import torch
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import torch.nn as nn
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from torch.autograd import Variable
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import math
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import torch.nn.functional as F
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# CNN Model (2 conv layer)
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class Simple_CNN(nn.Module):
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def __init__(self):
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super(Simple_CNN, self).__init__()
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self.layer1 = nn.Sequential(
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nn.Conv2d(3, 64, kernel_size=3, padding=0),
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nn.BatchNorm2d(64),
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nn.ReLU(),
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nn.MaxPool2d(2))
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self.layer2 = nn.Sequential(
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nn.Conv2d(64, 128, kernel_size=3, padding=0),
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nn.BatchNorm2d(128),
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nn.ReLU(),
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nn.MaxPool2d(2))
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self.layer3 = nn.Sequential(
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nn.Conv2d(128, 256, kernel_size=3, padding=0),
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nn.BatchNorm2d(256),
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nn.ReLU(),
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nn.MaxPool2d(2))
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self.layer4 = nn.Sequential(
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nn.Conv2d(256, 512, kernel_size=3, padding=0),
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nn.BatchNorm2d(512),
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nn.ReLU(),
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nn.MaxPool2d(2))
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self.fc = nn.Linear(17*17*512, 3)
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def forward(self, x):
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out = self.layer1(x)
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out = self.layer2(out)
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out = self.layer3(out)
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out = self.layer4(out)
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out = out.view(out.size(0), -1)
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out = self.fc(out)
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return out
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class Hopenet(nn.Module):
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# This is just Hopenet with 3 output layers for yaw, pitch and roll.
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def __init__(self, block, layers, num_bins, iter_ref):
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self.inplanes = 64
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super(Hopenet, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
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self.avgpool = nn.AvgPool2d(7)
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self.fc_yaw = nn.Linear(512 * block.expansion, num_bins)
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self.fc_pitch = nn.Linear(512 * block.expansion, num_bins)
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self.fc_roll = nn.Linear(512 * block.expansion, num_bins)
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self.softmax = nn.Softmax()
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self.fc_finetune = nn.Linear(512 * block.expansion + 3, 3)
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self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()
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self.iter_ref = iter_ref
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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m.weight.data.normal_(0, math.sqrt(2. / n))
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elif isinstance(m, nn.BatchNorm2d):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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def _make_layer(self, block, planes, blocks, stride=1):
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downsample = None
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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nn.Conv2d(self.inplanes, planes * block.expansion,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample))
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self.inplanes = planes * block.expansion
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for i in range(1, blocks):
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layers.append(block(self.inplanes, planes))
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return nn.Sequential(*layers)
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def forward(self, x):
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = x.view(x.size(0), -1)
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pre_yaw = self.fc_yaw(x)
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pre_pitch = self.fc_pitch(x)
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pre_roll = self.fc_roll(x)
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yaw = self.softmax(pre_yaw)
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yaw = Variable(torch.sum(yaw.data * self.idx_tensor.data, 1), requires_grad=True)
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pitch = self.softmax(pre_pitch)
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pitch = Variable(torch.sum(pitch.data * self.idx_tensor.data, 1), requires_grad=True)
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roll = self.softmax(pre_roll)
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roll = Variable(torch.sum(roll.data * self.idx_tensor.data, 1), requires_grad=True)
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yaw = yaw.view(yaw.size(0), 1)
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pitch = pitch.view(pitch.size(0), 1)
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roll = roll.view(roll.size(0), 1)
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angles = []
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preangles = torch.cat([yaw, pitch, roll], 1)
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angles.append(preangles)
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# angles predicts the residual
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for idx in xrange(self.iter_ref):
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angles.append(self.fc_finetune(torch.cat((preangles, x), 1)))
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return pre_yaw, pre_pitch, pre_roll, angles
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