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FRVSRGAN_Models.py
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FRVSRGAN_Models.py
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"""
This file contains implementation of FRVSR (FNet and SRNet) from https://arxiv.org/abs/1801.04590
Aman Chadha | aman@amanchadha.com
Adapted from FR-SRGAN, MIT 6.819 Advances in Computer Vision, Nov 2018
"""
import unittest
import torch
import torch.nn as nn
import torch.nn.functional as func
from torchvision.models import vgg16
class ResBlock(nn.Module):
def __init__(self, conv_dim):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(in_channels=conv_dim, out_channels=conv_dim,
kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=conv_dim, out_channels=conv_dim,
kernel_size=3, stride=1, padding=1)
def forward(self, input):
out = self.conv1(input)
out = func.relu(out)
out = self.conv2(out)
out = input + out
return out
class ConvLeaky(nn.Module):
def __init__(self, in_dim, out_dim):
super(ConvLeaky, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_dim, out_channels=out_dim,
kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=out_dim, out_channels=out_dim,
kernel_size=3, stride=1, padding=1)
def forward(self, input):
out = self.conv1(input)
out = func.leaky_relu(out, 0.2)
out = self.conv2(out)
out = func.leaky_relu(out, 0.2)
return out
class FNetBlock(nn.Module):
def __init__(self, in_dim, out_dim, typ):
super(FNetBlock, self).__init__()
self.convleaky = ConvLeaky(in_dim, out_dim)
if typ == "maxpool":
self.final = lambda x: func.max_pool2d(x, kernel_size=2)
elif typ == "bilinear":
self.final = lambda x: func.interpolate(x, scale_factor=2, mode="bilinear")
else:
raise Exception('Type does not match any of maxpool or bilinear')
def forward(self, input):
out = self.convleaky(input)
out = self.final(out)
return out
class SRNet(nn.Module):
def __init__(self, in_dim=51):
super(SRNet, self).__init__()
self.inputConv = nn.Conv2d(in_channels=in_dim, out_channels=64, kernel_size=3, stride=1, padding=1)
self.ResBlocks = nn.Sequential(*[ResBlock(64) for i in range(10)])
self.deconv1 = nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=3,
stride=2, padding=1, output_padding=1)
self.deconv2 = nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=3,
stride=2, padding=1, output_padding=1)
self.outputConv = nn.Conv2d(in_channels=64, out_channels=3, kernel_size=3, stride=1, padding=1)
# self.dropout = nn.Dropout(p = 0.5)
def forward(self, input):
out = self.inputConv(input)
out = self.ResBlocks(out)
out = self.deconv1(out)
out = func.relu(out)
out = self.deconv2(out)
out = func.relu(out)
out = self.outputConv(out)
return out
class FNet(nn.Module):
def __init__(self, in_dim=6):
super(FNet, self).__init__()
self.convPool1 = FNetBlock(in_dim, 32, typ="maxpool")
self.convPool2 = FNetBlock(32, 64, typ="maxpool")
self.convPool3 = FNetBlock(64, 128, typ="maxpool")
self.convBinl1 = FNetBlock(128, 256, typ="bilinear")
self.convBinl2 = FNetBlock(256, 128, typ="bilinear")
self.convBinl3 = FNetBlock(128, 64, typ="bilinear")
self.seq = nn.Sequential(self.convPool1, self.convPool2, self.convPool3,
self.convBinl1, self.convBinl2, self.convBinl3)
self.conv1 = nn.Conv2d(in_channels=64, out_channels=32, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=2, kernel_size=3, stride=1, padding=1)
def forward(self, input):
out = self.seq(input)
out = self.conv1(out)
out = func.leaky_relu(out, 0.2)
out = self.conv2(out)
self.out = torch.tanh(out)
self.out.retain_grad()
return self.out
# please ensure that input is (batch_size, depth, height, width)
# courtesy to Hung Nguyen at https://gist.github.com/jalola/f41278bb27447bed9cd3fb48ec142aec.
class SpaceToDepth(nn.Module):
def __init__(self, block_size):
super(SpaceToDepth, self).__init__()
self.block_size = block_size
self.block_size_sq = block_size * block_size
def forward(self, input):
output = input.permute(0, 2, 3, 1)
(batch_size, s_height, s_width, s_depth) = output.size()
d_depth = s_depth * self.block_size_sq
d_width = int(s_width / self.block_size)
d_height = int(s_height / self.block_size)
t_1 = output.split(self.block_size, 2)
stack = [t_t.reshape(batch_size, d_height, d_depth) for t_t in t_1]
output = torch.stack(stack, 1)
output = output.permute(0, 2, 1, 3)
output = output.permute(0, 3, 1, 2)
return output
# please ensure that lr_height and lr_width must be a multiple of 8.
class FRVSR(nn.Module):
def __init__(self, batch_size, lr_height, lr_width):
super(FRVSR, self).__init__()
FRVSR.SRFactor = 4
self.width = lr_width
self.height = lr_height
self.batch_size = batch_size
self.fnet = FNet()
self.todepth = SpaceToDepth(FRVSR.SRFactor)
self.srnet = SRNet(FRVSR.SRFactor * FRVSR.SRFactor * 3 + 3) # 3 is channel number
# make sure to call this before every batch train.
def init_hidden(self, device):
self.lastLrImg = torch.zeros([self.batch_size, 3, self.height, self.width]).to(device)
self.EstHrImg = torch.zeros([self.batch_size, 3, self.height * FRVSR.SRFactor, self.width * FRVSR.SRFactor]).to(device)
height_gap = 2 / (self.height - 1)
width_gap = 2 / (self.width - 1)
height, width = torch.meshgrid([torch.range(-1, 1, height_gap), torch.range(-1, 1, width_gap)])
self.lr_identity = torch.stack([width, height]).to(device)
height_gap = 2 / (self.height * self.SRFactor - 1)
width_gap = 2 / (self.width * self.SRFactor - 1)
height, width = torch.meshgrid([torch.range(-1, 1, height_gap), torch.range(-1, 1, width_gap)])
self.hr_identity = torch.stack([width, height]).to(device)
# x is a 4-d tensor of shape N×C×H×W
def forward(self, input):
# Apply FNet
# print(f'input.shape is {input.shape}, lastImg shape is {self.lastLrImg.shape}')
preflow = torch.cat((input, self.lastLrImg), dim=1)
flow = self.fnet(preflow)
relative_place = flow + self.lr_identity
self.EstLrImg = func.grid_sample(self.lastLrImg, relative_place.permute(0, 2, 3, 1))
# print(self.EstLrImg)
relative_placeNCHW = func.interpolate(relative_place, scale_factor=4, mode="bilinear")
relative_placeNWHC = relative_placeNCHW.permute(0, 2, 3, 1) # switch to channels-last notation for grid_sample()
afterWarp = func.grid_sample(self.EstHrImg, relative_placeNWHC)
self.afterWarp = afterWarp # for debugging, should be removed later.
depthImg = self.todepth(afterWarp)
# Apply SRNet
srInput = torch.cat((input, depthImg), dim=1)
estImg = self.srnet(srInput)
self.lastLrImg = input
self.EstHrImg = estImg
self.EstHrImg.retain_grad()
return self.EstHrImg, self.EstLrImg
def set_param(self, **kwargs):
for (key, val) in kwargs.items():
if key == 'batch_size':
self.batch_size = val
if key == 'height':
self.height = val
if key == 'width':
self.width = val
class Loss(nn.Module):
def __init__(self):
super(Loss, self).__init__()
vgg = vgg16(pretrained=True)
loss_network = nn.Sequential(*list(vgg.features)[:31]).eval()
for param in loss_network.parameters():
param.requires_grad = False
self.loss_network = loss_network
self.mse_loss = nn.MSELoss()
self.tv_loss = TVLoss()
def forward(self, out_images, target_images):
# Adversarial Loss
# adversarial_loss = torch.mean(1 - out_labels)
# Perception Loss
perception_loss = self.mse_loss(self.loss_network(out_images), self.loss_network(target_images))
# Image Loss
image_loss = self.mse_loss(out_images, target_images)
# TV Loss
tv_loss = self.tv_loss(out_images)
return image_loss + 0.006 * perception_loss + 2e-8 * tv_loss
class GeneratorLoss(nn.Module):
def __init__(self):
super(GeneratorLoss, self).__init__()
vgg = vgg16(pretrained=True)
loss_network = nn.Sequential(*list(vgg.features)[:31]).eval()
for param in loss_network.parameters():
param.requires_grad = False
self.loss_network = loss_network
self.mse_loss = nn.MSELoss()
self.tv_loss = TVLoss()
def forward(self, out_labels, hr_est, hr_img, lr_est, lr_img, idx):
# Adversarial Loss
adversarial_loss = -torch.mean(out_labels)
# Perception Loss
perception_loss = self.mse_loss(self.loss_network(hr_est), self.loss_network(hr_img))
# Image Loss
image_loss = self.mse_loss(hr_est, hr_img)
# TV Loss
tv_loss = self.tv_loss(hr_est)
# flow loss
if idx != 0:
flow_loss = self.mse_loss(lr_est, lr_img)
else:
flow_loss = 0
return image_loss + 0.001 * adversarial_loss + 0.006 * perception_loss + 2e-8 * tv_loss + 0.0001 * flow_loss
class TVLoss(nn.Module):
def __init__(self, tv_loss_weight=1):
super(TVLoss, self).__init__()
self.tv_loss_weight = tv_loss_weight
def forward(self, x):
batch_size = x.size()[0]
h_x = x.size()[2]
w_x = x.size()[3]
count_h = self.tensor_size(x[:, :, 1:, :])
count_w = self.tensor_size(x[:, :, :, 1:])
h_tv = torch.pow((x[:, :, 1:, :] - x[:, :, :h_x - 1, :]), 2).sum()
w_tv = torch.pow((x[:, :, :, 1:] - x[:, :, :, :w_x - 1]), 2).sum()
return self.tv_loss_weight * 2 * (h_tv / count_h + w_tv / count_w) / batch_size
@staticmethod
def tensor_size(t):
return t.size()[1] * t.size()[2] * t.size()[3]
#
# if __name__ == "__main__":
# g_loss = GeneratorLoss()
# print(g_loss)
# class FRVSR_Criterion(torch.autograd.Function):
# def __init__(self):
# super(FRVSR_Criterion, self).__init__()
#
# def forward(self, lr_est, lr_img, hr_est, hr_img):
# #= input[0], input[1], input[2], input[3]
# assert (lr_est.shape == lr_img.shape)
# assert (hr_est.shape == hr_img.shape)
# return nn.MSELoss(lr_est, lr_img) + nn.MSELoss(hr_est, hr_img)
# run tests make sure that output is correct.
class TestFRVSR(unittest.TestCase):
def testResBlock(self):
block = ResBlock(3)
input = torch.rand(2, 3, 64, 112)
output = block(input)
self.assertEqual(input.shape, output.shape)
def testConvLeaky(self):
block = ConvLeaky(3, 32)
input = torch.rand(2, 3, 64, 112)
output = block(input)
self.assertEqual(output.shape, torch.empty(2, 32, 64, 112).shape)
def testFNetBlockMaxPool(self):
block = FNetBlock(3, 32, "maxpool")
input = torch.rand(2, 3, 64, 112)
output = block(input)
self.assertEqual(output.shape, torch.empty(2, 32, 32, 56).shape)
def testFNetBlockInterPolate(self):
block = FNetBlock(3, 32, "bilinear")
input = torch.rand(2, 3, 32, 56)
output = block(input)
self.assertEqual(output.shape, torch.empty(2, 32, 64, 112).shape)
def testSRNet(self):
block = SRNet()
input = torch.rand(2, 51, 32, 56)
output = block(input)
self.assertEqual(output.shape, torch.empty(2, 3, 128, 224).shape)
block = SRNet()
input = torch.rand(2, 51, 64, 64)
output = block(input)
self.assertEqual(output.shape, torch.empty(2, 3, 256, 256).shape)
def testFNet(self):
block = FNet()
input = torch.rand(2, 6, 32, 56)
output = block(input)
self.assertEqual(output.shape, torch.empty(2, 2, 32, 56).shape)
def testFRVSR(self):
H = 16
W = 16
block = FRVSR(4, H, W)
input = torch.rand(7, 4, 3, H, W)
block.init_hidden(torch.device("cuda:0" if torch.cuda.is_available() else "cpu"))
for batch_frames in input:
output1, output2 = block(batch_frames)
self.assertEqual(output1.shape, torch.empty(4, 3, H * 4, W * 4).shape)
self.assertEqual(output2.shape, torch.empty(4, 3, H, W).shape)
# def testCriterion(self):
# H = 16
# W = 16
# input = torch.rand(7, 4, 3, H, W)
# output = torch.rand(4, 3, H * 4, W * 4)
# criterion = FRVSR_Criterion()
# self.assertIsInstance(criterion(input, input, output, output), type(0.1))
if __name__ == '__main__':
unittest.main()