def autoencoder_single_main(request: AESingleRequest):
item = particlePickingPool.get(request.path)
remove_particles = list(map(int, request.remove_particles.replace(',', ' ').split()))
pick_num = 100
item.pick.select(remove_particles, pick_num)
subvols_loc = os.path.join(
item.dump_folder, "selected_demo_single_particle_subvolumes.pickle")
output_dir = os.path.join(item.dump_folder, 'autoencoder_particle')
d = AIF.pickle_load(subvols_loc)
AE.encoder_simple_conv_test(d=d, pose=None, img_org_file=False, out_dir=output_dir, clus_num=1)
AE.kmeans_centers_plot(AE.op_join(output_dir, 'clus-center'))
return AESingleResponse()
def main(args):
'''
Step1:Prepare input dataset
You can download the example dataset from https://cmu.app.box.com/s/9hn3qqtqmivauus3kgtasg5uzlj53wxp/file/509296892992 into your present working directory.
Note: This is not the dataset used in the paper, which will be added in the future.
Here's four parameters and dataset format.
1.A python pickle data file of CECT small subvolumes, this data file should be prepared as follows:
d is the small subvolume data file.
d is a dictionary consists 'v_siz' and 'vs'.
d['v_siz'] is an numpy.ndarray specifying the shape of the small subvolume. For example, d['v_siz'] = array([32,32,32]).
d['vs'] is a dictionary with keys of uuids specifying each small subvolume.
d['vs'][an example uuid] is a dictionary consists 'center', 'id', and 'v'.
d['vs'][an example uuid]['center'] is the center of the small subvolume in the tomogram. For example, d['vs'][an example uuid]['center'] = [110,407,200].
d['vs'][an example uuid]['id'] is the specific uuid.
d['vs'][an example uuid]['v'] are voxel values of the small subvolume, which is an numpy.ndarray of shape d['v_siz'].
2.A tomogram file in .rec format, which is only required when performing pose normalization.
3.Whether the optional pose normalization step should be applied. Input should be True or False.
4.The number of clusters. This should be an positive integer such as 100.
'''
'''
Step2 Train the auto encoder. Given the example dataset, you can use parameters1 or parameters2.
'''
import aitom.classify.deep.unsupervised.autoencoder.autoencoder as AE
import time
s_time = time.time()
# Note: these two datasets are missing for now
# parameters1 = ["example/subvolumes_example_2.pickle", "None", "False", "4"]
# parameters2 = ["example/subvolumes_example_1.pickle", "example/tomogram.rec", "True", "100"]
# This example dataset is generated in the particle picking tutorial
single_particle_param = [
'data/demo_single_particle_subvolumes.pickle', 'None', "False", 4
]
out_dir = args.output_dir
if args.data:
single_particle_param[0] = args.data
if args.cluster_number:
single_particle_param[3] = args.cluster_number
# demo dataset format is different, which has 2 kinds of particle and their templates
multiple_particles_params = [
'data/aitom_demo_subtomograms.pickle', 'None', "False", 4
]
parameters_demo = single_particle_param # choose one of the above
import aitom.io.file as AIF
d = AIF.pickle_load(
parameters_demo[0]) # pickle data file of CECT small subvolumes
if parameters_demo == multiple_particles_params: # TODO add multiparticle tutorial
# choose one particle and convert the data format
# d = d['5T2C_data']
# from aitom.classify.deep.unsupervised.autoencoder.autoencoder_util import subtomograms_to_subvolumes
# d = subtomograms_to_subvolumes(d)
pass
img_org_file = parameters_demo[
1] #A tomogram file in .rec format, which can be None when pose normalization is not required
pose = eval(
parameters_demo[2]
) #Whether the optional pose normalization step should be applied True or False
clus_num = int(parameters_demo[3]) # The number of clusters
AE.encoder_simple_conv_test(d=d,
pose=pose,
img_org_file=img_org_file,
out_dir=out_dir,
clus_num=clus_num)
AE.kmeans_centers_plot(AE.op_join(out_dir, 'clus-center'))
'''
Step 3. Manual selection of small subvolume clusters
Autoencoder3D training step will have two output folders.
'model' directory saved the trained models
'clus-center' directory for the resulting clusters.
There should be two pickle files in 'clus-center'.
'kmeans.pickle' stores the uuids for each cluster.
'ccents.pickle' stores the decoded cluster centers.
The 'fig' folder under 'clus-center' directory contains the 2D slices of decoded cluster center.
User can use the figures as guide for manual selection.
Manual selection clues are provided in the folder 'fig' under 'clus-center'.
Each picture is a 2D slices presentation of a decoded small subvolume cluster center.
The picture name such as '035--47.png' refers to cluster 35 which consists 47 small subvolumes.
'''
'''
Step 4. Optional Encoder-decoder Semantic Segmentation 3D network training.
Based on the manual selection results, Encoder-decoder Semantic Segmentation 3D (EDSS3D) network can be trained and applied for another tomogram dataset.
'''
import aitom.classify.deep.unsupervised.autoencoder.seg_src as SEG
import os
from os.path import join as op_join
# sel_clus = {1: [3, 21, 28, 34, 38, 39, 43, 62, 63, 81, 86, 88],
# 2: [15, 25, 29, 33, 35, 66, 79, 90, 92, 98]} # an example of selected clusters for segmentation
# sel_clus is the selected clusters for segmentation, which can be multiple classes.
sel_clus = {0: [3, 21, 28, 34, 38, 39, 43, 62, 63, 81, 86, 88]}
import numpy as np
# sel_clus = {0: np.arange(100)}
data_dir = out_dir
data_file = op_join(
data_dir, parameters_demo[0]
) #here's the name of pickle data file of CECT small subvolumes
with open(data_file, 'rb') as f:
d = SEG.pickle.load(f, encoding='iso-8859-1')
SEG.decode_all_images(d, data_dir)
# The following files come from the previous Autoencoder3D results
with open(op_join(data_dir, 'clus-center', 'kmeans.pickle'), 'rb') as f:
km = SEG.pickle.load(f, encoding='iso-8859-1')
with open(op_join(data_dir, 'clus-center', 'ccents.pickle'), 'rb') as f:
cc = SEG.pickle.load(f, encoding='iso-8859-1')
with open(op_join(data_dir, 'decoded', 'decoded.pickle'), 'rb') as f:
vs_dec = SEG.pickle.load(f, encoding='iso-8859-1')
print('sel_clus', len(sel_clus))
print('km', len(km))
vs_lbl = SEG.image_label_prepare(sel_clus, km)
print('vs_lbl', len(vs_lbl))
# print(vs_lbl)
for key in vs_lbl:
vs_lbl[key] = 1
print('vs_lbl', len(vs_lbl))
# print(vs_lbl)
vs_seg = SEG.train_label_prepare(
vs_lbl=vs_lbl, vs_dec=vs_dec,
iso_value=0.5) # iso_value is the mask threshold for segmentation
print('vs_seg', len(vs_seg))
model_dir = op_join(data_dir, 'model-seg')
if not os.path.isdir(model_dir): os.makedirs(model_dir)
model_checkpoint_file = op_join(model_dir, 'model-seg--weights--best.h5')
model_file = op_join(model_dir, 'model-seg.h5')
if os.path.isfile(model_file):
print('use existing', model_file)
import keras.models as KM
model = KM.load_model(model_file)
else:
model = SEG.train_validate__reshape(
vs_lbl=vs_lbl,
vs=d['vs'],
vs_seg=vs_seg,
model_file=model_file,
model_checkpoint_file=model_checkpoint_file)
model.save(model_file)
# Segmentation prediction on new data
data_dir = out_dir # This should be the new data for prediction
data_file = op_join(data_dir, parameters_demo[0])
with open(data_file, 'rb') as f:
d = SEG.pickle.load(f, encoding='iso-8859-1')
prediction_dir = op_join(data_dir, 'prediction')
if not os.path.isdir(prediction_dir): os.makedirs(prediction_dir)
vs_p = SEG.predict__reshape(model, vs={_: d['vs'][_]['v'] for _ in vs_seg})
with open(op_join(prediction_dir, 'vs_p.pickle'), 'wb') as f:
SEG.pickle.dump(vs_p, f, protocol=-1)
print(time.time() - s_time, 's')
# d = subtomograms_to_subvolumes(d)
pass
img_org_file = parameters_demo[
1] #A tomogram file in .rec format, which can be None when pose normalization is not required
pose = eval(
parameters_demo[2]
) #Whether the optional pose normalization step should be applied True or False
clus_num = int(parameters_demo[3]) # The number of clusters
AE.encoder_simple_conv_test(d=d,
pose=pose,
img_org_file=img_org_file,
out_dir=AE.os.getcwd(),
clus_num=clus_num)
AE.kmeans_centers_plot(AE.op_join(AE.os.getcwd(), 'clus-center'))
'''
Step 3. Manual selection of small subvolume clusters
Autoencoder3D training step will have two output folders.
'model' directory saved the trained models
'clus-center' directory for the resulting clusters.
There should be two pickle files in 'clus-center'.
'kmeans.pickle' stores the uuids for each cluster.
'ccents.pickle' stores the decoded cluster centers.
The 'fig' folder under 'clus-center' directory contains the 2D slices of decoded cluster center.
User can use the figures as guide for manual selection.
Manual selection clues are provided in the folder 'fig' under 'clus-center'.