From f415df3448622f30c3d1eb680596871672b38dac Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 26 十月 2017 02:51:37 +0800
Subject: [PATCH] after fg
---
code/hopenet.py | 26 +++++++++++++-------------
1 files changed, 13 insertions(+), 13 deletions(-)
diff --git a/code/hopenet.py b/code/hopenet.py
index b2dd097..80160d9 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -339,13 +339,13 @@
preangles = torch.cat([yaw, pitch, roll], 1)
angles.append(preangles)
- return pre_yaw, pre_pitch, pre_roll, angles, sr_output
+ return pre_yaw, pre_pitch, pre_roll, angles, sr_y
-class Hopenet_LSTM(nn.Module):
+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_LSTM, self).__init__()
+ 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)
@@ -361,14 +361,11 @@
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.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()
-
- 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):
@@ -396,7 +393,6 @@
return nn.Sequential(*layers)
def forward(self, x):
-
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
@@ -405,6 +401,11 @@
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)
@@ -413,7 +414,6 @@
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)
@@ -425,8 +425,8 @@
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, :])
+ # 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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