import torch import torch.nn.functional as F from torch import nn # DropPath copied from timm library def drop_path( x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True ): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ # noqa: E501 if drop_prob == 0.0 or not training: return x keep_prob = 1 - drop_prob shape = (x.shape[0],) + (1,) * ( x.ndim - 1 ) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0 and scale_by_keep: random_tensor.div_(keep_prob) return x * random_tensor class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" # noqa: E501 def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True): super(DropPath, self).__init__() self.drop_prob = drop_prob self.scale_by_keep = scale_by_keep def forward(self, x): return drop_path(x, self.drop_prob, self.training, self.scale_by_keep) def extra_repr(self): return f"drop_prob={round(self.drop_prob,3):0.3f}" class LayerNorm(nn.Module): r"""LayerNorm that supports two data formats: channels_last (default) or channels_first. The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height, width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width). """ # noqa: E501 def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"): super().__init__() self.weight = nn.Parameter(torch.ones(normalized_shape)) self.bias = nn.Parameter(torch.zeros(normalized_shape)) self.eps = eps self.data_format = data_format if self.data_format not in ["channels_last", "channels_first"]: raise NotImplementedError self.normalized_shape = (normalized_shape,) def forward(self, x): if self.data_format == "channels_last": return F.layer_norm( x, self.normalized_shape, self.weight, self.bias, self.eps ) elif self.data_format == "channels_first": u = x.mean(1, keepdim=True) s = (x - u).pow(2).mean(1, keepdim=True) x = (x - u) / torch.sqrt(s + self.eps) x = self.weight[:, None] * x + self.bias[:, None] return x # ConvNeXt Block copied from https://github.com/fishaudio/fish-diffusion/blob/main/fish_diffusion/modules/convnext.py class ConvNeXtBlock(nn.Module): r"""ConvNeXt Block. There are two equivalent implementations: (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W) (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back We use (2) as we find it slightly faster in PyTorch Args: dim (int): Number of input channels. drop_path (float): Stochastic depth rate. Default: 0.0 layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6. mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.0. kernel_size (int): Kernel size for depthwise conv. Default: 7. dilation (int): Dilation for depthwise conv. Default: 1. """ # noqa: E501 def __init__( self, dim: int, drop_path: float = 0.0, layer_scale_init_value: float = 1e-6, mlp_ratio: float = 4.0, kernel_size: int = 7, dilation: int = 1, ): super().__init__() self.dwconv = nn.Conv1d( dim, dim, kernel_size=kernel_size, padding=int(dilation * (kernel_size - 1) / 2), groups=dim, ) # depthwise conv self.norm = LayerNorm(dim, eps=1e-6) self.pwconv1 = nn.Linear( dim, int(mlp_ratio * dim) ) # pointwise/1x1 convs, implemented with linear layers self.act = nn.GELU() self.pwconv2 = nn.Linear(int(mlp_ratio * dim), dim) self.gamma = ( nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True) if layer_scale_init_value > 0 else None ) self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() def forward(self, x, apply_residual: bool = True): input = x x = self.dwconv(x) x = x.permute(0, 2, 1) # (N, C, L) -> (N, L, C) x = self.norm(x) x = self.pwconv1(x) x = self.act(x) x = self.pwconv2(x) if self.gamma is not None: x = self.gamma * x x = x.permute(0, 2, 1) # (N, L, C) -> (N, C, L) x = self.drop_path(x) if apply_residual: x = input + x return x class ConvNeXtEncoder(nn.Module): def __init__( self, input_channels: int = 3, depths: list[int] = [3, 3, 9, 3], dims: list[int] = [96, 192, 384, 768], drop_path_rate: float = 0.0, layer_scale_init_value: float = 1e-6, kernel_size: int = 7, ): super().__init__() assert len(depths) == len(dims) self.downsample_layers = nn.ModuleList() stem = nn.Sequential( nn.Conv1d( input_channels, dims[0], kernel_size=kernel_size, padding=kernel_size // 2, padding_mode="zeros", ), LayerNorm(dims[0], eps=1e-6, data_format="channels_first"), ) self.downsample_layers.append(stem) for i in range(len(depths) - 1): mid_layer = nn.Sequential( LayerNorm(dims[i], eps=1e-6, data_format="channels_first"), nn.Conv1d(dims[i], dims[i + 1], kernel_size=1), ) self.downsample_layers.append(mid_layer) self.stages = nn.ModuleList() dp_rates = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))] cur = 0 for i in range(len(depths)): stage = nn.Sequential( *[ ConvNeXtBlock( dim=dims[i], drop_path=dp_rates[cur + j], layer_scale_init_value=layer_scale_init_value, kernel_size=kernel_size, ) for j in range(depths[i]) ] ) self.stages.append(stage) cur += depths[i] self.norm = LayerNorm(dims[-1], eps=1e-6, data_format="channels_first") self.apply(self._init_weights) def _init_weights(self, m): if isinstance(m, (nn.Conv1d, nn.Linear)): nn.init.trunc_normal_(m.weight, std=0.02) nn.init.constant_(m.bias, 0) def forward( self, x: torch.Tensor, ) -> torch.Tensor: for i in range(len(self.downsample_layers)): x = self.downsample_layers[i](x) x = self.stages[i](x) return self.norm(x)