"""Model definition not used in any publication"""
from functools import reduce
from typing import Any, Dict, Optional, Tuple, Union
import pytorch_lightning as pl
import torch
import torch.optim as optim
from torch import nn
from doc_octopy.models.components.activation_functions import SMU
from doc_octopy.models.components.utils import CircularPad
[docs]
class ElectrodeWiseDropout(torch.nn.Module):
def __init__(self, p: float):
super(ElectrodeWiseDropout, self).__init__()
self.p = p
if self.p < 0 or self.p > 1:
raise ValueError("p must be a probability")
[docs]
def forward(self, x: torch.Tensor) -> torch.Tensor:
if self.training:
for i in range(x.shape[0]):
x[i, :, torch.bernoulli(torch.full(x.shape[2:4], self.p, device=x.device)).to(torch.bool)] = 0
return x
[docs]
class RaulNetV6(pl.LightningModule):
"""Model definition not used in any publication
Attributes
----------
learning_rate : float
The learning rate.
nr_of_input_channels : int
The number of input channels.
nr_of_outputs : int
The number of outputs.
cnn_encoder_channels : Tuple[int, int, int]
Tuple containing 3 integers defining the cnn encoder channels.
mlp_encoder_channels : Tuple[int, int]
Tuple containing 2 integers defining the mlp encoder channels.
event_search_kernel_length : int
Integer that sets the length of the kernels searching for action potentials.
event_search_kernel_stride : int
Integer that sets the stride of the kernels searching for action potentials.
"""
def __init__(
self,
learning_rate: float,
nr_of_input_channels: int,
input_length__samples: int,
nr_of_outputs: int,
cnn_encoder_channels: Tuple[int, int, int],
mlp_encoder_channels: Tuple[int, int],
event_search_kernel_length: int,
event_search_kernel_stride: int,
):
super(RaulNetV6, self).__init__()
self.save_hyperparameters()
self.learning_rate = learning_rate
self.nr_of_input_channels = nr_of_input_channels
self.nr_of_outputs = nr_of_outputs
self.input_length__samples = input_length__samples
self.cnn_encoder_channels = cnn_encoder_channels
self.mlp_encoder_channels = mlp_encoder_channels
self.event_search_kernel_length = event_search_kernel_length
self.event_search_kernel_stride = event_search_kernel_stride
self.criterion = nn.L1Loss()
self.electrode_wise_dropout = ElectrodeWiseDropout(p=0.10)
self.cnn_encoder = nn.Sequential(
nn.Conv3d(
self.nr_of_input_channels,
self.cnn_encoder_channels[0],
kernel_size=(1, 1, self.event_search_kernel_length),
stride=(1, 1, self.event_search_kernel_stride),
),
SMU(),
nn.BatchNorm3d(self.cnn_encoder_channels[0]),
# nn.Dropout3d(p=0.25),
CircularPad(),
nn.Conv3d(
self.cnn_encoder_channels[0],
self.cnn_encoder_channels[1],
kernel_size=(5, 32, 18),
dilation=(1, 2, 1),
),
SMU(),
nn.BatchNorm3d(self.cnn_encoder_channels[1]),
nn.Conv3d(self.cnn_encoder_channels[1], self.cnn_encoder_channels[2], kernel_size=(5, 9, 1)),
SMU(),
nn.BatchNorm3d(self.cnn_encoder_channels[2]),
nn.Flatten(),
nn.Dropout(p=0.40),
)
self.mlp_encoder = nn.Sequential(
nn.Linear(
reduce(
lambda x, y: x * int(y),
self.cnn_encoder(
torch.rand((1, self.nr_of_input_channels, 5, 64, self.input_length__samples))
).shape[1:],
1,
),
self.mlp_encoder_channels[0],
),
SMU(),
nn.Linear(self.mlp_encoder_channels[0], self.mlp_encoder_channels[1]),
SMU(),
nn.Linear(self.mlp_encoder_channels[1], self.nr_of_outputs),
)
[docs]
def forward(self, inputs) -> torch.Tensor:
x = torch.stack(torch.split(inputs, 64, dim=2), dim=2)
x = self.electrode_wise_dropout(x)
x = self._normalize_input(x)
x = self.cnn_encoder(x)
x = self.mlp_encoder(x)
return x
def _normalize_input(self, inputs: torch.Tensor) -> torch.Tensor:
mins = torch.min(torch.min(inputs, dim=3, keepdim=True)[0], dim=4, keepdim=True)[0].expand(inputs.shape)
maxs = torch.max(torch.max(inputs, dim=3, keepdim=True)[0], dim=4, keepdim=True)[0].expand(inputs.shape)
return 2 * torch.div(torch.sub(inputs, mins), torch.sub(maxs, mins)) - 1
def configure_optimizers(self):
optimizer = optim.AdamW(self.parameters(), lr=self.learning_rate, amsgrad=True, weight_decay=0.01)
lr_scheduler = {
"scheduler": optim.lr_scheduler.OneCycleLR(
optimizer,
max_lr=self.learning_rate * (10**1.5),
total_steps=self.trainer.estimated_stepping_batches,
anneal_strategy="cos",
three_phase=False,
div_factor=10**1.5,
final_div_factor=1e3,
),
"name": "OncCycleLR",
"interval": "step",
"frequency": 1,
}
return [optimizer], [lr_scheduler]
def training_step(self, train_batch, batch_idx: int) -> Optional[Union[torch.Tensor, Dict[str, Any]]]:
inputs, ground_truths = train_batch
prediction = self(inputs)
scores_dict = {"loss": self.criterion(prediction, ground_truths)}
if scores_dict["loss"].isnan().item():
return None
self.log_dict(scores_dict, prog_bar=True, logger=False, on_epoch=True)
self.log_dict(
{f"train/{k}": v for k, v in scores_dict.items()}, prog_bar=False, logger=True, on_epoch=True, on_step=False
)
return scores_dict
def validation_step(self, batch, batch_idx) -> Optional[Union[torch.Tensor, Dict[str, Any]]]:
inputs, ground_truths = batch
prediction = self(inputs)
scores_dict = {"val_loss": self.criterion(prediction, ground_truths)}
self.log_dict(scores_dict, prog_bar=True, logger=False, on_epoch=True)
return scores_dict
def test_step(self, batch, batch_idx) -> Optional[Union[torch.Tensor, Dict[str, Any]]]:
inputs, ground_truths = batch
prediction = self(inputs)
scores_dict = {"loss": self.criterion(prediction, ground_truths)}
self.log_dict(scores_dict, prog_bar=True, logger=False, on_epoch=True)
self.log_dict(
{f"test/{k}": v for k, v in scores_dict.items()}, prog_bar=False, logger=True, on_epoch=False, on_step=True
)
return scores_dict