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natanielruiz

2017-10-30 b730bbd6ea565d7689964661c53a6074654b5d3b

 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

@@ -184,3 +149,43 @@
        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)
        return yaw, pitch, roll