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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)
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