def channel_shuffle(x, groups):
"""Shuffle the channel of features.
:param x: feature maps
:type x: tensor
:param groups: group number of channels
:type groups: int
:return: shuffled feature map
:rtype: tensor
"""
batchsize, num_channels, height, width = ops.get_shape(x)
channels_per_group = num_channels // groups
x = ops.View([batchsize, groups, channels_per_group, height, width])(x)
x = ops.Transpose(1, 2)(x)
x = ops.View([batchsize, num_channels, height, width])(x)
return x
def __call__(self, x):
"""Forward compute of MobileNetV3 for classification."""
x = self.features(x)
x = self.avgpool(x)
x = ops.View((x.shape[0], -1))(x)
x = self.classifier(x)
return x
def __init__(self,
cfgs,
mode='small',
input_channel=3,
feat_channels=16,
special_stride=1,
num_classes=10,
width_mult=1.,
block=InvertedResidualSE,
momentum=0.1,
is_prune_mode=False,
**kwargs):
"""Init MobileNetV3.
:params cfgs: cfgs for mobilenetv3
:type cfgs: list
:params special_stride: the stride of the first InvertedResidualSE block.
:type special_stride: int (1 for cifar10, 2 for imagenet)
"""
super(MobileNetV3, self).__init__()
self.cfgs = cfgs
# building first layer
if not is_prune_mode:
feat_channels = _make_divisible(feat_channels * width_mult, 8)
else:
feat_channels = int(feat_channels * width_mult)
layers = [
ConvBnAct(input_channel,
feat_channels,
kernel_size=3,
momentum=momentum,
stride=special_stride,
padding=1,
activation='hswish')
]
# buidling blocks
# kernel_size, expand_ratio, output_channels, use_se, use_hs, stride
for k, t, c, use_se, use_hs, s in self.cfgs:
output_channel = _make_divisible(
c * width_mult, 8) if not is_prune_mode else int(c *
width_mult)
hidden_dim = _make_divisible(t, 8) if not is_prune_mode else t
layers.append(
block(feat_channels, hidden_dim, output_channel, k, s, use_se,
use_hs, momentum))
feat_channels = output_channel
self.features = Sequential(*layers)
# building last linear layer
self.avgpool = ops.AdaptiveAvgPool2d((1, 1))
chn = 1280 if mode == 'large' else 1024
self.classifier = Sequential(ops.View(),
ops.Linear(feat_channels, chn),
ops.Hswish(), ops.Dropout(0.2),
ops.Linear(chn, num_classes))
self._initialize_weights()
def __init__(self, base_channel, num_classes):
"""Create layers.
:param base_channel: base_channel
:type base_channel: int
:param num_class: number of class
:type num_class: int
"""
super(LinearClassificationHead, self).__init__()
self.avgpool = ops.AdaptiveAvgPool2d(output_size=(1, 1))
self.view = ops.View()
self.linear = ops.Linear(in_features=base_channel,
out_features=num_classes)
def __init__(self, **desc):
"""Initialize."""
super(SimpleCnn, self).__init__()
desc = Config(**desc)
self.num_class = desc.num_class
self.fp16 = desc.get('fp16', False)
self.channels = desc.channels
self.conv1 = ops.Conv2d(3, 32, padding=1, kernel_size=3)
self.pool1 = ops.MaxPool2d(2, stride=2)
self.blocks = self._blocks(self.channels, desc.blocks)
self.pool2 = ops.MaxPool2d(2, stride=2)
self.conv2 = ops.Conv2d(self.channels, 64, padding=1, kernel_size=3)
self.global_conv = ops.Conv2d(64, 64, kernel_size=8)
self.view = ops.View()
self.fc = ops.Linear(64, self.num_class)
def __init__(self, encoding, n_class=1000):
super(DNet, self).__init__()
op_names = ["conv3", "conv1", "conv3_grp2", "conv3_grp4", "conv3_base1", "conv3_base32", "conv3_sep"]
block_str, num_channel, macro_str = encoding.split('_')
curr_channel, index = int(num_channel), 0
_big_model = "*" in block_str
if _big_model:
block_encoding_list = block_str.split('*')
# stem
self.layers = Sequential(
create_op('conv3', 3, curr_channel // 2, stride=2),
ops.Relu(),
create_op('conv3', curr_channel // 2, curr_channel // 2),
ops.Relu(),
create_op('conv3', curr_channel // 2, curr_channel, stride=2),
ops.Relu()
)
# body
if not _big_model:
while index < len(macro_str):
stride = 1
if macro_str[index] == '-':
stride = 2
index += 1
channel_increase = int(macro_str[index])
block = EncodedBlock(block_str, curr_channel, op_names, stride, channel_increase)
self.layers.append(block)
curr_channel *= channel_increase
index += 1
else:
block_encoding_index = 0
while index < len(macro_str):
stride = 1
if macro_str[index] == '-':
stride = 2
index += 1
block_encoding_index += 1
channel_increase = int(macro_str[index])
block_encoding = block_encoding_list[block_encoding_index]
block = EncodedBlock(block_encoding, curr_channel, op_names, stride, channel_increase)
self.layers.append(block)
curr_channel *= channel_increase
index += 1
self.layers.append(ops.AdaptiveAvgPool2d((1, 1)))
self.view = ops.View()
self.fc = ops.Linear(in_features=curr_channel, out_features=n_class)
def __init__(self, C, num_classes, input_size):
"""Init AuxiliaryHead."""
super(AuxiliaryHead, self).__init__()
stride = input_size - 5
self.relu1 = ops.Relu(inplace=True)
self.avgpool1 = ops.AvgPool2d(5,
stride=stride,
padding=0,
count_include_pad=False)
self.conv1 = ops.Conv2d(C, 128, 1, bias=False)
self.batchnorm1 = ops.BatchNorm2d(128)
self.relu2 = ops.Relu(inplace=True)
self.conv2 = ops.Conv2d(128, 768, 2, bias=False)
self.batchnorm2 = ops.BatchNorm2d(768)
self.relu3 = ops.Relu(inplace=True)
self.view = ops.View()
self.classifier = ops.Linear(768, num_classes)
def __call__(self, x):
"""Forward compute of SELayer."""
b, c, _, _ = x.shape
y = ops.View((b, c))(self.avg_pool(x))
y = ops.View((b, c, 1, 1))(self.fc(y))
return x * y
def __init__(self, encoding, n_class=1000):
super().__init__()
self.backbone = DNetBackbone(encoding)
self.view = ops.View()
self.fc = ops.Linear(out_features=n_class)
def __init__(self, encoding, n_class=1000):
super().__init__()
self.backbone = DNetBackbone(encoding)
self.view = ops.View()
out_plane = self.backbone.out_channels
self.fc = ops.Linear(in_features=out_plane, out_features=n_class)