1e-3)):
"""
Parameters
----------
width_stages: str
width (output channels) of each cell stage in the block
n_cell_stages: str
number of cells in each cell stage
stride_strages: str
stride of each cell stage in the block
width_mult : int
the scale factor of width
"""
super(SearchMobileNet, self).__init__()
input_channel = putils.make_divisible(32 * width_mult, 8)
first_cell_width = putils.make_divisible(16 * width_mult, 8)
for i in range(len(width_stages)):
width_stages[i] = putils.make_divisible(width_stages[i] * width_mult, 8)
# first conv
first_conv = ops.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 = ops.OPS['3x3_MBConv1'](input_channel, first_cell_width, 1)
first_block = first_block_conv
input_channel = first_cell_width
blocks = [first_block]
stage_cnt = 0
for width, n_cell, s in zip(width_stages, n_cell_stages, stride_stages):
for i in range(n_cell):
if i == 0:
stride = s
else:
stride = 1
op_candidates = [ops.OPS['3x3_MBConv3'](input_channel, width, stride),
ops.OPS['3x3_MBConv6'](input_channel, width, stride),
ops.OPS['5x5_MBConv3'](input_channel, width, stride),
ops.OPS['5x5_MBConv6'](input_channel, width, stride),
ops.OPS['7x7_MBConv3'](input_channel, width, stride),
ops.OPS['7x7_MBConv6'](input_channel, width, stride)]
if stride == 1 and input_channel == width:
# if it is not the first one
op_candidates += [ops.OPS['Zero'](input_channel, width, stride)]
conv_op = LayerChoice(op_candidates, label="s{}_c{}".format(stage_cnt, i))
else:
conv_op = LayerChoice(op_candidates, label="s{}_c{}".format(stage_cnt, i))
# shortcut
if stride == 1 and input_channel == width:
# if not first cell
shortcut = ops.IdentityLayer(input_channel, input_channel)
else:
shortcut = None
inverted_residual_block = ops.MobileInvertedResidualBlock(conv_op, shortcut, op_candidates)
blocks.append(inverted_residual_block)
input_channel = width
stage_cnt += 1
# feature mix layer
last_channel = putils.make_devisible(1280 * width_mult, 8) if width_mult > 1.0 else 1280
feature_mix_layer = ops.ConvLayer(input_channel, last_channel, kernel_size=1, use_bn=True, act_func='relu6', ops_order='weight_bn_act', )
classifier = ops.LinearLayer(last_channel, n_classes, dropout_rate=dropout_rate)
self.first_conv = first_conv
self.blocks = nn.ModuleList(blocks)
self.feature_mix_layer = feature_mix_layer
self.global_avg_pooling = nn.AdaptiveAvgPool2d(1)
self.classifier = classifier
# set bn param
self.set_bn_param(momentum=bn_param[0], eps=bn_param[1])
