def __init__(self):
super().__init__()
self.linear = nn.LayerChoice([
nn.Linear(3, nn.ValueChoice([10, 20])),
nn.Linear(3, nn.ValueChoice([30, 40]))
])
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.first_conv = ConvBNReLU(3,
widths[0],
stride=2,
norm_layer=nn.BatchNorm2d)
blocks = [
# first stage is fixed
SeparableConv(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 < 6:
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,
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),
)
feature_blocks = []
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)
mid_channels = cast(nn.MaybeChoice[int], 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}')
feature_blocks.append(choice_block)
self.features = nn.Sequential(*feature_blocks)
# 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):
super(Sequence, self).__init__()
self.lstm1 = nn.LSTMCell(1, 51)
self.lstm2 = nn.LSTMCell(51, 51)
self.linear = nn.Linear(51, 1)
def __init__(self, d_embed, d_proj):
super().__init__()
self.linear = nn.Linear(d_embed, d_proj)
def __init__(self):
super(MyModule, self).__init__()
self.fc1 = nn.Linear(4, 5, bias=False)
self.fc1.weight.data.fill_(2.)
self.fc2 = nn.Linear(5, 6, bias=False)
self.fc2.weight.data.fill_(3.)
def __init__(self):
super(Policy, self).__init__()
self.affine1 = nn.Linear(4, 128)
self.affine2 = nn.Linear(128, 2)
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):
super().__init__()
self.cell = nn.Cell([nn.Linear(16, 16), nn.Linear(16, 16, bias=False)],
num_nodes=4, num_ops_per_node=2, num_predecessors=2, merge_op='all')
def __init__(self, num_nodes):
super().__init__()
self.ops = nn.ModuleList()
self.num_nodes = num_nodes
for _ in range(num_nodes):
self.ops.append(nn.Linear(16, 16))
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
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(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):
super().__init__()
self.cell = nn.NasBench201Cell([
lambda x, y: nn.Linear(x, y),
lambda x, y: nn.Linear(x, y, bias=False)
], 10, 16)
def __init__(
self,
search_embed_dim: Tuple[int, ...] = (192, 216, 240),
search_mlp_ratio: Tuple[float, ...] = (3.5, 4.0),
search_num_heads: Tuple[int, ...] = (3, 4),
search_depth: Tuple[int, ...] = (12, 13, 14),
img_size: int = 224,
patch_size: int = 16,
in_chans: int = 3,
num_classes: int = 1000,
qkv_bias: bool = False,
drop_rate: float = 0.,
attn_drop_rate: float = 0.,
drop_path_rate: float = 0.,
pre_norm: bool = True,
global_pool: bool = False,
abs_pos: bool = True,
qk_scale: Optional[float] = None,
rpe: bool = True,
):
super().__init__()
embed_dim = nn.ValueChoice(list(search_embed_dim), label="embed_dim")
fixed_embed_dim = nn.ModelParameterChoice(
list(search_embed_dim), label="embed_dim")
depth = nn.ValueChoice(list(search_depth), label="depth")
self.patch_embed = nn.Conv2d(
in_chans,
cast(int, embed_dim),
kernel_size=patch_size,
stride=patch_size)
self.patches_num = int((img_size // patch_size) ** 2)
self.global_pool = global_pool
self.cls_token = nn.Parameter(torch.zeros(1, 1, cast(int, fixed_embed_dim)))
trunc_normal_(self.cls_token, std=.02)
dpr = [
x.item() for x in torch.linspace(
0,
drop_path_rate,
max(search_depth))] # stochastic depth decay rule
self.abs_pos = abs_pos
if self.abs_pos:
self.pos_embed = nn.Parameter(torch.zeros(
1, self.patches_num + 1, cast(int, fixed_embed_dim)))
trunc_normal_(self.pos_embed, std=.02)
self.blocks = nn.Repeat(lambda index: nn.LayerChoice([
TransformerEncoderLayer(embed_dim=embed_dim,
fixed_embed_dim=fixed_embed_dim,
num_heads=num_heads, mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, drop_rate=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[index],
rpe_length=img_size // patch_size,
qk_scale=qk_scale, rpe=rpe,
pre_norm=pre_norm,)
for mlp_ratio, num_heads in itertools.product(search_mlp_ratio, search_num_heads)
], label=f'layer{index}'), depth)
self.pre_norm = pre_norm
if self.pre_norm:
self.norm = nn.LayerNorm(cast(int, embed_dim))
self.head = nn.Linear(
cast(int, embed_dim),
num_classes) if num_classes > 0 else nn.Identity()
def __init__(self, foo, bar):
super().__init__()
self.foo = nn.Linear(foo, 3)
self.bar = nn.Dropout(bar)
def __init__(self, head_count):
super().__init__()
embed_dim = ValueChoice(candidates=[32, 64])
self.linear1 = nn.Linear(128, embed_dim)
self.mhatt = nn.MultiheadAttention(embed_dim, head_count)
self.linear2 = nn.Linear(embed_dim, 1)
def __init__(self):
super().__init__()
self.cell = nn.Cell([nn.Linear(16, 16), nn.Linear(16, 16, bias=False)], num_nodes=4)
def __init__(self,
alpha,
depths,
convops,
kernel_sizes,
num_layers,
skips,
num_classes=1000,
dropout=0.2):
super(MNASNet, self).__init__()
assert alpha > 0.0
assert len(depths) == len(convops) == len(kernel_sizes) == len(
num_layers) == len(skips) == 7
self.alpha = alpha
self.num_classes = num_classes
depths = _get_depths([_FIRST_DEPTH] + depths, alpha)
base_filter_sizes = [16, 24, 40, 80, 96, 192, 320]
exp_ratios = [3, 3, 3, 6, 6, 6, 6]
strides = [1, 2, 2, 2, 1, 2, 1]
layers = [
# First layer: regular conv.
nn.Conv2d(3, depths[0], 3, padding=1, stride=2, bias=False),
nn.BatchNorm2d(depths[0], momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
]
count = 0
#for conv, prev_depth, depth, ks, skip, stride, repeat, exp_ratio in \
# zip(convops, depths[:-1], depths[1:], kernel_sizes, skips, strides, num_layers, exp_ratios):
for filter_size, exp_ratio, stride in zip(base_filter_sizes,
exp_ratios, strides):
# TODO: restrict that "choose" can only be used within mutator
ph = nn.Placeholder(
label=f'mutable_{count}',
related_info={
'kernel_size_options': [1, 3, 5],
'n_layer_options': [1, 2, 3, 4],
'op_type_options': [
'__mutated__.base_mnasnet.RegularConv',
'__mutated__.base_mnasnet.DepthwiseConv',
'__mutated__.base_mnasnet.MobileConv'
],
#'se_ratio_options': [0, 0.25],
'skip_options': ['identity', 'no'],
'n_filter_options':
[int(filter_size * x) for x in [0.75, 1.0, 1.25]],
'exp_ratio':
exp_ratio,
'stride':
stride,
'in_ch':
depths[0] if count == 0 else None
})
layers.append(ph)
'''if conv == "mconv":
# MNASNet blocks: stacks of inverted residuals.
layers.append(_stack_inverted_residual(prev_depth, depth, ks, skip,
stride, exp_ratio, repeat, _BN_MOMENTUM))
else:
# Normal conv and depth-separated conv
layers += _stack_normal_conv(prev_depth, depth, ks, skip, conv == "dconv",
stride, repeat, _BN_MOMENTUM)'''
count += 1
if count >= 2:
break
layers += [
# Final mapping to classifier input.
nn.Conv2d(depths[7], 1280, 1, padding=0, stride=1, bias=False),
nn.BatchNorm2d(1280, momentum=_BN_MOMENTUM),
nn.ReLU(inplace=True),
]
self.layers = nn.Sequential(*layers)
self.classifier = nn.Sequential(nn.Dropout(p=dropout, inplace=True),
nn.Linear(1280, num_classes))
self._initialize_weights()
def __init__(self):
super().__init__()
self.m = nn.Linear(4, 5, bias=True)
