pytorch resnet inception v2

src/core/resnet_inception_v2.py

@@ -0,0 +1,279 @@
+import torch
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+import os
+import sys
+
+
+class BasicConv2d(nn.Module):
+    def __init__(self, in_planes, out_planes, kernel_size, stride, padding=0):
+        super(BasicConv2d, self).__init__()
+        self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
+                              bias=False)  # verify bias false
+        self.bn = nn.BatchNorm2d(out_planes, eps=0.001, momentum=0, affine=True)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Mixed_5b(nn.Module):
+    def __init__(self):
+        super(Mixed_5b, self).__init__()
+
+        self.branch0 = BasicConv2d(192, 96, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(192, 48, kernel_size=1, stride=1),
+            BasicConv2d(48, 64, kernel_size=5, stride=1, padding=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(192, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(192, 64, kernel_size=1, stride=1)
+        )
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Block35(nn.Module):
+    def __init__(self, scale=1.0):
+        super(Block35, self).__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(320, 32, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(320, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 32, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(320, 32, kernel_size=1, stride=1),
+            BasicConv2d(32, 48, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(48, 64, kernel_size=3, stride=1, padding=1)
+        )
+
+        self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Mixed_6a(nn.Module):
+    def __init__(self):
+        super(Mixed_6a, self).__init__()
+
+        self.branch0 = BasicConv2d(320, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(320, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 256, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Block17(nn.Module):
+    def __init__(self, scale=1.0):
+        super(Block17, self).__init__()
+
+        self.scale = scale
+
+        self.branch0 = BasicConv2d(1088, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1088, 128, kernel_size=1, stride=1),
+            BasicConv2d(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
+            BasicConv2d(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0))
+        )
+
+        self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
+        self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        out = self.relu(out)
+        return out
+
+
+class Mixed_7a(nn.Module):
+    def __init__(self):
+        super(Mixed_7a, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 384, kernel_size=3, stride=2)
+        )
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 288, kernel_size=3, stride=2)
+        )
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(1088, 256, kernel_size=1, stride=1),
+            BasicConv2d(256, 288, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(288, 320, kernel_size=3, stride=2)
+        )
+
+        self.branch3 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Block8(nn.Module):
+    def __init__(self, scale=1.0, noReLU=False):
+        super(Block8, self).__init__()
+
+        self.scale = scale
+        self.noReLU = noReLU
+
+        self.branch0 = BasicConv2d(2080, 192, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(2080, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
+            BasicConv2d(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
+        )
+
+        self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
+        if not self.noReLU:
+            self.relu = nn.ReLU(inplace=False)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        out = self.conv2d(out)
+        out = out * self.scale + x
+        if not self.noReLU:
+            out = self.relu(out)
+        return out
+
+
+class InceptionResnetV2(nn.Module):
+    def __init__(self, num_classes=1001):
+        super(InceptionResnetV2, self).__init__()
+        self.conv2d_1a = BasicConv2d(3, 32, kernel_size=3, stride=2)
+        self.conv2d_2a = BasicConv2d(32, 32, kernel_size=3, stride=1)
+        self.conv2d_2b = BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.maxpool_3a = nn.MaxPool2d(3, stride=2)
+        self.conv2d_3b = BasicConv2d(64, 80, kernel_size=1, stride=1)
+        self.conv2d_4a = BasicConv2d(80, 192, kernel_size=3, stride=1)
+        self.maxpool_5a = nn.MaxPool2d(3, stride=2)
+        self.mixed_5b = Mixed_5b()
+        self.repeat = nn.Sequential(
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17),
+            Block35(scale=0.17)
+        )
+        self.mixed_6a = Mixed_6a()
+        self.repeat_1 = nn.Sequential(
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10),
+            Block17(scale=0.10)
+        )
+        self.mixed_7a = Mixed_7a()
+        self.repeat_2 = nn.Sequential(
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20),
+            Block8(scale=0.20)
+        )
+        self.block8 = Block8(noReLU=True)
+        self.conv2d_7b = BasicConv2d(2080, 1536, kernel_size=1, stride=1)
+        self.avgpool_1a = nn.AvgPool2d(8, count_include_pad=False)
+        self.classif = nn.Linear(1536, num_classes)
+
+    def forward(self, x):
+        x = self.conv2d_1a(x)
+        x = self.conv2d_2a(x)
+        x = self.conv2d_2b(x)
+        x = self.maxpool_3a(x)
+        x = self.conv2d_3b(x)
+        x = self.conv2d_4a(x)
+        x = self.maxpool_5a(x)
+        x = self.mixed_5b(x)
+        x = self.repeat(x)
+        x = self.mixed_6a(x)
+        x = self.repeat_1(x)
+        x = self.mixed_7a(x)
+        x = self.repeat_2(x)
+        x = self.block8(x)
+        x = self.conv2d_7b(x)
+        x = self.avgpool_1a(x)
+        x = x.view(x.size(0), -1)
+        x = self.classif(x)
+        return x