def test_imgs():
root = '/home/bergstra/cvs/eccv12/eccv12/tests/data/'
saved_paths = np.load(os.path.join(root, 'fg11_Xraw0-4.npy'))
saved_processed_imgs = np.load(os.path.join(root, 'fg11_X0-4.npy'))
imgs = plugins.get_images('float32', None)
print saved_paths
print saved_processed_imgs.shape
assert saved_processed_imgs[0].shape == imgs[0].shape
# the saved_preprocessed_imgs were designed to include only images
# that appeared in view1, so it is normal that some images are omitted.
assert np.allclose(saved_processed_imgs[0], imgs[0])
assert np.allclose(saved_processed_imgs[1], imgs[1])
assert np.allclose(saved_processed_imgs[2], imgs[3])
if 0:
from skdata.utils.glviewer import glumpy_viewer, command, glumpy
glumpy_viewer(
#img_array=saved_processed_imgs,
img_array=imgs,
arrays_to_print=[saved_paths],
cmap=glumpy.colormap.Grey)
def test_cvprtop_img_features():
saved = np.load(os.path.join(data_root, 'fg11_features0-4.npy'))
imgs = plugins.get_images('float32', None) #--larray
desc = copy.deepcopy(model_params.cvpr_top,
memo={
id(model_params.null): None,
},)
feat_fn = SLMFunction(desc, imgs.shape[1:])
print saved[0].shape
print feat_fn(imgs[0]).shape
for ii, jj in (0, 0), (1, 1), (2, 3):
assert np.allclose(saved[ii], feat_fn(imgs[jj]))
def test_cvprtop_features_all():
filename='features_df448700aa91cef4c8bc666c75c393776a210177_0.dat'
saved = np.memmap(os.path.join(data_root, filename),
dtype='float32', mode='r',
shape=(4992, 16, 16, 256))
desc = copy.deepcopy(model_params.cvpr_top,
memo={
id(model_params.null): None,
},)
image_features = plugins.slm_memmap(
desc=desc,
X=plugins.get_images('float32', preproc=None),
name='cvprtop_features_all_img_feat')
vpairs_train = plugins.verification_pairs('DevTrain')
vpairs_test = plugins.verification_pairs('DevTest')
train_X, train_y = plugins.pairs_memmap(vpairs_train, image_features, 'mult', 'wtf_train')
test_X, test_y = plugins.pairs_memmap(vpairs_test , image_features, 'mult', 'wtf_test')
# -- evaluate the whole set of pairs
train_X = np.asarray(train_X)
test_X = np.asarray(test_X)
# -- check that there are 4992 valid entries in the image_features memmap
# and that our features match the saved ones
print np.sum(image_features._valid)
assert np.sum(image_features._valid) == 4992
jj = 0
for ii in range(4992):
if image_features._valid[ii]:
assert np.allclose(image_features._data[ii], saved[jj])
jj += 1
# -- check that our pair features match the saved ones
saved_train_pairs_X = np.memmap(
os.path.join(data_root,
'train_pairs_df448700aa91cef4c8bc666c75c393776a210177.dat'),
dtype='float32', mode='r',
shape=(2200, 65536))
saved_test_pairs_X = np.memmap(
os.path.join(data_root,
'test_pairs_df448700aa91cef4c8bc666c75c393776a210177.dat'),
dtype='float32', mode='r',
shape=(1000, 65536))
assert np.allclose(train_X, saved_train_pairs_X)
assert np.allclose(test_X, saved_test_pairs_X)
train_d = np.zeros(len(train_y), dtype='float32')
test_d = np.zeros(len(test_y), dtype='float32')
train_Xyd_n, test_Xyd_n = plugins.normalize_Xcols(
(train_X, train_y, train_d,),
(test_X, test_y, test_d,))
svm = plugins.train_svm(train_Xyd_n, l2_regularization=1e-3)
new_d_train = plugins.svm_decisions(svm, train_Xyd_n)
new_d_test = plugins.svm_decisions(svm, test_Xyd_n)
result = plugins.result_binary_classifier_stats(
train_Xyd_n,
test_Xyd_n,
new_d_train,
new_d_test,
result={})
print 'Train_accuracy', result['train_accuracy']
print 'Test accuracy', result['test_accuracy']
print 'loss', result['loss'], np.sqrt(result['loss'] * (1 -
result['loss']) / (len(test_y) - 1))
