Models:
Samuel Callahan
SCallahan/
lung-ultrasound-scan-targ
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[c1b1c5]: / ViTPose / tests / test_losses / test_regression_losses.py

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# Copyright (c) OpenMMLab. All rights reserved.

import torch



from mmpose.models import build_loss





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.))