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# Copyright 2024 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import torch
import einops
import torch.nn.functional as F
def get_mask(idx, array):
'''
array: b m, records # of elements to be masked
'''
b, m = array.shape
n = idx.size(-1)
A = torch.arange(n, dtype=idx.dtype, device=idx.device).unsqueeze(0).unsqueeze(0).expand(b, m, n) # 1 1 n -> b m n
mask = A < array.unsqueeze(-1)
return mask
def alloc(var, rest, budget, tp, maximum, times=0, fast=False):
'''
var: (b m) variance of each pixel POSITIVE VALUE
rest: (b m) list of already allocated budgets
budget: (b) remaining to be allocated
tp: mean type, plain/softmax
maximum: maximum budget for each pixel
'''
b, m = var.shape
if tp == 'plain':
var_p = var * (rest < maximum)
var_sum = var_p.sum(dim=-1, keepdim=True) # b 1
proportion = var_p / var_sum # b m
elif tp == 'softmax':
var_p = var.clone()
var_p[rest >= maximum] = -float('inf') # maximum
proportion = torch.nn.functional.softmax(var_p, dim=-1) # b m
allocation = torch.round(proportion * budget.unsqueeze(1)) # b m
new_rest = torch.clamp(rest + allocation, 0, maximum) # b m
remain_budget = budget - (new_rest - rest).sum(dim=-1) # b m allocated
negative_remain = (remain_budget < 0)
while negative_remain.sum() > 0:
offset = torch.eye(m, device=rest.device)[
torch.randint(m, (negative_remain.sum().int().item(),), device=rest.device)]
new_rest[negative_remain] = torch.clamp(new_rest[negative_remain] - offset, 1, maximum) # reduce by one
# update remain budget
remain_budget = budget - (new_rest - rest).sum(dim=-1) # b m allocated
negative_remain = (remain_budget < 0)
if (remain_budget > 0).sum() > 0:
if times < 3:
new_rest[remain_budget > 0] = alloc(var[remain_budget > 0], new_rest[remain_budget > 0],
remain_budget[remain_budget > 0], tp, maximum, times + 1, fast=fast)
elif not fast: # precise budget allocation
positive_remain = (remain_budget > 0)
while positive_remain.sum() > 0:
offset = torch.eye(m, device=rest.device)[
torch.randint(m, (positive_remain.sum().int().item(),), device=rest.device)]
new_rest[positive_remain] = torch.clamp(new_rest[positive_remain] + offset, 1, maximum) # add by one
# update remain budget
remain_budget = budget - (new_rest - rest).sum(dim=-1) # b m allocated
positive_remain = (remain_budget > 0)
return new_rest
def flex(D_: torch.Tensor, X: torch.Tensor, idx: torch.Tensor, flex_type, topk_, current_iter, total_iters, X_diff,
fast=False, return_maskarray=False):
'''
D: (b m n) Gram matrix, sorted on last dim, descending
X: (b numh numw he) c (sh sw) X_data
idx: (b m n) sorted index of D
x_size: (h, w) 2-tuple tensor
OUT: (b m n) Binary mask
'''
b, m, n = D_.shape
if flex_type is None or flex_type == 'none':
mask_array = topk_ * torch.ones((b, m), dtype=torch.int, device=D_.device)
elif flex_type == 'gsort':
D = D_.clone()
D -= (D == D.max(dim=-1, keepdim=True)) * 100000 # neglect max position
val, g_idx = torch.sort(D.view(b, -1), dim=-1, descending=True) # global sort
# g_idx: (b m*n)
g_idx += m * n * torch.arange(b, dtype=g_idx.dtype, device=g_idx.device).unsqueeze(-1) # b 1
non_topk_idx = g_idx[:, topk_ * (m - 1):] # select top k, neglect max
mask_ = torch.ones_like(D).bool()
mask_.view(-1)[non_topk_idx.reshape(-1)] = False # set to negative value
mask_array = mask_.sum(dim=-1)
mask_array += 1 # include max, ensure each pixel has at least one match
elif flex_type == 'interdiff_plain': # interpolate and diff
rest = torch.ones_like(X_diff)
budget = torch.ones(b, dtype=torch.int, device=idx.device) * (topk_ - 1) * idx.size(1)
mask_array = alloc(X_diff, rest, budget, tp='plain', maximum=idx.size(-1), fast=fast)
else:
raise NotImplementedError(f'Graph type {flex_type} not implemented...')
if return_maskarray:
return mask_array
mask = ~get_mask(idx, mask_array) # negated
return mask
def cossim(X_sample, Y_sample, graph=None):
if graph is not None:
return torch.einsum('a b m c, a b n c -> a b m n', F.normalize(X_sample, dim=-1),
F.normalize(Y_sample, dim=-1)) + (-100.) * (~graph)
return torch.einsum('a b m c, a b n c -> a b m n', F.normalize(X_sample, dim=-1), F.normalize(Y_sample, dim=-1))
def local_sampling(x, group_size, unfold_dict, output=0, tp='bhwc'):
'''
output:
x (grouped) [B, nn, c]
x_unfold [B, NN, C]
0/1/2: grouped, sampled, both
'''
if isinstance(group_size, int):
group_size = (group_size, group_size)
if output != 1:
if tp == 'bhwc':
x_grouped = einops.rearrange(x, 'b (numh sh) (numw sw) c-> (b numh numw) (sh sw) c', sh=group_size[0],
sw=group_size[1])
elif tp == 'bchw':
x_grouped = einops.rearrange(x, 'b c (numh sh) (numw sw)-> (b numh numw) (sh sw) c', sh=group_size[0],
sw=group_size[1])
if output == 0:
return x_grouped
if tp == 'bhwc':
x = einops.rearrange(x, 'b h w c -> b c h w')
x_sampled = einops.rearrange(F.unfold(x, **unfold_dict), 'b (c k0 k1) l -> (b l) (k0 k1) c',
k0=unfold_dict['kernel_size'][0], k1=unfold_dict['kernel_size'][1])
if output == 1:
return x_sampled
assert x_grouped.size(0) == x_sampled.size(0)
return x_grouped, x_sampled
def global_sampling(x, group_size, sample_size, output=0, tp='bhwc'):
'''
output:
x (grouped) [B, nn, c]
x_unfold [B, NN, C]
'''
if isinstance(group_size, int):
group_size = (group_size, group_size)
if isinstance(sample_size, int):
sample_size = (sample_size, sample_size)
if output != 1:
if tp == 'bchw':
x_grouped = einops.rearrange(x, 'b c (sh numh) (sw numw) -> (b numh numw) (sh sw) c', sh=group_size[0],
sw=group_size[1])
elif tp == 'bhwc':
x_grouped = einops.rearrange(x, 'b (sh numh) (sw numw) c -> (b numh numw) (sh sw) c', sh=group_size[0],
sw=group_size[1])
if output == 0:
return x_grouped
if tp == 'bchw':
x_sampled = einops.rearrange(x, 'b c (sh extrah numh) (sw extraw numw) -> b extrah numh extraw numw c sh sw',
sh=sample_size[0], sw=sample_size[1], extrah=1, extraw=1)
elif tp == 'bhwc':
x_sampled = einops.rearrange(x, 'b (sh extrah numh) (sw extraw numw) c -> b extrah numh extraw numw c sh sw',
sh=sample_size[0], sw=sample_size[1], extrah=1, extraw=1)
b_y, _, numh, _, numw, c_y, sh_y, sw_y = x_sampled.shape
ratio_h, ratio_w = sample_size[0] // group_size[0], sample_size[1] // group_size[1]
x_sampled = x_sampled.expand(b_y, ratio_h, numh, ratio_w, numw, c_y, sh_y, sw_y).reshape(-1, c_y,
sh_y * sw_y).permute(0, 2,
1)
if output == 1:
return x_sampled
assert x_grouped.size(0) == x_sampled.size(0)
return x_grouped, x_sampled |