From 0be0ecf0a8fc6df1f9e354f8aea12b7008f658f1 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 06:21:54 +0800
Subject: [PATCH] hopenet experiments
---
code/hopenet.py | 90 +++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 90 insertions(+), 0 deletions(-)
diff --git a/code/hopenet.py b/code/hopenet.py
index c6bf0db..de2f4ec 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -340,3 +340,93 @@
angles.append(preangles)
return pre_yaw, pre_pitch, pre_roll, angles, sr_output
+
+class Hopenet_new(nn.Module):
+ # This is just Hopenet with 3 output layers for yaw, pitch and roll.
+ def __init__(self, block, layers, num_bins):
+ self.inplanes = 64
+ super(Hopenet_new, self).__init__()
+ self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+ bias=False)
+ self.bn1 = nn.BatchNorm2d(64)
+ self.relu = nn.ReLU(inplace=True)
+ self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+ self.layer1 = self._make_layer(block, 64, layers[0])
+ self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+ self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+ self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+ self.avgpool = nn.AvgPool2d(7)
+ self.fc_yaw = nn.Linear(512 * block.expansion, num_bins)
+ self.fc_pitch = nn.Linear(512 * block.expansion, num_bins)
+ self.fc_roll = nn.Linear(512 * block.expansion, num_bins)
+
+ self.softmax = nn.Softmax()
+ self.fc_finetune_new = nn.Linear(512 * block.expansion + 256 * block.expansion + 3, 3)
+ self.conv1x1 = nn.Conv2d(1024, 64, kernel_size = 1, stride = 1, bias=False)
+ self.maxpool_interm = nn.MaxPool2d(kernel_size=5, stride=3, padding=1)
+
+ self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()
+
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+ m.weight.data.normal_(0, math.sqrt(2. / n))
+ elif isinstance(m, nn.BatchNorm2d):
+ m.weight.data.fill_(1)
+ m.bias.data.zero_()
+
+ def _make_layer(self, block, planes, blocks, stride=1):
+ downsample = None
+ if stride != 1 or self.inplanes != planes * block.expansion:
+ downsample = nn.Sequential(
+ nn.Conv2d(self.inplanes, planes * block.expansion,
+ kernel_size=1, stride=stride, bias=False),
+ nn.BatchNorm2d(planes * block.expansion),
+ )
+
+ layers = []
+ layers.append(block(self.inplanes, planes, stride, downsample))
+ self.inplanes = planes * block.expansion
+ for i in range(1, blocks):
+ layers.append(block(self.inplanes, planes))
+
+ return nn.Sequential(*layers)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = self.bn1(x)
+ x = self.relu(x)
+ x = self.maxpool(x)
+
+ x = self.layer1(x)
+ x = self.layer2(x)
+ x = self.layer3(x)
+ x_interm = self.conv1x1(x)
+ x_interm = self.relu(x_interm)
+ x_interm = self.maxpool_interm(x_interm)
+ x_interm = x_interm.view(x_interm.size(0), -1)
+
+ x = self.layer4(x)
+
+ x = self.avgpool(x)
+ x = x.view(x.size(0), -1)
+ pre_yaw = self.fc_yaw(x)
+ pre_pitch = self.fc_pitch(x)
+ pre_roll = self.fc_roll(x)
+
+ yaw = self.softmax(pre_yaw)
+ yaw = Variable(torch.sum(yaw.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99
+ pitch = self.softmax(pre_pitch)
+ pitch = Variable(torch.sum(pitch.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99
+ roll = self.softmax(pre_roll)
+ roll = Variable(torch.sum(roll.data * self.idx_tensor.data, 1), requires_grad=True) * 3 - 99
+ yaw = yaw.view(yaw.size(0), 1)
+ pitch = pitch.view(pitch.size(0), 1)
+ roll = roll.view(roll.size(0), 1)
+ preangles = torch.cat([yaw, pitch, roll], 1)
+
+ # angles predicts the residual
+ residuals = self.fc_finetune_new(torch.cat((preangles, x_interm, x), 1))
+ final_angles = preangles + residuals
+
+ return pre_yaw, pre_pitch, pre_roll, preangles, final_angles
--
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