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train_Sony.py
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train_Sony.py
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import os
import time
import numpy as np
import rawpy
import glob
import torch
import torch.nn as nn
import torch.optim as optim
from PIL import Image
from model import SeeInDark
input_dir = './dataset/Sony/short/'
gt_dir = './dataset/Sony/long/'
result_dir = './result_Sony/'
model_dir = './saved_model/'
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(f"Device: {device}")
#get train and test IDs
train_fns = glob.glob(gt_dir + '0*.ARW')
train_ids = []
for i in range(len(train_fns)):
_, train_fn = os.path.split(train_fns[i])
train_ids.append(int(train_fn[0:5]))
test_fns = glob.glob(gt_dir + '/1*.ARW')
test_ids = []
for i in range(len(test_fns)):
_, test_fn = os.path.split(test_fns[i])
test_ids.append(int(test_fn[0:5]))
ps = 512 #patch size for training
save_freq = 100
DEBUG = 0
if DEBUG == 1:
save_freq = 100
train_ids = train_ids[0:5]
test_ids = test_ids[0:5]
def pack_raw(raw):
#pack Bayer image to 4 channels
im = raw.raw_image_visible.astype(np.float32)
im = np.maximum(im - 512,0)/ (16383 - 512) #subtract the black level
im = np.expand_dims(im,axis=2)
img_shape = im.shape
H = img_shape[0]
W = img_shape[1]
out = np.concatenate((im[0:H:2,0:W:2,:],
im[0:H:2,1:W:2,:],
im[1:H:2,1:W:2,:],
im[1:H:2,0:W:2,:]), axis=2)
return out
def reduce_mean(out_im, gt_im):
return torch.abs(out_im - gt_im).mean()
#Raw data takes long time to load. Keep them in memory after loaded.
gt_images=[None]*6000
input_images = {}
input_images['300'] = [None]*len(train_ids)
input_images['250'] = [None]*len(train_ids)
input_images['100'] = [None]*len(train_ids)
g_loss = np.zeros((5000,1))
allfolders = glob.glob('./result/*0')
lastepoch = 0
for folder in allfolders:
lastepoch = np.maximum(lastepoch, int(folder[-4:]))
learning_rate = 1e-4
model = SeeInDark().to(device)
model._initialize_weights()
opt = optim.Adam(model.parameters(), lr = learning_rate)
for epoch in range(lastepoch,4001):
if os.path.isdir("result/%04d"%epoch):
continue
cnt=0
if epoch > 2000:
for g in opt.param_groups:
g['lr'] = 1e-5
for ind in np.random.permutation(len(train_ids)):
# get the path from image id
train_id = train_ids[ind]
in_files = glob.glob(input_dir + '%05d_00*.ARW'%train_id)
in_path = in_files[np.random.randint(0,len(in_files))]
_, in_fn = os.path.split(in_path)
gt_files = glob.glob(gt_dir + '%05d_00*.ARW'%train_id)
gt_path = gt_files[0]
_, gt_fn = os.path.split(gt_path)
in_exposure = float(in_fn[9:-5])
gt_exposure = float(gt_fn[9:-5])
ratio = min(gt_exposure/in_exposure,300)
st=time.time()
cnt+=1
if input_images[str(ratio)[0:3]][ind] is None:
raw = rawpy.imread(in_path)
input_images[str(ratio)[0:3]][ind] = np.expand_dims(pack_raw(raw),axis=0) *ratio
gt_raw = rawpy.imread(gt_path)
im = gt_raw.postprocess(use_camera_wb=True, half_size=False, no_auto_bright=True, output_bps=16)
gt_images[ind] = np.expand_dims(np.float32(im/65535.0),axis = 0)
#crop
H = input_images[str(ratio)[0:3]][ind].shape[1]
W = input_images[str(ratio)[0:3]][ind].shape[2]
xx = np.random.randint(0,W-ps)
yy = np.random.randint(0,H-ps)
input_patch = input_images[str(ratio)[0:3]][ind][:,yy:yy+ps,xx:xx+ps,:]
gt_patch = gt_images[ind][:,yy*2:yy*2+ps*2,xx*2:xx*2+ps*2,:]
if np.random.randint(2,size=1)[0] == 1: # random flip
input_patch = np.flip(input_patch, axis=1)
gt_patch = np.flip(gt_patch, axis=1)
if np.random.randint(2,size=1)[0] == 1:
input_patch = np.flip(input_patch, axis=2)
gt_patch = np.flip(gt_patch, axis=2)
if np.random.randint(2,size=1)[0] == 1: # random transpose
input_patch = np.transpose(input_patch, (0,2,1,3))
gt_patch = np.transpose(gt_patch, (0,2,1,3))
input_patch = np.minimum(input_patch,1.0)
gt_patch = np.maximum(gt_patch, 0.0)
in_img = torch.from_numpy(input_patch).permute(0,3,1,2).to(device)
gt_img = torch.from_numpy(gt_patch).permute(0,3,1,2).to(device)
model.zero_grad()
out_img = model(in_img)
loss = reduce_mean(out_img, gt_img)
loss.backward()
opt.step()
g_loss[ind]=loss.data.cpu()
mean_loss = np.mean(g_loss[np.where(g_loss)])
print(f"Epoch: {epoch} \t Count: {cnt} \t Loss={mean_loss:.3} \t Time={time.time()-st:.3}")
if epoch%save_freq==0:
epoch_result_dir = result_dir + f'{epoch:04}/'
if not os.path.isdir(epoch_result_dir):
os.makedirs(epoch_result_dir)
output = out_img.permute(0, 2, 3, 1).cpu().data.numpy()
output = np.minimum(np.maximum(output,0),1)
temp = np.concatenate((gt_patch[0,:,:,:], output[0,:,:,:]),axis=1)
Image.fromarray((temp*255).astype('uint8')).save(epoch_result_dir + f'{train_id:05}_00_train_{ratio}.jpg')
torch.save(model.state_dict(), model_dir+'checkpoint_sony_e%04d.pth'%epoch)