Delete CustomResNet.py

CustomResNet.py

@@ -1,104 +0,0 @@
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class BasicBlock(nn.Module):
-
-    def __init__(self, in_planes, planes, stride=1):
-        super(BasicBlock, self).__init__()
-        self.conv1 = nn.Conv2d(
-            in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
-        )
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(
-            planes, planes, kernel_size=3, stride=1, padding=1, bias=False
-        )
-        self.bn2 = nn.BatchNorm2d(planes)
-
-        self.shortcut = nn.Sequential()
-
-    def forward(self, x):
-        out = F.relu(self.bn1(self.conv1(x)))
-        out = self.bn2(self.conv2(out))
-        out += self.shortcut(x)
-        out = F.relu(out)
-        return out
-
-
-class CustomBlock(nn.Module):
-    def __init__(self, in_channels, out_channels):
-        super(CustomBlock, self).__init__()
-
-        self.inner_layer = nn.Sequential(
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=False,
-            ),
-            nn.MaxPool2d(kernel_size=2),
-            nn.BatchNorm2d(out_channels),
-            nn.ReLU(),
-        )
-
-        self.res_block = BasicBlock(out_channels, out_channels)
-
-    def forward(self, x):
-        x = self.inner_layer(x)
-        r = self.res_block(x)
-
-        out = x + r
-
-        return out
-
-
-class CustomResNet(nn.Module):
-    def __init__(self, num_classes=10):
-        super(CustomResNet, self).__init__()
-
-        self.prep_layer = nn.Sequential(
-            nn.Conv2d(
-                in_channels=3,
-                out_channels=64,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=False,
-            ),
-            nn.BatchNorm2d(64),
-            nn.ReLU(),
-        )
-
-        self.layer_1 = CustomBlock(in_channels=64, out_channels=128)
-
-        self.layer_2 = nn.Sequential(
-            nn.Conv2d(
-                in_channels=128,
-                out_channels=256,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=False,
-            ),
-            nn.MaxPool2d(kernel_size=2),
-            nn.BatchNorm2d(256),
-            nn.ReLU(),
-        )
-
-        self.layer_3 = CustomBlock(in_channels=256, out_channels=512)
-
-        self.max_pool = nn.Sequential(nn.MaxPool2d(kernel_size=4))
-
-        self.fc = nn.Linear(512, num_classes)
-
-    def forward(self, x):
-        x = self.prep_layer(x)
-        x = self.layer_1(x)
-        x = self.layer_2(x)
-        x = self.layer_3(x)
-        x = self.max_pool(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc(x)
-        return x