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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('rs_multiscale_super')
class RSMultiScaleSuper(nn.Module):
def __init__(self,
encoder_spec,
multiscale=False,
neck=None,
decoder=None,
has_bn=True,
input_rgb=False,
n_forward_times=1,
global_decoder=None,
encode_scale_ratio=False
):
super().__init__()
self.encoder = models.make(encoder_spec)
self.multiscale = multiscale
self.encoder_out_dim = self.encoder.out_dim
self.encode_scale_ratio = encode_scale_ratio
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')
if self.multiscale:
self.multiscale_layers = nn.ModuleList()
# 32->16->8->4
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
self.input_rgb = input_rgb
decoder_in_dim = 5 if self.input_rgb else 2
if encode_scale_ratio:
decoder_in_dim += 2
if decoder is not None:
self.decoder = models.make(decoder, args={'modulation_dim': modulation_dim, 'in_dim': decoder_in_dim})
if global_decoder is not None:
decoder_in_dim = 5 if self.input_rgb else 2
if encode_scale_ratio:
decoder_in_dim += 2
self.decoder_is_proj = global_decoder.get('is_proj', False)
self.grid_global = global_decoder.get('grid_global', 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.Sequential(
nn.Linear(modulation_dim, modulation_dim)
)
self.output_proj = nn.Sequential(
nn.Linear(3, 3)
)
def query_rgb(self, coord, cell=None):
feat = self.feat
if self.imnet is None:
ret = F.grid_sample(feat, coord.flip(-1).unsqueeze(1),
mode='nearest', align_corners=False)[:, :, 0, :] \
.permute(0, 2, 1)
return ret
if self.feat_unfold:
# if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2)
feat = F.unfold(feat, 3, padding=1).view(
feat.shape[0], feat.shape[1] * 9, feat.shape[2], feat.shape[3])
if self.local_ensemble:
vx_lst = [-1, 1]
vy_lst = [-1, 1]
eps_shift = 1e-6
else:
vx_lst, vy_lst, eps_shift = [0], [0], 0
# field radius (global: [-1, 1])
rx = 2 / feat.shape[-2] / 2
ry = 2 / feat.shape[-1] / 2
feat_coord = make_coord(feat.shape[-2:], flatten=False).cuda() \
.permute(2, 0, 1) \
.unsqueeze(0).expand(feat.shape[0], 2, *feat.shape[-2:])
preds = []
areas = []
for vx in vx_lst:
for vy in vy_lst:
coord_ = coord.clone()
coord_[:, :, 0] += vx * rx + eps_shift
coord_[:, :, 1] += vy * ry + eps_shift
coord_.clamp_(-1 + 1e-6, 1 - 1e-6)
q_feat = F.grid_sample(
feat, coord_.flip(-1).unsqueeze(1),
mode='nearest', align_corners=False)[:, :, 0, :] \
.permute(0, 2, 1)
q_coord = F.grid_sample(
feat_coord, coord_.flip(-1).unsqueeze(1),
mode='nearest', align_corners=False)[:, :, 0, :] \
.permute(0, 2, 1)
rel_coord = coord - q_coord
rel_coord[:, :, 0] *= feat.shape[-2]
rel_coord[:, :, 1] *= feat.shape[-1]
inp = torch.cat([q_feat, rel_coord], dim=-1)
if self.cell_decode:
rel_cell = cell.clone()
rel_cell[:, :, 0] *= feat.shape[-2]
rel_cell[:, :, 1] *= feat.shape[-1]
inp = torch.cat([inp, rel_cell], dim=-1)
bs, q = coord.shape[:2]
pred = self.imnet(inp.view(bs * q, -1)).view(bs, q, -1)
preds.append(pred)
area = torch.abs(rel_coord[:, :, 0] * rel_coord[:, :, 1])
areas.append(area + 1e-9)
tot_area = torch.stack(areas).sum(dim=0)
if self.local_ensemble:
t = areas[0]; areas[0] = areas[3]; areas[3] = t
t = areas[1]; areas[1] = areas[2]; areas[2] = t
ret = 0
for pred, area in zip(preds, areas):
ret = ret + pred * (area / tot_area).unsqueeze(-1)
return ret
def forward_step(self,
ori_img,
coord,
func_map,
global_content,
pred_rgb_value=None,
scale_ratio=None
):
weight_gen_func = 'bilinear' # 'bilinear'
# grid： 先x再y
coord_ = coord.clone().unsqueeze(1).flip(-1) # Bx1xNxC
funcs = F.grid_sample(
func_map, coord_, padding_mode='border', mode=weight_gen_func, align_corners=True).squeeze(2) # B C N
funcs = rearrange(funcs, 'B C N -> (B N) C')
feat_coord = to_coordinates(func_map.shape[-2:], return_map=True).to(func_map.device)
feat_coord = repeat(feat_coord, 'H W C -> B C H W', B=coord.size(0)) # 坐标是[y, x]
nearest_coord = F.grid_sample(feat_coord, coord_, mode='nearest', align_corners=True).squeeze(2) # B 2 N
nearest_coord = rearrange(nearest_coord, 'B C N -> B N C') # B N 2
relative_coord = coord - nearest_coord
relative_coord[:, :, 0] *= func_map.shape[-2]
relative_coord[:, :, 1] *= func_map.shape[-1]
relative_coord = rearrange(relative_coord, 'B N C -> (B N) C')
decoder_input = relative_coord
interpolated_rgb = None
if self.input_rgb:
if pred_rgb_value is not None:
interpolated_rgb = rearrange(pred_rgb_value, 'B N C -> (B N) C')
else:
interpolated_rgb = F.grid_sample(
ori_img, coord_, padding_mode='border', mode='bilinear', align_corners=True).squeeze(2) # B 3 N
interpolated_rgb = rearrange(interpolated_rgb, 'B C N -> (B N) C')
decoder_input = torch.cat((decoder_input, interpolated_rgb), dim=-1)
if self.encode_scale_ratio:
scale_ratio = rearrange(scale_ratio, 'B N C -> (B N) C')
decoder_input = torch.cat((decoder_input, scale_ratio), dim=-1)
decoder_output = self.decoder(decoder_input, funcs)
decoder_output = rearrange(decoder_output, '(B N) C -> B N C', B=func_map.size(0))
if hasattr(self, 'global_decoder'):
# coord: BxNx2
# global_content: Bx1xC
if self.decoder_is_proj:
global_content = self.input_proj(global_content) # B 1 C
global_funcs = repeat(global_content, 'B C -> B N C', N=coord.size(1))
global_funcs = rearrange(global_funcs, 'B N C -> (B N) C')
if self.grid_global:
global_decoder_input = decoder_input
else:
global_decoder_input = rearrange(coord, 'B N C -> (B N) C')
if self.input_rgb:
global_decoder_input = torch.cat((global_decoder_input, interpolated_rgb), dim=-1)
if self.encode_scale_ratio:
global_decoder_input = torch.cat((global_decoder_input, scale_ratio), dim=-1)
global_decoder_output = self.global_decoder(global_decoder_input, global_funcs)
global_decoder_output = rearrange(global_decoder_output, '(B N) C -> B N C', B=func_map.size(0))
if self.decoder_is_proj:
decoder_output = self.output_proj(global_decoder_output + decoder_output)
else:
decoder_output = global_decoder_output + decoder_output
return decoder_output
def forward_backbone(self, inp):
# inp: img-BxCxHxW
return self.encoder(inp)
def forward_multiscale(self, feats, keep_ori_featmap=False):
if keep_ori_featmap:
output_feats = feats
else:
output_feats = []
x = feats[0]
for layer in self.multiscale_layers:
x = layer(x)
output_feats.append(x)
return output_feats
def forward(self, inp, coord, scale_ratio=None):
output_feats = [self.forward_backbone(inp)]
if self.multiscale:
output_feats = self.forward_multiscale(output_feats)
if hasattr(self, 'neck'):
global_content, func_maps = self.neck(output_feats)
else:
global_content = None
func_maps = output_feats[0]
pred_rgb_value = None
return_pred_rgb_value = []
for n_time in range(self.n_forward_times):
pred_rgb_value = self.forward_step(inp, coord, func_maps, global_content, pred_rgb_value, scale_ratio)
return_pred_rgb_value.append(pred_rgb_value)
return return_pred_rgb_value
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