| | |
| | | # 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) |
| | |
| | | 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): |
| | |
| | | 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 |