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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmpose.models import build_loss
def test_rle_loss():
# test RLELoss without target weight(default None)
loss_cfg = dict(type='RLELoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 4))
fake_label = torch.zeros((1, 3, 2))
loss(fake_pred, fake_label)
# test RLELoss with Q(error) changed to "Gaussian"(default "Laplace")
loss_cfg = dict(type='RLELoss', q_dis='gaussian')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 4))
fake_label = torch.zeros((1, 3, 2))
loss(fake_pred, fake_label)
# test RLELoss._apply(fn)
loss_cfg = dict(type='RLELoss', size_average=False)
loss = build_loss(loss_cfg)
loss.cpu()
fake_pred = torch.zeros((1, 3, 4))
fake_label = torch.zeros((1, 3, 2))
loss(fake_pred, fake_label)
# test RLELoss with size_average(default True) changed to False
loss_cfg = dict(type='RLELoss', size_average=False)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 4))
fake_label = torch.zeros((1, 3, 2))
loss(fake_pred, fake_label)
# test RLELoss with residual(default True) changed to False
loss_cfg = dict(type='RLELoss', residual=False)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 4))
fake_label = torch.zeros((1, 3, 2))
loss(fake_pred, fake_label)
# test RLELoss with target weight
loss_cfg = dict(type='RLELoss', use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 4))
fake_label = torch.zeros((1, 3, 2))
loss(fake_pred, fake_label, torch.ones_like(fake_label))
fake_pred = torch.ones((1, 3, 4))
fake_label = torch.zeros((1, 3, 2))
loss(fake_pred, fake_label, torch.ones_like(fake_label))
def test_smooth_l1_loss():
# test SmoothL1Loss without target weight(default None)
loss_cfg = dict(type='SmoothL1Loss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(0.))
fake_pred = torch.ones((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(.5))
# test SmoothL1Loss with target weight
loss_cfg = dict(type='SmoothL1Loss', use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(
loss(fake_pred, fake_label, torch.ones_like(fake_label)),
torch.tensor(0.))
fake_pred = torch.ones((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(
loss(fake_pred, fake_label, torch.ones_like(fake_label)),
torch.tensor(.5))
def test_wing_loss():
# test WingLoss without target weight(default None)
loss_cfg = dict(type='WingLoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(0.))
fake_pred = torch.ones((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.gt(loss(fake_pred, fake_label), torch.tensor(.5))
# test WingLoss with target weight
loss_cfg = dict(type='WingLoss', use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(
loss(fake_pred, fake_label, torch.ones_like(fake_label)),
torch.tensor(0.))
fake_pred = torch.ones((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.gt(
loss(fake_pred, fake_label, torch.ones_like(fake_label)),
torch.tensor(.5))
def test_soft_wing_loss():
# test SoftWingLoss without target weight(default None)
loss_cfg = dict(type='SoftWingLoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(0.))
fake_pred = torch.ones((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.gt(loss(fake_pred, fake_label), torch.tensor(.5))
# test SoftWingLoss with target weight
loss_cfg = dict(type='SoftWingLoss', use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(
loss(fake_pred, fake_label, torch.ones_like(fake_label)),
torch.tensor(0.))
fake_pred = torch.ones((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.gt(
loss(fake_pred, fake_label, torch.ones_like(fake_label)),
torch.tensor(.5))
def test_mse_regression_loss():
# w/o target weight(default None)
loss_cfg = dict(type='MSELoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 3))
fake_label = torch.zeros((1, 3, 3))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(0.))
fake_pred = torch.ones((1, 3, 3))
fake_label = torch.zeros((1, 3, 3))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(1.))
# w/ target weight
loss_cfg = dict(type='MSELoss', use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 3))
fake_label = torch.zeros((1, 3, 3))
assert torch.allclose(
loss(fake_pred, fake_label, torch.ones_like(fake_label)),
torch.tensor(0.))
fake_pred = torch.ones((1, 3, 3))
fake_label = torch.zeros((1, 3, 3))
assert torch.allclose(
loss(fake_pred, fake_label, torch.ones_like(fake_label)),
torch.tensor(1.))
def test_bone_loss():
# w/o target weight(default None)
loss_cfg = dict(type='BoneLoss', joint_parents=[0, 0, 1])
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 3))
fake_label = torch.zeros((1, 3, 3))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(0.))
fake_pred = torch.tensor([[[0, 0, 0], [1, 1, 1], [2, 2, 2]]],
dtype=torch.float32)
fake_label = fake_pred * 2
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(3**0.5))
# w/ target weight
loss_cfg = dict(
type='BoneLoss', joint_parents=[0, 0, 1], use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 3))
fake_label = torch.zeros((1, 3, 3))
fake_weight = torch.ones((1, 2))
assert torch.allclose(
loss(fake_pred, fake_label, fake_weight), torch.tensor(0.))
fake_pred = torch.tensor([[[0, 0, 0], [1, 1, 1], [2, 2, 2]]],
dtype=torch.float32)
fake_label = fake_pred * 2
fake_weight = torch.ones((1, 2))
assert torch.allclose(
loss(fake_pred, fake_label, fake_weight), torch.tensor(3**0.5))
def test_semi_supervision_loss():
loss_cfg = dict(
type='SemiSupervisionLoss',
joint_parents=[0, 0, 1],
warmup_iterations=1)
loss = build_loss(loss_cfg)
unlabeled_pose = torch.rand((1, 3, 3))
unlabeled_traj = torch.ones((1, 1, 3))
labeled_pose = unlabeled_pose.clone()
fake_pred = dict(
labeled_pose=labeled_pose,
unlabeled_pose=unlabeled_pose,
unlabeled_traj=unlabeled_traj)
intrinsics = torch.tensor([[1, 1, 1, 1, 0.1, 0.1, 0.1, 0, 0]],
dtype=torch.float32)
unlabled_target_2d = loss.project_joints(unlabeled_pose + unlabeled_traj,
intrinsics)
fake_label = dict(
unlabeled_target_2d=unlabled_target_2d, intrinsics=intrinsics)
# test warmup
losses = loss(fake_pred, fake_label)
assert not losses
# test semi-supervised loss
losses = loss(fake_pred, fake_label)
assert torch.allclose(losses['proj_loss'], torch.tensor(0.))
assert torch.allclose(losses['bone_loss'], torch.tensor(0.))
def test_soft_weight_smooth_l1_loss():
loss_cfg = dict(
type='SoftWeightSmoothL1Loss', use_target_weight=False, beta=0.5)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(0.))
fake_pred = torch.ones((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(.75))
loss_cfg = dict(
type='SoftWeightSmoothL1Loss',
use_target_weight=True,
supervise_empty=True)
loss = build_loss(loss_cfg)
fake_pred = torch.ones((1, 3, 2))
fake_label = torch.zeros((1, 3, 2))
fake_weight = torch.arange(6).reshape(1, 3, 2).float()
assert torch.allclose(
loss(fake_pred, fake_label, fake_weight), torch.tensor(1.25))
loss_cfg = dict(
type='SoftWeightSmoothL1Loss',
use_target_weight=True,
supervise_empty=False)
loss = build_loss(loss_cfg)
assert torch.allclose(
loss(fake_pred, fake_label, fake_weight), torch.tensor(1.5))
with pytest.raises(ValueError):
_ = loss.smooth_l1_loss(fake_pred, fake_label, reduction='fake')
output = loss.smooth_l1_loss(fake_pred, fake_label, reduction='sum')
assert torch.allclose(output, torch.tensor(3.0))
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