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misc.py
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misc.py
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import torch
import os
import sys
from transforms.pix2pix import *
from skimage.transform import resize
def create_exp_dir(exp):
try:
os.makedirs(exp)
print('Creating exp dir: %s' % exp)
except OSError:
pass
return True
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
m.weight.data.normal_(0.0, 0.02)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
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
from torchvision import transforms
elif datasetName == 'pix2pix_val2':
# from datasets.pix2pix_val import pix2pix_val as commonDataset
# import transforms.pix2pix as transforms
from datasets.pix2pix_val2 import pix2pix_val2 as commonDataset
import transforms.pix2pix as transforms
if split == 'train':
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
# transforms.Scale(originalSize),
# transforms.CenterCrop(256),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# transforms.Normalize(mean, std),
]),
seed=seed)
print('split == train')
else:
dataset = commonDataset(root=dataroot,
transform = None,
# 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 getLoader1(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 == 'new':
# from datasets.pix2pix import pix2pix as commonDataset
# import transforms.pix2pix as transforms
from datasets.new import new as commonDataset
# import transforms.pix2pix as transforms
from torchvision import transforms
elif datasetName == 'pix2pix_val2':
# from datasets.pix2pix_val import pix2pix_val as commonDataset
# import transforms.pix2pix as transforms
from datasets.pix2pix_val2 import pix2pix_val2 as commonDataset
import transforms.pix2pix as transforms
if split == 'train':
dataset = commonDataset(root=dataroot,
transform=transforms.Compose([
# transforms.Scale(originalSize),
# transforms.CenterCrop(256),
# transforms.RandomHorizontalFlip(),
# transforms.ToTensor(),
# transforms.Normalize(mean, std),
]),
seed=seed)
print('split == train')
else:
dataset = commonDataset(root=dataroot,
transform = None,
# 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
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
import numpy as np
class ImagePool:
def __init__(self, pool_size=50):
self.pool_size = pool_size
if pool_size > 0:
self.num_imgs = 0
self.images = []
def query(self, image):
if self.pool_size == 0:
return image
if self.num_imgs < self.pool_size:
self.images.append(image.clone())
self.num_imgs += 1
return image
else:
if np.random.uniform(0,1) > 0.5:
random_id = np.random.randint(self.pool_size, size=1)[0]
tmp = self.images[random_id].clone()
self.images[random_id] = image.clone()
return tmp
else:
return image
def adjust_learning_rate(optimizer, init_lr, epoch, factor, every):
#import pdb; pdb.set_trace()
lrd = init_lr / every
old_lr = optimizer.param_groups[0]['lr']
# linearly decaying lr
lr = old_lr - lrd
if lr < 0: lr = 0
for param_group in optimizer.param_groups:
param_group['lr'] = lr