我正在尝试运行以下代码。 我收到一个错误。该代码来自从Github下载的Deep Image Prior。谁能告诉我为什么我收到此错误以及我在哪里出错？ 我读到一些关于它的内容，它说可以减小批量大小。如何减少呢？ 是因为.tif文件约为100 MB？ 错误是RuntimeError：CUDA错误：内存不足，我正在使用12 GB GPU和CUDA 9.2
from __future__ import print_function
import matplotlib.pyplot as plt
%matplotlib inline
import argparse
import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import numpy as np
from models import *
import torch
import torch.optim
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
import warnings
warnings.filterwarnings("ignore")
from skimage.measure import compare_psnr
from models.downsampler import Downsampler
from utils.sr_utils import *
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark =True
dtype = torch.cuda.FloatTensor
device = torch.device("cuda:0" if torch.cuda.is_available() else"cpu")
imsize = (819,819)
factor = 2 # 8
enforse_div32 = 'CROP' # we usually need the dimensions to be divisible by a power of two (32 in this case)
PLOT = True
# To produce images from the paper we took *_GT.png images from LapSRN viewer for corresponding factor,
# e.g. x4/zebra_GT.png for factor=4, and x8/zebra_GT.png for factor=8
with torch.no_grad():
path_to_image = '/home/smitha/deep-image-prior/resize.tif'
imgs = load_LR_HR_imgs_sr(path_to_image , imsize, factor, enforse_div32)
imgs['bicubic_np'], imgs['sharp_np'], imgs['nearest_np'] = get_baselines(imgs['LR_pil'], imgs['HR_pil'])
if PLOT:
plot_image_grid([imgs['HR_np'], imgs['bicubic_np'], imgs['sharp_np'], imgs['nearest_np']], 4,12);
print ('PSNR bicubic: %.4f PSNR nearest: %.4f' % (
compare_psnr(imgs['HR_np'], imgs['bicubic_np']),
compare_psnr(imgs['HR_np'], imgs['nearest_np'])))
input_depth = 8
INPUT = 'noise'
pad = 'reflection'
OPT_OVER = 'net'
KERNEL_TYPE='lanczos2'
LR = 1
tv_weight = 0.0
OPTIMIZER = 'adam'
if factor == 2:
num_iter = 10
reg_noise_std = 0.01
elif factor == 8:
num_iter = 40
reg_noise_std = 0.05
else:
assert False, 'We did not experiment with other factors'
net_input = get_noise(input_depth, INPUT, (imgs['HR_pil'].size[1], imgs['HR_pil'].size[0])).type(dtype).detach()
NET_TYPE = 'skip' # UNet, ResNet
net = get_net(input_depth, 'skip', pad,
skip_n33d=512,
skip_n33u=512,
skip_n11=4,
num_scales=5,
upsample_mode='bilinear').type(dtype)
# Losses
mse = torch.nn.MSELoss().type(dtype)
img_LR_var = np_to_torch(imgs['LR_np']).type(dtype)
downsampler = Downsampler(n_planes=3, factor=factor, kernel_type=KERNEL_TYPE, phase=0.5, preserve_size=True).type(dtype)
def closure():
global i, net_input
if reg_noise_std > 0:
net_input = net_input_saved + (noise.normal_() * reg_noise_std)
out_HR = net(net_input)
out_LR = downsampler(out_HR)
total_loss = mse(out_LR, img_LR_var)
if tv_weight > 0:
total_loss += tv_weight * tv_loss(out_HR)
total_loss.backward()
# Log
psnr_LR = compare_psnr(imgs['LR_np'], torch_to_np(out_LR))
psnr_HR = compare_psnr(imgs['HR_np'], torch_to_np(out_HR))
print ('Iteration %05d PSNR_LR %.3f PSNR_HR %.3f' % (i, psnr_LR, psnr_HR), '\r', end='')
# History
psnr_history.append([psnr_LR, psnr_HR])
if PLOT and i % 100 == 0:
out_HR_np = torch_to_np(out_HR)
plot_image_grid([imgs['HR_np'], imgs['bicubic_np'], np.clip(out_HR_np, 0, 1)], factor=13, nrow=3)
i += 1
return total_loss
psnr_history = []
net_input_saved = net_input.detach().clone()
noise = net_input.clone()
i = 0
p = get_params(OPT_OVER, net, net_input)
optimize(OPTIMIZER, p, closure, LR, num_iter)
out_HR_np = np.clip(torch_to_np(net(net_input)), 0, 1)
result_deep_prior = put_in_center(out_HR_np, imgs['orig_np'].shape[1:])
# For the paper we acually took `_bicubic.png` files from LapSRN viewer and used `result_deep_prior` as our result
plot_image_grid([imgs['HR_np'],
imgs['bicubic_np'],
out_HR_np], factor=4, nrow=1);