def test_generate_pastiche():
_, content = st.load_images()
pastiche = st.generate_pastiche(content)
assert isinstance(pastiche, Variable), 'pastice must be of type Variable'
assert pastiche.size() == content.size()
assert np.allclose(content.data.numpy(), pastiche.data.numpy())
assert content is not pastiche
assert content.data is not pastiche.data
def test_construct_style_loss_fns():
vgg_model = utils.load_vgg()
style_image, content_image = st.load_images()
pastiche = st.generate_pastiche(content_image)
style_layers = ['r11', 'r21', 'r31', 'r41', 'r51']
out = vgg_model(pastiche, style_layers)
loss_fns = st.construct_style_loss_fns(vgg_model, style_image,
style_layers)
assert len(loss_fns) == len(style_layers)
assert all([isinstance(loss_fn, st.StyleLoss) for loss_fn in loss_fns])
losses = [loss_fn(A).data[0] for loss_fn, A in zip(loss_fns, out)]
expected = [
95157.6953125, 8318182.5, 4280054.5, 213536288.0, 26124.064453125
]
assert np.allclose(
losses, expected), 'Expected: %s, Actual: %s' % (expected, losses)
def test_construct_content_loss_fns():
torch.manual_seed(0)
vgg_model = utils.load_vgg()
style_image, content_image = st.load_images()
pastiche = st.generate_pastiche(content_image) + Variable(
torch.randn(*content_image.size()))
content_layers = ['r42']
out = vgg_model(pastiche, content_layers)
loss_fns = st.construct_content_loss_fns(vgg_model, content_image,
content_layers)
assert len(loss_fns) == len(content_layers)
assert all([isinstance(loss_fn, st.ContentLoss) for loss_fn in loss_fns])
losses = [loss_fn(A).data[0] for loss_fn, A in zip(loss_fns, out)]
expected = [324.38946533203125]
assert np.allclose(
losses, expected), 'Expected: %s, Actual: %s' % (expected, losses)
def test_load_images():
style, content = st.load_images()
assert isinstance(style,
torch.FloatTensor), 'style_image must be FloatTensor'
assert isinstance(content,
torch.FloatTensor), 'content_image must be FloatTensor'
style_test = np.load('test_data/style_image_test.npy')
content_test = np.load('test_data/content_image_test.npy')
style_test, content_test = torch.FloatTensor(
style_test), torch.FloatTensor(content_test)
assert style_test.size() == style.size(), 'Expected: %s, Actual: %s' % (
style_test.size(), style.size())
assert content_test.size() == content.size(
), 'Expected: %s, Actual: %s' % (content_test.size(), content.size())
assert np.allclose(style_test.numpy(), style.numpy())
assert np.allclose(content_test.numpy(), content.numpy())
def test_extract_vgg_features():
style, content = st.load_images()
st.extract_vgg_features()
def test_generate_pastiche():
_, content = st.load_images()
pastiche = st.generate_pastiche(content)
assert pastiche.size() == content.size()
