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| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class MappingNet(nn.Module): | |
| def __init__(self, coeff_nc, descriptor_nc, layer, num_kp, num_bins): | |
| super( MappingNet, self).__init__() | |
| self.layer = layer | |
| nonlinearity = nn.LeakyReLU(0.1) | |
| self.first = nn.Sequential( | |
| torch.nn.Conv1d(coeff_nc, descriptor_nc, kernel_size=7, padding=0, bias=True)) | |
| for i in range(layer): | |
| net = nn.Sequential(nonlinearity, | |
| torch.nn.Conv1d(descriptor_nc, descriptor_nc, kernel_size=3, padding=0, dilation=3)) | |
| setattr(self, 'encoder' + str(i), net) | |
| self.pooling = nn.AdaptiveAvgPool1d(1) | |
| self.output_nc = descriptor_nc | |
| self.fc_roll = nn.Linear(descriptor_nc, num_bins) | |
| self.fc_pitch = nn.Linear(descriptor_nc, num_bins) | |
| self.fc_yaw = nn.Linear(descriptor_nc, num_bins) | |
| self.fc_t = nn.Linear(descriptor_nc, 3) | |
| self.fc_exp = nn.Linear(descriptor_nc, 3*num_kp) | |
| def forward(self, input_3dmm): | |
| out = self.first(input_3dmm) | |
| for i in range(self.layer): | |
| model = getattr(self, 'encoder' + str(i)) | |
| out = model(out) + out[:,:,3:-3] | |
| out = self.pooling(out) | |
| out = out.view(out.shape[0], -1) | |
| #print('out:', out.shape) | |
| yaw = self.fc_yaw(out) | |
| pitch = self.fc_pitch(out) | |
| roll = self.fc_roll(out) | |
| t = self.fc_t(out) | |
| exp = self.fc_exp(out) | |
| return {'yaw': yaw, 'pitch': pitch, 'roll': roll, 't': t, 'exp': exp} |