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View Code? Open in Web Editor NEWFast and differentiable MS-SSIM and SSIM for pytorch.
License: MIT License
Fast and differentiable MS-SSIM and SSIM for pytorch.
License: MIT License
Hello
I wanted to try to implement this SSMI for a 3D autoencoder due to the success I have had implementing the loss function in my 2D autoencoder. However, I notice the code is only implemented for 2D images as I get the error: expected stride to be a single integer value or a list of 2 values to match the convolution dimensions, but got stride=[1, 1, 1].
Is there a method where I can implement this loss function for 3D data as well as 2D?
The output of our CNN network is a non-negative tensor named D which dimension is [B,4,H,W]. B is batch size. For every sample, the output is a [4,H,W] tensor named Di. We want minimize the image structure similarity between channels of Di, so we define a custom loss function using SSIM. We calculate the SSIM value of each channel to the others , and take the sum as the final loss.
In the beginning, we did not concern about the different of value distribution between each channel, and the code is :
criterionSSIM = ssim.SSIM(data_range=1, channel=4) //Construct the SSIM criterion
T1 = D.clone().detach()
l1 = T1[:, 0, :, :]
l2 = T1[:, 1, :, :]
l3 = T1[:, 2, :, :]
l4 = T1[:, 3, :, :]
tmp1 = torch.stack([l2, l3, l4, l1], 1)
loss1 = criterionSSIM(fusion_out, tmp1)
tmp2 = torch.stack([l3, l4, l1, l2], 1)
loss2 = criterionSSIM(fusion_out, tmp2)
tmp3 = torch.stack([l4, l1, l2, l3], 1)
loss3 = criterionSSIM(fusion_out, tmp3)
lossSSIM = (loss1+loss2+loss3)
But we found that the SSIM loss go down below zero quickly. To avoid negative SSIM, we normalize every channel of Di to [0, 1], and the code changes to :
criterionSSIM = ssim.SSIM(data_range=1, channel=4) //Construct the SSIM criterion
B, C, H, W = D.shape
for b in range(0, B):
for c in range(0, C):
D[b][c] = D[b][c] / torch.max(D[b][c]) // normalize every channel to [0, 1]
T1 = D.clone().detach()
l1 = T1[:, 0, :, :]
l2 = T1[:, 1, :, :]
l3 = T1[:, 2, :, :]
l4 = T1[:, 3, :, :]
tmp1 = torch.stack([l2, l3, l4, l1], 1)
loss1 = criterionSSIM(fusion_out, tmp1)
tmp2 = torch.stack([l3, l4, l1, l2], 1)
loss2 = criterionSSIM(fusion_out, tmp2)
tmp3 = torch.stack([l4, l1, l2, l3], 1)
loss3 = criterionSSIM(fusion_out, tmp3)
lossSSIM = (loss1+loss2+loss3)
Then the complier reports:
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [224, 224]], which is output 0 of SelectBackward, is at version 128; expected version 127 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True).
We think this error is caused by the normalization action:
for b in range(0, B):
for c in range(0, C):
D[b][c] = D[b][c] / torch.max(D[b][c]) // normalize every channel to [0, 1]
But as a rookie, we don’t know how to fix it. I checked out#6934but got no clue. If anybody here can help us, that will be very appreciated and thankful.
Hi, thanks for the great implementation. I am currently working with greyscale images and implemented the loss for 1 channel images here. Would you mind looking at the implementation and see if it is accurate?
Thank you.
It doesnt work in my case
Example
https://colab.research.google.com/drive/1jK-2TAJs6W4vmjVdG_c_Fnjr1MG0USdd
I have a network that trains 64x64px images. I can't currently use MSSSIM as a loss function as the number of downsamples means I need a larger input image size as suggested by the error message,
Image size should be larger than 160 due to the 4 downsamplings in ms-ssim
What is the best way to deal with this? I have to keep the 64x64px input image size for various reasons. My immediate thoughts are to pad the input images with zero (or 0.5???) up to 160x160 prior to calculating the loss?
Is this a legitimate way to go?
Steve
Image size should be larger than 160 due to the 4 downsamplings in ms-ssim.How should I change it?
Hi @VainF
I am trying to train my cnn model with ssim loss.
So, I used 2 methods for training:
Method 1:
output_normalized = (output-min_val)/(max_val-min_val)
target_normalized = (target-min_val)/(max_val-min_val)
loss = 100*(1 - ssim(output_normalized, target_normalized, data_range=1, size_average=True))
Method 2:
loss = 100*(1 - ssim(output-min_val, target-min_val, data_range=max_val-min_val, size_average=True))
-Which method is better for training with ssim ?
-What is better:
to compute data_range for each output of the model
or
to use fixed data_range for all outputs ?
Thanks
What does T mean in (N,C,[T,]H,W) in ms_ssim?
Here is it, very noticeable artifacts
Here is demo
https://colab.research.google.com/drive/1unDKzCr2wIrTISc9PzbHlwtXb1LSeFq9?usp=sharing
In my test.
It looks a bit counterintuitive. However, separate operations are indeed more efficient than combining operations.
Use #3 test code.
replace
mu1, mu2, sigma1_sq, sigma2_sq, sigma12 = (
concat_out[:, idx*channel:(idx+1)*channel, :, :] for idx in range(5))
to
mu1, mu2, sigma1_sq, sigma2_sq, sigma12 = torch.chunk(concat_out, 5, 1)
can reduce the running time from 51s to 37s.
replace
concat_input = torch.cat([X, Y, X*X, Y*Y, X*Y], dim=1)
concat_win = win.repeat(5, 1, 1, 1).to(X.device, dtype=X.dtype)
concat_out = gaussian_filter(concat_input, concat_win)
mu1, mu2, sigma1_sq, sigma2_sq, sigma12 = (
concat_out[:, idx*channel:(idx+1)*channel, :, :] for idx in range(5))
to
win = win.to(X.device, dtype=X.dtype)
mu1 = gaussian_filter(X, win)
mu2 = gaussian_filter(Y, win)
sigma1_sq = gaussian_filter(X * X, win)
sigma2_sq = gaussian_filter(Y * Y, win)
sigma12 = gaussian_filter(X * Y, win)
can reduce the running time from 51s to 36s and reduce vram occupancy from 1G to 733MB.
first of all, thank you for sharing it with us!
while reading your code I've noticed in ssim.py line 195-6:
msssim_val = torch.prod((mcs[:-1] ** weights[:-1].unsqueeze(1)) * (ssim_val ** weights[-1]), dim=0) # (batch, )
as far as I understand, the ssim result is the product of l_m(p) and cs(p), it seems that your code will calculate:
I think that the correct code would be:
msssim_val = torch.prod((mcs ** weights.unsqueeze(1)), dim=0) * (ssim_val / cs) # (batch, )
MS_SSIM throw RuntimeError: Calculated padded input size per channel: (6 x 6). Kernel size: (1 x 11). Kernel size can't be greater than actual input size when input image size too small.
Maybe you can add a parameter to allow dynamic modification of the window size or need padding?
I'm using ssim loss for my autoencoder
seems like the result comes out real nice
but I see that the loss is bigger than one
whereas in the equation, I don't see how it can be bigger than zero
any help or intuition on this?
When I input 2D grayscale image ,i report this.
ValueError: Input images should be 4-d or 5-d tensors, but got torch.Size([256, 256])
Therefore,I change the input dimension to 4D(1,1,256,256),but i got this problem.
RuntimeError: Given groups=1, weight of size [3, 1, 1, 11], expected input[1, 3, 246, 256] to have 1 channels, but got 3 channels instead
Quick question on the output range. Does the SSIM implementation output number of range [0;1] or [-1;1] instead as described in the original SSIM paper http://www.cns.nyu.edu/pub/lcv/wang03-reprint.pdf ?
I am working on medical image. It is 5D tensor, such as BxNxDxHxW. Could you please write a test function for 5D tensor? I hear that we can use view function to convert 5D to 4D and then use your code but I am not sure about speed and how to do it? Thanks
pytorch-msssim/tests/tests_cuda.py
Line 9 in 3a42966
In additions, we have one bug when the channel is not 3. Let try the script
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
from pytorch_msssim import *
import torch
s = SSIM(data_range=1.)
a = torch.randint(0, 255, size=(10, 2, 64, 64, 64), dtype=torch.float32).cuda() / 255.
b = a * 0.5
B,c,d,h,w = a.size()
a = a.view(B,c,d,-1)
b = b.view(B,c,d,-1)
a.requires_grad = True
b.requires_grad = True
print(a.size(), b.size())
start_record = torch.cuda.Event(enable_timing=True)
end_record = torch.cuda.Event(enable_timing=True)
start_record.record()
for _ in range(500):
loss = s(a, b)
loss.backward()
end_record.record()
torch.cuda.synchronize()
print('cuda time: ', start_record.elapsed_time(end_record)/1000)
Bug is
File "/home/john/pytorch-msssim/pytorch_msssim/ssim.py", line 63, in _ssim
mu1 = gaussian_filter(X, win)
File "/home/john/pytorch-msssim/pytorch_msssim/ssim.py", line 34, in gaussian_filter
out = F.conv2d(input, win, stride=1, padding=0, groups=1)
RuntimeError: Given groups=1, weight of size [3, 1, 1, 11], expected input[2, 2, 64, 4096] to have 1 channels, but got 2 channels instead
Hi, I came across this while looking for a PyTorch implementation of SSIM. On the Tensorflow page for SSIM it's mentioned that "Note: The true SSIM is only defined on grayscale. This function does not perform any colorspace transform. (If the input is already YUV, then it will compute YUV SSIM average.)"
I was wondering if there are any conversions (either to YUV or to grayscale) that I would have to do to use this. My images are all RGB images.
Great repository! I am working on a deep learning application where about 70% of the pixels in my ground truth target images are masked, because they contain invalid pixels. Is there a way to extend this repository to calculate the SSIM only over valid pixels?
As a first step, it would be really helpful to add the torch argument: reduction = 'none' and have _ssim() return the ssim_map instead of the average across all pixels in the image. It's not perfect, but from there one could calculate an approximate MaskedSSIM by averaging SSIM only over valid pixels.
torch reduction argument for reference:
reduction (str, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'
So in the original SSIM implementation in skimage.metrics
, we can get the diff image which is the actual image differences between the two images. Do we have support to get the diff image in MSSSIM as well?
I found your code quite useful in my project. However, lines from 230 to 234 in ssim.py make the list mcs
contain only level-1
(M-1) times cs values, while the original equation requires M times values.
Hi , thanks for your share, I don't know how to use it to train ? My code is here, the loss (ssim_out) is decreasing , but the img2 is not correctly!!.
#import pytorch_ssim
from pytorch_msssim import ssim, ms_ssim, SSIM, MS_SSIM
import torch
from torch.autograd import Variable
from torch import optim
import cv2
import numpy as np
npImg1 = cv2.imread("einstein.png")[:,:,0]
npImg1 = npImg1[:,:, np.newaxis]
img1 = torch.from_numpy(np.rollaxis(npImg1, 2)).float().unsqueeze(0)/255.0
img2 = torch.rand(img1.size())
if torch.cuda.is_available():
img1 = img1.cuda()
img2 = img2.cuda()
img1 = Variable( img1, requires_grad=False)
img2 = Variable( img2, requires_grad = True)
# Functional: pytorch_ssim.ssim(img1, img2, window_size = 11, size_average = True)
#ssim_value = pytorch_ssim.ssim(img1, img2).item()
ssim_value = ssim(img1, img2).item()
print("Initial ssim:", ssim_value)
# Module: pytorch_ssim.SSIM(window_size = 11, size_average = True)
#ssim_loss = pytorch_ssim.SSIM()
ssim_loss = SSIM(win_size=11, win_sigma=1.5, data_range=255, size_average=True, channel=1)
optimizer = optim.Adam([img2], lr=0.01)
while ssim_value < 1.0:
optimizer.zero_grad()
ssim_out = ssim_loss(img1, img2)
#ssim_out = torch.nn.functional.mse_loss(img1, img2)
print(ssim_out.item())
ssim_out.backward()
optimizer.step()
img2_ = (img2 * 255.0).squeeze()
#np_img2 = img2_.data.numpy().transpose(1,2,0).astype(np.uint8)
np_img2 = img2_.data.numpy().astype(np.uint8)
cv2.imwrite("result.jpg", np_img2)
cv2.imshow("result",np_img2)
cv2.waitKey(5)
cv2.waitKey(0)
But when I use Other-SSIM, any img1 picture can trained and show correctly
train code here:
import pytorch_ssim
import torch
from torch.autograd import Variable
from torch import optim
import cv2
import numpy as np
npImg1 = cv2.imread("einstein.png")[:,:,0]
npImg1 = npImg1[:,:, np.newaxis]
img1 = torch.from_numpy(np.rollaxis(npImg1, 2)).float().unsqueeze(0)/255.0
img2 = torch.rand(img1.size())
if torch.cuda.is_available():
img1 = img1.cuda()
img2 = img2.cuda()
img1 = Variable( img1, requires_grad=False)
img2 = Variable( img2, requires_grad = True)
# Functional: pytorch_ssim.ssim(img1, img2, window_size = 11, size_average = True)
ssim_value = pytorch_ssim.ssim(img1, img2).item()
print("Initial ssim:", ssim_value)
# Module: pytorch_ssim.SSIM(window_size = 11, size_average = True)
ssim_loss = pytorch_ssim.SSIM()
optimizer = optim.Adam([img2], lr=0.01)
while ssim_value < 0.97:
optimizer.zero_grad()
ssim_out = -ssim_loss(img1, img2)
#ssim_out = -torch.nn.functional.mse_loss(img1, img2)
ssim_value = - ssim_out.item()
print(ssim_value)
ssim_out.backward()
optimizer.step()
img2_ = (img2 * 255.0).squeeze()
#np_img2 = img2_.data.numpy().transpose(1,2,0).astype(np.uint8)
np_img2 = img2_.data.numpy().astype(np.uint8)
cv2.imwrite("result.jpg", np_img2)
cv2.imshow("result",np_img2)
cv2.waitKey(5)
cv2.waitKey(0)
It seems that whether the class SSIM/MS_SSIM or the function ssim/msssim are all unsupported for the input with 1 channel. When I fed the ssim function with input array of size (1,1,1984,1984), I encountered the following error:
dim' is an invalid keyword argument for squeeze()
What's more, only 4D numpy array is supported, but not for 2D image, which requires expanding dimension first before being processed. This is also not very convenient. Please consider adding some parameters/functions for more versatile usage. Thanks for your efforts!
What are your thoughts about extending the library to include the following metrics:
From a quick look, the required changes are:
An implementation choice that I did not understand is why do you apply multiple 1D gaussian smoothings (here) instead of a 2D or 3D one. Could you please explain it?
What would you think about a collaboration to extend the codebase and improve it?
Hi,
Have you some plan to develop this function for c++ api of pytorch? I am interested to contribute.
Thanks
For 3d images was getting the error:
out = conv(out, weight=win.transpose(2 + i, -1), stride=1, padding=0, groups=C)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: expected stride to be a single integer value or a list of 2 values to match the convolution dimensions, but got stride=[1, 1, 1]
though weight was 4D? is this incorrect and requires and undate?
def gaussian_filter(input: Tensor, win: Tensor) -> Tensor:
r""" Blur input with 1-D kernel
Args:
input (torch.Tensor): a batch of tensors to be blurred
window (torch.Tensor): 1-D gauss kernel
Returns:
torch.Tensor: blurred tensors
"""
ndim = input.dim()
assert all([ws == 1 for ws in win.shape[1:-1]]), win.shape
if ndim == 4:
conv = F.conv2d
elif ndim == 5:
conv = F.conv3d
else:
raise NotImplementedError(input.shape)
C = input.shape[1]
out = input
for i, s in enumerate(input.shape[2:]):
if ndim == 5:
weight = win.view(1, 1, 1, 1, win.shape[-1])
elif ndim == 4:
weight = win.view(1, 1, 1, win.shape[-1])
if s >= win.shape[-1]:
out = conv(out, weight=weight, stride=1, padding=0, groups=C)
else:
warnings.warn(
f"Skipping Gaussian Smoothing at dimension 2+{i} for input: {input.shape} and win size: {win.shape[-1]}"
)
return out
Let me know,
I have 3 images: the inference
, img_1
, and img_2
.
I use this code to calculate SSIM value:
from pytorch_msssim import ssim
ssim_value = ssim(reference.unsqueeze(0), img_1.unsqueeze(0), data_range=255)
output_img = torch.cat((reference, img_1), dim=2)
plt.figure(figsize=(8, 4))
plt.imshow(output_img.numpy().transpose(1, 2, 0))
plt.axis('off')
plt.title(f'ssim = {ssim_value}')
plt.show()
ssim_value = ssim(reference.unsqueeze(0), img_2.unsqueeze(0), data_range=255)
output_img = torch.cat((reference, img_2), dim=2)
plt.figure(figsize=(8, 4))
plt.imshow(output_img.numpy().transpose(1, 2, 0))
plt.axis('off')
plt.title(f'ssim = {ssim_value}')
plt.show()
When i use SSIM with my autoencoder it produces greyscale images (or very close colors), yet mean square error gives me color. Is there a reason for this, i have tried taking SSIM per channel and i get the same issue.
I am using the loss as:
ssim_loss = SSIM(data_range=1.0, size_average=True, channel=3)
Any suggestions?
Hi Author,
You mentioned in pytorch zero-padding is used to replace symmetric padding.
I think replication padding in pytorch is the same as symmetric padding in tf. Would you take a look?
def gaussian_filter(input, win):
r""" Blur input with 1-D kernel
Args:
input (torch.Tensor):a batch of tensors to be blured
window (torch.Tensor): 1-D gauss kernel
Returns:
torch.Tensor: blured tensors
"""
N, C, H, W = input.shape
out = F.conv2d(input, win, stride=1, padding=0, groups=C)
# make it contiguous in y direction for memory efficiency
out = F.conv2d(out, win.transpose(2, 3), stride=1, padding=0, groups=C)
return out #.contiguous()
Just transpose conv kernel can reduce some ops. Maybe it can be faster.
Hi,
I used MS_SSIM with default win_size=11 and default weights = [0.0448, 0.2856, 0.3001, 0.2363, 0.1333]
my input image size is 64x64
So, I get this error :
RuntimeError: Calculated padded input size per channel: (8 x 8). Kernel size: (1 x 11). Kernel size can't be greater than actual input size
ms_ssim function outputs NaN when the input images are anti-correlated, i.e. when ssim would output values between -1 and 0.
Example:
X = torch.rand(3,1,190,190)
Y = 1 - X
print(ssim( X, Y, data_range=1, size_average=False))
print(ms_ssim( X, Y, data_range=1, size_average=False))
tensor([-0.9664, -0.9654, -0.9649])
tensor([nan, nan, nan])
Some times cs calculated by the _ssim will be negative, and let the result be NaN
Hi,
Thanks for this tool. I use both pytorch_mssim.ssim and skimage.measure.compare_ssim to compute ssim, but the results are different. For example, ssim evaluation on an image sequence:
pytorch_msssim.ssim: [0.9655, 0.9500, 0.9324, 0.9229, 0.9191, 0.9154]
skimage.measure.compare_ssim: [0.97794482, 0.96226299, 0.948432, 0.9386946, 0.93113704, 0.92531453]
Why will this happen?
Hi, I want to know where to download the CLIC dataset. I went to the website: http://clic.compression.cc/2019/challenge. But the file is no longer on the server. Does anyone has the copy of the dataset? Thank you!
Hi, I just test pytorch_msssim.ssim and skimage. When I compute ssim value with these two methods on 2d matrix, I got different results, I want to know what the problem is. Maybe I use ssim in a wrong way? Here are my codes and results.
code:
`import torch
from skimage.metrics import structural_similarity
from pytorch_msssim import ssim
m0 = torch.ones(7, 7, dtype=torch.float) # OR matrix
m0[4:7, 0:3] = 0
m_sig = torch.ones(7, 7, dtype=torch.float)
m_sig[4:7, 0] = 0
ssim_out = ssim(m0.reshape(1, 1, 7, 7), m_sig.reshape(1, 1, 7, 7))
print('pytorch_ssim_value = ' + str(ssim_out.numpy()))
ssim_value2 = structural_similarity(m_sig.numpy(), m0.numpy())
print('ssim_value = ' + str(ssim_value2))`
results:
ytorch_ssim_value = 0.9836789
ssim_value = 0.4858374093651142
`from torchvision import transforms
from PIL import Image
from pytorch_msssim import ssim
import glob
import os
def load_image(image_path):
transform = transforms.Compose([
#transforms.Resize((256, 256)),
transforms.ToTensor()
])
image = Image.open(image_path).convert('RGB')
return transform(image).unsqueeze(0)
def calculate_ssim(image1, image2):
image1 = image1.cuda()
image2 = image2.cuda()
ssim_value = ssim(image1, image2, data_range=1.0, size_average=False)
return ssim_value.item()
def process_directory(hazy_dir, clear_dir):
hazy_images = glob.glob(os.path.join(hazy_dir, '*'))
clear_images = glob.glob(os.path.join(clear_dir, '*'))
ssim_scores = []
for hazy_image_path, clear_image_path in zip(hazy_images, clear_images):
img1 = load_image(hazy_image_path)
img2 = load_image(clear_image_path)
ssim_score = calculate_ssim(img1, img2)
ssim_scores.append(ssim_score)
print(f"SSIM for {os.path.basename(hazy_image_path)} and {os.path.basename(clear_image_path)}: {ssim_score:.4f}")
if ssim_scores:
average_ssim = sum(ssim_scores) / len(ssim_scores)
print(f"Average SSIM: {average_ssim:.4f}")
else:
print("No images to process.")
if name == 'main':
input_file = '../../test/O-HAZY/GT/'
output_file = '../../results/defog-spa/'
process_directory(input_file, output_file)`
This is Skimage
`import glob
import os
import numpy as np
from skimage.metrics import peak_signal_noise_ratio as psnr
from skimage.metrics import structural_similarity as ssim
from PIL import Image
def calculate_metrics(clean_image_path, dehazed_image_path):
clean_img = Image.open(clean_image_path)
dehazed_img = Image.open(dehazed_image_path)
clean_data = np.asarray(clean_img).astype(np.float32)
dehazed_data = np.asarray(dehazed_img).astype(np.float32)
psnr_value = psnr(clean_data, dehazed_data, data_range=255)
ssim_value = ssim(clean_data, dehazed_data, data_range=255, multichannel=True)
return psnr_value, ssim_value`
But the average result pytorch_msssim is 0.6136
Skimage is 0.5985
If the window is modified to 11, the result is 0.5864
pytorch-msssim/pytorch_msssim/ssim.py
Line 135 in d23a69e
pytorch-msssim/pytorch_msssim/ssim.py
Line 183 in d23a69e
I think it's not necessary to judge if X.type() == Y.type(), or mixed precision training will not be supported.
I see this implamentation is slower than skimage.measure.compare_ssim
in Tests
I would like to ask about the reproducibility issues with SSIM and MS-SSIM. I am considering using MS-SSIM as a loss function to supervise my model's training. However, I have noticed that when using pytorch_msssim for SSIM and MS-SSIM, the training loss fluctuates with each run. In contrast, using the SSIM implementation from this source does not result in such fluctuations. Have you encountered similar issues, or do you have any suggestions to ensure consistent loss during training?
Hi, I'm not sure if this behavior is intended or not but for a batch of 64 images, when I set size_average=False, i get a tensor of size 64 with individual values:
tensor([1.0004, 0.9843, 0.9976, 0.9938, 0.9879, 0.9989, 0.9916, 0.9976, 1.0069,
0.9847, 0.9832, 0.9844, 0.9757, 0.9914, 0.9717, 1.0027, 1.0106, 0.9889,
0.9885, 0.9949, 0.9996, 0.9965, 0.9801, 0.9887, 0.9879, 0.9804, 0.9926,
0.9856, 0.9896, 0.9936, 0.9950, 0.9941, 0.9911, 0.9860, 0.9886, 0.9949,
0.9881, 0.9898, 0.9934, 0.9825, 0.9939, 0.9912, 0.9955, 0.9937, 1.0005,
0.9975, 0.9831, 1.0005, 0.9970, 0.9953, 0.9855, 1.0001, 1.0101, 0.9862,
0.9960]
The usual approach in PyTorch is:
size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
Source: https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html
您好,根据您的提示:https://www.compression.cc/challenge/ 这个页面下找不到数据集,请问有没有别的方式可以获取到数据集呢?
I am now investigating motion artifact correction in MRI images. which metric should I use to evaluate the performance of my model, SSIM or MS-SSIM?
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