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