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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 | from itertools import product
from typing import Dict, Optional, List
import torch
from torch import nn, Tensor
from .base_model import AutoCfdModel
from .ffn import Ffn
from .act_fn import get_act_fn
from .loss import MseLoss
class AutoEDeepONet(AutoCfdModel):
"""
EDeepONet for autoregressive generation. The two input functions are
the previous field (flattened) and the case parameters.
Branch net accepts the boundary and physics properties as inputs.
Trunk net accepts the query location (t, x, y) as input.
"""
def __init__(
self,
dim_branch1: int,
dim_branch2: int,
trunk_dim: int, # (x, y)
loss_fn: MseLoss,
num_label_samples: int = 1000,
branch_depth: int = 4,
trunk_depth: int = 4,
width: int = 100,
act_name: str = "relu",
act_norm: bool = False,
act_on_output: bool = False,
):
super().__init__(loss_fn)
self.dim_branch1 = dim_branch1
self.dim_branch2 = dim_branch2
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.branch1_dims = [dim_branch1] + [width] * branch_depth
self.branch2_dims = [dim_branch2] + [width] * branch_depth
self.trunk_dims = [trunk_dim] + [width] * trunk_depth
print(self.trunk_dims)
act_fn = get_act_fn(act_name, act_norm)
self.branch1 = Ffn(
self.branch1_dims, act_fn=act_fn, act_on_output=self.act_on_output
)
self.branch2 = Ffn(
self.branch2_dims, act_fn=act_fn, act_on_output=self.act_on_output
)
# we will be using an entire frame as label.
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, # (b, d1), previous field u(t-1)
case_params: Tensor, # (b, d2), physical properties
label: Optional[Tensor] = None,
mask: Optional[Tensor] = None, # NOTE: not used
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, num_chan, height, width = inputs.shape
# Only use the u channel, because using more channels is to expensive
inputs = inputs[:, 0] # (B, h, w)
# Flatten
flat_inputs = inputs.view(batch_size, -1) # (B, h * w)
b1 = self.branch1(flat_inputs) # (b, p)
b2 = self.branch2(case_params) # (b, p)
x_branch = b1 * b2
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)
# Normalize query location
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 input field at query points as residuals
residuals = inputs[:, query_idxs[:, 0], query_idxs[:, 1]] # (b, k)
preds = preds + residuals
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,
)
preds.view(-1, 1, height, width)
return dict(preds=preds)
def generate(
self,
inputs: Tensor,
case_params: Tensor,
mask: Optional[Tensor] = None,
) -> Tensor:
"""
Generate one frame at time t.
Args:
- x: Tensor, (b, field dim)
- case_params: Tensor, (b, case params dim)
- t: Tensor, (b)
- height: int
- width: int
Returns:
(b, c, h, w)
"""
batch_size, num_chan, height, width = inputs.shape
# Create 2D lattice of query points to infer the frame.
query_idxs = torch.tensor(
list(product(range(height), range(width))),
dtype=torch.long,
device=inputs.device,
) # (h * w, 2)
# (b, 1, h * w)
preds = self.forward(
inputs=inputs, case_params=case_params, query_idxs=query_idxs
)["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.
F
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=None
)
preds.append(cur_frame)
return preds
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