File size: 8,701 Bytes
484b847 | 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 | import math
from functools import partial
from os.path import join
from typing import Callable, List
import h5py
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from neuralop.models import FNO
from torch import vmap
from torch.distributions import Categorical
from pdeinvbench.data.transforms import (
collapse_time_and_channels,
expand_time_and_channels,
)
from pdeinvbench.data.utils import unnormalize_params
from pdeinvbench.losses import get_pde_residual_function
from pdeinvbench.models import MLP, ConvHead
from pdeinvbench.utils.types import (
PDE,
PDE_NUM_CHANNELS,
PDE_NUM_SPATIAL,
PDE_PARAM_VALUES,
PDE_SPATIAL_SIZE,
)
class InverseModel(nn.Module):
"""
Model that predicts the parameters of a PDE given a set of conditioning frames.
:param paramnet: Model that predicts the parameters.
"""
def __init__(
self,
paramnet: nn.Module,
):
super(InverseModel, self).__init__()
self.paramnet = paramnet
self.pde = paramnet.pde
self.param_values = paramnet.param_values
self.pde_residual = get_pde_residual_function(self.pde)
self.params_to_predict = self.paramnet.params_to_predict
self.use_partials = self.paramnet.encoder.use_partials
self.dropout = self.paramnet.dropout
# Set up data transform and detransform functions
self.data_transform = None
self.data_detransform = None
if PDE_NUM_SPATIAL[self.pde] > 1:
self.data_transform = vmap(collapse_time_and_channels)
self.data_detransform = vmap(
partial(
expand_time_and_channels, num_channels=PDE_NUM_CHANNELS[self.pde]
)
)
def forward(self, solution_field, true_params, spatial_grid, t, gumbel=False):
# get the partial derivatives and true pde residual, and append to the input if needed
with torch.no_grad():
# PDE residual function expects time and channel dimensions to be uncollapsed
sf = (
solution_field
if self.data_transform is None
else self.data_detransform(solution_field)
)
true_residual, partials = self.pde_residual(
sf, true_params, spatial_grid, t, return_partials=True
)
if self.use_partials:
partials = (
partials
if self.data_transform is None
else self.data_transform(partials)
)
new_solution_field = torch.cat([solution_field, partials], dim=1)
else:
new_solution_field = solution_field
# predict parameters
pred_params = self.paramnet(new_solution_field, gumbel=gumbel, t=t)
# compute PDE residual with predicted params (replace unpredicted params with true params)
combined_params = {**true_params, **pred_params}
pred_residual = self.pde_residual(sf, combined_params, spatial_grid, t)
return combined_params, pred_residual, true_residual
class ParameterNet(nn.Module):
"""
Model that predicts PDE parameters given conditioning frames.
:param params_to_predict: list of strings of parameters to predict.
:param pde: enum of the PDE to use for the residual calculation.
:param encoder: Encoder model. Can be FNO or e.g ResNet
:param downsampler: Convolutional downsampler model.
:param mlp_hidden_size: Hidden size of the MLP.
:param mlp_layers: Number of layers in the MLP.
:param mlp_activation: Activation function in the MLP.
:param logspace: Whether to predict the parameters in log space.
:param normalize: Whether to normalize the parameters.
:param condition_on_time: Whether or not to provide $t$ as input to the encoder
:param mlp_type: Alternatively, may be "conv" which denotes pointwise convolution of darcy flow
"""
def __init__(
self,
params_to_predict: List[str],
pde: PDE,
encoder: nn.Module,
downsampler: nn.Module,
mlp_hidden_size: int,
mlp_layers: int,
mlp_activation: str,
mlp_dropout: float,
logspace: bool,
normalize: bool,
downsample_factor: int,
condition_on_time: bool = False,
mlp_type: str = "mlp",
):
super(ParameterNet, self).__init__()
self.encoder = encoder
self.downsampler = downsampler
self.params_to_predict = params_to_predict
self.logspace = logspace
self.normalize = normalize
self.pde = pde
self.input_size = PDE_SPATIAL_SIZE[pde]
self.condition_on_time = condition_on_time
self.param_values = None
self.dropout = mlp_dropout
if self.pde != PDE.DarcyFlow2D:
self.param_values = {
param: torch.tensor(PDE_PARAM_VALUES[pde][param]).to(
torch.device(torch.cuda.current_device())
)
for param in params_to_predict
}
# Consistency checks
assert not (
logspace and normalize
), "Cannot use logspace and normalize together."
# get the input shape into the MLP by running a dummy forward pass
with torch.no_grad():
dummy = torch.randn(1, self.encoder.in_channels, *self.input_size)
dummy_time = (
torch.ones(1, self.encoder.__dict__.get("n_past", 1))
if self.condition_on_time
else None
)
dummy = self.encoder(dummy, t=dummy_time)
dummy = self.downsampler(dummy)
encoder_out_channels = dummy.shape[1]
dummy = torch.flatten(dummy, start_dim=1)
mlp_input_size = dummy.shape[1]
self.heads: nn.ModuleList
# The following calls set the heads
if mlp_type == "mlp":
self.generate_mlp_heads(
mlp_input_size=mlp_input_size,
mlp_hidden_size=mlp_hidden_size,
mlp_dropout=mlp_dropout,
mlp_layers=mlp_layers,
mlp_activation=mlp_activation,
params_to_predict=self.params_to_predict,
)
elif mlp_type == "conv":
self.generate_conv_heads(
in_dim=encoder_out_channels,
hidden_dim=mlp_hidden_size,
n_layers=mlp_layers,
dropout=mlp_dropout,
activation=mlp_activation,
params_to_predict=self.params_to_predict,
)
def generate_conv_heads(
self,
in_dim: int,
hidden_dim: int,
n_layers: int,
dropout: float,
activation: str,
params_to_predict,
):
self.heads = nn.ModuleList(
[
ConvHead(
in_dim=in_dim,
hidden_dim=hidden_dim,
n_layers=n_layers,
dropout=dropout,
activation=activation,
)
for param in params_to_predict
]
)
def generate_mlp_heads(
self,
mlp_input_size: int,
mlp_hidden_size: int,
mlp_dropout: float,
mlp_layers: int,
mlp_activation,
params_to_predict,
):
self.heads = nn.ModuleList(
[
MLP(
in_dim=int(mlp_input_size),
hidden_size=mlp_hidden_size,
dropout=mlp_dropout,
out_dim=1,
num_layers=mlp_layers,
activation=mlp_activation,
)
for param in params_to_predict
]
)
def forward(self, x, gumbel=False, t=None):
if self.condition_on_time:
x = self.encoder(x, t=t)
else:
x = self.encoder(x)
x = self.downsampler(x)
# We follow different paths depending on the PDE
if self.pde == PDE.DarcyFlow2D:
preds = [head(x) for head in self.heads]
else:
x = torch.flatten(x, start_dim=1)
# combine output from each head
preds = [head(x) for head in self.heads]
if self.logspace:
preds = [torch.exp(pred) for pred in preds]
# convert to a dictionary
preds = {k: v for k, v in zip(self.params_to_predict, preds)}
if self.normalize:
preds = unnormalize_params(preds, self.pde)
return preds
|