def getLoader(datasetName,
dataroot,
originalSize,
imageSize,
batchSize=64,
workers=4,
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
split='train',
shuffle=True,
seed=None):
#import pdb; pdb.set_trace()
if datasetName == 'pix2pix':
# from datasets.pix2pix import pix2pix as commonDataset
# import transforms.pix2pix as transforms
from datasets.pix2pix import pix2pix as commonDataset
import transforms.pix2pix as transforms
elif datasetName == 'pix2pix_val':
# from datasets.pix2pix_val import pix2pix_val as commonDataset
# import transforms.pix2pix as transforms
from datasets.pix2pix_val import pix2pix_val as commonDataset
import transforms.pix2pix as transforms
if datasetName == 'pix2pix_class':
# from datasets.pix2pix import pix2pix as commonDataset
# import transforms.pix2pix as transforms
from datasets.pix2pix_class import pix2pix as commonDataset
import transforms.pix2pix as transforms
if split == 'train':
dataset = commonDataset(
root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
#transforms.RandomCrop(imageSize),
#transforms.CenterCrop(imageSize),
#transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
else:
dataset = commonDataset(
root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
#transforms.CenterCrop(imageSize),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batchSize,
shuffle=shuffle,
num_workers=int(workers))
return dataloader
def getLoader(datasetName,
dataroot,
originalSize,
imageSize,
batchSize=64,
workers=4,
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
split='train',
shuffle=True,
seed=None):
if datasetName == 'pix2pix_val':
from pix2pix_val import pix2pix_val as commonDataset
import transforms.pix2pix as transforms
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batchSize,
shuffle=shuffle,
num_workers=int(workers))
return dataloader
def getLoader(datasetName,
dataroot,
originalSize,
imageSize,
batchSize=64,
workers=4,
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5),
split='train',
shuffle=True,
seed=None,
pre="",
label_file=""):
if datasetName == 'my_loader':
from datasets.my_loader import my_loader as commonDataset
import transforms.pix2pix as transforms
if split == 'train':
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
transforms.RandomCrop(imageSize),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed,
pre=pre,
label_file=label_file)
else:
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
transforms.Scale(originalSize),
transforms.CenterCrop(imageSize),
transforms.ToTensor(),
transforms.Normalize(mean, std),
]),
seed=seed,
pre=pre,
label_file=label_file)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batchSize,
shuffle=shuffle,
num_workers=int(workers))
return dataloader
def deblur_images(images):
opt = Opt()
create_exp_dir(opt.exp)
opt.manualSeed = random.randint(1, 10000)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
torch.cuda.manual_seed_all(opt.manualSeed)
print("Random Seed: ", opt.manualSeed)
dataset = imageCustomDataset(
images=images,
transform=transforms.Compose([
transforms.Scale(opt.originalSize),
#transforms.CenterCrop(imageSize),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
]),
seed=opt.manualSeed)
valDataloader = torch.utils.data.DataLoader(dataset,
batch_size=opt.valBatchSize,
shuffle=False,
num_workers=int(opt.workers))
# get logger
trainLogger = open('%s/train.log' % opt.exp, 'w')
ngf = opt.ngf
ndf = opt.ndf
inputChannelSize = opt.inputChannelSize
outputChannelSize = opt.outputChannelSize
# Load Pre-trained derain model
netS = net.Segmentation()
netG = net.Deblur_segdl()
#netC.apply(weights_init)
netG.apply(weights_init)
if opt.netG != '':
state_dict_g = torch.load(opt.netG, map_location='cpu')
new_state_dict_g = {}
for k, v in state_dict_g.items():
name = k[7:] # remove `module.`
#print(k)
new_state_dict_g[name] = v
# load params
netG.load_state_dict(new_state_dict_g)
#netG.load_state_dict(torch.load(opt.netG))
# print(netG)
netG.eval()
#netS.apply(weights_init)
netS.load_state_dict(
torch.load('./pretrained_models/SMaps_Best.pth', map_location='cpu'))
#netS.eval()
netS.cpu()
netG.cpu()
# Initialize testing data
target = torch.FloatTensor(opt.batchSize, outputChannelSize, opt.imageSize,
opt.imageSize)
input = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize,
opt.imageSize)
val_target = torch.FloatTensor(opt.valBatchSize, outputChannelSize,
opt.imageSize, opt.imageSize)
val_input = torch.FloatTensor(opt.valBatchSize, inputChannelSize,
opt.imageSize, opt.imageSize)
label_d = torch.FloatTensor(opt.batchSize)
target = torch.FloatTensor(opt.batchSize, outputChannelSize, opt.imageSize,
opt.imageSize)
input = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize,
opt.imageSize)
depth = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize,
opt.imageSize)
ato = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize,
opt.imageSize)
val_target = torch.FloatTensor(opt.valBatchSize, outputChannelSize,
opt.imageSize, opt.imageSize)
val_input = torch.FloatTensor(opt.valBatchSize, inputChannelSize,
opt.imageSize, opt.imageSize)
val_depth = torch.FloatTensor(opt.valBatchSize, inputChannelSize,
opt.imageSize, opt.imageSize)
val_ato = torch.FloatTensor(opt.valBatchSize, inputChannelSize,
opt.imageSize, opt.imageSize)
target_128 = torch.FloatTensor(opt.batchSize, outputChannelSize,
(opt.imageSize // 4), (opt.imageSize // 4))
input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize,
(opt.imageSize // 4), (opt.imageSize // 4))
target_256 = torch.FloatTensor(opt.batchSize, outputChannelSize,
(opt.imageSize // 2), (opt.imageSize // 2))
input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize,
(opt.imageSize // 2), (opt.imageSize // 2))
val_target_128 = torch.FloatTensor(opt.batchSize, outputChannelSize,
(opt.imageSize // 4),
(opt.imageSize // 4))
val_input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize,
(opt.imageSize // 4),
(opt.imageSize // 4))
val_target_256 = torch.FloatTensor(opt.batchSize, outputChannelSize,
(opt.imageSize // 2),
(opt.imageSize // 2))
val_input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize,
(opt.imageSize // 2),
(opt.imageSize // 2))
target, input, depth, ato = target.cpu(), input.cpu(), depth.cpu(
), ato.cpu()
val_target, val_input, val_depth, val_ato = val_target.cpu(
), val_input.cpu(), val_depth.cpu(), val_ato.cpu()
target = Variable(target, volatile=True)
input = Variable(input, volatile=True)
depth = Variable(depth, volatile=True)
ato = Variable(ato, volatile=True)
target_128, input_128 = target_128.cpu(), input_128.cpu()
val_target_128, val_input_128 = val_target_128.cpu(), val_input_128.cpu()
target_256, input_256 = target_256.cpu(), input_256.cpu()
val_target_256, val_input_256 = val_target_256.cpu(), val_input_256.cpu()
target_128 = Variable(target_128)
input_128 = Variable(input_128)
target_256 = Variable(target_256)
input_256 = Variable(input_256)
label_d = Variable(label_d.cpu())
optimizerG = optim.Adam(netG.parameters(),
lr=opt.lrG,
betas=(opt.beta1, 0.999),
weight_decay=0.00005)
result_image = []
for epoch in range(1):
heavy, medium, light = 200, 200, 200
for i, data in enumerate(valDataloader, 0):
if 1:
import time
data_val = data
t0 = time.time()
val_input_cpu, val_target_cpu = data_val
val_target_cpu, val_input_cpu = val_target_cpu.float().cpu(
), val_input_cpu.float().cpu()
val_batch_output = torch.FloatTensor(val_input.size()).fill_(0)
val_input.resize_as_(val_input_cpu).copy_(val_input_cpu)
val_target = Variable(val_target_cpu, volatile=True)
z = 0
with torch.no_grad():
for idx in range(val_input.size(0)):
single_img = val_input[idx, :, :, :].unsqueeze(0)
val_inputv = Variable(single_img, volatile=True)
# print (val_inputv.size())
# val_inputv = val_inputv.float().cuda()
val_inputv_256 = torch.nn.functional.interpolate(
val_inputv, scale_factor=0.5)
val_inputv_128 = torch.nn.functional.interpolate(
val_inputv, scale_factor=0.25)
## Get de-rained results ##
#residual_val, x_hat_val, x_hatlv128, x_hatvl256 = netG(val_inputv, val_inputv_256, val_inputv_128)
t1 = time.time()
print('running time:' + str(t1 - t0))
from PIL import Image
#x_hat_val = netG(val_inputv)
#smaps_vl = netS(val_inputv)
#S_valinput = torch.cat([smaps_vl,val_inputv],1)
"""smaps,smaps64 = netS(val_inputv,val_inputv_256)
S_input = torch.cat([smaps,val_inputv],1)
x_hat_val, x_hat_val64 = netG(val_inputv,val_inputv_256,smaps,smaps64)"""
#x_hatcls1,x_hatcls2,x_hatcls3,x_hatcls4,x_lst1,x_lst2,x_lst3,x_lst4 = netG(val_inputv,val_inputv_256,smaps_i,smaps_i64,class1,class2,class3,class4)
smaps, smaps64 = netS(val_inputv, val_inputv_256)
class1 = torch.zeros([1, 1, 128, 128],
dtype=torch.float32)
class1[:, 0, :, :] = smaps[:, 0, :, :]
class2 = torch.zeros([1, 1, 128, 128],
dtype=torch.float32)
class2[:, 0, :, :] = smaps[:, 1, :, :]
class3 = torch.zeros([1, 1, 128, 128],
dtype=torch.float32)
class3[:, 0, :, :] = smaps[:, 2, :, :]
class4 = torch.zeros([1, 1, 128, 128],
dtype=torch.float32)
class4[:, 0, :, :] = smaps[:, 3, :, :]
class_msk1 = torch.zeros([1, 3, 128, 128],
dtype=torch.float32)
class_msk1[:, 0, :, :] = smaps[:, 0, :, :]
class_msk1[:, 1, :, :] = smaps[:, 0, :, :]
class_msk1[:, 2, :, :] = smaps[:, 0, :, :]
class_msk2 = torch.zeros([1, 3, 128, 128],
dtype=torch.float32)
class_msk2[:, 0, :, :] = smaps[:, 1, :, :]
class_msk2[:, 1, :, :] = smaps[:, 1, :, :]
class_msk2[:, 2, :, :] = smaps[:, 1, :, :]
class_msk3 = torch.zeros([1, 3, 128, 128],
dtype=torch.float32)
class_msk3[:, 0, :, :] = smaps[:, 2, :, :]
class_msk3[:, 1, :, :] = smaps[:, 2, :, :]
class_msk3[:, 2, :, :] = smaps[:, 2, :, :]
class_msk4 = torch.zeros([1, 3, 128, 128],
dtype=torch.float32)
class_msk4[:, 0, :, :] = smaps[:, 3, :, :]
class_msk4[:, 1, :, :] = smaps[:, 3, :, :]
class_msk4[:, 2, :, :] = smaps[:, 3, :, :]
class1 = class1.float().cpu()
class2 = class2.float().cpu()
class3 = class3.float().cpu()
class4 = class4.float().cpu()
class_msk4 = class_msk4.float().cpu()
class_msk3 = class_msk3.float().cpu()
class_msk2 = class_msk2.float().cpu()
class_msk1 = class_msk1.float().cpu()
x_hat_val, x_hat_val64, xmask1, xmask2, xmask3, xmask4, xcl_class1, xcl_class2, xcl_class3, xcl_class4 = netG(
val_inputv, val_inputv_256, smaps, class1, class2,
class3, class4, val_inputv, class_msk1, class_msk2,
class_msk3, class_msk4)
# x_hat1,x_hat64,xmask1,xmask2,xmask3,xmask4,xcl_class1,xcl_class2,xcl_class3,xcl_class4 = netG(input,input_256,smaps_i,class1,class2,class3,class4,target,class_msk1,class_msk2,class_msk3,class_msk4)
#x_hat_val.data
#val_batch_output[idx,:,:,:].copy_(x_hat_val.data[0,1,:,:])
# print(torch.mean(xmask1),torch.mean(xmask2),torch.mean(xmask3),torch.mean(xmask4))
print(smaps.size())
tensor = x_hat_val.data.cpu()
tensor = torch.squeeze(tensor)
tensor = norm_range(tensor, None)
# print(tensor.min(),tensor.max())
ndarr = tensor.mul(255).clamp(0, 255).byte().permute(
1, 2, 0).numpy()
im = Image.fromarray(ndarr)
result_image.append(im)
# im.save(filename)
return result_image