File size: 5,670 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 | from itertools import product
from typing import List, Optional
import torch
from torch import Tensor
from .ffn import Ffn
from .base_model import AutoCfdModel
from .act_fn import get_act_fn
from .loss import MseLoss
class AutoFfn(AutoCfdModel):
"""
Equivalent to autoregressive data-driven PINN.
"""
def __init__(
self,
input_field_dim: int,
num_case_params: int,
query_dim: int,
loss_fn: MseLoss,
num_label_samples: int = 1000,
depth: int = 8,
width: int = 100,
act_norm: bool = False,
act_name="relu",
):
"""
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.input_field_dim = input_field_dim
self.num_case_params = num_case_params
self.query_dim = query_dim
self.depth = depth
self.width = width
self.act_name = act_name
self.act_norm = act_norm
self.num_label_samples = num_label_samples
self.in_dim = input_field_dim + num_case_params + query_dim
act_fn = get_act_fn(act_name, act_norm)
self.widths = [self.in_dim] + [width] * depth + [1]
self.ffn = Ffn(
self.widths,
act_fn=act_fn,
act_on_output=False,
)
def forward(
self,
inputs: Tensor,
case_params: Tensor,
label: Optional[Tensor] = None,
mask: Optional[Tensor] = None, # NOTE: Not used
query_idxs: Optional[Tensor] = None,
):
"""
Here, we just randomly sample some points, and use the label values on
those points as the label.
### Parameters
- `inputs: Tensor` -- (b, c, h, w)
- `labels: Tensor` -- (b, c, h, w)
- `query_idxs: Tensor` -- (k, 2), k is the number of query points,
each is an (x, y) coordinate.
- `mask: Tensor` -- Not used.
### Function
Input: [b, branch_dim + trunk_dim]
Output: [b, 1]
"""
batch_size, _num_chan, height, width = inputs.shape
# Only use the u channel
inputs = inputs[:, 0] # (B, h, w)
# Flatten
flat_inputs = inputs.view(batch_size, -1) # (B, h * w)
flat_inputs = torch.cat(
[flat_inputs, case_params], dim=1
) # (B, h * w + 2)
if query_idxs is None:
query_idxs = torch.tensor(
list(product(range(height), range(width))),
dtype=torch.long,
device=flat_inputs.device,
) # (k=h * w, 2)
n_queries = query_idxs.shape[0]
# For each combination of (input, query_point), we have a sample.
# Repeat tensors such that we get (b * k) samples
# (b, k, h * w)
flat_inputs = flat_inputs.repeat(n_queries, 1) # (b * k, h * w)
batch_query_idxs = query_idxs.repeat(batch_size, 1) # (b * k, 2)
# (b * k, h * w + 2)
flat_inputs = torch.cat([flat_inputs, batch_query_idxs.float()], dim=1)
preds = self.ffn(flat_inputs) # (b * k, 1)
preds = preds.view(batch_size, -1) # (b, k)
# Use values of the input field at query points as residuals
residuals = inputs[:, query_idxs[:, 0], query_idxs[:, 1]] # (b, k)
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)
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
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)
return preds
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