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train_pretrain.py
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from __future__ import division
import os, time, scipy.io
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import glob
import cv2
import argparse
from PIL import Image
from skimage.measure import compare_psnr,compare_ssim
from tensorboardX import SummaryWriter
from models import RViDeNet
from utils import *
parser = argparse.ArgumentParser(description='Pretrain denoising model')
parser.add_argument('--gpu_id', dest='gpu_id', type=int, default=0, help='gpu id')
parser.add_argument('--num_epochs', dest='num_epochs', type=int, default=33, help='num_epochs')
parser.add_argument('--patch_size', dest='patch_size', type=int, default=128, help='patch_size')
parser.add_argument('--batch_size', dest='batch_size', type=int, default=1, help='batch_size')
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu_id)
save_dir = './pretrain_model'
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
gt_paths1 = glob.glob('./data/SRVD_data/raw_clean/MOT17-02_raw/*.tiff')
gt_paths2 = glob.glob('./data/SRVD_data/raw_clean/MOT17-09_raw/*.tiff')
gt_paths3 = glob.glob('./data/SRVD_data/raw_clean/MOT17-10_raw/*.tiff')
gt_paths4 = glob.glob('./data/SRVD_data/raw_clean/MOT17-11_raw/*.tiff')
gt_paths = gt_paths1 + gt_paths2 + gt_paths3 + gt_paths4
ps = args.patch_size # patch size for training
batch_size = args.batch_size # batch size for training
log_dir = './logs/pretrain'
if not os.path.isdir(log_dir):
os.makedirs(log_dir)
writer = SummaryWriter(log_dir)
learning_rate = 1e-4
isp = torch.load('isp/ISP_CNN.pth').cuda()
for k,v in isp.named_parameters():
v.requires_grad=False
predenoiser = torch.load('./predenoising/PreDenoising.pth')
for k,v in predenoiser.named_parameters():
v.requires_grad=False
denoiser = RViDeNet(predenoiser=predenoiser).cuda()
initial_epoch = findLastCheckpoint(save_dir=save_dir)
if initial_epoch > 0:
print('resuming by loading epoch %03d' % initial_epoch)
denoiser = torch.load(os.path.join(save_dir, 'model_epoch%d.pth' % initial_epoch))
initial_epoch += 1
opt = optim.Adam(denoiser.parameters(), lr = learning_rate)
# Raw data takes long time to load. Keep them in memory after loaded.
gt_raws = [None] * len(gt_paths)
iso_list = [1600,3200,6400,12800,25600]
a_list = [3.513262,6.955588,13.486051,26.585953,52.032536]
g_noise_var_list = [11.917691,38.117816,130.818508,484.539790,1819.818657]
if initial_epoch==0:
step=0
else:
step = (initial_epoch-1)*int(len(gt_paths)/batch_size)
temporal_frames_num = 3
for epoch in range(initial_epoch, args.num_epochs+1):
cnt = 0
if epoch > 20:
for g in opt.param_groups:
g['lr'] = 1e-5
for batch_id in range(int(len(gt_paths)/batch_size)):
input_batch_list = []
gt_batch_list = []
batch_num = 0
while batch_num<batch_size:
ind = np.random.randint(0,len(gt_paths))
gt_path = gt_paths[ind]
#select center frame
gt_fn = os.path.basename(gt_path)
if gt_fn[2]!='0':
frame_id = int(gt_fn[2:6])
elif gt_fn[3]!='0':
frame_id = int(gt_fn[3:6])
elif gt_fn[4]!='0':
frame_id = int(gt_fn[4:6])
else:
frame_id = int(gt_fn[5])
if 'MOT17-02_raw' in gt_path:
if frame_id<2 or frame_id > len(gt_paths1)-2:
continue
if 'MOT17-09_raw' in gt_path:
if frame_id<2 or frame_id > len(gt_paths2)-2:
continue
if 'MOT17-10_raw' in gt_path:
if frame_id<2 or frame_id > len(gt_paths3)-2:
continue
if 'MOT17-11_raw' in gt_path:
if frame_id<2 or frame_id > len(gt_paths4)-2:
continue
batch_num += 1
noisy_level = np.random.randint(1,5+1)
a = a_list[noisy_level-1]
g_noise_var = g_noise_var_list[noisy_level-1]
input_pack_list = []
gt_pack_list = []
H = 1080
W = 1920
xx = np.random.randint(0, W - ps*2+1)
while xx%2!=0:
xx = np.random.randint(0, W - ps*2+1)
yy = np.random.randint(0, H - ps*2+1)
while yy%2!=0:
yy = np.random.randint(0, H - ps*2+1)
for shift in range(-1,2):
gt_frame_name = generate_name(frame_id+shift)
gt_frame_path = list(gt_path)
gt_frame_path[-len(gt_fn):] = gt_frame_name
gt_frame_path = ''.join(gt_frame_path)
scene_id = gt_paths.index(gt_frame_path)
if gt_raws[scene_id] is None:
gt_raw = cv2.imread(gt_frame_path,-1)
gt_raws[scene_id] = gt_raw
gt_raw_full = gt_raws[scene_id]
gt_patch = gt_raw_full[yy:yy + ps*2, xx:xx + ps*2]
gt_pack = np.expand_dims(pack_gbrg_raw(gt_patch), axis=0)
#generate noisy raw
noisy_raw = generate_noisy_raw(gt_patch.astype(np.float32), a, g_noise_var)
input_pack = np.expand_dims(pack_gbrg_raw(noisy_raw), axis=0)
input_pack = np.minimum(input_pack, 1.0)
input_pack_list.append(input_pack)
gt_pack_list.append(gt_pack)
input_pack_frames = np.concatenate(input_pack_list, axis=3)
gt_pack = gt_pack_list[1]
input_batch_list.append(input_pack_frames)
gt_batch_list.append(gt_pack)
input_batch = np.concatenate(input_batch_list, axis=0)
gt_batch = np.concatenate(gt_batch_list, axis=0)
in_data = torch.from_numpy(input_batch.copy()).permute(0,3,1,2).cuda()
gt_data = torch.from_numpy(gt_batch.copy()).permute(0,3,1,2).cuda()
denoiser.train()
opt.zero_grad()
denoised_out = denoiser(in_data.reshape(batch_size,3,4,ps,ps))
l1_loss = reduce_mean(denoised_out, gt_data)
loss = l1_loss
loss.backward()
opt.step()
cnt += 1
step += 1
writer.add_scalar('loss', loss.item(), step)
writer.add_scalar('l1_loss', l1_loss.item(), step)
print("epoch:%d iter%d loss=%.6f" % (epoch, cnt, loss.data))
if epoch%1==0:
torch.save(denoiser, os.path.join(save_dir, 'model_epoch%d.pth' % epoch))