From 6dd2ff502947ec809d420e2baefa023d821a8bb1 Mon Sep 17 00:00:00 2001
From: natanielruiz <nataniel777@hotmail.com>
Date: 星期四, 07 九月 2017 07:26:35 +0800
Subject: [PATCH] Omg
---
code/hopenet.py | 149 +++++++++++++++++++++++++++++++++++++++++++++++++
1 files changed, 149 insertions(+), 0 deletions(-)
diff --git a/code/hopenet.py b/code/hopenet.py
index e6f8f50..1b94fa1 100644
--- a/code/hopenet.py
+++ b/code/hopenet.py
@@ -2,6 +2,7 @@
import torch.nn as nn
import torchvision.datasets as dsets
from torch.autograd import Variable
+import math
# CNN Model (2 conv layer)
class Simple_CNN(nn.Module):
@@ -37,3 +38,151 @@
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):
+ self.inplanes = 64
+ super(Hopenet, 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)
+
+ 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)
+ yaw = self.fc_yaw(x)
+ pitch = self.fc_pitch(x)
+ roll = self.fc_roll(x)
+
+ return yaw, pitch, roll
+
+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):
+ self.inplanes = 64
+ super(Hopenet_shape, 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.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)
+
+ 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)
+ 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
--
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