Official TensorFlow code for paper "Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks".
Home Page: https://www.mdpi.com/2072-4292/12/14/2207
License: Apache License 2.0
Hello! I am getting these warnings while running the test scripts.
As a result, the final images are quite grayish and not looking well too.
Can you help with this, I am using the code without any changes.
@fsalv @EscVM Could you please have a look at it. :)
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer
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WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-56.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-57.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-58.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-59.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-60.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-61.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-62.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-63.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-64.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-65.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-66.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-67.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-68.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-69.layer.bias
WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer's state 'v' for (root).model.layer_with_weights-70.layer.bias
WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
After preprocessing the default data, i'm trying to run the train_RAMS_network notebook.
When calling the fit() function, I'm getting an
InternalError: Failed copying input tensor from /job:localhost/replica:0/task:0/device:CPU:0 to /job:localhost/replica:0/task:0/device:GPU:0 in order to run _EagerConst: Dst tensor is not initialized.
From what I could find, it means it was impossible to allocate enough GPU memory for the tensor, and decreasing batch size might help - unfortunately it didn't, I'm getting the same error even after decreasing it to 1.
I'm using a GTX1660Ti and the script allocates about 4.7 out of 6GB of its RAM before the error, is there any way I can get it to run on this hardware?
Excuse me, what kind of files(in ZIP) need to submit in PROBA-V Super Resolution post mortem?
I have an account but don't know how to use it. Thank you!
Hi! Thanks for your work!
I have another question for help. There are many different ckpt files uploaded, what models do these files represent?
I'd like to compare your methods named RAMS and RAMS+20 in the validation set you provide.
I also used the same training set and validation set. Now I need to compare all of the verification scenes.
Thanks a lot!!
I'm not familiar with jupyter also I really need to run the script through python, but I receive that error.
Don't you have a version launchable through python directly ?
Hey guys,
Validation loss and Validation PSNR in train_RAMS is coming consistently as 0 in the progress bar, as visible in the attached image.
(a) Could you tell us where it might be going wrong?
(b) Does it impact the overall training process?
Hello @fsalv ,
I am trying to reproduce your results training a model from scratch and am having some trouble. One thing I've noticed is this:
The register_imgset function L160 returns the input imgset instead of the registered version of the images imgset_reg. See here:
Line 160 in bc16e52
To me this seems like a mistake. Maybe I am missing something?
If this is a mistake, could you please advise if you think your pretrained models were affected?
RuntimeError Traceback (most recent call last)
Cell In[6], line 2
1 # train registration
----> 2 X_RED_train, X_RED_train_masks = register_dataset(X_RED_train, X_RED_train_masks)
3 X_NIR_train, X_NIR_train_masks = register_dataset(X_NIR_train, X_NIR_train_masks)
File c:\Users\Юнсок\Desktop\Research\MISR_research\preprocessing.py:129, in register_dataset(X, masks)
126 masks_reg = []
128 for i in tqdm(range(len(X))):
--> 129 img_reg,m_reg = register_imgset(X[i], masks[i])
130 X_reg.append(img_reg)
131 masks_reg.append(m_reg)
File c:\Users\Юнсок\Desktop\Research\MISR_research\preprocessing.py:155, in register_imgset(imgset, mask)
153 x = imgset[...,i]; m = mask[...,i]
154 s = phase_cross_correlation(ref, x, reference_mask = m)
--> 155 x = shift(x, s, mode='reflect')
156 m = shift(m, s, mode='constant', cval=0)
157 imgset_reg[...,i] = x
File c:\Users\Юнсок\AppData\Local\Programs\Python\Python310\lib\site-packages\scipy\ndimage_interpolation.py:684, in shift(input, shift, output, order, mode, cval, prefilter)
682 filtered = input
683 mode = _ni_support._extend_mode_to_code(mode)
--> 684 shift = _ni_support._normalize_sequence(shift, input.ndim)
...
---> 67 raise RuntimeError(err)
68 else:
69 normalized = [input] * rank
RuntimeError: sequence argument must have length equal to input rank
Anyone accounted such a problem?
There is an indentation error present in the file mentioned above. just above the line 219.
Hello,
One perk I am interested in from your network is to deal with multiple bands at once.
I am trying to train and use the RAMS with multiband inputs (i.e. RGB).
After reading your paper and checking carefully your code I have realized that the architecture of the network is theoretically designed to work with multiple bands, however, since it was used by separated bands in the ESA competition, the present code is not suitable for multiband.
What I did so far is to rearrange the patches generation so that it has an additional dimension for the bands. My array of patches are ( N , 32, 32, 9, 3) Which is NxHxWxTxC
Then I changed this line of code to img_inputs = Input(shape=(None,None,channels,bands))
I observed how you expand dimension just after the input.(code here) Is this to add the extra Bands dimension? (in your case C=1 ?)
I have observed that the RTAB is hard-coded to 9, see this line of code. Following the paper I should set it to 9x3=27, right? Also, before this layer, I am resizeing (following the paper) to (NxHxWx(T·C))
Is there anything else that I am missing?
I am wondering if you have got another version of the code that is not hardcoded for single bands or if it is feasible to arrange it for multiple bands.
Many thanks for all your work, it is just fantastic,
pau
Hi,
Thank you for your work! I am working on a new framework to solve the MISR problem.
Since I am not deeply involved in this field, I would like to add some experiments to make my work more abundant.
I'd like to ask you, is there any other public dataset for satellite multi-image super-resolution.
Thank you!!
Hello sir,
I tried your algorithm with predefined probv dataset and it works fine. Thank you for the code sir.
I wanted to create my own dataset, so I made one HR and temporal several LRs LR000,LR001,LR002 etc..But I am not sure how to get the QM000.png, QM001.png (the quality maps) and the SM.png (status map). Is there any software where I can make them in batch? what's the difference between the quality map and the status map?. I read your paper, so it it like cloud and cloud shadows removal?, what else could be removed sir?
Am new to image domain so any explanation would be of great help
@fsalv @EscVM
With reference to #8 (comment)
Matrix of how many ones should I set cropped_y_mask as ?
cropped_y_mask = tf.cast([1,1,1,1,1], tf.float32)
cropped_predictions_masked = tf.cast(
cropped_predictions, tf.float32)*cropped_y_mask
cropped_labels_masked = cropped_labels*cropped_y_mask
total_pixels_masked = tf.reduce_sum(cropped_y_mask, axis=[1, 2])
I am getting error
InvalidArgumentError: Invalid reduction dimension (1 for input with 1 dimension(s) [Op:Sum]
D:\SuperResolution\RAMS\utils\loss.py in l1_loss(y_true, y_pred, HR_SIZE)
50 cropped_labels_masked = cropped_labels*cropped_y_mask
51
---> 52 total_pixels_masked = tf.reduce_sum(cropped_y_mask, axis=[1, 2])
53
54 # bias brightness
I tried all combos
cropped_y_mask =[1,1,1,1,1]
cropped_y_mask =[1]
cropped_y_mask =[1,1]
FullError.txt
Any help appreciated
Hello @fsalv ,
I'm curious about the random flips and rotations.
In training.py, LL119-121:
if data_aug:
train_ds.map(random_rotate, num_parallel_calls=tf.data.experimental.AUTOTUNE)
train_ds.map(random_flip, num_parallel_calls=tf.data.experimental.AUTOTUNE)I believe .map is NOT an in-place operator so I don't think this is doing anything. To check this, we can adapt the example from the Tensorflow documentation here: https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map
from tensorflow.data import Dataset
dataset = Dataset.range(1, 6) # ==> [ 1, 2, 3, 4, 5 ]
dataset.map(lambda x: x + 1)
list(dataset.as_numpy_iterator()) # [1, 2, 3, 4, 5] map had no effect
dataset = Dataset.range(1, 6) # ==> [ 1, 2, 3, 4, 5 ]
dataset = dataset.map(lambda x: x + 1)
list(dataset.as_numpy_iterator()) # [2, 3, 4, 5, 6] expected outputDoes this seem like a bug to you? If so, could you please advise if you think your pretrained models were affected?
Hi @bibuzz!
Any help would be appreciated
Please clone the repository again, there was an issue in how our code was managing an even number of temporal frames. Now it should work with your 4 channels. Anyway, note that out Temporal Reduction Block works better with an odd number (minimum of 3), so you should consider adding a temporal step to your dataset.
#-------------
MEAN = 7433.6436 # mean of the proba-v dataset
STD = 2353.0723 # std of proba-v the dataset
Since I am using sentinel images and not of prob-v dataset, then the MEAN and STD will be different ? How do I calculate them?
Those two values are used in the normalization and denormalization layers and they were simply computed on all the training images of the dataset. Just compute mean and standard deviation of your images pixel values.
Originally posted by @fsalv in #7 (comment)
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