我正在尝试运行Deep Image Prior的超级分辨率。在运行它时，出现此错误。谁能告诉我我要去哪里错了？我正在使用CUDA-9.2，GPU 12GB，足以运行此代码。有人告诉我要减小批量大小，我不知道该怎么做。我是新来的。
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
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
imsize = -1
factor = 4 # 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
path_to_image = '/home/smitha/Documents/Falcon.png'
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 = 32
INPUT = 'noise'
pad = 'reflection'
OPT_OVER = 'net'
KERNEL_TYPE='lanczos2'
LR = 0.01
tv_weight = 0.0
OPTIMIZER = 'adam'
if factor == 4:
num_iter = 2000
reg_noise_std = 0.03
elif factor == 8:
num_iter = 4000
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=128,
skip_n33u=128,
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.detach().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);
错误是：
Starting optimization with ADAM
/home/smitha/anaconda3/lib/python3.6/site-packages/torch/nn/modules/upsampling.py:122: UserWarning: nn.Upsampling is deprecated. Use nn.functional.interpolate instead.
warnings.warn("nn.Upsampling is deprecated. Use nn.functional.interpolate instead.")
/home/smitha/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py:1961: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-6-0fe781a02812> in <module>()
5 i = 0
6 p = get_params(OPT_OVER, net, net_input)
----> 7 optimize(OPTIMIZER, p, closure, LR, num_iter)
~/Documents/deep-image-prior/utils/common_utils.py in optimize(optimizer_type, parameters, closure, LR, num_iter)
227 for j in range(num_iter):
228 optimizer.zero_grad()
--> 229 closure()
230 optimizer.step()
231 else:
<ipython-input-5-887ba7755977> in closure()
5 net_input = net_input_saved + (noise.normal_() * reg_noise_std)
6
----> 7 out_HR = net(net_input)
8 out_LR = downsampler(out_HR)
9
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
475 result = self._slow_forward(*input, **kwargs)
476 else:
--> 477 result = self.forward(*input, **kwargs)
478 for hook in self._forward_hooks.values():
479 hook_result = hook(self, input, result)
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
89 def forward(self, input):
90 for module in self._modules.values():
---> 91 input = module(input)
92 return input
93
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
475 result = self._slow_forward(*input, **kwargs)
476 else:
--> 477 result = self.forward(*input, **kwargs)
478 for hook in self._forward_hooks.values():
479 hook_result = hook(self, input, result)
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/container.py in forward(self, input)
89 def forward(self, input):
90 for module in self._modules.values():
---> 91 input = module(input)
92 return input
93
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
475 result = self._slow_forward(*input, **kwargs)
476 else:
--> 477 result = self.forward(*input, **kwargs)
478 for hook in self._forward_hooks.values():
479 hook_result = hook(self, input, result)
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/conv.py in forward(self, input)
299 def forward(self, input):
300 return F.conv2d(input, self.weight, self.bias, self.stride,
--> 301 self.padding, self.dilation, self.groups)
302
303
RuntimeError: CUDA error: out of memory
如果减小输入图像的大小，则会出现此错误。
RuntimeError Traceback (most recent call last)
<ipython-input-6-0fe781a02812> in <module>()
5 i = 0
6 p = get_params(OPT_OVER, net, net_input)
----> 7 optimize(OPTIMIZER, p, closure, LR, num_iter)
~/Documents/deep-image-prior/utils/common_utils.py in optimize(optimizer_type, parameters, closure, LR, num_iter)
227 for j in range(num_iter):
228 optimizer.zero_grad()
--> 229 closure()
230 optimizer.step()
231 else:
<ipython-input-5-887ba7755977> in closure()
8 out_LR = downsampler(out_HR)
9
---> 10 total_loss = mse(out_LR, img_LR_var)
11
12 if tv_weight > 0:
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
475 result = self._slow_forward(*input, **kwargs)
476 else:
--> 477 result = self.forward(*input, **kwargs)
478 for hook in self._forward_hooks.values():
479 hook_result = hook(self, input, result)
~/anaconda3/lib/python3.6/site-packages/torch/nn/modules/loss.py in forward(self, input, target)
419
420 def forward(self, input, target):
--> 421 return F.mse_loss(input, target, reduction=self.reduction)
422
423
~/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py in mse_loss(input, target, size_average, reduce, reduction)
1714 else:
1715 reduction = _Reduction.get_enum(reduction)
-> 1716 return _pointwise_loss(lambda a, b: (a - b) ** 2, torch._C._nn.mse_loss, input, target, reduction)
1717
1718
~/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py in _pointwise_loss(lambd, lambd_optimized, input, target, reduction)
1672 return torch.mean(d) if reduction == 'elementwise_mean' else torch.sum(d)
1673 else:
-> 1674 return lambd_optimized(input, target, reduction)
1675
1676
RuntimeError: input and target shapes do not match: input [1 x 3 x 64 x 64], target [1 x 1 x 64 x 64] at /opt/conda/conda-bld/pytorch_1532502421238/work/aten/src/THCUNN/generic/MSECriterion.cu:12