From b74b9c54247177c82493f180617d7551de8e2bb1 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期二, 26 九月 2017 03:47:06 +0800
Subject: [PATCH] Before SR experiments
---
code/hopenet.py | 112 +++++++++++++++++++++++++++++++++++++------------------
1 files changed, 75 insertions(+), 37 deletions(-)
diff --git a/code/hopenet.py b/code/hopenet.py
index 1b94fa1..7b5f764 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -1,8 +1,8 @@
import torch
import torch.nn as nn
-import torchvision.datasets as dsets
from torch.autograd import Variable
import math
+import torch.nn.functional as F
# CNN Model (2 conv layer)
class Simple_CNN(nn.Module):
@@ -41,7 +41,7 @@
class Hopenet(nn.Module):
# This is just Hopenet with 3 output layers for yaw, pitch and roll.
- def __init__(self, block, layers, num_bins):
+ def __init__(self, block, layers, num_bins, iter_ref):
self.inplanes = 64
super(Hopenet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
@@ -58,6 +58,13 @@
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.iter_ref = iter_ref
+
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
@@ -96,17 +103,34 @@
x = self.avgpool(x)
x = x.view(x.size(0), -1)
- yaw = self.fc_yaw(x)
- pitch = self.fc_pitch(x)
- roll = self.fc_roll(x)
+ pre_yaw = self.fc_yaw(x)
+ pre_pitch = self.fc_pitch(x)
+ pre_roll = self.fc_roll(x)
- return yaw, pitch, roll
+ yaw = self.softmax(pre_yaw)
+ yaw = Variable(torch.sum(yaw.data * self.idx_tensor.data, 1), requires_grad=True)
+ pitch = self.softmax(pre_pitch)
+ pitch = Variable(torch.sum(pitch.data * self.idx_tensor.data, 1), requires_grad=True)
+ roll = self.softmax(pre_roll)
+ roll = Variable(torch.sum(roll.data * self.idx_tensor.data, 1), requires_grad=True)
+ 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)
-class Hopenet_shape(nn.Module):
- # This is just Hopenet with 3 output layers for yaw, pitch and roll.
- def __init__(self, block, layers, num_bins, shape_bins):
+ # angles predicts the residual
+ for idx in xrange(self.iter_ref):
+ angles.append(self.fc_finetune(torch.cat((preangles, x), 1)))
+
+ return pre_yaw, pre_pitch, pre_roll, angles
+
+class ResNet(nn.Module):
+
+ def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
- super(Hopenet_shape, self).__init__()
+ super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
@@ -117,19 +141,7 @@
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.fc_shape_0 = nn.Linear(512 * block.expansion, shape_bins)
- self.fc_shape_1 = nn.Linear(512 * block.expansion, shape_bins)
- self.fc_shape_2 = nn.Linear(512 * block.expansion, shape_bins)
- self.fc_shape_3 = nn.Linear(512 * block.expansion, shape_bins)
- self.fc_shape_4 = nn.Linear(512 * block.expansion, shape_bins)
- self.fc_shape_5 = nn.Linear(512 * block.expansion, shape_bins)
- self.fc_shape_6 = nn.Linear(512 * block.expansion, shape_bins)
- self.fc_shape_7 = nn.Linear(512 * block.expansion, shape_bins)
- self.fc_shape_8 = nn.Linear(512 * block.expansion, shape_bins)
- self.fc_shape_9 = nn.Linear(512 * block.expansion, shape_bins)
+ self.fc_angles = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
@@ -169,20 +181,46 @@
x = self.avgpool(x)
x = x.view(x.size(0), -1)
+ x = self.fc_angles(x)
+
+ return x
+
+class AlexNet(nn.Module):
+
+ def __init__(self, num_bins):
+ super(AlexNet, self).__init__()
+ self.features = nn.Sequential(
+ nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
+ nn.ReLU(inplace=True),
+ nn.MaxPool2d(kernel_size=3, stride=2),
+ nn.Conv2d(64, 192, kernel_size=5, padding=2),
+ nn.ReLU(inplace=True),
+ nn.MaxPool2d(kernel_size=3, stride=2),
+ nn.Conv2d(192, 384, kernel_size=3, padding=1),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(384, 256, kernel_size=3, padding=1),
+ nn.ReLU(inplace=True),
+ nn.Conv2d(256, 256, kernel_size=3, padding=1),
+ nn.ReLU(inplace=True),
+ nn.MaxPool2d(kernel_size=3, stride=2),
+ )
+ self.classifier = nn.Sequential(
+ nn.Dropout(),
+ nn.Linear(256 * 6 * 6, 4096),
+ nn.ReLU(inplace=True),
+ nn.Dropout(),
+ nn.Linear(4096, 4096),
+ nn.ReLU(inplace=True),
+ )
+ self.fc_yaw = nn.Linear(4096, num_bins)
+ self.fc_pitch = nn.Linear(4096, num_bins)
+ self.fc_roll = nn.Linear(4096, num_bins)
+
+ def forward(self, x):
+ x = self.features(x)
+ x = x.view(x.size(0), 256 * 6 * 6)
+ x = self.classifier(x)
yaw = self.fc_yaw(x)
pitch = self.fc_pitch(x)
roll = self.fc_roll(x)
-
- shape = []
- shape.append(self.fc_shape_0(x))
- shape.append(self.fc_shape_1(x))
- shape.append(self.fc_shape_2(x))
- shape.append(self.fc_shape_3(x))
- shape.append(self.fc_shape_4(x))
- shape.append(self.fc_shape_5(x))
- shape.append(self.fc_shape_6(x))
- shape.append(self.fc_shape_7(x))
- shape.append(self.fc_shape_8(x))
- shape.append(self.fc_shape_9(x))
-
- return yaw, pitch, roll, shape
+ return yaw, pitch, roll
--
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