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 figure_3_d(content_image, style_image):
content_image_yuv = rgb_to_yuv(content_image)
content_luminance = content_image_yuv[:, :1].repeat(1, 3, 1, 1)
content_chromaticity = content_image_yuv[:, 1:]
style_luminance = rgb_to_grayscale(style_image, num_output_channels=3)
print("Replicating Figure 3 (d)")
output_luminance = paper.nst(
content_luminance,
style_luminance,
impl_params=args.impl_params,
)
output_luminance = torch.mean(output_luminance, dim=1, keepdim=True)
output_chromaticity = resize(content_chromaticity,
output_luminance.size()[2:])
output_image_yuv = torch.cat((output_luminance, output_chromaticity),
dim=1)
output_image = yuv_to_rgb(output_image_yuv)
filename = make_output_filename(
"gatys_et_al_2017",
"fig_3",
"d",
impl_params=args.impl_params,
)
output_file = path.join(args.image_results_dir, filename)
save_result(output_image, output_file)
def figure_2_d(content_image, style_image):
print("Replicating Figure 2 (d)")
output_image = paper.nst(
content_image,
style_image,
impl_params=args.impl_params,
)
output_file = path.join(args.image_results_dir, "fig_2__d.jpg")
save_result(output_image, output_file)
def figure_3_e(content_image, style_image, method="cholesky"):
style_image = match_channelwise_statistics(style_image, content_image,
method)
print("Replicating Figure 3 (e)")
output_image = paper.nst(
content_image,
style_image,
impl_params=args.impl_params,
)
output_file = path.join(args.image_results_dir, "fig_3__e.jpg")
save_result(output_image, output_file)
def figure_3_c(content_image, style_image):
print("Replicating Figure 3 (c)")
output_image = paper.nst(
content_image,
style_image,
impl_params=args.impl_params,
)
filename = make_output_filename(
"gatys_et_al_2017",
"fig_3",
"c",
impl_params=args.impl_params,
)
output_file = path.join(args.image_results_dir, filename)
save_result(output_image, output_file)
def figure_3_e(content_image, style_image, method="cholesky"):
style_image = match_channelwise_statistics(style_image, content_image,
method)
print("Replicating Figure 3 (e)")
output_image = paper.nst(
content_image,
style_image,
impl_params=args.impl_params,
)
filename = make_output_filename(
"gatys_et_al_2017",
"fig_3",
"e",
impl_params=args.impl_params,
)
output_file = path.join(args.image_results_dir, filename)
save_result(output_image, output_file)