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fbpn_sr_rbpn_v3.py
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import os
import torch.nn as nn
import torch.optim as optim
from base_networks import *
from torchvision.transforms import *
import torch.nn.functional as F
from rbpn import Net as RBPN
from autoencoder_v4 import UNet
from torch.autograd import Variable
class Net(nn.Module):
def __init__(self, base_filter, feat, num_stages, n_resblock, scale_factor, pretrained=True, freeze=False):
super(Net, self).__init__()
if scale_factor == 2:
kernel = 6
stride = 2
padding = 2
elif scale_factor == 4:
kernel = 8
stride = 4
padding = 2
elif scale_factor == 8:
kernel = 12
stride = 8
padding = 2
#Initial Feature Extraction
self.motion_feat = ConvBlock(4, base_filter, 3, 1, 1, activation='lrelu', norm=None)
###INTERPOLATION
#Interp_block
motion_net = [
ResnetBlock(base_filter, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \
for _ in range(2)]
motion_net.append(ConvBlock(base_filter, feat, 3, 1, 1, activation='lrelu', norm=None))
self.motion = nn.Sequential(*motion_net)
t_net1 = [
ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \
for _ in range(2)]
t_net1.append(ConvBlock(feat*5, feat*2, 3, 1, 1, activation='lrelu', norm=None))
self.t_net_lr = nn.Sequential(*t_net1)
interp_b = [
ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \
for _ in range(n_resblock)]
interp_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None))
self.interp_block = nn.Sequential(*interp_b)
###ITERATIVE REFINEMENT
#Motion Up FORWARD
modules_up_f = [
ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \
for _ in range(n_resblock)]
modules_up_f.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None))
self.motion_up_f = nn.Sequential(*modules_up_f)
#Motion Up BACKWARD
modules_up_b = [
ResnetBlock(feat*5, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \
for _ in range(n_resblock)]
modules_up_b.append(DeconvBlock(feat*5, feat, kernel, stride, padding, activation='lrelu', norm=None))
self.motion_up_b = nn.Sequential(*modules_up_b)
#Motion Down
modules_down = [
ResnetBlock(feat, kernel_size=3, stride=1, padding=1, bias=True, activation='lrelu', norm=None) \
for _ in range(2)]
modules_down.append(ConvBlock(feat, feat*2, kernel, stride, padding, activation='lrelu', norm=None))
self.motion_down = nn.Sequential(*modules_down)
self.relu_bp = torch.nn.LeakyReLU(negative_slope=0.1, inplace=True)#torch.nn.PReLU()
#Reconstruction
self.reconstruction_l = ConvBlock(feat*2, 3, 3, 1, 1, activation=None, norm=None)
self.reconstruction_h = ConvBlock(feat, 3, 3, 1, 1, activation=None, norm=None)
####ALIGNMENT
###RBPN
self.RBPN = RBPN(num_channels=3, base_filter=base_filter, feat = feat, num_stages=num_stages, n_resblock=5, nFrames=2, scale_factor=scale_factor)
for m in self.modules():
classname = m.__class__.__name__
if classname.find('Conv2d') != -1:
torch.nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.zero_()
elif classname.find('ConvTranspose2d') != -1:
torch.nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.zero_()
if pretrained:
if scale_factor == 4:
self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_4x.pth", map_location=lambda storage, loc: storage))
elif scale_factor == 2:
self.RBPN.load_state_dict(torch.load("weights/pretrained/rbpn_pretrained_F2_2x.pth", map_location=lambda storage, loc: storage))
if freeze:
self.freeze_model(self.RBPN)
def freeze_model(self, model):
for child in model.children():
for param in child.parameters():
param.requires_grad = False
def forward(self, t_im1, t_im2, t_flow_f, t_flow_b, train=True):
result_l = []
result_h1 = []
result_ht = []
result_h2 = []
###ALIGNMENT
aux_H1, H1 = self.RBPN(t_im1,[t_im2],[t_flow_f])
aux_H2, H2 = self.RBPN(t_im2,[t_im1],[t_flow_b])
L1 = self.motion_down(H1)
L2 = self.motion_down(H2)
###MOTION & DEPTH
motion_feat0 = self.motion_feat(torch.cat((t_flow_f, t_flow_b),1))
M = self.motion(motion_feat0)
motion_feat1 = self.motion_feat(torch.cat((t_flow_f/2.0, t_flow_b/2.0),1))
M_half = self.motion(motion_feat1)
###INTERPOLATION
Ht = self.interp_block(torch.cat((L1,L2,M),1))
L = self.motion_down(Ht)
L = L + self.relu_bp(L - self.t_net_lr(torch.cat((L1,L2,M),1)))
aux_Ht = self.reconstruction_h(Ht)
aux_L = self.reconstruction_l(L)
result_l.append(aux_L)
result_h1.append(aux_H1)
result_ht.append(aux_Ht)
result_h2.append(aux_H2)
####Projection
backward1 = torch.cat((L1, L, M_half),1)
H_b = self.motion_up_b(backward1)
H1 = H1 + self.relu_bp(H1 - H_b)
L1 = L1 + self.relu_bp(L1 - self.motion_down(H_b))
forwardd2 = torch.cat((L, L2, M_half),1)
H_f = self.motion_up_f(forwardd2)
H2 = H2 + self.relu_bp(H2 - H_f)
L2 = L2 + self.relu_bp(L2 - self.motion_down(H_f))
forwardd = torch.cat((L1, L, M_half),1)
H_t_f = self.motion_up_f(forwardd)
Ht = Ht + self.relu_bp(Ht - H_t_f)
L = L + self.relu_bp(L - self.motion_down(H_t_f))
backward = torch.cat((L, L2, M_half),1)
H_t_b = self.motion_up_b(backward)
Ht = Ht + self.relu_bp(Ht - H_t_b)
L = L + self.relu_bp(L - self.motion_down(H_t_b))
output_ht = self.reconstruction_h(Ht)
output_h1 = self.reconstruction_h(H1)
output_h2 = self.reconstruction_h(H2)
output_l = self.reconstruction_l(L)
result_l.append(output_l)
result_h1.append(output_h1)
result_ht.append(output_ht)
result_h2.append(output_h2)
if train:
return result_ht, result_h1, result_h2, result_l
else:
return output_ht, output_h1, output_h2, output_l
class FeatureExtractor(nn.Module):
def __init__(self, cnn, feature_layer=35):
super(FeatureExtractor, self).__init__()
self.features = nn.Sequential(*list(cnn.features.children())[:(feature_layer+1)])
def forward(self, x):
return self.features(x)