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engine_abmil.py
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engine_abmil.py
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from typing import Optional
import torch
import torch.nn.functional as F
from engine_base import BaseEngine
from model_abmil import ABMIL
class EngineABMIL(BaseEngine):
def __init__(
self,
in_channels: int,
intermediate_dim: int,
n_classes: int, stain_info: bool,
dropout: bool,
) -> None:
super().__init__(in_channels, intermediate_dim,
n_classes, stain_info, dropout)
# Init model
self.model = ABMIL(
in_channels=in_channels,
intermediate_dim=intermediate_dim,
n_classes=n_classes,
stain_info=stain_info,
dropout=dropout
)
def training_step(self, batch, batch_idx):
x = batch['img']
x_fname = batch['filename']
y = batch['label']
wsi_logits, _, _ = self.model(x, x_fname)
total_loss = self.abmil_loss(wsi_logits, y)
self.log("train_loss",
total_loss,
on_step=False,
on_epoch=True,
# prog_bar=True,
logger=True,
batch_size=1
)
return {
"loss": total_loss,
}
@torch.no_grad()
def validation_step(self, batch, batch_idx):
x = batch['img']
x_fname = batch['filename']
y = batch['label']
wsi_logits, _, _ = self.model(x, x_fname)
total_loss = self.abmil_loss(wsi_logits, y)
self.log("val_loss",
total_loss,
on_step=False,
on_epoch=True,
# prog_bar=True,
logger=True,
batch_size=1)
return {
'loss': total_loss,
}
@torch.no_grad()
def test_step(self, batch, batch_idx):
x = batch['img']
x_fname = batch['filename']
y = batch['label']
wsi_logits, A_raw, top_ids = self.model(x, x_fname)
total_loss = self.abmil_loss(wsi_logits, y)
y_pred = torch.topk(wsi_logits, 1, dim=1)[1]
y_pred = y_pred.squeeze(0)
y_prob = F.softmax(wsi_logits, dim=1)
return {
'loss': total_loss,
'y_pred': y_pred,
'y_prob': y_prob,
'target': y,
'top_ids': top_ids,
'A_raw': A_raw,
'filename': x_fname
}
def abmil_loss(self, logits, label):
total_loss = self.bag_loss_fn(logits, label)
total_loss = total_loss.unsqueeze_(0)
return total_loss