Update core/networks.py

core/networks.py

@@ -1,355 +1,360 @@
-# Copyright (C) 2021 * Ltd. All rights reserved.
-# author : Sanghyeon Jo <josanghyeokn@gmail.com>
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from torchvision import models
-import torch.utils.model_zoo as model_zoo
-
-from .arch_resnet import resnet
-from .arch_resnest import resnest
-from .abc_modules import ABC_Model
-
-from .deeplab_utils import ASPP, Decoder
-from .aff_utils import PathIndex
-from .puzzle_utils import tile_features, merge_features
-
-from tools.ai.torch_utils import resize_for_tensors
-
-#######################################################################
-# Normalization
-#######################################################################
-from .sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
-
-class FixedBatchNorm(nn.BatchNorm2d):
-    def forward(self, x):
-        return F.batch_norm(x, self.running_mean, self.running_var, self.weight, self.bias, training=False, eps=self.eps)
-
-def group_norm(features):
-    return nn.GroupNorm(4, features)
-#######################################################################
-
-class Backbone(nn.Module, ABC_Model):
-    def __init__(self, model_name, state_path, num_classes=20, mode='fix', segmentation=False):
-        super().__init__()
-
-        self.mode = mode
-
-        if self.mode == 'fix': 
-            self.norm_fn = FixedBatchNorm
-        else:
-            self.norm_fn = nn.BatchNorm2d
-        
-        if 'resnet' in model_name:
-            self.model = resnet.ResNet(resnet.Bottleneck, resnet.layers_dic[model_name], strides=(2, 2, 2, 1), batch_norm_fn=self.norm_fn)
-
-            state_dict = torch.load(state_path)
-            self.model.load_state_dict(state_dict, strict=False)
-        else:
-            if segmentation:
-                dilation, dilated = 4, True
-            else:
-                dilation, dilated = 2, False
-
-            self.model = eval("resnest." + model_name)(pretrained=True, dilated=dilated, dilation=dilation, norm_layer=self.norm_fn)
-
-            del self.model.avgpool
-            del self.model.fc
-
-        self.stage1 = nn.Sequential(self.model.conv1, 
-                                    self.model.bn1, 
-                                    self.model.relu, 
-                                    self.model.maxpool)
-        self.stage2 = nn.Sequential(self.model.layer1)
-        self.stage3 = nn.Sequential(self.model.layer2)
-        self.stage4 = nn.Sequential(self.model.layer3)
-        self.stage5 = nn.Sequential(self.model.layer4)
-
-class Classifier(Backbone):
-    def __init__(self, model_name, state_path, num_classes=20, mode='fix'):
-        super().__init__(model_name, state_path, num_classes, mode)
-        
-        self.classifier = nn.Conv2d(2048, num_classes, 1, bias=False)
-        self.num_classes = num_classes
-
-        self.initialize([self.classifier])
-    
-    def forward(self, x, with_cam=False):
-        x = self.stage1(x)
-        x = self.stage2(x)
-        x = self.stage3(x)
-        x = self.stage4(x)
-        x = self.stage5(x)
-        
-        if with_cam:
-            features = self.classifier(x)
-            logits = self.global_average_pooling_2d(features)
-            return logits, features
-        else:
-            x = self.global_average_pooling_2d(x, keepdims=True) 
-            logits = self.classifier(x).view(-1, self.num_classes)
-            return logits
-
-class Classifier_For_Positive_Pooling(Backbone):
-    def __init__(self, model_name, num_classes=20, mode='fix'):
-        super().__init__(model_name, num_classes, mode)
-        
-        self.classifier = nn.Conv2d(2048, num_classes, 1, bias=False)
-        self.num_classes = num_classes
-        
-        self.initialize([self.classifier])
-    
-    def forward(self, x, with_cam=False):
-        x = self.stage1(x)
-        x = self.stage2(x)
-        x = self.stage3(x)
-        x = self.stage4(x)
-        x = self.stage5(x)
-        
-        if with_cam:
-            features = self.classifier(x)
-            logits = self.global_average_pooling_2d(features)
-            return logits, features
-        else:
-            x = self.global_average_pooling_2d(x, keepdims=True) 
-            logits = self.classifier(x).view(-1, self.num_classes)
-            return logits
-
-class Classifier_For_Puzzle(Classifier):
-    def __init__(self, model_name, num_classes=20, mode='fix'):
-        super().__init__(model_name, num_classes, mode)
-        
-    def forward(self, x, num_pieces=1, level=-1):
-        batch_size = x.size()[0]
-        
-        output_dic = {}
-        layers = [self.stage1, self.stage2, self.stage3, self.stage4, self.stage5, self.classifier]
-
-        for l, layer in enumerate(layers):
-            l += 1
-            if level == l:
-                x = tile_features(x, num_pieces)
-
-            x = layer(x)
-            output_dic['stage%d'%l] = x
-        
-        output_dic['logits'] = self.global_average_pooling_2d(output_dic['stage6'])
-
-        for l in range(len(layers)):
-            l += 1
-            if l >= level:
-                output_dic['stage%d'%l] = merge_features(output_dic['stage%d'%l], num_pieces, batch_size)
-
-        if level is not None:
-            output_dic['merged_logits'] = self.global_average_pooling_2d(output_dic['stage6'])
-
-        return output_dic
-        
-class AffinityNet(Backbone):
-    def __init__(self, model_name, path_index=None):
-        super().__init__(model_name, None, 'fix')
-
-        if '50' in model_name:
-            fc_edge1_features = 64
-        else:
-            fc_edge1_features = 128
-
-        self.fc_edge1 = nn.Sequential(
-            nn.Conv2d(fc_edge1_features, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge2 = nn.Sequential(
-            nn.Conv2d(256, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge3 = nn.Sequential(
-            nn.Conv2d(512, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge4 = nn.Sequential(
-            nn.Conv2d(1024, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge5 = nn.Sequential(
-            nn.Conv2d(2048, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge6 = nn.Conv2d(160, 1, 1, bias=True)
-
-        self.backbone = nn.ModuleList([self.stage1, self.stage2, self.stage3, self.stage4, self.stage5])
-        self.edge_layers = nn.ModuleList([self.fc_edge1, self.fc_edge2, self.fc_edge3, self.fc_edge4, self.fc_edge5, self.fc_edge6])
-
-        if path_index is not None:
-            self.path_index = path_index
-            self.n_path_lengths = len(self.path_index.path_indices)
-            for i, pi in enumerate(self.path_index.path_indices):
-                self.register_buffer("path_indices_" + str(i), torch.from_numpy(pi))
-    
-    def train(self, mode=True):
-        super().train(mode)
-        self.backbone.eval()
-
-    def forward(self, x, with_affinity=False):
-        x1 = self.stage1(x).detach()
-        x2 = self.stage2(x1).detach()
-        x3 = self.stage3(x2).detach()
-        x4 = self.stage4(x3).detach()
-        x5 = self.stage5(x4).detach()
-        
-        edge1 = self.fc_edge1(x1)
-        edge2 = self.fc_edge2(x2)
-        edge3 = self.fc_edge3(x3)[..., :edge2.size(2), :edge2.size(3)]
-        edge4 = self.fc_edge4(x4)[..., :edge2.size(2), :edge2.size(3)]
-        edge5 = self.fc_edge5(x5)[..., :edge2.size(2), :edge2.size(3)]
-
-        edge = self.fc_edge6(torch.cat([edge1, edge2, edge3, edge4, edge5], dim=1))
-
-        if with_affinity:
-            return edge, self.to_affinity(torch.sigmoid(edge))
-        else:
-            return edge
-
-    def get_edge(self, x, image_size=512, stride=4):
-        feat_size = (x.size(2)-1)//stride+1, (x.size(3)-1)//stride+1
-
-        x = F.pad(x, [0, image_size-x.size(3), 0, image_size-x.size(2)])
-        edge_out = self.forward(x)
-        edge_out = edge_out[..., :feat_size[0], :feat_size[1]]
-        edge_out = torch.sigmoid(edge_out[0]/2 + edge_out[1].flip(-1)/2)
-        
-        return edge_out
-    
-    """
-    aff = self.to_affinity(torch.sigmoid(edge_out))
-    pos_aff_loss = (-1) * torch.log(aff + 1e-5)
-    neg_aff_loss = (-1) * torch.log(1. + 1e-5 - aff)
-    """
-    def to_affinity(self, edge):
-        aff_list = []
-        edge = edge.view(edge.size(0), -1)
-        
-        for i in range(self.n_path_lengths):
-            ind = self._buffers["path_indices_" + str(i)]
-            ind_flat = ind.view(-1)
-            dist = torch.index_select(edge, dim=-1, index=ind_flat)
-            dist = dist.view(dist.size(0), ind.size(0), ind.size(1), ind.size(2))
-            aff = torch.squeeze(1 - F.max_pool2d(dist, (dist.size(2), 1)), dim=2)
-            aff_list.append(aff)
-        aff_cat = torch.cat(aff_list, dim=1)
-        return aff_cat
-
-class DeepLabv3_Plus(Backbone):
-    def __init__(self, model_name, num_classes=21, mode='fix', use_group_norm=False):
-        super().__init__(model_name, num_classes, mode, segmentation=False)
-        
-        if use_group_norm:
-            norm_fn_for_extra_modules = group_norm
-        else:
-            norm_fn_for_extra_modules = self.norm_fn
-        
-        self.aspp = ASPP(output_stride=16, norm_fn=norm_fn_for_extra_modules)
-        self.decoder = Decoder(num_classes, 256, norm_fn_for_extra_modules)
-        
-    def forward(self, x, with_cam=False):
-        inputs = x
-
-        x = self.stage1(x)
-        x = self.stage2(x)
-        x_low_level = x
-        
-        x = self.stage3(x)
-        x = self.stage4(x)
-        x = self.stage5(x)
-        
-        x = self.aspp(x)
-        x = self.decoder(x, x_low_level)
-        x = resize_for_tensors(x, inputs.size()[2:], align_corners=True)
-
-        return x
-
-class Seg_Model(Backbone):
-    def __init__(self, model_name, num_classes=21):
-        super().__init__(model_name, num_classes, mode='fix', segmentation=False)
-        
-        self.classifier = nn.Conv2d(2048, num_classes, 1, bias=False)
-    
-    def forward(self, inputs):
-        x = self.stage1(inputs)
-        x = self.stage2(x)
-        x = self.stage3(x)
-        x = self.stage4(x)
-        x = self.stage5(x)
-        
-        logits = self.classifier(x)
-        # logits = resize_for_tensors(logits, inputs.size()[2:], align_corners=False)
-        
-        return logits
-
-class CSeg_Model(Backbone):
-    def __init__(self, model_name, num_classes=21):
-        super().__init__(model_name, num_classes, 'fix')
-
-        if '50' in model_name:
-            fc_edge1_features = 64
-        else:
-            fc_edge1_features = 128
-
-        self.fc_edge1 = nn.Sequential(
-            nn.Conv2d(fc_edge1_features, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge2 = nn.Sequential(
-            nn.Conv2d(256, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge3 = nn.Sequential(
-            nn.Conv2d(512, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge4 = nn.Sequential(
-            nn.Conv2d(1024, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge5 = nn.Sequential(
-            nn.Conv2d(2048, 32, 1, bias=False),
-            nn.GroupNorm(4, 32),
-            nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False),
-            nn.ReLU(inplace=True),
-        )
-        self.fc_edge6 = nn.Conv2d(160, num_classes, 1, bias=True)
-
-    def forward(self, x):
-        x1 = self.stage1(x)
-        x2 = self.stage2(x1)
-        x3 = self.stage3(x2)
-        x4 = self.stage4(x3)
-        x5 = self.stage5(x4)
-        
-        edge1 = self.fc_edge1(x1)
-        edge2 = self.fc_edge2(x2)
-        edge3 = self.fc_edge3(x3)[..., :edge2.size(2), :edge2.size(3)]
-        edge4 = self.fc_edge4(x4)[..., :edge2.size(2), :edge2.size(3)]
-        edge5 = self.fc_edge5(x5)[..., :edge2.size(2), :edge2.size(3)]
-
-        logits = self.fc_edge6(torch.cat([edge1, edge2, edge3, edge4, edge5], dim=1))
-        # logits = resize_for_tensors(logits, x.size()[2:], align_corners=True)
-        
-        return logits
+# Copyright (C) 2021 * Ltd. All rights reserved.
+# author : Sanghyeon Jo <josanghyeokn@gmail.com>
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from torchvision import models
+import torch.utils.model_zoo as model_zoo
+
+from .arch_resnet import resnet
+from .arch_resnest import resnest
+from .abc_modules import ABC_Model
+
+from .deeplab_utils import ASPP, Decoder
+from .aff_utils import PathIndex
+from .puzzle_utils import tile_features, merge_features
+
+from tools.ai.torch_utils import resize_for_tensors
+
+#######################################################################
+# Normalization
+#######################################################################
+from .sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
+
+class FixedBatchNorm(nn.BatchNorm2d):
+    def forward(self, x):
+        return F.batch_norm(x, self.running_mean, self.running_var, self.weight, self.bias, training=False, eps=self.eps)
+
+def group_norm(features):
+    return nn.GroupNorm(4, features)
+#######################################################################
+
+class Backbone(nn.Module, ABC_Model):
+        def __init__(self, model_name, num_classes=20, mode='fix', segmentation=False):
+        super().__init__()
+
+        self.mode = mode
+
+        if self.mode == 'fix':
+            self.norm_fn = FixedBatchNorm
+        else:
+            self.norm_fn = nn.BatchNorm2d
+
+        if 'resnet' in model_name:
+            self.model = resnet.ResNet(resnet.Bottleneck, resnet.layers_dic[model_name], strides=(2, 2, 2, 1),
+                                       batch_norm_fn=self.norm_fn)
+
+            state_dict = model_zoo.load_url(resnet.urls_dic[model_name])
+            state_dict.pop('fc.weight')
+            state_dict.pop('fc.bias')
+
+            self.model.load_state_dict(state_dict)
+        else:
+            if segmentation:
+                dilation, dilated = 4, True
+            else:
+                dilation, dilated = 2, False
+
+            self.model = eval("resnest." + model_name)(pretrained=True, dilated=dilated, dilation=dilation,
+                                                       norm_layer=self.norm_fn)
+
+            del self.model.avgpool
+            del self.model.fc
+
+        self.stage1 = nn.Sequential(self.model.conv1,
+                                    self.model.bn1,
+                                    self.model.relu,
+                                    self.model.maxpool)
+        self.stage2 = nn.Sequential(self.model.layer1)
+        self.stage3 = nn.Sequential(self.model.layer2)
+        self.stage4 = nn.Sequential(self.model.layer3)
+        self.stage5 = nn.Sequential(self.model.layer4)
+
+class Classifier(Backbone):
+    def __init__(self, model_name, state_path, num_classes=20, mode='fix'):
+        super().__init__(model_name, state_path, num_classes, mode)
+        
+        self.classifier = nn.Conv2d(2048, num_classes, 1, bias=False)
+        self.num_classes = num_classes
+
+        self.initialize([self.classifier])
+    
+    def forward(self, x, with_cam=False):
+        x = self.stage1(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = self.stage5(x)
+        
+        if with_cam:
+            features = self.classifier(x)
+            logits = self.global_average_pooling_2d(features)
+            return logits, features
+        else:
+            x = self.global_average_pooling_2d(x, keepdims=True) 
+            logits = self.classifier(x).view(-1, self.num_classes)
+            return logits
+
+class Classifier_For_Positive_Pooling(Backbone):
+    def __init__(self, model_name, num_classes=20, mode='fix'):
+        super().__init__(model_name, num_classes, mode)
+        
+        self.classifier = nn.Conv2d(2048, num_classes, 1, bias=False)
+        self.num_classes = num_classes
+        
+        self.initialize([self.classifier])
+    
+    def forward(self, x, with_cam=False):
+        x = self.stage1(x)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = self.stage5(x)
+        
+        if with_cam:
+            features = self.classifier(x)
+            logits = self.global_average_pooling_2d(features)
+            return logits, features
+        else:
+            x = self.global_average_pooling_2d(x, keepdims=True) 
+            logits = self.classifier(x).view(-1, self.num_classes)
+            return logits
+
+class Classifier_For_Puzzle(Classifier):
+    def __init__(self, model_name, num_classes=20, mode='fix'):
+        super().__init__(model_name, num_classes, mode)
+        
+    def forward(self, x, num_pieces=1, level=-1):
+        batch_size = x.size()[0]
+        
+        output_dic = {}
+        layers = [self.stage1, self.stage2, self.stage3, self.stage4, self.stage5, self.classifier]
+
+        for l, layer in enumerate(layers):
+            l += 1
+            if level == l:
+                x = tile_features(x, num_pieces)
+
+            x = layer(x)
+            output_dic['stage%d'%l] = x
+        
+        output_dic['logits'] = self.global_average_pooling_2d(output_dic['stage6'])
+
+        for l in range(len(layers)):
+            l += 1
+            if l >= level:
+                output_dic['stage%d'%l] = merge_features(output_dic['stage%d'%l], num_pieces, batch_size)
+
+        if level is not None:
+            output_dic['merged_logits'] = self.global_average_pooling_2d(output_dic['stage6'])
+
+        return output_dic
+        
+class AffinityNet(Backbone):
+    def __init__(self, model_name, path_index=None):
+        super().__init__(model_name, None, 'fix')
+
+        if '50' in model_name:
+            fc_edge1_features = 64
+        else:
+            fc_edge1_features = 128
+
+        self.fc_edge1 = nn.Sequential(
+            nn.Conv2d(fc_edge1_features, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge2 = nn.Sequential(
+            nn.Conv2d(256, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge3 = nn.Sequential(
+            nn.Conv2d(512, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge4 = nn.Sequential(
+            nn.Conv2d(1024, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge5 = nn.Sequential(
+            nn.Conv2d(2048, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge6 = nn.Conv2d(160, 1, 1, bias=True)
+
+        self.backbone = nn.ModuleList([self.stage1, self.stage2, self.stage3, self.stage4, self.stage5])
+        self.edge_layers = nn.ModuleList([self.fc_edge1, self.fc_edge2, self.fc_edge3, self.fc_edge4, self.fc_edge5, self.fc_edge6])
+
+        if path_index is not None:
+            self.path_index = path_index
+            self.n_path_lengths = len(self.path_index.path_indices)
+            for i, pi in enumerate(self.path_index.path_indices):
+                self.register_buffer("path_indices_" + str(i), torch.from_numpy(pi))
+    
+    def train(self, mode=True):
+        super().train(mode)
+        self.backbone.eval()
+
+    def forward(self, x, with_affinity=False):
+        x1 = self.stage1(x).detach()
+        x2 = self.stage2(x1).detach()
+        x3 = self.stage3(x2).detach()
+        x4 = self.stage4(x3).detach()
+        x5 = self.stage5(x4).detach()
+        
+        edge1 = self.fc_edge1(x1)
+        edge2 = self.fc_edge2(x2)
+        edge3 = self.fc_edge3(x3)[..., :edge2.size(2), :edge2.size(3)]
+        edge4 = self.fc_edge4(x4)[..., :edge2.size(2), :edge2.size(3)]
+        edge5 = self.fc_edge5(x5)[..., :edge2.size(2), :edge2.size(3)]
+
+        edge = self.fc_edge6(torch.cat([edge1, edge2, edge3, edge4, edge5], dim=1))
+
+        if with_affinity:
+            return edge, self.to_affinity(torch.sigmoid(edge))
+        else:
+            return edge
+
+    def get_edge(self, x, image_size=512, stride=4):
+        feat_size = (x.size(2)-1)//stride+1, (x.size(3)-1)//stride+1
+
+        x = F.pad(x, [0, image_size-x.size(3), 0, image_size-x.size(2)])
+        edge_out = self.forward(x)
+        edge_out = edge_out[..., :feat_size[0], :feat_size[1]]
+        edge_out = torch.sigmoid(edge_out[0]/2 + edge_out[1].flip(-1)/2)
+        
+        return edge_out
+    
+    """
+    aff = self.to_affinity(torch.sigmoid(edge_out))
+    pos_aff_loss = (-1) * torch.log(aff + 1e-5)
+    neg_aff_loss = (-1) * torch.log(1. + 1e-5 - aff)
+    """
+    def to_affinity(self, edge):
+        aff_list = []
+        edge = edge.view(edge.size(0), -1)
+        
+        for i in range(self.n_path_lengths):
+            ind = self._buffers["path_indices_" + str(i)]
+            ind_flat = ind.view(-1)
+            dist = torch.index_select(edge, dim=-1, index=ind_flat)
+            dist = dist.view(dist.size(0), ind.size(0), ind.size(1), ind.size(2))
+            aff = torch.squeeze(1 - F.max_pool2d(dist, (dist.size(2), 1)), dim=2)
+            aff_list.append(aff)
+        aff_cat = torch.cat(aff_list, dim=1)
+        return aff_cat
+
+class DeepLabv3_Plus(Backbone):
+    def __init__(self, model_name, num_classes=21, mode='fix', use_group_norm=False):
+        super().__init__(model_name, num_classes, mode, segmentation=False)
+        
+        if use_group_norm:
+            norm_fn_for_extra_modules = group_norm
+        else:
+            norm_fn_for_extra_modules = self.norm_fn
+        
+        self.aspp = ASPP(output_stride=16, norm_fn=norm_fn_for_extra_modules)
+        self.decoder = Decoder(num_classes, 256, norm_fn_for_extra_modules)
+        
+    def forward(self, x, with_cam=False):
+        inputs = x
+
+        x = self.stage1(x)
+        x = self.stage2(x)
+        x_low_level = x
+        
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = self.stage5(x)
+        
+        x = self.aspp(x)
+        x = self.decoder(x, x_low_level)
+        x = resize_for_tensors(x, inputs.size()[2:], align_corners=True)
+
+        return x
+
+class Seg_Model(Backbone):
+    def __init__(self, model_name, num_classes=21):
+        super().__init__(model_name, num_classes, mode='fix', segmentation=False)
+        
+        self.classifier = nn.Conv2d(2048, num_classes, 1, bias=False)
+    
+    def forward(self, inputs):
+        x = self.stage1(inputs)
+        x = self.stage2(x)
+        x = self.stage3(x)
+        x = self.stage4(x)
+        x = self.stage5(x)
+        
+        logits = self.classifier(x)
+        # logits = resize_for_tensors(logits, inputs.size()[2:], align_corners=False)
+        
+        return logits
+
+class CSeg_Model(Backbone):
+    def __init__(self, model_name, num_classes=21):
+        super().__init__(model_name, num_classes, 'fix')
+
+        if '50' in model_name:
+            fc_edge1_features = 64
+        else:
+            fc_edge1_features = 128
+
+        self.fc_edge1 = nn.Sequential(
+            nn.Conv2d(fc_edge1_features, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge2 = nn.Sequential(
+            nn.Conv2d(256, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge3 = nn.Sequential(
+            nn.Conv2d(512, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.Upsample(scale_factor=2, mode='bilinear', align_corners=False),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge4 = nn.Sequential(
+            nn.Conv2d(1024, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge5 = nn.Sequential(
+            nn.Conv2d(2048, 32, 1, bias=False),
+            nn.GroupNorm(4, 32),
+            nn.Upsample(scale_factor=4, mode='bilinear', align_corners=False),
+            nn.ReLU(inplace=True),
+        )
+        self.fc_edge6 = nn.Conv2d(160, num_classes, 1, bias=True)
+
+    def forward(self, x):
+        x1 = self.stage1(x)
+        x2 = self.stage2(x1)
+        x3 = self.stage3(x2)
+        x4 = self.stage4(x3)
+        x5 = self.stage5(x4)
+        
+        edge1 = self.fc_edge1(x1)
+        edge2 = self.fc_edge2(x2)
+        edge3 = self.fc_edge3(x3)[..., :edge2.size(2), :edge2.size(3)]
+        edge4 = self.fc_edge4(x4)[..., :edge2.size(2), :edge2.size(3)]
+        edge5 = self.fc_edge5(x5)[..., :edge2.size(2), :edge2.size(3)]
+
+        logits = self.fc_edge6(torch.cat([edge1, edge2, edge3, edge4, edge5], dim=1))
+        # logits = resize_for_tensors(logits, x.size()[2:], align_corners=True)
+        
+        return logits