File size: 9,008 Bytes
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 | from itertools import product
from typing import Dict, Optional
import torch
from torch import nn, Tensor
from .base_model import CfdModel
from .ffn import Ffn
from .act_fn import get_act_fn
from .loss import MseLoss
class DeepONet(CfdModel):
"""
DeepONet for CFD.
Branch net accepts the boundary and physics properties as inputs.
Trunk net accepts the query location (t, x, y) as input.
"""
def __init__(
self,
branch_dim: int,
trunk_dim: int, # (t, x, y)
loss_fn: MseLoss,
num_label_samples: int = 1000,
branch_depth: int = 4,
trunk_depth: int = 3,
width: int = 100,
act_name: str = "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.loss_fn = loss_fn
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
self.branch_dims = [branch_dim] + [width] * branch_depth
act_fn = get_act_fn(act_name, act_norm)
self.branch_net = Ffn(
self.branch_dims, act_fn=act_fn, act_on_output=self.act_on_output
)
# t and x, y uses separate fc layers because during training
# we will be using an entire frame as label.
self.fc_trunk_t = nn.Linear(1, width)
self.fc_trunk_xy = nn.Linear(2, width)
self.trunk_dims = [width] * trunk_depth
self.trunk_net = Ffn(self.trunk_dims, act_fn=act_fn)
# self.params = self.__init_params()
self.bias = nn.Parameter(torch.zeros(1)) # type: ignore
# self.__initialize()
# NOTE: This is no long used!!
# This is the implementation of the ordinary DeepONet, but it is very slow.
# Our `forward` is much faster because it re-uses the output
# of the branch net for different queries in one batch.
def forward_vanilla(
self,
x_branch: Tensor,
x_trunk: Tensor,
label: Optional[Tensor] = None,
query_idxs: Optional[Tensor] = None,
):
"""
NOTE: This is no long used!!
This is the implementation of the ordinary DeepONet, but it is very
slow! Our `forward` is much faster because it re-uses the output
of the branch net for different queries in one batch.
Args:
- x_branch: (b, branch_dim), input to the branch net.
- x_trunk: (b), input to the trunk net, a batch of (t, x, y)
- label: (b), the label for the query location.
Return:
- if label is None:
- preds: (b, k), the prediction for the query location.
- else:
- preds: (b, k), the prediction for the query location.
- loss: (b, k), the loss for the query location.
"""
# Create k query locations.
# (b, k, 3), where each element is (t, x, y)
if query_idxs is None:
assert label is not None
height, width = label.shape[-2:]
query_idxs = torch.stack(
[
torch.randint(
0,
height,
(self.num_label_samples,),
device=x_trunk.device,
),
torch.randint(
0,
width,
(self.num_label_samples,),
device=x_trunk.device,
),
],
dim=-1,
) # (k, 2)
t = x_trunk.unsqueeze(1).float() # (b, 1)
x_trunk_t = self.fc_trunk_t(t) # (b, p)
# Normalize query location
x_trunk_xy = (
query_idxs.float() - 32.0
) / 64.0 # (k, 2) # TODO: update this
x_trunk_xy = self.fc_trunk_xy(x_trunk_xy) # (k, p)
x_trunk_t = x_trunk_t.unsqueeze(1) # (b, 1, p)
x_trunk_xy = x_trunk_xy.unsqueeze(0) # (1, k, p)
x_trunk = x_trunk_t + x_trunk_xy # (b, k, p)
x_branch = self.branch_net(x_branch) # (b, p)
x_trunk = self.trunk_net(x_trunk) # (b, 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)
print(preds.dtype)
if label is not None:
print(label.dtype)
# Use only the u channel
label = label[:, 0] # (B, w, h)
# 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)
# assert preds.shape == label.shape, f"{preds.shape} {label.shape}"
loss = self.loss_fn(preds=preds, labels=labels) # (b, k)
print(labels.dtype)
print(loss.dtype)
return preds, loss
return preds
def forward(
self,
case_params: Tensor, # Case parameters
t: Tensor,
label: Optional[Tensor] = None,
query_idxs: Optional[Tensor] = None,
) -> Dict[str, Tensor]:
"""
A faster forward by using all the points in the frame (`label`) at
time step `t` as training examples.
Args:
- x_branch: (b, branch_dim), input to the branch net.
- t: (b), input to the trunk net, a batch of t
- label: (b, w, h), the frame to be predicted.
- query_idxs: (b, k, 2), the query locations.
"""
if query_idxs is None:
# Create k query locations on a lattice.
# (b, k, 2), where each element is (x, y)
assert label is not None
height, width = label.shape[-2:]
query_idxs = torch.stack(
[
torch.randint(
0,
height,
(self.num_label_samples,),
device=label.device,
),
torch.randint(
0,
width,
(self.num_label_samples,),
device=label.device,
),
],
dim=-1,
) # (k, 2)
x_trunk_t = self.fc_trunk_t(t) # (b, p)
# Normalize query location
x_trunk_xy = query_idxs.float() # (k, 2)
x_trunk_xy = self.fc_trunk_xy(x_trunk_xy) # (k, p)
x_trunk_t = x_trunk_t.unsqueeze(1) # (b, 1, p)
x_trunk_xy = x_trunk_xy.unsqueeze(0) # (1, k, p)
x_trunk = x_trunk_t + x_trunk_xy # (b, k, p)
case_params = self.branch_net(case_params) # (b, p)
x_trunk = self.trunk_net(x_trunk) # (b, k, p)
case_params = case_params.unsqueeze(1) # (b, 1, p)
preds = torch.sum(case_params * x_trunk, dim=-1) + self.bias # (b, k)
if label is not None:
# Only predict u
label = label[:, 0] # (B, w, h)
# 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)
assert (
preds.shape == labels.shape
), f"{preds.shape}, {labels.shape}"
loss = self.loss_fn(preds=preds, labels=labels) # (b, k)
return dict(
preds=preds,
loss=loss,
)
return dict(
preds=preds,
)
def generate_one(
self, case_params: Tensor, t: Tensor, height: int, width: int
) -> Tensor:
"""
Generate one frame at time t.
Args:
- x_branch: Tensor, (b, branch_dim)
- t: Tensor, (b)
- height: int
- width: int
Returns:
(b, c, h, w)
"""
if len(case_params.shape) == 1:
case_params = case_params.unsqueeze(0)
if len(t.shape) == 0:
t = t.unsqueeze(0).unsqueeze(0)
elif len(t.shape) == 1:
t = t.unsqueeze(0)
# Create 2D lattice of query points to infer the frame.
query_idxs = torch.tensor(
list(product(range(height), range(width))),
# dtype=torch.long,
device=case_params.device,
) # (h * w, 2)
# query_points = query_points / 100
# (b, 1, h * w)
output = self.forward(case_params, t=t, query_idxs=query_idxs)["preds"]
output = output.view(-1, 1, height, width) # (b, 1, h, w)
return output
|