From b730bbd6ea565d7689964661c53a6074654b5d3b Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期一, 30 十月 2017 05:30:52 +0800
Subject: [PATCH] next
---
code/hopenet.py | 37 +------------------------------------
1 files changed, 1 insertions(+), 36 deletions(-)
diff --git a/code/hopenet.py b/code/hopenet.py
index 7b5f764..129ff63 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -4,41 +4,6 @@
import math
import torch.nn.functional as F
-# CNN Model (2 conv layer)
-class Simple_CNN(nn.Module):
- def __init__(self):
- super(Simple_CNN, self).__init__()
- self.layer1 = nn.Sequential(
- nn.Conv2d(3, 64, kernel_size=3, padding=0),
- nn.BatchNorm2d(64),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.layer2 = nn.Sequential(
- nn.Conv2d(64, 128, kernel_size=3, padding=0),
- nn.BatchNorm2d(128),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.layer3 = nn.Sequential(
- nn.Conv2d(128, 256, kernel_size=3, padding=0),
- nn.BatchNorm2d(256),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.layer4 = nn.Sequential(
- nn.Conv2d(256, 512, kernel_size=3, padding=0),
- nn.BatchNorm2d(512),
- nn.ReLU(),
- nn.MaxPool2d(2))
- self.fc = nn.Linear(17*17*512, 3)
-
- def forward(self, x):
- out = self.layer1(x)
- out = self.layer2(out)
- out = self.layer3(out)
- out = self.layer4(out)
- out = out.view(out.size(0), -1)
- out = self.fc(out)
- return out
-
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):
@@ -122,7 +87,7 @@
# angles predicts the residual
for idx in xrange(self.iter_ref):
- angles.append(self.fc_finetune(torch.cat((preangles, x), 1)))
+ angles.append(self.fc_finetune(torch.cat((angles[idx], x), 1)))
return pre_yaw, pre_pitch, pre_roll, angles
--
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