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video_f2f_offline_with_teacher.py
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val = 800 #images will be cropped [:val, :val] in order to reduce the memory.
import argparse
import iio
import numpy as np
import os
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
from scipy.ndimage.morphology import binary_dilation
from model.models import FastDVDnet
from functions import *
cuda = torch.cuda.is_available()
if cuda:
device = torch.device('cuda')
print("CUDA is available")
#interp = 'bilinear'
interp = 'bicubic'
class Loss(nn.Module):
fff = 0
def __init__(self):
super(Loss, self).__init__()
self.criterion = nn.L1Loss(reduce=True)
def cubic_interpolation(self, A, B, C, D, x):
a,b,c,d = A.size()
x = x.view(a,1,c,d).repeat(1,3,1,1)
return B + 0.5*x*(C - A + x*(2.*A - 5.*B + 4.*C - D + x*(3.*(B - C) + D - A)))
def bicubic_interpolation(self, im, grid):
# Assume B == 1
B, C, H, W = im.size()
x0 = torch.floor(grid[0, 0, :, :] - 1).long()
y0 = torch.floor(grid[0, 1, :, :] - 1).long()
x1 = x0 + 1
y1 = y0 + 1
x2 = x0 + 2
y2 = y0 + 2
x3 = x0 + 3
y3 = y0 + 3
outsideX = torch.max(torch.gt(-x0, 0), torch.gt(x3, W-1)).repeat((B,C,1,1))
outsideY = torch.max(torch.gt(-y0, 0), torch.gt(y3, H-1)).repeat((B,C,1,1))
mask = torch.Tensor(np.ones((B, C, H, W))).cuda()
mask[outsideX] = 0
mask[outsideY] = 0
x0 = x0.clamp(0, W-1)
y0 = y0.clamp(0, H-1)
x1 = x1.clamp(0, W-1)
y1 = y1.clamp(0, H-1)
x2 = x2.clamp(0, W-1)
y2 = y2.clamp(0, H-1)
x3 = x3.clamp(0, W-1)
y3 = y3.clamp(0, H-1)
A = self.cubic_interpolation(im[:, :, y0, x0], im[:, :, y1, x0], im[:, :, y2, x0],
im[:, :, y3, x0], grid[:, 1, :, :] - torch.floor(grid[:, 1, :, :]))
B = self.cubic_interpolation(im[:, :, y0, x1], im[:, :, y1, x1], im[:, :, y2, x1],
im[:, :, y3, x1], grid[:, 1, :, :] - torch.floor(grid[:, 1, :, :]))
C = self.cubic_interpolation(im[:, :, y0, x2], im[:, :, y1, x2], im[:, :, y2, x2],
im[:, :, y3, x2], grid[:, 1, :, :] - torch.floor(grid[:, 1, :, :]))
D = self.cubic_interpolation(im[:, :, y0, x3], im[:, :, y1, x3], im[:, :, y2, x3],
im[:, :, y3, x3], grid[:, 1, :, :] - torch.floor(grid[:, 1, :, :]))
return self.cubic_interpolation(A, B, C, D, grid[:, 0, :, :] - torch.floor(grid[:, 0, :, :])), mask
# Function heavily inspired by the code for PWCnet by JinWei GU and Zhile Ren available at https://github.com/NVlabs/PWC-Net
# Warps the image 'x' using the optical flow defined by 'flo'
def warp(self, x, flo):
"""
warp an image/tensor (im2) back to im1, according to the optical flow
x: [B, C, H, W] (im2)
flo: [B, 2, H, W] flow
"""
B, C, H, W = x.size()
# mesh grid
xx = torch.arange(0, W).view(1, -1).repeat(H, 1)
yy = torch.arange(0, H).view(-1, 1).repeat(1, W)
xx = xx.view(1, 1, H, W).repeat(B, 1, 1, 1)
yy = yy.view(1, 1, H, W).repeat(B, 1, 1, 1)
grid = torch.cat((xx, yy), 1).float()
grid = grid.cuda()
vgrid = Variable(grid) + flo.cuda()
if interp == 'bicubic':
output, mask = self.bicubic_interpolation(x, vgrid)
else: # if interp == 'bilinear':
# scale grid to [-1,1]
vgrid[:, 0, :, :] = 2.0*vgrid[:, 0, :, :]/max(W-1, 1)-1.0
vgrid[:, 1, :, :] = 2.0*vgrid[:, 1, :, :]/max(H-1, 1)-1.0
vgrid = vgrid.permute(0, 2, 3, 1)
output = nn.functional.grid_sample(x, vgrid)
# Define a first mask containing pixels that wasn't properly interpolated
mask = torch.autograd.Variable(torch.ones(x.size()))
mask = nn.functional.grid_sample(mask.cuda(), vgrid)
mask[mask < 0.9999] = 0
mask[mask > 0] = 1
return output, mask
# Computes the occlusion map based on the optical flow
def occlusion_mask(self, warped, of, old_mask):
"""
Computes an occlusion mask based on the optical flow
warped: [B, C, H, W] warped frame (only used for size)
of: [B, 2, H, W] flow
old_mask: [B, C, H, W] first estimate of the mask
"""
B,C,H,W = warped.size() # Suppose B==1
a = np.zeros((1,1,H,W))
b = np.zeros((1,1,H,W))
a[:, :, :-1, :] = (of[0, 0, 1:, :] - of[0, 0, :-1, :])
b[:, :, :, :-1] = (of[0, 1, :, 1:] - of[0, 1, :, :-1])
mask = np.abs(a + b) > 0.75
#mask = old_mask[0,0,:,:].detach().cpu().numpy()
#mask = 1 - mask
#mask = np.expand_dims(mask,0)
#mask = np.expand_dims(mask,0)
if interp == 'bicubic':
# Slighlty dilates the occlusion map to remove pixels estimated with wrong values
# bicubic interpolation uses a 4x4 kernel
boule = np.ones((4, 4))
mask[0, 0, :, :] = binary_dilation(mask[0, 0, :, :], boule)
# Remove the boundaries (values extrapolated on the boundaries)
mask[:, :, 1, :] = 1
mask[:, :, mask.shape[2]-2, :] = 1
mask[:, :, :, 1] = 1
mask[:, :, :, mask.shape[3]-2] = 1
mask[:, :, 0, :] = 1
mask[:, :, mask.shape[2]-1, :] = 1
mask[:, :, :, 0] = 1
mask[:, :, :, mask.shape[3]-1] = 1
else:
# Slighlty dilates the occlusion map to remove pixels estimated with wrong values
# bilinear interpolation uses a 2x2 kernel
boule = np.ones((2, 2))
mask[0, 0, :, :] = binary_dilation(mask[0, 0, :, :], boule)
# Remove the boundaries (values extrapolated on the boundaries)
mask[:, :, 0, :] = 1
mask[:, :, mask.shape[2]-1, :] = 1
mask[:, :, :, 0] = 1
mask[:, :, :, mask.shape[3]-1] = 1
# Invert the mask because we want a mask of good pixels
mask = torch.Tensor(1-mask).cuda()
mask = mask.view(1,1,H,W).repeat(1,C,1,1)
mask = old_mask * mask
return mask
#return torch.ones(warped.size()).cuda()
def forward(self, input1, input2, prev_frame1, flow1, mask1_0, exclusive_mask1, fastdvdnet):
prev1 = prev_frame1.unsqueeze(0)
# Warp input on target
warped1, mask1 = self.warp(input1, flow1)
# Compute the occlusion mask
mask1 = self.occlusion_mask(warped1, flow1, mask1)
Mask1 = mask1_0*mask1
warp1, m1 = self.warp(input2, flow1)
m1 = self.occlusion_mask(warp1, flow1, m1)
wfastdvdnet, m11 = self.warp(fastdvdnet, flow1)
m11 = self.occlusion_mask(wfastdvdnet, flow1, m11)
m1 = m1 * m11
self.loss = self.criterion(exclusive_mask1*Mask1*warped1, exclusive_mask1*Mask1*prev1) + self.criterion( (m1*exclusive_mask1*(1.-Mask1)*warp1), (m1*exclusive_mask1*(1.-Mask1)*wfastdvdnet) )
return self.loss
def MF2F(**args):
"""
Main function
args: Parameters
"""
################
# LOAD THE MODEL
################
if args['network'] == "model/model.pth":
print("Loading model a pre-trained FastDVDnet \n")
model = FastDVDnet( num_input_frames=5)
#Load saved weights
state_temp_dict = torch.load(args['network'])
if cuda:
device = torch.device("cuda")
device_ids = [0]
model = nn.DataParallel(model, device_ids = device_ids).cuda()
model.load_state_dict(state_temp_dict)
else:
model_fn = args['network']
model_fn = os.path.join(os.path.abspath(os.path.dirname(__file__)), model_fn)
model = torch.load(model_fn)[0]
model.cuda()
device=torch.device("cuda")
#################
# DEFINE THE LOSS
#################
# The loss needs to be changed when used with different networks
lr = args['lr']
weight_decay = 0.00001
criterion = Loss()
criterion.cuda()
optimizer = optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=weight_decay, amsgrad=False)
##### Useful thinks #####
list_PSNR_training = []
list_PSNR_eval = []
#Initialisation
frame = iio.read(args['input'] % (args['first']))
H, W, C = frame.shape
H = H if H < val else val
W = W if W < val else val
noise_std = Variable(torch.FloatTensor(np.array(args['noise_level']/255.)))
noise_map = noise_std.expand((1, 1, H, W))
# Write the psnr per frame in this file
output_path = os.path.dirname(args['output']) +"/"
path_psnr = output_path + "PSNR.txt"
path_ssim = output_path + "SSIM.txt"
path_psnr_tot = output_path + "PSNR_total.txt"
path_ssim_tot = output_path + "SSIM_total.txt"
path_psnr_training = output_path + "PSNR_training.txt"
path_ssim_training = output_path + "SSIM_training.txt"
plot_psnr = open(path_psnr, 'w')
plot_ssim = open(path_ssim, 'w')
plot_psnr_tot = open(path_psnr_tot, 'w')
plot_ssim_tot = open(path_ssim_tot, 'w')
plot_psnr_training = open(path_psnr_training, 'w')
plot_ssim_training = open(path_ssim_training, 'w')
optimizer.zero_grad()
###########
# MAIN LOOP
###########
for training in range(args['nb_trainings']):
i = np.random.randint(args['first']+4, args['last']-3) #index of center frame (ut use for the training)
ut_moins_4 = reads_image(args['input'] % (i-4), H, W)
ut_moins_3 = reads_image(args['input'] % (i-3), H, W)
ut_moins_2 = reads_image(args['input'] % (i-2), H, W)
ut_moins_1 = reads_image(args['input'] % (i-1), H, W)
ut = reads_image(args['input'] % (i), H, W)
ut_plus_1 = reads_image(args['input'] % (i+1), H, W)
ut_plus_2 = reads_image(args['input'] % (i+2), H, W)
ut_plus_3 = reads_image(args['input'] % (i+3), H, W)
ut_plus_4 = reads_image(args['input'] % (i+4), H, W)
#Creation of the stack
inframes = [ut_moins_4, ut_moins_2, ut, ut_plus_2, ut_plus_4]
stack = torch.stack(inframes, dim=0).contiguous().view((1, 5*C, H, W)).cuda()
flow = gives_flow(args['flow'] % (i-1), H, W)
mask, exclusive_mask = gives_masks(args['mask_collision']%(i-1), args['mask_warping_res']%(i-1), H, W)
inframes_ori = [ut_moins_2, ut_moins_1, ut, ut_plus_1, ut_plus_2]
stack_ori = torch.stack(inframes_ori, dim=0).contiguous().view((1, 5*C, H, W)).cuda()
fastdvdnet = iio.read(args['teacher_outputs']%i)[:val, :val] / 255.
fastdvdnet = fastdvdnet.transpose(2,0,1)
fastdvdnet = np.expand_dims(fastdvdnet, 0)
fastdvdnet = torch.tensor(fastdvdnet).cuda()
model.eval()
out_train1 = temp_denoise(model, stack , noise_map)
out_train2 = temp_denoise(model, stack_ori, noise_map)
loss = criterion(out_train1, out_train2, ut_moins_1, flow, mask, exclusive_mask, fastdvdnet)
loss.backward()
## Do the backward and step everyloss.backward()
if training%args['nb_trainings_before_step'] == 0 and training>=1:
optimizer.step()
del loss
optimizer.zero_grad()
color = '\033[1;31;40m'
else:
color = '\033[0;37;40m'
#Compute and save the denoising
model.eval()
with torch.no_grad():
#denoise with the training stack :
outimg = temp_denoise(model, stack, noise_map)
outimg = tensor_to_image(outimg)
#denoise with the natural stack
inframes = [ut_moins_2, ut_moins_1, ut, ut_plus_1, ut_plus_2]
stack = torch.stack(inframes, dim=0).contiguous().view((1, 5*C, H, W)).cuda()
outimg2 = temp_denoise(model, stack, noise_map)
outimg2 = tensor_to_image(outimg2)
# Load frame to compute the PSNR
ref_frame = iio.read(args['ref'] % (i))[:val, :val, :]
# Compute the PSNR according to the reference frame
quant_our_stack = psnr(ref_frame.astype(outimg.dtype)/255, outimg)
quant_Tassano_stack = psnr(ref_frame.astype(outimg2.dtype)/255., outimg2)
if quant_Tassano_stack > quant_our_stack:
value = 1
else:
value = 0
ssim_our = ssim(outimg*255 , ref_frame)
ssim_Tassano = ssim(outimg2*255, ref_frame)
plot_psnr_tot.write(str(quant_Tassano_stack)+'\n')
plot_ssim_tot.write(str(ssim_Tassano)+'\n')
print(color + "Paires = {:03d}-{:03d}, PSNR our stack = {:5.3f}, PSNR Tassano's stack = {:5.3f}, SSIM our {:4.3f}, SSIM Tassano's = {:4.3f} {:1d} {:4d}/{:04d}".format(i-1, i, quant_our_stack, quant_Tassano_stack, ssim_our, ssim_Tassano, value, training, args['nb_trainings']))
## Save the offline fine-tuned network
torch.save([model, optimizer], output_path + "final_mf2f.pth")
print("")
print("")
print(" Process the entire video with the final offline fine-tuned network")
for i in range(args['first']+4, args['last']-3):
ut_moins_4 = reads_image(args['input'] % (i-4), H, W)
ut_moins_3 = reads_image(args['input'] % (i-3), H, W)
ut_moins_2 = reads_image(args['input'] % (i-2), H, W)
ut_moins_1 = reads_image(args['input'] % (i-1), H, W)
ut = reads_image(args['input'] % (i), H, W)
ut_plus_1 = reads_image(args['input'] % (i+1), H, W)
ut_plus_2 = reads_image(args['input'] % (i+2), H, W)
ut_plus_3 = reads_image(args['input'] % (i+3), H, W)
ut_plus_4 = reads_image(args['input'] % (i+4), H, W)
#Creation of the stack
inframes = [ut_moins_4, ut_moins_2, ut, ut_plus_2, ut_plus_4]
stack = torch.stack(inframes, dim=0).contiguous().view((1, 5*C, H, W)).cuda()
#Compute and save the denoising
model.eval()
with torch.no_grad():
#denoise with the training stack :
outimg = temp_denoise(model, stack, noise_map)
outimg = tensor_to_image(outimg)
#denoise with the natural stack
inframes = [ut_moins_2, ut_moins_1, ut, ut_plus_1, ut_plus_2]
stack = torch.stack(inframes, dim=0).contiguous().view((1, 5*C, H, W)).cuda()
outimg2 = temp_denoise(model, stack, noise_map)
outimg2 = tensor_to_image(outimg2)
# Load frame to compute the PSNR
ref_frame = iio.read(args['ref'] % (i))[:val, :val, :]
# Compute the PSNR according to the reference frame
quant_our_stack = psnr(ref_frame.astype(outimg.dtype)/255, outimg)
quant_Tassano_stack = psnr(ref_frame.astype(outimg2.dtype)/255., outimg2)
if quant_Tassano_stack > quant_our_stack:
value = 1
else:
value = 0
ssim_our = ssim(outimg*255 , ref_frame)
ssim_Tassano = ssim(outimg2*255, ref_frame)
print("Itération = {:03d}, PSNR our stack = {:5.3f}, PSNR Tassano's stack = {:5.3f}, SSIM our {:4.3f}, SSIM Tassano's = {:4.3f} {:1d}".format(i, quant_our_stack, quant_Tassano_stack, ssim_our, ssim_Tassano, value))
# Store the results
iio.write(output_path + "training_{:03d}.png".format(i), 255*outimg)
iio.write(args['output']%(i), 255*outimg2)
list_PSNR_training.append(quant_our_stack)
list_PSNR_eval.append(quant_Tassano_stack)
plot_psnr.write(str(quant_Tassano_stack)+'\n')
plot_ssim.write(str(ssim_Tassano)+'\n')
plot_psnr_training.write(str(quant_our_stack)+'\n')
plot_ssim_training.write(str(ssim_our)+'\n')
tab_PSNR_training = np.array(list_PSNR_training)
tab_PSNR_eval = np.array(list_PSNR_eval)
print("Average PSNR: training stack = {:5.3f}, natural stack = {:5.3f}".format(np.mean(tab_PSNR_training), np.mean(tab_PSNR_eval)))
plot_psnr.close()
plot_ssim.close()
plot_psnr_tot.close()
plot_ssim_tot.close()
plot_psnr_training.close()
plot_ssim_training.close()
if __name__ == "__main__":
# Parse arguments
parser = argparse.ArgumentParser(description="MF2F offline with teacher")
parser.add_argument("--input" , type=str , default="" , help='path to input frames (C type)')
parser.add_argument("--ref" , type=str , default="" , help='path to reference frames (C type), against which the psnr is going to be computed')
parser.add_argument("--flow" , type=str , default="" , help='path to optical flow (C type)')
parser.add_argument("--mask_collision" , type=str , default="" , help='path to collision mask (C type)')
parser.add_argument("--mask_warping_res" , type=str , default="" , help='path to warping res mask (C type)')
parser.add_argument("--teacher_outputs" , type=str , default="" , help='path to teacher outputs (have to be previously precomputed) (C type)')
parser.add_argument("--output" , type=str , default="./%03d.png" , help='path to output image (C type)')
parser.add_argument("--first" , type=int , default=1 , help='index first frame')
parser.add_argument("--last" , type=int , default=300 , help='index last frame')
parser.add_argument("--nb_trainings" , type=int , default=4002 , help='number of trainings')
parser.add_argument("--nb_trainings_before_step", type=int , default=20 , help='number of trainings before each step')
parser.add_argument("--network" , type=str , default="model/model.pth", help='path to the network')
parser.add_argument("--noise_level" , type=int , default=25 , help='sigma standard deviation of the noise')
parser.add_argument("--lr" , type=float, default=0.00001 , help='learning rate')
argspar = parser.parse_args()
print("\n### MF2F offline with teacher ###")
print("> Parameters:")
for p, v in zip(argspar.__dict__.keys(), argspar.__dict__.values()):
print('\t{}: {}'.format(p, v))
print('\n')
MF2F(**vars(argspar))