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from model import net
import torch.nn as nn
import torch
import torch.nn.functional as F
from torchvision.transforms import Resize
try:
from resize_right import resize
except:
from .resize_right import resize
try:
from .interp_methods import *
except:
from interp_methods import *
from torchvision.models import vgg19
from torchvision.models.feature_extraction import create_feature_extractor
import tinycudann as tcnn
from torchvision.utils import save_image
import torchvision.transforms as transforms
from torchviz import make_dot
def make_coord(shape, ranges=None, flatten=True):
""" Make coordinates at grid centers.
"""
coord_seqs = []
for i, n in enumerate(shape):
if ranges is None:
v0, v1 = -1, 1
else:
v0, v1 = ranges[i]
r = (v1 - v0) / (2 * n)
seq = v0 + r + (2 * r) * torch.arange(n).float()
coord_seqs.append(seq)
ret = torch.stack(torch.meshgrid(*coord_seqs), dim=-1)
if flatten:
ret = ret.view(-1, ret.shape[-1])
return ret
def get_local_grid(img):
local_grid = make_coord(img.shape[-2:], flatten=False).cuda()
local_grid = local_grid.permute(2, 0, 1).unsqueeze(0)
local_grid = local_grid.expand(img.shape[0], 2, *img.shape[-2:])
return local_grid
def creat_coord(x):
b = x.shape[0]
coord = make_coord(x.shape[-2:], flatten=False)
coord = coord.permute(2, 0, 1).contiguous().unsqueeze(0)
coord = coord.expand(b, 2, *coord.shape[-2:])
coord_ = coord.clone()
coord_ = coord_.clamp_(-1 + 1e-6, 1 - 1e-6)
coord_ = coord_.permute(0, 2, 3, 1).contiguous()
coord_ = coord_.view(b, -1, coord.size(1))
return coord.cuda(), coord_.cuda()
def get_cell(img, local_grid):
cell = torch.ones_like(local_grid)
cell[:, 0] *= 2 / img.size(2)
cell[:, 1] *= 2 / img.size(3)
return cell
class TcnnFCBlock(tcnn.Network):
def __init__(
self, in_features, out_features,
num_hidden_layers, hidden_features,
activation: str = 'LeakyRelu', last_activation: str = 'None',
seed=42):
assert hidden_features in [16, 32, 64, 128], "hidden_features can only be 16, 32, 64, or 128."
super().__init__(in_features, out_features, network_config={
"otype": "FullyFusedMLP", # Component type.
"activation": activation, # Activation of hidden layers.
"output_activation": last_activation, # Activation of the output layer.
"n_neurons": hidden_features, # Neurons in each hidden layer. # May only be 16, 32, 64, or 128.
"n_hidden_layers": num_hidden_layers, # Number of hidden layers.
}, seed=seed)
def forward(self, x: torch.Tensor):
prefix = x.shape[:-1]
return super().forward(x.flatten(0, -2)).unflatten(0, prefix)
class LMAR_model(nn.Module):
def __init__(self, args):
super().__init__()
self.resume_flag = args.resume["flag"]
self.load_path = args.resume["checkpoint"]
if self.resume_flag and self.load_path:
self.model = net(args)
checkpoint = torch.load(self.load_path)
self.model.load_state_dict(checkpoint["state_dict"])
for param in self.model.parameters():
param.requires_grad_(False)
self.in_channel = 3
self.out_channel = 3
self.kernel_size = 3
self.imnet = TcnnFCBlock(7, self.in_channel * self.out_channel * self.kernel_size * self.kernel_size, 5,
128).cuda()
self.mid_nodes = {"hr_backbone.skip2": "bottom"}
self.extractor_mid = create_feature_extractor(self.model, self.mid_nodes)
self.modulation = nn.Conv2d(6, 3, 1, 1, 0)
# self.projection = nn.Conv2d()
def forward(self, x, down_size, up_size, test_flag=False):
if test_flag:
up_out, _ = self.inference(x, down_size, up_size)
return up_out, _
else:
down_x, hr_feature, new_lr_feature, ori_lr_feature, residual, res = self.train_model(x, down_size, up_size)
return down_x, hr_feature, new_lr_feature, ori_lr_feature, residual, res
def train_model(self, x, down_size, up_size):
# down_sizer = transforms.Resize(size=down_size,
# interpolation=transforms.InterpolationMode.BILINEAR)
# up_sizer = transforms.Resize(size=up_size,
# interpolation=transforms.InterpolationMode.BILINEAR)
b = x.shape[0]
# down_x = down_sizer(x)
down_x = resize(x, out_shape=down_size, antialiasing=False)
# down_x = resize(x, out_shape=down_size, antialiasing=True)
hr_feature = self.extractor_mid(x)["bottom"]
# feature_sizer = transforms.Resize(size=(hr_feature.shape[2], hr_feature.shape[3]),
# interpolation=transforms.InterpolationMode.BILINEAR)
hr_coord, hr_coord_ = self.creat_coord(x)
lr_coord, _ = self.creat_coord(down_x)
q_coord = F.grid_sample(lr_coord, hr_coord_.flip(-1).unsqueeze(1), mode='nearest', align_corners=False)
q_coord = q_coord.view(b, -1, hr_coord.size(2) * hr_coord.size(3)).permute(0, 2, 1).contiguous()
# test_coord = F.grid_sample(lr_coord, hr_coord.permute(0, 2, 3, 1), mode='bilinear', align_corners=False)
# test_rel_coord = hr_coord - test_coord
# test_rel_coord = test_rel_coord.view(b, -1, 2)
# test_rel_coord[:, :, 0] *= down_x.shape[-2]
# test_rel_coord[:, :, 1] *= down_x.shape[-1]
rel_coord = hr_coord_ - q_coord
rel_coord[:, :, 0] *= down_x.shape[-2]
rel_coord[:, :, 1] *= down_x.shape[-1]
laplacian = x - resize(down_x, out_shape=up_size, antialiasing=False)
# laplacian = x - resize(down_x, out_shape=up_size, antialiasing=True)
laplacian = laplacian.reshape(b, laplacian.size(1), -1).permute(0, 2, 1).contiguous()
# cell
hr_grid = self.get_local_grid(x)
hr_cell = self.get_cell(x, hr_grid)
hr_cell_ = hr_cell.clone()
hr_cell_ = hr_cell_.permute(0, 2, 3, 1).contiguous()
rel_cell = hr_cell_.view(b, -1, hr_cell.size(1))
rel_cell[:, :, 0] *= down_x.shape[-2]
rel_cell[:, :, 1] *= down_x.shape[-1]
inp = torch.cat([rel_coord.cuda(), rel_cell.cuda(), laplacian], dim=-1)
local_weight = self.imnet(inp)
local_weight = local_weight.type(torch.float32)
local_weight = local_weight.view(b, -1, x.shape[1] * 9, 3).contiguous()
unfolded_x = F.unfold(x, 3, padding=1).view(b, -1, x.shape[2] * x.shape[3]).permute(0, 2, 1).contiguous()
cols = unfolded_x.unsqueeze(2)
out = torch.matmul(cols, local_weight).squeeze(2).permute(0, 2, 1).contiguous().view(b, -1, x.size(2),
x.size(3))
out = resize(out, out_shape=down_size, antialiasing=False)
# out = resize(out, out_shape=down_size, antialiasing=True)
# out = down_sizer(out)
# ori
ori_lr_feature = self.extractor_mid(down_x)["bottom"]
ori_lr_feature = resize(ori_lr_feature, out_shape=(hr_feature.shape[2], hr_feature.shape[3]),
antialiasing=False)
# ori_lr_feature = resize(ori_lr_feature, out_shape=(hr_feature.shape[2], hr_feature.shape[3]), antialiasing=True)
# ori_lr_feature = feature_sizer(ori_lr_feature)
# new
down_x = self.modulation(torch.cat([down_x, out], dim=1))
new_lr_feature = self.extractor_mid(down_x)["bottom"]
new_lr_feature = resize(new_lr_feature, out_shape=(hr_feature.shape[2], hr_feature.shape[3]),
antialiasing=False)
# new_lr_feature = resize(new_lr_feature, out_shape=(hr_feature.shape[2], hr_feature.shape[3]), antialiasing=True)
# new_lr_feature = feature_sizer(new_lr_feature)
# res = resize(self.model(self.modulation(torch.cat([down_x, out], dim=1))), out_shape=up_size,
# antialiasing=False)
# res = up_sizer(self.model(self.modulation(torch.cat([down_x, out], dim=1))))
res = 0
return down_x, hr_feature, \
new_lr_feature, ori_lr_feature, out, res
def inference(self, x, down_size, up_size):
b = x.shape[0]
down_x = resize(x, out_shape=down_size, antialiasing=False)
hr_coord, hr_coord_ = self.creat_coord(x)
lr_coord, _ = self.creat_coord(down_x)
q_coord = F.grid_sample(lr_coord, hr_coord_.flip(-1).unsqueeze(1), mode='nearest', align_corners=False)
q_coord = q_coord.view(b, -1, hr_coord.size(2) * hr_coord.size(3)).permute(0, 2, 1).contiguous()
rel_coord = hr_coord_ - q_coord
rel_coord[:, :, 0] *= down_x.shape[-2]
rel_coord[:, :, 1] *= down_x.shape[-1]
hr_grid = self.get_local_grid(x)
hr_cell = self.get_cell(x, hr_grid)
hr_cell_ = hr_cell.clone()
hr_cell_ = hr_cell_.permute(0, 2, 3, 1).contiguous()
rel_cell = hr_cell_.view(b, -1, hr_cell.size(1))
rel_cell[:, :, 0] *= down_x.shape[-2]
rel_cell[:, :, 1] *= down_x.shape[-1]
laplacian = x - resize(down_x, out_shape=up_size, antialiasing=False)
# laplacian = x - resize(down_x, out_shape=up_size, antialiasing=True)
laplacian = laplacian.reshape(b, laplacian.size(1), -1).permute(0, 2, 1).contiguous()
# laplacian = F.unfold(laplacian, 3, padding=1).view(b, -1, laplacian.shape[2] * laplacian.shape[3]).permute(0, 2, 1).contiguous()
inp = torch.cat([rel_coord.cuda(), rel_cell.cuda(), laplacian], dim=-1)
local_weight = self.imnet(inp)
local_weight = local_weight.type(torch.float32)
local_weight = local_weight.view(b, -1, x.shape[1] * 9, 3)
unfolded_x = F.unfold(x, 3, padding=1).view(b, -1, x.shape[2] * x.shape[3]).permute(0, 2, 1).contiguous()
cols = unfolded_x.unsqueeze(2)
out = torch.matmul(cols, local_weight).squeeze(2).permute(0, 2, 1).contiguous().view(b, -1, x.size(2),
x.size(3))
out = resize(out, out_shape=down_size, antialiasing=False)
down_x = self.modulation(torch.cat([down_x, out], dim=1))
res = resize(self.model(down_x), out_shape=up_size, antialiasing=False)
return res, down_x
def creat_coord(self, x):
b = x.shape[0]
coord = make_coord(x.shape[-2:], flatten=False)
coord = coord.permute(2, 0, 1).contiguous().unsqueeze(0)
coord = coord.expand(b, 2, *coord.shape[-2:])
coord_ = coord.clone()
coord_ = coord_.clamp_(-1 + 1e-6, 1 - 1e-6)
coord_ = coord_.permute(0, 2, 3, 1).contiguous()
coord_ = coord_.view(b, -1, coord.size(1))
return coord.cuda(), coord_.cuda()
def get_local_grid(self, img):
local_grid = make_coord(img.shape[-2:], flatten=False).cuda()
local_grid = local_grid.permute(2, 0, 1).unsqueeze(0)
local_grid = local_grid.expand(img.shape[0], 2, *img.shape[-2:])
return local_grid
def get_cell(self, img, local_grid):
cell = torch.ones_like(local_grid)
cell[:, 0] *= 2 / img.size(2)
cell[:, 1] *= 2 / img.size(3)
return cell
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