CellProfiler-plugins
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CellProfiler
Cellprofiler 4.0.7 and CellPose 3.0.7 - RunCellpose is out of date
Firstly, my versioning information:
CellProfiler version: 4.2.7
cellpose version: 3.0.7
platform: win32
python version: 3.8.19
torch version: 2.4.0
conda version: 24.7.1
I am using the Cuda SDK v12.4, and torch version 2.4.0
I am using a conda environment with both CellProfiler and Cellpose running in it. I have installed the GUI version of CellPose, and it runs well with CUDA/GPU processing enabled.
However, there are issues with the RunCellpose plugin in CellProfiler. runcellpose.py was pulled via git clone today, 8th August 2024.
Issues:
-
In the CellProfiler project pipeline, the RunCellpose items are crossed ❌ with the "Value not in decimal format" notification. With the workaround I have, that issue doesn't affect the processing.
-
The models in the Cellpose 2.x are now mostly removed, but are referenced by the current runcellpose.py.
-
net_avgused by the plugin is no longer used by CellPose 3.0.7, so the plugin throws an error and exits.
(cellpose) C:\Users\user\anaconda3\envs\cellpose\Scripts\CellProfiler_Plugins>cellprofiler
Progress Counter({'Unprocessed': 1})
Starting workers on address tcp://127.0.0.1:62355
Worker 0: C:\Users\usert\anaconda3\envs\cellpose\lib\site-packages\cellprofiler_core\worker\__init__.py:25: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
Worker 0: import pkg_resources
Worker 0: Starting job
Progress Counter({'InProcess': 1})
Worker 0: Doing job: 1
Worker 0: Running module Images 1
Worker 0: Thu Aug 8 11:31:18 2024: Image # 1, module Images # 1: CPU_time = 0.00 secs, Wall_time = 0.00 secs
Worker 0: Running module Metadata 2
Worker 0: Thu Aug 8 11:31:18 2024: Image # 1, module Metadata # 2: CPU_time = 0.00 secs, Wall_time = 0.00 secs
Worker 0: Running module NamesAndTypes 3
Worker 0: Thu Aug 8 11:31:18 2024: Image # 1, module NamesAndTypes # 3: CPU_time = 0.25 secs, Wall_time = 0.50 secs
Worker 0: Running module Groups 4
Worker 0: Thu Aug 8 11:31:19 2024: Image # 1, module Groups # 4: CPU_time = 0.00 secs, Wall_time = 0.00 secs
Worker 0: Running module RunCellpose 5
Worker 0: >>>> loading model c:\users\user\Desktop\CellPose Models\StellarisDapi_20240807_131156
Worker 0: >>>> using CPU
Worker 0: C:\Users\user\anaconda3\envs\cellpose\lib\site-packages\cellpose\resnet_torch.py:298: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
Worker 0: state_dict = torch.load(filename, map_location=torch.device("cpu"))
Worker 0: >>>> model diam_mean = 30.000 (ROIs rescaled to this size during training)
Worker 0: >>>> model diam_labels = 31.044 (mean diameter of training ROIs)
Worker 0: Unable to create masks. Check your module settings. eval() got an unexpected keyword argument 'net_avg'
Worker 0: run_image_set_exception, get_always_continue False
Worker 0: Error detected during run of module RunCellpose#5
Worker 0: Traceback (most recent call last):
Worker 0: File "C:\Users\user\anaconda3\envs\cellpose\lib\site-packages\cellprofiler_core\pipeline\_pipeline.py", line 1208, in run_image_set
Worker 0: self.run_module(module, workspace)
Worker 0: File "C:\Users\user\anaconda3\envs\cellpose\lib\site-packages\cellprofiler_core\pipeline\_pipeline.py", line 1349, in run_module
Worker 0: module.run(workspace)
Worker 0: File "C:\Users\user\anaconda3\envs\cellpose\Scripts\CellProfiler_Plugins\CellProfiler-plugins\active_plugins\runcellpose.py", line 638, in run
Worker 0: y.segmented = y_data
Worker 0: UnboundLocalError: local variable 'y_data' referenced before assignment
Progress Counter({'FinishedWaitingMeasurements': 1})
Progress Counter({'Done': 1})
Finished!
Worker 0: Cancelling worker
If I comment out net_avg lines 536, 551, and 610. The plugin works, but with CPU only (see next).
- The Test GPU button does not work. The GPU detection fails (the reference to
core.use_gpuon line 717 in the script fails, because there's no reference tocore:
File "C:\Users\user\anaconda3\envs\cellpose\Scripts\CellProfiler_Plugins\CellProfiler-plugins\active_plugins\runcellpose.py", line 722, in do_check_gpu
GPU_works = core.use_gpu(use_torch=torch_installed)
NameError: name 'core' is not defined
- When using the GPU anyway, the process fails even though that there is a line that says:
****Worker 0: TORCH CUDA version installed and working. ****:
Progress Counter({'Unprocessed': 1})
Starting workers on address tcp://127.0.0.1:64668
Worker 0: C:\Users\user\anaconda3\envs\cellpose\lib\site-packages\cellprofiler_core\worker\__init__.py:25: DeprecationWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html
Worker 0: import pkg_resources
Worker 0: Starting job
Progress Counter({'InProcess': 1})
Worker 0: Doing job: 1
Worker 0: Running module Images 1
Worker 0: Thu Aug 8 11:44:16 2024: Image # 1, module Images # 1: CPU_time = 0.00 secs, Wall_time = 0.00 secs
Worker 0: Running module Metadata 2
Worker 0: Thu Aug 8 11:44:16 2024: Image # 1, module Metadata # 2: CPU_time = 0.00 secs, Wall_time = 0.00 secs
Worker 0: Running module NamesAndTypes 3
Worker 0: Thu Aug 8 11:44:16 2024: Image # 1, module NamesAndTypes # 3: CPU_time = 0.38 secs, Wall_time = 0.49 secs
Worker 0: Running module Groups 4
Worker 0: Thu Aug 8 11:44:16 2024: Image # 1, module Groups # 4: CPU_time = 0.00 secs, Wall_time = 0.00 secs
Worker 0: Running module RunCellpose 5
Worker 0: >>>> loading model c:\users\userDesktop\CellPose Models\StellarisDapi_20240807_131156
Worker 0: ** TORCH CUDA version installed and working. **
Worker 0: >>>> using GPU
Worker 0: C:\Users\user\anaconda3\envs\cellpose\lib\site-packages\cellpose\resnet_torch.py:294: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
Worker 0: state_dict = torch.load(filename, map_location=device)
Worker 0: >>>> model diam_mean = 30.000 (ROIs rescaled to this size during training)
Worker 0: >>>> model diam_labels = 31.044 (mean diameter of training ROIs)
Worker 0: run_image_set_exception, get_always_continue False
Worker 0: Error detected during run of module RunCellpose#5
Worker 0: Traceback (most recent call last):
Worker 0: File "C:\Users\user\anaconda3\envs\cellpose\lib\site-packages\cellprofiler_core\pipeline\_pipeline.py", line 1208, in run_image_set
Worker 0: self.run_module(module, workspace)
Worker 0: File "C:\Users\user\anaconda3\envs\cellpose\lib\site-packages\cellprofiler_core\pipeline\_pipeline.py", line 1349, in run_module
Worker 0: module.run(workspace)
Worker 0: File "C:\Users\user\anaconda3\envs\cellpose\Scripts\CellProfiler_Plugins\CellProfiler-plugins\active_plugins\runcellpose.py", line 526, in run
Worker 0: if self.use_gpu.value and model.torch:
Worker 0: AttributeError: 'CellposeModel' object has no attribute 'torch'
Worker 0: Cancelling worker
Worker 0: Aborting job after cancellation
Cancelled!
So in summary, there are a few major issues with runcellpose.py and CellPose 3.0.7. My current edits to remove net_avg, make it work, but only via CPU. I am using custom trained models generated in CellPose 3.0.7.
I hope these issues can be resolved. It would be good to get GPU processing working. I'll continue to look at the python script to see what I can do.
And the available switches for CellPose 3.0.7:
usage: cellpose [-h] [--version] [--verbose] [--Zstack] [--use_gpu] [--gpu_device GPU_DEVICE] [--check_mkl]
[--dir DIR] [--image_path IMAGE_PATH] [--look_one_level_down] [--img_filter IMG_FILTER]
[--channel_axis CHANNEL_AXIS] [--z_axis Z_AXIS] [--chan CHAN] [--chan2 CHAN2] [--invert]
[--all_channels] [--pretrained_model PRETRAINED_MODEL] [--restore_type RESTORE_TYPE] [--chan2_restore]
[--add_model ADD_MODEL] [--no_resample] [--no_interp] [--no_norm] [--do_3D] [--diameter DIAMETER]
[--stitch_threshold STITCH_THRESHOLD] [--min_size MIN_SIZE] [--flow_threshold FLOW_THRESHOLD]
[--cellprob_threshold CELLPROB_THRESHOLD] [--niter NITER] [--anisotropy ANISOTROPY]
[--exclude_on_edges] [--augment] [--save_png] [--save_tif] [--no_npy] [--savedir SAVEDIR]
[--dir_above] [--in_folders] [--save_flows] [--save_outlines] [--save_rois] [--save_txt] [--save_mpl]
[--train] [--train_size] [--test_dir TEST_DIR] [--mask_filter MASK_FILTER] [--diam_mean DIAM_MEAN]
[--learning_rate LEARNING_RATE] [--weight_decay WEIGHT_DECAY] [--n_epochs N_EPOCHS]
[--batch_size BATCH_SIZE] [--min_train_masks MIN_TRAIN_MASKS] [--SGD SGD] [--save_every SAVE_EVERY]
[--model_name_out MODEL_NAME_OUT]
Cellpose Command Line Parameters
optional arguments:
-h, --help show this help message and exit
--version show cellpose version info
--verbose show information about running and settings and save to log
--Zstack run GUI in 3D mode
Hardware Arguments:
--use_gpu use gpu if torch with cuda installed
--gpu_device GPU_DEVICE
which gpu device to use, use an integer for torch, or mps for M1
--check_mkl check if mkl working
Input Image Arguments:
--dir DIR folder containing data to run or train on.
--image_path IMAGE_PATH
if given and --dir not given, run on single image instead of folder (cannot train with this
option)
--look_one_level_down
run processing on all subdirectories of current folder
--img_filter IMG_FILTER
end string for images to run on
--channel_axis CHANNEL_AXIS
axis of image which corresponds to image channels
--z_axis Z_AXIS axis of image which corresponds to Z dimension
--chan CHAN channel to segment; 0: GRAY, 1: RED, 2: GREEN, 3: BLUE. Default: 0
--chan2 CHAN2 nuclear channel (if cyto, optional); 0: NONE, 1: RED, 2: GREEN, 3: BLUE. Default: 0
--invert invert grayscale channel
--all_channels use all channels in image if using own model and images with special channels
Model Arguments:
--pretrained_model PRETRAINED_MODEL
model to use for running or starting training
--restore_type RESTORE_TYPE
model to use for image restoration
--chan2_restore use nuclei restore model for second channel
--add_model ADD_MODEL
model path to copy model to hidden .cellpose folder for using in GUI/CLI
Algorithm Arguments:
--no_resample disable dynamics on full image (makes algorithm faster for images with large diameters)
--no_interp do not interpolate when running dynamics (was default)
--no_norm do not normalize images (normalize=False)
--do_3D process images as 3D stacks of images (nplanes x nchan x Ly x Lx
--diameter DIAMETER cell diameter, if 0 will use the diameter of the training labels used in the model, or with
built-in model will estimate diameter for each image
--stitch_threshold STITCH_THRESHOLD
compute masks in 2D then stitch together masks with IoU>0.9 across planes
--min_size MIN_SIZE minimum number of pixels per mask, can turn off with -1
--flow_threshold FLOW_THRESHOLD
flow error threshold, 0 turns off this optional QC step. Default: 0.4
--cellprob_threshold CELLPROB_THRESHOLD
cellprob threshold, default is 0, decrease to find more and larger masks
--niter NITER niter, number of iterations for dynamics for mask creation, default of 0 means it is
proportional to diameter, set to a larger number like 2000 for very long ROIs
--anisotropy ANISOTROPY
anisotropy of volume in 3D
--exclude_on_edges discard masks which touch edges of image
--augment tiles image with overlapping tiles and flips overlapped regions to augment
Output Arguments:
--save_png save masks as png and outlines as text file for ImageJ
--save_tif save masks as tif and outlines as text file for ImageJ
--no_npy suppress saving of npy
--savedir SAVEDIR folder to which segmentation results will be saved (defaults to input image directory)
--dir_above save output folders adjacent to image folder instead of inside it (off by default)
--in_folders flag to save output in folders (off by default)
--save_flows whether or not to save RGB images of flows when masks are saved (disabled by default)
--save_outlines whether or not to save RGB outline images when masks are saved (disabled by default)
--save_rois whether or not to save ImageJ compatible ROI archive (disabled by default)
--save_txt flag to enable txt outlines for ImageJ (disabled by default)
--save_mpl save a figure of image/mask/flows using matplotlib (disabled by default). This is slow,
especially with large images.
Training Arguments:
--train train network using images in dir
--train_size train size network at end of training
--test_dir TEST_DIR folder containing test data (optional)
--mask_filter MASK_FILTER
end string for masks to run on. use '_seg.npy' for manual annotations from the GUI. Default:
_masks
--diam_mean DIAM_MEAN
mean diameter to resize cells to during training -- if starting from pretrained models it
cannot be changed from 30.0
--learning_rate LEARNING_RATE
learning rate. Default: 0.2
--weight_decay WEIGHT_DECAY
weight decay. Default: 1e-05
--n_epochs N_EPOCHS number of epochs. Default: 500
--batch_size BATCH_SIZE
batch size. Default: 8
--min_train_masks MIN_TRAIN_MASKS
minimum number of masks a training image must have to be used. Default: 5
--SGD SGD use SGD
--save_every SAVE_EVERY
number of epochs to skip between saves. Default: 100
--model_name_out MODEL_NAME_OUT
Name of model to save as, defaults to name describing model architecture. Model is saved in
the folder specified by --dir in models subfolder.
This will be fixed by #276 in which we add support for Cellpose 3 and Cellpose4 (SAM)
