def main():
''' pipeline for testing and evaluation '''
# preset parameters
save_path = '../data_viper/model_feat/'
# load data
# imL = 48
bs = 100
datafile_viper = '../data_viper/viper.pkl'
viper = loadfile(datafile_viper)
# load model
modelfile_viper = '../data_viper/model_feat/model.pkl'
model = loadfile(modelfile_viper)
# evaluation and testing
# test_x = viper.test_x.get_value(borrow=True)
test_x = np.asarray(viper.test_feat)
test_y = viper.test_y
n_test = test_x.shape[0]
test_ypred = model.predict(viper.test_feat)
test_ypred = np.asarray(test_ypred).flatten()
# test_ims = test_x.reshape((n_test, imL, imL, -1))
# assign predicted scores to images
h, w = viper.imgs[0].shape[:2]
mh, mw = len(np.unique(viper.yy)), len(np.unique(viper.xx))
msk0 = np.zeros(mh*mw).astype(np.uint8)
msks = [msk0.copy() for im in viper.imgs]
showlist = []
for i in range(n_test):
imgid = viper.test_imgids[i]
patid = viper.test_ctrids[i]
score = test_ypred[i]
msks[imgid][patid] = score*255
# resize predicted salience map to match image size
msks_rs = [imresize(msk.reshape((mw, mh)).T, size=(h, w))/255. for msk in msks]
# save salience map for comparison
test_imids = np.asarray(np.unique(viper.test_imgids))
salmap_gt = np.asarray(viper.salmsks) #np.asarray([viper.salmsks[imid] for imid in test_imids])
salmap_pred = np.asarray(msks_rs) #np.asarray([msks_rs[imid]/255. for imid in test_imids])
savefile(save_path+'salmaps_comparison.pkl', [test_imids, salmap_gt, salmap_pred])
# quantize to show different test patches
# kmeans = KMeans(init='k-means++', n_clusters=10, n_init=10)
# kmeans.fit(test_ypred.reshape(n_test, 1))
# # save to result folder
# for i in range(10):
# idx = kmeans.labels_== i
# if any(idx):
# im = immontage(list(test_ims[idx]))
# imsave(save_path+'{}.jpg'.format(kmeans.cluster_centers_[i]), im)
print 'testing finished'
def main():
''' pipeline for supervised salience training '''
if os.path.isdir('../data_viper/'):
datapath = '../data_viper/'
else:
datapath = '../data/'
save_path = '../data_viper/model_feat/'
DATA_OPT = 'feat' # feature type
TRAIN_OPT = 'SVR' # training model option
TRAIN = True # wheather re-train the model
# prepare training data for supervised salience training
#=======================================================
datafile_viper = datapath + 'viper.pkl'
if not os.path.isfile(datafile_viper):
viper = DataMan_viper_small()
viper.make_data()
savefile(datafile_viper, viper)
else:
viper = loadfile(datafile_viper)
viper = preprocess_data(viper, DATA_OPT)
# training
# ==============
modelfile = datapath + 'model_feat/model.pkl'
if TRAIN:
tic = time.clock()
model = train_model(viper, TRAIN_OPT)
toc = time.clock()
print 'Elapsed training time: {0:.2f} min'.format((toc-tic)/60.)
savefile(modelfile, model)
os.system('ls -lh ' + modelfile)
else:
model = loadfile(modelfile)
## validation
#=========================================
print 'validating trained model'
nValid = 5000
valididx = np.random.permutation(viper.valid_feat.shape[0])[:nValid]
# valid_ypred = model.predict(viper.valid_feat[valididx])
valid_ypred = predict(model, viper.valid_feat[valididx], viper.yy[viper.valid_ctrids][valididx], viper.imH)
#- quantize patches based on testing scores
kmeans = KMeans(init='k-means++', n_clusters=10, n_init=10, verbose=1)
kmeans.fit(valid_ypred.reshape(nValid, 1))
#- crop patches for testing image
valid_patset = np.asarray(viper.get_patchset('valid'))[valididx]
#- save to result folder
os.system('rm '+save_path+'*.jpg')
for i in range(10):
idx = kmeans.labels_== i
if any(idx):
pats = immontage(list(valid_patset[idx]))
imsave(save_path+'{}.jpg'.format(kmeans.cluster_centers_[i]), pats)
print 'patchset {} saved'.format(i)
### testing
#===============
print 'testing'
# test_ypred = model.predict(viper.test_feat)
test_ypred = predict(model, viper.test_feat, viper.yy_test[viper.test_ctrids], viper.imH)
## assign predicted scores to images
h, w = viper.imgs[0].shape[:2]
mh, mw = len(np.unique(viper.yy_test)), len(np.unique(viper.xx_test))
msk0 = np.zeros(mh*mw, dtype=np.float32)
msks = [msk0.copy() for im in viper.imgs]
showlist = []
n_test = len(test_ypred)
for i in range(n_test):
imgid = viper.test_imgids[i]
patid = viper.test_ctrids[i]
score = test_ypred[i]
msks[imgid][patid] = score
# resize predicted salience map to match image size
msks_rs = [mapresize(msk.reshape((mw, mh)).T, size=(h, w)) for msk in msks]
# msks_rs = msks
# save salience map for comparison
test_imids = np.asarray(np.unique(viper.test_imgids))
salmap_gt = np.asarray(viper.salmsks) #np.asarray([viper.salmsks[imid] for imid in test_imids])
salmap_pred = np.asarray(msks_rs) #np.asarray([msks_rs[imid]/255. for imid in test_imids])
savefile(save_path+'salmaps_comparison.pkl', [test_imids, salmap_gt, salmap_pred])
def main():
''' pipeline for supervised salience training '''
if os.path.isdir('../data_viper/'):
datapath = '../data_viper/'
else:
datapath = '../data/'
# prepare training data for supervised salience training
datafile_viper = datapath + 'viper.pkl'
if not os.path.isfile(datafile_viper):
viper = DataMan_viper_small()
viper.make_data()
savefile(datafile_viper, viper)
viper = loadfile(datafile_viper)
viper = change_label(viper)
viper.train_feat = viper.get_pixeldata('train')
viper.valid_feat = viper.get_pixeldata('valid')
viper.test_feat = viper.get_pixeldata('test')
bs = 100
imL = 10
nfilter1 = 16
filterL = 3
x = T.tensor4(name='x', dtype=theano.config.floatX)
y = T.ivector(name='y')
# layer0 = x.reshape((bs, 3, imL, imL))
conv1 = ConvLayer(input=x, image_shape=(bs, 3, imL, imL),
filter_shape=(nfilter1, 3, filterL, filterL),
flatten=True,
actfun=tanh,
tag='_convpool1')
# outL = np.floor((imL-filterL+1.)/recfield).astype(np.int)
outL = imL-filterL+1
# nfilter3 = 16
# filterL3 = 3
# conv3 = ConvLayer(input=conv2.output(), image_shape=(bs, nfilter2, outL2, outL2),
# filter_shape=(nfilter3, nfilter2, filterL3, filterL3),
# flatten=True,
# actfun=tanh,
# tag='_conv3')
#
# outL3 = outL2-filterL3+1
fc2 = FCLayer(input=conv1.output(), n_in=nfilter1*outL*outL, n_out=256, actfun=tanh, tag='_fc2')
fc3 = FCLayer(input=fc2.output(), n_in=256, n_out=10, actfun=sigmoid, tag='_fc3')
params_cmb = conv1.params + fc2.params + fc3.params
# ypred = fc3.output().flatten()
ypred = fc3.output()
model = GeneralModel(input=x, data=viper, output=ypred,
target=y, params=params_cmb,
regularizers=0,
cost_func=negative_log_likelihood,
error_func=sqr_error,
batch_size=bs)
sgd = sgd_optimizer(data=viper,
model=model,
batch_size=bs,
learning_rate=0.001,
n_epochs=500)
sgd.fit_viper()
filepath = datapath + 'model/model.pkl'
savefile(filepath, model)
os.system('ls -lh ' + filepath)