def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, norm_type='Unknown'): super(_DenseLayer, self).__init__() self.add_module('norm1', get_norm(norm_type, num_input_features)), self.add_module('relu1', nn.ReLU(inplace=True)), self.add_module( 'conv1', nn.Conv2d(num_input_features, bn_size * growth_rate, kernel_size=1, stride=1, bias=False)), self.add_module('norm2', get_norm(norm_type, bn_size * growth_rate)), self.add_module('relu2', nn.ReLU(inplace=True)), self.add_module( 'conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, kernel_size=3, stride=1, padding=1, bias=False)), self.drop_rate = drop_rate
def __init__(self, in_channels, out_channels, norm_type='Unknown', **kwargs): super(BasicConv2d, self).__init__() self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) self.norm = get_norm(norm_type, out_channels, eps=0.001)
def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=0, bias=True, norm_type='Unknown'): super(Conv2dNormRelu, self).__init__() self.conv = nn.Sequential( nn.Conv2d(in_ch, out_ch, kernel_size, stride, padding, bias=bias), get_norm(norm_type, out_ch), nn.ReLU(inplace=True))
def __init__(self, num_input_features, num_output_features, norm_type='Unknown'): super(_Transition, self).__init__() self.add_module('norm', get_norm(norm_type, num_input_features)) self.add_module('relu', nn.ReLU(inplace=True)) self.add_module( 'conv', nn.Conv2d( num_input_features, num_output_features, # noqa kernel_size=1, stride=1, bias=False)) self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
def make_layers(cfg, batch_norm=False, norm_type='Unknown'): layers = [] in_channels = 3 for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [ conv2d, get_norm(norm_type, v), nn.ReLU(inplace=True) ] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v return nn.Sequential(*layers)
def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16), norm_type='Unknown', num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000): # noqa super(DenseNet, self).__init__() # First convolution self.features = nn.Sequential( OrderedDict([ ('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)), # noqa ('norm0', get_norm(norm_type, num_init_features)), ('relu0', nn.ReLU(inplace=True)), ('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)), ])) # Each denseblock num_features = num_init_features for i, num_layers in enumerate(block_config): block = _DenseBlock( num_layers=num_layers, num_input_features=num_features, norm_type=norm_type, # noqa bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate) # noqa self.features.add_module('denseblock%d' % (i + 1), block) num_features = num_features + num_layers * growth_rate if i != len(block_config) - 1: trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2, norm_type=norm_type) # noqa self.features.add_module('transition%d' % (i + 1), trans) num_features = num_features // 2 # Final batch norm self.features.add_module('norm5', get_norm(norm_type, num_features)) # Linear layer self.classifier = nn.Linear(num_features, num_classes) self.num_features = num_features # Official init from torch repo. for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.Linear): nn.init.constant_(m.bias, 0) elif isinstance(m, nn.GroupNorm): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) elif isinstance(m, nn.InstanceNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0)