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utils function for inference
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import pytorch_lightning as pl
from . import config
from .utils import (
check_class_accuracy,
get_evaluation_bboxes,
mean_average_precision,
plot_couple_examples,
)
class PlotTestExamplesCallback(pl.Callback):
def __init__(self, every_n_epochs: int = 1) -> None:
super().__init__()
self.every_n_epochs = every_n_epochs
def on_train_epoch_end(self, trainer:pl.Trainer, pl_module:pl.LightningModule) -> None:
if (trainer.current_epoch + 1) % self.every_n_epochs == 0:
plot_couple_examples(
model=pl_module,
loader=trainer.datamodule.test_dataloader(),
thresh=0.6,
iou_thresh=0.5,
anchors=pl_module.scaled_anchors
)
class CheckClassAccuracyCallback(pl.Callback):
def __init__(
self, train_every_n_epochs: int = 1, test_every_n_epochs: int = 3
) -> None:
super().__init__()
self.train_every_n_epochs = train_every_n_epochs
self.test_every_n_epochs = test_every_n_epochs
def on_train_epoch_end(
self, trainer: pl.Trainer, pl_module: pl.LightningModule
) -> None:
if (trainer.current_epoch + 1) % self.train_every_n_epochs == 0:
print("+++ TRAIN ACCURACIES")
class_acc, no_obj_acc, obj_acc = check_class_accuracy(
model=pl_module,
loader=trainer.datamodule.train_dataloader(),
threshold=config.CONF_THRESHOLD,
)
pl_module.log_dict(
{
"train_class_acc": class_acc,
"train_no_obj_acc": no_obj_acc,
"train_obj_acc": obj_acc,
},
logger=True,
)
if (trainer.current_epoch + 1) % self.test_every_n_epochs == 0:
print("+++ TEST ACCURACIES")
class_acc, no_obj_acc, obj_acc = check_class_accuracy(
model=pl_module,
loader=trainer.datamodule.test_dataloader(),
threshold=config.CONF_THRESHOLD,
)
pl_module.log_dict(
{
"test_class_acc": class_acc,
"test_no_obj_acc": no_obj_acc,
"test_obj_acc": obj_acc,
},
logger=True,
)
class MAPCallback(pl.Callback):
def __init__(self, every_n_epochs: int = 3) -> None:
super().__init__()
self.every_n_epochs = every_n_epochs
def on_train_epoch_end(
self, trainer: pl.Trainer, pl_module: pl.LightningModule
) -> None:
if (trainer.current_epoch + 1) % self.every_n_epochs == 0:
pred_boxes, true_boxes = get_evaluation_bboxes(
loader=trainer.datamodule.test_dataloader(),
model=pl_module,
iou_threshold=config.NMS_IOU_THRESH,
anchors=config.ANCHORS,
threshold=config.CONF_THRESHOLD,
device=config.DEVICE,
)
map_val = mean_average_precision(
pred_boxes=pred_boxes,
true_boxes=true_boxes,
iou_threshold=config.MAP_IOU_THRESH,
box_format="midpoint",
num_classes=config.NUM_CLASSES,
)
print("+++ MAP: ", map_val.item())
pl_module.log(
"MAP",
map_val.item(),
logger=True,
)
pl_module.train()