class BatchLoader(object):
"""
This class abstracts away the loading of images.
Images can either be loaded singly, or in a batch. The latter is used for
the asyncronous data layer to preload batches while other processing is
performed.
"""
def __init__(self, params, result):
self.result = result
#load data
if params['source'][-4:] == '.mat':
self.img_data, self.img_labels = mat2py_imdb(params['source'])
self.img_data = np.rollaxis(self.img_data,2) #swap dims to have Num x height x width
elif os.path.isdir(params['source']): #assume it is lmdb
lmdb_ = lmdbpy()
self.img_data, self.img_labels = lmdb_.read(params['source']) #use read_encoded if the img is encoded
self.img_data = self.img_data.squeeze()
else:
assert 0, 'Invalid format for source {}\n\
need either lmdb or .mat data'.format(params['source'])
if params['mean_file']==0:
self.img_mean = 0
elif params['mean_file'][-4:] == '.mat':
self.img_mean = mat2py_mean(params['mean_file'])
elif params['mean_file'][-12:] == '.binaryproto':
self.img_mean = biproto2py(params['mean_file']).squeeze()
else:
assert 0, 'Invalid format for mean_file {}'.format(params['mean_file'])
self.indexlist = range(len(self.img_labels))
shuffle(self.indexlist)
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'])
print "\nBatchLoader initialized with {} images".format(
len(self.img_labels))
def load_next_image(self):
"""
Load the next image in a batch.
"""
# Did we finish an epoch?
if self._cur == len(self.indexlist):
self._cur = 0
shuffle(self.indexlist)
# Load a datum
index = self.indexlist[self._cur] # Get the index
img = self.img_data[index]
label = self.img_labels[index]
self._cur += 1
return (self.img_augment.augment(img), label)
class BatchLoader(object):
"""
This class abstracts away the loading of images.
Images can either be loaded singly, or in a batch. The latter is used for
the asyncronous data layer to preload batches while other processing is
performed.
"""
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
def load_next_pair(self, l):
"""
Load the next pair in a batch.
"""
# Did we finish an epoch?
l.acquire() #5
if self._cur == len(self.indexlist):
self._cur = 0
#self.shuffle_keeping_min_classes_per_batch()
shuffle(self.indexlist)
if self._cur_img == len(self.indexlist_img):
self._cur_img = 0
#self.shuffle_keeping_min_classes_per_batch()
shuffle(self.indexlist_img)
# Load a sketch
index = self.indexlist[self._cur] # Get the sketch index
index_img = self.indexlist_img[self._cur_img]
self._cur += 1
self._cur_img += 1
l.release() #6
skt = self.skt_lmdb.get_datum(index).squeeze()
label = self.skt_labels[index]
img_i = self.img_lmdb.get_datum(index_img).squeeze()
label_i = self.img_labels[index_img]
#==============================================================================
# #randomly select paired image
# diff_label = np.random.choice(2)
# label_i = label
# if self.hard_sel == 0: #hard selection turned off
# if diff_label: #paired image has different label
# while label_i == label:
# label_i = np.random.choice(self.classes)
# index_i = np.random.choice(self.img_labels_dict[str(label_i)])
# elif self.hard_sel == 1: #hard positive selection
# if diff_label:
# while label_i == label:
# label_i = np.random.choice(self.classes)
# index_i = np.random.choice(self.img_labels_dict[str(label_i)])
# else:
# index_i = np.random.choice(self.hard_pos[index])
# elif self.hard_sel == 2: #hard neg
# if diff_label:
# index_i = np.random.choice(self.hard_neg[index])
# else:
# index_i = np.random.choice(self.img_labels_dict[str(label_i)])
# else: #hard pos and neg
# if diff_label:
# index_i = np.random.choice(self.hard_neg[index])
# else:
# index_i = np.random.choice(self.hard_pos[index])
#
# img_i = self.img_lmdb.get_image_deprocess(index_i)
#==============================================================================
res = dict(sketch=self.img_augment.augment(skt),
image=self.img_augment.augment(img_i),
label_s=label,
label_i=label_i)
return res
def load_next_batch(self):
res = {}
#7
lock = Lock()
threads = [self.pool.apply_async(self.load_next_pair,(lock,)) for \
i in range (self.batch_size)]
thread_res = [thread.get() for thread in threads]
res['data_s'] = np.asarray([tri['sketch']
for tri in thread_res])[:, None, :, :]
res['data_i'] = np.asarray([tri['image']
for tri in thread_res])[:, None, :, :]
res['label_s'] = np.asarray([tri['label_s'] for tri in thread_res],
dtype=np.float32)[:, None]
res['label_i'] = np.asarray([tri['label_i'] for tri in thread_res],
dtype=np.float32)[:, None]
return res
#==============================================================================
# res['data_s'] = np.zeros((self.batch_size,1,self.outshape[0],\
# self.outshape[1]),dtype = np.float32)
# res['data_i'] = np.zeros_like(res['data_a'],dtype=np.float32)
# res['label'] = np.zeros((self.batch_size,1),dtype = np.float32)
# for itt in range(self.batch_size):
# trp = self.load_next_pair(1)
# res['data_s'][itt,...] = trp['sketch']
# res['data_i'][itt,...] = trp['image']
# res['label'][itt,...] = trp['label']
# return res
#==============================================================================
def shuffle_keeping_min_classes_per_batch(self):
shuffle(self.indexlist)
# sort index list by class
# (using lambda to restrict sorting to just first element of tuple
sort_indexlist = sorted(self.indexlist,
key=lambda index: self.skt_labels[index])
# make it 2D based on classes
sort_indexlist = np.array(sort_indexlist, dtype=np.int64)
sort_indexlist = sort_indexlist.reshape(self.num_classes,
self.skt_per_class)
# permute the class positions and flat it (make it 1D again)
sort_indexlist = np.random.permutation(sort_indexlist)
sort_indexlist = sort_indexlist.reshape(self.num_classes *
self.skt_per_class)
temp_indexlist = np.array([], dtype=np.int64)
skt_per_batch_per_class = int(self.batch_size / self.classes_per_batch)
# apply dark magic
# (slices of classes sliced, appended together until list is over)
for k in range(0, self.skt_per_class, skt_per_batch_per_class):
temp_indexlist = np.append(temp_indexlist, [
sort_indexlist[(i * self.skt_per_class) + j]
for i in range(self.num_classes)
for j in range(k, k + skt_per_batch_per_class)
])
# convert back to list
self.indexlist = temp_indexlist.tolist()
class BatchLoader(object):
"""
This class abstracts away the loading of images.
Images can either be loaded singly, or in a batch. The latter is used for
the asyncronous data layer to preload batches while other processing is
performed.
"""
def __init__(self, params):
#load data
self.batch_size = params['batch_size']
self.outshape = params['shape']
if params['source'][-4:] == '.mat':
self.img_data, self.img_labels = mat2py_imdb(params['source'])
self.img_data = self.img_data.transpose(
2, 0, 1) #swap dims to have Num x height x width
elif os.path.isdir(params['source']): #assume it is lmdb
lmdb_ = lmdbpy()
self.img_data, self.img_labels = lmdb_.read(
params['source']) #use read_encoded if the img is encoded
self.img_data = self.img_data.squeeze()
else:
assert 0, 'Invalid format for source {}\n\
need either lmdb or .mat data'.format(params['source'])
label_ids = list(set(self.img_labels))
NCATS = len(label_ids)
if label_ids[0] != 0 or label_ids[-1] != NCATS - 1:
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
]
if params['mean_file'] == 0:
self.img_mean = 0
elif params['mean_file'][-4:] == '.mat':
self.img_mean = mat2py_mean(params['mean_file'])
elif params['mean_file'][-12:] == '.binaryproto':
self.img_mean = biproto2py(params['mean_file']).squeeze()
else:
assert 0, 'Invalid format for mean_file {}'.format(
params['mean_file'])
self.indexlist = range(len(self.img_labels))
shuffle(self.indexlist)
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'])
print "\nBatchLoader initialized with {} images".format(
len(self.img_labels))
self.pool = ThreadPool() #4
def load_next_image(self, l):
"""
Load the next image in a batch.
"""
# Did we finish an epoch?
l.acquire() #5
if self._cur == len(self.indexlist):
self._cur = 0
shuffle(self.indexlist)
# Load a datum
index = self.indexlist[self._cur] # Get the index
self._cur += 1
l.release() #6
img = self.img_data[index]
label = self.img_labels[index]
return (self.img_augment.augment(img), label)
def load_next_batch(self):
res = {}
#7
lock = Lock()
threads = [self.pool.apply_async(self.load_next_image,(lock,)) for \
i in range (self.batch_size)]
thread_res = [thread.get() for thread in threads]
res['data'] = np.asarray([datum[0]
for datum in thread_res])[:, None, :, :]
res['label'] = np.asarray([datum[1] for datum in thread_res],
dtype=np.float32)
return res
class BatchLoader(object):
"""
This class abstracts away the loading of images.
Images can either be loaded singly, or in a batch. The latter is used for
the asyncronous data layer to preload batches while other processing is
performed.
"""
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 load_next_triplet(self, l):
"""
Load the next triplet in a batch.
"""
# Did we finish an epoch?
l.acquire() #5
if self._cur == len(self.indexlist):
self._cur = 0
self.shuffle_keeping_min_classes_per_batch()
# Load a sketch
index = self.indexlist[self._cur] # Get the sketch index
self._cur += 1
l.release() #6
skt = self.skt_lmdb.get_datum(index).squeeze()
label = self.skt_labels[index]
#randomly select pos and neg img
index_p = np.random.choice(self.img_labels_dict[str(label)])
label_n = label
while label_n == label:
label_n = np.random.choice(self.classes)
index_n = np.random.choice(self.img_labels_dict[str(label_n)])
img_p = self.img_lmdb.get_datum(index_p).squeeze()
img_n = self.img_lmdb.get_datum(index_n).squeeze()
res = dict(anchor=self.img_augment.augment(skt),
pos=self.img_augment.augment(img_p),
neg=self.img_augment.augment(img_n),
label_a=label,
label_n=label_n)
return res
def load_next_batch(self):
res = {}
#7
lock = Lock()
threads = [self.pool.apply_async(self.load_next_triplet,(lock,)) for \
i in range (self.batch_size)]
thread_res = [thread.get() for thread in threads]
res['data_a'] = np.asarray([tri['anchor']
for tri in thread_res])[:, None, :, :]
res['data_p'] = np.asarray([tri['pos']
for tri in thread_res])[:, None, :, :]
res['data_n'] = np.asarray([tri['neg']
for tri in thread_res])[:, None, :, :]
res['label_a'] = np.asarray([tri['label_a'] for tri in thread_res],
dtype=np.float32)
res['label_n'] = np.asarray([tri['label_n'] for tri in thread_res],
dtype=np.float32)
return res
#==============================================================================
# res['data_a'] = np.zeros((self.batch_size,1,self.outshape[0],\
# self.outshape[1]),dtype = np.float32)
# res['data_p'] = np.zeros_like(res['data_a'],dtype=np.float32)
# res['data_n'] = np.zeros_like(res['data_a'],dtype=np.float32)
# res['label'] = np.zeros((self.batch_size,3),dtype = np.float32)
# for itt in range(self.batch_size):
# trp = self.load_next_triplet(1)
# res['data_a'][itt,...] = trp['anchor']
# res['data_p'][itt,...] = trp['pos']
# res['data_n'][itt,...] = trp['neg']
# res['label'][itt,...] = trp['label']
# return res
#==============================================================================
def shuffle_keeping_min_classes_per_batch(self):
shuffle(self.indexlist)
# sort index list by class
# (using lambda to restrict sorting to just first element of tuple
sort_indexlist = sorted(self.indexlist,
key=lambda index: self.skt_labels[index])
# make it 2D based on classes
sort_indexlist = np.array(sort_indexlist, dtype=np.int64)
sort_indexlist = sort_indexlist.reshape(self.num_classes,
self.skt_per_class)
# permute the class positions and flat it (make it 1D again)
sort_indexlist = np.random.permutation(sort_indexlist)
sort_indexlist = sort_indexlist.reshape(self.num_classes *
self.skt_per_class)
temp_indexlist = np.array([], dtype=np.int64)
skt_per_batch_per_class = int(self.batch_size / self.classes_per_batch)
# apply dark magic
# (slices of classes sliced, appended together until list is over)
for k in range(0, self.skt_per_class, skt_per_batch_per_class):
temp_indexlist = np.append(temp_indexlist, [
sort_indexlist[(i * self.skt_per_class) + j]
for i in range(self.num_classes)
for j in range(k, k + skt_per_batch_per_class)
])
# convert back to list
self.indexlist = temp_indexlist.tolist()