| from __future__ import print_function, division, absolute_import |
| import torch |
| import torch.nn as nn |
| 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) |
| self.bn = nn.BatchNorm2d(out_planes, |
| eps=0.001, |
| momentum=0.1, |
| 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=50): |
| super(InceptionResNetV2, self).__init__() |
| |
| self.num_classes = num_classes |
| self.input_space = None |
| self.input_size = (299, 299, 3) |
| self.mean = None |
| self.std = None |
| |
| 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.AdaptiveAvgPool2d((1, 1)) |
| self.last_linear = nn.Linear(1536, num_classes) |
| |
| self.branch = self._make_branch(320, 1536, 3) |
| self.branch1 = self._make_branch(1088, 1536, 3) |
| self.branch2 = self._make_branch(2080, 1536, 3) |
| |
| def _make_branch(self, channel_in, channel_out, kernel_size): |
| middle_channel = channel_out // 4 |
| return nn.Sequential( |
| nn.Conv2d(channel_in, middle_channel, kernel_size=1, stride=1), |
| nn.BatchNorm2d(middle_channel), |
| nn.ReLU(), |
| |
| nn.Conv2d(middle_channel, middle_channel, kernel_size=kernel_size, stride=kernel_size), |
| nn.BatchNorm2d(middle_channel), |
| nn.ReLU(), |
| |
| nn.Conv2d(middle_channel, channel_out, kernel_size=1, stride=1), |
| nn.BatchNorm2d(channel_out), |
| nn.ReLU(), |
| |
| nn.AdaptiveAvgPool2d((1,1)), |
| nn.Flatten(), |
| nn.Linear(channel_out, self.num_classes) |
| ) |
| |
|
|
| def features(self, input): |
| x = self.conv2d_1a(input) |
| 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) |
| x1 = self.branch(x) |
| |
| x = self.mixed_6a(x) |
| x = self.repeat_1(x) |
| x2 = self.branch1(x) |
| |
| x = self.mixed_7a(x) |
| x = self.repeat_2(x) |
| x3 = self.branch2(x) |
| |
| x = self.block8(x) |
| x = self.conv2d_7b(x) |
| return x, x1, x2, x3 |
|
|
| def logits(self, features): |
| x = self.avgpool_1a(features) |
| x = x.view(x.size(0), -1) |
| out = self.last_linear(x) |
| return out |
|
|
|
|
| def forward(self, input): |
| x, x1, x2, x3, = self.features(input) |
| out = self.logits(x) |
| return {'outputs': [out, x1, x2, x3]} |
|
|
|
|
| def test(): |
| net = InceptionResNetV2().cuda() |
| y = net(torch.randn(1,3,227,227).cuda()) |
| print(y.size()) |
| |
