Yeah, that might be problematic.
Have a look at this small example:
x = np.zeros((500, 500, 3))
x[200:300, 200:300, :] = 1.
plt.imshow(x)

x_reshaped = np.resize(x, (3, 500, 500))
print(x_reshaped.shape)
> (3, 500, 500)
print(x_reshaped.sum(1).sum(1))
> [    0. 30000.     0.]

x_transposed = x.transpose(2, 0, 1)
plt.imshow(x_transposed[0])
print(x_transposed.sum(1).sum(1))
> [10000. 10000. 10000.]

As you can see, the reshape operation will put all ones into the second channel, which is wrong.
Use transpose in numpy or permute in pytorch instead.