From 2f6778c2db9ce1a887f04fdc85ad0d5db4ba84b8 Mon Sep 17 00:00:00 2001
From: natanielruiz <nruiz9@gatech.edu>
Date: 星期一, 30 十月 2017 06:15:30 +0800
Subject: [PATCH] Cleaned up a bit
---
code/hopenet.py | 24 +++++++++---------------
1 files changed, 9 insertions(+), 15 deletions(-)
diff --git a/code/hopenet.py b/code/hopenet.py
index 129ff63..0a98a66 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -5,8 +5,9 @@
import torch.nn.functional as F
class Hopenet(nn.Module):
- # This is just Hopenet with 3 output layers for yaw, pitch and roll.
- def __init__(self, block, layers, num_bins, iter_ref):
+ # Hopenet with 3 output layers for yaw, pitch and roll
+ # Predicts Euler angles by binning and regression with the expected value
+ def __init__(self, block, layers, num_bins):
self.inplanes = 64
super(Hopenet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
@@ -23,12 +24,11 @@
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)
+ # Used to get the expected value of angle from bins
+ self.softmax = nn.Softmax()
self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()
-
- self.iter_ref = iter_ref
for m in self.modules():
if isinstance(m, nn.Conv2d):
@@ -81,18 +81,12 @@
yaw = yaw.view(yaw.size(0), 1)
pitch = pitch.view(pitch.size(0), 1)
roll = roll.view(roll.size(0), 1)
- angles = []
preangles = torch.cat([yaw, pitch, roll], 1)
- angles.append(preangles)
- # angles predicts the residual
- for idx in xrange(self.iter_ref):
- angles.append(self.fc_finetune(torch.cat((angles[idx], x), 1)))
-
- return pre_yaw, pre_pitch, pre_roll, angles
+ return pre_yaw, pre_pitch, pre_roll, preangles
class ResNet(nn.Module):
-
+ # ResNet for regression of 3 Euler angles.
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
@@ -147,11 +141,11 @@
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc_angles(x)
-
return x
class AlexNet(nn.Module):
-
+ # AlexNet laid out as a Hopenet - classify Euler angles in bins and
+ # regress the expected value.
def __init__(self, num_bins):
super(AlexNet, self).__init__()
self.features = nn.Sequential(
--
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