def __init__(self, params):
self.batch_size = params['batch_size']
self.outshape = params['shape']
self.classes_per_batch = params['classes_per_batch']
self.img_lmdb = lmdbs(params['img_source'])
if params['skt_source'].endswith('.pkl'):
self.skt_lmdb = svgs(params['skt_source'])
else:
self.skt_lmdb = lmdbs(params['skt_source'])
self.img_labels = self.img_lmdb.get_label_list()
self.skt_labels = self.skt_lmdb.get_label_list()
self.img_mean = biproto2py(params['mean_file']).squeeze()
self.num_classes = len(set(self.skt_labels))
assert len(self.skt_labels)%self.num_classes==0, \
'Unequal sketch training samples for each class'
self.skt_per_class = len(self.skt_labels) / self.num_classes
if 'hard_pos' in params:
self.hard_sel = 'hard_pos'
self.hard_sel_file = params['hard_pos']
elif 'hard_neg' in params:
self.hard_sel = 'hard_neg'
self.hard_sel_file = params['hard_neg']
elif 'hard_pn' in params:
self.hard_sel = 'hard_pn'
self.hard_sel_file = params['hard_pn']
else:
assert False, 'Hard selection must be on'
self.watchChange = WatchDog(
self.hard_sel_file) #check if file has been updated
self.hardsel_tab = np.load(self.hard_sel_file)
self.hard_pos = self.hardsel_tab['pos']
self.hard_neg = self.hardsel_tab['neg']
self.img_labels_dict, self.classes = vec2dic(self.img_labels)
self.NSKTS = len(self.skt_labels)
self.indexlist = range(self.NSKTS)
self.shuffle_keeping_min_classes_per_batch()
self._cur = 0 # current image
# this class does some simple data-manipulations
self.img_augment = SimpleAugment(mean=self.img_mean,
shape=params['shape'],
scale=params['scale'],
rot=params['rot'])
if 'verbose' not in params:
print "BatchLoader initialized with {} sketches, {} images of {} classes".format(
len(self.skt_labels), len(self.img_labels), self.num_classes)
print('Hard selection: {}'.format(self.hard_sel))
#create threadpools for parallel augmentation
self.pool = ThreadPool() #4
def __init__(self, params):
self.batch_size = params['batch_size']
self.outshape = params['shape']
self.classes_per_batch = params['classes_per_batch']
self.img_lmdb = lmdbs(params['img_source'])
if params['skt_source'].endswith('.pkl'):
self.skt_lmdb = svgs(params['skt_source'])
else:
self.skt_lmdb = lmdbs(params['skt_source'])
self.img_labels = self.img_lmdb.get_label_list()
self.skt_labels = self.skt_lmdb.get_label_list()
self.img_mean = biproto2py(params['mean_file']).squeeze()
self.num_classes = len(set(self.skt_labels))
assert len(self.skt_labels)%self.num_classes==0, \
'Unequal sketch training samples for each class'
self.skt_per_class = len(self.skt_labels) / self.num_classes
self.hard_sel = 0
self.img_labels_dict, self.classes = vec2dic(self.img_labels)
self.indexlist = range(len(self.skt_labels))
self.shuffle_keeping_min_classes_per_batch()
self._cur = 0 # current image
# this class does some simple data-manipulations
self.img_augment = SimpleAugment(mean=self.img_mean,
shape=params['shape'],
scale=params['scale'],
rot=params['rot'])
if 'verbose' not in params:
print "BatchLoader initialized with {} sketches, {} images of {} classes".format(
len(self.skt_labels), len(self.img_labels), self.num_classes)
#create threadpools for parallel augmentation
self.pool = ThreadPool() #4
def __init__(self, params):
self.batch_size = params['batch_size']
self.img_shape = params['shape']
self.classes_per_batch = params['classes_per_batch']
self.img_lmdb = lmdbs(params['img_source'])
if params['skt_source'].endswith('.pkl'):
self.skt_lmdb = svgs(params['skt_source'])
else:
self.skt_lmdb = lmdbs(params['skt_source'])
self.img_labels = self.img_lmdb.get_label_list()
self.skt_labels = self.skt_lmdb.get_label_list()
label_ids = list(set(self.img_labels))
NCATS = len(label_ids)
if label_ids[0] != 0 or label_ids[-1] != NCATS - 1:
if 'verbose' not in params:
print 'Your data labels are not [0:{}]. Converting label ...'.format(
NCATS - 1)
self.img_labels = [
label_ids.index(label) for label in self.img_labels
]
self.skt_labels = [
label_ids.index(label) for label in self.skt_labels
]
self.img_mean = biproto2py(params['mean_file']).squeeze()
#self.skt_mean = biproto2py(params['skt_mean']).squeeze()
self.num_classes = len(set(self.skt_labels))
assert self.num_classes == NCATS, 'XX!!Sketch & image datasets unequal #categories'
assert len(self.skt_labels)%self.num_classes==0, \
'Unequal sketch training samples for each class'
self.skt_per_class = len(self.skt_labels) / self.num_classes
if 'hard_pos' in params:
self.hard_sel = 1
self.hard_pos = np.load(params['hard_pos'])['pos']
elif 'hard_neg' in params:
self.hard_sel = 2
self.hard_neg = np.load(params['hard_neg'])['neg']
elif 'hard_pn' in params:
self.hard_sel = 3
tmp = np.load(params['hard_pn'])
self.hard_pos = tmp['pos']
self.hard_neg = tmp['neg']
else: #hard selection turn off
self.hard_sel = 0
#self.img_labels_dict, self.classes = vec2dic(self.img_labels)
self.indexlist = range(len(self.skt_labels))
self.indexlist_img = range(len(self.img_labels))
#self.shuffle_keeping_min_classes_per_batch()
shuffle(self.indexlist)
shuffle(self.indexlist_img)
self._cur = 0 # current image
self._cur_img = 0
# this class does some simple data-manipulations
self.img_augment = SimpleAugment(mean=self.img_mean,
shape=self.img_shape,
scale=params['scale'],
rot=params['rot'])
print "BatchLoader initialized with {} sketches, {} images of {} classes".format(
len(self.skt_labels), len(self.img_labels), self.num_classes)
#create threadpools for parallel augmentation
self.pool = ThreadPool() #4