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"""
Contains the implementation of autoregressive DeepONet
"""
from itertools import product
from typing import List, Optional
import torch
from torch import nn, Tensor
from .ffn import Ffn
from .base_model import AutoCfdModel
from .act_fn import get_act_fn
from .loss import MseLoss
class AutoDeepONet(AutoCfdModel):
"""
Auto-regressive DeepONet for CFD.
Our task is different from the one that the original DeepONet. In the
original DeepONet, the input function (input to the branch net)
is the initial condition (IC), but here, we have a fixed (zero) IC.
Instead, we have different boundary conditions (BCs), but we also
want the model to predict the next time step given the current time step.
Ideally, we should have two different branch nets, one accepting the
BCs, one accepting the current time step.
Here, we assume that the current time step includes the information about
BCs (which are the values on the bounds), so we just feed
the current time step to one branch net.
"""
def __init__(
self,
branch_dim: int,
trunk_dim: int,
loss_fn: MseLoss,
num_label_samples: int = 1000,
branch_depth: int = 4,
trunk_depth: int = 4,
width: int = 100,
act_name="relu",
act_norm: bool = False,
act_on_output: bool = False,
):
"""
Args:
- branch_dim: int, the dimension of the branch net input.
- trunk_dim: int, the dimension of the trunk net input.
"""
super().__init__(loss_fn)
self.branch_dim = branch_dim
self.trunk_dim = trunk_dim
self.branch_depth = branch_depth
self.trunk_depth = trunk_depth
self.width = width
self.act_name = act_name
self.act_norm = act_norm
self.act_on_output = act_on_output
self.num_label_samples = num_label_samples
act_fn = get_act_fn(act_name, act_norm)
self.branch_dims = [branch_dim] + [width] * branch_depth
self.trunk_dims = [trunk_dim] + [width] * trunk_depth
self.branch_net = Ffn(
self.branch_dims,
act_fn=act_fn,
act_on_output=act_on_output,
)
self.trunk_net = Ffn(self.trunk_dims, act_fn=act_fn)
self.bias = nn.Parameter(torch.zeros(1)) # type: ignore
def forward(
self,
inputs: Tensor,
case_params: Tensor,
label: Optional[Tensor] = None,
mask: Optional[Tensor] = None,
query_idxs: Optional[Tensor] = None,
):
"""
Here, we just randomly sample some points, and use the label values on
those points as the label.
### Args
- inputs: (b, c, h, w)
- case_params: (b, p)
- labels: (b, c, h, w)
- query_point: (k, 2), k is the number of query points, each is
an (x, y) coordinate.
- masks: For future use.
### Returns
Output: Tensor, if query_points is not None, the shape is (b, k).
Else, the shape is (b, c, h, w).
Notations:
- b: batch size
- c: number of channels
- h: height
- w: width
- p: number of case parameters
- k: number of query points
"""
batch_size, num_chan, height, width = inputs.shape
# Only use the u channel
inputs = inputs[:, 0] # (B, h, w)
# Flatten
flat_inputs = inputs.view(batch_size, -1) # (B, h * w)
# Simple prepend physical properties to the input field.
# (B, h * w + 2)
flat_inputs = torch.cat([flat_inputs, case_params], dim=1)
x_branch = self.branch_net(flat_inputs)
if query_idxs is None:
query_idxs = torch.tensor(
list(product(range(height), range(width))),
dtype=torch.long,
device=flat_inputs.device,
) # (h * w, 2)
# Input to the trunk net
x_trunk = (query_idxs.float() - 50) / 100 # (k, 2)
x_trunk = self.trunk_net(x_trunk) # (k, p)
x_trunk = x_trunk.unsqueeze(0) # (1, k, p)
x_branch = x_branch.unsqueeze(1) # (b, 1, p)
preds = torch.sum(x_branch * x_trunk, dim=-1) + self.bias # (b, k)
# Use values of the input field at query points as residuals
residuals = inputs[:, query_idxs[:, 0], query_idxs[:, 1]] # (b, k)
preds += residuals
if label is not None:
label = label[:, 0] # (B, 1, h, w) # Predict only u
labels = label[:, query_idxs[:, 0], query_idxs[:, 1]] # (b, k)
loss = self.loss_fn(labels=labels, preds=preds) # (b, k)
return dict(
preds=preds,
loss=loss,
)
preds = preds.view(-1, 1, height, width) # (b, 1, h, w)
return dict(preds=preds)
def generate(
self, inputs: Tensor, case_params: Tensor, mask: Tensor
) -> Tensor:
"""
x: (c, h, w) or (B, c, h, w)
Returns:
(b, c, h, w)
"""
if inputs.dim() == 3:
inputs = inputs.unsqueeze(0) # (1, c, h, w)
batch_size, num_chan, height, width = inputs.shape
query_idxs = torch.tensor(
list(product(range(height), range(width))),
dtype=torch.long,
device=inputs.device,
) # (h * w, 2)
# query_points = query_points / 100
# (b, 1, h * w)
preds = self.forward(
inputs, case_params=case_params, query_idxs=query_idxs, mask=mask
)["preds"]
preds = preds.view(-1, 1, height, width) # (b, 1, h, w)
return preds
def generate_many(
self, inputs: Tensor, case_params: Tensor, mask: Tensor, steps: int
) -> List[Tensor]:
"""
Args:
x: (c, h, w) or (B, c, h, w)
mask: (h, w). 1 for interior, 0 for boundaries.
steps: int, number of steps to generate.
Returns:
list of tensors, each of shape (b, c, h, w)
"""
assert len(inputs.shape) == len(case_params.shape) + 2
if inputs.dim() == 3:
inputs = inputs.unsqueeze(0) # (1, c, h, w)
case_params = case_params.unsqueeze(0) # (1, p)
mask = mask.unsqueeze(0) # (1, h, w)
assert inputs.shape[0] == case_params.shape[0]
cur_frame = inputs
preds = []
for _ in range(steps):
# (b, c, h, w)
cur_frame = self.generate(
inputs=cur_frame, case_params=case_params, mask=mask
)
preds.append(cur_frame)
return preds
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