def __init__(self, inp, hidden_dim, oup, kernel_size, stride, use_se=False, use_hs=False, momentum=0.1): """Init InvertedResidualSE.""" super(InvertedResidualSE, self).__init__() self.identity = stride == 1 and inp == oup self.ir_block = Sequential( # pw ops.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False), ops.BatchNorm2d(hidden_dim, momentum=momentum), ops.Hswish() if use_hs else ops.Relu(inplace=True), # dw ops.Conv2d(hidden_dim, hidden_dim, kernel_size, stride, (kernel_size - 1) // 2, groups=hidden_dim, bias=False), ops.BatchNorm2d(hidden_dim, momentum=momentum), # Squeeze-and-Excite SELayer(hidden_dim) if use_se else Sequential(), ops.Hswish() if use_hs else ops.Relu(inplace=True), # pw-linear ops.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False), ops.BatchNorm2d(oup, momentum=momentum), )
def __init__(self, in_chnls, cardinality, group_depth, stride): super(ResNeXt_Block, self).__init__() self.group_chnls = cardinality * group_depth self.conv1 = BN_Conv2d(in_chnls, self.group_chnls, 1, stride=1, padding=0) self.conv2 = BN_Conv2d(self.group_chnls, self.group_chnls, 3, stride=stride, padding=1, groups=cardinality) self.conv3 = ops.Conv2d(self.group_chnls, self.group_chnls * 2, 1, stride=1, padding=0) self.bn = ops.BatchNorm2d(self.group_chnls * 2) if stride != 1 or in_chnls != self.group_chnls * 2: self.short_cut = Sequential( ops.Conv2d(in_chnls, self.group_chnls * 2, 1, stride, bias=False), ops.BatchNorm2d(self.group_chnls * 2)) else: self.short_cut = None
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False): """Init Bottleneck.""" super(Bottleneck, self).__init__() assert style in ['pytorch', 'caffe'] self.inplanes = inplanes self.planes = planes self.stride = stride self.dilation = dilation self.style = style self.with_cp = with_cp self.norm1 = ops.BatchNorm2d(planes) self.norm2 = ops.BatchNorm2d(planes) self.norm3 = ops.BatchNorm2d(planes * self.expansion) self.conv1 = ops.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.with_modulated_dcn = False self.conv2 = ops.Conv2d( planes, planes, kernel_size=3, stride=stride, padding=dilation, dilation=dilation, bias=False, ) self.conv3 = ops.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False) self.relu = ops.Relu(inplace=True) if stride > 1 or downsample is not None: conv_layer = ops.Conv2d(inplanes, planes * self.expansion, kernel_size=1, stride=stride, bias=False) norm_layer = ops.BatchNorm2d(planes * self.expansion) self.downsample = Sequential(conv_layer, norm_layer) else: self.downsample = None
def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation, affine=True): """Construct SepConv class.""" super(DilConv, self).__init__() self.relu = ops.Relu(inplace=False) self.conv1 = ops.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=C_in, bias=False) self.conv2 = ops.Conv2d(C_in, C_out, kernel_size=1, padding=0, bias=False) self.batch = ops.BatchNorm2d(C_out, affine=affine)
def _make_stem_layer(self): """Make stem layer.""" self.conv1 = ops.Conv2d( 3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False) self.norm1 = ops.BatchNorm2d(64) self.relu = ops.Relu(inplace=True) self.maxpool = ops.MaxPool2d(kernel_size=3, stride=2, padding=1)
def __init__(self, C_in, C_out, kernel_size, stride, padding, dilation=1, affine=True, repeats=1): """Construct SepConv class.""" super(SeparatedConv, self).__init__() for idx in range(repeats): self.add_module( '{}_conv1'.format(idx), ops.Conv2d(C_in, C_in, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=C_in, bias=False)) self.add_module( '{}_conv2'.format(idx), ops.Conv2d(C_in, C_in, kernel_size=1, padding=0, bias=False)) self.add_module('{}_batch'.format(idx), ops.BatchNorm2d(C_in, affine=affine)) self.add_module('{}_relu'.format(idx), ops.Relu(inplace=False))
def __init__(self, C_in, C_out, kernel_size, stride, padding, bias=False, momentum=0.1, affine=True, activation='relu', inplace=True): """Construct ConvBnAct class.""" super(ConvBnAct, self).__init__() self.conv2d = ops.Conv2d(C_in, C_out, kernel_size, stride, padding, bias=bias) self.batch_norm2d = ops.BatchNorm2d(C_out, affine=affine, momentum=momentum) if activation == 'hswish': self.act = ops.Hswish(inplace=inplace) elif activation == 'hsigmoid': self.act = ops.Hsigmoid(inplace=inplace) elif activation == 'relu6': self.act = ops.Relu6(inplace=inplace) else: self.act = ops.Relu(inplace=inplace)
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, groups=1, bias=False): super(BN_Conv2d, self).__init__() self.seq = Sequential( ops.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias), ops.BatchNorm2d(out_channels), ops.Relu() )
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 __init__(self, in_planes, planes, inner_plane, stride=1): """Create BottleConv layer.""" super(PruneBasicConv, self).__init__() self.conv1 = ops.Conv2d(in_planes, inner_plane, kernel_size=3, stride=stride, padding=1, bias=False) self.bn1 = ops.BatchNorm2d(inner_plane) self.relu = ops.Relu() self.conv2 = ops.Conv2d(inner_plane, planes, kernel_size=3, stride=1, padding=1, bias=False) self.bn2 = ops.BatchNorm2d(planes) self.relu2 = ops.Relu()
def __init__(self, inp, oup, stride, kernel=3, expand_ratio=1): """Construct InvertedResidual class. :param inp: input channel :param oup: output channel :param stride: stride :param kernel: kernel :param expand_ratio: channel increase multiplier """ super(InvertedConv, self).__init__() hidden_dim = round(inp * expand_ratio) conv = [] if expand_ratio > 1: conv = [ ops.Conv2d(in_channels=inp, out_channels=hidden_dim, kernel_size=1, stride=1, padding=0, bias=False), ops.BatchNorm2d(num_features=hidden_dim), ops.Relu6(inplace=True) ] conv = conv + [ ops.Conv2d(in_channels=hidden_dim, out_channels=hidden_dim, kernel_size=kernel, stride=stride, padding=kernel // 2, groups=hidden_dim, bias=False, depthwise=True), ops.BatchNorm2d(num_features=hidden_dim), ops.Relu6(inplace=True), ops.Conv2d(in_channels=hidden_dim, out_channels=oup, kernel_size=1, stride=1, padding=0, bias=False), ops.BatchNorm2d(num_features=oup) ] self.models = Sequential(*conv)
def __init__(self, C_in, C_out, kernel_size, stride, padding, affine=True): """Init ReLUConvBN.""" super(ReLUConvBN, self).__init__() self.relu = ops.Relu(inplace=False) self.conv = ops.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False) self.bn = ops.BatchNorm2d(C_out, affine=affine)
def __init__(self, C, stride, ops_cands): """Init MixedOp.""" super(MixedOp, self).__init__() if not isinstance(ops_cands, list): # train self.add_module(ops_cands, OPS[ops_cands](C, stride, True)) else: # search for primitive in ops_cands: op = OPS[primitive](C, stride, False) if 'pool' in primitive: op = Seq(op, ops.BatchNorm2d(C, affine=False)) self.add_module(primitive, op)
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False): """Init BasicBlock.""" super(BasicBlock, self).__init__() self.expansion = 1 self.norm1 = ops.BatchNorm2d(planes) self.norm2 = ops.BatchNorm2d(planes) self.conv1 = ops.Conv2d(inplanes, planes, 3, stride=stride, padding=dilation, dilation=dilation, bias=False) self.conv2 = ops.Conv2d(planes, planes, 3, padding=1, bias=False) self.relu = ops.Relu(inplace=True) if stride > 1 or downsample is not None: conv_layer = ops.Conv2d(inplanes, planes * self.expansion, kernel_size=1, stride=stride, bias=False) norm_layer = ops.BatchNorm2d(planes) self.downsample = Sequential(conv_layer, norm_layer) else: self.downsample = None self.inplanes = inplanes self.planes = planes self.stride = stride self.dilation = dilation self.style = style assert not with_cp
def _transform_op(init_layer): """Transform the torch op to Vega op.""" if isinstance(init_layer, nn.Conv2d): in_channels = init_layer.in_channels out_channels = init_layer.out_channels kernel_size = init_layer.kernel_size[0] stride = init_layer.stride padding = init_layer.padding # bias = init_layer.bias new_layer = ops.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, bias=False) elif isinstance(init_layer, nn.BatchNorm2d): num_features = init_layer.num_features new_layer = ops.BatchNorm2d(num_features=num_features) elif isinstance(init_layer, nn.ReLU): new_layer = ops.Relu() elif isinstance(init_layer, nn.MaxPool2d): kernel_size = init_layer.kernel_size stride = init_layer.stride # padding = init_layer.padding new_layer = ops.MaxPool2d(kernel_size=kernel_size, stride=stride) elif isinstance(init_layer, nn.AvgPool2d): kernel_size = init_layer.kernel_size stride = init_layer.stride padding = init_layer.padding new_layer = ops.AvgPool2d(kernel_size=kernel_size, stride=stride, padding=padding) elif isinstance(init_layer, P.ReduceMean): new_layer = ops.AdaptiveAvgPool2d() elif isinstance(init_layer, nn.Dense): in_features = init_layer.in_channels out_features = init_layer.out_channels # use_bias = init_layer.bias new_layer = ops.Linear(in_features=in_features, out_features=out_features) elif isinstance(init_layer, nn.Dropout): prob = init_layer.p inplace = init_layer.inplace new_layer = ops.Dropout(prob=prob, inplace=inplace) elif isinstance(init_layer, nn.Flatten): new_layer = ops.View() else: raise ValueError("The op {} is not supported.".format( type(init_layer))) return new_layer
def _blocks(self, out_channels, desc_blocks): blocks = ModuleList() in_channels = 32 for i in range(desc_blocks): blocks.append( Sequential( ops.Conv2d(in_channels, out_channels, padding=1, kernel_size=3), ops.BatchNorm2d(out_channels), ops.Relu(inplace=True), )) in_channels = out_channels return blocks
def __init__(self, init_plane): """Create SmallInputInitialBlock layer. :param init_plane: input channel. :type init_plane: int """ super(SmallInputInitialBlock, self).__init__() self.conv = ops.Conv2d(in_channels=3, out_channels=init_plane, kernel_size=3, stride=1, padding=1, bias=False) self.bn = ops.BatchNorm2d(num_features=init_plane) self.relu = ops.Relu()
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias='auto', activation='relu', inplace=True, activate_last=True): """Init Conv Module with Normalization.""" super(ConvModule, self).__init__() self.activation = activation self.inplace = inplace self.activate_last = activate_last self.with_norm = True self.with_activatation = activation is not None if bias == 'auto': bias = False if self.with_norm else True self.with_bias = bias self.conv = ops.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias) self.in_channels = self.conv.in_channels self.out_channels = self.conv.out_channels self.kernel_size = self.conv.kernel_size self.stride = self.conv.stride self.padding = self.conv.padding self.dilation = self.conv.dilation self.transposed = self.conv.transposed self.output_padding = self.conv.output_padding self.groups = self.conv.groups if self.with_norm: norm_channels = out_channels if self.activate_last else in_channels self.norm = ops.BatchNorm2d(norm_channels) if self.with_activatation: if self.activation not in ['relu']: raise ValueError('{} is currently not supported.'.format( self.activation)) if self.activation == 'relu': self.activate = ops.Relu(inplace=inplace)
def __init__(self, init_plane): """Create InitialBlock layer. :param init_plane: input channel. :type init_plane: int """ super(InitialBlock, self).__init__() self.conv = ops.Conv2d(in_channels=3, out_channels=init_plane, kernel_size=7, stride=2, padding=3, bias=False) self.batch = ops.BatchNorm2d(num_features=init_plane) self.relu = ops.Relu() self.maxpool2d = ops.MaxPool2d(kernel_size=3, stride=2, padding=1)
def build_norm_layer(features, norm_type='BN', **kwargs): """Build norm layers according to their type. :param features: input tensor. :param norm_type: type of norm layer. :param **kwargs: other optional parameters. """ if norm_type == 'BN': return ops.BatchNorm2d(features, **kwargs) elif norm_type == 'GN': assert 'num_groups' in kwargs.keys( ), 'num_groups is required for group normalization' num_groups = kwargs.pop('num_groups') return ops.GroupNorm(num_groups, features, **kwargs) elif norm_type == 'Sync': return ops.SyncBatchNorm(features, **kwargs) else: raise ValueError('norm type {} is not defined'.format(norm_type))
def __init__(self, C_in, C_out, kernel_size, stride, padding, Conv2d='Conv2d', affine=True, use_relu6=False, norm_layer='BN', has_bn=True, has_relu=True, **kwargs): """Construct ConvBnRelu class.""" super(ConvBnRelu, self).__init__() if Conv2d == 'Conv2d': self.conv2d = ops.Conv2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False) elif Conv2d == 'ConvWS2d': self.conv2d = ops.ConvWS2d(C_in, C_out, kernel_size, stride=stride, padding=padding, bias=False) if has_bn: if norm_layer == 'BN': self.batch_norm2d = ops.BatchNorm2d(C_out, affine=affine) elif norm_layer == 'GN': num_groups = kwargs.pop('num_groups') self.batch_norm2d = ops.GroupNorm(num_groups, C_out, affine=affine) elif norm_layer == 'Sync': self.batch_norm2d = ops.SyncBatchNorm(C_out, affine=affine) if has_relu: if use_relu6: self.relu = ops.Relu6(inplace=False) else: self.relu = ops.Relu(inplace=False)
def __init__(self, C_in, C_out, affine=True): """Construct FactorizedReduce class. :param C_in: input channel :param C_out: output channel :param affine: whether to use affine in BN """ super(FactorizedReduce, self).__init__() assert C_out % 2 == 0 self.relu = ops.Relu(inplace=False) self.conv_1 = ops.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False) self.conv_2 = ops.Conv2d(C_in, C_out // 2, 1, stride=2, padding=0, bias=False) self.bn = ops.BatchNorm2d(C_out, affine=affine)
def make_res_layer(block, inplanes, planes, blocks, stride=1, dilation=1, style='pytorch', with_cp=False): """Build resnet layer.""" downsample = None if stride != 1 or inplanes != planes * block.expansion: conv_layer = ops.Conv2d(inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False) norm_layer = ops.BatchNorm2d(planes * block.expansion) downsample = Sequential(conv_layer, norm_layer) layers = [] layers.append( block(inplanes=inplanes, planes=planes, stride=stride, dilation=dilation, downsample=downsample, style=style, with_cp=with_cp)) inplanes = planes * block.expansion for i in range(1, blocks): layers.append( block(inplanes=inplanes, planes=planes, stride=1, dilation=dilation, style=style, with_cp=with_cp)) return Sequential(*layers)
def create_op(opt_name, in_channel, out_channel, stride=1): """Create op.""" layer = OPS[opt_name](in_channel, out_channel, stride) bn = ops.BatchNorm2d(out_channel) return Sequential(layer, bn)
lambda C, stride, affine, repeats=1: SeparatedConv( C, C, 7, stride, 3, affine=affine), 'dil_conv_3x3': lambda C, stride, affine, repeats=1: DilConv( C, C, 3, stride, 2, 2, affine=affine), 'dil_conv_5x5': lambda C, stride, affine, repeats=1: DilConv( C, C, 5, stride, 4, 2, affine=affine), 'conv_7x1_1x7': lambda C, stride, affine, repeats=1: Seq( ops.Relu(inplace=False), ops.Conv2d( C, C, (1, 7), stride=(1, stride), padding=(0, 3), bias=False), ops.Conv2d( C, C, (7, 1), stride=(stride, 1), padding=(3, 0), bias=False), ops.BatchNorm2d(C, affine=affine)), 'conv1x1': lambda C, stride, affine, repeats=1: Seq(conv1X1(C, C, stride=stride), ops.BatchNorm2d(C, affine=affine), ops.Relu(inplace=False)), 'conv3x3': lambda C, stride, affine, repeats=1: Seq(conv3x3(C, C, stride=stride), ops.BatchNorm2d(C, affine=affine), ops.Relu(inplace=False)), 'conv5x5': lambda C, stride, affine, repeats=1: Seq(conv5x5(C, C, stride=stride), ops.BatchNorm2d(C, affine=affine), ops.Relu(inplace=False)), 'conv7x7': lambda C, stride, affine, repeats=1: Seq(conv7x7(C, C, stride=stride), ops.BatchNorm2d(C, affine=affine),
def __init__(self, init_channels, stem_multi): """Init PreOneStem.""" super(PreOneStem, self).__init__() self._c_curr = init_channels * stem_multi self.conv2d = ops.Conv2d(3, self._c_curr, 3, padding=1, bias=False) self.batchNorm2d = ops.BatchNorm2d(self._c_curr)