def __init__(self, num_classes=10):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=2),
nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2),
nn.Conv2d(64, 192, kernel_size=3,
padding=2), nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True), nn.Conv2d(384,
256,
kernel_size=3,
padding=1), nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3,
padding=1), nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2))
self.fc_layers = nn.Sequential(
nn.Dropout(0.6),
nn.Linear(4096, 2048),
nn.ReLU(inplace=True),
nn.Dropout(0.6),
nn.Linear(2048, 2048),
nn.ReLU(inplace=True),
nn.Linear(2048, num_classes),
)
def __init__(self, in_planes, planes, stride=1):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=1,
padding=1,
bias=False)
if stride != 1 or in_planes != planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes,
planes,
kernel_size=1,
stride=stride,
bias=False))
# SE layers
self.fc1 = nn.Conv2d(planes, planes // 16, kernel_size=1)
self.fc2 = nn.Conv2d(planes // 16, planes, kernel_size=1)
def __init__(self, channels):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(channels)
self.prelu = nn.PReLU()
self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(channels)
def __init__(self, in_planes, out_planes, stride=1):
super(CellB, self).__init__()
self.stride = stride
# Left branch
self.sep_conv1 = SepConv(in_planes,
out_planes,
kernel_size=7,
stride=stride)
self.sep_conv2 = SepConv(in_planes,
out_planes,
kernel_size=3,
stride=stride)
# Right branch
self.sep_conv3 = SepConv(in_planes,
out_planes,
kernel_size=5,
stride=stride)
if stride == 2:
self.conv1 = nn.Conv2d(in_planes,
out_planes,
kernel_size=1,
stride=1,
padding=0,
bias=False)
self.bn1 = nn.BatchNorm2d(out_planes)
# Reduce channels
self.conv2 = nn.Conv2d(2 * out_planes,
out_planes,
kernel_size=1,
stride=1,
padding=0,
bias=False)
self.bn2 = nn.BatchNorm2d(out_planes)
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16*5*5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def __init__(self, inplanes, planes, stride=1, dilation=1):
super(BottleneckX, self).__init__()
cardinality = BottleneckX.cardinality
bottle_planes = planes * cardinality // 32
self.conv1 = nn.Conv2d(inplanes,
bottle_planes,
kernel_size=1,
bias=False)
self.bn1 = BatchNorm(bottle_planes)
self.conv2 = nn.Conv2d(
bottle_planes,
bottle_planes,
kernel_size=3,
stride=stride,
padding=dilation,
bias=False,
dilation=dilation,
groups=cardinality,
)
self.bn2 = BatchNorm(bottle_planes)
self.conv3 = nn.Conv2d(bottle_planes,
planes,
kernel_size=1,
bias=False)
self.bn3 = BatchNorm(planes)
self.relu = nn.ReLU(inplace=True)
self.stride = stride
def __init__(self, inp: int, oup: int, stride: int) -> None:
super().__init__()
if not (1 <= stride <= 3):
raise ValueError("illegal stride value")
self.stride = stride
branch_features = oup // 2
assert (self.stride != 1) or (inp == branch_features << 1)
if self.stride > 1:
self.branch1 = nn.Sequential(
self.depthwise_conv(inp,
inp,
kernel_size=3,
stride=self.stride,
padding=1),
nn.BatchNorm2d(inp),
nn.Conv2d(inp,
branch_features,
kernel_size=1,
stride=1,
padding=0,
bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
)
else:
self.branch1 = nn.Sequential()
self.branch2 = nn.Sequential(
nn.Conv2d(
inp if (self.stride > 1) else branch_features,
branch_features,
kernel_size=1,
stride=1,
padding=0,
bias=False,
),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
self.depthwise_conv(
branch_features,
branch_features,
kernel_size=3,
stride=self.stride,
padding=1,
),
nn.BatchNorm2d(branch_features),
nn.Conv2d(
branch_features,
branch_features,
kernel_size=1,
stride=1,
padding=0,
bias=False,
),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
)
def __init__(self,
in_planes,
cardinality=32,
bottleneck_width=4,
stride=1):
super(Block, self).__init__()
group_width = cardinality * bottleneck_width
self.conv1 = nn.Conv2d(in_planes,
group_width,
kernel_size=1,
bias=False)
self.bn1 = nn.BatchNorm2d(group_width)
self.conv2 = nn.Conv2d(group_width,
group_width,
kernel_size=3,
stride=stride,
padding=1,
groups=cardinality,
bias=False)
self.bn2 = nn.BatchNorm2d(group_width)
self.conv3 = nn.Conv2d(group_width,
self.expansion * group_width,
kernel_size=1,
bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion * group_width)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * group_width:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes,
self.expansion * group_width,
kernel_size=1,
stride=stride,
bias=False),
nn.BatchNorm2d(self.expansion * group_width))
def __init__(self, w_in, w_out, stride, group_width, bottleneck_ratio,
se_ratio):
super(Block, self).__init__()
# 1x1
w_b = int(round(w_out * bottleneck_ratio))
self.conv1 = nn.Conv2d(w_in, w_b, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(w_b)
# 3x3
num_groups = w_b // group_width
self.conv2 = nn.Conv2d(w_b,
w_b,
kernel_size=3,
stride=stride,
padding=1,
groups=num_groups,
bias=False)
self.bn2 = nn.BatchNorm2d(w_b)
# se
self.with_se = se_ratio > 0
if self.with_se:
w_se = int(round(w_in * se_ratio))
self.se = SE(w_b, w_se)
# 1x1
self.conv3 = nn.Conv2d(w_b, w_out, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(w_out)
self.shortcut = nn.Sequential()
if stride != 1 or w_in != w_out:
self.shortcut = nn.Sequential(
nn.Conv2d(w_in,
w_out,
kernel_size=1,
stride=stride,
bias=False), nn.BatchNorm2d(w_out))
def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride,
first_layer):
super(Bottleneck, self).__init__()
self.out_planes = out_planes
self.dense_depth = dense_depth
self.conv1 = nn.Conv2d(last_planes,
in_planes,
kernel_size=1,
bias=False)
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv2 = nn.Conv2d(in_planes,
in_planes,
kernel_size=3,
stride=stride,
padding=1,
groups=32,
bias=False)
self.bn2 = nn.BatchNorm2d(in_planes)
self.conv3 = nn.Conv2d(in_planes,
out_planes + dense_depth,
kernel_size=1,
bias=False)
self.bn3 = nn.BatchNorm2d(out_planes + dense_depth)
self.shortcut = nn.Sequential()
if first_layer:
self.shortcut = nn.Sequential(
nn.Conv2d(last_planes,
out_planes + dense_depth,
kernel_size=1,
stride=stride,
bias=False),
nn.BatchNorm2d(out_planes + dense_depth))
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes,
planes,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes,
planes,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes,
self.expansion * planes,
kernel_size=1,
stride=stride,
bias=False), nn.BatchNorm2d(self.expansion * planes))
def __init__(self,
in_ch,
out_ch,
kernel_size,
stride,
expansion_factor,
bn_momentum=0.1):
super(_InvertedResidual, self).__init__()
assert stride in [1, 2]
assert kernel_size in [3, 5]
mid_ch = in_ch * expansion_factor
self.apply_resudual = in_ch == out_ch and stride == 1
self.layers = nn.Sequential(
# Pointwise
nn.Conv2d(in_ch, mid_ch, 1, bias=False),
nn.BatchNorm2d(mid_ch, momentum=bn_momentum),
nn.ReLU(inplace=True),
# Depthwise
nn.Conv2d(
mid_ch,
mid_ch,
kernel_size,
padding=kernel_size // 2,
stride=stride,
groups=mid_ch,
bias=False,
),
nn.BatchNorm2d(mid_ch, momentum=bn_momentum),
nn.ReLU(inplace=True),
# Linear pointwise, Note that there's no activation
nn.Conv2d(mid_ch, out_ch, 1, bias=False),
nn.BatchNorm2d(out_ch, momentum=bn_momentum),
)
def __init__(self, in_planes, out_planes, stride, groups):
super(Bottleneck, self).__init__()
self.stride = stride
mid_planes = out_planes // 4
g = 1 if in_planes == 24 else groups
self.conv1 = nn.Conv2d(in_planes,
mid_planes,
kernel_size=1,
groups=g,
bias=False)
self.bn1 = nn.BatchNorm2d(mid_planes)
self.shuffle1 = ShuffleBlock(groups=g)
self.conv2 = nn.Conv2d(mid_planes,
mid_planes,
kernel_size=3,
stride=stride,
padding=1,
groups=mid_planes,
bias=False)
self.bn2 = nn.BatchNorm2d(mid_planes)
self.conv3 = nn.Conv2d(mid_planes,
out_planes,
kernel_size=1,
groups=groups,
bias=False)
self.bn3 = nn.BatchNorm2d(out_planes)
self.shortcut = nn.Sequential()
if stride == 2:
self.shortcut = nn.Sequential(nn.AvgPool2d(3, stride=2, padding=1))
def __init__(self):
super(Discriminator, self).__init__()
self.net = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, padding=1),
nn.LeakyReLU(0.2),
nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(64),
nn.LeakyReLU(0.2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
nn.Conv2d(128, 128, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(128),
nn.LeakyReLU(0.2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2),
nn.Conv2d(256, 256, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(256),
nn.LeakyReLU(0.2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2),
nn.Conv2d(512, 512, kernel_size=3, stride=2, padding=1),
nn.BatchNorm2d(512),
nn.LeakyReLU(0.2),
nn.AdaptiveAvgPool2d(1),
nn.Conv2d(512, 1024, kernel_size=1),
nn.LeakyReLU(0.2),
nn.Conv2d(1024, 1, kernel_size=1),
)
def __init__(self, inplanes, planes, stride=1, dilation=1):
super(Bottleneck, self).__init__()
expansion = Bottleneck.expansion
bottle_planes = planes // expansion
self.conv1 = nn.Conv2d(inplanes,
bottle_planes,
kernel_size=1,
bias=False)
self.bn1 = BatchNorm(bottle_planes)
self.conv2 = nn.Conv2d(
bottle_planes,
bottle_planes,
kernel_size=3,
stride=stride,
padding=dilation,
bias=False,
dilation=dilation,
)
self.bn2 = BatchNorm(bottle_planes)
self.conv3 = nn.Conv2d(bottle_planes,
planes,
kernel_size=1,
bias=False)
self.bn3 = BatchNorm(planes)
self.relu = nn.ReLU(inplace=True)
self.stride = stride
def __init__(self, num_classes: int = 1000) -> None:
super(QuantizationAlexNet, self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(64, 192, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(192, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
)
self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
self.classifier = nn.Sequential(
nn.Dropout(),
nn.Linear(256 * 6 * 6, 4096),
nn.ReLU(inplace=True),
nn.Dropout(),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Linear(4096, num_classes),
)
def __init__(self, block=BasicBlock, num_classes=10):
super(DLA, self).__init__()
self.base = nn.Sequential(
nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(16),
nn.ReLU(True)
)
self.layer1 = nn.Sequential(
nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(16),
nn.ReLU(True)
)
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.ReLU(True)
)
self.layer3 = Tree(block, 32, 64, level=1, stride=1)
self.layer4 = Tree(block, 64, 128, level=2, stride=2)
self.layer5 = Tree(block, 128, 256, level=2, stride=2)
self.layer6 = Tree(block, 256, 512, level=1, stride=2)
self.linear = nn.Linear(512, num_classes)
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def __init__(self, input_channels: int, squeeze_factor: int = 4):
super().__init__()
squeeze_channels = _make_divisible(input_channels // squeeze_factor, 8)
self.fc1 = nn.Conv2d(input_channels, squeeze_channels, 1)
self.relu = nn.ReLU(inplace=True)
self.fc2 = nn.Conv2d(squeeze_channels, input_channels, 1)
self.adaptive_avg_pool2d = nn.AdaptiveAvgPool2d(1)
self.hardsigmoid = nn.Hardsigmoid(inplace=True)
def __init__(
self,
in_chs,
mid_chs,
out_chs,
dw_kernel_size=3,
stride=1,
act_layer=nn.ReLU,
se_ratio=0.0,
):
super(GhostBottleneck, self).__init__()
has_se = se_ratio is not None and se_ratio > 0.0
self.stride = stride
# Point-wise expansion
self.ghost1 = GhostModule(in_chs, mid_chs, relu=True)
# Depth-wise convolution
if self.stride > 1:
self.conv_dw = nn.Conv2d(
mid_chs,
mid_chs,
dw_kernel_size,
stride=stride,
padding=(dw_kernel_size - 1) // 2,
groups=mid_chs,
bias=False,
)
self.bn_dw = nn.BatchNorm2d(mid_chs)
# Squeeze-and-excitation
if has_se:
self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio)
else:
self.se = None
# Point-wise linear projection
self.ghost2 = GhostModule(mid_chs, out_chs, relu=False)
# shortcut
if in_chs == out_chs and self.stride == 1:
self.shortcut = nn.Sequential()
else:
self.shortcut = nn.Sequential(
nn.Conv2d(
in_chs,
in_chs,
dw_kernel_size,
stride=stride,
padding=(dw_kernel_size - 1) // 2,
groups=in_chs,
bias=False,
),
nn.BatchNorm2d(in_chs),
nn.Conv2d(in_chs, out_chs, 1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(out_chs),
)
def __init__(self, input_shape=(80, 64), residual_in_channels=256):
super(Discriminator, self).__init__()
self.convLayer1 = nn.Sequential(
nn.Conv2d(
in_channels=1,
out_channels=residual_in_channels // 2,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
),
GLU(),
)
# Downsampling Layers
self.downSample1 = self.downsample(
in_channels=residual_in_channels // 2,
out_channels=residual_in_channels,
kernel_size=(3, 3),
stride=(2, 2),
padding=1,
)
self.downSample2 = self.downsample(
in_channels=residual_in_channels,
out_channels=residual_in_channels * 2,
kernel_size=(3, 3),
stride=[2, 2],
padding=1,
)
self.downSample3 = self.downsample(
in_channels=residual_in_channels * 2,
out_channels=residual_in_channels * 4,
kernel_size=[3, 3],
stride=[2, 2],
padding=1,
)
self.downSample4 = self.downsample(
in_channels=residual_in_channels * 4,
out_channels=residual_in_channels * 4,
kernel_size=[1, 10],
stride=(1, 1),
padding=(0, 2),
)
# Conv Layer
self.outputConvLayer = nn.Sequential(
nn.Conv2d(
in_channels=residual_in_channels * 4,
out_channels=1,
kernel_size=(1, 3),
stride=[1, 1],
padding=[0, 1],
)
)
def __init__(self):
super(Discriminator, self).__init__()
self.convLayer1 = nn.Sequential(
nn.Conv2d(
in_channels=1,
out_channels=128,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
),
GLU(),
)
# DownSample Layer
self.downSample1 = self.downSample(
in_channels=128,
out_channels=256,
kernel_size=(3, 3),
stride=(2, 2),
padding=1,
)
self.downSample2 = self.downSample(
in_channels=256,
out_channels=512,
kernel_size=(3, 3),
stride=[2, 2],
padding=1,
)
self.downSample3 = self.downSample(
in_channels=512,
out_channels=1024,
kernel_size=[3, 3],
stride=[2, 2],
padding=1,
)
self.downSample4 = self.downSample(
in_channels=1024,
out_channels=1024,
kernel_size=[1, 5],
stride=(1, 1),
padding=(0, 2),
)
# Conv Layer
self.outputConvLayer = nn.Sequential(
nn.Conv2d(
in_channels=1024,
out_channels=1,
kernel_size=(1, 3),
stride=[1, 1],
padding=[0, 1],
))
def __init__(self, in_channels, out_channels):
super().__init__()
self.double_conv = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
)
def __init__(self, in_channels, se_channels):
super(SE, self).__init__()
self.se1 = nn.Conv2d(in_channels,
se_channels,
kernel_size=1,
bias=True)
self.se2 = nn.Conv2d(se_channels,
in_channels,
kernel_size=1,
bias=True)
def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2):
super(GhostNet, self).__init__()
# setting of inverted residual blocks
self.cfgs = cfgs
self.dropout = dropout
# building first layer
output_channel = _make_divisible(16 * width, 4)
self.conv_stem = nn.Conv2d(3, output_channel, 3, 2, 1, bias=False)
self.bn1 = nn.BatchNorm2d(output_channel)
self.act1 = nn.ReLU(inplace=True)
input_channel = output_channel
# building inverted residual blocks
stages = []
block = GhostBottleneck
for cfg in self.cfgs:
layers = []
for k, exp_size, c, se_ratio, s in cfg:
output_channel = _make_divisible(c * width, 4)
hidden_channel = _make_divisible(exp_size * width, 4)
layers.append(
block(
input_channel,
hidden_channel,
output_channel,
k,
s,
se_ratio=se_ratio,
))
input_channel = output_channel
stages.append(nn.Sequential(*layers))
output_channel = _make_divisible(exp_size * width, 4)
stages.append(
nn.Sequential(ConvBnAct(input_channel, output_channel, 1)))
input_channel = output_channel
self.blocks = nn.Sequential(*stages)
# building last several layers
output_channel = 1280
self.global_pool = nn.AdaptiveAvgPool2d((1, 1))
self.conv_head = nn.Conv2d(input_channel,
output_channel,
1,
1,
0,
bias=True)
self.act2 = nn.ReLU(inplace=True)
self.classifier = nn.Linear(output_channel, num_classes)
self.dropout = nn.Dropout(p=self.dropout)
def __init__(self, in_planes, growth_rate):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes,
4 * growth_rate,
kernel_size=1,
bias=False)
self.bn2 = nn.BatchNorm2d(4 * growth_rate)
self.conv2 = nn.Conv2d(4 * growth_rate,
growth_rate,
kernel_size=3,
padding=1,
bias=False)
def __init__(
self,
stages_repeats: List[int],
stages_out_channels: List[int],
num_classes: int = 1000,
inverted_residual: Callable[..., nn.Module] = InvertedResidual,
) -> None:
super().__init__()
if len(stages_repeats) != 3:
raise ValueError(
"expected stages_repeats as list of 3 positive ints")
if len(stages_out_channels) != 5:
raise ValueError(
"expected stages_out_channels as list of 5 positive ints")
self._stage_out_channels = stages_out_channels
input_channels = 3
output_channels = self._stage_out_channels[0]
self.conv1 = nn.Sequential(
nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True),
)
input_channels = output_channels
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# Static annotations for mypy
self.stage2: nn.Sequential
self.stage3: nn.Sequential
self.stage4: nn.Sequential
stage_names = ["stage{}".format(i) for i in [2, 3, 4]]
for name, repeats, output_channels in zip(
stage_names, stages_repeats, self._stage_out_channels[1:]):
seq = [inverted_residual(input_channels, output_channels, 2)]
for i in range(repeats - 1):
seq.append(
inverted_residual(output_channels, output_channels, 1))
setattr(self, name, nn.Sequential(*seq))
input_channels = output_channels
output_channels = self._stage_out_channels[-1]
self.conv5 = nn.Sequential(
nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True),
)
self.fc = nn.Linear(output_channels, num_classes)
def build_conv_block(self, dim, padding_type, norm_layer, use_dropout,
use_bias):
"""Construct a convolutional block.
Parameters:
dim (int) -- the number of channels in the conv layer.
padding_type (str) -- the name of padding layer: reflect | replicate | zero
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
use_bias (bool) -- if the conv layer uses bias or not
Returns a conv block (with a conv layer, a normalization layer, and a non-linearity layer (ReLU))
"""
conv_block = []
p = 0
if padding_type == "reflect":
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == "replicate":
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == "zero":
p = 1
else:
raise NotImplementedError("padding [%s] is not implemented" %
padding_type)
conv_block += [
nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
norm_layer(dim),
nn.ReLU(True),
]
if use_dropout:
conv_block += [nn.Dropout(0.5)]
p = 0
if padding_type == "reflect":
conv_block += [nn.ReflectionPad2d(1)]
elif padding_type == "replicate":
conv_block += [nn.ReplicationPad2d(1)]
elif padding_type == "zero":
p = 1
else:
raise NotImplementedError("padding [%s] is not implemented" %
padding_type)
conv_block += [
nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias),
norm_layer(dim),
]
return nn.Sequential(*conv_block)
def __init__(self, in_channel, out_channel, kernel, stride, padding):
super(Down2d, self).__init__()
self.c1 = nn.Conv2d(in_channel,
out_channel,
kernel_size=kernel,
stride=stride,
padding=padding)
self.n1 = nn.InstanceNorm2d(out_channel)
self.c2 = nn.Conv2d(in_channel,
out_channel,
kernel_size=kernel,
stride=stride,
padding=padding)
self.n2 = nn.InstanceNorm2d(out_channel)
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
expand_ratio=1,
se_ratio=0.,
drop_rate=0.):
super(Block, self).__init__()
self.stride = stride
self.drop_rate = drop_rate
self.expand_ratio = expand_ratio
# Expansion
channels = expand_ratio * in_channels
self.conv1 = nn.Conv2d(in_channels,
channels,
kernel_size=1,
stride=1,
padding=0,
bias=False)
self.bn1 = nn.BatchNorm2d(channels)
# Depthwise conv
self.conv2 = nn.Conv2d(channels,
channels,
kernel_size=kernel_size,
stride=stride,
padding=(1 if kernel_size == 3 else 2),
groups=channels,
bias=False)
self.bn2 = nn.BatchNorm2d(channels)
# SE layers
se_channels = int(in_channels * se_ratio)
self.se = SE(channels, se_channels)
# Output
self.conv3 = nn.Conv2d(channels,
out_channels,
kernel_size=1,
stride=1,
padding=0,
bias=False)
self.bn3 = nn.BatchNorm2d(out_channels)
# Skip connection if in and out shapes are the same (MV-V2 style)
self.has_skip = (stride == 1) and (in_channels == out_channels)