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add mmcv.jit #4192

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merged 5 commits into from
Feb 21, 2021
Merged

add mmcv.jit #4192

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c0723bb
add mmcv.jit
Nov 27, 2020
030b568
only use derivate and coderize
Nov 30, 2020
5d55288
fix for isort
Feb 3, 2021
93ffd9a
merge master
Feb 3, 2021
ef8afdb
small modify for decorator order
Feb 3, 2021
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small modify for decorator order
  • Loading branch information
limaolin committed Feb 3, 2021
commit ef8afdbc2dec915822a85e5b0e0b7a8fbc0b767f
2 changes: 1 addition & 1 deletion mmdet/models/losses/accuracy.py
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import torch.nn as nn


@mmcv.jit(derivate=True, coderize=True)
@mmcv.jit(coderize=True)
def accuracy(pred, target, topk=1, thresh=None):
"""Calculate accuracy according to the prediction and target.

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2 changes: 1 addition & 1 deletion mmdet/models/losses/balanced_l1_loss.py
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from .utils import weighted_loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,
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2 changes: 1 addition & 1 deletion mmdet/models/losses/gaussian_focal_loss.py
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from .utils import weighted_loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
"""`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian
distribution.
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4 changes: 2 additions & 2 deletions mmdet/models/losses/gfocal_loss.py
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from .utils import weighted_loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def quality_focal_loss(pred, target, beta=2.0):
r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning
Qualified and Distributed Bounding Boxes for Dense Object Detection
Expand Down Expand Up @@ -51,8 +51,8 @@ def quality_focal_loss(pred, target, beta=2.0):
return loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def distribution_focal_loss(pred, label):
r"""Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning
Qualified and Distributed Bounding Boxes for Dense Object Detection
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10 changes: 5 additions & 5 deletions mmdet/models/losses/iou_loss.py
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from .utils import weighted_loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def iou_loss(pred, target, linear=False, eps=1e-6):
"""IoU loss.

Expand All @@ -36,8 +36,8 @@ def iou_loss(pred, target, linear=False, eps=1e-6):
return loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
"""BIoULoss.

Expand Down Expand Up @@ -82,8 +82,8 @@ def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
return loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def giou_loss(pred, target, eps=1e-7):
r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding
Box Regression <https://arxiv.org/abs/1902.09630>`_.
Expand All @@ -102,8 +102,8 @@ def giou_loss(pred, target, eps=1e-7):
return loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def diou_loss(pred, target, eps=1e-7):
r"""`Implementation of Distance-IoU Loss: Faster and Better
Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.
Expand Down Expand Up @@ -157,8 +157,8 @@ def diou_loss(pred, target, eps=1e-7):
return loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def ciou_loss(pred, target, eps=1e-7):
r"""`Implementation of paper `Enhancing Geometric Factors into
Model Learning and Inference for Object Detection and Instance
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4 changes: 2 additions & 2 deletions mmdet/models/losses/smooth_l1_loss.py
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from .utils import weighted_loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def smooth_l1_loss(pred, target, beta=1.0):
"""Smooth L1 loss.

Expand All @@ -28,8 +28,8 @@ def smooth_l1_loss(pred, target, beta=1.0):
return loss


@weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def l1_loss(pred, target):
"""L1 loss.

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