nasa/pretrained-microscopy-models

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pretrained-microscopy-models's Issues

Bug in `image-micronet` weights download.

The issue is on this line of code:

pretrained-microscopy-models/pretrained_microscopy_models/segmentation_training.py

Line 99 in 543e4b0

if encoder_weights in ['micronet', 'imagemicronet']:

If we request image-micronet weights they are going to be ignored - this if statement returns false. If I instead request imagemicronet weights the code crashes as there is no such variant available.

Possible fix would be to modify the list:
if encoder_weights in ['micronet', 'image-micronet']:

Load pretrained EfficientNet models

Hi!

Thanks for sharing your pretrained models!
Can you provide an example code for loading your pretrained EfficientNet models? Maybe I am missing something, but I think they cannot be loaded by torch.hub.load from torchvision repository.

Thanks in advance!

Training not converge on EBC subsets

Thank you so much for your kind reply before. I have successfully reproduced results on superalloyed subsets (Super1-4). However, when it comes to EBC subsets. There are still some problems:

1. The format of annotation files in EBC subsets seem to be different from those in Super1-4. EBC's annotations are already within the range [0-1] so the logic in io.py might need to be changed. (here, and here). Otherwise the visualization of GT will be a completely purple image like this:
   

2. I simply followed the example, and comment the lines and changed the path from Super1 to EBC1 to let it work for EBC1. The GT visualization seems to be normal now, however, the loss value didn't converge.

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Best model saved!

Epoch: 1, lr: 0.00020000, time: 16.11 seconds, patience step: 0, best iou: 0.1046

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Epoch: 2, lr: 0.00020000, time: 7.93 seconds, patience step: 1, best iou: 0.1046

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Epoch: 3, lr: 0.00020000, time: 7.82 seconds, patience step: 2, best iou: 0.1046

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Epoch: 4, lr: 0.00020000, time: 7.93 seconds, patience step: 3, best iou: 0.1046

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Epoch: 5, lr: 0.00020000, time: 7.93 seconds, patience step: 4, best iou: 0.1046

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Epoch: 6, lr: 0.00020000, time: 7.74 seconds, patience step: 5, best iou: 0.1046

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Epoch: 7, lr: 0.00020000, time: 7.84 seconds, patience step: 6, best iou: 0.1046

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Epoch: 8, lr: 0.00020000, time: 7.42 seconds, patience step: 7, best iou: 0.1046

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Epoch: 9, lr: 0.00020000, time: 7.84 seconds, patience step: 8, best iou: 0.1046

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Epoch: 10, lr: 0.00020000, time: 8.09 seconds, patience step: 9, best iou: 0.1046

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Epoch: 11, lr: 0.00020000, time: 7.98 seconds, patience step: 10, best iou: 0.1046

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Epoch: 12, lr: 0.00020000, time: 6.91 seconds, patience step: 11, best iou: 0.1046

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train:  33%|███▎      | 1/3 [00:00<00:01,  1.99it/s, DiceBCELoss - 0.7798, iou_score - 6.275e-06]
train:  67%|██████▋   | 2/3 [00:00<00:00,  2.03it/s, DiceBCELoss - 0.7798, iou_score - 6.275e-06]
train:  67%|██████▋   | 2/3 [00:01<00:00,  2.03it/s, DiceBCELoss - 0.7859, iou_score - 4.183e-06]
train: 100%|██████████| 3/3 [00:01<00:00,  2.15it/s, DiceBCELoss - 0.7859, iou_score - 4.183e-06]
train: 100%|██████████| 3/3 [00:01<00:00,  2.11it/s, DiceBCELoss - 0.7859, iou_score - 4.183e-06]

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valid: 100%|██████████| 1/1 [00:00<00:00,  9.93it/s, DiceBCELoss - 0.7631, iou_score - 1.905e-13]

Epoch: 13, lr: 0.00020000, time: 7.74 seconds, patience step: 12, best iou: 0.1046

train:   0%|          | 0/3 [00:00<?, ?it/s]
train:   0%|          | 0/3 [00:00<?, ?it/s, DiceBCELoss - 0.7984, iou_score - 1.309e-13]
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train:  33%|███▎      | 1/3 [00:00<00:01,  1.93it/s, DiceBCELoss - 0.7794, iou_score - 1.119e-13]
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train:  67%|██████▋   | 2/3 [00:01<00:00,  2.04it/s, DiceBCELoss - 0.7861, iou_score - 1.186e-13]
train: 100%|██████████| 3/3 [00:01<00:00,  2.03it/s, DiceBCELoss - 0.7861, iou_score - 1.186e-13]
train: 100%|██████████| 3/3 [00:01<00:00,  2.02it/s, DiceBCELoss - 0.7861, iou_score - 1.186e-13]

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valid: 100%|██████████| 1/1 [00:00<00:00, 10.29it/s, DiceBCELoss - 0.7631, iou_score - 1.905e-13]

Epoch: 14, lr: 0.00020000, time: 7.82 seconds, patience step: 13, best iou: 0.1046

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valid: 100%|██████████| 1/1 [00:00<00:00,  8.44it/s, DiceBCELoss - 0.7631, iou_score - 1.905e-13]

Epoch: 15, lr: 0.00020000, time: 7.83 seconds, patience step: 14, best iou: 0.1046

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train:  33%|███▎      | 1/3 [00:00<00:01,  1.97it/s, DiceBCELoss - 0.791, iou_score - 1.233e-13] 
train:  67%|██████▋   | 2/3 [00:00<00:00,  2.10it/s, DiceBCELoss - 0.791, iou_score - 1.233e-13]
train:  67%|██████▋   | 2/3 [00:01<00:00,  2.10it/s, DiceBCELoss - 0.7859, iou_score - 1.175e-13]
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Epoch: 16, lr: 0.00020000, time: 7.21 seconds, patience step: 15, best iou: 0.1046

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Epoch: 17, lr: 0.00020000, time: 7.76 seconds, patience step: 16, best iou: 0.1046

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Epoch: 18, lr: 0.00020000, time: 7.74 seconds, patience step: 17, best iou: 0.1046

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Epoch: 19, lr: 0.00020000, time: 7.98 seconds, patience step: 18, best iou: 0.1046

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Epoch: 20, lr: 0.00020000, time: 7.22 seconds, patience step: 19, best iou: 0.1046

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train:  33%|███▎      | 1/3 [00:00<00:01,  1.88it/s, DiceBCELoss - 0.7835, iou_score - 1.144e-13]
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Epoch: 21, lr: 0.00020000, time: 7.81 seconds, patience step: 20, best iou: 0.1046

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train:  67%|██████▋   | 2/3 [00:01<00:00,  2.01it/s, DiceBCELoss - 0.7857, iou_score - 1.162e-13]
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train: 100%|██████████| 3/3 [00:01<00:00,  1.97it/s, DiceBCELoss - 0.7857, iou_score - 1.162e-13]

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valid: 100%|██████████| 1/1 [00:00<00:00,  9.83it/s, DiceBCELoss - 0.7631, iou_score - 1.905e-13]

Epoch: 22, lr: 0.00020000, time: 7.90 seconds, patience step: 21, best iou: 0.1046

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Feature Extraction

I am trying to use your pretrained microscopy model for feature extraction, but I am running into some issues. Do you have any suggestions?

I have tried using the pmm_model.predict(test_image); however the formatting of the test_image is incorrect for the model. I have the model working on my own training images already, but I am additionally trying to extract the features for use in another classifier. Any help would be appreciated!

Error in preprocessing_fn: `AttributeError: 'Image' object has no attribute 'max'`

I went to run the example I had before again but with classes=1 per #4; however, it seemed to error out in a different spot. I didn't think I had changed the code, but at the same time the most recent commit predates when I shared the reproducer notebook.

Here's another reproducer (pinned to the most recent commit) for a different error.

AttributeError                            Traceback (most recent call last)
[<ipython-input-7-15a16e61f48a>](https://localhost:8080/#) in <module>()
      2 preprocessing_fn = smp.encoders.get_preprocessing_fn('resnet50', 'imagenet')
      3 img = np.asarray(img)
----> 4 pred = pmm.segmentation_training.segmentation_models_inference(cropped_img, model, preprocessing_fn, batch_size=4, patch_size=224, device='cpu', probabilities=None)

1 frames
[/usr/local/lib/python3.7/dist-packages/pretrained_microscopy_models/segmentation_training.py](https://localhost:8080/#) in segmentation_models_inference(io, model, preprocessing_fn, device, batch_size, patch_size, num_classes, probabilities)
    204     # This will not output the first class and assumes that the first class is wherever the other classes are not!
    205 
--> 206     io = preprocessing_fn(io)
    207     io_shape_orig = np.array(io.shape)
    208     stride_size = patch_size // 2

[/usr/local/lib/python3.7/dist-packages/segmentation_models_pytorch/encoders/_preprocessing.py](https://localhost:8080/#) in preprocess_input(x, mean, std, input_space, input_range, **kwargs)
     10 
     11     if input_range is not None:
---> 12         if x.max() > 1 and input_range[1] == 1:
     13             x = x / 255.0
     14 

AttributeError: 'Image' object has no attribute 'max'AttributeError                            Traceback (most recent call last)
[<ipython-input-7-15a16e61f48a>](https://localhost:8080/#) in <module>()
      2 preprocessing_fn = smp.encoders.get_preprocessing_fn('resnet50', 'imagenet')
      3 img = np.asarray(img)
----> 4 pred = pmm.segmentation_training.segmentation_models_inference(cropped_img, model, preprocessing_fn, batch_size=4, patch_size=224, device='cpu', probabilities=None)

1 frames
[/usr/local/lib/python3.7/dist-packages/pretrained_microscopy_models/segmentation_training.py](https://localhost:8080/#) in segmentation_models_inference(io, model, preprocessing_fn, device, batch_size, patch_size, num_classes, probabilities)
    204     # This will not output the first class and assumes that the first class is wherever the other classes are not!
    205 
--> 206     io = preprocessing_fn(io)
    207     io_shape_orig = np.array(io.shape)
    208     stride_size = patch_size // 2

[/usr/local/lib/python3.7/dist-packages/segmentation_models_pytorch/encoders/_preprocessing.py](https://localhost:8080/#) in preprocess_input(x, mean, std, input_space, input_range, **kwargs)
     10 
     11     if input_range is not None:
---> 12         if x.max() > 1 and input_range[1] == 1:
     13             x = x / 255.0
     14 

AttributeError: 'Image' object has no attribute 'max'

Randomness in validation loss?

I see a worryingly high fluctuation in validation loss that never seem to stabilize. Even if I set LR = 1e-99 in multiclass_segmentation_materials.ipynb in the finetuning step it's varying between 0.95 down to 0.5 between epochs, why is there any variation at all?

Class labels and dataset download

Hi,
I have two questions:

1. Is the MicroNet dataset publically available? I could not find a link to it in the paper/code/from googling
2. What are the class labels in the classfication task? I could not find them in the paper/code. The example simply outputs an index.

Thanks!
Kevin

`get_segmentation_model()` nowhere to be found other than README instructions

https://github.com/nasa/pretrained-microscopy-models/search?q=get_segmentation_model

Since GitHub search is sometimes unreliable at finding every instance, took a look at a local fork in VS Code as well:

Issue on SE_ResNet-50 MicroNet Pre-trained model

Hello,

I am currently facing a problem when I try to use the SE_ResNet-50 MicroNet pre-trained model. I encountered an HTTPS error during the process of downloading the model.

Thanks

Can this repo directly inference on unlabelled raw images?

Dear authors,

I now have a set of images but do not have any annotated mask for now. I want to use your pre-trained segmentation model to do inference on the images.

1. However, it seems like this repo only contains the model for the encoder, but does not contain the complete model for segmentation. Am I missing something?

2. I tried to follow multiclass_segmentation_example.ipynb but it seems like we need a dataset in DATA_DIR = 'Super1/'. Could you also tell me where we can get these? (You have mentioned that 1/3 of the data is available in #11 (comment))

Best,

Looking forward to trying this on some monophase GB segmentation!

Great presentation at the TechBlick virtual conf

when `classes==2`, `"AssertionError: Two classes is binary classification. Just specify the posative class value"`

import pretrained_microscopy_models as pmm
# setup a UNet model with a ResNet50 backbone.
model = pmm.segmentation_training.create_segmentation_model('Unet', 'resnet50', 'micronet', classes=2)

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
[<ipython-input-18-e7bebb562564>](https://localhost:8080/#) in <module>()
      2 
      3 # setup a UNet model with a ResNet50 backbone.
----> 4 model = pmm.segmentation_training.create_segmentation_model('Unet', 'resnet50', 'micronet', classes=2)

[/usr/local/lib/python3.7/dist-packages/pretrained_microscopy_models/segmentation_training.py](https://localhost:8080/#) in create_segmentation_model(architecture, encoder, encoder_weights, classes, activation)
     75     # setup and check parameters
     76     assert classes != 2, "Two classes is binary classification.  \
---> 77         Just specify the posative class value"
     78 
     79     if activation is None:

AssertionError: Two classes is binary classification.          Just specify the posative class value

I'm not sure what is meant by "just specify the [positive] class value". Is the suggestion to use a different function?

reproducer: https://colab.research.google.com/drive/1eGvA7_q1dgIEZjHJvugZ0aa-koY3Vg_c#revisionId=0B0MK2L13BxuWaktYK3BYT3RscWxuVXBUQkZIbHNia2M4eCs0PQ

How to segment an unlabeled image, colab example

Could you help me fill in the blank here?

https://colab.research.google.com/drive/1eGvA7_q1dgIEZjHJvugZ0aa-koY3Vg_c?usp=sharing