| |
| |
| |
|
|
| import numpy as np |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| class AvgPool2d(nn.Module): |
| def __init__(self, kernel_size=None, base_size=None, auto_pad=True, fast_imp=False, train_size=None): |
| super().__init__() |
| self.kernel_size = kernel_size |
| self.base_size = base_size |
| self.auto_pad = auto_pad |
|
|
| |
| self.fast_imp = fast_imp |
| self.rs = [5, 4, 3, 2, 1] |
| self.max_r1 = self.rs[0] |
| self.max_r2 = self.rs[0] |
| self.train_size = train_size |
|
|
| def extra_repr(self) -> str: |
| return 'kernel_size={}, base_size={}, stride={}, fast_imp={}'.format( |
| self.kernel_size, self.base_size, self.kernel_size, self.fast_imp |
| ) |
|
|
| def forward(self, x): |
| if self.kernel_size is None and self.base_size: |
| train_size = self.train_size |
| if isinstance(self.base_size, int): |
| self.base_size = (self.base_size, self.base_size) |
| self.kernel_size = list(self.base_size) |
| self.kernel_size[0] = x.shape[2] * self.base_size[0] // train_size[-2] |
| self.kernel_size[1] = x.shape[3] * self.base_size[1] // train_size[-1] |
|
|
| |
| self.max_r1 = max(1, self.rs[0] * x.shape[2] // train_size[-2]) |
| self.max_r2 = max(1, self.rs[0] * x.shape[3] // train_size[-1]) |
|
|
| if self.kernel_size[0] >= x.size(-2) and self.kernel_size[1] >= x.size(-1): |
| return F.adaptive_avg_pool2d(x, 1) |
|
|
| if self.fast_imp: |
| h, w = x.shape[2:] |
| if self.kernel_size[0] >= h and self.kernel_size[1] >= w: |
| out = F.adaptive_avg_pool2d(x, 1) |
| else: |
| r1 = [r for r in self.rs if h % r == 0][0] |
| r2 = [r for r in self.rs if w % r == 0][0] |
| |
| r1 = min(self.max_r1, r1) |
| r2 = min(self.max_r2, r2) |
| s = x[:, :, ::r1, ::r2].cumsum(dim=-1).cumsum(dim=-2) |
| n, c, h, w = s.shape |
| k1, k2 = min(h - 1, self.kernel_size[0] // r1), min(w - 1, self.kernel_size[1] // r2) |
| out = (s[:, :, :-k1, :-k2] - s[:, :, :-k1, k2:] - s[:, :, k1:, :-k2] + s[:, :, k1:, k2:]) / (k1 * k2) |
| out = torch.nn.functional.interpolate(out, scale_factor=(r1, r2)) |
| else: |
| n, c, h, w = x.shape |
| s = x.cumsum(dim=-1).cumsum_(dim=-2) |
| s = torch.nn.functional.pad(s, (1, 0, 1, 0)) |
| k1, k2 = min(h, self.kernel_size[0]), min(w, self.kernel_size[1]) |
| s1, s2, s3, s4 = s[:, :, :-k1, :-k2], s[:, :, :-k1, k2:], s[:, :, k1:, :-k2], s[:, :, k1:, k2:] |
| out = s4 + s1 - s2 - s3 |
| out = out / (k1 * k2) |
|
|
| if self.auto_pad: |
| n, c, h, w = x.shape |
| _h, _w = out.shape[2:] |
| |
| pad2d = ((w - _w) // 2, (w - _w + 1) // 2, (h - _h) // 2, (h - _h + 1) // 2) |
| out = torch.nn.functional.pad(out, pad2d, mode='replicate') |
|
|
| return out |
|
|
| def replace_layers(model, base_size, train_size, fast_imp, **kwargs): |
| for n, m in model.named_children(): |
| if len(list(m.children())) > 0: |
| |
| replace_layers(m, base_size, train_size, fast_imp, **kwargs) |
|
|
| if isinstance(m, nn.AdaptiveAvgPool2d): |
| pool = AvgPool2d(base_size=base_size, fast_imp=fast_imp, train_size=train_size) |
| assert m.output_size == 1 |
| setattr(model, n, pool) |
|
|
|
|
| ''' |
| ref. |
| @article{chu2021tlsc, |
| title={Revisiting Global Statistics Aggregation for Improving Image Restoration}, |
| author={Chu, Xiaojie and Chen, Liangyu and and Chen, Chengpeng and Lu, Xin}, |
| journal={arXiv preprint arXiv:2112.04491}, |
| year={2021} |
| } |
| ''' |
| class Local_Base(): |
| def convert(self, *args, train_size, **kwargs): |
| replace_layers(self, *args, train_size=train_size, **kwargs) |
| imgs = torch.rand(train_size) |
| with torch.no_grad(): |
| self.forward(imgs) |
|
|
