584 lines
21 KiB
Python
584 lines
21 KiB
Python
import collections.abc
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import math
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import torch
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import warnings
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from itertools import repeat
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from torch import nn as nn
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from torch.nn import functional as F
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from torch.nn import init as init
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from torch.nn.modules.batchnorm import _BatchNorm
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from einops import rearrange
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import numbers
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@torch.no_grad() # @ is a decorator, which takes the decorated function as the argument of the function after @
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def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
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"""Initialize network weights.
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Args:
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module_list (list[nn.Module] | nn.Module): Modules to be initialized.
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scale (float): Scale initialized weights, especially for residual
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blocks. Default: 1.
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bias_fill (float): The value to fill bias. Default: 0
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kwargs (dict): Other arguments for initialization function.
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"""
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if not isinstance(module_list, list):
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module_list = [module_list]
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for module in module_list:
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for m in module.modules():
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if isinstance(m, nn.Conv2d):
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init.kaiming_normal_(m.weight, **kwargs)
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m.weight.data *= scale
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if m.bias is not None:
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m.bias.data.fill_(bias_fill)
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elif isinstance(m, nn.Linear):
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init.kaiming_normal_(m.weight, **kwargs)
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m.weight.data *= scale
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if m.bias is not None:
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m.bias.data.fill_(bias_fill)
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elif isinstance(m, _BatchNorm):
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init.constant_(m.weight, 1)
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if m.bias is not None:
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m.bias.data.fill_(bias_fill)
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def make_layer(basic_block, num_basic_block, **kwarg):
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"""Make layers by stacking the same blocks.
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Args:
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basic_block (nn.module): nn.module class for basic block.
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num_basic_block (int): number of blocks.
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Returns:
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nn.Sequential: Stacked blocks in nn.Sequential.
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"""
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layers = []
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for _ in range(num_basic_block):
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layers.append(basic_block(**kwarg))
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return nn.Sequential(*layers)
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class ResidualBlockNoBN(nn.Module):
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"""Residual block without BN.
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It has a style of:
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---Conv-ReLU-Conv-+-
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|________________|
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Args:
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num_feat (int): Channel number of intermediate features.
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Default: 64.
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res_scale (float): Residual scale. Default: 1.
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pytorch_init (bool): If set to True, use pytorch default init,
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otherwise, use default_init_weights. Default: False.
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"""
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def __init__(self, num_feat=64, res_scale=1, pytorch_init=False):
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super(ResidualBlockNoBN, self).__init__()
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self.res_scale = res_scale
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self.conv1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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self.conv2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1, bias=True)
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self.relu = nn.ReLU(inplace=True)
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if not pytorch_init:
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default_init_weights([self.conv1, self.conv2], 0.1)
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def forward(self, x):
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identity = x
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out = self.conv2(self.relu(self.conv1(x)))
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return identity + out * self.res_scale
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class Upsample(nn.Sequential):
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"""Upsample module.
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Args:
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scale (int): Scale factor. Supported scales: 2^n and 3.
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num_feat (int): Channel number of intermediate features.
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"""
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def __init__(self, scale, num_feat):
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m = []
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if (scale & (scale - 1)) == 0: # scale = 2^n
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for _ in range(int(math.log(scale, 2))):
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m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
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m.append(nn.PixelShuffle(2))
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elif scale == 3:
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m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
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m.append(nn.PixelShuffle(3))
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else:
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raise ValueError(f'scale {scale} is not supported. Supported scales: 2^n and 3.')
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super(Upsample, self).__init__(*m)
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def flow_warp(x, flow, interp_mode='bilinear', padding_mode='zeros', align_corners=True):
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"""Warp an image or feature map with optical flow.
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Args:
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x (Tensor): Tensor with size (n, c, h, w).
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flow (Tensor): Tensor with size (n, h, w, 2), normal value.
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interp_mode (str): 'nearest' or 'bilinear'. Default: 'bilinear'.
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padding_mode (str): 'zeros' or 'border' or 'reflection'.
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Default: 'zeros'.
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align_corners (bool): Before pytorch 1.3, the default value is
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align_corners=True. After pytorch 1.3, the default value is
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align_corners=False. Here, we use the True as default.
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Returns:
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Tensor: Warped image or feature map.
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"""
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assert x.size()[-2:] == flow.size()[1:3]
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_, _, h, w = x.size()
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# create mesh grid
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grid_y, grid_x = torch.meshgrid(torch.arange(0, h).type_as(x), torch.arange(0, w).type_as(x))
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grid = torch.stack((grid_x, grid_y), 2).float() # W(x), H(y), 2 current position
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grid.requires_grad = False
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vgrid = grid + flow # for all n batches do the same current position --> warped position
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# scale grid to [-1,1]
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vgrid_x = 2.0 * vgrid[:, :, :, 0] / max(w - 1, 1) - 1.0
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vgrid_y = 2.0 * vgrid[:, :, :, 1] / max(h - 1, 1) - 1.0
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vgrid_scaled = torch.stack((vgrid_x, vgrid_y), dim=3)
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output = F.grid_sample(x, vgrid_scaled, mode=interp_mode, padding_mode=padding_mode, align_corners=align_corners)
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# TODO, what if align_corners=False
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return output
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def resize_flow(flow, size_type, sizes, interp_mode='bilinear', align_corners=False):
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"""Resize a flow according to ratio or shape.
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Args:
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flow (Tensor): Precomputed flow. shape [N, 2, H, W].
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size_type (str): 'ratio' or 'shape'.
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sizes (list[int | float]): the ratio for resizing or the final output
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shape.
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1) The order of ratio should be [ratio_h, ratio_w]. For
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downsampling, the ratio should be smaller than 1.0 (i.e., ratio
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< 1.0). For upsampling, the ratio should be larger than 1.0 (i.e.,
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ratio > 1.0).
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2) The order of output_size should be [out_h, out_w].
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interp_mode (str): The mode of interpolation for resizing.
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Default: 'bilinear'.
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align_corners (bool): Whether align corners. Default: False.
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Returns:
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Tensor: Resized flow.
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"""
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_, _, flow_h, flow_w = flow.size()
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if size_type == 'ratio':
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output_h, output_w = int(flow_h * sizes[0]), int(flow_w * sizes[1])
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elif size_type == 'shape':
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output_h, output_w = sizes[0], sizes[1]
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else:
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raise ValueError(f'Size type should be ratio or shape, but got type {size_type}.')
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input_flow = flow.clone()
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ratio_h = output_h / flow_h
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ratio_w = output_w / flow_w
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input_flow[:, 0, :, :] *= ratio_w
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input_flow[:, 1, :, :] *= ratio_h
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resized_flow = F.interpolate(
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input=input_flow, size=(output_h, output_w), mode=interp_mode) #, align_corners=align_corners)
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return resized_flow
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# TODO: may write a cpp file
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def pixel_unshuffle(x, scale):
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""" Pixel unshuffle.
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Args:
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x (Tensor): Input feature with shape (b, c, hh, hw).
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scale (int): Downsample ratio.
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Returns:
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Tensor: the pixel unshuffled feature.
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"""
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b, c, hh, hw = x.size()
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out_channel = c * (scale**2)
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assert hh % scale == 0 and hw % scale == 0
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h = hh // scale
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w = hw // scale
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x_view = x.view(b, c, h, scale, w, scale)
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return x_view.permute(0, 1, 3, 5, 2, 4).reshape(b, out_channel, h, w)
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def _no_grad_trunc_normal_(tensor, mean, std, a, b):
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# From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
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# Cut & paste from PyTorch official master until it's in a few official releases - RW
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# Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
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def norm_cdf(x):
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# Computes standard normal cumulative distribution function
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return (1. + math.erf(x / math.sqrt(2.))) / 2.
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if (mean < a - 2 * std) or (mean > b + 2 * std):
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warnings.warn(
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'mean is more than 2 std from [a, b] in nn.init.trunc_normal_. '
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'The distribution of values may be incorrect.',
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stacklevel=2)
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with torch.no_grad():
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# Values are generated by using a truncated uniform distribution and
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# then using the inverse CDF for the normal distribution.
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# Get upper and lower cdf values
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low = norm_cdf((a - mean) / std)
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up = norm_cdf((b - mean) / std)
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# Uniformly fill tensor with values from [low, up], then translate to
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# [2l-1, 2u-1].
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tensor.uniform_(2 * low - 1, 2 * up - 1)
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# Use inverse cdf transform for normal distribution to get truncated
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# standard normal
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tensor.erfinv_()
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# Transform to proper mean, std
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tensor.mul_(std * math.sqrt(2.))
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tensor.add_(mean)
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# Clamp to ensure it's in the proper range
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tensor.clamp_(min=a, max=b)
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return tensor
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def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
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r"""Fills the input Tensor with values drawn from a truncated
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normal distribution.
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From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/weight_init.py
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The values are effectively drawn from the
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normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
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with values outside :math:`[a, b]` redrawn until they are within
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the bounds. The method used for generating the random values works
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best when :math:`a \leq \text{mean} \leq b`.
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Args:
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tensor: an n-dimensional `torch.Tensor`
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mean: the mean of the normal distribution
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std: the standard deviation of the normal distribution
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a: the minimum cutoff value
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b: the maximum cutoff value
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Examples:
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>>> w = torch.empty(3, 5)
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>>> nn.init.trunc_normal_(w)
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"""
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return _no_grad_trunc_normal_(tensor, mean, std, a, b)
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# From PyTorch
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def _ntuple(n):
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def parse(x):
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if isinstance(x, collections.abc.Iterable):
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return x
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return tuple(repeat(x, n))
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return parse
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to_1tuple = _ntuple(1)
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to_2tuple = _ntuple(2)
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to_3tuple = _ntuple(3)
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to_4tuple = _ntuple(4)
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to_ntuple = _ntuple
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class ConvResidualBlocks(nn.Module):
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"""Conv and residual block used in BasicVSR.
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Args:
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num_in_ch (int): Number of input channels. Default: 3.
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num_out_ch (int): Number of output channels. Default: 64.
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num_block (int): Number of residual blocks. Default: 15.
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"""
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def __init__(self, num_in_ch=3, num_out_ch=64, num_block=15):
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super().__init__()
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self.main = nn.Sequential(
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nn.Conv2d(num_in_ch, num_out_ch, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.1, inplace=True),
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make_layer(ResidualBlockNoBN, num_block, num_feat=num_out_ch))
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def forward(self, fea):
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return self.main(fea)
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class ResidualBlocks(nn.Module):
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"""Residual block without shallow conv
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Args:
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num_in_ch (int): Number of input channels. Default: 3.
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num_out_ch (int): Number of output channels. Default: 64.
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num_block (int): Number of residual blocks. Default: 15.
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"""
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def __init__(self, num_out_ch=64, num_block=15):
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super().__init__()
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self.main = make_layer(ResidualBlockNoBN, num_block, num_feat=num_out_ch)
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def forward(self, fea):
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return self.main(fea)
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class Conv2dwithActication(nn.Module):
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""" Conv2d layers with activation
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"""
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def __init__(self, num_in_ch, num_out_ch, num_layer=1):
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super().__init__()
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main = [nn.Conv2d(num_in_ch, num_out_ch, 3, 1, 1, bias=True), nn.LeakyReLU(negative_slope=0.1, inplace=True)]
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for _ in range(num_layer-1):
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main.append(nn.Conv2d(num_out_ch, num_out_ch, 3, 1, 1, bias=True))
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main.append(nn.LeakyReLU(negative_slope=0.1, inplace=True))
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self.main = nn.Sequential(*main)
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def forward(self, fea):
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return self.main(fea)
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##########################################################################
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## LayerNorm
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def to_3d(x):
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return rearrange(x, 'b c h w -> b (h w) c')
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def to_4d(x,h,w):
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return rearrange(x, 'b (h w) c -> b c h w',h=h,w=w)
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class BiasFree_LayerNorm(nn.Module):
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def __init__(self, normalized_shape):
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super(BiasFree_LayerNorm, self).__init__()
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if isinstance(normalized_shape, numbers.Integral):
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normalized_shape = (normalized_shape,)
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normalized_shape = torch.Size(normalized_shape)
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assert len(normalized_shape) == 1
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self.weight = nn.Parameter(torch.ones(normalized_shape))
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self.normalized_shape = normalized_shape
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def forward(self, x):
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sigma = x.var(-1, keepdim=True, unbiased=False)
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return x / torch.sqrt(sigma+1e-5) * self.weight
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class WithBias_LayerNorm(nn.Module):
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def __init__(self, normalized_shape):
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super(WithBias_LayerNorm, self).__init__()
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if isinstance(normalized_shape, numbers.Integral):
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normalized_shape = (normalized_shape,)
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normalized_shape = torch.Size(normalized_shape)
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assert len(normalized_shape) == 1
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self.weight = nn.Parameter(torch.ones(normalized_shape))
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self.bias = nn.Parameter(torch.zeros(normalized_shape))
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self.normalized_shape = normalized_shape
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def forward(self, x):
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mu = x.mean(-1, keepdim=True)
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sigma = x.var(-1, keepdim=True, unbiased=False)
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return (x - mu) / torch.sqrt(sigma+1e-5) * self.weight + self.bias
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class LayerNorm(nn.Module):
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def __init__(self, dim, LayerNorm_type):
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super(LayerNorm, self).__init__()
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if LayerNorm_type =='BiasFree':
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self.body = BiasFree_LayerNorm(dim)
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else:
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self.body = WithBias_LayerNorm(dim)
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def forward(self, x):
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h, w = x.shape[-2:]
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return to_4d(self.body(to_3d(x)), h, w)
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##########################################################################
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## Multi-DConv Head Transposed Self-Attention (MDTA)
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class Attention(nn.Module):
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def __init__(self, dim_q, dim_k, num_heads, bias):
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super(Attention, self).__init__()
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self.num_heads = num_heads
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self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
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self.kv = nn.Conv2d(dim_k, dim_k*2, kernel_size=1, bias=bias)
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self.kv_dwconv = nn.Conv2d(dim_k*2, dim_k*2, kernel_size=3, stride=1, padding=1, groups=dim_k*2, bias=bias)
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self.q = nn.Conv2d(dim_q, dim_q, kernel_size=3, stride=1, padding=1, bias=bias)
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self.project_out = nn.Conv2d(dim_q, dim_q, kernel_size=1, bias=bias)
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def forward(self, x, y):
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b,c_kv,h,w = x.shape
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kv = self.kv_dwconv(self.kv(x))
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k,v = kv.chunk(2, dim=1)
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# c_q = y.size(1)
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q = self.q(y)
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q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
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k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
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v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
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q = torch.nn.functional.normalize(q, dim=-1)
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k = torch.nn.functional.normalize(k, dim=-1)
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attn = (q @ k.transpose(-2, -1)) * self.temperature # c_q, c_kv (64, 32)
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attn = attn.softmax(dim=-1)
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out = (attn @ v)
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out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)
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out = self.project_out(out) # (b, 64, h, w)
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return out
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##########################################################################
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# Attention block with layer norm
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class AttentionBlock(nn.Module):
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def __init__(self, dim_q, dim_kv, num_heads, bias, LayerNorm_type=None, reduction='sum'):
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super(AttentionBlock, self).__init__()
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if LayerNorm_type is not None:
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self.norm_q = LayerNorm(dim_q, LayerNorm_type)
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self.norm_kv = LayerNorm(dim_kv, LayerNorm_type)
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self.attn = Attention(dim_q, dim_kv, num_heads, bias)
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if reduction == 'cat':
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self.fusion = nn.Conv2d(dim_q*2, dim_q, kernel_size=1, bias=bias)
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def forward(self, x, y):
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if hasattr(self, 'norm_kv'):
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x = self.norm_kv(x)
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if hasattr(self, 'norm_q'):
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y = self.norm_q(y)
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v = self.attn(x, y) ## ()
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if hasattr(self, 'fusion'):
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return self.fusion(torch.cat((y, v), dim=1))
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else:
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return y + v
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##########################################################################
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## Simple attention function (parameter-free)
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def cross_attention(x, ref):
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b,c0,h,w = x.shape
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_,c1,_,_ = ref.shape
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q,k,v = x.clone(), ref.clone(), ref.clone()
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q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=1)
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k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=1)
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v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=1)
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q = torch.nn.functional.normalize(q, dim=-1)
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k = torch.nn.functional.normalize(k, dim=-1)
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attn = (q @ k.transpose(-2, -1)) * 1 # self.temperature [c0, c1]
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max_, _ = torch.max(attn, dim=-1, keepdim=True)
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attn_submax = attn - max_
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attn = attn.softmax(dim=-1)
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attn = attn * torch.lt(attn_submax, 0).int()
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attn = attn.softmax(dim=-1)
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out = (attn @ v) # [c0, hw]
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out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=1, h=h, w=w)
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# out = self.project_out(out)
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return out
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##########################################################################
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## Multi-DConv Head Cross-Attention (MDCA)
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class CrossChannelAttention(nn.Module):
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def __init__(self, dim_q, dim_k, num_heads, bias):
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super(CrossChannelAttention, self).__init__()
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self.num_heads = num_heads
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self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
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|
|
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self.kv = nn.Conv2d(dim_k, dim_k*2, kernel_size=1, bias=bias)
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self.kv_dwconv = nn.Conv2d(dim_k*2, dim_k*2, kernel_size=3, stride=1, padding=1, groups=dim_k*2, bias=bias)
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self.q = nn.Conv2d(dim_q, dim_q, kernel_size=3, stride=1, padding=1, bias=bias)
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self.project_out = nn.Conv2d(dim_q, dim_q, kernel_size=1, bias=bias)
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|
|
|
def forward(self, x, y, return_attn=False):
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b,c_kv,h,w = x.shape
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|
|
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kv = self.kv_dwconv(self.kv(y))
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k,v = kv.chunk(2, dim=1)
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|
|
|
# c_q = y.size(1)
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|
q = self.q(x)
|
|
|
|
q = rearrange(q, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
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|
k = rearrange(k, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
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|
v = rearrange(v, 'b (head c) h w -> b head c (h w)', head=self.num_heads)
|
|
|
|
q = torch.nn.functional.normalize(q, dim=-1)
|
|
k = torch.nn.functional.normalize(k, dim=-1)
|
|
|
|
attn = (q @ k.transpose(-2, -1)) * self.temperature
|
|
|
|
attn = attn.softmax(dim=-1)
|
|
out = (attn @ v)
|
|
|
|
out = rearrange(out, 'b head c (h w) -> b (head c) h w', head=self.num_heads, h=h, w=w)
|
|
|
|
out = self.project_out(out) # (b, 64, h, w)
|
|
if return_attn:
|
|
return out, attn
|
|
else:
|
|
return out
|
|
|
|
##########################################################################
|
|
# Channel-wise Attention block with layer norm
|
|
class ChannelAttentionBlock(nn.Module):
|
|
def __init__(self, dim_q, dim_kv, dim_out,num_heads, bias, LayerNorm_type=None,
|
|
reduction=True, ch_compress=False, squeeze_factor=1):
|
|
super(ChannelAttentionBlock, self).__init__()
|
|
self.ch_compress = ch_compress and (squeeze_factor > 1)
|
|
if self.ch_compress:
|
|
if LayerNorm_type is not None:
|
|
self.norm_q = LayerNorm(dim_q//squeeze_factor, LayerNorm_type)
|
|
self.norm_kv = LayerNorm(dim_kv//squeeze_factor, LayerNorm_type)
|
|
self.attn = CrossChannelAttention(dim_q//squeeze_factor, dim_kv//squeeze_factor, num_heads, bias)
|
|
else:
|
|
if LayerNorm_type is not None:
|
|
self.norm_q = LayerNorm(dim_q, LayerNorm_type)
|
|
self.norm_kv = LayerNorm(dim_kv, LayerNorm_type)
|
|
self.attn = CrossChannelAttention(dim_q, dim_kv, num_heads, bias)
|
|
self.reduction = reduction
|
|
if reduction:
|
|
self.norm_out = LayerNorm(dim_kv, LayerNorm_type)
|
|
self.ffn = nn.Sequential(nn.Conv2d(dim_kv+dim_q, dim_out, kernel_size=1, bias=bias),
|
|
nn.Conv2d(dim_out, dim_out, kernel_size=3, stride=1, padding=1, groups=dim_out, bias=bias),
|
|
nn.GELU(),
|
|
nn.Conv2d(dim_out, dim_out, kernel_size=1, bias=bias))
|
|
|
|
if self.ch_compress:
|
|
self.compress_q = nn.Conv2d(dim_q, dim_q//squeeze_factor, kernel_size=3, stride=1, padding=1, bias=bias)
|
|
self.compress_kv = nn.Conv2d(dim_kv, dim_kv//squeeze_factor, kernel_size=3, stride=1, padding=1, bias=bias)
|
|
self.expand = nn.Conv2d(dim_q//squeeze_factor, dim_q, kernel_size=3, stride=1, padding=1, bias=bias)
|
|
|
|
def forward(self, x, y, return_attn=False):
|
|
|
|
if self.ch_compress:
|
|
x_compressed = self.compress_q(x)
|
|
y_compressed = self.compress_kv(y)
|
|
if hasattr(self, 'norm_kv'):
|
|
y_compressed = self.norm_kv(y_compressed)
|
|
if hasattr(self, 'norm_q'):
|
|
x_compressed = self.norm_q(x_compressed)
|
|
v = self.expand(self.attn(x_compressed, y_compressed, return_attn))
|
|
else:
|
|
if hasattr(self, 'norm_kv'):
|
|
y = self.norm_kv(y)
|
|
if hasattr(self, 'norm_q'):
|
|
x = self.norm_q(x)
|
|
v = self.attn(x, y, return_attn) ## ()
|
|
if self.reduction:
|
|
out = self.ffn(torch.cat([x,self.norm_out(v)], dim=1))
|
|
return out
|