def test_BranchBlock(subtests, input_image):
deep_branch = nn.Conv2d(3, 3, 1)
shallow_branch = nn.Conv2d(3, 3, 1)
block = paper.BranchBlock(deep_branch, shallow_branch)
with subtests.test("deep"):
assert block.deep == deep_branch
with subtests.test("shallow"):
assert block.shallow == shallow_branch
with subtests.test("join_block"):
assert isinstance(block.join, paper.JoinBlock)
with subtests.test("out_channels"):
assert (
block.out_channels == deep_branch.out_channels + shallow_branch.out_channels
)
with subtests.test("forward"):
actual = block(input_image)
assert isinstance(actual, torch.Tensor)
momentum = block.join.norm_modules[0].momentum
inputs = (deep_branch(input_image), shallow_branch(input_image))
desired_inputs = tuple(
F.instance_norm(image, momentum=momentum) for image in inputs
)
desired = torch.cat(desired_inputs, 1)
ptu.assert_allclose(actual, desired)
def test_extract_normalized_patches2d_no_overlap(subtests):
height = 4
width = 4
patch_size = 2
stride = 2
input = torch.ones(1, 1, height, width).requires_grad_(True)
input_normalized = torch.ones(1, 1, height, width).requires_grad_(True)
target = torch.zeros(1, 1, height, width).detach()
input_patches = pystiche.extract_patches2d(input,
patch_size=patch_size,
stride=stride)
input_patches_normalized = paper.extract_normalized_patches2d(
input_normalized, patch_size=patch_size, stride=stride)
target_patches = pystiche.extract_patches2d(target,
patch_size=patch_size,
stride=stride)
loss = 0.5 * torch.sum((input_patches - target_patches)**2.0)
loss.backward()
loss_normalized = 0.5 * torch.sum(
(input_patches_normalized - target_patches)**2.0)
loss_normalized.backward()
with subtests.test("forward"):
ptu.assert_allclose(input_patches_normalized, input_patches)
with subtests.test("backward"):
ptu.assert_allclose(input_normalized.grad, input.grad)
def test_Uint8ToFloatRange():
image = torch.tensor(255.0)
transform = transforms.Uint8ToFloatRange()
actual = transform(image)
desired = image / 255.0
ptu.assert_allclose(actual, desired)
def test_FloatToUint8Range():
image = torch.tensor(1.0)
transform = transforms.FloatToUint8Range()
actual = transform(image)
desired = image * 255.0
ptu.assert_allclose(actual, desired)
def test_nst_smoke(subtests, default_image_pyramid_optim_loop_patch,
content_image, style_image):
patch = default_image_pyramid_optim_loop_patch
paper.nst(content_image, style_image)
args, kwargs = patch.call_args
input_image, criterion, image_pyramid = args
get_optimizer = kwargs["get_optimizer"]
preprocessor = kwargs["preprocessor"]
postprocessor = kwargs["postprocessor"]
with subtests.test("input_image"):
ptu.assert_allclose(input_image,
image_pyramid[-1].resize_image(content_image))
with subtests.test("criterion"):
assert isinstance(criterion, type(paper.perceptual_loss()))
with subtests.test("image_pyramid"):
assert isinstance(image_pyramid, type(paper.image_pyramid()))
with subtests.test("optimizer"):
assert is_callable(get_optimizer)
optimizer = get_optimizer(input_image)
assert isinstance(optimizer, type(paper.optimizer(input_image)))
with subtests.test("preprocessor"):
assert isinstance(preprocessor, type(paper.preprocessor()))
with subtests.test("postprocessor"):
assert isinstance(postprocessor, type(paper.postprocessor()))
def test_stylization_smoke(stylization, postprocessor_mocks, input_image):
hyper_parameters = paper.hyper_parameters()
edge_size = hyper_parameters.content_transform.edge_size
_, _, output_image = stylization(input_image)
ptu.assert_allclose(output_image,
F.resize(input_image, (edge_size, edge_size)) + 0.5,
rtol=1e-6)
def test_call_guided(self, encoder):
patch_size = 2
stride = 2
torch.manual_seed(0)
target_image = torch.rand(1, 3, 32, 32)
input_image = torch.rand(1, 3, 32, 32)
target_guide = torch.cat(
(torch.zeros(1, 1, 16, 32), torch.ones(1, 1, 16, 32)), dim=2
)
input_guide = target_guide.flip(2)
loss = loss_.MRFLoss(encoder, patch_size, stride=stride)
loss.set_target_image(target_image, guide=target_guide)
loss.set_input_guide(input_guide)
actual = loss(input_image)
input_enc = encoder(input_image)[:, :, :16, :]
target_enc = encoder(target_image)[:, :, 16:, :]
desired = F.mrf_loss(
pystiche.extract_patches2d(input_enc, patch_size, stride=stride),
pystiche.extract_patches2d(target_enc, patch_size, stride=stride),
batched_input=True,
)
ptu.assert_allclose(actual, desired)
def test_content_transform_grayscale_image(subtests, content_image,
impl_params, instance_norm):
content_image = F.rgb_to_grayscale(content_image)
edge_size = 16
hyper_parameters = paper.hyper_parameters(impl_params=impl_params,
instance_norm=instance_norm)
hyper_parameters.content_transform.edge_size = edge_size
content_transform = paper.content_transform(
impl_params=impl_params,
instance_norm=instance_norm,
hyper_parameters=hyper_parameters,
)
if instance_norm:
utils.make_reproducible()
actual = content_transform(content_image)
if impl_params:
if instance_norm:
# Since the transform involves an uncontrollable random component, we can't
# do a reference test here
return
transform_image = F.resize(content_image, edge_size)
else:
transform = transforms.CenterCrop(edge_size)
transform_image = transform(content_image)
desired = transform_image.repeat(1, 3, 1, 1)
ptu.assert_allclose(actual, desired)
def test_MRFOperator_call_guided():
patch_size = 2
stride = 2
torch.manual_seed(0)
target_image = torch.rand(1, 3, 32, 32)
input_image = torch.rand(1, 3, 32, 32)
target_guide = torch.cat(
(torch.zeros(1, 1, 16, 32), torch.ones(1, 1, 16, 32)), dim=2)
input_guide = target_guide.flip(2)
encoder = enc.SequentialEncoder((nn.Conv2d(3, 3, 1), ))
op = ops.MRFOperator(encoder, patch_size, stride=stride)
op.set_target_guide(target_guide)
op.set_target_image(target_image)
op.set_input_guide(input_guide)
actual = op(input_image)
input_enc = encoder(input_image)[:, :, :16, :]
target_enc = encoder(target_image)[:, :, 16:, :]
desired = F.mrf_loss(
pystiche.extract_patches2d(input_enc, patch_size, stride=stride),
pystiche.extract_patches2d(target_enc, patch_size, stride=stride),
)
ptu.assert_allclose(actual, desired)
def test_MultiLayerEncoder_encode(forward_pass_counter):
torch.manual_seed(0)
conv = nn.Conv2d(3, 1, 1)
relu = nn.ReLU(inplace=False)
input = torch.rand(1, 3, 128, 128)
modules = (("count", forward_pass_counter), ("conv", conv), ("relu", relu))
multi_layer_encoder = enc.MultiLayerEncoder(modules)
layers = ("conv", "relu")
multi_layer_encoder.registered_layers.update(layers)
multi_layer_encoder.encode(input)
encs = multi_layer_encoder(input, layers)
actual = encs[0]
desired = conv(input)
ptu.assert_allclose(actual, desired)
actual = encs[1]
desired = relu(conv(input))
ptu.assert_allclose(actual, desired)
actual = forward_pass_counter.count
desired = 1
assert actual == desired
def test_PixelComparisonOperator_call_guided():
class TestOperator(ops.PixelComparisonOperator):
def target_image_to_repr(self, image):
repr = image + 1.0
return repr, None
def input_image_to_repr(self, image, ctx):
return image + 2.0
def calculate_score(self, input_repr, target_repr, ctx):
return input_repr * target_repr
torch.manual_seed(0)
target_image = torch.rand(1, 3, 32, 32)
target_guide = torch.rand(1, 1, 32, 32)
input_image = torch.rand(1, 3, 32, 32)
input_guide = torch.rand(1, 1, 32, 32)
test_op = TestOperator()
test_op.set_target_guide(target_guide)
test_op.set_target_image(target_image)
test_op.set_input_guide(input_guide)
actual = test_op(input_image)
desired = (TestOperator.apply_guide(target_image, target_guide) + 1.0) * (
TestOperator.apply_guide(input_image, input_guide) + 2.0)
ptu.assert_allclose(actual, desired)
def test_segmentation_to_guides():
guides, _ = get_test_guides()
segmentation, region_map = get_test_segmentation()
actual = image.segmentation_to_guides(segmentation, region_map=region_map)
desired = guides
ptu.assert_allclose(actual, desired)
def test_PixelComparisonOperator_set_target_guide():
class TestOperator(ops.PixelComparisonOperator):
def target_image_to_repr(self, image):
repr = image * 2.0
ctx = torch.norm(image)
return repr, ctx
def input_image_to_repr(self, image, ctx):
pass
def calculate_score(self, input_repr, target_repr, ctx):
pass
torch.manual_seed(0)
image = torch.rand(1, 3, 32, 32)
guide = torch.rand(1, 1, 32, 32)
test_op = TestOperator()
test_op.set_target_image(image)
assert not test_op.has_target_guide
test_op.set_target_guide(guide)
assert test_op.has_target_guide
actual = test_op.target_guide
desired = guide
ptu.assert_allclose(actual, desired)
actual = test_op.target_image
desired = image
ptu.assert_allclose(actual, desired)
def test_EncodingComparisonOperator_call_guided():
class TestOperator(ops.EncodingComparisonOperator):
def target_enc_to_repr(self, image):
repr = image + 1.0
return repr, None
def input_enc_to_repr(self, image, ctx):
return image + 2.0
def calculate_score(self, input_repr, target_repr, ctx):
return input_repr * target_repr
torch.manual_seed(0)
target_image = torch.rand(1, 3, 32, 32)
input_image = torch.rand(1, 3, 32, 32)
target_guide = torch.rand(1, 1, 32, 32)
input_guide = torch.rand(1, 1, 32, 32)
encoder = enc.SequentialEncoder((nn.Conv2d(3, 3, 1), ))
target_enc_guide = encoder.propagate_guide(target_guide)
input_enc_guide = encoder.propagate_guide(input_guide)
test_op = TestOperator(encoder)
test_op.set_target_guide(target_guide)
test_op.set_target_image(target_image)
test_op.set_input_guide(input_guide)
actual = test_op(input_image)
desired = (
TestOperator.apply_guide(encoder(target_image), target_enc_guide) + 1.0
) * (TestOperator.apply_guide(encoder(input_image), input_enc_guide) + 2.0)
ptu.assert_allclose(actual, desired)
def test_guides_to_segmentation():
guides, color_map = get_test_guides()
segmentation, _ = get_test_segmentation()
actual = image.guides_to_segmentation(guides, color_map=color_map)
desired = segmentation
ptu.assert_allclose(actual, desired)
def test_AutoPadAvgPool1d_count_include_pad():
kernel_size = 5
from pystiche.misc import to_1d_arg
from pystiche_papers.utils.modules import _AutoPadAvgPoolNdMixin
class AutoPadAvgPool1d(_AutoPadAvgPoolNdMixin, nn.AvgPool1d):
def __init__(
self,
kernel_size,
stride=None,
**kwargs,
) -> None:
kernel_size = to_1d_arg(kernel_size)
stride = kernel_size if stride is None else to_1d_arg(stride)
super().__init__(kernel_size, stride=stride, **kwargs)
torch.manual_seed(0)
input = torch.rand(1, 1, 32)
manual = nn.AvgPool1d(kernel_size,
stride=1,
padding=(kernel_size - 1) // 2,
count_include_pad=False)
auto = AutoPadAvgPool1d(kernel_size, stride=1, count_include_pad=False)
ptu.assert_allclose(auto(input), manual(input), rtol=1e-6)
def test_backward(self):
losses = [
torch.tensor(val, dtype=torch.float, requires_grad=True)
for val in range(3)
]
def zero_grad():
for loss in losses:
loss.grad = None
def extract_grads():
return [loss.grad.clone() for loss in losses]
zero_grad()
loss_dict = pystiche.LossDict([(str(idx), loss)
for idx, loss in enumerate(losses)])
loss_dict.backward()
actuals = extract_grads()
zero_grad()
total = sum(losses)
total.backward()
desireds = extract_grads()
for actual, desired in zip(actuals, desireds):
ptu.assert_allclose(actual, desired)
def test_nst_smoke(subtests, mocker, content_image, style_image):
mock = mocker.patch("pystiche.optim.image_optimization")
paper.nst(content_image, style_image)
args, kwargs = mock.call_args
input_image, criterion = args
optimizer = kwargs["optimizer"]
num_steps = kwargs["num_steps"]
preprocessor = kwargs["preprocessor"]
postprocessor = kwargs["postprocessor"]
hyper_parameters = paper.hyper_parameters().nst
with subtests.test("input_image"):
ptu.assert_allclose(input_image, content_image)
with subtests.test("criterion"):
assert isinstance(criterion, type(paper.perceptual_loss()))
with subtests.test("optimizer"):
assert optimizer is paper.optimizer
with subtests.test("num_steps"):
assert num_steps == hyper_parameters.num_steps
with subtests.test("preprocessor"):
assert isinstance(preprocessor, type(paper.preprocessor()))
with subtests.test("postprocessor"):
assert isinstance(postprocessor, type(paper.postprocessor()))
def test_set_target_guide_without_recalc(self):
class TestOperator(ops.PixelComparisonOperator):
def target_image_to_repr(self, image):
repr = image * 2.0
ctx = torch.norm(image)
return repr, ctx
def input_image_to_repr(self, image, ctx):
pass
def calculate_score(self, input_repr, target_repr, ctx):
pass
torch.manual_seed(0)
image = torch.rand(1, 3, 32, 32)
guide = torch.rand(1, 1, 32, 32)
test_op = TestOperator()
test_op.set_target_image(image)
desired = test_op.target_repr.clone()
test_op.set_target_guide(guide, recalc_repr=False)
actual = test_op.target_repr
ptu.assert_allclose(actual, desired)
def test_read_rel_path(self, test_image_file, test_image):
root, filename = path.dirname(test_image_file), path.basename(test_image_file)
image = data.LocalImage(filename)
actual = image.read(root)
desired = test_image
ptu.assert_allclose(actual, desired)
def test_EncodingComparisonOperator_set_target_guide_without_recalc():
class TestOperator(ops.EncodingComparisonOperator):
def target_enc_to_repr(self, image):
repr = image * 2.0
ctx = torch.norm(image)
return repr, ctx
def input_enc_to_repr(self, image, ctx):
pass
def calculate_score(self, input_repr, target_repr, ctx):
pass
torch.manual_seed(0)
repr = torch.rand(1, 3, 32, 32)
guide = torch.rand(1, 1, 32, 32)
encoder = enc.SequentialEncoder((nn.Conv2d(3, 3, 1), ))
test_op = TestOperator(encoder)
test_op.register_buffer("target_repr", repr)
test_op.set_target_guide(guide, recalc_repr=False)
actual = test_op.target_repr
desired = repr
ptu.assert_allclose(actual, desired)
def test_CaffePostprocessing(pixel, caffe_mean, caffe_std):
processing = enc.CaffePostprocessing()
expected = pixel
input = pixel.sub(caffe_mean).div(caffe_std).mul(255).flip(1)
actual = processing(input)
ptu.assert_allclose(actual, expected, rtol=1e-6)
def test_main(self, tmpdir, test_image_url, test_image):
images = {"test_image": data.DownloadableImage(test_image_url)}
collection = data.DownloadableImageCollection(images,)
collection.download(root=tmpdir)
actual = collection["test_image"].read(root=tmpdir)
desired = test_image
ptu.assert_allclose(actual, desired)
def test_enc_to_repr_guided_varying(self, mrf_op):
enc_ = torch.rand(1, 4, 8, 8)
actual = mrf_op.enc_to_repr(enc_, is_guided=True)
desired = pystiche.extract_patches2d(
enc_, mrf_op.patch_size[0], stride=mrf_op.stride[0]
)
ptu.assert_allclose(actual, desired)
def test_SequentialWithOutChannels_forward_behaviour(input_image):
sequential_modules = (nn.Conv2d(3, 3, 1), nn.Conv2d(3, 5, 1))
sequential = utils.SequentialWithOutChannels(*sequential_modules)
actual = sequential(input_image)
desired = input_image
for module in sequential_modules:
desired = module(desired)
ptu.assert_allclose(actual, desired)
def test_Operator_apply_guide():
torch.manual_seed(0)
image = torch.rand(1, 3, 32, 32)
guide = torch.rand(1, 1, 32, 32)
actual = ops.Operator.apply_guide(image, guide)
desired = image * guide
ptu.assert_allclose(actual, desired)
def test_TorchPreprocessing(pixel, torch_mean, torch_std):
processing = enc.TorchPreprocessing()
expected = pixel
input = pixel.mul(torch_std).add(torch_mean)
actual = processing(input)
ptu.assert_allclose(actual, expected, rtol=1e-6)
def test_TorchPostprocessing(pixel, torch_mean, torch_std):
processing = enc.TorchPostprocessing()
expected = pixel
input = pixel.sub(torch_mean).div(torch_std)
actual = processing(input)
ptu.assert_allclose(actual, expected, rtol=1e-6)
def test_read(self, tmpdir, test_image_url, test_image):
names = [str(idx) for idx in range(3)]
collection = data.DownloadableImageCollection(
{name: data.DownloadableImage(test_image_url) for name in names}
)
images = collection.read(tmpdir)
ptu.assert_allclose(images, dict(zip(names, [test_image] * len(names))))
def test_AutoPadConvTranspose2d_state_dict():
kwargs = dict(in_channels=1, out_channels=2, kernel_size=3, bias=True)
conv = nn.ConvTranspose2d(**kwargs)
auto_pad_conv = utils.AutoPadConvTranspose2d(**kwargs)
state_dict = conv.state_dict()
auto_pad_conv.load_state_dict(state_dict)
ptu.assert_allclose(auto_pad_conv.state_dict(), state_dict)
