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_wing_loss():
from mmpose.models import build_loss
# test WingLoss without target weight
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, None), 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, None), 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_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_bce_loss():
from mmpose.models import build_loss
# test BCE loss without target weight(None)
loss_cfg = dict(type='BCELoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 2))
fake_label = torch.zeros((1, 2))
assert torch.allclose(loss(fake_pred, fake_label), torch.tensor(0.))
fake_pred = torch.ones((1, 2)) * 0.5
fake_label = torch.zeros((1, 2))
assert torch.allclose(loss(fake_pred, fake_label),
-torch.log(torch.tensor(0.5)))
# test BCE loss with target weight
loss_cfg = dict(type='BCELoss', use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.ones((1, 2)) * 0.5
fake_label = torch.zeros((1, 2))
fake_weight = torch.ones((1, 2))
assert torch.allclose(loss(fake_pred, fake_label, fake_weight),
-torch.log(torch.tensor(0.5)))
fake_weight[:, 0] = 0
assert torch.allclose(loss(fake_pred, fake_label, fake_weight),
-0.5 * torch.log(torch.tensor(0.5)))
fake_weight = torch.ones(1)
assert torch.allclose(loss(fake_pred, fake_label, fake_weight),
-torch.log(torch.tensor(0.5)))
def test_mse_loss():
from mmpose.models import build_loss
# test MSE loss without target weight
loss_cfg = dict(type='JointsMSELoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(0.))
fake_pred = torch.ones((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(1.))
fake_pred = torch.zeros((1, 2, 64, 64))
fake_pred[0, 0] += 1
fake_label = torch.zeros((1, 2, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(0.5))
with pytest.raises(ValueError):
loss_cfg = dict(type='JointsOHKMMSELoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None),
torch.tensor(0.))
with pytest.raises(AssertionError):
loss_cfg = dict(type='JointsOHKMMSELoss', topk=-1)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None),
torch.tensor(0.))
loss_cfg = dict(type='JointsOHKMMSELoss', topk=2)
loss = build_loss(loss_cfg)
fake_pred = torch.ones((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(1.))
loss_cfg = dict(type='JointsOHKMMSELoss', topk=2)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_pred[0, 0] += 1
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(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_smoothl1_loss():
# test MSE loss without target weight
loss_cfg = dict(type='SmoothL1Loss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3))
fake_label = torch.zeros((1, 3))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(0.))
def test_adaptive_wing_loss():
# test Adaptive WingLoss without target weight
loss_cfg = dict(type='AdaptiveWingLoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(0.))
# test WingLoss with target weight
loss_cfg = dict(type='AdaptiveWingLoss', use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.ones((1, 3, 64, 64))
fake_label = torch.ones((1, 3, 64, 64))
assert torch.allclose(
loss(fake_pred, fake_label, torch.ones([1, 3, 1])), torch.tensor(0.))
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_multi_loss_factory():
from mmpose.models import build_loss
# test heatmap loss
loss_cfg = dict(type='HeatmapLoss')
loss = build_loss(loss_cfg)
with pytest.raises(AssertionError):
fake_pred = torch.zeros((2, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
fake_mask = torch.zeros((1, 64, 64))
loss(fake_pred, fake_label, fake_mask)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
fake_mask = torch.zeros((1, 64, 64))
assert torch.allclose(
loss(fake_pred, fake_label, fake_mask), torch.tensor(0.))
fake_pred = torch.ones((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
fake_mask = torch.zeros((1, 64, 64))
assert torch.allclose(
loss(fake_pred, fake_label, fake_mask), torch.tensor(0.))
fake_pred = torch.ones((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
fake_mask = torch.ones((1, 64, 64))
assert torch.allclose(
loss(fake_pred, fake_label, fake_mask), torch.tensor(1.))
# test AE loss
fake_tags = torch.zeros((1, 18, 1))
fake_joints = torch.zeros((1, 3, 2, 2), dtype=torch.int)
loss_cfg = dict(type='AELoss', loss_type='exp')
loss = build_loss(loss_cfg)
assert torch.allclose(loss(fake_tags, fake_joints)[0], torch.tensor(0.))
assert torch.allclose(loss(fake_tags, fake_joints)[1], torch.tensor(0.))
fake_tags[0, 0, 0] = 1.
fake_tags[0, 10, 0] = 0.
fake_joints[0, 0, 0, :] = torch.IntTensor((0, 1))
fake_joints[0, 0, 1, :] = torch.IntTensor((10, 1))
loss_cfg = dict(type='AELoss', loss_type='exp')
loss = build_loss(loss_cfg)
assert torch.allclose(loss(fake_tags, fake_joints)[0], torch.tensor(0.))
assert torch.allclose(loss(fake_tags, fake_joints)[1], torch.tensor(0.25))
fake_tags[0, 0, 0] = 0
fake_tags[0, 7, 0] = 1.
fake_tags[0, 17, 0] = 1.
fake_joints[0, 1, 0, :] = torch.IntTensor((7, 1))
fake_joints[0, 1, 1, :] = torch.IntTensor((17, 1))
loss_cfg = dict(type='AELoss', loss_type='exp')
loss = build_loss(loss_cfg)
assert torch.allclose(loss(fake_tags, fake_joints)[1], torch.tensor(0.))
loss_cfg = dict(type='AELoss', loss_type='max')
loss = build_loss(loss_cfg)
assert torch.allclose(loss(fake_tags, fake_joints)[0], torch.tensor(0.))
with pytest.raises(ValueError):
loss_cfg = dict(type='AELoss', loss_type='min')
loss = build_loss(loss_cfg)
loss(fake_tags, fake_joints)
# test MultiLossFactory
with pytest.raises(AssertionError):
loss_cfg = dict(
type='MultiLossFactory',
num_joints=2,
num_stages=1,
ae_loss_type='exp',
with_ae_loss=True,
push_loss_factor=[0.001],
pull_loss_factor=[0.001],
with_heatmaps_loss=[True],
heatmaps_loss_factor=[1.0])
loss = build_loss(loss_cfg)
with pytest.raises(AssertionError):
loss_cfg = dict(
type='MultiLossFactory',
num_joints=2,
num_stages=1,
ae_loss_type='exp',
with_ae_loss=[True],
push_loss_factor=0.001,
pull_loss_factor=[0.001],
with_heatmaps_loss=[True],
heatmaps_loss_factor=[1.0])
loss = build_loss(loss_cfg)
with pytest.raises(AssertionError):
loss_cfg = dict(
type='MultiLossFactory',
num_joints=2,
num_stages=1,
ae_loss_type='exp',
with_ae_loss=[True],
push_loss_factor=[0.001],
pull_loss_factor=0.001,
with_heatmaps_loss=[True],
heatmaps_loss_factor=[1.0])
loss = build_loss(loss_cfg)
with pytest.raises(AssertionError):
loss_cfg = dict(
type='MultiLossFactory',
num_joints=2,
num_stages=1,
ae_loss_type='exp',
with_ae_loss=[True],
push_loss_factor=[0.001],
pull_loss_factor=[0.001],
with_heatmaps_loss=True,
heatmaps_loss_factor=[1.0])
loss = build_loss(loss_cfg)
with pytest.raises(AssertionError):
loss_cfg = dict(
type='MultiLossFactory',
num_joints=2,
num_stages=1,
ae_loss_type='exp',
with_ae_loss=[True],
push_loss_factor=[0.001],
pull_loss_factor=[0.001],
with_heatmaps_loss=[True],
heatmaps_loss_factor=1.0)
loss = build_loss(loss_cfg)
loss_cfg = dict(
type='MultiLossFactory',
num_joints=17,
num_stages=1,
ae_loss_type='exp',
with_ae_loss=[False],
push_loss_factor=[0.001],
pull_loss_factor=[0.001],
with_heatmaps_loss=[False],
heatmaps_loss_factor=[1.0])
loss = build_loss(loss_cfg)
fake_outputs = [torch.zeros((1, 34, 64, 64))]
fake_heatmaps = [torch.zeros((1, 17, 64, 64))]
fake_masks = [torch.ones((1, 64, 64))]
fake_joints = [torch.zeros((1, 30, 17, 2))]
heatmaps_losses, push_losses, pull_losses = \
loss(fake_outputs, fake_heatmaps, fake_masks, fake_joints)
assert heatmaps_losses == [None]
assert pull_losses == [None]
assert push_losses == [None]
loss_cfg = dict(
type='MultiLossFactory',
num_joints=17,
num_stages=1,
ae_loss_type='exp',
with_ae_loss=[True],
push_loss_factor=[0.001],
pull_loss_factor=[0.001],
with_heatmaps_loss=[True],
heatmaps_loss_factor=[1.0])
loss = build_loss(loss_cfg)
heatmaps_losses, push_losses, pull_losses = \
loss(fake_outputs, fake_heatmaps, fake_masks, fake_joints)
assert len(heatmaps_losses) == 1
def test_gan_loss():
"""test gan loss."""
with pytest.raises(NotImplementedError):
loss_cfg = dict(type='GANLoss',
gan_type='test',
real_label_val=1.0,
fake_label_val=0.0,
loss_weight=1)
_ = build_loss(loss_cfg)
input_1 = torch.ones(1, 1)
input_2 = torch.ones(1, 3, 6, 6) * 2
# vanilla
loss_cfg = dict(type='GANLoss',
gan_type='vanilla',
real_label_val=1.0,
fake_label_val=0.0,
loss_weight=2.0)
gan_loss = build_loss(loss_cfg)
loss = gan_loss(input_1, True, is_disc=False)
assert_almost_equal(loss.item(), 0.6265233)
loss = gan_loss(input_1, False, is_disc=False)
assert_almost_equal(loss.item(), 2.6265232)
loss = gan_loss(input_1, True, is_disc=True)
assert_almost_equal(loss.item(), 0.3132616)
loss = gan_loss(input_1, False, is_disc=True)
assert_almost_equal(loss.item(), 1.3132616)
# lsgan
loss_cfg = dict(type='GANLoss',
gan_type='lsgan',
real_label_val=1.0,
fake_label_val=0.0,
loss_weight=2.0)
gan_loss = build_loss(loss_cfg)
loss = gan_loss(input_2, True, is_disc=False)
assert_almost_equal(loss.item(), 2.0)
loss = gan_loss(input_2, False, is_disc=False)
assert_almost_equal(loss.item(), 8.0)
loss = gan_loss(input_2, True, is_disc=True)
assert_almost_equal(loss.item(), 1.0)
loss = gan_loss(input_2, False, is_disc=True)
assert_almost_equal(loss.item(), 4.0)
# wgan
loss_cfg = dict(type='GANLoss',
gan_type='wgan',
real_label_val=1.0,
fake_label_val=0.0,
loss_weight=2.0)
gan_loss = build_loss(loss_cfg)
loss = gan_loss(input_2, True, is_disc=False)
assert_almost_equal(loss.item(), -4.0)
loss = gan_loss(input_2, False, is_disc=False)
assert_almost_equal(loss.item(), 4)
loss = gan_loss(input_2, True, is_disc=True)
assert_almost_equal(loss.item(), -2.0)
loss = gan_loss(input_2, False, is_disc=True)
assert_almost_equal(loss.item(), 2.0)
# hinge
loss_cfg = dict(type='GANLoss',
gan_type='hinge',
real_label_val=1.0,
fake_label_val=0.0,
loss_weight=2.0)
gan_loss = build_loss(loss_cfg)
loss = gan_loss(input_2, True, is_disc=False)
assert_almost_equal(loss.item(), -4.0)
loss = gan_loss(input_2, False, is_disc=False)
assert_almost_equal(loss.item(), -4.0)
loss = gan_loss(input_2, True, is_disc=True)
assert_almost_equal(loss.item(), 0.0)
loss = gan_loss(input_2, False, is_disc=True)
assert_almost_equal(loss.item(), 3.0)
def test_mesh_loss():
"""test mesh loss."""
loss_cfg = dict(type='MeshLoss',
joints_2d_loss_weight=1,
joints_3d_loss_weight=1,
vertex_loss_weight=1,
smpl_pose_loss_weight=1,
smpl_beta_loss_weight=1,
img_res=256,
focal_length=5000)
loss = build_loss(loss_cfg)
smpl_pose = torch.zeros([1, 72], dtype=torch.float32)
smpl_rotmat = batch_rodrigues(smpl_pose.view(-1, 3)).view(-1, 24, 3, 3)
smpl_beta = torch.zeros([1, 10], dtype=torch.float32)
camera = torch.tensor([[1, 0, 0]], dtype=torch.float32)
vertices = torch.rand([1, 6890, 3], dtype=torch.float32)
joints_3d = torch.ones([1, 24, 3], dtype=torch.float32)
joints_2d = loss.project_points(joints_3d, camera) + (256 - 1) / 2
fake_pred = {}
fake_pred['pose'] = smpl_rotmat
fake_pred['beta'] = smpl_beta
fake_pred['camera'] = camera
fake_pred['vertices'] = vertices
fake_pred['joints_3d'] = joints_3d
fake_gt = {}
fake_gt['pose'] = smpl_pose
fake_gt['beta'] = smpl_beta
fake_gt['vertices'] = vertices
fake_gt['has_smpl'] = torch.ones(1, dtype=torch.float32)
fake_gt['joints_3d'] = joints_3d
fake_gt['joints_3d_visible'] = torch.ones([1, 24, 1], dtype=torch.float32)
fake_gt['joints_2d'] = joints_2d
fake_gt['joints_2d_visible'] = torch.ones([1, 24, 1], dtype=torch.float32)
losses = loss(fake_pred, fake_gt)
assert torch.allclose(losses['vertex_loss'], torch.tensor(0.))
assert torch.allclose(losses['smpl_pose_loss'], torch.tensor(0.))
assert torch.allclose(losses['smpl_beta_loss'], torch.tensor(0.))
assert torch.allclose(losses['joints_3d_loss'], torch.tensor(0.))
assert torch.allclose(losses['joints_2d_loss'], torch.tensor(0.))
fake_pred = {}
fake_pred['pose'] = smpl_rotmat + 1
fake_pred['beta'] = smpl_beta + 1
fake_pred['camera'] = camera
fake_pred['vertices'] = vertices + 1
fake_pred['joints_3d'] = joints_3d.clone()
joints_3d_t = joints_3d.clone()
joints_3d_t[:, 0] = joints_3d_t[:, 0] + 1
fake_gt = {}
fake_gt['pose'] = smpl_pose
fake_gt['beta'] = smpl_beta
fake_gt['vertices'] = vertices
fake_gt['has_smpl'] = torch.ones(1, dtype=torch.float32)
fake_gt['joints_3d'] = joints_3d_t
fake_gt['joints_3d_visible'] = torch.ones([1, 24, 1], dtype=torch.float32)
fake_gt['joints_2d'] = joints_2d + (256 - 1) / 2
fake_gt['joints_2d_visible'] = torch.ones([1, 24, 1], dtype=torch.float32)
losses = loss(fake_pred, fake_gt)
assert torch.allclose(losses['vertex_loss'], torch.tensor(1.))
assert torch.allclose(losses['smpl_pose_loss'], torch.tensor(1.))
assert torch.allclose(losses['smpl_beta_loss'], torch.tensor(1.))
assert torch.allclose(losses['joints_3d_loss'], torch.tensor(0.5 / 24))
assert torch.allclose(losses['joints_2d_loss'], torch.tensor(0.5))
def test_mse_loss():
# test MSE loss without target weight
loss_cfg = dict(type='JointsMSELoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(0.))
fake_pred = torch.ones((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(1.))
fake_pred = torch.zeros((1, 2, 64, 64))
fake_pred[0, 0] += 1
fake_label = torch.zeros((1, 2, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(0.5))
with pytest.raises(ValueError):
loss_cfg = dict(type='JointsOHKMMSELoss')
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(
loss(fake_pred, fake_label, None), torch.tensor(0.))
with pytest.raises(AssertionError):
loss_cfg = dict(type='JointsOHKMMSELoss', topk=-1)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(
loss(fake_pred, fake_label, None), torch.tensor(0.))
loss_cfg = dict(type='JointsOHKMMSELoss', topk=2)
loss = build_loss(loss_cfg)
fake_pred = torch.ones((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(1.))
loss_cfg = dict(type='JointsOHKMMSELoss', topk=2)
loss = build_loss(loss_cfg)
fake_pred = torch.zeros((1, 3, 64, 64))
fake_pred[0, 0] += 1
fake_label = torch.zeros((1, 3, 64, 64))
assert torch.allclose(loss(fake_pred, fake_label, None), torch.tensor(0.5))
loss_cfg = dict(type='CombinedTargetMSELoss', use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.ones((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
target_weight = torch.ones((1, 1, 1))
assert torch.allclose(
loss(fake_pred, fake_label, target_weight), torch.tensor(0.5))
loss_cfg = dict(type='CombinedTargetMSELoss', use_target_weight=True)
loss = build_loss(loss_cfg)
fake_pred = torch.ones((1, 3, 64, 64))
fake_label = torch.zeros((1, 3, 64, 64))
target_weight = torch.zeros((1, 1, 1))
assert torch.allclose(
loss(fake_pred, fake_label, target_weight), torch.tensor(0.))
