pipeline:window:sp_cryolo_train

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 — pipeline:window:sp_cryolo_train [2019/04/02 11:40] (current)lusnig created 2019/04/02 11:40 lusnig created 2019/04/02 11:40 lusnig created Line 1: Line 1: + ~~NOTOC~~ + + ===== sp_cryolo_train ===== + crYOLO - training: Training of crYOLO, a deep learning high accuracy particle picking procedure. + + \\ + ===== Usage ===== + + Usage in command line + + sp_cryolo_train.py particle_diameter training_dir annot_dir --cryolo_train_path=CRYOLO_PATH --architecture=architecture --input_size=input_size --num_patches=num_patches --overlap_patches=overlap_patches --train_times=train_times --pretrained_weights_name=PRETRAINED_NAME --saved_weights_name=SAVE_WEIGHTS_NAME --batch_size=batch_size --learning_rate=learning_rate --np_epoch=np_epoch --object_scale=object_scale --no_object_scale=no_object_scale --coord_scale=coord_scale --valid_image_dir=valid_image_dir --valid_annot_dir=valid_annot_dir --warmup=warmup --gpu=gpu --fine_tune --gpu_fraction=GPU_FRACTION --num_cpu=NUM_CPU + + \\ + ===== Typical usage ===== + + To train crYOLO for a specific dataset, one have to specify the path to training data in the config file. + Then the training typcial happens in two steps: + + \\ __1. Warmup__: + + sp_cryolo_train.py particle_diameter training_dir annot_dir --architecture="​YOLO"​ --warmup=5 + + \\ __2. Actual training__: + + sp_cryolo_train.py --conf=config_path --warmup=0 --gpu=0 + + \\ + ===== Input ===== + === Main Parameters === + ; %%--%%cryolo_train_path : crYOLO train executeable : Path to the crYOLO executeable (default none) + ; particle_diameter : Particle diameter [Pixel] : Particle diameter in pixel. This size will be used for as box size for picking. Should be as small as possible. (default required int) + ; training_dir : Training image directory : Folder which contain all images. (default required string) + ; annot_dir : Annotation directory : Box or star files used for training. The should have the same name as the images. (default required string) + + + \\ + === Advanced Parameters === + ; %%--%%architecture : Network architecture:​ Type of network that is trained. ​ (default PhosaurusNet) + ; %%--%%input_size : Input image dimension [Pixel] : Dimension of the image used as input to network. (default 1024) + ; %%--%%num_patches : Number of patches : The number of patches (e.g 2x2) the image is divided and classified separately. (default 1) + ; %%--%%overlap_patches:​ Patch overlap [Pixel]: The amount of overlap the patches will overlap (default 0) + ; %%--%%train_times : Repeat images :  How often a images is augmented and repeadet in one epoch. (default 10) + ; %%--%%pretrained_weights_name:​ Pretrained weights name : Name of the pretrained model (default cryolo_model.h5) + ; %%--%%saved_weights_name:​ Saved weights name : Name of the model to save (default cryolo_model.h5) + ; %%--%%batch_size : Batch size : How many patches are processed in parallel. (default 5) + ; %%--%%fine_tune : Fine tune mode : Set it to true if you only want to use the fine tune mode. (default False) + ; %%--%%learning_rate : Learning rate : Learning rate used during training. (default 0.0001) + ; %%--%%np_epoch : Number of epochs : Maximum number of epochs. (default 100) + ; %%--%%object_scale : Object loss scale : Loss scale for object. (default 5.0) + ; %%--%%no_object_scale:​ Background loss scale: Loss scale for background. (default 1.0) + ; %%--%%coord_scale:​ Coordinates loss scale: Loss scale for coordinates. (default 1.0) + ; %%--%%valid_image_dir : Path to validation images : Images used (default none) + ; %%--%%valid_annot_dir : Path to validation annotations : Path to the validation box files (default none) + ; %%--%%warmup : Warm up epochs : Number of warmup epochs. (default 5) + ; %%--%%gpu: GPUs : List of GPUs to use. (default 0) + ; %%--%%gpu_fraction:​ GPU memory fraction : Specify the fraction of memory per GPU used by crYOLO during training. Only values between 0.0 and 1.0 are allowed. (default 1.0) + ; %%--%%num_cpu:​ Number of CPUs : Number of CPUs used during training. By default it will use half of the available CPUs. (default -1) + + \\ + ===== Output ===== + It will write a .h5 file (default yolo_model.h5) into your project directory. + + + \\ + ===== Description ===== + The training is divided into two parts. 1. Warmup: It prepares the network with a few epochs of training without actually estimating the size of the particle. + 2. Actual training: The training will stop when the loss on the validation data stops to improve. + + \\ + ==== Method ==== + See the reference below. + + \\ + ==== Time and Memory === + Training needs a GPU with ~8GB memory. Training on 20 micrographs typicall needs ~20 minutes. + + + \\ + ==== Developer Notes ==== + === 2019/09/19 Thorsten Wagner === + * Initial creation of the document + + \\ + ==== Reference ==== + ​https://​doi.org/​10.1101/​356584 + + \\ + ==== Author / Maintainer ==== + Thorsten Wagner + + \\ + ==== Keywords ==== + Category 1:: APPLICATIONS + + \\ + ==== Files ==== + sparx/​bin/​sp_cryolo_train.py + + \\ + ==== See also ==== + [[pipeline:​window:​cryolo|crYOLO]] + + \\ + ==== Maturity ==== + Stable + + \\ + ==== Bugs ==== + None right now. + + \\
• pipeline/window/sp_cryolo_train.txt