Upload New CNN

networks/convnet.py

+import math
+import torch
+import torch.nn as nn
+import torch.nn.init as init 
+import torch.nn.functional as F
+import torch.optim as optim
+
+def call_bn(bn, x):
+    return bn(x)
+
+class ConvNet(nn.Module):
+    def __init__(self, input_channel=3, n_outputs=10, dropout_rate=0.25, top_bn=False):
+        self.dropout_rate = dropout_rate
+        self.top_bn = top_bn
+        super(ConvNet, self).__init__()
+        self.c1=nn.Conv2d(input_channel,128,kernel_size=3,stride=1, padding=1)
+        self.c2=nn.Conv2d(128,128,kernel_size=3,stride=1, padding=1)
+        self.c3=nn.Conv2d(128,128,kernel_size=3,stride=1, padding=1)
+        self.c4=nn.Conv2d(128,256,kernel_size=3,stride=1, padding=1)
+        self.c5=nn.Conv2d(256,256,kernel_size=3,stride=1, padding=1)
+        self.c6=nn.Conv2d(256,256,kernel_size=3,stride=1, padding=1)
+        self.c7=nn.Conv2d(256,512,kernel_size=3,stride=1, padding=0)
+        self.c8=nn.Conv2d(512,256,kernel_size=3,stride=1, padding=0)
+        self.c9=nn.Conv2d(256,128,kernel_size=3,stride=1, padding=0)
+        self.l_c1=nn.Linear(128,n_outputs)
+        self.bn1=nn.BatchNorm2d(128)
+        self.bn2=nn.BatchNorm2d(128)
+        self.bn3=nn.BatchNorm2d(128)
+        self.bn4=nn.BatchNorm2d(256)
+        self.bn5=nn.BatchNorm2d(256)
+        self.bn6=nn.BatchNorm2d(256)
+        self.bn7=nn.BatchNorm2d(512)
+        self.bn8=nn.BatchNorm2d(256)
+        self.bn9=nn.BatchNorm2d(128)
+
+    def forward(self, x,):
+        h=x
+        h=self.c1(h)
+        h=F.leaky_relu(call_bn(self.bn1, h), negative_slope=0.01)
+        h=self.c2(h)
+        h=F.leaky_relu(call_bn(self.bn2, h), negative_slope=0.01)
+        h=self.c3(h)
+        h=F.leaky_relu(call_bn(self.bn3, h), negative_slope=0.01)
+        h=F.max_pool2d(h, kernel_size=2, stride=2)
+        h=F.dropout2d(h, p=self.dropout_rate)
+
+        h=self.c4(h)
+        h=F.leaky_relu(call_bn(self.bn4, h), negative_slope=0.01)
+        h=self.c5(h)
+        h=F.leaky_relu(call_bn(self.bn5, h), negative_slope=0.01)
+        h=self.c6(h)
+        h=F.leaky_relu(call_bn(self.bn6, h), negative_slope=0.01)
+        h=F.max_pool2d(h, kernel_size=2, stride=2)
+        h=F.dropout2d(h, p=self.dropout_rate)
+
+        h=self.c7(h)
+        h=F.leaky_relu(call_bn(self.bn7, h), negative_slope=0.01)
+        h=self.c8(h)
+        h=F.leaky_relu(call_bn(self.bn8, h), negative_slope=0.01)
+        h=self.c9(h)
+        h=F.leaky_relu(call_bn(self.bn9, h), negative_slope=0.01)
+        h=F.avg_pool2d(h, kernel_size=h.data.shape[2])
+
+        h = h.view(h.size(0), h.size(1))
+        logit=self.l_c1(h)
+        if self.top_bn:
+            logit=call_bn(self.bn_c1, logit)
+        return logit