def load_images_and_evaluate1(model, args, dataFolder, numImages=None):
#load images
if numImages == None:
#Load all images
image_files, im = lm.load_im(dataFolder)
label = lm.load_label(image_files)
else:
image_files, im_all = lm.load_im(dataFolder)
im = im_all[0:numImages, :, :]
label_all = lm.load_label(image_files)
label = label_all[0:numImages, :, :]
print 'Image Shape:' + str(im.shape)
print 'Label Shape:' + str(label.shape)
predicted = model.predict(im, batch_size=10)
print 'Predicted Results Shape:' + str(predicted.shape)
mse = find_mse(label, predicted)
sortedIndices = np.argsort(mse)
topImage = im[sortedIndices[0]]
topLabel = label[sortedIndices[0]]
topPredicted = predicted[0]
topResults = np.concatenate((topImage, topLabel, topPredicted), axis=2)
bottomImage = im[sortedIndices[sortedIndices.size - 1]]
bottomLabel = label[sortedIndices[sortedIndices.size - 1]]
bottomPredicted = predicted[sortedIndices[sortedIndices.size - 1]]
bottomResults = np.concatenate((bottomImage, bottomLabel, bottomPredicted),
axis=2)
return topResults, bottomResults, im, label
def load_model_images_and_evaluate(model,
dataFolderPath,
dataFolder='im',
labelFolder='label',
numImages=None):
#load images
print dataFolder
print labelFolder
if numImages == None:
#Load all images
image_files, im = lm.load_im(dataFolderPath, dataFolder)
label = lm.load_label(image_files, dataFolder, labelFolder)
else:
image_files, im_all = lm.load_im(dataFolderPath, dataFolder, numImages)
im = im_all[0:numImages, :, :]
label_all = lm.load_label(image_files, dataFolder, labelFolder)
label = label_all[0:numImages, :, :]
print 'Image Shape:' + str(im.shape)
print 'Label Shape:' + str(label.shape)
predicted = model.predict(im, batch_size=10)
imageCountNan = 0
imageCountInf = 0
for i in range(predicted.shape[0]):
nancount = anynan(np.squeeze(predicted[i, :, :, :]))
infcount = anyinf(np.squeeze(predicted[i, :, :, :]))
if (nancount > 0):
#print('predicted contains nan:'+str(nancount));
imageCountNan = imageCountNan + 1
if (infcount > 0):
#print('predicted contains inf:'+str(infcount));
imageCountInf = imageCountInf + 1
print("Number of image with predicted containing Nan:" +
str(imageCountNan))
print("Number of image with predicted containing Inf:" +
str(imageCountInf))
print 'Predicted datatype'
print predicted.dtype
return predicted, im, label, image_files
def main():
"Initialize the model"
if hasattr(args, 'load_path') and args.load_path is not None:
print("Loading Model from" + args.load_path);
fileName = args.load_path + '/' +'Keras_model_weights.h5';
model = load_model(fileName);
print("Model Loaded");
else:
print("Creating new model");
model = create_model_seg();
#set training parameters
sgd = opt.SGD(lr=0.0001, decay=0.0005, momentum=0.9, nesterov=True);
model.compile(loss='mean_squared_error', optimizer='sgd');
#load images
dataFolder = os.getcwd() +'/data_prev';
image_files,im = lm.load_im(dataFolder);
print im.shape;
label = lm.load_label(image_files);
print label.shape;
losses = model.evaluate(im,label,batch_size=10);
predicted = model.predict(im,batch_size=10);
#lm.save_results( predicted,image_files);
print(predicted.shape);
mse = find_mse(label,predicted);
sortedIndices = np.argsort(mse);
resultsFolder = dataFolder + '/results';
errorFile = resultsFolder + '/mse.csv';
if not os.path.exists(resultsFolder):
print 'Creating folder:' + resultsFolder;
create_results_folder(resultsFolder);
ef = open(errorFile,'w');
for i in range(sortedIndices.size):
print >> ef, image_files[sortedIndices[i]]+','+ str(mse[sortedIndices[i]]);
ef.close();
#save predicted images for topk and bottomk
topkFolderName = dataFolder + '/topk_predicted';
if not os.path.exists(topkFolderName) :
create_results_folder(topkFolderName);
topkIndices = sortedIndices[sortedIndices.size-10:sortedIndices.size ];
print topkIndices;
lm.save_results(predicted,image_files,topkIndices,'topk_predicted' );
bottomkFolderName = dataFolder + '/bottomk_predicted';
if not os.path.exists(bottomkFolderName) :
create_results_folder(bottomkFolderName)
lm.save_results(predicted,image_files,sortedIndices[0:9 ],'bottomk_predicted' );
#save predicted images for topk and bottomk
topkFolderName = dataFolder + '/topk_im';
if not os.path.exists(topkFolderName) :
create_results_folder(topkFolderName);
topkIndices = sortedIndices[sortedIndices.size-10:sortedIndices.size ];
print topkIndices;
lm.save_results(im,image_files,topkIndices,'topk_im' );
bottomkFolderName = dataFolder + '/bottomk_im';
if not os.path.exists(bottomkFolderName) :
create_results_folder(bottomkFolderName)
lm.save_results(im,image_files,sortedIndices[0:9 ],'bottomk_im' );
def main():
#load/create model
if hasattr(args, 'load_path') and args.load_path is not None:
print("Loading Model from" + args.load_path);
fileName = args.load_path + '/' +'Keras_model_weights.h5';
model = load_model(fileName);
print("Model Loaded");
else:
print("Creating new model");
model = create_model_seg();
#set training parameters
sgd = opt.SGD(lr=0.0001, decay=0.0005, momentum=0.9, nesterov=True);
model.compile(loss='mean_squared_error', optimizer='sgd');
#load images
dataFolder = os.getcwd() + '/data_prev';
image_files,im_all = lm.load_im(dataFolder);
numImages = 20;
im= im_all[0:numImages,:,:];
print 'Image Shape:' + str(im.shape);
label_all = lm.load_label(image_files);
label = label_all[0:numImages,:,:]
print 'Label Shape:' + str(label.shape);
losses = model.evaluate(im,label,batch_size=10);
predicted = model.predict(im,batch_size=10);
#lm.save_results( predicted,image_files);
print 'Predicted Results Shape:' + str(predicted.shape);
mse = find_mse(label,predicted);
sortedIndices = np.argsort(mse);
#Display image, label and predicted output for the image with highest and lowest error
topImage = im[sortedIndices[0]];
topLabel = label[sortedIndices[0]];
topPredicted = predicted[0];
bottomImage = im[sortedIndices[sortedIndices.size - 1]];
bottomLabel = label[sortedIndices[sortedIndices.size - 1]];
bottomPredicted = predicted[sortedIndices[sortedIndices.size - 1]];
pl.figure(1,figsize=(15,15));
pl.title('Results');
top = np.zeros([400,200,3]);
top[:,:,0] = np.squeeze( topImage );
top[:,:,1] = np.squeeze(topLabel );
top[:,:,2] = np.squeeze(topPredicted );
bottom = np.zeros([400,200,3]);
bottom[:,:,0] = np.squeeze( bottomImage );
bottom[:,:,1] = np.squeeze( bottomLabel );
bottom[:,:,2] = np.squeeze( bottomPredicted );
pl.subplot(2,3,1);
pl.imshow(top[:,:,0],cmap=cm.binary);
pl.subplot(2,3,2);
pl.imshow(top[:,:,1],cmap=cm.binary);
pl.subplot(2,3,3);
pl.imshow(top[:,:,2],cmap=cm.binary);
pl.subplot(2,3,4);
pl.imshow(bottom[:,:,0],cmap=cm.binary);
pl.subplot(2,3,5);
pl.imshow(bottom[:,:,1],cmap=cm.binary);
pl.subplot(2,3,6);
pl.imshow(bottom[:,:,2],cmap=cm.binary);
layer = 1;
layer_out = model.layers[layer];
inputs = [backend.learning_phase()] + model.inputs;
_convout1_f = backend.function(inputs, layer_out.output);
def convout1_f(X):
# The [0] is to disable the training phase flag
return _convout1_f( [0] + [X] )
imagesToVisualize = np.zeros([2,400,200,1]);
imagesToVisualize[0,:,:,0] = np.squeeze( topImage );
imagesToVisualize[1,:,:,0] = np.squeeze( bottomImage );
convout1 = np.squeeze(convout1_f(imagesToVisualize));
print 'Output shape of layer ' +str(layer)+ ':' +str(convout1.shape);
numFilters = convout1.shape[3];
numImages = imagesToVisualize.shape[0];
pl.figure(1, figsize = (15,15));
pl.title('Output of layer ' +str(layer));
filterNum = 0;
imageNum = 0;
position = 1;
print 'Number of filters:' + str(numFilters)
while imageNum < numImages:
pl.subplot(numImages,numFilters+1,position);
pl.imshow(np.squeeze(imagesToVisualize[imageNum,:,:,0]),cmap = cm.binary);
position = position + 1;
while filterNum < numFilters :
pl.subplot(numImages,numFilters+1,position);
pl.imshow( np.squeeze(convout1[imageNum,:,:,filterNum] ),cmap = cm.binary);
position = position + 1;
filterNum = filterNum + 1;
imageNum = imageNum + 1;
filterNum = 0;
## nice_imshow(pl.gca(),make_mosaic(convout6,10,10),cmap=cm.binary);
##
# plot the model weights
layer = 3;
W = model.layers[layer].W.get_value(borrow=True)
W = np.squeeze(W)
print("W shape : ", W.shape);
print("Dimension : ", len(W.shape));
pl.figure(2,figsize=(15, 15));
pl.title('Convoution layer:'+ str(layer)+' weights');
nice_imshow(pl.gca(), make_mosaic(W, 10,10), cmap=cm.binary);
figFileName = args.load_path + '/' + 'layer_'+str(layer)+'.png';
pl.savefig(figFileName);
freq,values = np.histogram(W,bins=1000);
pl.figure(3,figsize=(15,15));
pl.plot(values[1:len(values)],freq);
#open files
weightsFile = args.load_path + '/' + 'layer_'+str(layer)+'.txt';
weightsStatisticsFile = args.load_path + '/' + 'layer_stats_'+str(layer)+'.txt';
wf = open(weightsFile, 'w+'); #weights file
wfs = open(weightsStatisticsFile, 'w+'); #weights statistics file
print >> wfs,'Overall Minimum= '+str(np.amin(W));
print >> wfs,'Overall Maximum= '+str(np.amax(W));
print >> wfs,'Overall Mean= '+ str(np.mean(W));
print >> wfs,'Overall variance= '+ str(np.var(W));
## print >> wfs,'Histogram';
## print >> wfs, 'Frequency';
## print >> wfs, freq;
## print >> wfs, 'Values';
## print >> wfs, values[len(values) -10: len(values) - 1];
pruningThreshold = 15;
""" Print all filter numbers whose weights lies in the interval [pruningThreshold,0]
"""
if len(W.shape) == 3 :
numFilter = W.shape[2];
numChannel = 1;
else:
numFilter = W.shape[3];
numChannel = W.shape[2];
numberOfFilters = numFilter * numChannel;
percent = (100.0 * getNumberOfFiltersToBePruned(W, pruningThreshold,0) ) / numberOfFilters;
## while percent < 90:
## pruningThreshold = pruningThreshold + 1;
## percent = ( 100.0 * getNumberOfFiltersToBePruned(W, pruningThreshold,0) ) /numberOfFilters;
## print percent;
#print >> wfs, 'Total Number of channels = ' + str(getNumberOfFiltersToBePruned(W));
#print >> wfs, 'Channels and filters in which weights lies in the interval ['+str(pruningThreshold)+',0]';
print >> wfs,'(channel,filter)';
numberOfFiltersToPrune =0 ;
if len(W.shape) == 4:
for i in range(W.shape[2]) :
for j in range(W.shape[3]) :
weightMatrix = W[:,:,i,j];
maxW = np.amax( W[:,:,i,j] );
minW = np.amin( W[:,:,i,j] );
if abs(minW) < pruningThreshold and abs(maxW) < pruningThreshold:
print >> wfs, '('+str(i) +','+str(j) +')';
numberOfFiltersToPrune = numberOfFiltersToPrune + 1;
print >> wfs, 'Total Number of channels= ' + str(numberOfFiltersToPrune);
if len(W.shape) == 4:
for i in range(W.shape[2]) :
for j in range(W.shape[3]) :
weightMatrix = W[:,:,i,j];
print >> wfs, 'i=' + str(i) + ' j=' + str(j);
print >> wf,weightMatrix;
maxVector = np.amax(W[:,:,i,j],axis=1);
minVector = np.amin(W[:,:,i,j],axis=1);
print >> wfs,'Minimum='+str(np.amin(minVector));
print >> wfs,'Maximum='+str(np.amax(maxVector));
print >> wfs,'Mean='+str(np.mean(weightMatrix));
print >> wfs,'Variance='+str(np.var(weightMatrix));
print >> wfs,maxVector;
print >> wfs,minVector;
else:
for i in range(W.shape[2]):
print >> wfs,W[:,:,i];
print >> wfs,np.amax(W[:,:,i],axis=1);
wf.close();
wfs.close();
pl.show();
from lib_metrics import find_mse import load_and_save_images as lm from sklearn.metrics import mean_squared_error from math import sqrt import numpy as np import os ##y_actual = np.array([[1,2,3],[4,5,0]]); ##y_predicted = np.array([[0,0,0],[0,0,0]]); ##rms = mean_squared_error(y_actual, y_predicted); ##print(rms); dataFolder = os.getcwd() + '/data_prev' image_files, im = lm.load_im(dataFolder) print 'Image Shape:' + str(im.shape) label = lm.load_label(image_files) print 'Label Shape:' + str(label.shape) mse = find_mse(label, label) print mse