Delete animation/SadTalker directory

animation/SadTalker/src/audio2pose_models/discriminator.py

@@ -1,76 +0,0 @@
-import torch
-import torch.nn.functional as F
-from torch import nn
-
-class ConvNormRelu(nn.Module):
-    def __init__(self, conv_type='1d', in_channels=3, out_channels=64, downsample=False,
-                 kernel_size=None, stride=None, padding=None, norm='BN', leaky=False):
-        super().__init__()
-        if kernel_size is None:
-            if downsample:
-                kernel_size, stride, padding = 4, 2, 1
-            else:
-                kernel_size, stride, padding = 3, 1, 1
-
-        if conv_type == '2d':
-            self.conv = nn.Conv2d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                stride,
-                padding,
-                bias=False,
-            )
-            if norm == 'BN':
-                self.norm = nn.BatchNorm2d(out_channels)
-            elif norm == 'IN':
-                self.norm = nn.InstanceNorm2d(out_channels)
-            else:
-                raise NotImplementedError
-        elif conv_type == '1d':
-            self.conv = nn.Conv1d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                stride,
-                padding,
-                bias=False,
-            )
-            if norm == 'BN':
-                self.norm = nn.BatchNorm1d(out_channels)
-            elif norm == 'IN':
-                self.norm = nn.InstanceNorm1d(out_channels)
-            else:
-                raise NotImplementedError
-        nn.init.kaiming_normal_(self.conv.weight)
-
-        self.act = nn.LeakyReLU(negative_slope=0.2, inplace=False) if leaky else nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        x = self.conv(x)
-        if isinstance(self.norm, nn.InstanceNorm1d):
-            x = self.norm(x.permute((0, 2, 1))).permute((0, 2, 1))  # normalize on [C]
-        else:
-            x = self.norm(x)
-        x = self.act(x)
-        return x
-
-
-class PoseSequenceDiscriminator(nn.Module):
-    def __init__(self, cfg):
-        super().__init__()
-        self.cfg = cfg
-        leaky = self.cfg.MODEL.DISCRIMINATOR.LEAKY_RELU
-
-        self.seq = nn.Sequential(
-            ConvNormRelu('1d', cfg.MODEL.DISCRIMINATOR.INPUT_CHANNELS, 256, downsample=True, leaky=leaky),  # B, 256, 64
-            ConvNormRelu('1d', 256, 512, downsample=True, leaky=leaky),  # B, 512, 32
-            ConvNormRelu('1d', 512, 1024, kernel_size=3, stride=1, padding=1, leaky=leaky),  # B, 1024, 16
-            nn.Conv1d(1024, 1, kernel_size=3, stride=1, padding=1, bias=True)  # B, 1, 16
-        )
-
-    def forward(self, x):
-        x = x.reshape(x.size(0), x.size(1), -1).transpose(1, 2)
-        x = self.seq(x)
-        x = x.squeeze(1)
-        return x