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"""Time-Depth Separable Convolutions.
References:
https://arxiv.org/abs/1904.02619
https://arxiv.org/pdf/2010.01003.pdf
Code stolen from:
https://github.com/facebookresearch/gtn_applications
"""
from typing import List, Tuple
from einops import rearrange
import gtn
import numpy as np
import torch
from torch import nn
from torch import Tensor
class TDSBlock2d(nn.Module):
"""Internal block of a 2D TDSC network."""
def __init__(
self,
in_channels: int,
img_depth: int,
kernel_size: Tuple[int],
dropout_rate: float,
) -> None:
super().__init__()
self.in_channels = in_channels
self.img_depth = img_depth
self.kernel_size = kernel_size
self.dropout_rate = dropout_rate
self.fc_dim = in_channels * img_depth
# Network placeholders.
self.conv = None
self.mlp = None
self.instance_norm = None
self._build_block()
def _build_block(self) -> None:
# Convolutional block.
self.conv = nn.Sequential(
nn.Conv3d(
in_channels=self.in_channels,
out_channels=self.in_channels,
kernel_size=(1, self.kernel_size[0], self.kernel_size[1]),
padding=(0, self.kernel_size[0] // 2, self.kernel_size[1] // 2),
),
nn.ReLU(inplace=True),
nn.Dropout(self.dropout_rate),
)
# MLP block.
self.mlp = nn.Sequential(
nn.Linear(self.fc_dim, self.fc_dim),
nn.ReLU(inplace=True),
nn.Dropout(self.dropout_rate),
nn.Linear(self.fc_dim, self.fc_dim),
nn.Dropout(self.dropout_rate),
)
# Instance norm.
self.instance_norm = nn.ModuleList(
[
nn.InstanceNorm2d(self.fc_dim, affine=True),
nn.InstanceNorm2d(self.fc_dim, affine=True),
]
)
def forward(self, x: Tensor) -> Tensor:
"""Forward pass.
Args:
x (Tensor): Input tensor.
Shape:
- x: :math: `(B, CD, H, W)`
Returns:
Tensor: Output tensor.
"""
B, CD, H, W = x.shape
C, D = self.in_channels, self.img_depth
residual = x
x = rearrange(x, "b (c d) h w -> b c d h w", c=C, d=D)
x = self.conv(x)
x = rearrange(x, "b c d h w -> b (c d) h w")
x += residual
x = self.instance_norm[0](x)
x = self.mlp(x.transpose(1, 3)).transpose(1, 3) + x
x + self.instance_norm[1](x)
# Output shape: [B, CD, H, W]
return x
class TDS2d(nn.Module):
"""TDS Netowrk.
Structure is the following:
Downsample layer -> TDS2d group -> ... -> Linear output layer
"""
def __init__(
self,
input_dim: int,
output_dim: int,
depth: int,
tds_groups: Tuple[int],
kernel_size: Tuple[int],
dropout_rate: float,
in_channels: int = 1,
) -> None:
super().__init__()
self.in_channels = in_channels
self.input_dim = input_dim
self.output_dim = output_dim
self.depth = depth
self.tds_groups = tds_groups
self.kernel_size = kernel_size
self.dropout_rate = dropout_rate
self.tds = None
self.fc = None
self._build_network()
def _build_network(self) -> None:
in_channels = self.in_channels
modules = []
stride_h = np.prod([grp["stride"][0] for grp in self.tds_groups])
if self.input_dim % stride_h:
raise RuntimeError(
f"Image height not divisible by total stride {stride_h}."
)
for tds_group in self.tds_groups:
# Add downsample layer.
out_channels = self.depth * tds_group["channels"]
modules.extend(
[
nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=self.kernel_size,
padding=(self.kernel_size[0] // 2, self.kernel_size[1] // 2),
stride=tds_group["stride"],
),
nn.ReLU(inplace=True),
nn.Dropout(self.dropout_rate),
nn.InstanceNorm2d(out_channels, affine=True),
]
)
for _ in range(tds_group["num_blocks"]):
modules.append(
TDSBlock2d(
tds_group["channels"],
self.depth,
self.kernel_size,
self.dropout_rate,
)
)
in_channels = out_channels
self.tds = nn.Sequential(*modules)
self.fc = nn.Linear(in_channels * self.input_dim // stride_h, self.output_dim)
def forward(self, x: Tensor) -> Tensor:
"""Forward pass.
Args:
x (Tensor): Input tensor.
Shape:
- x: :math: `(B, H, W)`
Returns:
Tensor: Output tensor.
"""
if len(x.shape) == 4:
x = x.squeeze(1) # Squeeze the channel dim away.
B, H, W = x.shape
x = rearrange(
x, "b (h1 h2) w -> b h1 h2 w", h1=self.in_channels, h2=H // self.in_channels
)
x = self.tds(x)
# x shape: [B, C, H, W]
x = rearrange(x, "b c h w -> b w (c h)")
return self.fc(x)
|
