def test_TotalVariationLoss(input_image):
loss = paper.TotalVariationLoss()
actual = loss(input_image)
desired = F.total_variation_loss(input_image, reduction="sum")
assert actual == ptu.approx(desired)
def test_MultiLayerEncodingLoss(subtests, multi_layer_encoder_with_layer,
target_image, input_image):
multi_layer_encoder, layer = multi_layer_encoder_with_layer
encoder = multi_layer_encoder.extract_encoder(layer)
target_repr = pystiche.gram_matrix(encoder(target_image), normalize=True)
input_repr = pystiche.gram_matrix(encoder(input_image), normalize=True)
configs = ((True, 1.0), (False, 1.0 / 4.0))
for impl_params, score_correction_factor in configs:
with subtests.test(impl_params=impl_params):
loss = paper.MultiLayerEncodingLoss(
multi_layer_encoder,
(layer, ),
lambda encoder, layer_weight: pystiche.loss.GramLoss(
encoder, score_weight=layer_weight),
impl_params=impl_params,
)
loss.set_target_image(target_image)
actual = loss(input_image)
score = mse_loss(
input_repr,
target_repr,
)
desired = score * score_correction_factor
assert actual == ptu.approx(desired)
def test_MRFLoss(subtests, multi_layer_encoder_with_layer, target_image,
input_image):
multi_layer_encoder, layer = multi_layer_encoder_with_layer
encoder = multi_layer_encoder.extract_encoder(layer)
target_enc = encoder(target_image)
input_enc = encoder(input_image)
patch_size = 3
stride = 1
configs = ((True, 1.0 / 2.0), (False, 1.0))
for (
impl_params,
score_correction_factor,
) in configs:
with subtests.test(impl_params=impl_params):
loss = paper.MRFLoss(encoder,
patch_size,
impl_params=impl_params,
stride=stride)
loss.set_target_image(target_image)
actual = loss(input_image)
extract_patches2d = (paper.extract_normalized_patches2d if
impl_params else pystiche.extract_patches2d)
target_repr = extract_patches2d(target_enc, patch_size, stride)
input_repr = extract_patches2d(input_enc, patch_size, stride)
score = F.mrf_loss(input_repr,
target_repr,
reduction="sum",
batched_input=True)
desired = score * score_correction_factor
assert actual == ptu.approx(desired)
def test_TotalVariationLoss(subtests, input_image):
configs = ((True, 1.0 / 2.0), (False, 1.0))
for impl_params, score_correction_factor in configs:
with subtests.test(impl_params=impl_params):
loss = paper.TotalVariationLoss(impl_params=impl_params, )
actual = loss(input_image)
score = F.total_variation_loss(input_image,
exponent=loss.exponent,
reduction="sum")
desired = score * score_correction_factor
assert actual == ptu.approx(desired)
def test_FeatureReconstructionLoss(subtests, multi_layer_encoder_with_layer,
target_image, input_image):
multi_layer_encoder, layer = multi_layer_encoder_with_layer
encoder = multi_layer_encoder.extract_encoder(layer)
target_enc = encoder(target_image)
input_enc = encoder(input_image)
configs = ((True, "mean"), (False, "sum"))
for impl_params, loss_reduction in configs:
with subtests.test(impl_params=impl_params):
loss = paper.FeatureReconstructionLoss(
encoder,
impl_params=impl_params,
)
loss.set_target_image(target_image)
actual = loss(input_image)
desired = mse_loss(input_enc, target_enc, reduction=loss_reduction)
assert actual == ptu.approx(desired)
def test_main(self, subtests, multi_layer_encoder_with_layer, target_image,
input_image):
multi_layer_encoder, layer = multi_layer_encoder_with_layer
encoder = multi_layer_encoder.extract_encoder(layer)
target_enc = encoder(target_image)
input_enc = encoder(input_image)
configs = ((True, "mean", 1.0), (False, "sum", 1.0 / 2.0))
for impl_params, loss_reduction, score_correction_factor in configs:
with subtests.test(impl_params=impl_params):
loss = paper.FeatureReconstructionLoss(encoder,
impl_params=impl_params)
loss.set_target_image(target_image)
actual = loss(input_image)
score = mse_loss(input_enc,
target_enc,
reduction=loss_reduction)
desired = score * score_correction_factor
assert actual == ptu.approx(desired)
def test_GramLoss(subtests, multi_layer_encoder_with_layer, target_image,
input_image):
multi_layer_encoder, layer = multi_layer_encoder_with_layer
encoder = multi_layer_encoder.extract_encoder(layer)
configs = ((True, True), (False, False))
for (impl_params, normalize_by_num_channels) in configs:
with subtests.test(impl_params=impl_params):
target_repr = pystiche.gram_matrix(encoder(target_image),
normalize=True)
input_repr = pystiche.gram_matrix(encoder(input_image),
normalize=True)
intern_target_repr = (target_repr / target_repr.size()[-1] if
normalize_by_num_channels else target_repr)
intern_input_repr = (input_repr / input_repr.size()[-1]
if normalize_by_num_channels else input_repr)
loss = paper.GramLoss(encoder, impl_params=impl_params)
loss.set_target_image(target_image)
actual = loss(input_image)
desired = mse_loss(intern_input_repr, intern_target_repr)
assert actual == ptu.approx(desired, rel=1e-3)
def test_GramLoss(subtests, multi_layer_encoder_with_layer, target_image,
input_image):
multi_layer_encoder, layer = multi_layer_encoder_with_layer
encoder = multi_layer_encoder.extract_encoder(layer)
target_repr = pystiche.gram_matrix(encoder(target_image), normalize=True)
input_repr = pystiche.gram_matrix(encoder(input_image), normalize=True)
configs = ((True, target_repr.size()[1]), (False, 1.0))
for impl_params, extra_num_channels_normalization in configs:
with subtests.test(impl_params=impl_params):
loss = paper.GramLoss(
encoder,
impl_params=impl_params,
)
loss.set_target_image(target_image)
actual = loss(input_image)
score = mse_loss(
input_repr,
target_repr,
)
desired = score / extra_num_channels_normalization**2
assert actual == ptu.approx(desired, rel=1e-3)