From c13dba86b2dbe581353b72602d7fa6e40991964c Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期三, 27 九月 2017 04:11:23 +0800
Subject: [PATCH] next
---
code/hopenet.py | 90 ---------------------------------------------
1 files changed, 0 insertions(+), 90 deletions(-)
diff --git a/code/hopenet.py b/code/hopenet.py
index b2dd097..c6bf0db 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -340,93 +340,3 @@
angles.append(preangles)
return pre_yaw, pre_pitch, pre_roll, angles, sr_output
-
-class Hopenet_LSTM(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_LSTM, 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 = nn.Linear(512 * block.expansion + 3, 3)
-
- self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()
-
- self.lstm = nn.LSTM(512 * block.expansion + 3, 256 * block.expansion, 2, batch_first=True)
- self.fc_lstm = nn.Linear(256 * block.expansion, 3)
-
- self.block_expansion = block.expansion
-
- 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 = 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, pitch, roll
- 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)
-
- residuals, _ = self.lstm(torch.cat((preangles, x), 1), (h0, c0))
- residuals = self.fc_lstm(residuals[:, -1, :])
- final_angles = preangles + residuals
-
- return pre_yaw, pre_pitch, pre_roll, preangles, final_angles
--
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