I wanted to combine two grids from make_grid. One for the source images, and another from model predictions.
Is it possible to apply a cmap to the masks?
I pasted a few relevant parts of the code‹ below:
from torchvision.utils import make_grid
...
def display_volumes(
img_vol, pred_vol,
):
def show(img, label=None, alpha=0.5):
npimg = img.numpy()
plt.imshow(
np.transpose(npimg, (1, 2, 0)), interpolation="none"
)
if label is not None:
lbimg = label.numpy()
plt.imshow(
np.transpose(lbimg, (1, 2, 0)),
cmap="jet", # cmap doesn't appear to do anything!
alpha=alpha,
interpolation="none",
)
x = torch.from_numpy(img_vol)
y = torch.from_numpy(pred_vol)
show(make_grid(x), make_grid(y), alpha=0.5)
plt.show()