CryoDRGN is a neural network based algorithm for heterogeneous cryo-EM reconstruction. In particular, the method models a continuous distribution over 3D structures by using a neural network based representation for the volume.
CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks. Ellen D. Zhong, Tristan Bepler, Bonnie Berger*, Joseph H. Davis*. https://www.nature.com/articles/s41592-020-01049-4
Reconstructing continuous distributions of 3D protein structure from cryo-EM images. Ellen D. Zhong, Tristan Bepler, Joseph H. Davis*, Bonnie Berger*. ICLR 2020, Spotlight presentation, https://arxiv.org/abs/1909.05215
The latest documentation for cryoDRGN is available here. This includes an overview and walkthrough of cryoDRGN installation, training and analysis.
A more in-depth manuscript version of the tutorial is available here.
Old Documentation pages are available at notion.so.
A quick start is provided below.
Post any questions as an Github issue or to our google group: https://groups.google.com/g/cryodrgn.
Updated default parameters for cryodrgn train_vae with modified positional encoding, larger model architecture, and accelerated mixed-precision training turned on by default:
- Mixed precision training is now turned on by default (Use
--no-ampto revert to single precision training) - Encoder/decoder architecture is now 1024x3 by default (Use
--enc-dim 256and--dec-dim 256to revert) - Gaussian Fourier featurization for faster training and higher resolution density maps (Use
--pe-type geom_lowfto revert)
The official version 1.0 release. This version introduces several new tools for analysis of the reconstructed ensembles, and adds functionality for calling utility scripts with cryodrgn_utils <command>.
- NEW:
cryodrgn analyze_landscapeandcryodrgn analyze_landscape_fullfor automatic assignment of classes and conformational landscape visualization. Documentation for this new feature is here: https://www.notion.so/cryodrgn-conformational-landscape-analysis-a5af129288d54d1aa95388bdac48235a. - NEW: Faster training and higher resolution model with Gaussian Fourier featurization (Use
--pe-type gaussian) - NEW:
cryodrgn_utils <command> -hfor standalone utility scripts - NEW:
cryodrgn_utils write_starfor converting cryoDRGN particle selections to.starfiles - Add pytorch native mixed precision training and fix support for pytorch 1.9+
Version 0.3.4
- FIX: Bug in
write_starfile.pywhen provided particle stack is chunked (.txt file) - Support micrograph coordinates and additional column headers to
write_starfile.py - New helper scripts:
analyze_convergence.py(in beta testing) contributed by Barrett Powell (thanks!) andmake_random_selection.pyfor splitting up particle stacks for training
Version 0.3.3
- Faster image preprocessing and smaller memory footprint
- New:
cryodrgn preprocessfor large datasets (in beta testing - see this Notion doc for details) - Known issue with PyTorch version 1.9+
Version 0.3.2
* New: cryoDRGN_filtering.ipynb for interactive filtering/selection of images from the dataset * New: `cryodrgn view_config` * Minor performance improvements and compatibility fixesVersion 0.3.1
- New: Script
write_starfile.pyto convert (filtered) particle selection to a .star file - More visualizations in
cryodrgn analyze
Version 0.3.0
- New: GPU parallelization with flag
--multigpu - New: Mode for accelerated mixed precision training with flag
--amp, available for NVIDIA tensor core GPUs - Interface update:
- Renamed encoder arguments
--qdimand--qlayersto--enc-dimand--enc-layers - Renamed decoder arguments
--pdimand--playersto--dec-dimand--dec-layers
- Renamed encoder arguments
- Argument default changes:
- Flipped the default for
--invert-datato True by default - Flipped the default for
--windowto True by default
- Flipped the default for
- Updated training recommendations in below quick start guide
- Updates to cryodrgn analyze
- More visualizations
- Order kmeans volumes according to distances in latent space (previously random)
- More features for particle selection and filtering in the Jupiter notebook
Version 0.2.1
- New: Parsing of RELION 3.1 files
- Fix: Compatibility with pytorch 1.5
Version 0.2.0
-
New interface and proper python packaging with
setup.py. This version has identical functionality and argument usage as previous versions, however tools are now available from a common entry point. See:$ cryodrgn <command> -h -
New analysis pipeline
cryodrgn analyze -
New latent space traversal scripts with
cryodrgn graph_traversalandcryodrgn pc_traversal.
To install cryoDRGN, git clone the source code and install the following dependencies with anaconda:
# Create conda environment
conda create --name cryodrgn1 python=3.9
conda activate cryodrgn1
# Install dependencies
conda install pytorch -c pytorch
conda install pandas
# Install dependencies for latent space visualization
conda install seaborn scikit-learn
conda install umap-learn jupyterlab ipywidgets cufflinks-py "nodejs>=15.12.0" -c conda-forge
jupyter labextension install @jupyter-widgets/jupyterlab-manager --no-build
jupyter labextension install jupyterlab-plotly --no-build
jupyter labextension install plotlywidget --no-build
jupyter lab build
# Clone source code and install
git clone https://github.com/zhonge/cryodrgn.git
cd cryodrgn
git checkout 1.1.0 # or latest version
pip install .
A detailed installation and testing guide is provided here: https://www.notion.so/cryoDRGN-installation-with-anaconda-4cff0367d9b241bb8d902efe339d01e6
First resize your particle images using the cryodrgn downsample command:
$ cryodrgn downsample -h
usage: cryodrgn downsample [-h] -D D -o MRCS [--is-vol] [--chunk CHUNK]
[--datadir DATADIR]
mrcs
Downsample an image stack or volume by clipping fourier frequencies
positional arguments:
mrcs Input particles or volume (.mrc, .mrcs, .star, or .txt)
optional arguments:
-h, --help show this help message and exit
-D D New box size in pixels, must be even
-o MRCS Output projection stack (.mrcs)
--is-vol Flag if input .mrc is a volume
--chunk CHUNK Chunksize (in # of images) to split particle stack when
saving
--relion31 Flag for relion3.1 star format
--datadir DATADIR Optionally provide path to input .mrcs if loading from a
.star or .cs file
Since larger images take (much) longer to train, we recommend first downsampling images to 128x128:
$ cryodrgn downsample [input particle stack] -D 128 -o particles.128.mrcs
The maximum recommended image size is D=256, so we also recommend downsampling your images to D=256 if your images are larger than 256x256:
$ cryodrgn downsample [input particle stack] -D 256 -o particles.256.mrcs
The input file format can be a single .mrcs file, a .txt file containing paths to multiple .mrcs files, a .star file, or a cryoSPARC .cs file. For the latter two options, if the relative paths to the .mrcs are broken, the argument --datadir can be used to supply the path to where the .mrcs files are located.
If there are memory issues with downsampling large particle stacks, add the --chunk 10000 argument to save images as separate .mrcs files of 10k images.
CryoDRGN expects image poses to be stored in a binary pickle format (.pkl). Use the parse_pose_star or parse_pose_csparc command to extract the poses from a .star file or a .cs file, respectively.
Example usage to parse image poses from a RELION 3.1 starfile:
$ cryodrgn parse_pose_star particles.star -o pose.pkl -D 300
Example usage to parse image poses from a cryoSPARC homogeneous refinement particles.cs file:
$ cryodrgn parse_pose_csparc cryosparc_P27_J3_005_particles.cs -o pose.pkl -D 300
The -D argument should be set to the box size of the original consensus reconstruction (before any downsampling).
Note: Poses should be obtained from a C1 consensus refinement! (See ml-struct-bio#21)
CryoDRGN expects CTF parameters in be stored a binary pickle format (.pkl). Use the parse_ctf_star or parse_ctf_csparc command to extract the relevant CTF parameters from a .star file or a .cs file, respectively.
Example usage for a .star file:
$ cryodrgn parse_ctf_star particles.star -D 300 --Apix 1.03 -o ctf.pkl
The -D and --Apix arguments should be set to the box size and Angstrom/pixel of the original .mrcs file (before any downsampling).
Example usage for a .cs file:
$ cryodrgn parse_ctf_csparc cryosparc_P27_J3_005_particles.cs -o ctf.pkl
Next, test that pose and CTF parameters were parsed correctly using the voxel-based backprojection script. The goal is to quickly verify that there are no major problems with the extracted values and that the output structure resembles the structure from the consensus reconstruction before beginning training.
Example usage:
$ cryodrgn backproject_voxel projections.128.mrcs \
--poses pose.pkl \
--ctf ctf.pkl \
-o backproject.128.mrc
The output structure backproject.128.mrc will not match the consensus reconstruction exactly as the backproject_voxel command backprojects phase-flipped particles onto the voxel grid, and by default only uses the first 10k images. If the structure is too noisy, you can increase the number of images that are used with the --first argument.
Note: If the volume does not resemble your structure, you may need to use the flag --uninvert-data. This flips the data sign (e.g. light-on-dark or dark-on-light), which may be needed depending on the convention used in upstream processing tools.
When the input images (.mrcs), poses (.pkl), and CTF parameters (.pkl) have been prepared, a cryoDRGN model can be trained with following command:
$ cryodrgn train_vae -h
usage: cryodrgn train_vae [-h] -o OUTDIR --zdim ZDIM --poses POSES [--ctf pkl]
[--load WEIGHTS.PKL] [--checkpoint CHECKPOINT]
[--log-interval LOG_INTERVAL] [-v] [--seed SEED]
[--uninvert-data] [--no-window] [--ind PKL] [--lazy]
[--datadir DATADIR] [--relion31] [--tilt TILT]
[--tilt-deg TILT_DEG] [-n NUM_EPOCHS]
[-b BATCH_SIZE] [--wd WD] [--lr LR] [--beta BETA]
[--beta-control BETA_CONTROL] [--norm NORM NORM]
[--amp] [--multigpu] [--do-pose-sgd]
[--pretrain PRETRAIN] [--emb-type {s2s2,quat}]
[--pose-lr POSE_LR] [--enc-layers QLAYERS]
[--enc-dim QDIM]
[--encode-mode {conv,resid,mlp,tilt}]
[--enc-mask ENC_MASK] [--use-real]
[--dec-layers PLAYERS] [--dec-dim PDIM]
[--pe-type {geom_ft,geom_full,geom_lowf,geom_nohighf,linear_lowf,none}]
[--pe-dim PE_DIM] [--domain {hartley,fourier}]
[--activation {relu,leaky_relu}]
particles
Train a VAE for heterogeneous reconstruction with known pose
positional arguments:
particles Input particles (.mrcs, .star, .cs, or .txt)
optional arguments:
-h, --help show this help message and exit
-o OUTDIR, --outdir OUTDIR
Output directory to save model
--zdim ZDIM Dimension of latent variable
--poses POSES Image poses (.pkl)
--ctf pkl CTF parameters (.pkl)
--load WEIGHTS.PKL Initialize training from a checkpoint
--checkpoint CHECKPOINT
Checkpointing interval in N_EPOCHS (default: 1)
--log-interval LOG_INTERVAL
Logging interval in N_IMGS (default: 1000)
-v, --verbose Increaes verbosity
--seed SEED Random seed
--relion31 Flag if relion3.1 star format
Dataset loading:
--uninvert-data Do not invert data sign
--no-window Turn off real space windowing of dataset
--ind PKL Filter particle stack by these indices
--lazy Lazy loading if full dataset is too large to fit in
memory
--datadir DATADIR Path prefix to particle stack if loading relative
paths from a .star or .cs file
Tilt series:
--tilt TILT Particles (.mrcs)
--tilt-deg TILT_DEG X-axis tilt offset in degrees (default: 45)
Training parameters:
-n NUM_EPOCHS, --num-epochs NUM_EPOCHS
Number of training epochs (default: 20)
-b BATCH_SIZE, --batch-size BATCH_SIZE
Minibatch size (default: 8)
--wd WD Weight decay in Adam optimizer (default: 0)
--lr LR Learning rate in Adam optimizer (default: 0.0001)
--beta BETA Choice of beta schedule or a constant for KLD weight
(default: 1/zdim)
--beta-control BETA_CONTROL
KL-Controlled VAE gamma. Beta is KL target. (default:
None)
--norm NORM NORM Data normalization as shift, 1/scale (default: 0, std
of dataset)
--amp Use mixed-precision training
--multigpu Parallelize training across all detected GPUs
Pose SGD:
--do-pose-sgd Refine poses with gradient descent
--pretrain PRETRAIN Number of epochs with fixed poses before pose SGD
(default: 1)
--emb-type {s2s2,quat}
SO(3) embedding type for pose SGD (default: quat)
--pose-lr POSE_LR Learning rate for pose optimizer (default: 0.0003)
Encoder Network:
--enc-layers QLAYERS Number of hidden layers (default: 3)
--enc-dim QDIM Number of nodes in hidden layers (default: 256)
--encode-mode {conv,resid,mlp,tilt}
Type of encoder network (default: resid)
--enc-mask ENC_MASK Circular mask of image for encoder (default: D/2; -1
for no mask)
--use-real Use real space image for encoder (for convolutional
encoder)
Decoder Network:
--dec-layers PLAYERS Number of hidden layers (default: 3)
--dec-dim PDIM Number of nodes in hidden layers (default: 256)
--pe-type {geom_ft,geom_full,geom_lowf,geom_nohighf,linear_lowf,none}
Type of positional encoding (default: geom_lowf)
--pe-dim PE_DIM Num features in positional encoding (default: image D)
--domain {hartley,fourier}
Decoder representation domain (default: fourier)
--activation {relu,leaky_relu}
Activation (default: relu)
Many of the parameters of this script have sensible defaults. The required arguments are:
- an input image stack (
.mrcsor other listed file types) --poses, image poses (.pkl) that correspond to the input images--ctf, ctf parameters (.pkl), unless phase-flipped images are used--zdim, the dimension of the latent variable-o, a clean output directory for storing results
Additional parameters which are typically set include:
-n, Number of epochs to train--uninvert-data, Use if particles are dark on light (negative stain format)- Architecture parameters with
--enc-layers,--enc-dim,--dec-layers,--dec-dim --multigputo enable parallelized training across multiple GPUs
- It is highly recommended to first train on lower resolution images (e.g. D=128) to sanity check results and perform any particle filtering.
Example command to train a cryoDRGN model for 25 epochs on an image dataset projections.128.mrcs with poses pose.pkl and ctf parameters ctf.pkl:
# 8-D latent variable model, small images
$ cryodrgn train_vae projections.128.mrcs \
--poses pose.pkl \
--ctf ctf.pkl \
--zdim 8 -n 25 \
-o 00_cryodrgn128
- After validation, pose optimization, and any necessary particle filtering, then train on the full resolution images (up to D=256):
Example command to train a cryoDRGN model for 25 epochs on an image dataset projections.256.mrcs with poses pose.pkl and ctf parameters ctf.pkl:
# 8-D latent variable model, larger images
$ cryodrgn train_vae projections.256.mrcs \
--poses pose.pkl \
--ctf ctf.pkl \
--zdim 8 -n 25 \
-o 01_cryodrgn256
The number of epochs -n refers to the number of full passes through the dataset for training, and should be modified depending on the number of particles in the dataset. For a 100k particle dataset on 1 V100 GPU, the above settings required ~12 min/epoch for D=128 images and ~47 min/epoch for D=256 images.
If you would like to train longer, a training job can be extended with the --load argument. For example to extend the training of the previous example to 50 epochs:
$ cryodrgn train_vae projections.256.mrcs \
--poses pose.pkl \
--ctf ctf.pkl \
--zdim 8 -n 50 \
-o 01_cryodrgn256 \
--load 01_cryodrgn256/weights.24.pkl # 0-based indexing
Use cryoDRGN's --multigpu flag to enable parallelized training across all detected GPUs on the machine. To select specific GPUs for cryoDRGN to run on, use the environmental variable CUDA_VISIBLE_DEVICES, e.g.:
$ cryodrgn train_vae ... # Run on GPU 0
$ cryodrgn train_vae ... --multigpu # Run on all GPUs on the machine
$ CUDA_VISIBLE_DEVICES=0,3 cryodrgn train_vae ... --multigpu # Run on GPU 0,3
When training is parallelized across multiple GPUs, the batch size (number of images trained in each mini-batch of SGD; default -b 8) will be automatically scaled by the number of available GPUs to better take advantage of parallelization. Depending on your compute resources, GPU utilization may be improved with -b 16. However, note that GPU parallelization, while leading to a faster wall-clock time per epoch, may require increasing the total number of epochs, since the training dynamics are affected (fewer model updates per epoch with larger -b).
Note: We recommend using --multigpu for large images, e.g. D=256. GPU computation may not be the training bottleneck for smaller images (D=128). In this case, GPU parallelization may have a limited effect on the wall clock training time (while taking up additional compute resources).
Depending on the quality of the consensus reconstruction, image poses may contain errors.
Image poses may be locally refined using the --do-pose-sgd flag. Please consult Ellen Zhong (zhonge@princeton.edu) for details.
Once the model has finished training, the output directory will contain a configuration file config.pkl, neural network weights weights.pkl, image poses (if performing pose sgd) pose.pkl, and the latent embeddings for each image z.pkl. The latent embeddings are provided in the same order as the input particles. To analyze these results, use the cryodrgn analyze command to visualize the latent space and generate structures. cryodrgn analyze will also provide a template jupyter notebook for further interactive visualization and analysis.
$ cryodrgn analyze -h
usage: cryodrgn analyze [-h] [--device DEVICE] [-o OUTDIR] [--skip-vol]
[--skip-umap] [--Apix APIX] [--flip] [-d DOWNSAMPLE]
[--pc PC] [--ksample KSAMPLE]
workdir epoch
Visualize latent space and generate volumes
positional arguments:
workdir Directory with cryoDRGN results
epoch Epoch number N to analyze (0-based indexing,
corresponding to z.N.pkl, weights.N.pkl)
optional arguments:
-h, --help show this help message and exit
--device DEVICE Optionally specify CUDA device
-o OUTDIR, --outdir OUTDIR
Output directory for analysis results (default:
[workdir]/analyze.[epoch])
--skip-vol Skip generation of volumes
--skip-umap Skip running UMAP
Extra arguments for volume generation:
--Apix APIX Pixel size to add to .mrc header (default: 1 A/pix)
--flip Flip handedness of output volume
-d DOWNSAMPLE, --downsample DOWNSAMPLE
Downsample volumes to this box size (pixels)
--pc PC Number of principal component traversals to generate
(default: 2)
--ksample KSAMPLE Number of kmeans samples to generate (default: 20)
This script runs a series of standard analyses:
- PCA visualization of the latent embeddings
- UMAP visualization of the latent embeddings
- Generation of volumes. See note [1].
- Generation of trajectories along the first and second principal components of the latent embeddings
- Generation of a template jupyter notebook that may be used for further interactive analyses, visualization, and volume generation
- Generation of a template jupyter notebook for particle filtering and selection
Example usage to analyze results from the direction 01_cryodrgn256 containing results after 25 epochs of training:
$ cryodrgn analyze 01_cryodrgn256 24 --Apix 1.31
Notes:
[1] Volumes are generated after k-means clustering of the latent embeddings with k=20 by default. Note that we use k-means clustering here not to identify clusters, but to segment the latent space and generate structures from different regions of the latent space. The number of structures that are generated may be increased with the option --ksample.
[2] The cryodrgn analyze command chains together a series of calls to cryodrgn eval_vol and scripts that can be run separately for more flexibility. These scripts are located in the analysis_scripts directory within the source code.
Additional structures may be generated using cryodrgn eval_vol:
$ cryodrgn eval_vol -h
usage: cryodrgn eval_vol [-h] -c PKL -o O [--prefix PREFIX] [-v]
[-z [Z [Z ...]]] [--z-start [Z_START [Z_START ...]]]
[--z-end [Z_END [Z_END ...]]] [-n N] [--zfile ZFILE]
[--Apix APIX] [--flip] [-d DOWNSAMPLE]
[--norm NORM NORM] [-D D] [--enc-layers QLAYERS]
[--enc-dim QDIM] [--zdim ZDIM]
[--encode-mode {conv,resid,mlp,tilt}]
[--dec-layers PLAYERS] [--dec-dim PDIM]
[--enc-mask ENC_MASK]
[--pe-type {geom_ft,geom_full,geom_lowf,geom_nohighf,linear_lowf,none}]
[--pe-dim PE_DIM] [--domain {hartley,fourier}]
[--l-extent L_EXTENT]
[--activation {relu,leaky_relu}]
weights
Evaluate the decoder at specified values of z
positional arguments:
weights Model weights
optional arguments:
-h, --help show this help message and exit
-c PKL, --config PKL CryoDRGN config.pkl file
-o O Output .mrc or directory
--prefix PREFIX Prefix when writing out multiple .mrc files (default:
vol_)
-v, --verbose Increaes verbosity
Specify z values:
-z [Z [Z ...]] Specify one z-value
--z-start [Z_START [Z_START ...]]
Specify a starting z-value
--z-end [Z_END [Z_END ...]]
Specify an ending z-value
-n N Number of structures between [z_start, z_end]
--zfile ZFILE Text file with z-values to evaluate
Volume arguments:
--Apix APIX Pixel size to add to .mrc header (default: 1 A/pix)
--flip Flip handedness of output volume
-d DOWNSAMPLE, --downsample DOWNSAMPLE
Downsample volumes to this box size (pixels)
Overwrite architecture hyperparameters in config.pkl:
--norm NORM NORM
-D D Box size
--enc-layers QLAYERS Number of hidden layers
--enc-dim QDIM Number of nodes in hidden layers
--zdim ZDIM Dimension of latent variable
--encode-mode {conv,resid,mlp,tilt}
Type of encoder network
--dec-layers PLAYERS Number of hidden layers
--dec-dim PDIM Number of nodes in hidden layers
--enc-mask ENC_MASK Circular mask radius for image encoder
--pe-type {geom_ft,geom_full,geom_lowf,geom_nohighf,linear_lowf,none}
Type of positional encoding
--pe-dim PE_DIM Num sinusoid features in positional encoding (default:
D/2)
--domain {hartley,fourier}
--l-extent L_EXTENT Coordinate lattice size
--activation {relu,leaky_relu}
Activation (default: relu)
Example usage:
To generate a volume at a single value of the latent variable:
$ cryodrgn eval_vol [YOUR_WORKDIR]/weights.pkl --config [YOUR_WORKDIR]/config.pkl -z ZVALUE -o reconstruct.mrc
The number of inputs for -z must match the dimension of your latent variable.
Or to generate a trajectory of structures from a defined start and ending point, use the --z-start and --z-end arugments:
$ cryodrgn eval_vol [YOUR_WORKDIR]/weights.pkl --config [YOUR_WORKDIR]/config.pkl --z-start -3 --z-end 3 -n 20 -o [WORKDIR]/trajectory
This example generates 20 structures at evenly spaced values between z=[-3,3], assuming a 1-dimensional latent variable model.
Finally, a series of structures can be generated using values of z given in a file specified by the arugment --zfile:
$ cryodrgn eval_vol [WORKDIR]/weights.pkl --config [WORKDIR]/config.pkl --zfile zvalues.txt -o [WORKDIR]/trajectory
The input to --zfile is expected to be an array of dimension (N_volumes x zdim), loaded with np.loadtxt.
Two additional commands can be used in conjunction with cryodrgn eval_vol to generate trajectories:
$ cryodrgn pc_traversal -h
$ cryodrgn graph_traversal -h
These scripts produce a text file of z values that can be input to cryodrgn eval_vol to generate a series of structures that can be visualized as a trajectory in ChimeraX (https://www.cgl.ucsf.edu/chimerax).
An example usage of the graph traversal algorithm is here (ml-struct-bio#16 (comment)).
To perform ab initio heterogeneous reconstruction, use cryodrgn abinit_het. The arguments are similar to cryodrgn train_vae, but the --poses argument is not required.
The defaults match the settings reported in the CryoDRGN2 manuscript.
usage: cryodrgn abinit_het [-h] -o OUTDIR --zdim ZDIM [--ctf pkl]
[--load LOAD] [--load-poses LOAD_POSES]
[--checkpoint CHECKPOINT]
[--log-interval LOG_INTERVAL] [-v] [--seed SEED]
[--ind PKL] [--uninvert-data] [--no-window]
[--window-r WINDOW_R] [--datadir DATADIR]
[--lazy-single] [--lazy] [--preprocessed]
[--max-threads MAX_THREADS] [--tilt TILT]
[--tilt-deg TILT_DEG] [--enc-only] [-n NUM_EPOCHS]
[-b BATCH_SIZE] [--wd WD] [--lr LR] [--beta BETA]
[--beta-control BETA_CONTROL]
[--equivariance EQUIVARIANCE]
[--eq-start-it EQ_START_IT] [--eq-end-it EQ_END_IT]
[--norm NORM NORM] [--l-ramp-epochs L_RAMP_EPOCHS]
[--l-ramp-model L_RAMP_MODEL]
[--reset-model-every RESET_MODEL_EVERY]
[--reset-optim-every RESET_OPTIM_EVERY]
[--reset-optim-after-pretrain RESET_OPTIM_AFTER_PRETRAIN]
[--l-start L_START] [--l-end L_END] [--niter NITER]
[--t-extent T_EXTENT] [--t-ngrid T_NGRID]
[--t-xshift T_XSHIFT] [--t-yshift T_YSHIFT]
[--pretrain PRETRAIN] [--ps-freq PS_FREQ]
[--nkeptposes NKEPTPOSES]
[--base-healpy BASE_HEALPY]
[--pose-model-update-freq POSE_MODEL_UPDATE_FREQ]
[--enc-layers QLAYERS] [--enc-dim QDIM]
[--encode-mode {conv,resid,mlp,tilt}]
[--enc-mask ENC_MASK] [--use-real]
[--dec-layers PLAYERS] [--dec-dim PDIM]
[--pe-type {geom_ft,geom_full,geom_lowf,geom_nohighf,linear_lowf,gaussian,none}]
[--feat-sigma FEAT_SIGMA] [--pe-dim PE_DIM]
[--domain {hartley,fourier}]
[--activation {relu,leaky_relu}]
particles
Heterogeneous NN reconstruction with hierarchical pose optimization
positional arguments:
particles Input particles (.mrcs, .txt or .star)
optional arguments:
-h, --help show this help message and exit
-o OUTDIR, --outdir OUTDIR
Output directory to save model
--zdim ZDIM Dimension of latent variable
--ctf pkl CTF parameters (.pkl)
--load LOAD Initialize training from a checkpoint
--load-poses LOAD_POSES
Initialize training from a checkpoint
--checkpoint CHECKPOINT
Checkpointing interval in N_EPOCHS (default: 1)
--log-interval LOG_INTERVAL
Logging interval in N_IMGS (default: 1000)
-v, --verbose Increaes verbosity
--seed SEED Random seed
Dataset loading:
--ind PKL Filter particle stack by these indices
--uninvert-data Do not invert data sign
--no-window Turn off real space windowing of dataset
--window-r WINDOW_R Windowing radius (default: 0.85)
--datadir DATADIR Path prefix to particle stack if loading relative
paths from a .star or .cs file
--lazy-single Lazy loading if full dataset is too large to fit in
memory
--lazy Memory efficient training by loading data in chunks
--preprocessed Skip preprocessing steps if input data is from
cryodrgn preprocess_mrcs
--max-threads MAX_THREADS
Maximum number of CPU cores for FFT parallelization
(default: 16)
Tilt series:
--tilt TILT Particle stack file (.mrcs)
--tilt-deg TILT_DEG X-axis tilt offset in degrees (default: 45)
--enc-only Use the tilt pair only in VAE and not in BNB search
Training parameters:
-n NUM_EPOCHS, --num-epochs NUM_EPOCHS
Number of training epochs (default: 30)
-b BATCH_SIZE, --batch-size BATCH_SIZE
Minibatch size (default: 8)
--wd WD Weight decay in Adam optimizer (default: 0)
--lr LR Learning rate in Adam optimizer (default: 0.0001)
--beta BETA Choice of beta schedule or a constant for KLD weight
(default: 1.0)
--beta-control BETA_CONTROL
KL-Controlled VAE gamma. Beta is KL target. (default:
None)
--equivariance EQUIVARIANCE
Strength of equivariance loss (default: None)
--eq-start-it EQ_START_IT
It at which equivariance turned on (default: 100000)
--eq-end-it EQ_END_IT
It at which equivariance max (default: 200000)
--norm NORM NORM Data normalization as shift, 1/scale (default: mean,
std of dataset)
--l-ramp-epochs L_RAMP_EPOCHS
default: 0
--l-ramp-model L_RAMP_MODEL
If 1, then during ramp only train the model up to
l-max
--reset-model-every RESET_MODEL_EVERY
If set, reset the model every N epochs
--reset-optim-every RESET_OPTIM_EVERY
If set, reset the optimizer every N epochs
--reset-optim-after-pretrain RESET_OPTIM_AFTER_PRETRAIN
If set, reset the optimizer every N epochs
Pose Search parameters:
--l-start L_START Starting L radius (default: 12)
--l-end L_END End L radius (default: 32)
--niter NITER Number of iterations of grid subdivision
--t-extent T_EXTENT +/- pixels to search over translations (default: 10)
--t-ngrid T_NGRID Initial grid size for translations
--t-xshift T_XSHIFT
--t-yshift T_YSHIFT
--pretrain PRETRAIN Number of initial iterations with random poses
(default: 10000)
--ps-freq PS_FREQ Frequency of pose inference (default: every 5 epochs)
--nkeptposes NKEPTPOSES
Number of poses to keep at each refinement interation
during branch and bound
--base-healpy BASE_HEALPY
Base healpy grid for pose search. Higher means
exponentially higher resolution.
--pose-model-update-freq POSE_MODEL_UPDATE_FREQ
If set, only update the model used for pose search
every N examples.
Encoder Network:
--enc-layers QLAYERS Number of hidden layers (default: 3)
--enc-dim QDIM Number of nodes in hidden layers (default: 256)
--encode-mode {conv,resid,mlp,tilt}
Type of encoder network (default: resid)
--enc-mask ENC_MASK Circular mask of image for encoder (default: D/2; -1
for no mask)
--use-real Use real space image for encoder (for convolutional
encoder)
Decoder Network:
--dec-layers PLAYERS Number of hidden layers (default: 3)
--dec-dim PDIM Number of nodes in hidden layers (default: 256)
--pe-type {geom_ft,geom_full,geom_lowf,geom_nohighf,linear_lowf,gaussian,none}
Type of positional encoding (default: gaussian)
--feat-sigma FEAT_SIGMA
Scale for random Gaussian features (default: 0.5)
--pe-dim PE_DIM Num features in positional encoding (default: image D)
--domain {hartley,fourier}
Decoder representation domain (default: fourier)
--activation {relu,leaky_relu}
Activation (default: relu)
For homogeneous reconstruction, run cryodrgn abinit_homo.
Please submit any bug reports, feature requests, or general usage feedback as a github issue, or start a github discussion!