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data_iterators.py
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data_iterators.py
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import numpy as np
import itertools
import pathfinder
import utils
import app
import buffering
import os
class DataGenerator(object):
def __init__(self, dataset, batch_size, img_ids, p_transform, data_prep_fun, label_prep_fun, rng,
random, infinite, full_batch, override_patch_size=None, version=1, **kwargs):
self.dataset = dataset
self.img_ids = img_ids
self.nsamples = len(img_ids)
self.batch_size = batch_size
self.p_transform = p_transform
self.data_prep_fun = data_prep_fun
self.label_prep_fun = label_prep_fun
self.rng = rng
self.random = random
self.infinite = infinite
self.full_batch = full_batch
self.override_patch_size = override_patch_size
if override_patch_size:
self.patch_size = override_patch_size
else:
self.patch_size = self.p_transform['patch_size']
self.labels = app.get_labels_array(version=version)
def generate(self):
while True:
rand_idxs = np.arange(len(self.img_ids))
if self.random:
self.rng.shuffle(rand_idxs)
for pos in xrange(0, len(rand_idxs), self.batch_size):
idxs_batch = rand_idxs[pos:pos + self.batch_size]
nb = len(idxs_batch)
# allocate batches
if self.p_transform['channels']:
x_batch = np.zeros((nb,self.p_transform['channels'],) + self.patch_size, dtype='float32')
else:
x_batch = np.zeros((nb,) + self.patch_size, dtype='float32')
if self.p_transform['n_labels']>1:
y_batch = np.zeros((nb, self.p_transform['n_labels']), dtype='float32')
else:
y_batch = np.zeros((nb,), dtype='float32')
batch_ids = []
for i, idx in enumerate(idxs_batch):
img_id = self.img_ids[idx]
batch_ids.append(img_id)
try:
img = app.read_compressed_image(self.dataset, img_id)
except Exception:
print 'cannot open ', img_id
x_batch[i] = self.data_prep_fun(x=img)
if 'train' in self.dataset:
y_batch[i] = self.label_prep_fun(self.labels[img_id])
#print 'i', i, 'img_id', img_id, y_batch[i]
if self.full_batch:
if nb == self.batch_size:
yield x_batch, y_batch, batch_ids
else:
yield x_batch, y_batch, batch_ids
if not self.infinite:
break
class AutoEncoderDataGenerator(object):
def __init__(self, batch_size, img_paths, labeled_img_paths, p_transform, data_prep_fun, label_prep_fun, rng,
random, infinite, full_batch, **kwargs):
self.img_paths = img_paths
self.labeled_img_paths = labeled_img_paths
self.nsamples = len(img_paths)
self.batch_size = batch_size
self.p_transform = p_transform
self.data_prep_fun = data_prep_fun
self.label_prep_fun = label_prep_fun
self.rng = rng
self.random = random
self.infinite = infinite
self.full_batch = full_batch
self.labels = app.get_labels_array()
def generate(self):
while True:
rand_idxs = np.arange(len(self.img_paths))
if self.random:
self.rng.shuffle(rand_idxs)
for pos in xrange(0, len(rand_idxs), self.batch_size):
idxs_batch = rand_idxs[pos:pos + self.batch_size]
nb = len(idxs_batch)
# allocate batches
if self.p_transform['channels']:
x_batch = np.zeros((nb,self.p_transform['channels'],) + self.p_transform['patch_size'], dtype='float32')
else:
x_batch = np.zeros((nb,) + self.p_transform['patch_size'], dtype='float32')
if self.p_transform['n_labels']>1:
y_batch = np.zeros((nb, self.p_transform['n_labels']), dtype='float32')
else:
y_batch = np.zeros((nb,), dtype='float32')
z_batch = np.zeros((nb,), dtype='float32')
batch_ids = []
for i, idx in enumerate(idxs_batch):
img_path = self.img_paths[idx]
batch_ids.append(img_path)
try:
img = app.read_image_from_path(img_path)
except Exception:
print 'cannot open ', img_id
x_batch[i] = self.data_prep_fun(x=img)
if img_path in self.labeled_img_paths:
z_batch[i] = 1.
img_id = app.get_id_from_path(img_path)
y_batch[i] = self.label_prep_fun(self.labels[img_id])
#print 'i', i, 'img_id', img_id, y_batch[i]
if self.full_batch:
if nb == self.batch_size:
yield x_batch, y_batch, z_batch, batch_ids
else:
yield x_batch, y_batch, z_batch, batch_ids
if not self.infinite:
break
class SlimDataGenerator(object):
def __init__(self, dataset, batch_size, img_ids, p_transform, data_prep_fun, label_prep_fun, rng,
random, infinite, full_batch, **kwargs):
self.dataset = dataset
self.img_ids = img_ids
self.nsamples = len(img_ids)
self.batch_size = batch_size
self.p_transform = p_transform
self.data_prep_fun = data_prep_fun
self.label_prep_fun = label_prep_fun
self.rng = rng
self.random = random
self.infinite = infinite
self.full_batch = full_batch
self.labels = app.get_labels_array()
def generate(self):
while True:
rand_idxs = np.arange(len(self.img_ids))
if self.random:
self.rng.shuffle(rand_idxs)
for pos in xrange(0, len(rand_idxs), self.batch_size):
idxs_batch = rand_idxs[pos:pos + self.batch_size]
nb = len(idxs_batch)
# allocate batches
x_batch = []
y_batch = []
batch_ids = []
for i, idx in enumerate(idxs_batch):
img_id = self.img_ids[idx]
batch_ids.append(img_id)
try:
img = app.read_compressed_image(self.dataset, img_id)
except Exception:
print 'cannot open ', img_id
x_batch.append(self.data_prep_fun(x=img))
if 'train' in self.dataset:
y_batch.append(self.label_prep_fun(self.labels[img_id]))
if self.full_batch:
if nb == self.batch_size:
yield x_batch, y_batch, batch_ids
else:
yield x_batch, y_batch, batch_ids
if not self.infinite:
break
def _test_data_generator():
#testing data iterator
p_transform = {'patch_size': (256, 256),
'channels': 4,
'n_labels': 17}
rng = np.random.RandomState(42)
def data_prep_fun(x):
x = np.array(x)
x = np.swapaxes(x,0,2)
return x
def label_prep_fun(labels):
return labels
folds = app.make_stratified_split(no_folds=5)
all_ids = folds[0] + folds[1] + folds[2] + folds[3] +folds[4]
bad_ids = [18772, 28173, 5023]
img_ids = [x for x in all_ids if x not in bad_ids]
dg = DataGenerator(dataset='train-jpg',
batch_size=10,
img_ids = img_ids,
p_transform=p_transform,
data_prep_fun = data_prep_fun,
label_prep_fun = label_prep_fun,
rng=rng,
full_batch=True, random=False, infinite=False)
for (x_chunk, y_chunk, id_train) in buffering.buffered_gen_threaded(dg.generate()):
print x_chunk.shape, y_chunk.shape, id_train
def _test_simple_data_generator():
#testing data iterator
p_transform = {'patch_size': (256, 256),
'channels': 4,
'n_labels': 1}
label_id = 4
rng = np.random.RandomState(42)
def data_prep_fun(x):
return x
def label_prep_fun(labels):
print labels
return labels[label_id]
folds = app.make_stratified_split(no_folds=5)
all_ids = folds[0] + folds[1] + folds[2] + folds[3] +folds[4]
bad_ids = []
img_ids = [x for x in all_ids if x not in bad_ids]
dg = SlimDataGenerator(dataset='train-jpg',
batch_size=10,
label_id = label_id,
img_ids = all_ids,
p_transform=p_transform,
data_prep_fun = data_prep_fun,
label_prep_fun = label_prep_fun,
rng=rng,
full_batch=True,
random=False,
infinite=False)
print 'start'
avgs = []
stds = []
ch_avgs = [[],[],[],[]]
ch_stds = [[],[],[],[]]
for (x_chunk, y_chunk, id_train) in dg.generate():
x_chunk = np.stack(x_chunk)
#x_chunk = x_chunk/255.
avgs.append(np.mean(x_chunk))
stds.append(np.std(x_chunk))
for ch in range(4):
ch_avgs[ch].append(np.mean(x_chunk[:,ch]))
ch_stds[ch].append(np.std(x_chunk[:,ch]))
print 'avgs', np.mean(np.stack(avgs))
print 'stds', np.mean(np.stack(stds))
for ch in range(4):
print 'ch', str(ch)
print 'mean of avgs', np.mean(ch_avgs[ch])
print 'mean of stds', np.mean(ch_stds[ch])
if __name__ == "__main__":
_test_simple_data_generator()