alpha.unsqueeze(0).expand(
(len(images), int(params.n_attr))).cuda())
stack.append(ae.decode(enc_outputs, alpha)[-1])
#pdb.set_trace()
outputs.append(
torch.cat([x.unsqueeze(1).unsqueeze(1) for x in stack], 1))
return torch.cat(outputs, 2).data.cpu()
interpolations = []
for k in range(0, params.n_images, 100):
i = params.offset + k
j = params.offset + min(params.n_images, k + 100)
images, attributes = test_data.eval_batch(i, j)
interpolations.append(
get_interpolations_2dim(ae, images, attributes, params))
#interpolations = torch.cat(interpolations, 0)
assert interpolations[0].size() == (params.n_images, params.n_interpolations,
params.n_interpolations + 2, 3,
params.img_sz, params.img_sz)
def get_grid(images, row_wise, plot_size=5):
"""
Create a grid with all images.
"""
n_images, n_columns, img_fm, img_sz, _ = images.size()
if not row_wise:
def interpolate(ae, n_epoch):
def get_interpolations(ae, images, attributes, params):
"""
Reconstruct images / create interpolations
"""
assert len(images) == len(attributes)
enc_outputs = ae.encode(images)
# interpolation values
alphas = np.linspace(1 - params.alpha_min, params.alpha_max,
params.n_interpolations)
alphas = [torch.FloatTensor([1 - alpha, alpha]) for alpha in alphas]
# original image / reconstructed image / interpolations
outputs = []
outputs.append(images)
outputs.append(ae.decode(enc_outputs, attributes)[-1])
for alpha in alphas:
alpha = Variable(
alpha.unsqueeze(0).expand((len(images), 2)).cuda())
outputs.append(ae.decode(enc_outputs, alpha)[-1])
# return stacked images
return torch.cat([x.unsqueeze(1) for x in outputs], 1).data.cpu()
params = parameters.interpolateParams()
# ae = torch.load(params.model_path).eval()
params.debug = True
params.batch_size = 50
params.v_flip = False
params.h_flip = False
params.img_sz = ae.img_sz
params.attr = ae.attr
params.n_attr = ae.n_attr
if not (len(params.attr) == 1 and params.n_attr == 2):
raise Exception("The model must use a single boolean attribute only.")
data, attributes = load_images(params)
test_data = DataSampler(data[2], attributes[2], params)
interpolations = []
for k in range(0, params.n_images, 100):
i = params.offset + k
j = params.offset + min(params.n_images, k + 100)
images, attributes = test_data.eval_batch(i, j)
interpolations.append(
get_interpolations(ae, images, attributes, params))
interpolations = torch.cat(interpolations, 0)
assert interpolations.size() == (params.n_images,
2 + params.n_interpolations, 3,
params.img_sz, params.img_sz)
def get_grid(images, row_wise, plot_size=5):
"""
Create a grid with all images.
"""
n_images, n_columns, img_fm, img_sz, _ = images.size()
if not row_wise:
images = images.transpose(0, 1).contiguous()
images = images.view(n_images * n_columns, img_fm, img_sz, img_sz)
images.add_(1).div_(2.0)
return make_grid(images, nrow=(n_columns if row_wise else n_images))
# generate the grid / save it to a PNG file
grid = get_grid(interpolations, params.row_wise, params.plot_size)
matplotlib.image.imsave(params.output_path + str(n_epoch) + ".png",
grid.numpy().transpose((1, 2, 0)))