File size: 8,874 Bytes
02c5426 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange, repeat
import models
from models import register
from utils import make_coord, to_coordinates
from mmcv.cnn import ConvModule
from .blocks.CSPLayer import CSPLayer
@register('funsr')
class FUNSR(nn.Module):
def __init__(self,
encoder_spec,
has_multiscale=False,
neck=None,
decoder=None,
global_decoder=None,
encoder_rgb=False,
n_forward_times=1,
encode_hr_coord=False,
has_bn=True,
encode_scale_ratio=False,
local_unfold=False,
weight_gen_func='nearest-exact',
return_featmap=False,
):
super().__init__()
self.weight_gen_func = weight_gen_func # 'bilinear', 'nearest-exact'
self.encoder = models.make(encoder_spec)
self.encoder_out_dim = self.encoder.out_dim
self.encode_scale_ratio = encode_scale_ratio
self.has_multiscale = has_multiscale
self.encoder_rgb = encoder_rgb
self.encode_hr_coord = encode_hr_coord
self.local_unfold = local_unfold
self.return_featmap = return_featmap
self.multiscale_layers = nn.ModuleList()
if self.has_multiscale:
# 48->24->12->6
conv_cfg = None
if has_bn:
norm_cfg = dict(type='BN', momentum=0.03, eps=0.001)
else:
norm_cfg = None
act_cfg = dict(type='ReLU')
num_blocks = [2, 4, 6]
for n_idx in range(3):
conv_layer = ConvModule(
self.encoder_out_dim,
self.encoder_out_dim*2,
3,
stride=2,
padding=1,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg
)
csp_layer = CSPLayer(
self.encoder_out_dim*2,
self.encoder_out_dim,
num_blocks=num_blocks[n_idx],
add_identity=True,
use_depthwise=False,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg,
act_cfg=act_cfg)
self.multiscale_layers.append(nn.Sequential(conv_layer, csp_layer))
if neck is not None:
self.neck = models.make(neck, args={'in_dim': self.encoder_out_dim})
modulation_dim = self.neck.d_dim
else:
modulation_dim = self.encoder_out_dim
self.n_forward_times = n_forward_times
decoder_in_dim = 2
if self.encode_scale_ratio:
decoder_in_dim += 2
if self.encode_hr_coord:
decoder_in_dim += 2
if self.encoder_rgb:
decoder_in_dim += 3
if decoder is not None:
if self.local_unfold:
self.down_dim_layer = nn.Conv2d(modulation_dim * 9, modulation_dim, 1)
self.decoder = models.make(decoder, args={'modulation_dim': modulation_dim, 'in_dim': decoder_in_dim})
if global_decoder is not None:
decoder_in_dim = 2
if self.encode_scale_ratio:
decoder_in_dim += 2
if self.encoder_rgb:
decoder_in_dim += 3
self.decoder_is_proj = global_decoder.get('is_proj', False)
self.global_decoder = models.make(global_decoder, args={'modulation_dim': modulation_dim, 'in_dim': decoder_in_dim})
if self.decoder_is_proj:
self.input_proj = nn.Linear(modulation_dim, modulation_dim)
# self.output_proj = nn.Conv2d(6, 3, kernel_size=3, padding=1)
self.output_proj = nn.Conv2d(6, 3, kernel_size=1)
def forward_step(self,
lr_img,
func_map,
global_func,
rel_coord,
lr_coord,
hr_coord,
scale_ratio_map=None,
pred_rgb_value=None
):
# Expand funcmap
if self.local_unfold:
b, c, h, w = func_map.shape
func_map = F.unfold(func_map, 3, padding=1).view(b, c * 9, h, w)
func_map = self.down_dim_layer(func_map)
local_func_map = F.interpolate(func_map, size=hr_coord.shape[-2:], mode=self.weight_gen_func)
rel_coord = repeat(rel_coord, 'b c h w -> (B b) c h w', B=lr_img.size(0))
hr_coord = repeat(hr_coord, 'c h w -> B c h w', B=lr_img.size(0))
local_input = rel_coord
if self.encode_scale_ratio:
local_input = torch.cat([local_input, scale_ratio_map], dim=1)
if self.encode_hr_coord:
local_input = torch.cat([local_input, hr_coord], dim=1)
if self.encoder_rgb:
if pred_rgb_value is None:
pred_rgb_value = F.interpolate(lr_img, size=hr_coord.shape[-2:], mode='bicubic', align_corners=True)
local_input = torch.cat((local_input, pred_rgb_value), dim=1)
decoder_output = self.decoder(local_input, local_func_map)
if hasattr(self, 'global_decoder'):
if self.decoder_is_proj:
global_func = self.input_proj(global_func) # B C
global_func = repeat(global_func, 'B C -> B C H W', H=hr_coord.shape[2], W=hr_coord.shape[3])
global_input = hr_coord
if self.encode_scale_ratio:
global_input = torch.cat([global_input, scale_ratio_map], dim=1)
if self.encoder_rgb:
if pred_rgb_value is None:
pred_rgb_value = F.interpolate(lr_img, size=hr_coord.shape[-2:], mode='bicubic',
align_corners=True)
global_input = torch.cat((global_input, pred_rgb_value), dim=1)
global_decoder_output = self.global_decoder(global_input, global_func)
returned_featmap = None
if self.decoder_is_proj:
if self.return_featmap:
returned_featmap = torch.cat((global_decoder_output, decoder_output), dim=1)
decoder_output = self.output_proj(torch.cat((global_decoder_output, decoder_output), dim=1))
else:
decoder_output = global_decoder_output + decoder_output
return decoder_output, returned_featmap
def forward_backbone(self, x, keep_ori_feat=True):
# x: img-BxCxHxW
x = self.encoder(x)
output_feats = []
if keep_ori_feat:
output_feats.append(x)
for layer in self.multiscale_layers:
x = layer(x)
output_feats.append(x)
return output_feats
def get_coordinate_map(self, x, hr_size):
B, C, H, W = x.shape
H_up, W_up = hr_size
x_coord = to_coordinates(x.shape[-2:], return_map=True).to(x.device).permute(2, 0, 1)
hr_coord = to_coordinates(hr_size, return_map=True).to(x.device).permute(2, 0, 1)
# important! mode='nearest' gives inconsistent results
# import pdb
# pdb.set_trace()
rel_grid = hr_coord - F.interpolate(x_coord.unsqueeze(0), size=hr_size, mode='nearest-exact')
rel_grid[:, 0, :, :] *= H
rel_grid[:, 1, :, :] *= W
return rel_grid.contiguous().detach(), x_coord.contiguous().detach(), hr_coord.contiguous().detach()
def forward(self, x, out_size):
B, C, H_lr, W_lr = x.shape
output_feats = self.forward_backbone(x) # List
if hasattr(self, 'neck'):
global_content, func_map = self.neck(output_feats)
else:
global_content = None
func_map = output_feats[0]
rel_coord, lr_coord, hr_coord = self.get_coordinate_map(x, out_size)
scale_ratio_map = None
if self.encode_scale_ratio:
h_ratio = x.shape[2] / out_size[0]
w_ratio = x.shape[3] / out_size[1]
scale_ratio_map = torch.tensor([h_ratio, w_ratio]).view(1, -1, 1, 1).expand(B, -1, *out_size).to(x.device)
pred_rgb_value = None
return_pred_rgb_value = []
for n_time in range(self.n_forward_times):
pred_rgb_value, returned_featmaps = self.forward_step(
x,
func_map,
global_content,
rel_coord,
lr_coord,
hr_coord,
scale_ratio_map,
pred_rgb_value
)
return_pred_rgb_value.append(pred_rgb_value)
if self.return_featmap:
return return_pred_rgb_value, returned_featmaps
return return_pred_rgb_value
|