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unet_test.py
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unet_test.py
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#!/usr/bin/env python
u"""
unet_test.py
by Yara Mohajerani (Last Update 11/2018)
Test U-Net model in frontlearn_unet.py
Update History
11/2018 Fork from unet_train
"""
import os
import numpy as np
import keras
from keras.preprocessing import image
import imp
import sys
from glob import glob
from PIL import Image,ImageOps
from keras import backend as K
from tensorflow.python.client import device_lib
from keras.callbacks import ReduceLROnPlateau, EarlyStopping
from sklearn.utils import class_weight
#-- Print backend information
print(device_lib.list_local_devices())
print(K.tensorflow_backend._get_available_gpus())
#-- read in images
def load_data(suffix,tst_dir,n_layers):
#-- make subdirectories for input images
tst_subdir = os.path.join(tst_dir,'images%s'%(suffix))
#-- get a list of the input files
tst_list = glob(os.path.join(tst_subdir,'*.png'))
#-- get just the file names
tst_files = [os.path.basename(i) for i in tst_list]
#-- read training data
n = len(tst_files)
#-- get dimensions, force to 1 b/w channel
im_shape = np.array(Image.open(tst_list[0]).convert('L')).shape
h,w = im_shape
# pad height and width until it's at least divisible by the right number for the given
# network depth
n_div = 2**(n_layers-1)
if h%n_div != 0:
h_pad = h+n_div-(h%n_div)
else:
h_pad = np.copy(h)
if w%n_div != 0:
w_pad = w+n_div-(w%n_div)
else:
w_pad = np.copy(w)
#-- get the test data
n_test = len(tst_files)
test_img = np.ones((n_test,h_pad,w_pad))
for i in range(n_test):
test_img[i][:im_shape[0],:im_shape[1]] = np.array(Image.open(tst_list[i]).convert('L'))/255.
return {'tst_img':test_img.reshape(n_test,h_pad,w_pad,1),'tst_names':tst_files,'orig_shape':im_shape}
#-- train model and make predictions
def train_model(parameters):
glacier = parameters['GLACIER_NAME']
model_glacier =parameters['MODEL_DIR']
n_batch = int(parameters['BATCHES'])
n_epochs = int(parameters['EPOCHS'])
n_layers = int(parameters['LAYERS_DOWN'])
n_init = int(parameters['N_INIT'])
suffix = parameters['SUFFIX']
drop = float(parameters['DROPOUT'])
imb_str = '_%.2fweight'%(float(parameters['imb_str']))
at = float(parameters['THRESHOLD'])
if at != 0:
threshold_str = '%.2fthreshold'%at
else:
threshold_str = 'nothreshold'
#-- set up configurations based on parameters
if parameters['AUGMENT'] in ['Y','y']:
aug_config = np.int(parameters['AUG_CONFIG'])
aug_str = '_augment-x%i'%aug_config
else:
aug_config = 0
aug_str = ''
if parameters['CROP'] in ['Y','y']:
crop_str = '_cropped'
else:
crop_str = ''
if parameters['NORMALIZE'] in ['y','Y']:
normalize = True
norm_str = '_normalized'
else:
normalize = False
norm_str = ''
if parameters['LINEAR'] in ['Y','Y']:
linear = True
lin_str = '_linear'
else:
linear = False
lin_str = ''
drop_str = ''
if drop>0:
drop_str = '_w%.1fdrop'%drop
#-- width of labels (pixels)
#-- don't label 3-pix width to be consistent with old results
if parameters['LABEL_WIDTH'] == '3':
lbl_width = ''
else:
lbl_width = '_%ipx'%int(parameters['LABEL_WIDTH'])
if (normalize) and (drop!=0):
sys.exit('Both batch normalization and dropout are selecte. Choose one.')
#-- directory setup
#- current directory
current_dir = os.path.dirname(os.path.realpath(__file__))
main_dir = os.path.join(current_dir,'..','FrontLearning_data')
glacier_ddir = os.path.join(main_dir,'%s.dir'%glacier)
model_dir = os.path.join(main_dir,'%s.dir'%model_glacier)
data_dir = os.path.join(glacier_ddir, 'data')
trn_dir = os.path.join(data_dir,'train')
tst_dir = os.path.join(data_dir,'test')
#-- load images
data = load_data(suffix,tst_dir,n_layers)
n,height,width,channels=data['tst_img'].shape
print('width=%i'%width)
print('height=%i'%height)
#-- import mod
unet = imp.load_source('unet_model', os.path.join(current_dir,'unet_model.py'))
if normalize:
if linear:
model = unet.unet_model_linear_normalized(height=height,width=width,channels=channels,\
n_init=n_init,n_layers=n_layers)
print('importing unet_model_linear_normalized')
else:
model = unet.unet_model_double_normalized(height=height,width=width,channels=channels,\
n_init=n_init,n_layers=n_layers)
print('importing unet_model_double_normalized')
else:
if linear:
model = unet.unet_model_linear_dropout(height=height,width=width,channels=channels,\
n_init=n_init,n_layers=n_layers,drop=drop)
print('importing unet_model_linear_dropout')
else:
model = unet.unet_model_double_dropout(height=height,width=width,channels=channels,\
n_init=n_init,n_layers=n_layers,drop=drop)
print('importing unet_model_double_dropout')
#-- checkpoint file
chk_file = os.path.join(model_dir,'unet_model_weights_%ibatches_%iepochs_%ilayers_%iinit%s%s%s%s%s%s%s%s.h5'\
%(n_batch,n_epochs,n_layers,n_init,lin_str,imb_str,drop_str,norm_str,aug_str,suffix,crop_str,lbl_width))
print('model file:%s'%chk_file)
#-- if file exists, read model from file
if os.path.isfile(chk_file):
print('Check point exists; loading model from file.')
# load weights
model.load_weights(chk_file)
else:
sys.exit('Model not found.')
print('Running on test data...')
out_imgs = model.predict(data['tst_img'], batch_size=1, verbose=1)
print out_imgs.shape
out_imgs = out_imgs.reshape(out_imgs.shape[0],height,width,out_imgs.shape[2])
print out_imgs.shape
#-- make output directory
out_subdir = 'output_%s_%ibatches_%iepochs_%ilayers_%iinit%s%s%s%s%s%s%s%s'\
%(model_glacier,n_batch,n_epochs,n_layers,n_init,lin_str,imb_str,drop_str,norm_str,aug_str,suffix,crop_str,lbl_width)
if (not os.path.isdir(os.path.join(tst_dir,out_subdir))):
os.mkdir(os.path.join(tst_dir,out_subdir))
#-- save the test image
for i in range(len(out_imgs)):
if at != 0.:
#-- clean up points below the threshold
img_flat = out_imgs[i].flatten()
ind_black = np.squeeze(np.nonzero(img_flat <= at))
ind_white = np.squeeze(np.nonzero(img_flat > at))
img_flat[ind_black] = 0.
img_flat[ind_white] = 1.
img_array = img_flat.reshape(out_imgs[i].shape)
#-- convert back to original dimension
img_final = img_array[:data['orig_shape'][0],:data['orig_shape'][1]]
else:
img_final = out_imgs[i][:data['orig_shape'][0],:data['orig_shape'][1]]
#-- convert to image
im = image.array_to_img(img_final)
#im = ImageOps.autocontrast(image.array_to_img(out_imgs[i]))
out_name = '%s_%s.png'%((data['tst_names'][i].replace('_Subset',''))[:-4],threshold_str)
#out_name = '%s.png'%((data['tst_names'][i].replace('_Subset',''))[:-4])
print(os.path.join(tst_dir,out_subdir,out_name))
im.save(os.path.join(tst_dir,out_subdir,out_name))
#-- main function to get parameters and pass them along to fitting function
def main():
if (len(sys.argv) == 1):
sys.exit('You need to input at least one parameter file to set run configurations.')
else:
#-- Input Parameter Files (sys.argv[0] is the python code)
input_files = sys.argv[1:]
#-- for each input parameter file
for file in input_files:
#-- keep track of progress
print(os.path.basename(file))
#-- variable with parameter definitions
parameters = {}
#-- Opening parameter file and assigning file ID number (fid)
fid = open(file, 'r')
#-- for each line in the file will extract the parameter (name and value)
for fileline in fid:
#-- Splitting the input line between parameter name and value
part = fileline.split()
#-- filling the parameter definition variable
parameters[part[0]] = part[1]
#-- close the parameter file
fid.close()
#-- pass parameters to training function
train_model(parameters)
if __name__ == '__main__':
main()