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stg2_DN_train.py
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stg2_DN_train.py
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# -*- coding: utf-8 -*-
"""
## Pseudo-ISP: Learning Pseudo In-camera Signal Processing Pipeline from A Color Image Denoiser
## Yue Cao, Xiaohe Wu, Shuran Qi, Xiao Liu, Zhongqin Wu, Wangmeng Zuo
## Thank the Professor Wangmeng Zuo for his guidance and help in this work.
## If you use our code, please cite our paper. Thank you.
## If you have a question or comment about our paper, please send me an email. cscaoyue@gamil.com
"""
import os
import random
import glob
import re
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import DataLoader
import scipy.io as sio
from net.pseudoisp import RGB2PACK, PACK2RGB, Noise_Model_Network
from skimage.io import imread
from stg2_DN_options import opt
from h5_files.h5_dataset import Dataset # for loading .h5 data
import math
import h5py
import stg2_DN_valid
# You can also choose other networks MWCNN RIDNet PT-MWRN and so on
from net.CBDNet import Net
random.seed()
def get_gaussian_kernel(kernel_size=21, sigma=5, channels=3):
#if not kernel_size: kernel_size = int(2*np.ceil(2*sigma)+1)
#print("Kernel is: ",kernel_size)
#print("Sigma is: ",sigma)
padding = kernel_size//2
# Create a x, y coordinate grid of shape (kernel_size, kernel_size, 2)
x_coord = torch.arange(kernel_size)
x_grid = x_coord.repeat(kernel_size).view(kernel_size, kernel_size)
y_grid = x_grid.t()
xy_grid = torch.stack([x_grid, y_grid], dim=-1).float()
mean = (kernel_size - 1)/2.
variance = sigma**2.
# Calculate the 2-dimensional gaussian kernel which is
# the product of two gaussian distributions for two different
# variables (in this case called x and y)
gaussian_kernel = (1./(2.*math.pi*variance)) *\
torch.exp(
-torch.sum((xy_grid - mean)**2., dim=-1) /\
(2*variance)
)
# Make sure sum of values in gaussian kernel equals 1.
gaussian_kernel = gaussian_kernel / torch.sum(gaussian_kernel)
# Reshape to 2d depthwise convolutional weight
gaussian_kernel = gaussian_kernel.view(1, 1, kernel_size, kernel_size)
gaussian_kernel = gaussian_kernel.repeat(channels, 1, 1, 1)
gaussian_filter = nn.Conv2d(in_channels=channels, out_channels=channels,
kernel_size=kernel_size, groups=channels, bias=False)
gaussian_filter.weight.data = gaussian_kernel
gaussian_filter.weight.requires_grad = False
return gaussian_filter, padding
def findLastCheckpoint(save_dir, save_pre):
file_list = glob.glob(os.path.join(save_dir, save_pre + '*.pth'))
if file_list:
epochs_exist = []
for file_ in file_list:
result = re.findall(".*" + save_pre +"(.*).pth.*", file_)
epochs_exist.append(int(result[0]))
initial_epoch = max(epochs_exist)
else:
initial_epoch = 0
return initial_epoch
def data_augmentation(image, mode):
r"""Performs dat augmentation of the input image
Args:
image: a cv2 (OpenCV) image
mode: int. Choice of transformation to apply to the image
0 - no transformation
1 - flip up and down
2 - rotate counterwise 90 degree
3 - rotate 90 degree and flip up and down
4 - rotate 180 degree
5 - rotate 180 degree and flip
6 - rotate 270 degree
7 - rotate 270 degree and flip
"""
out = image.copy()
if mode == 0:
# original
out = out
elif mode == 1:
# flip up and down
out = np.flipud(out)
elif mode == 2:
# rotate counterwise 90 degree
out = np.rot90(out)
elif mode == 3:
# rotate 90 degree and flip up and down
out = np.rot90(out)
out = np.flipud(out)
elif mode == 4:
# rotate 180 degree
out = np.rot90(out, k=2)
elif mode == 5:
# rotate 180 degree and flip
out = np.rot90(out, k=2)
out = np.flipud(out)
elif mode == 6:
# rotate 270 degree
out = np.rot90(out, k=3)
elif mode == 7:
# rotate 270 degree and flip
out = np.rot90(out, k=3)
out = np.flipud(out)
else:
raise Exception('Invalid choice of image transformation')
return out
# Synthesis Dataset Generation
def Generate_Synthesis_Dataset(args):
# Synthesis
# args.traindbf = './dataset_h5py/Synthesis_Div_p160_s60_srgb_uint8.h5'
train_num = 0
patch_size = 512
patchsize = 160
if os.path.exists(args.traindbf):
os.remove(args.traindbf)
else:
print('no such file:%s' % args.traindbf)
with h5py.File(args.traindbf, 'w') as h5f:
# 100 no same random number
resultList = random.sample(range(1, 800), 200)
for tt in range(200):
image_num = resultList[tt]
image_name = '%04d.png' % (image_num)
gt = np.array(imread(args.Div_path + image_name), dtype=np.uint8)
if not os.path.exists(args.Div_path + image_name):
print("Please download DIV2K datase")
assert (os.path.exists(args.Div_path + image_name))
if gt.shape[2] != 3:
image_num = random.randint(1, 800)
image_name = '%04d.png' % (image_num)
gt = np.array(imread(args.Div_path + image_name), dtype=np.uint8)
if gt.shape[2] != 3:
image_num = random.randint(1, 800)
image_name = '%04d.png' % (image_num)
gt = np.array(imread(args.Div_path + image_name), dtype=np.uint8)
if gt.shape[2] != 3:
image_num = random.randint(1, 800)
image_name = '%04d.png' % (image_num)
gt = np.array(imread(args.Div_path + image_name), dtype=np.uint8)
if gt.shape[2] != 3:
image_num = random.randint(1, 800)
image_name = '%04d.png' % (image_num)
gt = np.array(imread(args.Div_path + image_name), dtype=np.uint8)
img_w = gt.shape[0]
img_h = gt.shape[1]
if img_h > 511 and img_w > 511 and gt.shape[2] == 3:
w_num = int(np.ceil(img_w / patch_size))
h_num = int(np.ceil(img_h / patch_size))
for w_index in range(w_num):
for h_index in range(h_num):
start_x = w_index * patch_size
end_x = start_x + patch_size - 1
if end_x > img_w - 1:
end_x = img_w - 1
start_x = end_x - patch_size + 1
start_y = h_index * patch_size
end_y = start_y + patch_size - 1
if end_y > img_h - 1:
end_y = img_h - 1
start_y = end_y - patch_size + 1
label = gt[start_x:end_x + 1, start_y:end_y + 1, :]
# get input numpy
Iclean_crop = np.float32(label / 255.0)
# 1 * 512 * 512 * 3
Iclean_crop_exp = np.expand_dims(Iclean_crop, 0)
# 1 * 3 * 512 * 512
input = np.transpose(Iclean_crop_exp, (0, 3, 1, 2))
n = np.random.randint(0, args.ISP_num)
print("PseudoISP No. : %d\n" % (n + 1))
RGB2PACK_model = nn.DataParallel(RGB2PACK()).cuda()
PACK2RGB_model = nn.DataParallel(PACK2RGB()).cuda()
Noise_Model_Network_model = nn.DataParallel(Noise_Model_Network()).cuda()
load_model_dir = args.PseudoISP_path + '%04d_PseudoISP.pth' % (n + 1)
tmp_ckpt = torch.load(load_model_dir)
# RGB2PACK_model
pretrained_dict = tmp_ckpt['state_dict_RGB2PACK']
model_dict = RGB2PACK_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
RGB2PACK_model.load_state_dict(model_dict)
# PACK2RGB_model
pretrained_dict = tmp_ckpt['state_dict_PACK2RGB']
model_dict = PACK2RGB_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
PACK2RGB_model.load_state_dict(model_dict)
# Noise_Model_Network_model
pretrained_dict = tmp_ckpt['state_dict_Noise_Model_Network_model']
model_dict = Noise_Model_Network_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert (len(pretrained_dict) == len(pretrained_dict_update))
assert (len(pretrained_dict_update) == len(model_dict))
model_dict.update(pretrained_dict_update)
Noise_Model_Network_model.load_state_dict(model_dict)
with torch.no_grad():
RGB2PACK_model.eval()
PACK2RGB_model.eval()
Noise_Model_Network_model.eval()
# 1 * 3 * 512 * 512
input = torch.Tensor(input)
blur, pad = get_gaussian_kernel(kernel_size=5, sigma=1)
input = F.pad(input, (pad, pad, pad, pad), mode='reflect')
X_rgb = blur(input)
# 1 * 3 * 512 * 512
X_rgb = X_rgb.cuda()
X_pack, Y_pack = RGB2PACK_model(X_rgb, X_rgb)
Output = Noise_Model_Network_model(X_pack)
gamma_int = random.randint(90, 111)
# 0.9 ~ 1.1
gamma = gamma_int / 100.0
noise_level = Output * gamma
torch.manual_seed(0)
noise_map = torch.randn(X_pack.size()).cuda() * noise_level
noisy_image = X_pack + noise_map
# 1 * 3 * 512 * 512
X_output, Y_output = PACK2RGB_model(noisy_image, noisy_image)
img = Y_output.cpu().detach().clamp(0., 1.).numpy().astype(np.float32)
img = np.squeeze(img)
img = img.transpose(1, 2, 0)
img = np.uint8(np.round(img * 255.0))
img_gt = X_rgb.cpu().detach().clamp(0., 1.).numpy().astype(np.float32)
img_gt = np.squeeze(img_gt)
img_gt = img_gt.transpose(1, 2, 0)
img_gt = np.uint8(np.round(img_gt * 255.0))
count = 0
data_noisy = []
data_gt = []
stride = 120
for ii in range(10, img.shape[0] - patchsize - 10, stride):
for jj in range(10, img.shape[1] - patchsize -10, stride):
x = img[ii:ii + patchsize, jj:jj + patchsize, :]
y = img_gt[ii:ii + patchsize, jj:jj + patchsize, :]
data_noisy.append(x)
data_gt.append(y)
count = count + 1
data_noisy = np.array(data_noisy)
data_gt = np.array(data_gt)
for nx in range(count):
input = data_noisy[nx, :, :, :].copy()
target = data_gt[nx, :, :, :].copy()
input = np.transpose(input, (2, 0, 1))
target = np.transpose(target, (2, 0, 1))
h5f.create_dataset(str(train_num), data=(input, target))
train_num += 1
# Pseudo Dataset Generation
def Pseudo_Paired_Dataset(args):
train_num = 0
patch_size, stride = 160, 60
step1, step2 = 0, 0
train_num = 0
with h5py.File(args.traindbf_pre, 'w') as h5f:
for n in range(50):
for k in range(20):
print("Cell No. : %d\n" % (k + 1))
# 512 * 512 * 3
noisy_valid_dir = args.dataset_path + args.Noisy_path + '%04d_%02d.mat' % (n + 1, k + 1)
if not os.path.exists(noisy_valid_dir):
print("Please download dataset from my github")
print("The default path is " + args.dataset_path + args.Noisy_path)
print("You can change the path (args.dataset_path + args.Noisy_path)")
assert (os.path.exists(noisy_valid_dir))
mat_file = sio.loadmat(noisy_valid_dir)
# get input numpy
Inoisy_crop = np.float32(np.array(mat_file['Inoisy_crop']))
img = np.uint8(np.round(Inoisy_crop * 255.0))
# 512 * 512 * 3
denoised_valid_dir = args.dataset_path + args.Denoised_path + '%04d_%02d.mat' % (n + 1, k + 1)
if not os.path.exists(denoised_valid_dir):
print("Please download denoised result from my github or your denoised result")
print("The default path is " + args.dataset_path + args.Denoised_path)
print("You can change the path ( args.dataset_path + args.Denoised_path)")
assert (os.path.exists(denoised_valid_dir))
mat_file = sio.loadmat(denoised_valid_dir)
Iclean_crop = np.float32(np.array(mat_file['Idenoised_crop']))
# 512 * 512 * 3
Iclean_crop = np.clip(Iclean_crop, 0., 1.)
label = np.uint8(np.round(Iclean_crop * 255.0))
count = 0
data_noisy = []
data_gt = []
for ii in range(step1, img.shape[0] - patch_size + 1, stride):
for jj in range(step2, img.shape[1] - patch_size + 1, stride):
x = img[ii:ii + patch_size, jj:jj + patch_size, :]
y = label[ii:ii + patch_size, jj:jj + patch_size, :]
data_noisy.append(x)
data_gt.append(y)
count = count + 1
print(str(count))
data_noisy = np.array(data_noisy)
data_gt = np.array(data_gt)
for nx in range(count):
input = data_noisy[nx, :, :, :].copy()
target = data_gt[nx, :, :, :].copy()
input = np.transpose(input, (2, 0, 1))
target = np.transpose(target, (2, 0, 1))
h5f.create_dataset(str(train_num), data=(input, target))
train_num += 1
# We use our denoising results (PT-MWRN*) as a validation set
# You don't have to use it
def load_valid(args):
# 20 * 3 * 512 * 512
noisy_data = np.zeros((10, 3, 512, 512), dtype=np.float32)
denoised_data = np.zeros((10, 3, 512, 512), dtype=np.float32)
for i in range(10):
if i == 0:
n = 1
k = 18
if i == 1:
n = 2
k = 19
if i == 2:
n = 6
k = 6
if i == 3:
n = 16
k = 9
if i == 4:
n = 17
k = 3
if i == 5:
n = 26
k = 2
if i == 6:
n = 34
k = 9
if i == 7:
n = 39
k = 16
if i == 8:
n = 44
k = 1
if i == 9:
n = 50
k = 15
noisy_valid_dir = args.datacroproot + 'noisy_part_mat/%04d_%02d.mat' % (n, k)
mat_file = sio.loadmat(noisy_valid_dir)
# 512 * 512 * 3
Inoisy_crop = np.float32(np.array(mat_file['Inoisy_crop']))
# 1 * 512 * 512 * 3
Inoisy_crop_exp = np.expand_dims(Inoisy_crop, 0)
# 1 * 3 * 512 * 512
input = np.transpose(Inoisy_crop_exp, (0, 3, 1, 2))
denoised_valid_dir = args.datacroproot + 'denoised_part_mat/%04d_%02d.mat' % (n, k)
mat_file = sio.loadmat(denoised_valid_dir)
# 512 * 512 * 3
Iclean_crop = np.float32(np.array(mat_file['Idenoised_crop']))
# 1 * 512 * 512 * 3
Iclean_crop_exp = np.expand_dims(Iclean_crop, 0)
# 1 * 3 * 512 * 512
target = np.transpose(Iclean_crop_exp, (0, 3, 1, 2))
noisy_data[i, :, :, :] = input
denoised_data[i, :, :, :] = target
# print(i)
return noisy_data, denoised_data
def main(args):
# with or without Pseudo paried as validation ?
if args.valid_flag == 1:
noisy_data, denoised_data = load_valid(args)
if not os.path.exists('test_epoch_psnr.mat') and args.valid_flag == 1:
s = {}
s["tep"] = np.zeros((7, 1))
sio.savemat('test_epoch_psnr.mat', s)
if not os.path.exists(args.save_path):
os.makedirs(args.save_path)
initial_epoch = findLastCheckpoint(save_dir=args.save_path, save_pre = args.save_prefix)
if initial_epoch > 0:
print('resuming by loading epoch %03d' % initial_epoch)
args.resume = "continue"
args.last_ckpt = args.save_path + args.save_prefix + str(initial_epoch) + '.pth'
# net architecture
dn_net = Net()
# loss function
criterion = nn.MSELoss(reduction='sum').cuda()
dn_model = nn.DataParallel(dn_net).cuda()
# Optimizer
training_params = None
optimizer_dn = None
# load old params, optimizer, state
if args.resume == "continue":
tmp_ckpt = torch.load(args.last_ckpt)
start_epoch = initial_epoch + 1
# Initialize dn_model
pretrained_dict = tmp_ckpt['state_dict']
model_dict=dn_model.state_dict()
pretrained_dict_update = {k: v for k, v in pretrained_dict.items() if k in model_dict}
assert(len(pretrained_dict)==len(pretrained_dict_update))
assert(len(pretrained_dict_update)==len(model_dict))
model_dict.update(pretrained_dict_update)
dn_model.load_state_dict(model_dict)
optimizer_dn = optim.Adam(dn_model.parameters(), lr=args.learning_rate_dn)
optimizer_dn.load_state_dict(tmp_ckpt['optimizer_state'])
schedule_dn = torch.optim.lr_scheduler.MultiStepLR(optimizer_dn, milestones=args.steps, gamma=args.decay_rate)
elif args.resume == "new":
start_epoch = 1
optimizer_dn = optim.Adam(dn_model.parameters(), lr=args.learning_rate_dn)
schedule_dn = torch.optim.lr_scheduler.MultiStepLR(optimizer_dn, milestones=[20, 80], gamma=args.decay_rate)
if args.resume=="continue" and args.valid_flag == 1:
stg2_DN_valid.DN_valid(args, noisy_data.copy(), denoised_data.copy())
# set training set DataLoader
if not os.path.exists('./dataset_h5py/'):
os.makedirs('./dataset_h5py/')
# Synthesis Dataset Generation
Generate_Synthesis_Dataset(args)
# Pseudo Dataset Generation
Pseudo_Paired_Dataset(args)
dataset_train = Dataset(args.traindbf, shuffle=False, close_everytime=False, aug_mode=True)
loader_train = DataLoader(dataset=dataset_train, num_workers=args.load_thread, batch_size=args.Synthesis_size,
shuffle=True, pin_memory=True, timeout=0)
print("Number of Synthesis paried training images: %d\n" % int(len(dataset_train)))
# Previous Dataset Generation
if args.batch_size != args.Synthesis_size:
dataset_previous = Dataset(args.traindbf_pre, shuffle=False, close_everytime=False, aug_mode=True)
print("Number of Pseudo paried training images: %d\n" % int(len(dataset_previous)))
total_step = len(loader_train)
# training
for epoch in range(start_epoch, args.epoch+1):
# Synthesis Dataset Generation
# if (epoch - start_epoch) % 30 == 0:
schedule_dn.step()
dn_model.train()
# train
tt = 0
if args.batch_size != args.Synthesis_size:
resultList = random.sample(range(0, int(len(dataset_previous))), int(len(dataset_previous)))
i = 0
for img_noise_1, img_train_1 in loader_train:
batch, C, H, W = img_train_1.size()
if batch == args.Synthesis_size:
img_noise_1 = img_noise_1.cuda().float().div(255)
img_train_1 = img_train_1.cuda().float().div(255)
img_noise = torch.zeros(args.batch_size, C, H, W, device='cuda')
img_train = torch.zeros(args.batch_size, C, H, W, device='cuda')
bb_num = args.batch_size - args.Synthesis_size
cc = 0
for bb in range(bb_num):
tt = (tt + 1) % int(len(dataset_previous))
step_ram = resultList[tt]
img_noise_temp, img_train_temp = dataset_previous.__getitem__(step_ram)
img_noise[cc, :, :, :] = torch.unsqueeze(img_noise_temp, 0).cuda().float().div(255)
img_train[cc, :, :, :] = torch.unsqueeze(img_train_temp, 0).cuda().float().div(255)
cc = cc + 1
# batch Simulation
img_noise[cc:args.batch_size, :, :, :] = img_noise_1
img_train[cc:args.batch_size, :, :, :] = img_train_1
# 32 * 3 * 160 * 160
optimizer_dn.zero_grad()
output = dn_model(img_noise)
loss = criterion(img_train, output)
i = i + 1
print("Epoch:[{}/{}] Batch: [{}/{}] loss = {:.4f}".format(epoch, args.epoch, i, total_step,
loss.item() / batch))
loss = loss / (2*batch)
loss.backward()
optimizer_dn.step()
# save model and checkpoint
save_dict = {'state_dict': dn_model.state_dict(),
'optimizer_state': optimizer_dn.state_dict(),
'schedule_state': schedule_dn.state_dict()}
torch.save(save_dict, os.path.join(args.save_path + args.save_prefix + '{}.pth'.format(epoch)))
del save_dict
if epoch % args.save_every_epochs == 0 and args.valid_flag == 1:
stg2_DN_valid.DN_valid(args, noisy_data.copy(), denoised_data.copy())
if __name__ == "__main__":
main(opt)
exit(0)