File size: 5,731 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 | from typing import Optional, Dict, List
from itertools import product
from torch import nn, Tensor
import torch
from .base_model import CfdModel
from .loss import MseLoss
from .act_fn import get_act_fn
class Ffn(nn.Module):
"""
A general fully connected multi-layer neural network.
"""
def __init__(
self, dims: list, act_fn: nn.Module, act_on_output: bool = False
):
super().__init__()
self.dims = dims
layers = []
for i in range(len(dims) - 2):
layers.append(nn.Linear(dims[i], dims[i + 1]))
layers.append(act_fn)
# layers.append(NormAct(nn.Tanh()))
layers.append(nn.Linear(dims[-2], dims[-1]))
if act_on_output:
layers.append(act_fn)
self.layers = nn.Sequential(*layers)
def forward(self, x: Tensor) -> Tensor:
x = self.layers(x)
return x
class FfnModel(CfdModel):
"""
Non-autoregressive FNN for data-driven 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,
loss_fn: MseLoss,
widths: List[int],
act_name: str = "relu",
act_norm: bool = True,
act_on_output: bool = False,
num_label_samples: int = 1000,
):
"""
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.loss_fn = loss_fn
self.widths = widths
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.ffn = Ffn(
self.widths, act_fn=act_fn, act_on_output=self.act_on_output
)
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.
"""
batch_size, dim_in = case_params.shape
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)
# Concatenate query spatial coordinates with time coordinate
coords = query_idxs.unsqueeze(0) # (1, k, 2)
coords = coords.repeat(batch_size, 1, 1) # (b, k, 2)
num_queries = coords.shape[1]
t = t.unsqueeze(-1) # (b, 1)
t = t.repeat(1, num_queries, 1) # (b, k)
coords = torch.cat([coords, t], dim=-1) # (b, k, 3)
# Concatenate case params with queries
case_params = case_params.unsqueeze(1) # (b, 1, p)
case_params = case_params.repeat(1, num_queries, 1) # (b, k, p)
inp = torch.cat([case_params, coords], dim=-1) # (b, k, p + 3)
inp = inp.view(batch_size * num_queries, -1) # (bk, p + 3)
preds = self.ffn(inp) # (bk, out_dim)
preds = preds.view(batch_size, num_queries) # (b, k)
if label is not None:
# Use only the u channel
label = label[:, 0] # (B, w, h)
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, (branch_dim)
- t: Tensor, (1,)
- 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
|