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from math import ceil
from typing import List
import torch
import torch.nn as nn
from jaxtyping import Float
from neuralop.models import FNO
from scOT.model import ScOT, ScOTConfig, ScOTOutput
from torchvision.models.resnet import BasicBlock, Bottleneck, ResNet
from pdeinvbench.utils.types import (
PDE,
PDE_NUM_PARAMETERS,
PDE_NUM_SPATIAL,
PDE_PARTIALS,
PDE_SPATIAL_SIZE,
PDE_TRAJ_LEN,
)
def resolve_number_input_channels(
n_past: int, data_channels: int, use_partials: bool, pde: PDE
) -> int:
"""
Returns the number of input channels for a pde given args:
- n_past
- data_channels
- use_partials
"""
num_partials = PDE_PARTIALS[pde]
if use_partials:
# each timestep gets partials appended to it
data_channels += num_partials * data_channels
in_channels = n_past * data_channels
return in_channels
class FNOEncoder(FNO):
"""
Wrapper around FNO that figures out the input channels based
on the number of past frames and partial derivatives.
:param n_modes: Number of modes to use in the FNO.
:param n_layers: Number of layers in the FNO.
:param n_past: Number of past frames to use.
:param pde: PDE to use for the partial derivatives.
:param data_channels: Number of channels per timestep in the native input data.
:param hidden_channels: Number of channels in the hidden layers.
:param use_partials: Whether to use partial derivatives as input (only applicable to the inverse problem)
"""
def __init__(
self,
n_modes: int,
n_layers: int,
n_past: int,
n_future: int,
pde: PDE,
data_channels: int,
hidden_channels: int,
use_partials: bool,
batch_size: int,
):
if use_partials:
# if using partials, we are in inverse model mode
# therefore, there will be a downsampler after the encoder,
# need to preserve the number of channels
out_channels = hidden_channels
else:
out_channels = hidden_channels
# figure out the number of input channels
self.use_partials = use_partials
in_channels = resolve_number_input_channels(
n_past=n_past,
data_channels=data_channels,
use_partials=use_partials,
pde=pde,
)
# expand modes based on dimensionality of PDE
n_modes = [n_modes] * PDE_NUM_SPATIAL[pde]
self.batch_size = batch_size
super(FNOEncoder, self).__init__(
n_modes=n_modes,
n_layers=n_layers,
in_channels=in_channels,
hidden_channels=hidden_channels,
out_channels=out_channels,
)
def forward(self, x, **kwargs):
return super().forward(x)
class Resnet(nn.Module):
"""
Wrapper around FNO replacing FNO convolution blocks with Resnet Blocks.
"""
def __init__(self, *args, **kwargs):
# super().__init__(*args, **kwargs)
super(Resnet, self).__init__()
self.in_channels = kwargs["in_channels"]
self.hidden_channels = kwargs["hidden_channels"]
self.n_layers = kwargs["n_layers"]
self.batch_size = kwargs["batch_size"]
self.in_block = BasicBlock(
inplanes=self.in_channels,
planes=self.hidden_channels,
stride=1,
downsample=None,
groups=1,
base_width=64,
dilation=1,
norm_layer=nn.BatchNorm2d,
)
self.in_block = nn.Sequential(
nn.Conv2d(
self.in_channels,
self.hidden_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(
self.hidden_channels,
eps=1e-05,
momentum=0.1,
affine=True,
track_running_stats=True,
),
nn.ReLU(inplace=True),
nn.Conv2d(
self.hidden_channels,
self.hidden_channels,
kernel_size=(3, 3),
stride=(1, 1),
padding=(1, 1),
bias=False,
),
nn.BatchNorm2d(
self.hidden_channels,
eps=1e-05,
momentum=0.1,
affine=True,
track_running_stats=True,
),
)
self.resnet_blocks = nn.ModuleList(
[
BasicBlock(
inplanes=self.hidden_channels,
planes=self.hidden_channels,
stride=1,
downsample=None,
groups=1,
base_width=64,
dilation=1,
norm_layer=nn.BatchNorm2d,
)
for _ in range(kwargs["n_layers"])
]
)
def forward(self, x, output_shape=None, **kwargs):
"""CN-Resnet's forward pass
Parameters
----------
x : tensor
input tensor
output_shape : {tuple, tuple list, None}, default is None
Gives the option of specifying the exact output shape for odd shaped inputs.
* If None, don't specify an output shape
* If tuple, specifies the output-shape of the **last** FNO Block
* If tuple list, specifies the exact output-shape of each FNO Block
"""
x = self.in_block(x)
for layer_idx in range(self.n_layers):
x = self.resnet_blocks[layer_idx](x)
return x
class ResnetEncoder(Resnet):
"""
Wrapper around Resnet that figures out the input channels based
on the number of past frames and partial derivatives.
:param n_layers: Number of layers in the Resnet.
:param n_past: Number of past frames to use.
:param pde: PDE to use for the partial derivatives.
:param data_channels: Number of channels per timestep in the native input data.
:param hidden_channels: Number of channels in the hidden layers.
:param use_partials: Whether to use partial derivatives as input (only applicable to the inverse problem)
:param mode: One of "oneshot", "autoregressive", "grid_to_soln"
"""
def __init__(
self,
n_layers: int,
n_past: int,
n_future: int,
pde: PDE,
data_channels: int,
hidden_channels: int,
use_partials: bool,
batch_size: int,
):
# figure out the number of output channels
if use_partials:
# if using partials, we are in inverse model mode
# therefore, there will be a downsampler after the encoder,
# need to preserve the number of channels
out_channels = hidden_channels
else:
out_channels = hidden_channels # data_channels
self.use_partials = use_partials
self.pde = pde
in_channels = resolve_number_input_channels(
n_past=n_past,
data_channels=data_channels,
use_partials=use_partials,
pde=pde,
)
super(ResnetEncoder, self).__init__(
n_layers=n_layers,
in_channels=in_channels,
hidden_channels=hidden_channels,
out_channels=out_channels,
batch_size=batch_size,
pde=pde,
)
def forward(self, x, **kwargs):
if self.pde == PDE.KortewegDeVries1D:
x = x.unsqueeze(2)
x = super().forward(x)
return x[:, :, 0, :]
return super().forward(x)
class ScOTEncoder(nn.Module):
config: ScOTConfig
backbone: ScOT
n_past: int
in_channels: int
use_partials: bool
patch_size: int
padding_mode: str = "constant"
def __init__(
self,
# backbone args
embed_dim: int, # patch embedding
n_layers: int,
hidden_size: int,
patch_size: int,
num_heads: list[int],
skip_connections: list[int],
depths: list[int],
# Our args
use_partials: bool,
data_channels: bool,
n_past: int,
pde: PDE,
**kwargs,
):
super(ScOTEncoder, self).__init__()
self.n_past = n_past
self.use_partials = use_partials
self.patch_size = patch_size
self.in_channels = resolve_number_input_channels(
n_past=self.n_past,
use_partials=self.use_partials,
data_channels=data_channels,
pde=pde,
)
# All pdes are on square grids
self.spatial_size = PDE_SPATIAL_SIZE[pde][0]
self.pde = pde
self.config = ScOTConfig(
num_layers=n_layers,
num_channels=self.in_channels,
num_out_channels=hidden_size,
depths=depths,
num_heads=num_heads,
skip_connections=skip_connections,
patch_size=self.patch_size,
embed_dim=embed_dim,
image_size=self.spatial_size,
**kwargs,
)
self.backbone = ScOT(self.config)
def _pad_input(
self, x: Float[torch.Tensor, "batch channels nx ny"]
) -> tuple[
Float[torch.Tensor, "batch channels nx ny"], tuple[int, int, int, int] | None
]:
_, _, nx, ny = x.shape
assert nx == ny, f"Non-square solutions not supported nx={nx}, ny={ny}"
total_pad: int = (self.patch_size - (nx % self.patch_size)) % self.patch_size
left_pad, right_pad = total_pad // 2, ceil(total_pad / 2)
assert (
left_pad + right_pad == total_pad
), f"Incorrect swin padding {left_pad} + {right_pad} = {total_pad}"
if left_pad or right_pad:
pad_vals = (left_pad, right_pad, left_pad, right_pad)
return (
torch.nn.functional.pad(
x,
pad_vals,
mode=self.padding_mode,
value=0,
),
pad_vals,
)
return x, None
def forward(
self,
x: Float[torch.Tensor, "batch channels nx ny"],
t: Float[torch.Tensor, "batch nt"] | None = None,
) -> Float[torch.Tensor, "batch outdim nx ny"]:
# Check if we need to pad the input
if self.pde == PDE.KortewegDeVries1D:
x = x.unsqueeze(2)
x = x.repeat(1, 1, x.shape[-1], 1)
x, pad_vals = self._pad_input(x)
output: ScOTOutput = self.backbone(pixel_values=x, time=t).output
if pad_vals:
# undo padding
l, r, _, _ = pad_vals
output = output[..., l:-r, l:-r]
if self.pde == PDE.KortewegDeVries1D:
output = output[:, :, :1, :]
return output
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