ltt :调通了deeplab, 修改了backbone/mobilenet中的问题，添加了输入batch处理，待完成train,test…

…,evaluate模块

.gitignore

@@ -0,0 +1,3 @@
+*.pyc
+*.pth
+/__pycache__

README.md

@@ -1,3 +1,2 @@
-# -BUAA_FRB_-
-
-这里是由阳神带领的冯如小队，完毕！
+# FRB_lttprivate
+save code of myself

deeplab.py

@@ -61,6 +61,14 @@ def get_10x_lr_params(self):
                             yield p
 
 
+def getinput():
+   root_path = "../data20200111/"
+   img_path = "img/"
+   fea_path = "feature/"
+   label_path = "label/"
+
+
+
 if __name__ == "__main__":
     model = DeepLab(backbone='mobilenet', output_stride=16)
     model.eval()

modeling/aspp.py

@@ -0,0 +1,95 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from modeling.sync_batchnorm.batchnorm import SynchronizedBatchNorm2d
+
+class _ASPPModule(nn.Module):
+    def __init__(self, inplanes, planes, kernel_size, padding, dilation, BatchNorm):
+        super(_ASPPModule, self).__init__()
+        self.atrous_conv = nn.Conv2d(inplanes, planes, kernel_size=kernel_size,
+                                            stride=1, padding=padding, dilation=dilation, bias=False)
+        self.bn = BatchNorm(planes)
+        self.relu = nn.ReLU()
+
+        self._init_weight()
+
+    def forward(self, x):
+        x = self.atrous_conv(x)
+        x = self.bn(x)
+
+        return self.relu(x)
+
+    def _init_weight(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                torch.nn.init.kaiming_normal_(m.weight)
+            elif isinstance(m, SynchronizedBatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+class ASPP(nn.Module):
+    def __init__(self, backbone, output_stride, BatchNorm):
+        super(ASPP, self).__init__()
+        if backbone == 'drn':
+            inplanes = 512
+        elif backbone == 'mobilenet':
+            inplanes = 320
+        else:
+            inplanes = 2048
+        if output_stride == 16:
+            dilations = [1, 6, 12, 18]
+        elif output_stride == 8:
+            dilations = [1, 12, 24, 36]
+        else:
+            raise NotImplementedError
+
+        self.aspp1 = _ASPPModule(inplanes, 256, 1, padding=0, dilation=dilations[0], BatchNorm=BatchNorm)
+        self.aspp2 = _ASPPModule(inplanes, 256, 3, padding=dilations[1], dilation=dilations[1], BatchNorm=BatchNorm)
+        self.aspp3 = _ASPPModule(inplanes, 256, 3, padding=dilations[2], dilation=dilations[2], BatchNorm=BatchNorm)
+        self.aspp4 = _ASPPModule(inplanes, 256, 3, padding=dilations[3], dilation=dilations[3], BatchNorm=BatchNorm)
+
+        self.global_avg_pool = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
+                                             nn.Conv2d(inplanes, 256, 1, stride=1, bias=False),
+                                             BatchNorm(256),
+                                             nn.ReLU())
+        self.conv1 = nn.Conv2d(1280, 256, 1, bias=False)
+        self.bn1 = BatchNorm(256)
+        self.relu = nn.ReLU()
+        self.dropout = nn.Dropout(0.5)
+        self._init_weight()
+
+    def forward(self, x):
+        x1 = self.aspp1(x)
+        x2 = self.aspp2(x)
+        x3 = self.aspp3(x)
+        x4 = self.aspp4(x)
+        x5 = self.global_avg_pool(x)
+        x5 = F.interpolate(x5, size=x4.size()[2:], mode='bilinear', align_corners=True)
+        x = torch.cat((x1, x2, x3, x4, x5), dim=1)
+
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+
+        return self.dropout(x)
+
+    def _init_weight(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                # m.weight.data.normal_(0, math.sqrt(2. / n))
+                torch.nn.init.kaiming_normal_(m.weight)
+            elif isinstance(m, SynchronizedBatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+
+
+def build_aspp(backbone, output_stride, BatchNorm):
+    return ASPP(backbone, output_stride, BatchNorm)

modeling/backbone/__init__.py

@@ -0,0 +1,13 @@
+from modeling.backbone import resnet, xception, drn, mobilenet
+
+def build_backbone(backbone, output_stride, BatchNorm):
+    if backbone == 'resnet':
+        return resnet.ResNet101(output_stride, BatchNorm)
+    elif backbone == 'xception':
+        return xception.AlignedXception(output_stride, BatchNorm)
+    elif backbone == 'drn':
+        return drn.drn_d_54(BatchNorm)
+    elif backbone == 'mobilenet':
+        return mobilenet.MobileNetV2(output_stride, BatchNorm)
+    else:
+        raise NotImplementedError