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338c3e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 | 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 CnnBranch(nn.Module):
def __init__(
self, in_chan: int, kernel_size: int, padding: int, depth: int = 4
):
super().__init__()
self.in_chan = in_chan
self.in_conv = nn.Conv2d(
in_chan, 32, kernel_size=kernel_size, padding=padding
)
self.out_conv = nn.Conv2d(
32, 32, kernel_size=kernel_size, padding=padding
)
blocks = []
for i in range(depth):
blocks += [
nn.Conv2d(32, 32, kernel_size, padding=padding),
nn.MaxPool2d(2),
nn.ReLU(),
]
self.blocks = nn.Sequential(*blocks)
def forward(self, x: Tensor) -> Tensor:
x = self.in_conv(x) # (b, 16, h, w)
x = self.blocks(x) # (b, 32, h/16=4, w/16=4)
x = self.out_conv(x) # (b, 32, 4, 4)
return x
class AutoDeepONetCnn(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,
in_chan: int,
query_dim: int,
loss_fn: MseLoss,
height: int = 100,
width: int = 100,
num_case_params: int = 5,
trunk_depth: int = 4,
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.in_chan = in_chan
self.query_dim = query_dim
self.num_case_params = num_case_params
self.trunk_depth = trunk_depth
self.height = height
self.width = width
self.act_name = act_name
self.act_norm = act_norm
self.act_on_output = act_on_output
act_fn = get_act_fn(act_name, act_norm)
self.trunk_dims = [query_dim] + [100] * trunk_depth + [4 * 4 * 32]
# + 1 for mask
self.branch_net = CnnBranch(
in_chan + 1 + num_case_params, kernel_size=5, padding=2
)
self.trunk_net = Ffn(
self.trunk_dims, act_fn=act_fn, act_on_output=False
)
self.out_ffn = Ffn(
[32 * 4 * 4] * 3 + [1], act_fn=act_fn, act_on_output=False
)
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.
x: (b, c, h, w)
labels: (b, c, h, w)
query_point: (k, 2), k is the number of query points, each is an (x, y)
coordinate.
Goal:
Input: [b, branch_dim + trunk_dim]
Output: [b, 1]
"""
# Add mask to input as additional channels
if mask is not None:
if mask.dim() == 3:
mask = mask.unsqueeze(1) # (B, 1, h, w)
inputs = torch.cat([inputs, mask], dim=1) # (B, c + 1, h, w)
batch_size, num_chan, height, width = inputs.shape
# Only use the u channel
residuals = inputs[:, :] # (B, c, h, w)
# Add case params as additional channels
case_params = case_params.unsqueeze(-1).unsqueeze(-1) # (B, c, 1, 1)
# (B, n_params, h, w)
case_params = case_params.expand(
-1, -1, inputs.shape[-2], inputs.shape[-1]
)
inputs = torch.cat(
[inputs, case_params], dim=1
) # (B, c + n_params, h, w)
x_branch = self.branch_net(inputs) # (b, 32, h/16=4, w/16=4)
x_branch = x_branch.view(batch_size, -1) # (b, 32 * 4 * 4 = 512)
if query_idxs is None:
query_idxs = torch.tensor(
list(product(range(height), range(width))),
dtype=torch.long,
device=x_branch.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, 32, p)
# preds = torch.sum(x_branch * x_trunk, dim=-1) + self.bias # (b, k)
preds = x_branch * x_trunk # (b, k, h)
preds = self.out_ffn(preds) # (b, k, 1)
preds = preds.squeeze(-1) # (b, k)
# Use values of the input field at query points as residuals
residuals = residuals[
:, 0, query_idxs[:, 0], query_idxs[:, 1]
] # (b, c, k)
# print(residuals.shape, preds.shape)
preds += residuals
if label is not None:
label = label[:, 0] # (B, 1, h, w) # Predict only u
# we have labels[i, j] = label[
# i, query_points[i, j, 0], query_points[i, j, 1]]
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)
case_params = case_params.unsqueeze(0) # (1, p)
mask = mask.unsqueeze(0) # (1, 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, query_idxs=query_idxs, case_params=case_params, 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]:
"""
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)
"""
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)
cur_frame = inputs
p = inputs[:, -1:]
preds = []
for _ in range(steps):
# (b, c, h, w)
cur_frame = self.generate(
cur_frame, case_params=case_params, mask=mask
)
preds.append(cur_frame)
cur_frame = torch.cat([cur_frame, p], dim=1)
return preds
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