from icevision.models.mmdet.models.vfnet import backbones
from icevision.models.mmdet.common.bbox.single_stage import *
from icevision.models.interpretation import Interpretation, _move_to_device
from icevision.models.mmdet.common.interpretation_utils import (
sum_losses_mmdet,
loop_mmdet,
)
_LOSSES_DICT = {
"loss_cls": [],
"loss_bbox": [],
"loss_total": [],
}
interp = Interpretation(
losses_dict=_LOSSES_DICT,
valid_dl=valid_dl,
infer_dl=infer_dl,
predict_from_dl=predict_from_dl,
)
interp._loop = loop_mmdet
loss = compute_loss(preds, y)[0]
loss = {
"loss_yolo": float(loss.cpu().numpy()),
"loss_total": float(loss.cpu().numpy()),
}
for l in losses_stats.keys():
losses_stats[l].append(loss[l])
loss_comp = LossesRecordComponent()
loss_comp.set_losses(loss)
sample[0].add_component(loss_comp)
sample[0].set_img(tensor_to_image(x[0]))
samples_plus_losses.append(sample[0])
return samples_plus_losses, losses_stats
_LOSSES_DICT = {
"loss_yolo": [],
"loss_total": [],
}
interp = Interpretation(
losses_dict=_LOSSES_DICT,
valid_dl=valid_dl,
infer_dl=infer_dl,
predict_from_dl=predict_from_dl,
)
interp._loop = loop_yolo
"loss_unet": float(loss.cpu().numpy()),
"loss_total": float(loss.cpu().numpy()),
}
for l in losses_stats.keys():
losses_stats[l].append(loss[l])
loss_comp = LossesRecordComponent()
loss_comp.set_losses(loss)
sample[0].add_component(loss_comp)
sample[0].set_img(tensor_to_image(x[0]))
sample[0].segmentation.set_mask_array(
MaskArray(y[0].detach().cpu().numpy()))
samples_plus_losses.append(sample[0])
return samples_plus_losses, losses_stats
_LOSSES_DICT = {
"loss_unet": [],
"loss_total": [],
}
interp = Interpretation(
losses_dict=_LOSSES_DICT,
valid_dl=valid_dl,
infer_dl=infer_dl,
predict_from_dl=predict_from_dl,
)
interp._loop = loop_unet