def main(force):
cub_head = CUB_200_2011(settings.CUB_ROOT, images_folder_name='images_b_head')
cub_body = CUB_200_2011(settings.CUB_ROOT, images_folder_name='images_b_body')
cub_crop = CUB_200_2011(settings.CUB_ROOT, images_folder_name='images_b_cropped')
st_head = datastore(settings.storage('bmbh'))
st_body = datastore(settings.storage('bmbb'))
st_crop = datastore(settings.storage('bmbcflp'))
ext_head = Berkeley_Extractor(st_head, pretrained_file=settings.BERKELEY_HEAD_PRET)
ext_body = Berkeley_Extractor(st_body, pretrained_file=settings.BERKELEY_BODY_PRET)
ext_crop = Berkeley_Extractor(st_crop, pretrained_file=settings.BERKELEY_CROP_PRET)
number_of_images_in_dataset = sum(1 for _ in cub_crop.get_all_images())
bar = pyprind.ProgBar(number_of_images_in_dataset, width=80)
for t, des in ext_crop.extract_all(cub_crop.get_all_images(), flip=True, force=force):
bar.update()
print 'DONE CROP'
bar = pyprind.ProgBar(number_of_images_in_dataset, width=80)
for t, des in ext_head.extract_all(cub_head.get_all_images(), force=force):
bar.update()
print 'DONE HEAD'
bar = pyprind.ProgBar(number_of_images_in_dataset, width=80)
for t, des in ext_body.extract_all(cub_body.get_all_images(), force=force):
bar.update()
print 'DONE BODY'
def main(model_name, iteration, storage_name):
name = '%s-%s' % (model_name, iteration)
print settings.model(name), settings.pretrained(name)
safe = datastore(settings.storage(storage_name))
safe.super_name = 'features'
safe.sub_name = name
layer_names = ['fc7', 'fc6', 'pool5', 'conv5', 'conv4', 'conv3']
layer_dims = [4096, 4096, 9216, 43264, 64896, 64896]
net = caffe.Classifier(settings.model(name), settings.pretrained(name), mean=np.load(settings.ILSVRC_MEAN), channel_swap=(2, 1, 0), raw_scale=255)
net.set_mode_gpu()
net.set_phase_test()
cub = CUB_200_2011(settings.CUB_ROOT)
dataset_size = sum(1 for _ in cub.get_all_images())
instance = {}
for layer, dim in zip(layer_names, layer_dims):
instance[layer] = np.zeros((dataset_size, dim))
print instance[layer].shape
for i, info in enumerate(cub.get_all_images(cropped=True)):
print info['img_id']
img = caffe.io.load_image(info['img_file'])
net.predict([img], oversample=False)
for layer in layer_names:
instance[layer][i, :] = net.blobs[layer].data[0].flatten()
for layer in layer_names:
safe.save_large_instance(safe.get_instance_path(safe.super_name, safe.sub_name, 'feat_cache_%s' % layer), instance[layer], 4)
def main(out_path, part, random_state, pgs, net_name):
utils.ensure_dir(out_path)
cub = CUB_200_2011(settings.CUB_ROOT)
lfrg = rects.BerkeleyRG(settings.BERKELEY_ANNOTATION_BASE_PATH, cub, part)
RG = rects.RandomForestRG(datastore(settings.storage('rf')),
lfrg,
cub_utils.DeepHelper.get_custom_net(
settings.model(net_name),
settings.pretrained(net_name)),
net_name,
cub,
random_state=random_state,
point_gen_strategy=pgs,
use_seg=True,
pt_n_part=20,
pt_n_bg=100)
RG.setup()
for i, image in enumerate(cub.get_all_images()):
print i
image_path = image['img_file']
img_id = int(image['img_id'])
rel_image_path = image['img_file_rel']
o_image = cv2.imread(image_path)
rect = RG.generate(img_id)
t_img_part = rect.get_rect(o_image)
out_image_path = os.path.join(out_path, rel_image_path)
utils.ensure_dir(os.path.dirname(out_image_path))
cv2.imwrite(out_image_path, t_img_part)
print 'Done'
def main(sname, iteration, cropped, full, flipped, force, dataset, storage_name):
new_name = '%s-%d' % (sname, iteration)
if dataset == 'segmented':
cub = CUB_200_2011_Segmented(settings.CUB_ROOT, full=full)
elif dataset == 'part-head':
cub = CUB_200_2011_Parts_Head(settings.CUB_ROOT, full=full)
elif dataset == 'part-body':
cub = CUB_200_2011_Parts_Body(settings.CUB_ROOT, full=full)
elif dataset == 'part-head-rf-new':
cub = CUB_200_2011(settings.CUB_ROOT, 'images_head_rf_new')
elif dataset == 'part-body-rf-new':
cub = CUB_200_2011(settings.CUB_ROOT, 'images_body_rf_new')
else:
cub = CUB_200_2011(settings.CUB_ROOT, images_folder_name=dataset, full=full)
if not storage_name:
ft_storage = datastore(settings.storage(new_name))
else:
ft_storage = datastore(settings.storage(storage_name))
ft_extractor = CNN_Features_CAFFE_REFERENCE(ft_storage, model_file=settings.model(new_name), pretrained_file=settings.pretrained(new_name), full=full, crop_index=0)
number_of_images_in_dataset = sum(1 for _ in cub.get_all_images())
bar = pyprind.ProgBar(number_of_images_in_dataset, width=80)
for t, des in ft_extractor.extract_all(cub.get_all_images(), flip=flipped, crop=cropped, bbox=cub.get_bbox(), force=force):
bar.update()
print 'DONE'
def main(sname, iteration, cropped, full, flipped, force, dataset,
storage_name):
new_name = '%s-%d' % (sname, iteration)
if dataset == 'segmented':
cub = CUB_200_2011_Segmented(settings.CUB_ROOT, full=full)
elif dataset == 'part-head':
cub = CUB_200_2011_Parts_Head(settings.CUB_ROOT, full=full)
elif dataset == 'part-body':
cub = CUB_200_2011_Parts_Body(settings.CUB_ROOT, full=full)
elif dataset == 'part-head-rf-new':
cub = CUB_200_2011(settings.CUB_ROOT, 'images_head_rf_new')
elif dataset == 'part-body-rf-new':
cub = CUB_200_2011(settings.CUB_ROOT, 'images_body_rf_new')
else:
cub = CUB_200_2011(settings.CUB_ROOT,
images_folder_name=dataset,
full=full)
if not storage_name:
ft_storage = datastore(settings.storage(new_name))
else:
ft_storage = datastore(settings.storage(storage_name))
ft_extractor = CNN_Features_CAFFE_REFERENCE(
ft_storage,
model_file=settings.model(new_name),
pretrained_file=settings.pretrained(new_name),
full=full,
crop_index=0)
number_of_images_in_dataset = sum(1 for _ in cub.get_all_images())
bar = pyprind.ProgBar(number_of_images_in_dataset, width=80)
for t, des in ft_extractor.extract_all(cub.get_all_images(),
flip=flipped,
crop=cropped,
bbox=cub.get_bbox(),
force=force):
bar.update()
print 'DONE'
def main(sname, svm_c, segmented):
if segmented:
cub = CUB_200_2011_Segmented(settings.CUB_ROOT)
else:
cub = CUB_200_2011(settings.CUB_ROOT)
ft_storage = datastore(settings.storage(sname))
ft_extractor = CNN_Features_CAFFE_REFERENCE(ft_storage, make_net=False)
Xtrain, ytrain, Xtest, ytest = cub.get_train_test(ft_extractor.extract_one)
model = svm.LinearSVC(C=svm_c)
model.fit(Xtrain, ytrain)
predictions = model.predict(Xtest)
print 'accuracy', accuracy_score(ytest, predictions)
print 'mean accuracy', utils.mean_accuracy(ytest, predictions)
pred_storage = datastore(settings.PREDICTIONS_BASE, global_key='preds')
storage_path = pred_storage.get_instance_path('preds', sname, '%s.mat' % sname)
pred_storage.ensure_dir(os.path.dirname(storage_path))
pred_storage.save_instance(storage_path, predictions)
def main(sname, svm_c, segmented):
if segmented:
cub = CUB_200_2011_Segmented(settings.CUB_ROOT)
else:
cub = CUB_200_2011(settings.CUB_ROOT)
ft_storage = datastore(settings.storage(sname))
ft_extractor = CNN_Features_CAFFE_REFERENCE(ft_storage, make_net=False)
Xtrain, ytrain, Xtest, ytest = cub.get_train_test(ft_extractor.extract_one)
model = svm.LinearSVC(C=svm_c)
model.fit(Xtrain, ytrain)
predictions = model.predict(Xtest)
print 'accuracy', accuracy_score(ytest, predictions)
print 'mean accuracy', utils.mean_accuracy(ytest, predictions)
pred_storage = datastore(settings.PREDICTIONS_BASE, global_key='preds')
storage_path = pred_storage.get_instance_path('preds', sname,
'%s.mat' % sname)
pred_storage.ensure_dir(os.path.dirname(storage_path))
pred_storage.save_instance(storage_path, predictions)
def main(model_name, iteration, storage_name):
name = '%s-%s' % (model_name, iteration)
print settings.model(name), settings.pretrained(name)
safe = datastore(settings.storage(storage_name))
safe.super_name = 'features'
safe.sub_name = name
layer_names = ['fc7', 'fc6', 'pool5', 'conv5', 'conv4', 'conv3']
layer_dims = [4096, 4096, 9216, 43264, 64896, 64896]
net = caffe.Classifier(settings.model(name),
settings.pretrained(name),
mean=np.load(settings.ILSVRC_MEAN),
channel_swap=(2, 1, 0),
raw_scale=255)
net.set_mode_gpu()
net.set_phase_test()
cub = CUB_200_2011(settings.CUB_ROOT)
dataset_size = sum(1 for _ in cub.get_all_images())
instance = {}
for layer, dim in zip(layer_names, layer_dims):
instance[layer] = np.zeros((dataset_size, dim))
print instance[layer].shape
for i, info in enumerate(cub.get_all_images(cropped=True)):
print info['img_id']
img = caffe.io.load_image(info['img_file'])
net.predict([img], oversample=False)
for layer in layer_names:
instance[layer][i, :] = net.blobs[layer].data[0].flatten()
for layer in layer_names:
safe.save_large_instance(
safe.get_instance_path(safe.super_name, safe.sub_name,
'feat_cache_%s' % layer), instance[layer],
4)
def main(out_path, part, random_state, pgs, net_name):
utils.ensure_dir(out_path)
cub = CUB_200_2011(settings.CUB_ROOT)
lfrg = rects.BerkeleyRG(settings.BERKELEY_ANNOTATION_BASE_PATH, cub, part)
RG = rects.RandomForestRG(datastore(settings.storage('rf')), lfrg, cub_utils.DeepHelper.get_custom_net(settings.model(net_name), settings.pretrained(net_name)), net_name, cub, random_state=random_state, point_gen_strategy=pgs, use_seg=True, pt_n_part=20, pt_n_bg=100)
RG.setup()
for i, image in enumerate(cub.get_all_images()):
print i
image_path = image['img_file']
img_id = int(image['img_id'])
rel_image_path = image['img_file_rel']
o_image = cv2.imread(image_path)
rect = RG.generate(img_id)
t_img_part = rect.get_rect(o_image)
out_image_path = os.path.join(out_path, rel_image_path)
utils.ensure_dir(os.path.dirname(out_image_path))
cv2.imwrite(out_image_path, t_img_part)
print 'Done'
def main(c, f):
instance_split = 10
feat_layer = 'fc7'
load_rf_test = False
recalculate_training = True
C = c
force = f
dh = cub_utils.DeepHelper()
cub = CUB_200_2011(settings.CUB_ROOT)
cub_parts = cub.get_parts()
IDtrain, IDtest = cub.get_train_test_id()
all_image_infos = cub.get_all_image_infos()
all_segmentaion_infos = cub.get_all_segmentation_infos()
rf_safe = datastore(settings.storage('rf'))
rf_safe.super_name = 'features'
rf_safe.sub_name = 'head-points'
rf_safe.other_sub_name = 'head-final-features'
Xtrain_rf_ip = rf_safe.get_instance_path(rf_safe.super_name, rf_safe.sub_name, 'Xtrain_rf')
Xtest_rf_ip = rf_safe.get_instance_path(rf_safe.super_name, rf_safe.sub_name, 'Xtest_rf')
ytrain_rf_ip = rf_safe.get_instance_path(rf_safe.super_name, rf_safe.sub_name, 'ytrain_rf.mat')
ytest_rf_ip = rf_safe.get_instance_path(rf_safe.super_name, rf_safe.sub_name, 'ytest_rf.mat')
Xtrain_ip = rf_safe.get_instance_path(rf_safe.super_name, rf_safe.other_sub_name, 'Xtrain')
Xtest_ip = rf_safe.get_instance_path(rf_safe.super_name, rf_safe.other_sub_name, 'Xtest')
tic = time()
if rf_safe.check_exists(ytrain_rf_ip) and not force:
print 'loading'
Xtrain_rf = rf_safe.load_large_instance(Xtrain_rf_ip, instance_split)
ytrain_rf = rf_safe.load_instance(ytrain_rf_ip)
ytrain_rf = ytrain_rf[0, :]
else:
print 'calculating'
Xtrain_rf, ytrain_rf = dh.part_features_for_rf(all_image_infos, all_segmentaion_infos, cub_parts, IDtrain, Parts.HEAD_PART_NAMES)
rf_safe.save_large_instance(Xtrain_rf_ip, Xtrain_rf, instance_split)
rf_safe.save_instance(ytrain_rf_ip, ytrain_rf)
if load_rf_test:
if rf_safe.check_exists(ytest_rf_ip) and not force:
Xtest_rf = rf_safe.load_large_instance(Xtest_rf_ip, instance_split)
ytest_rf = rf_safe.load_instance(ytest_rf_ip)
ytest_rf = ytest_rf[0, :]
else:
Xtest_rf, ytest_rf = dh.part_features_for_rf(all_image_infos, all_segmentaion_infos, cub_parts, IDtest, Parts.HEAD_PART_NAMES)
rf_safe.save_large_instance(Xtest_rf_ip, Xtest_rf, instance_split)
rf_safe.save_instance(ytest_rf_ip, ytest_rf)
toc = time()
print 'loaded or calculated in', toc - tic
tic = time()
model_rf = sklearn.ensemble.RandomForestClassifier(n_estimators=10, bootstrap=False, max_depth=10, n_jobs=3, random_state=None, verbose=0)
model_rf.fit(Xtrain_rf, ytrain_rf)
toc = time()
print 'fitted rf model in', toc - tic
dense_points = gen_dense_points(227, 227)
# load whole and bbox and head part data
# load data
tic = time()
features_storage_r = datastore(settings.storage('ccrft2st-10000'))
feature_extractor_r = CNN_Features_CAFFE_REFERENCE(features_storage_r, make_net=False)
features_storage_c = datastore(settings.storage('cccft2st-50000'))
feature_extractor_c = CNN_Features_CAFFE_REFERENCE(features_storage_c, make_net=False)
features_storage_p_h = datastore(settings.storage('ccpheadft-100000'))
feature_extractor_p_h = CNN_Features_CAFFE_REFERENCE(features_storage_p_h, make_net=False)
Xtrain_r, ytrain_r, Xtest_r, ytest_r = cub.get_train_test(feature_extractor_r.extract_one)
Xtrain_c, ytrain_c, Xtest_c, ytest_c = cub.get_train_test(feature_extractor_c.extract_one)
Xtrain_p_h, ytrain_p_h, Xtest_p_h, ytest_p_h = cub.get_train_test(feature_extractor_p_h.extract_one)
toc = time()
print 'loaded whole and bbox and head part data in', toc - tic
def compute_estimated_part_data(model_name, shape, IDS, model_rf):
net = caffe.Classifier(settings.model(model_name), settings.pretrained(model_name), mean=np.load(settings.ILSVRC_MEAN), channel_swap=(2, 1, 0), raw_scale=255)
net.set_phase_test()
net.set_mode_gpu()
# compute estimated head data
new_Xtest_part = np.zeros(shape)
for i, t_id in enumerate(IDS):
print i
img = caffe.io.load_image(all_image_infos[t_id])
dh.init_with_image(img)
X = dh.features(dense_points)
preds_prob = model_rf.predict_proba(X)
max_prob = np.max(preds_prob[:, 1])
preds_prob = preds_prob[:, 1].reshape((227, 227)).T
preds = preds_prob >= (max_prob/2)
preds = skimage.morphology.closing(preds, skimage.morphology.square(10))
preds = skimage.morphology.remove_small_objects(preds, min_size=10, connectivity=1)
L, N = skimage.measure.label(preds, return_num=True, background=0)
L_no_bg = L[L != -1].flatten()
vals, counts = scipy.stats.mode(L_no_bg)
part_label = int(vals[0])
indices = np.where(L == part_label)
xmin = indices[0].min()
xmax = indices[0].max()
ymin = indices[1].min()
ymax = indices[1].max()
pmin = Part(-1, '?', -1, xmin, ymin, 1)
pmax = Part(-1, '?', -1, xmax, ymax, 1)
rect_parts = Parts(parts=[pmin, pmax])
rect_parts.denorm_for_size(img.shape[0], img.shape[1], size=227)
rect_info = rect_parts[0].x, rect_parts[1].x, rect_parts[0].y, rect_parts[1].y
t_img_part = Parts().get_rect(img, rect_info=rect_info)
try:
net.predict([t_img_part], oversample=False)
except Exception:
print '------', t_id, '----------'
new_Xtest_part[i, :] = net.blobs[feat_layer].data[0].flatten()
return new_Xtest_part
tic = time()
if rf_safe.check_exists_large(Xtest_ip) and not force:
print 'loading test'
Xtest_p_h = rf_safe.load_large_instance(Xtest_ip, instance_split)
else:
print 'calculating test'
Xtest_p_h = compute_estimated_part_data('ccpheadrfft-100000', Xtest_p_h.shape, IDtest, model_rf)
rf_safe.save_large_instance(Xtest_ip, Xtest_p_h, instance_split)
if recalculate_training:
if rf_safe.check_exists_large(Xtrain_ip) and not force:
print 'loading train'
Xtrain_p_h = rf_safe.load_large_instance(Xtrain_ip, instance_split)
else:
print 'calculating train'
Xtrain_p_h = compute_estimated_part_data('ccpheadrfft-100000', Xtrain_p_h.shape, IDtrain, model_rf)
rf_safe.save_large_instance(Xtrain_ip, Xtrain_p_h, instance_split)
toc = time()
print 'features loaded or calculated in', toc - tic
Xtrain = np.concatenate((Xtrain_r, Xtrain_c, Xtrain_p_h), axis=1)
Xtest = np.concatenate((Xtest_r, Xtest_c, Xtest_p_h), axis=1)
ytrain = ytrain_r
ytest = ytest_r
print Xtrain.shape, Xtest.shape
# do classification
tic = time()
model = sklearn.svm.LinearSVC(C=C)
model.fit(Xtrain, ytrain)
predictions = model.predict(Xtest)
toc = time() - tic
print 'classification in', toc
print '--------------------'
print 'C:', C
print '--------------------'
print 'accuracy', sklearn.metrics.accuracy_score(ytest, predictions), 'mean accuracy', utils.mean_accuracy(ytest, predictions)
print '===================='
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from dataset import CUB_200_2011
from storage import datastore
from deep_extractor import CNN_Features_CAFFE_REFERENCE
from datetime import datetime as dt
import settings
import utils
import numpy as np
cub = CUB_200_2011(settings.CUB_ROOT)
features_storage_r = datastore(settings.storage('ccrft'))
feature_extractor_r = CNN_Features_CAFFE_REFERENCE(features_storage_r,
make_net=False)
features_storage_c = datastore(settings.storage('cccft'))
feature_extractor_c = CNN_Features_CAFFE_REFERENCE(features_storage_c,
make_net=False)
features_storage_p_h = datastore(settings.storage('ccpheadft-100000'))
feature_extractor_p_h = CNN_Features_CAFFE_REFERENCE(features_storage_p_h,
make_net=False)
features_storage_p_h = datastore(settings.storage('ccpheadft-100000'))
feature_extractor_p_h = CNN_Features_CAFFE_REFERENCE(features_storage_p_h,
make_net=False)
features_storage_p_b = datastore(settings.storage('ccpbodyft-10000'))
feature_extractor_p_b = CNN_Features_CAFFE_REFERENCE(features_storage_p_b,
def main(storage_name, layer, model, iteration, normalize_feat, n_neighbors,
parts, feat_layer, add_noise, to_oracle, noise_std_c, noise_std_d,
augment_training, augmentation_fold, augmentation_noise):
if len(parts) == 0:
print 'no parts where needed'
exit()
name = '%s-%s' % (model, iteration)
nn_storage_name = 'nn-parts'
nn_storage = datastore(settings.storage(nn_storage_name))
nn_storage.super_name = '%s_%s' % (storage_name, name)
nn_storage.sub_name = layer
nn_storage.instance_name = 'norm_%s.mat' % str(normalize_feat)
nn_storage.instance_path = nn_storage.get_instance_path(
nn_storage.super_name, nn_storage.sub_name, nn_storage.instance_name)
cub = CUB_200_2011(settings.CUB_ROOT)
safe = datastore(settings.storage(storage_name))
safe.super_name = 'features'
safe.sub_name = name
instance_path = safe.get_instance_path(safe.super_name, safe.sub_name,
'feat_cache_%s' % layer)
feat = safe.load_large_instance(instance_path, 4)
# should we normalize the feats?
if normalize_feat:
# snippet from : http://stackoverflow.com/a/8904762/428321
# I've went for l2 normalization.
# row_sums = feat.sum(axis=1)
row_norms = np.linalg.norm(feat, axis=1)
new_feat = feat / row_norms[:, np.newaxis]
feat = new_feat
IDtrain, IDtest = cub.get_train_test_id()
# the following line is not really a good idea. Only works for this dataset.
Xtrain = feat[IDtrain - 1, :]
Xtest = feat[IDtest - 1, :]
print 'init load done'
if not nn_storage.check_exists(nn_storage.instance_path):
print 'calculating'
# the actual NN search
nn_model = sklearn.neighbors.NearestNeighbors(n_neighbors=n_neighbors,
algorithm='ball_tree',
metric='minkowski',
p=2)
tic = time()
nn_model.fit(Xtrain)
toc = time() - tic
print 'fitted in: ', toc
tic = time()
NNS = nn_model.kneighbors(Xtest, 1, return_distance=False)
toc = time() - tic
print 'found in: ', toc
nn_storage.save_instance(nn_storage.instance_path, NNS)
else:
# load the NNS
NNS = nn_storage.load_instance(nn_storage.instance_path)
print 'loaded'
# convert (N, 1) to (N,)
NNS = NNS.T[0]
# transfer part locations
all_parts_cub = cub.get_parts()
estimated_test_parts = Parts()
all_image_infos = cub.get_all_image_infos()
bbox = cub.get_bbox()
tic = time()
# estimate test parts with NN part transfer
for i in range(IDtest.shape[0]):
test_id = IDtest[i]
nn_id = IDtrain[NNS[i]]
nn_parts = all_parts_cub.for_image(nn_id)
test_bbox = bbox[test_id - 1]
nn_bbox = bbox[nn_id - 1]
estimated_parts = nn_parts.transfer(nn_bbox, test_bbox)
estimated_parts.set_for(test_id)
estimated_test_parts.appends(estimated_parts)
toc = time() - tic
print 'transfered in', toc
# load data
tic = time()
features_storage_r = datastore(settings.storage('ccrft'))
feature_extractor_r = CNN_Features_CAFFE_REFERENCE(features_storage_r,
make_net=False)
features_storage_c = datastore(settings.storage('cccft'))
feature_extractor_c = CNN_Features_CAFFE_REFERENCE(features_storage_c,
make_net=False)
if 'head' in parts:
features_storage_p_h = datastore(settings.storage('ccpheadft-100000'))
feature_extractor_p_h = CNN_Features_CAFFE_REFERENCE(
features_storage_p_h, make_net=False)
if 'body' in parts:
features_storage_p_b = datastore(settings.storage('ccpbodyft-100000'))
feature_extractor_p_b = CNN_Features_CAFFE_REFERENCE(
features_storage_p_b, make_net=False)
Xtrain_r, ytrain_r, Xtest_r, ytest_r = cub.get_train_test(
feature_extractor_r.extract_one)
Xtrain_c, ytrain_c, Xtest_c, ytest_c = cub.get_train_test(
feature_extractor_c.extract_one)
if 'head' in parts:
Xtrain_p_h, ytrain_p_h, Xtest_p_h, ytest_p_h = cub.get_train_test(
feature_extractor_p_h.extract_one)
if 'body' in parts:
Xtrain_p_b, ytrain_p_b, Xtest_p_b, ytest_p_b = cub.get_train_test(
feature_extractor_p_b.extract_one)
toc = time() - tic
print 'loaded data in', toc
def compute_estimated_part_data(model_name, shape, IDS,
part_names_to_filter, add_noise,
noise_std_c, noise_std_d):
net = caffe.Classifier(settings.model(model_name),
settings.pretrained(model_name),
mean=np.load(settings.ILSVRC_MEAN),
channel_swap=(2, 1, 0),
raw_scale=255)
net.set_phase_test()
net.set_mode_gpu()
# compute estimated head data
new_Xtest_part = np.zeros(shape)
for i, t_id in enumerate(IDS):
if to_oracle:
t_parts = all_parts_cub.for_image(t_id)
else:
t_parts = estimated_test_parts.for_image(t_id)
t_img_addr = all_image_infos[t_id]
t_img = caffe.io.load_image(t_img_addr)
t_parts_part = t_parts.filter_by_name(part_names_to_filter)
t_img_part = t_parts_part.get_rect(t_img,
add_noise=add_noise,
noise_std_c=noise_std_c,
noise_std_d=noise_std_d)
try:
net.predict([t_img_part], oversample=False)
except Exception, e:
print '------', t_id, '----------'
print part_names_to_filter
print t_img_addr
print '------------'
print t_img.shape
print t_parts
print '------------'
print t_img_part.shape
print t_parts_part
raise e
new_Xtest_part[i, :] = net.blobs[feat_layer].data[0].flatten()
return new_Xtest_part
def main(storage_name, layer, model, iteration, normalize_feat, n_neighbors, parts, feat_layer, add_noise, to_oracle, noise_std_c, noise_std_d, augment_training, augmentation_fold, augmentation_noise):
if len(parts) == 0:
print 'no parts where needed'
exit()
name = '%s-%s' % (model, iteration)
nn_storage_name = 'nn-parts'
nn_storage = datastore(settings.storage(nn_storage_name))
nn_storage.super_name = '%s_%s' % (storage_name, name)
nn_storage.sub_name = layer
nn_storage.instance_name = 'norm_%s.mat' % str(normalize_feat)
nn_storage.instance_path = nn_storage.get_instance_path(nn_storage.super_name, nn_storage.sub_name, nn_storage.instance_name)
cub = CUB_200_2011(settings.CUB_ROOT)
safe = datastore(settings.storage(storage_name))
safe.super_name = 'features'
safe.sub_name = name
instance_path = safe.get_instance_path(safe.super_name, safe.sub_name, 'feat_cache_%s' % layer)
feat = safe.load_large_instance(instance_path, 4)
# should we normalize the feats?
if normalize_feat:
# snippet from : http://stackoverflow.com/a/8904762/428321
# I've went for l2 normalization.
# row_sums = feat.sum(axis=1)
row_norms = np.linalg.norm(feat, axis=1)
new_feat = feat / row_norms[:, np.newaxis]
feat = new_feat
IDtrain, IDtest = cub.get_train_test_id()
# the following line is not really a good idea. Only works for this dataset.
Xtrain = feat[IDtrain-1, :]
Xtest = feat[IDtest-1, :]
print 'init load done'
if not nn_storage.check_exists(nn_storage.instance_path):
print 'calculating'
# the actual NN search
nn_model = sklearn.neighbors.NearestNeighbors(n_neighbors=n_neighbors, algorithm='ball_tree', metric='minkowski', p=2)
tic = time()
nn_model.fit(Xtrain)
toc = time() - tic
print 'fitted in: ', toc
tic = time()
NNS = nn_model.kneighbors(Xtest, 1, return_distance=False)
toc = time() - tic
print 'found in: ', toc
nn_storage.save_instance(nn_storage.instance_path, NNS)
else:
# load the NNS
NNS = nn_storage.load_instance(nn_storage.instance_path)
print 'loaded'
# convert (N, 1) to (N,)
NNS = NNS.T[0]
# transfer part locations
all_parts_cub = cub.get_parts()
estimated_test_parts = Parts()
all_image_infos = cub.get_all_image_infos()
bbox = cub.get_bbox()
tic = time()
# estimate test parts with NN part transfer
for i in range(IDtest.shape[0]):
test_id = IDtest[i]
nn_id = IDtrain[NNS[i]]
nn_parts = all_parts_cub.for_image(nn_id)
test_bbox = bbox[test_id - 1]
nn_bbox = bbox[nn_id - 1]
estimated_parts = nn_parts.transfer(nn_bbox, test_bbox)
estimated_parts.set_for(test_id)
estimated_test_parts.appends(estimated_parts)
toc = time() - tic
print 'transfered in', toc
# load data
tic = time()
features_storage_r = datastore(settings.storage('ccrft'))
feature_extractor_r = CNN_Features_CAFFE_REFERENCE(features_storage_r, make_net=False)
features_storage_c = datastore(settings.storage('cccft'))
feature_extractor_c = CNN_Features_CAFFE_REFERENCE(features_storage_c, make_net=False)
if 'head' in parts:
features_storage_p_h = datastore(settings.storage('ccpheadft-100000'))
feature_extractor_p_h = CNN_Features_CAFFE_REFERENCE(features_storage_p_h, make_net=False)
if 'body' in parts:
features_storage_p_b = datastore(settings.storage('ccpbodyft-100000'))
feature_extractor_p_b = CNN_Features_CAFFE_REFERENCE(features_storage_p_b, make_net=False)
Xtrain_r, ytrain_r, Xtest_r, ytest_r = cub.get_train_test(feature_extractor_r.extract_one)
Xtrain_c, ytrain_c, Xtest_c, ytest_c = cub.get_train_test(feature_extractor_c.extract_one)
if 'head' in parts:
Xtrain_p_h, ytrain_p_h, Xtest_p_h, ytest_p_h = cub.get_train_test(feature_extractor_p_h.extract_one)
if 'body' in parts:
Xtrain_p_b, ytrain_p_b, Xtest_p_b, ytest_p_b = cub.get_train_test(feature_extractor_p_b.extract_one)
toc = time() - tic
print 'loaded data in', toc
def compute_estimated_part_data(model_name, shape, IDS, part_names_to_filter, add_noise, noise_std_c, noise_std_d):
net = caffe.Classifier(settings.model(model_name), settings.pretrained(model_name), mean=np.load(settings.ILSVRC_MEAN), channel_swap=(2, 1, 0), raw_scale=255)
net.set_phase_test()
net.set_mode_gpu()
# compute estimated head data
new_Xtest_part = np.zeros(shape)
for i, t_id in enumerate(IDS):
if to_oracle:
t_parts = all_parts_cub.for_image(t_id)
else:
t_parts = estimated_test_parts.for_image(t_id)
t_img_addr = all_image_infos[t_id]
t_img = caffe.io.load_image(t_img_addr)
t_parts_part = t_parts.filter_by_name(part_names_to_filter)
t_img_part = t_parts_part.get_rect(t_img, add_noise=add_noise, noise_std_c=noise_std_c, noise_std_d=noise_std_d)
try:
net.predict([t_img_part], oversample=False)
except Exception, e:
print '------', t_id, '----------'
print part_names_to_filter
print t_img_addr
print '------------'
print t_img.shape
print t_parts
print '------------'
print t_img_part.shape
print t_parts_part
raise e
new_Xtest_part[i, :] = net.blobs[feat_layer].data[0].flatten()
return new_Xtest_part
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from dataset import CUB_200_2011
from storage import datastore
from deep_extractor import CNN_Features_CAFFE_REFERENCE
from datetime import datetime as dt
import settings
import utils
import numpy as np
cub = CUB_200_2011(settings.CUB_ROOT)
features_storage_r = datastore(settings.storage('ccrft'))
feature_extractor_r = CNN_Features_CAFFE_REFERENCE(features_storage_r, make_net=False)
features_storage_c = datastore(settings.storage('cccft'))
feature_extractor_c = CNN_Features_CAFFE_REFERENCE(features_storage_c, make_net=False)
features_storage_p_h = datastore(settings.storage('ccpheadft-100000'))
feature_extractor_p_h = CNN_Features_CAFFE_REFERENCE(features_storage_p_h, make_net=False)
features_storage_p_h = datastore(settings.storage('ccpheadft-100000'))
feature_extractor_p_h = CNN_Features_CAFFE_REFERENCE(features_storage_p_h, make_net=False)
features_storage_p_b = datastore(settings.storage('ccpbodyft-10000'))
feature_extractor_p_b = CNN_Features_CAFFE_REFERENCE(features_storage_p_b, make_net=False)
Xtrain_r, ytrain_r, Xtest_r, ytest_r = cub.get_train_test(feature_extractor_r.extract_one)
Xtrain_c, ytrain_c, Xtest_c, ytest_c = cub.get_train_test(feature_extractor_c.extract_one)
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from dataset import CUB_200_2011
from storage import datastore
from deep_extractor import CNN_Features_CAFFE_REFERENCE
from datetime import datetime as dt
import numpy
import settings
import utils
cub_full = CUB_200_2011(settings.CUB_ROOT, full=True)
cub = CUB_200_2011(settings.CUB_ROOT, full=False)
features_storage = datastore(settings.storage('ccc'))
feature_extractor_full = CNN_Features_CAFFE_REFERENCE(features_storage,
full=True)
feature_extractor = CNN_Features_CAFFE_REFERENCE(features_storage, full=False)
features_storage_f = datastore(settings.storage('ccfc'))
feature_extractor_full_f = CNN_Features_CAFFE_REFERENCE(features_storage_f,
full=True)
feature_extractor_f = CNN_Features_CAFFE_REFERENCE(features_storage_f,
full=False)
Xtrain, ytrain, Xtest, ytest = cub_full.get_train_test(
feature_extractor_full.extract_one, feature_extractor.extract_one)
Xtrain_f, ytrain_f, Xtest_f, ytest_f = cub_full.get_train_test(
feature_extractor_full_f.extract_one, feature_extractor_f.extract_one)
print Xtrain.shape, ytrain.shape
def main(c, f):
instance_split = 10
feat_layer = 'fc7'
load_rf_test = False
recalculate_training = True
C = c
force = f
dh = cub_utils.DeepHelper()
cub = CUB_200_2011(settings.CUB_ROOT)
cub_parts = cub.get_parts()
IDtrain, IDtest = cub.get_train_test_id()
all_image_infos = cub.get_all_image_infos()
all_segmentaion_infos = cub.get_all_segmentation_infos()
rf_safe = datastore(settings.storage('rf'))
rf_safe.super_name = 'features'
rf_safe.sub_name = 'head-points'
rf_safe.other_sub_name = 'head-final-features'
Xtrain_rf_ip = rf_safe.get_instance_path(rf_safe.super_name,
rf_safe.sub_name, 'Xtrain_rf')
Xtest_rf_ip = rf_safe.get_instance_path(rf_safe.super_name,
rf_safe.sub_name, 'Xtest_rf')
ytrain_rf_ip = rf_safe.get_instance_path(rf_safe.super_name,
rf_safe.sub_name, 'ytrain_rf.mat')
ytest_rf_ip = rf_safe.get_instance_path(rf_safe.super_name,
rf_safe.sub_name, 'ytest_rf.mat')
Xtrain_ip = rf_safe.get_instance_path(rf_safe.super_name,
rf_safe.other_sub_name, 'Xtrain')
Xtest_ip = rf_safe.get_instance_path(rf_safe.super_name,
rf_safe.other_sub_name, 'Xtest')
tic = time()
if rf_safe.check_exists(ytrain_rf_ip) and not force:
print 'loading'
Xtrain_rf = rf_safe.load_large_instance(Xtrain_rf_ip, instance_split)
ytrain_rf = rf_safe.load_instance(ytrain_rf_ip)
ytrain_rf = ytrain_rf[0, :]
else:
print 'calculating'
Xtrain_rf, ytrain_rf = dh.part_features_for_rf(all_image_infos,
all_segmentaion_infos,
cub_parts, IDtrain,
Parts.HEAD_PART_NAMES)
rf_safe.save_large_instance(Xtrain_rf_ip, Xtrain_rf, instance_split)
rf_safe.save_instance(ytrain_rf_ip, ytrain_rf)
if load_rf_test:
if rf_safe.check_exists(ytest_rf_ip) and not force:
Xtest_rf = rf_safe.load_large_instance(Xtest_rf_ip, instance_split)
ytest_rf = rf_safe.load_instance(ytest_rf_ip)
ytest_rf = ytest_rf[0, :]
else:
Xtest_rf, ytest_rf = dh.part_features_for_rf(
all_image_infos, all_segmentaion_infos, cub_parts, IDtest,
Parts.HEAD_PART_NAMES)
rf_safe.save_large_instance(Xtest_rf_ip, Xtest_rf, instance_split)
rf_safe.save_instance(ytest_rf_ip, ytest_rf)
toc = time()
print 'loaded or calculated in', toc - tic
tic = time()
model_rf = sklearn.ensemble.RandomForestClassifier(n_estimators=10,
bootstrap=False,
max_depth=10,
n_jobs=3,
random_state=None,
verbose=0)
model_rf.fit(Xtrain_rf, ytrain_rf)
toc = time()
print 'fitted rf model in', toc - tic
dense_points = gen_dense_points(227, 227)
# load whole and bbox and head part data
# load data
tic = time()
features_storage_r = datastore(settings.storage('ccrft2st-10000'))
feature_extractor_r = CNN_Features_CAFFE_REFERENCE(features_storage_r,
make_net=False)
features_storage_c = datastore(settings.storage('cccft2st-50000'))
feature_extractor_c = CNN_Features_CAFFE_REFERENCE(features_storage_c,
make_net=False)
features_storage_p_h = datastore(settings.storage('ccpheadft-100000'))
feature_extractor_p_h = CNN_Features_CAFFE_REFERENCE(features_storage_p_h,
make_net=False)
Xtrain_r, ytrain_r, Xtest_r, ytest_r = cub.get_train_test(
feature_extractor_r.extract_one)
Xtrain_c, ytrain_c, Xtest_c, ytest_c = cub.get_train_test(
feature_extractor_c.extract_one)
Xtrain_p_h, ytrain_p_h, Xtest_p_h, ytest_p_h = cub.get_train_test(
feature_extractor_p_h.extract_one)
toc = time()
print 'loaded whole and bbox and head part data in', toc - tic
def compute_estimated_part_data(model_name, shape, IDS, model_rf):
net = caffe.Classifier(settings.model(model_name),
settings.pretrained(model_name),
mean=np.load(settings.ILSVRC_MEAN),
channel_swap=(2, 1, 0),
raw_scale=255)
net.set_phase_test()
net.set_mode_gpu()
# compute estimated head data
new_Xtest_part = np.zeros(shape)
for i, t_id in enumerate(IDS):
print i
img = caffe.io.load_image(all_image_infos[t_id])
dh.init_with_image(img)
X = dh.features(dense_points)
preds_prob = model_rf.predict_proba(X)
max_prob = np.max(preds_prob[:, 1])
preds_prob = preds_prob[:, 1].reshape((227, 227)).T
preds = preds_prob >= (max_prob / 2)
preds = skimage.morphology.closing(preds,
skimage.morphology.square(10))
preds = skimage.morphology.remove_small_objects(preds,
min_size=10,
connectivity=1)
L, N = skimage.measure.label(preds, return_num=True, background=0)
L_no_bg = L[L != -1].flatten()
vals, counts = scipy.stats.mode(L_no_bg)
part_label = int(vals[0])
indices = np.where(L == part_label)
xmin = indices[0].min()
xmax = indices[0].max()
ymin = indices[1].min()
ymax = indices[1].max()
pmin = Part(-1, '?', -1, xmin, ymin, 1)
pmax = Part(-1, '?', -1, xmax, ymax, 1)
rect_parts = Parts(parts=[pmin, pmax])
rect_parts.denorm_for_size(img.shape[0], img.shape[1], size=227)
rect_info = rect_parts[0].x, rect_parts[1].x, rect_parts[
0].y, rect_parts[1].y
t_img_part = Parts().get_rect(img, rect_info=rect_info)
try:
net.predict([t_img_part], oversample=False)
except Exception:
print '------', t_id, '----------'
new_Xtest_part[i, :] = net.blobs[feat_layer].data[0].flatten()
return new_Xtest_part
tic = time()
if rf_safe.check_exists_large(Xtest_ip) and not force:
print 'loading test'
Xtest_p_h = rf_safe.load_large_instance(Xtest_ip, instance_split)
else:
print 'calculating test'
Xtest_p_h = compute_estimated_part_data('ccpheadrfft-100000',
Xtest_p_h.shape, IDtest,
model_rf)
rf_safe.save_large_instance(Xtest_ip, Xtest_p_h, instance_split)
if recalculate_training:
if rf_safe.check_exists_large(Xtrain_ip) and not force:
print 'loading train'
Xtrain_p_h = rf_safe.load_large_instance(Xtrain_ip, instance_split)
else:
print 'calculating train'
Xtrain_p_h = compute_estimated_part_data('ccpheadrfft-100000',
Xtrain_p_h.shape, IDtrain,
model_rf)
rf_safe.save_large_instance(Xtrain_ip, Xtrain_p_h, instance_split)
toc = time()
print 'features loaded or calculated in', toc - tic
Xtrain = np.concatenate((Xtrain_r, Xtrain_c, Xtrain_p_h), axis=1)
Xtest = np.concatenate((Xtest_r, Xtest_c, Xtest_p_h), axis=1)
ytrain = ytrain_r
ytest = ytest_r
print Xtrain.shape, Xtest.shape
# do classification
tic = time()
model = sklearn.svm.LinearSVC(C=C)
model.fit(Xtrain, ytrain)
predictions = model.predict(Xtest)
toc = time() - tic
print 'classification in', toc
print '--------------------'
print 'C:', C
print '--------------------'
print 'accuracy', sklearn.metrics.accuracy_score(
ytest,
predictions), 'mean accuracy', utils.mean_accuracy(ytest, predictions)
print '===================='
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
import settings
from dataset import CUB_200_2011, CUB_200_2011_Parts_Head, CUB_200_2011_Parts_Body
from storage import datastore
from deep_extractor import CNN_Features_CAFFE_REFERENCE
import pyprind
cub = CUB_200_2011(settings.CUB_ROOT, full=False)
cub_head = CUB_200_2011_Parts_Head(settings.CUB_ROOT)
cub_body = CUB_200_2011_Parts_Body(settings.CUB_ROOT)
features_storage = datastore(settings.storage('ccr'))
feature_extractor = CNN_Features_CAFFE_REFERENCE(features_storage, full=False)
features_storage_ft = datastore(settings.storage('ccrft'))
feature_extractor_ft = CNN_Features_CAFFE_REFERENCE(features_storage_ft,
full=True)
features_storage_flipped = datastore(settings.storage('ccf'))
feature_extractor_flipped = CNN_Features_CAFFE_REFERENCE(
features_storage_flipped, full=False)
features_storage_flipped_ft = datastore(settings.storage('ccfft'))
feature_extractor_flipped_ft = CNN_Features_CAFFE_REFERENCE(
features_storage_flipped_ft, full=True)
features_storage_cropped = datastore(settings.storage('ccc'))
feature_extractor_cropped = CNN_Features_CAFFE_REFERENCE(
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from dataset import CUB_200_2011
from storage import datastore
from deep_extractor import CNN_Features_CAFFE_REFERENCE
from datetime import datetime as dt
import numpy
import settings
import utils
cub_full = CUB_200_2011(settings.CUB_ROOT, full=True)
cub = CUB_200_2011(settings.CUB_ROOT, full=False)
features_storage = datastore(settings.storage('ccc'))
feature_extractor_full = CNN_Features_CAFFE_REFERENCE(features_storage, full=True)
feature_extractor = CNN_Features_CAFFE_REFERENCE(features_storage, full=False)
features_storage_f = datastore(settings.storage('ccfc'))
feature_extractor_full_f = CNN_Features_CAFFE_REFERENCE(features_storage_f, full=True)
feature_extractor_f = CNN_Features_CAFFE_REFERENCE(features_storage_f, full=False)
Xtrain, ytrain, Xtest, ytest = cub_full.get_train_test(feature_extractor_full.extract_one, feature_extractor.extract_one)
Xtrain_f, ytrain_f, Xtest_f, ytest_f = cub_full.get_train_test(feature_extractor_full_f.extract_one, feature_extractor_f.extract_one)
print Xtrain.shape, ytrain.shape
print Xtest.shape, ytest.shape
from sklearn import svm
from sklearn.metrics import accuracy_score
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from dataset import CUB_200_2011
from storage import datastore
from deep_extractor import CNN_Features_CAFFE_REFERENCE
from datetime import datetime as dt
import numpy
import settings
import utils
cub = CUB_200_2011(settings.CUB_ROOT)
features_storage = datastore(settings.storage('ccrft'))
feature_extractor = CNN_Features_CAFFE_REFERENCE(features_storage)
features_storage_c = datastore(settings.storage('cccft'))
feature_extractor_c = CNN_Features_CAFFE_REFERENCE(features_storage_c)
features_storage_f = datastore(settings.storage('ccfft'))
feature_extractor_f = CNN_Features_CAFFE_REFERENCE(features_storage_f)
features_storage_fc = datastore(settings.storage('ccfcft'))
feature_extractor_fc = CNN_Features_CAFFE_REFERENCE(features_storage_fc)
Xtrain, ytrain, Xtest, ytest = cub.get_train_test(
feature_extractor.extract_one)
Xtrain_c, ytrain_c, Xtest_c, ytest_c = cub.get_train_test(
feature_extractor_c.extract_one)
Xtrain_f, ytrain_f, Xtest_f, ytest_f = cub.get_train_test(
feature_extractor_f.extract_one)
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
import settings
from dataset import CUB_200_2011, CUB_200_2011_Parts_Head, CUB_200_2011_Parts_Body
from storage import datastore
from deep_extractor import CNN_Features_CAFFE_REFERENCE
import pyprind
cub = CUB_200_2011(settings.CUB_ROOT, full=False)
cub_head = CUB_200_2011_Parts_Head(settings.CUB_ROOT)
cub_body = CUB_200_2011_Parts_Body(settings.CUB_ROOT)
features_storage = datastore(settings.storage('ccr'))
feature_extractor = CNN_Features_CAFFE_REFERENCE(features_storage, full=False)
features_storage_ft = datastore(settings.storage('ccrft'))
feature_extractor_ft = CNN_Features_CAFFE_REFERENCE(features_storage_ft, full=True)
features_storage_flipped = datastore(settings.storage('ccf'))
feature_extractor_flipped = CNN_Features_CAFFE_REFERENCE(features_storage_flipped, full=False)
features_storage_flipped_ft = datastore(settings.storage('ccfft'))
feature_extractor_flipped_ft = CNN_Features_CAFFE_REFERENCE(features_storage_flipped_ft, full=True)
features_storage_cropped = datastore(settings.storage('ccc'))
feature_extractor_cropped = CNN_Features_CAFFE_REFERENCE(features_storage_cropped, full=False)
features_storage_cropped_ft = datastore(settings.storage('cccft'))
feature_extractor_cropped_ft = CNN_Features_CAFFE_REFERENCE(features_storage_cropped_ft, full=True)
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from dataset import CUB_200_2011
from storage import datastore
from deep_extractor import CNN_Features_CAFFE_REFERENCE
from datetime import datetime as dt
import settings
import utils
cub = CUB_200_2011(settings.CUB_ROOT)
features_storage = datastore(settings.storage('cccft'))
feature_extractor = CNN_Features_CAFFE_REFERENCE(features_storage)
Xtrain, ytrain, Xtest, ytest = cub.get_train_test(feature_extractor.extract_one)
print Xtrain.shape, ytrain.shape
print Xtest.shape, ytest.shape
from sklearn import svm
from sklearn.metrics import accuracy_score
a = dt.now()
model = svm.LinearSVC(C=0.0001)
model.fit(Xtrain, ytrain)
b = dt.now()
print 'fitted in: %s' % (b - a)
import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(__file__)))
from dataset import CUB_200_2011
from storage import datastore
from deep_extractor import CNN_Features_CAFFE_REFERENCE
from datetime import datetime as dt
import numpy
import settings
import utils
cub = CUB_200_2011(settings.CUB_ROOT)
features_storage = datastore(settings.storage('ccrft'))
feature_extractor = CNN_Features_CAFFE_REFERENCE(features_storage)
features_storage_c = datastore(settings.storage('cccft'))
feature_extractor_c = CNN_Features_CAFFE_REFERENCE(features_storage_c)
features_storage_f = datastore(settings.storage('ccfft'))
feature_extractor_f = CNN_Features_CAFFE_REFERENCE(features_storage_f)
features_storage_fc = datastore(settings.storage('ccfcft'))
feature_extractor_fc = CNN_Features_CAFFE_REFERENCE(features_storage_fc)
Xtrain, ytrain, Xtest, ytest = cub.get_train_test(feature_extractor.extract_one)
Xtrain_c, ytrain_c, Xtest_c, ytest_c = cub.get_train_test(feature_extractor_c.extract_one)
Xtrain_f, ytrain_f, Xtest_f, ytest_f = cub.get_train_test(feature_extractor_f.extract_one)
Xtrain_fc, ytrain_fc, Xtest_fc, ytest_fc = cub.get_train_test(feature_extractor_fc.extract_one)
