Finetune layer working

natanielruiz

2017-09-08 0b8e19c1cc8ad03805d4ca68f32df6e4806a36e8

 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):
@@ -58,6 +58,11 @@
        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)

        self.idx_tensor = Variable(torch.FloatTensor(range(66))).cuda()

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
@@ -83,6 +88,12 @@

        return nn.Sequential(*layers)

    def get_expectation(angle):
        angle_pred = F.softmax(angle)

        angle_pred = torch.sum(angle_pred.data * self.idx_tensor, 1)
        return angle_pred

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
@@ -96,17 +107,32 @@

        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 = []
        angles.append(torch.cat([yaw, pitch, roll], 1))

        for idx in xrange(1):
            angles.append(self.fc_finetune(torch.cat((angles[-1], x), 1)))

        return pre_yaw, pre_pitch, pre_roll, angles

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, self).__init__()
        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)
@@ -120,7 +146,16 @@
        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):
@@ -163,6 +198,17 @@
        yaw = self.fc_yaw(x)
        pitch = self.fc_pitch(x)
        roll = self.fc_roll(x)
        shape_1 = self.fc_shape_1(x)

        return yaw, pitch, roll, shape_1
        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