def __init__(self, configer):
super(asymmetric_non_local_network, self).__init__()
self.configer = configer
self.num_classes = self.configer.get('data', 'num_classes')
self.backbone = ModuleHelper.get_backbone(
backbone=self.configer.get('network.backbone'),
pretrained=self.configer.get('network.pretrained')
)
# low_in_channels, high_in_channels, out_channels, key_channels, value_channels, dropout
self.fusion = AFNB(1024, 2048, 2048, 256, 256, dropout=0.05, sizes=([1]), norm_type=self.configer.get('network', 'norm_type'))
# extra added layers
self.context = nn.Sequential(
nn.Conv2d(2048, 512, kernel_size=3, stride=1, padding=1),
ModuleHelper.BNReLU(512, norm_type=self.configer.get('network', 'norm_type')),
APNB(in_channels=512, out_channels=512, key_channels=256, value_channels=256,
dropout=0.05, sizes=([1]), norm_type=self.configer.get('network', 'norm_type'))
)
self.cls = nn.Conv2d(512, self.num_classes, kernel_size=1, stride=1, padding=0, bias=True)
self.dsn = nn.Sequential(
nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
ModuleHelper.BNReLU(512, norm_type=self.configer.get('network', 'norm_type')),
nn.Dropout2d(0.05),
nn.Conv2d(512, self.num_classes, kernel_size=1, stride=1, padding=0, bias=True)
)
self.valid_loss_dict = configer.get('loss', 'loss_weights', configer.get('loss.loss_type'))
def __init__(self, configer):
super(BaseModel, self).__init__()
self.configer = configer
self.net = ModuleHelper.get_backbone(
backbone=configer.get('network.{}backbone'.format(self.flag)),
pretrained=configer.get('network.{}pretrained'.format(self.flag)))
self.valid_loss_dict = configer.get('loss.loss_weights',
configer.get('loss.loss_type'))
def __init__(self, configer):
super(OpenPose, self).__init__()
self.configer = configer
self.backbone = ModuleHelper.get_backbone(
backbone=self.configer.get('network.backbone'),
pretrained=self.configer.get('network.pretrained'))
self.pose_model = PoseModel(configer, self.backbone.get_num_features())
self.valid_loss_dict = configer.get('loss', 'loss_weights',
configer.get('loss.loss_type'))
def __init__(self, configer):
super(DarkNetYolov3, self).__init__()
self.configer = configer
self.backbone = ModuleHelper.get_backbone(
backbone=configer.get('network.backbone'),
pretrained=configer.get('network.pretrained', default=None))
self.yolov3_head = Yolov3Head(configer,
out_filters=self.backbone.num_features)
self.yolo_detection_layer = YOLODetectionLayer(self.configer)
def __init__(self, configer):
super(ResSFNet, self).__init__()
self.configer = configer
self.num_classes = self.configer.get('data', 'num_classes')
base = ModuleHelper.get_backbone(
backbone=self.configer.get('network.backbone'),
pretrained=self.configer.get('network.pretrained'))
self.stage1 = nn.Sequential(base.conv1, base.bn1, base.relu1,
base.conv2, base.bn2, base.relu2,
base.conv3, base.bn3, base.relu3,
base.maxpool, base.layer1)
self.stage2 = base.layer2
self.stage3 = base.layer3
self.stage4 = base.layer4
num_features = 512 if 'resnet18' in self.configer.get(
'network.backbone') else 2048
fpn_dim = max(num_features // 8, 128)
self.head = AlignHead(num_features, fpn_dim=fpn_dim)
self.dsn = nn.Sequential(
nn.Conv2d(num_features // 2,
max(num_features // 4, 256),
kernel_size=3,
stride=1,
padding=1),
ModuleHelper.BNReLU(max(num_features // 4, 256),
norm_type="batchnorm"), nn.Dropout2d(0.1),
nn.Conv2d(max(num_features // 4, 256),
self.num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))
self.conv_last = nn.Sequential(
conv3x3_bn_relu(4 * fpn_dim, fpn_dim, 1),
nn.Conv2d(fpn_dim, self.num_classes, kernel_size=1))
self.fpn_dsn = nn.ModuleList()
for i in range(len([2, 4, 8])):
self.fpn_dsn.append(
nn.Sequential(
nn.Conv2d(fpn_dim,
fpn_dim,
kernel_size=3,
stride=1,
padding=1),
ModuleHelper.BNReLU(fpn_dim, norm_type="batchnorm"),
nn.Dropout2d(0.1),
nn.Conv2d(fpn_dim,
self.num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True)))
self.valid_loss_dict = configer.get('loss', 'loss_weights',
configer.get('loss.loss_type'))
def __init__(self, configer):
super(DenseASPP, self).__init__()
self.configer = configer
dropout0 = MODEL_CONFIG['dropout0']
dropout1 = MODEL_CONFIG['dropout1']
d_feature0 = MODEL_CONFIG['d_feature0']
d_feature1 = MODEL_CONFIG['d_feature1']
self.backbone = ModuleHelper.get_backbone(
backbone=self.configer.get('network.backbone'),
pretrained=self.configer.get('network.pretrained')
)
num_features = self.backbone.get_num_features()
self.trans = _Transition(num_input_features=self.num_features,
num_output_features=self.num_features // 2,
norm_type=self.configer.get('network', 'norm_type'))
self.num_features = self.num_features // 2
self.ASPP_3 = _DenseAsppBlock(input_num=num_features, num1=d_feature0, num2=d_feature1,
dilation_rate=3, drop_out=dropout0,
norm_type=self.configer.get('network', 'norm_type'))
self.ASPP_6 = _DenseAsppBlock(input_num=num_features + d_feature1 * 1, num1=d_feature0, num2=d_feature1,
dilation_rate=6, drop_out=dropout0,
norm_type=self.configer.get('network', 'norm_type'))
self.ASPP_12 = _DenseAsppBlock(input_num=num_features + d_feature1 * 2, num1=d_feature0, num2=d_feature1,
dilation_rate=12, drop_out=dropout0,
norm_type=self.configer.get('network', 'norm_type'))
self.ASPP_18 = _DenseAsppBlock(input_num=num_features + d_feature1 * 3, num1=d_feature0, num2=d_feature1,
dilation_rate=18, drop_out=dropout0,
norm_type=self.configer.get('network', 'norm_type'))
self.ASPP_24 = _DenseAsppBlock(input_num=num_features + d_feature1 * 4, num1=d_feature0, num2=d_feature1,
dilation_rate=24, drop_out=dropout0,
norm_type=self.configer.get('network', 'norm_type'))
num_features = num_features + 5 * d_feature1
self.classification = nn.Sequential(
nn.Dropout2d(p=dropout1),
nn.Conv2d(num_features, self.configer.get('data', 'num_classes'), kernel_size=1, padding=0)
)
self.valid_loss_dict = configer.get('loss', 'loss_weights', configer.get('loss.loss_type'))
def __init__(self, configer):
super(PSPNet, self).__init__()
self.configer = configer
self.num_classes = self.configer.get('data', 'num_classes')
base = ModuleHelper.get_backbone(
backbone=self.configer.get('network.backbone'),
pretrained=self.configer.get('network.pretrained'))
self.stage1 = nn.Sequential(base.conv1, base.bn1, base.relu1,
base.conv2, base.bn2, base.relu2,
base.conv3, base.bn3, base.relu3,
base.maxpool, base.layer1, base.layer2,
base.layer3)
self.stage2 = base.layer4
num_features = 512 if 'resnet18' in self.configer.get(
'network.backbone') else 2048
self.dsn = nn.Sequential(
_ConvBatchNormReluBlock(num_features // 2,
num_features // 4,
3,
1,
norm_type=self.configer.get(
'network', 'norm_type')),
nn.Dropout2d(0.1),
nn.Conv2d(num_features // 4, self.num_classes, 1, 1, 0))
self.ppm = PPMBilinearDeepsup(fc_dim=num_features,
norm_type=self.configer.get(
'network', 'norm_type'))
self.cls = nn.Sequential(
nn.Conv2d(num_features + 4 * 512,
512,
kernel_size=3,
padding=1,
bias=False),
ModuleHelper.BNReLU(512,
norm_type=self.configer.get(
'network', 'norm_type')),
nn.Dropout2d(0.1), nn.Conv2d(512, self.num_classes, kernel_size=1))
self.valid_loss_dict = configer.get('loss', 'loss_weights',
configer.get('loss.loss_type'))
def __init__(self, configer, loss_dict=None, flag=""):
super(ClsModel, self).__init__()
self.configer = configer
self.flag = flag if len(flag) == 0 else "{}_".format(flag)
self.backbone = ModuleHelper.get_backbone(
backbone=configer.get('network.{}backbone'.format(self.flag)),
pretrained=configer.get('network.{}pretrained'.format(self.flag)))
self.reduction = None
fc_dim_out = configer.get('network.{}fc_dim'.format(self.flag),
default=None)
fc_dim = self.backbone.num_features
if fc_dim_out is not None:
self.reduction = nn.Conv2d(self.backbone.num_features, fc_dim_out,
1)
fc_dim = fc_dim_out
self.linear_list = nn.ModuleList()
linear_type = configer.get('network',
'{}linear_type'.format(self.flag),
default='linear')
self.fc = LINEAR_DICT[linear_type](fc_dim,
configer.get('data.num_classes'))
self.embed = None
if configer.get('network.{}embed'.format(self.flag), default=False):
feat_dim = configer.get('network', '{}feat_dim'.format(self.flag))
self.embed = nn.Sequential(nn.Linear(fc_dim, feat_dim),
nn.BatchNorm1d(feat_dim))
self.bn = None
if configer.get('network.{}bn'.format(self.flag), default=False):
self.bn = nn.BatchNorm1d(fc_dim)
nn.init.zeros_(self.bn.bias)
self.bn.bias.requires_grad = False
self.valid_loss_dict = configer.get(
'loss.loss_weights',
configer.get('loss.loss_type')) if loss_dict is None else loss_dict
def __init__(self, configer):
self.inplanes = 128
super(DeepLabV3, self).__init__()
self.configer = configer
self.num_classes = self.configer.get('data', 'num_classes')
base = ModuleHelper.get_backbone(
backbone=self.configer.get('network.backbone'),
pretrained=self.configer.get('network.pretrained'))
self.stage1 = nn.Sequential(base.conv1, base.bn1, base.relu1,
base.conv2, base.bn2, base.relu2,
base.conv3, base.bn3, base.relu3,
base.maxpool, base.layer1, base.layer2,
base.layer3)
self.stage2 = base.layer4
num_features = 512 if 'resnet18' in self.configer.get(
'network.backbone') else 2048
self.head = nn.Sequential(
ASPPModule(num_features,
norm_type=self.configer.get('network', 'norm_type')),
nn.Conv2d(512,
self.num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))
self.dsn = nn.Sequential(
nn.Conv2d(1024, 512, kernel_size=3, stride=1, padding=1),
ModuleHelper.BNReLU(512,
norm_type=self.configer.get(
'network', 'norm_type')),
nn.Dropout2d(0.1),
nn.Conv2d(512,
self.num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))
self.valid_loss_dict = configer.get('loss', 'loss_weights',
configer.get('loss.loss_type'))