Spaces:
Running
Running
| # https://github.com/joonson/syncnet_python/blob/master/SyncNetModel.py | |
| import torch | |
| import torch.nn as nn | |
| def save(model, filename): | |
| with open(filename, "wb") as f: | |
| torch.save(model, f) | |
| print("%s saved." % filename) | |
| def load(filename): | |
| net = torch.load(filename) | |
| return net | |
| class S(nn.Module): | |
| def __init__(self, num_layers_in_fc_layers=1024): | |
| super(S, self).__init__() | |
| self.__nFeatures__ = 24 | |
| self.__nChs__ = 32 | |
| self.__midChs__ = 32 | |
| self.netcnnaud = nn.Sequential( | |
| nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), | |
| nn.BatchNorm2d(64), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=(1, 1), stride=(1, 1)), | |
| nn.Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), | |
| nn.BatchNorm2d(192), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=(3, 3), stride=(1, 2)), | |
| nn.Conv2d(192, 384, kernel_size=(3, 3), padding=(1, 1)), | |
| nn.BatchNorm2d(384), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(384, 256, kernel_size=(3, 3), padding=(1, 1)), | |
| nn.BatchNorm2d(256), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(256, 256, kernel_size=(3, 3), padding=(1, 1)), | |
| nn.BatchNorm2d(256), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)), | |
| nn.Conv2d(256, 512, kernel_size=(5, 4), padding=(0, 0)), | |
| nn.BatchNorm2d(512), | |
| nn.ReLU(), | |
| ) | |
| self.netfcaud = nn.Sequential( | |
| nn.Linear(512, 512), | |
| nn.BatchNorm1d(512), | |
| nn.ReLU(), | |
| nn.Linear(512, num_layers_in_fc_layers), | |
| ) | |
| self.netfclip = nn.Sequential( | |
| nn.Linear(512, 512), | |
| nn.BatchNorm1d(512), | |
| nn.ReLU(), | |
| nn.Linear(512, num_layers_in_fc_layers), | |
| ) | |
| self.netcnnlip = nn.Sequential( | |
| nn.Conv3d(3, 96, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=0), | |
| nn.BatchNorm3d(96), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2)), | |
| nn.Conv3d(96, 256, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 1, 1)), | |
| nn.BatchNorm3d(256), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)), | |
| nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)), | |
| nn.BatchNorm3d(256), | |
| nn.ReLU(inplace=True), | |
| nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)), | |
| nn.BatchNorm3d(256), | |
| nn.ReLU(inplace=True), | |
| nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)), | |
| nn.BatchNorm3d(256), | |
| nn.ReLU(inplace=True), | |
| nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2)), | |
| nn.Conv3d(256, 512, kernel_size=(1, 6, 6), padding=0), | |
| nn.BatchNorm3d(512), | |
| nn.ReLU(inplace=True), | |
| ) | |
| def forward_aud(self, x): | |
| mid = self.netcnnaud(x) | |
| # N x ch x 24 x M | |
| mid = mid.view((mid.size()[0], -1)) | |
| # N x (ch x 24) | |
| out = self.netfcaud(mid) | |
| return out | |
| def forward_lip(self, x): | |
| mid = self.netcnnlip(x) | |
| mid = mid.view((mid.size()[0], -1)) | |
| # N x (ch x 24) | |
| out = self.netfclip(mid) | |
| return out | |
| def forward_lipfeat(self, x): | |
| mid = self.netcnnlip(x) | |
| out = mid.view((mid.size()[0], -1)) | |
| # N x (ch x 24) | |
| return out | |