diff --git a/py/bnk_tiling.py b/py/bnk_tiling.py new file mode 100644 index 0000000..e5f6ef9 --- /dev/null +++ b/py/bnk_tiling.py @@ -0,0 +1,175 @@ +import torch +import itertools +import numpy as np + +def grouper(n, iterable): + it = iter(iterable) + while True: + chunk = list(itertools.islice(it, n)) + if not chunk: + return + yield chunk + +def create_batches(n, iterable): + groups = itertools.groupby(iterable, key= lambda x: (x[1], x[3])) + for _, x in groups: + for y in grouper(n, x): + yield y + + +def get_slice(tensor, h, h_len, w, w_len): + t = tensor.narrow(-2, h, h_len) + t = t.narrow(-1, w, w_len) + return t + +def set_slice(tensor1,tensor2, h, h_len, w, w_len, mask=None): + if mask is not None: + tensor1[:,:,h:h+h_len,w:w+w_len] = tensor1[:,:,h:h+h_len,w:w+w_len] * (1 - mask) + tensor2 * mask + else: + tensor1[:,:,h:h+h_len,w:w+w_len] = tensor2 + +def get_tiles_and_masks_simple(steps, latent_shape, tile_height, tile_width): + latent_size_h = latent_shape[-2] + latent_size_w = latent_shape[-1] + tile_size_h = int(tile_height // 8) + tile_size_w = int(tile_width // 8) + + h = np.arange(0,latent_size_h, tile_size_h) + w = np.arange(0,latent_size_w, tile_size_w) + + def create_tile(hs, ws, i, j): + h = int(hs[i]) + w = int(ws[j]) + h_len = min(tile_size_h, latent_size_h - h) + w_len = min(tile_size_w, latent_size_w - w) + return (h, h_len, w, w_len, steps, None) + + passes = [ + [[create_tile(h, w, i, j) for i in range(len(h)) for j in range(len(w))]], + ] + return passes + +def get_tiles_and_masks_padded(steps, latent_shape, tile_height, tile_width): + batch_size = latent_shape[0] + latent_size_h = latent_shape[-2] + latent_size_w = latent_shape[-1] + + tile_size_h = int(tile_height // 8) + tile_size_h = int((tile_size_h // 4) * 4) + tile_size_w = int(tile_width // 8) + tile_size_w = int((tile_size_w // 4) * 4) + + #masks + mask_h = [0,tile_size_h // 4, tile_size_h - tile_size_h // 4, tile_size_h] + mask_w = [0,tile_size_w // 4, tile_size_w - tile_size_w // 4, tile_size_w] + masks = [[] for _ in range(3)] + for i in range(3): + for j in range(3): + mask = torch.zeros((batch_size,1,tile_size_h, tile_size_w), dtype=torch.float32, device='cpu') + mask[:,:,mask_h[i]:mask_h[i+1],mask_w[j]:mask_w[j+1]] = 1.0 + masks[i].append(mask) + + def create_mask(h_ind, w_ind, h_ind_max, w_ind_max, mask_h, mask_w, h_len, w_len): + mask = masks[1][1] + if not (h_ind == 0 or h_ind == h_ind_max or w_ind == 0 or w_ind == w_ind_max): + return get_slice(mask, 0, h_len, 0, w_len) + mask = mask.clone() + if h_ind == 0 and mask_h: + mask += masks[0][1] + if h_ind == h_ind_max and mask_h: + mask += masks[2][1] + if w_ind == 0 and mask_w: + mask += masks[1][0] + if w_ind == w_ind_max and mask_w: + mask += masks[1][2] + if h_ind == 0 and w_ind == 0 and mask_h and mask_w: + mask += masks[0][0] + if h_ind == 0 and w_ind == w_ind_max and mask_h and mask_w: + mask += masks[0][2] + if h_ind == h_ind_max and w_ind == 0 and mask_h and mask_w: + mask += masks[2][0] + if h_ind == h_ind_max and w_ind == w_ind_max and mask_h and mask_w: + mask += masks[2][2] + return get_slice(mask, 0, h_len, 0, w_len) + + h = np.arange(0,latent_size_h, tile_size_h) + h_shift = np.arange(tile_size_h // 2, latent_size_h - tile_size_h // 2, tile_size_h) + w = np.arange(0,latent_size_w, tile_size_w) + w_shift = np.arange(tile_size_w // 2, latent_size_w - tile_size_h // 2, tile_size_w) + + + def create_tile(hs, ws, mask_h, mask_w, i, j): + h = int(hs[i]) + w = int(ws[j]) + h_len = min(tile_size_h, latent_size_h - h) + w_len = min(tile_size_w, latent_size_w - w) + mask = create_mask(i,j,len(hs)-1, len(ws)-1, mask_h, mask_w, h_len, w_len) + return (h, h_len, w, w_len, steps, mask) + + passes = [ + [[create_tile(h, w, True, True, i, j) for i in range(len(h)) for j in range(len(w))]], + [[create_tile(h_shift, w, False, True, i, j) for i in range(len(h_shift)) for j in range(len(w))]], + [[create_tile(h, w_shift, True, False, i, j) for i in range(len(h)) for j in range(len(w_shift))]], + [[create_tile(h_shift, w_shift, False, False, i,j) for i in range(len(h_shift)) for j in range(len(w_shift))]], + ] + + return passes + +def mask_at_boundary(h, h_len, w, w_len, tile_size_h, tile_size_w, latent_size_h, latent_size_w, mask, device='cpu'): + tile_size_h = int(tile_size_h // 8) + tile_size_w = int(tile_size_w // 8) + + if (h_len == tile_size_h or h_len == latent_size_h) and (w_len == tile_size_w or w_len == latent_size_w): + return h, h_len, w, w_len, mask + h_offset = min(0, latent_size_h - (h + tile_size_h)) + w_offset = min(0, latent_size_w - (w + tile_size_w)) + new_mask = torch.zeros((1,1,tile_size_h, tile_size_w), dtype=torch.float32, device=device) + new_mask[:,:,-h_offset:h_len if h_offset == 0 else tile_size_h, -w_offset:w_len if w_offset == 0 else tile_size_w] = 1.0 if mask is None else mask + return h + h_offset, tile_size_h, w + w_offset, tile_size_w, new_mask + +def get_tiles_and_masks_rgrid(steps, latent_shape, tile_height, tile_width, generator): + + def calc_coords(latent_size, tile_size, jitter): + tile_coords = int((latent_size + jitter - 1) // tile_size + 1) + tile_coords = [np.clip(tile_size * c - jitter, 0, latent_size) for c in range(tile_coords + 1)] + tile_coords = [(c1, c2-c1) for c1, c2 in zip(tile_coords, tile_coords[1:])] + return tile_coords + + #calc stuff + batch_size = latent_shape[0] + latent_size_h = latent_shape[-2] + latent_size_w = latent_shape[-1] + tile_size_h = int(tile_height // 8) + tile_size_w = int(tile_width // 8) + + tiles_all = [] + + for s in range(steps): + rands = torch.rand((2,), dtype=torch.float32, generator=generator, device='cpu').numpy() + + jitter_w1 = int(rands[0] * tile_size_w) + jitter_w2 = int(((rands[0] + .5) % 1.0) * tile_size_w) + jitter_h1 = int(rands[1] * tile_size_h) + jitter_h2 = int(((rands[1] + .5) % 1.0) * tile_size_h) + + #calc number of tiles + tiles_h = [ + calc_coords(latent_size_h, tile_size_h, jitter_h1), + calc_coords(latent_size_h, tile_size_h, jitter_h2) + ] + tiles_w = [ + calc_coords(latent_size_w, tile_size_w, jitter_w1), + calc_coords(latent_size_w, tile_size_w, jitter_w2) + ] + + tiles = [] + if s % 2 == 0: + for i, h in enumerate(tiles_h[0]): + for w in tiles_w[i%2]: + tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None)) + else: + for i, w in enumerate(tiles_w[0]): + for h in tiles_h[i%2]: + tiles.append((int(h[0]), int(h[1]), int(w[0]), int(w[1]), 1, None)) + tiles_all.append(tiles) + return [tiles_all] \ No newline at end of file