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| import torch | |
| from functools import reduce | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from functools import partial | |
| class FeedForward(nn.Module): | |
| def __init__(self, frame_hidden, mlp_ratio, n_vecs, t2t_params, p): | |
| """ | |
| Args: | |
| frame_hidden: hidden size of frame features | |
| mlp_ratio: mlp ratio in the middle layer of the transformers | |
| n_vecs: number of vectors in the transformer | |
| t2t_params: dictionary -> {'kernel_size': kernel_size, 'stride': stride, 'padding': padding, 'output_size': output_shape} | |
| p: dropout rate, 0 by default | |
| """ | |
| super(FeedForward, self).__init__() | |
| self.conv = nn.Sequential( | |
| nn.Linear(frame_hidden, frame_hidden * mlp_ratio), | |
| nn.ReLU(inplace=True), | |
| nn.Dropout(p), | |
| nn.Linear(frame_hidden * mlp_ratio, frame_hidden), | |
| nn.Dropout(p) | |
| ) | |
| def forward(self, x, n_vecs=0, output_h=0, output_w=0): | |
| x = self.conv(x) | |
| return x | |
| class FusionFeedForward(nn.Module): | |
| def __init__(self, frame_hidden, mlp_ratio, n_vecs, t2t_params, p): | |
| super(FusionFeedForward, self).__init__() | |
| self.kernel_shape = reduce((lambda x, y: x * y), t2t_params['kernel_size']) | |
| self.t2t_params = t2t_params | |
| hidden_size = self.kernel_shape * mlp_ratio | |
| self.conv1 = nn.Linear(frame_hidden, hidden_size) | |
| self.conv2 = nn.Sequential( | |
| nn.ReLU(inplace=True), | |
| nn.Dropout(p), | |
| nn.Linear(hidden_size, frame_hidden), | |
| nn.Dropout(p) | |
| ) | |
| assert t2t_params is not None and n_vecs is not None | |
| tp = t2t_params.copy() | |
| self.fold = nn.Fold(**tp) | |
| del tp['output_size'] | |
| self.unfold = nn.Unfold(**tp) | |
| self.n_vecs = n_vecs | |
| def forward(self, x, n_vecs=0, output_h=0, output_w=0): | |
| x = self.conv1(x) | |
| b, n, c = x.size() | |
| if n_vecs != 0: | |
| normalizer = x.new_ones(b, n, self.kernel_shape).view(-1, n_vecs, self.kernel_shape).permute(0, 2, 1) | |
| x = self.unfold(F.fold(x.view(-1, n_vecs, c).permute(0, 2, 1), output_size=(output_h, output_w), | |
| kernel_size=self.t2t_params['kernel_size'], stride=self.t2t_params['stride'], | |
| padding=self.t2t_params['padding']) / F.fold(normalizer, | |
| output_size=(output_h, output_w), | |
| kernel_size=self.t2t_params[ | |
| 'kernel_size'], | |
| stride=self.t2t_params['stride'], | |
| padding=self.t2t_params[ | |
| 'padding'])).permute(0, | |
| 2, | |
| 1).contiguous().view( | |
| b, n, c) | |
| else: | |
| normalizer = x.new_ones(b, n, self.kernel_shape).view(-1, self.n_vecs, self.kernel_shape).permute(0, 2, 1) | |
| x = self.unfold(self.fold(x.view(-1, self.n_vecs, c).permute(0, 2, 1)) / self.fold(normalizer)).permute(0, | |
| 2, | |
| 1).contiguous().view( | |
| b, n, c) | |
| x = self.conv2(x) | |
| return x | |
| class ResidualBlock_noBN(nn.Module): | |
| """Residual block w/o BN | |
| ---Conv-ReLU-Conv-+- | |
| |________________| | |
| """ | |
| def __init__(self, nf=64): | |
| super(ResidualBlock_noBN, self).__init__() | |
| self.conv1 = nn.Conv2d(nf, nf, kernel_size=3, stride=1, padding=1, bias=True) | |
| self.conv2 = nn.Conv2d(nf, nf, kernel_size=3, stride=1, padding=1, bias=True) | |
| self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True) | |
| def forward(self, x): | |
| """ | |
| Args: | |
| x: with shape of [b, c, t, h, w] | |
| Returns: processed features with shape [b, c, t, h, w] | |
| """ | |
| identity = x | |
| out = self.lrelu(self.conv1(x)) | |
| out = self.conv2(out) | |
| out = identity + out | |
| # Remove ReLU at the end of the residual block | |
| # http://torch.ch/blog/2016/02/04/resnets.html | |
| return out | |
| def make_layer(block, n_layers): | |
| layers = [] | |
| for _ in range(n_layers): | |
| layers.append(block()) | |
| return nn.Sequential(*layers) | |