def calc(I):
model = dp.trainmodel(Data_compact,
TrainList,
EvalList,
retrain=1,
epochs=100,
batch_size=100,
ModelType=I)
dp.testmodel(model, Data_compact, TestList, ModelType=I)
import DeePore as dp
# Part #1 A quick start
# Feed your porous material image to see its properties predicted
# 1. load the trained model
model = dp.loadmodel()
# 2. read and transform your data into initial feature maps
# in this example, the data is a 400 x 400 x 400 binary MATLAB mat file in which 0 indicates void and 1 indicates solid space
A = dp.feedsampledata(FileName="Data/Sample_large.mat")
# 3. show feature maps (optional)
dp.show_feature_maps(A)
# 4. predict properties
all_preds = dp.predict(
model, A,
res=4.8) # res is the spatial resolution of image in micron/pixel
# 5. save results into a text file and also print it in console
dp.prettyresult(all_preds, 'results.txt')
# Part #2 Compatibility
# 1. you can try to load numpy 3-d arrays with the same manner
A = dp.feedsampledata(FileName="Data/Sample.npy")
# 2. also 2-d images with formats of jpg and png are welcome
# if you import a 2-d image, the code creates 3 arbitrary mid-slices by flipping the 2-d image
A = dp.feedsampledata(FileName="Data/Sample.jpg")
A = dp.feedsampledata(FileName="Data/Sample.png")
# 3. when your image is larger than 256 x 256 x 256, the code automatically consider sliding windows to cover the whole image and report back to you the averaged predictions
A = dp.feedsampledata(FileName="Data/Sample_large.mat")
# when the data is loaded and transformed to the initial feature maps using this function, you are good to go and find its properties as shown above.
import DeePore as dp
# Explore the dataset:
# If you want to open images of dataset and visualize them
# 1. check or download the complete dataset
Data_complete='Data\DeePore_Dataset.h5'
# Data_complete='..\..\..\BigData\DeePore\DeePore_Dataset.h5'
dp.check_get('https://zenodo.org/record/4297035/files/DeePore_Dataset.h5?download=1',Data_complete)
# 2. read the first image out of 17700
A=dp.readh5slice(Data_complete,'X',[0])
# 3. show mid-slices of the loaded image
dp.showentry(A)
# 4. show and save the properties of this image which assumed to be the ground truth as text file
props=dp.readh5slice(Data_complete,'Y',[0])
dp.prettyresult(props,'sample_gt.txt',units='px')
import DeePore as dp
# Comparing statistics of the training, validation and testing data:
# 1. check or download the compact data
Data_compact = 'Data\DeePore_Compact_Data.h5'
# Data_compact='..\..\..\BigData\DeePore\DeePore_Compact_Data.h5'
dp.check_get(
'https://zenodo.org/record/4297035/files/DeePore_Compact_Data.h5?download=1',
Data_compact)
# 2. prepare the dataset by removing outliers and creating list of training, validation and test samples
List = dp.prep(Data_compact)
TrainList, EvalList, TestList = dp.splitdata(List)
# 3. read datasets 'Y' into arrays
Data_Eval = dp.readh5slice(Data_compact, 'Y', EvalList)
Data_Train = dp.readh5slice(Data_compact, 'Y', TrainList)
Data_Test = dp.readh5slice(Data_compact, 'Y', TestList)
# exporting to MATLAB for extra postprocessing if you needed
# import scipy.io as sio
# sio.savemat('All_Data.mat',{'train':Data_Train,'eval':Data_Eval,'test':Data_Test})
# 4. plot histograms
import matplotlib.pyplot as plt
FN = 5 # feature id number, you can select 0 to 14
h = plt.hist(Data_Eval[:, FN, 0],
50,
histtype='step',
density=True,
label='validation')
h = plt.hist(Data_Train[:, FN, 0],
50,
import DeePore as dp
# Comparing different model architectures:
# 1. check or download the compact data
Data_compact = 'Data\DeePore_Compact_Data.h5'
Data_compact = '..\..\..\BigData\DeePore\DeePore_Compact_Data.h5'
dp.check_get(
'https://zenodo.org/record/4297035/files/DeePore_Compact_Data.h5?download=1',
Data_compact)
# 2. prepare the dataset by removing outliers and creating list of training, evaluation and test samples
List = dp.prep(Data_compact)
# 3. shuffling the dataset
List = dp.shuf(List)
# List=List[1:1000] #uncomment for a smaller dataset for test purposes
TrainList, EvalList, TestList = dp.splitdata(List)
# 4. defining the training and testing workflows
def calc(I):
model = dp.trainmodel(Data_compact,
TrainList,
EvalList,
retrain=1,
epochs=100,
batch_size=100,
ModelType=I)
dp.testmodel(model, Data_compact, TestList, ModelType=I)
import DeePore as dp
# Retrain and test the model:
# If you want to try you own architecture of neural network or retrain the present one
# 1. check or download the compact data
Data_compact = 'Data\DeePore_Compact_Data.h5'
# Data_compact='..\..\..\BigData\DeePore\DeePore_Compact_Data.h5'
dp.check_get(
'https://zenodo.org/record/4297035/files/DeePore_Compact_Data.h5?download=1',
Data_compact)
# 2. prepare the dataset by removing outliers and creating list of training, evaluation and test samples
List = dp.prep(Data_compact)
TrainList, EvalList, TestList = dp.splitdata(List)
# 3. retrain the model
model = dp.trainmodel(Data_compact,
TrainList,
EvalList,
retrain=0,
epochs=50,
batch_size=100,
ModelType=3)
# 4. test the model
dp.testmodel(model, Data_compact, TestList)