|
|
import pdb |
|
|
from typing import Dict, Tuple |
|
|
|
|
|
import jaxtyping |
|
|
import torch |
|
|
import typeguard |
|
|
from jaxtyping import Float, jaxtyped |
|
|
|
|
|
""" |
|
|
Set of utility functions for data transformations. |
|
|
""" |
|
|
|
|
|
|
|
|
@jaxtyped(typechecker=typeguard.typechecked) |
|
|
def collapse_time_and_channels( |
|
|
x: Float[torch.Tensor, "time channel xspace yspace"], |
|
|
) -> Float[torch.Tensor, "time*channel xspace yspace"]: |
|
|
""" |
|
|
Collapses the time and channel dimensions of a tensor into a single dimension. |
|
|
NOTE: This is only applicable to 2D systems and this is NOT batched! |
|
|
We do this to be compatible with FNO. FNO can't handle multiple function outputs |
|
|
at once since we're already using the channel dimension to represent time. |
|
|
:param x: Input tensor of shape (time, channel, xspace, yspace). |
|
|
:return: Output tensor of shape (time*channel, xspace, yspace). |
|
|
""" |
|
|
x_flattened = torch.flatten(x, start_dim=0, end_dim=1) |
|
|
return x_flattened |
|
|
|
|
|
|
|
|
@jaxtyped(typechecker=typeguard.typechecked) |
|
|
def collapse_time_and_channels_torch_transform( |
|
|
batch: Tuple[ |
|
|
Float[torch.Tensor, "time_n_past in_channels xspace yspace"], |
|
|
Float[torch.Tensor, "time_n_fut out_channels xspace yspace"], |
|
|
Dict[ |
|
|
str, Float[torch.Tensor, "param"] | Float[torch.Tensor, "xspace yspace 1"] |
|
|
], |
|
|
], |
|
|
) -> Tuple[ |
|
|
Float[torch.Tensor, "time_n_past*in_channels xspace yspace"], |
|
|
Float[torch.Tensor, "time_n_fut*out_channels xspace yspace"], |
|
|
Dict[str, Float[torch.Tensor, "param"] | Float[torch.Tensor, "xspace yspace 1"]], |
|
|
]: |
|
|
""" |
|
|
Wrapper for ```collapse_time_and_channels``` to be used with PyTorch's dataloader transforms. |
|
|
Accepts a batch and for the first two elements of the batch, collapses the time and channel dimensions. |
|
|
:param batch: Tuple of (input, target, pde_params). |
|
|
:return: Tuple of (input, target, pde_params) |
|
|
""" |
|
|
input, target, pde_params = batch |
|
|
input = collapse_time_and_channels(input) |
|
|
target = collapse_time_and_channels(target) |
|
|
return input, target, pde_params |
|
|
|
|
|
|
|
|
@jaxtyped(typechecker=typeguard.typechecked) |
|
|
def expand_time_and_channels( |
|
|
x: Float[torch.Tensor, "timexchannel xspace yspace"], |
|
|
num_channels: int = -1, |
|
|
num_timesteps: int = -1, |
|
|
) -> Float[torch.Tensor, "time channel xspace yspace"]: |
|
|
""" |
|
|
Expands the time and channel dimensions of a tensor into separate dimensions. |
|
|
Either number of channels or number of timesteps must be specified. |
|
|
NOTE: This is only applicable to 2D systems. |
|
|
:param x: Input tensor of shape (time*channel, xspace, yspace). |
|
|
:param num_channels: Number of channels to expand to. OPTIONAL if num_timesteps is specified. |
|
|
:param num_timesteps: Number of timesteps to expand to. OPTIONAL if num_channels is specified. |
|
|
:return: Output tensor of shape (time, channel, xspace, yspace). |
|
|
""" |
|
|
assert ( |
|
|
num_channels != -1 or num_timesteps != -1 |
|
|
), "Either num_channels or num_timesteps must be specified!" |
|
|
if num_channels != -1: |
|
|
|
|
|
x_unflattened = torch.unflatten(x, 0, (-1, num_channels)) |
|
|
else: |
|
|
|
|
|
x_unflattened = torch.unflatten(x, 0, (num_timesteps, -1)) |
|
|
return x_unflattened |
|
|
|
