the code used for the preprint Embedding-based Instance Segmentation of Microscopy Images.

Hi.

I tried separately ran the notebook[bbbc010-2012] for inference provided by this repo but I had a memory allocation issue. I used batch size as 1.

Is there any other parameters to reduce memory requirement?

Also I set normalization_factor = 32767 if data_type=='8-bit' else 255 instead of normalization_factor = 65535 if data_type=='16-bit' else 255.

But nothing changed.

Hi, again..

When I run begin_evaluating(test_configs, verbose = False, avg_bg= avg_bg/normalization_factor) at predict notebook, I got the following error:

2-D `test` dataloader created! Accessing data from ../../../data/bbbc010-2012/test/
Number of images in `test` directory is 50
Number of instances in `test` directory is 50
Number of center images in `test` directory is 0
*************************
Creating branched erfnet with [4, 1] classes

0%| | 0/50 [00:01<?, ?it/s]

---

RuntimeError Traceback (most recent call last) /tmp/ipykernel_33/4185926816.py in ----> 1 begin_evaluating(test_configs, verbose = False, avg_bg= avg_bg/normalization_factor)

/kaggle/input/embedsegv1/EmbedSeg/test.py in begin_evaluating(test_configs, verbose, mask_region, mask_intensity, avg_bg) 62 test(verbose = verbose, grid_x = test_configs['grid_x'], grid_y = test_configs['grid_y'], 63 pixel_x = test_configs['pixel_x'], pixel_y = test_configs['pixel_y'], ---> 64 one_hot = test_configs['dataset']['kwargs']['one_hot'], avg_bg = avg_bg, n_sigma=n_sigma) 65 elif(test_configs['name']=='3d'): 66 test_3d(verbose=verbose,

/kaggle/input/embedsegv1/EmbedSeg/test.py in test(verbose, grid_y, grid_x, pixel_y, pixel_x, one_hot, avg_bg, n_sigma) 126 127 center_x, center_y, samples_x, samples_y, sample_spatial_embedding_x, sample_spatial_embedding_y, sigma_x, sigma_y,
--> 128 color_sample_dic, color_embedding_dic = prepare_embedding_for_test_image(instance_map = instance_map, output = output, grid_x = grid_x, grid_y = grid_y, pixel_x = pixel_x, pixel_y =pixel_y, predictions =predictions, n_sigma = n_sigma) 129 130 base, _ = os.path.splitext(os.path.basename(sample['im_name'][0]))

/kaggle/input/embedsegv1/EmbedSeg/utils/utils.py in prepare_embedding_for_test_image(instance_map, output, grid_x, grid_y, pixel_x, pixel_y, predictions, n_sigma) 483 sample_spatial_embedding_y[id.item()] = add_samples(samples_spatial_embeddings, 1, grid_y - 1, pixel_y) 484 center_image = predictions[id.item() - 1]['center-image'] # predictions is a list! --> 485 center_mask = in_mask & center_image.byte() 486 487

RuntimeError: result type Byte can't be cast to the desired output type Bool

Hi,

I am trying to run the firs example notebook, and I am failing at the very first cell...

miniconda installation, creating the environment from your directions.

conda env create -f EmbedSeg_environment.yml
conda activate EmbedSegEnv
python3 -m pip install -e .
python3 -m ipykernel install --sys-prefix  --name EmbedSegEnv --display-name "EmbedSegEnv"

(instead of --user to install it into the virtualenv instead of $HOME/.local)

(EmbedSegEnv) [[email protected] EmbedSeg]$ pip3 list |grep numpy
numpy                             1.19.4
(EmbedSegEnv) [[email protected] EmbedSeg]$ pip3 list |grep hdm
hdmedians                         0.14.1

from tqdm import tqdm

from glob import glob

import tifffile

import numpy as np

import os

from EmbedSeg.utils.preprocess_data import extract_data, split_train_val

from EmbedSeg.utils.generate_crops import *

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-1-54e5f42b447e> in <module>
      5 import os
      6 from EmbedSeg.utils.preprocess_data import extract_data, split_train_val
----> 7 from EmbedSeg.utils.generate_crops import *

~/git/github/juglab/EmbedSeg/EmbedSeg/utils/generate_crops.py in <module>
      5 from scipy.ndimage.morphology import binary_fill_holes
      6 from scipy.spatial import distance_matrix
----> 7 import hdmedians as hd
      8 from numba import jit
      9 

/c7/home/tru/miniconda3/envs/EmbedSegEnv/lib/python3.7/site-packages/hdmedians/__init__.py in <module>
      4 
      5 from .medoid import medoid, nanmedoid
----> 6 from .geomedian import geomedian, nangeomedian

hdmedians/geomedian.pyx in init hdmedians.geomedian()

ValueError: numpy.ndarray size changed, may indicate binary incompatibility. Expected 88 from C header, got 80 from PyObject

Hello again.

I have a question about your elaborate notebook. I get stacked one section when loading cmap_60.npy.

When I tried to load it, I got FileNotFoundError: [Errno 2] No such file or directory: '../../../cmaps/cmap_60.npy'.

How can I prepare it?

Hi.

I have a question about implementation about medoid which is mentioned in the paper. I found the calculation of it is done at https://github.com/juglab/EmbedSeg/blob/50f23233cf9564ff443c67c45a611ce665571c12/EmbedSeg/utils/generate_crops.py#L84

But I could not found the clue that this funtion is called from any other python scripts.

Could you tell me how the medoid is used in the code?

Hi, I am trying to create/generate prediction (part 3) but my dataset lacks validation files which prevent me from going further. I was wondering is there a specific function or code that can be implemented to tackle the issue or by default, validation files are required to generate prediction?

embedseg seems promising,

• why not use bsd or apache for license
• how does embedseg compares to DenoiSeg in segmenting connected components, performance, efficiency, etc...

Describe the bug Only 1 thread (core) is used for the dataloaders.

To Reproduce Steps to reproduce the behavior:

1. Spin up any of the training examples
2. Set batch_size to something respectable, like 512
3. Adjust workers dataloader parameter
4. Examine CPU utilization

Expected behavior Multiple cores get engaged and are used to feed the GPU(s).

Screenshots

Desktop (please complete the following information):

• OS: Ubuntu 20.04.2 LTS
• Graphics 2x GeForce GTX 3090

Additional context

train_dataset_dict = create_dataset_dict(
	data_dir = data_dir, 
	project_name = project_name,  
	center = center, 
	size = train_size, 
	batch_size = train_batch_size, 
	virtual_batch_multiplier = virtual_train_batch_multiplier, 
	normalization_factor= normalization_factor,
	one_hot = one_hot,
	workers=16,
	type = 'train'
)

To help debug, from the same virtual environment I put together this dummy script:

import random
import numpy as np
from torch.utils.data import Dataset
import torch
from tqdm.auto import tqdm

class TestDS(Dataset):
    def __len__(self):
        return 5000

    def __getitem__(self, index):
        z = np.zeros((256*256))
        for i in range(256*256): z[i] = i
        return z
        

val_dataset = TestDS()
val_dataset_it = torch.utils.data.DataLoader(
    val_dataset,
    batch_size=32,
    shuffle=True,
    drop_last=True,
    num_workers=12,
    pin_memory=True
)

while True:
    for i, sample in enumerate(tqdm(val_dataset_it)):
        sample = sample.to('cuda:1')

Running the above results in proper core utilization:

Even adding the following code at the head of EmbSeg training script does not help:

import os
os.environ["MKL_NUM_THREADS"] = "20"
os.environ["OMP_NUM_THREADS"] = "20"

• Add Arabidopsis-Cells-CAM notebooks
• Add stitch_2d and stitch_3d functions
• Introduce num_workers while creating test_configs_dict
• Correct path to labkit wiki

• Fix resume path
• Add updated docstrings
• Hide display tags, save_images and virtual_batch_multiplier from 2d notebooks
• Set drop_last=False while creating val_dataset_it (this helps if number of val crops is less than val_batch_size)

• Make min-max-percentile normalization default
• Update README
• Better Visualization of crops and model predictions
• Reduce text in train notebooks
• Take away virtual_batch_multiplier as a user-defined attribute

Hello,

I found your links of pretrained models in this project page are 404. Do they still available? I want to try your models on our private dataset of 3D nuclei instance segmentation.

Thank you! Best wishes.

Describe the bug I am trying the example notebooks and successfully ran 01-data However, when I try the training notebook and being training the model, it takes a long time to initialise and then I get the following error: cublas runtime error : the GPU program failed to execute at C:/w/1/s/tmp_conda_3.7_044431/conda/conda-bld/pytorch_1556686009173/work/aten/src/THC/THCBlas.cu:259

Desktop (please complete the following information):

• OS: Tried this on Window 10 and Windows 11
• Graphics NVIDIA RTX 3080

Additional context Not sure if its a compatibility issue with RTX 30 series cards. I found a similar error for RTX 2080 cards on older pytorch https://github.com/pytorch/pytorch/issues/17334

Hello. I tried tutorial of bbbc010-2012 Jupyter notebooks, but this error happend and I don't know solution. Could you tell me what I should do ?

I ran 01-data.ipynb and 02-train.ipynb. When I ran 「begin_training(train_dataset_dict, val_dataset_dict, model_dict, loss_dict, configs, color_map=new_cmap)」, the following error happend.

Environment

• OS: Ubuntu 18.04
• GPU:Tesla -python3.7 torch 1.1.0 torchvision 0.3.0 cuda=10.0

I have 4 images, and batch size is only 1. but when I start the begin_training(train_dataset_dict, val_dataset_dict, model_dict, loss_dict, configs), I have RuntimeError: CUDA out of memory. Tried to allocate 128.00 MiB (GPU 0; 31.75 GiB total capacity; 30.71 GiB already allocated; 62.50 MiB free; 12.93 MiB cached). Please let me know how can I solve it. Thanks