def load_dataset():
# We first define a download function, supporting both Python 2 and 3.
if sys.version_info[0] == 2:
from urllib import urlretrieve
else:
from urllib.request import urlretrieve
def check_download(filename,
source='http://www.aaalgo.com/picpac/datasets/cifar/'):
if not os.path.exists(filename):
print("Downloading %s" % filename)
urlretrieve(source + filename, filename)
config = dict(seed=1996,
resize_width=32,
resize_height=32,
batch=batch_size,
channels=3,
channel_first=False,
onehot=nb_classes)
train_config = dict(
shuffle=True,
reshuffle=True,
split=10,
split_fold=1,
stratify=True,
pert_color1=10,
pert_color2=10,
pert_color3=10,
pert_angle=15,
pert_min_scale=0.7,
pert_max_scale=1.2,
)
train_config.update(config)
# We can now download and read the training and test set images and labels.
check_download('cifar10-train.picpac')
check_download('cifar10-test.picpac')
train = picpac.ImageStream('cifar10-train.picpac',
loop=True,
split_negate=False,
perturb=data_augmentation,
**train_config)
val = picpac.ImageStream('cifar10-train.picpac',
loop=False,
split_negate=True,
perturb=False,
**train_config)
test = picpac.ImageStream('cifar10-test.picpac',
loop=False,
perturb=False,
**config)
return train, val, test
def create_picpac_stream (path, is_training):
assert os.path.exists(path)
config = {"db": path,
"loop": is_training,
"shuffle": is_training,
"reshuffle": is_training,
"annotate": [1],
"channels": 3,
"stratify": is_training,
"dtype": "float32",
"batch": BATCH,
"colorspace": 'BGR',
"cache": 0,
"order": "NCHW", # specific to caffe
"transforms": [
{"type": "resize", "width": WIDTH, "height": HEIGHT},
{"type": "augment.flip", "horizontal": True, "vertical": False, "transpose": False},
{"type": "augment.scale", "min":0.8, "max":1.2},
{"type": "augment.rotate", "range": 10},
{"type": "augment.add", "range":20},
{"type": "clip", "width": WIDTH, "height": HEIGHT},
{"type": "rasterize"},
]
}
if not is_training:
config['threads'] = 1
return picpac.ImageStream(config)
def create_picpac_stream (db_path, is_training):
assert os.path.exists(db_path)
augments = []
if is_training:
augments = [
{"type": "augment.flip", "horizontal": True, "vertical": True},
{"type": "augment.rotate", "min":-10, "max":10},
{"type": "augment.scale", "min":0.9, "max":1.1},
{"type": "augment.add", "range":20},
]
else:
augments = []
config = {"db": db_path,
"loop": is_training,
"shuffle": is_training,
"reshuffle": is_training,
"annotate": True,
"channels": FLAGS.channels,
"stratify": is_training,
"dtype": "float32",
"batch": FLAGS.batch,
"colorspace": COLORSPACE,
"transforms": augments + [
{"type": "clip", "round": FLAGS.stride},
{"type": "rasterize"},
]
}
if is_training and not FLAGS.mixin is None:
print("mixin support is incomplete in new picpac.")
# assert os.path.exists(FLAGS.mixin)
# picpac_config['mixin'] = FLAGS.mixin
# picpac_config['mixin_group_delta'] = 1
# pass
return picpac.ImageStream(config)
def create_picpac_stream(db_path, is_training):
assert os.path.exists(db_path)
augments = []
shift = 0
if is_training:
shift = FLAGS.shift
if FLAGS.augments:
with open(FLAGS.augments, 'r') as f:
augments = json.loads(f.read())
print("Using augments:")
print(json.dumps(augments))
else:
augments = [
{
"type": "augment.flip",
"horizontal": True,
"vertical": False
},
]
config = {
"db":
db_path,
"loop":
is_training,
"shuffle":
is_training,
"reshuffle":
is_training,
"annotate":
False,
"channels":
FLAGS.channels,
"stratify":
is_training,
"dtype":
"float32",
"batch":
FLAGS.batch,
"cache":
FLAGS.cache,
"transforms":
augments + [
#{"type": "resize", "size": FLAGS.size},
{
"type": "clip",
"size": FLAGS.size,
"shift": shift,
"border_type": "replicate"
},
]
}
if is_training and not FLAGS.mixin is None:
print("mixin support is incomplete in new picpac.")
assert os.path.exists(FLAGS.mixin)
config['mixin'] = FLAGS.mixin
config['mixin_group_reset'] = 0
config['mixin_group_delta'] = 1
pass
return picpac.ImageStream(config)
def main(_):
assert FLAGS.out
assert FLAGS.db and os.path.exists(FLAGS.db)
picpac_config = dict(
seed=2016,
#loop=True,
shuffle=True,
reshuffle=True,
#resize_width=256,
#resize_height=256,
round_div=FLAGS.stride,
batch=1,
split=1,
split_fold=0,
annotate='json',
channels=FLAGS.channels,
stratify=True,
pert_color1=20,
pert_angle=20,
pert_min_scale=0.8,
pert_max_scale=1.2,
#pad=False,
pert_hflip=True,
pert_vflip=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
stream = picpac.ImageStream(FLAGS.db,
perturb=False,
loop=False,
**picpac_config)
gal = Gallery(FLAGS.out)
cc = 0
with Model(FLAGS.model, name=FLAGS.name, prob=True) as model:
for images, _, _ in stream:
#images *= 600.0/1500
#images -= 800
#images *= 3000 /(2000-800)
_, H, W, _ = images.shape
if FLAGS.max_size:
if max(H, W) > FLAGS.max_size:
continue
if FLAGS.patch:
stch = Stitcher(images, FLAGS.patch)
probs = stch.stitch(model.apply(stch.split()))
else:
probs = model.apply(images)
cc += 1
save(gal.next(), images, probs)
if FLAGS.max and cc >= FLAGS.max:
break
gal.flush()
pass
def main(_):
X = tf.placeholder(tf.float32, shape=(None, None, None, 3), name="images")
is_training = tf.placeholder(tf.bool, name="is_training")
anchor_th = tf.constant(FLAGS.anchor_th, tf.float32)
nms_max = tf.constant(FLAGS.nms_max, tf.int32)
nms_th = tf.constant(FLAGS.nms_th, tf.float32)
model = Model(X, anchor_th, nms_max, nms_th, is_training, FLAGS.model,
'xxx')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model.loader(sess)
if FLAGS.input:
assert os.path.exists(FLAGS.input)
image = cv2.imread(FLAGS.input, cv2.IMREAD_COLOR)
batch = np.expand_dims(image, axis=0).astype(dtype=np.float32)
boxes, probs = sess.run([model.boxes, model.probs],
feed_dict={
X: batch,
is_training: False
})
save_prediction_image(FLAGS.input + '.prob.png', image, boxes,
probs)
if FLAGS.input_db:
assert os.path.exists(FLAGS.input_db)
import picpac
from gallery import Gallery
picpac_config = {
"db": FLAGS.input_db,
"loop": False,
"shuffle": False,
"reshuffle": False,
"annotate": False,
"channels": 3,
"stratify": False,
"dtype": "float32",
"batch": 1,
"transforms": []
}
stream = picpac.ImageStream(picpac_config)
gal = Gallery(FLAGS.input_db + '.out')
C = 0
for _, images in stream:
boxes, probs = sess.run([model.boxes, model.probs],
feed_dict={
X: images,
is_training: False
})
save_prediction_image(gal.next(), images[0], boxes, probs)
C += 1
if FLAGS.max and C >= FLAGS.max:
break
pass
pass
gal.flush()
pass
def main (_):
assert FLAGS.out
assert FLAGS.db and os.path.exists(FLAGS.db)
picpac_config = dict(seed=2016,
#loop=True,
shuffle=True,
reshuffle=True,
max_size = 400,
#resize_width=256,
#resize_height=256,
round_div = FLAGS.stride,
batch=1,
split=1,
split_fold=0,
annotate='json',
channels=FLAGS.channels,
stratify=True,
#pad=False,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
stream = picpac.ImageStream(FLAGS.db, perturb=False, loop=False, **picpac_config)
gal = Gallery(FLAGS.out, score=True)
cc = 0
with Model(FLAGS.model, prob=True) as model:
for images, _, _ in stream:
#images *= 600.0/1500
#images -= 800
#images *= 3000 /(2000-800)
_, H, W, _ = images.shape
if FLAGS.max_size:
if max(H, W) > FLAGS.max_size:
continue
print(images.shape)
# fcn-cls do not have patch
# if FLAGS.patch:
# stch = Stitcher(images, FLAGS.patch)
# probs = stch.stitch(model.apply(stch.split()))
# else:
# probs = model.apply(images)
probs, scores = model.apply(images)
cc += 1
save(gal.next(score=scores[0]), images, probs)
if FLAGS.max and cc >= FLAGS.max:
break
gal.flush(rank=True)
pass
def __init__(self, path, **kwargs):
super(ImageStream, self).__init__()
kwargs['loop'] = False
kwargs.setdefault('batch', 1)
self.stream = picpac.ImageStream(path, **kwargs)
self.batch_size = kwargs['batch']
self.data = None
self.label = None
self.pad = None
self.peek = self.stream.next()
self.provide_data = [('data', self.peek[0].shape)]
self.provide_label = [('softmax_label', self.peek[1].shape)]
pass
def create_picpac_stream(db_path, is_training):
assert os.path.exists(db_path)
augments = []
shift = 0
if is_training:
shift = FLAGS.shift
augments = [
{
"type": "augment.flip",
"horizontal": True,
"vertical": False
},
]
config = {
"db":
db_path,
"loop":
is_training,
"shuffle":
is_training,
"reshuffle":
is_training,
"annotate":
False,
"channels":
FLAGS.channels,
"stratify":
is_training,
"dtype":
"float32",
"batch":
FLAGS.batch,
"transforms":
augments + [
#{"type": "resize", "size": FLAGS.size},
{
"type": "clip",
"size": FLAGS.size,
"shift": shift,
"border_type": "replicate"
},
]
}
if is_training and not FLAGS.mixin is None:
print("mixin support is incomplete in new picpac.")
# assert os.path.exists(FLAGS.mixin)
# picpac_config['mixin'] = FLAGS.mixin
# picpac_config['mixin_group_delta'] = 1
# pass
return picpac.ImageStream(config)
def main (_):
assert FLAGS.db and os.path.exists(FLAGS.db)
assert FLAGS.model and os.path.exists(FLAGS.model + '.meta')
L = tf.placeholder(tf.float32, shape=(None, None, None, 1))
mg = meta_graph.read_meta_graph_file(FLAGS.model + '.meta')
logits, = tf.import_graph_def(mg.graph_def, name='colorize',
#input_map={'L:0':L},
input_map={'fifo_queue_Dequeue:0':L},
return_elements=['logits:0'])
prob = tf.nn.softmax(logits)
saver = tf.train.Saver(saver_def=mg.saver_def, name='colorize')
picpac_config = dict(seed=2016,
cache=False,
max_size=200,
min_size=192,
crop_width=192,
crop_height=192,
shuffle=True,
reshuffle=True,
batch=1,
round_div=FLAGS.stride,
channels=3,
stratify=False,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
stream = picpac.ImageStream(FLAGS.db, perturb=False, loop=False, **picpac_config)
with tf.Session() as sess:
tf.global_variables_initializer().run()
saver.restore(sess, FLAGS.model)
gallery = Gallery(FLAGS.output, cols=2, header=['groundtruth', 'prediction'])
c = 0
for images, _, _ in stream:
if FLAGS.max and (c >= FLAGS.max):
break
l, ab, w = _pic2pic.encode_lab(images.copy(), FLAGS.downsize)
ab_p, = sess.run([prob], feed_dict={L: l})
y_p = decode_lab(l, ab_p, T=FLAGS.T)
cv2.imwrite(gallery.next(), images[0])
cv2.imwrite(gallery.next(), y_p[0])
c += 1
print('%d/%d' % (c, FLAGS.max))
pass
gallery.flush()
pass
pass
def main(_):
X = tf.placeholder(tf.float32, shape=(None, None, None, 1), name="images")
model = Model(X, FLAGS.model, 'xxx')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
model.loader(sess)
# 模型至此导入完毕
# 注意如果要读入图片预测，需要进行下述预处理
# 1. 转成灰度图, 比如用cv2.imread(..., cv2.IMREAD_GRAYSCALE)
# 2. resize成固定大小，和train.sh中的参数匹配
# 3. 比如灰度图是image, 那么对应的batch = image[np.newaxis, :, :, np.newaxis], 就可以送入tf.session预测了
gal = Gallery('output', cols=2, ext='.jpg')
CC = 0
stream = picpac.ImageStream({
'db':
FLAGS.db,
'loop':
False,
'channels':
1,
'threads':
1,
'shuffle':
False,
'transforms': [{
"type": "resize",
"width": FLAGS.width,
"height": FLAGS.height
}]
})
for meta, batch in stream:
if CC > FLAGS.max:
break
print(meta.ids)
image = batch[0]
logits = sess.run(model.logits, feed_dict={X: batch})
# 把logits转换成字符串
label = logits2text(logits)
'''END INFERENCE'''
save_prediction_image(gal, image, label)
CC += 1
gal.flush()
pass
def create_picpac_stream (db_path, is_training):
assert os.path.exists(db_path)
augments = []
if is_training:
if FLAGS.augments:
with open(FLAGS.augments, 'r') as f:
augments = json.loads(f.read())
print("Using augments:")
print(json.dumps(augments))
else:
augments = [
#{"type": "augment.flip", "horizontal": True, "vertical": False},
{"type": "augment.rotate", "min":-10, "max":10},
{"type": "augment.scale", "min":0.9, "max":1.1},
{"type": "augment.add", "range":20},
]
else:
augments = []
picpac_config = {"db": db_path,
"loop": is_training,
"shuffle": is_training,
"reshuffle": is_training,
"annotate": True,
"channels": FLAGS.channels,
"stratify": is_training,
"dtype": "float32",
"batch": FLAGS.batch,
"colorspace": COLORSPACE,
"cache": FLAGS.cache,
"transforms": augments + [
{"type": "clip", "round": FLAGS.backbone_stride},
{"type": "anchors.dense.box", 'downsize': FLAGS.anchor_stride},
{"type": "box_feature"},
{"type": "rasterize", "use_tag": True, "dtype": "float32"}
]
}
if is_training and not FLAGS.mixin is None:
#print("mixin support is incomplete in new picpac.")
assert os.path.exists(FLAGS.mixin)
picpac_config['mixin'] = FLAGS.mixin
picpac_config['mixin_group_reset'] = 0
picpac_config['mixin_group_delta'] = 1
return picpac.ImageStream(picpac_config)
def __init__(self, path, **kwargs):
super(ImageStream, self).__init__()
kwargs.setdefault('batch', 1)
kwargs.setdefault('loop', True)
self.stream = picpac.ImageStream(path, **kwargs)
self.batch_size = kwargs['batch']
self.nclass = kwargs['onehot']
self.loop = kwargs['loop']
self.ndata = self.stream.size()
self.peek = self.stream.next()
self.shape = tuple(self.peek[0].shape[1:])
self.xdim = reduce(mul, self.shape)
yshape = tuple(self.peek[1].shape[1:])
if self.nclass:
assert len(yshape) == 1
self.ydim = reduce(mul, yshape)
self.Xbuf = self.be.iobuf(self.xdim)
self.Ybuf = self.be.iobuf(self.ydim)
pass
def create_picpac_stream(path, is_training, extra_config):
assert os.path.exists(path)
print("CACHE:", FLAGS.cache)
# check db size, warn not to cache if file is big
statinfo = os.stat(path)
if statinfo.st_size > 0x40000000 and FLAGS.cache:
print_red(
"DB is probably too big too be cached, consider adding --cache 0")
config = {
"db": path,
"loop": is_training,
"shuffle": is_training,
"reshuffle": is_training,
"annotate": [],
"channels": FLAGS.channels,
"stratify": is_training,
"dtype": "float32",
"batch": FLAGS.batch,
"colorspace": FLAGS.colorspace,
"cache": FLAGS.cache,
"transforms": []
}
if is_training:
config[
"dump"] = FLAGS.picpac_dump # dump 20 training samples for debugging and see
else:
config['threads'] = 1
if is_training and not FLAGS.mixin is None:
print("mixin support is incomplete in new picpac.")
assert os.path.exists(FLAGS.mixin)
config['mixin'] = FLAGS.mixin
config['mixin_group_reset'] = 0
config['mixin_group_delta'] = 1
pass
config.update(extra_config)
return picpac.ImageStream(config)
def main(_):
picpac_config = dict(
seed=2016,
shuffle=True,
batch=1,
annotate='json',
channels=1,
perturb=False,
loop=False,
stratify=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
tr_stream = picpac.ImageStream(FLAGS.db, **picpac_config)
gal = Gallery('sample', ext='.gif')
cc = 0
for image, label, _ in tr_stream:
cc += 1
visualize(gal.next(), image[0], label[0, :, :, 0])
if cc >= FLAGS.classes - 1:
break
pass
gal.flush()
def main(_):
logging.basicConfig(level=FLAGS.verbose)
try:
os.makedirs(FLAGS.model)
except:
pass
assert FLAGS.pos and os.path.exists(FLAGS.pos)
assert FLAGS.neg and os.path.exists(FLAGS.neg)
X = tf.placeholder(tf.float32,
shape=(None, None, None, FLAGS.channels),
name="images")
Y_fcn = tf.placeholder(tf.float32, shape=(None, None, None, 1))
Y_cls = tf.placeholder(tf.float32, shape=(None, ))
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose, slim.max_pool2d],
padding=FLAGS.padding):
logits_fcn, logits_cls, stride = getattr(fcn_cls_nets, FLAGS.net)(X)
loss_fcn = fcn_loss(logits_fcn, Y_fcn)
loss_cls, accuracy_cls = cls_loss(logits_cls, Y_cls)
#tf.summary.scalar("loss", loss)
loss_total = tf.identity(loss_fcn + FLAGS.W * loss_cls, name='loss')
metrics = [loss_total, loss_fcn, loss_cls, accuracy_cls]
metric_names = [x.name[:-2] for x in metrics]
rate = FLAGS.learning_rate
if FLAGS.opt == 'adam':
rate /= 100
global_step = tf.Variable(0, name='global_step', trainable=False)
if FLAGS.decay:
rate = tf.train.exponential_decay(rate,
global_step,
FLAGS.decay_steps,
FLAGS.decay_rate,
staircase=True)
if FLAGS.opt == 'adam':
optimizer = tf.train.AdamOptimizer(rate)
elif FLAGS.opt == 'mom':
optimizer = tf.train.MomentumOptimizer(rate, FLAGS.momentum)
else:
optimizer = tf.train.GradientDescentOptimizer(rate)
pass
train_op = optimizer.minimize(loss_total, global_step=global_step)
picpac_config_cls = dict(
seed=2016,
shuffle=True,
reshuffle=True,
#max_size = 300,
#resize_width=256,
#resize_height=256,
split=FLAGS.split,
split_fold=FLAGS.split_fold,
batch=1,
pert_angle=15,
pert_hflip=True,
pert_vflip=False,
pert_color1=20,
pert_color2=20,
pert_color3=20,
pert_min_scale=0.8,
pert_max_scale=1.2,
channels=FLAGS.channels,
#mixin = FLAGS.mixin,
stratify=True,
#pad=False,
channel_first=False, # this is tensorflow specific
)
picpac_config_fcn = dict(
annotate='json',
round_div=stride,
)
picpac_config_fcn.update(picpac_config_cls)
tr_stream0 = picpac.ImageStream(FLAGS.neg,
split_negate=False,
perturb=True,
loop=True,
**picpac_config_fcn)
tr_stream1 = picpac.ImageStream(FLAGS.pos,
split_negate=False,
perturb=True,
loop=True,
**picpac_config_fcn)
te_streams = []
if FLAGS.test_epochs > 0:
# testing stream, "negate" inverts the image selection specified by split & split_fold
# so different images are used for training and testing
if FLAGS.test_db:
if FLAGS.split > 1:
print(
"Cannot use cross-validation & evaluation db at the same time"
)
print("If --test-db is specified, do not set --split")
raise Exception("bad parameters")
te_streams.append((0,
picpac.ImageStream(FLAGS.test_db,
perturb=False,
loop=False,
**picpac_config_cls)))
elif FLAGS.split > 1:
te_streams.append((0,
picpac.ImageStream(FLAGS.neg,
split_negate=True,
perturb=False,
loop=False,
**picpac_config_cls)))
te_streams.append((1,
picpac.ImageStream(FLAGS.pos,
split_negate=True,
perturb=False,
loop=False,
**picpac_config_cls)))
pass
pass
init = tf.global_variables_initializer()
saver = tf.train.Saver(max_to_keep=FLAGS.max_to_keep)
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
with tf.Session(config=config) as sess:
sess.run(init)
if FLAGS.resume:
saver.restore(sess, FLAGS.resume)
step = 0
epoch = 0
global_start_time = time.time()
while step < FLAGS.max_steps:
start_time = time.time()
avg = np.array([0] * len(metrics), dtype=np.float32)
for _ in tqdm(range(FLAGS.epoch_steps), leave=False):
# train FCN
images, labels, _ = tr_stream0.next()
#print('xxx', images.shape)
feed_dict = {X: images, Y_fcn: labels, Y_cls: [0]}
mm, _ = sess.run([metrics, train_op], feed_dict=feed_dict)
avg += np.array(mm)
images, labels, _ = tr_stream1.next()
#print('xxx', images.shape)
feed_dict = {X: images, Y_fcn: labels, Y_cls: [1]}
mm, _ = sess.run([metrics, train_op], feed_dict=feed_dict)
avg += np.array(mm)
step += 1
pass
avg /= FLAGS.epoch_steps * 2
stop_time = time.time()
txt = ', '.join(
['%s=%.4f' % (a, b) for a, b in zip(metric_names, list(avg))])
print('step %d: elapsed=%.4f time=%.4f, %s' %
(step, (stop_time - global_start_time),
(stop_time - start_time), txt))
epoch += 1
if epoch and (epoch % FLAGS.ckpt_epochs == 0):
ckpt_path = '%s/%d' % (FLAGS.model, step)
start_time = time.time()
saver.save(sess, ckpt_path)
stop_time = time.time()
print('epoch %d step %d, saving to %s in %.4fs.' %
(epoch, step, ckpt_path, stop_time - start_time))
if epoch and (epoch % FLAGS.test_epochs
== 0) and len(te_streams) > 0:
cmatrix = np.zeros((2, 2))
total = 0
acc_sum = 0
for delta, te_stream in te_streams:
te_stream.reset()
for images, labels, _ in te_stream:
total += 1
labels += delta
feed_dict = {X: images, Y_cls: labels}
acc, ll = sess.run([accuracy_cls, logits_cls],
feed_dict=feed_dict)
acc_sum += acc
cmatrix[int(labels),
np.where(ll == np.max(ll))[1]] += np.divide(
float(1),
np.size(np.where(ll == np.max(ll))[1]))
pass
rowsum = np.sum(cmatrix, axis=1)
colsum = np.sum(cmatrix, axis=0)
#print(total)
#print(np.tile(rowsum,(FLAGS.classes,1)).transpose())
#print(np.tile(colsum,(FLAGS.classes,1)))
print('row---label; colum ---predict')
print(total)
print('evaluation: accuracy = %.4f' % (acc_sum / total))
print('accuracy from confusion matrix = %.4f' %
np.divide(np.trace(cmatrix), float(total)))
print('absolute confusion matrix:')
print(cmatrix)
#print('confusion matrix divided by total:')
#print(np.divide(cmatrix,float(total)))
print('confusion matrix divided by col sum:')
print(np.divide(cmatrix, np.tile(colsum, (2, 1))))
print('confusion matrix divided by row sum:')
print(np.divide(cmatrix, np.tile(rowsum, (2, 1)).transpose()))
pass
pass
pass
def main(_):
logging.basicConfig(level=FLAGS.verbose)
try:
os.makedirs(FLAGS.model)
except:
pass
assert FLAGS.db and os.path.exists(FLAGS.db)
rate = tf.constant(FLAGS.learning_rate)
global_step = tf.Variable(0, name='global_step', trainable=False)
if FLAGS.decay:
rate = tf.train.exponential_decay(rate,
global_step,
FLAGS.decay_steps,
FLAGS.decay_rate,
staircase=True)
tf.summary.scalar('learning_rate', rate)
optimizer = tf.train.AdamOptimizer(rate)
enc_GRAY = tf.placeholder(tf.float32,
shape=(None, None, None, 1)) # inference input
enc_GTCOLOR = tf.placeholder(tf.float32, shape=(None, None, None,
3)) # inference output
enc_W = tf.placeholder(tf.float32, shape=(None, None, None,
1)) # inference per-pixel weight
queue = tf.FIFOQueue(128, (tf.float32, tf.float32, tf.float32))
enc = queue.enqueue((enc_GRAY, enc_GTCOLOR, enc_W))
GRAY, GTCOLOR, W = queue.dequeue()
GRAY.set_shape(enc_GRAY.get_shape())
# we need to name this so as to map the variable for prediction
GRAY = tf.identity(GRAY, name='gray')
GTCOLOR.set_shape(enc_GTCOLOR.get_shape())
W.set_shape(enc_W.get_shape())
COLOR, stride, downsize = generator(GRAY)
COLOR = tf.identity(COLOR, name='color')
#tf.summary.image('low_res', BGR2RGB(X), max_outputs=3)
#tf.summary.image('hi_res', BGR2RGB(Y), max_outputs=3)
#tf.summary.image('predict', BGR2RGB(G), max_outputs=3)
tf.summary.image('input', BGR2RGB(GTCOLOR), max_outputs=5)
tf.summary.image('output', BGR2RGB(COLOR), max_outputs=5)
# X and G need to go through the same feature extracting network
# so we concatenate them first and then split the results
L, H = feature_extractor(tf.concat([GTCOLOR, COLOR], axis=0))
L1, L2 = tf.split(L, 2) # low-level feature, 2 is prediction
H1, H2 = tf.split(H, 2) # high-level feature, 2 is prediction
loss_perceptual = tf.reduce_mean(tf.square(L1 - L2), name='pe')
sub = tf.shape(COLOR) - tf.constant([0, 1, 1, 0], dtype=tf.int32)
G0 = tf.slice(COLOR, [0, 0, 0, 0], sub)
Gx = tf.slice(COLOR, [0, 0, 1, 0], sub)
Gy = tf.slice(COLOR, [0, 1, 0, 0], sub)
loss_smooth = tf.identity(
tf.reduce_mean(tf.pow(tf.square(G0 - Gx) + tf.square(G0 - Gy), 1.25)) /
529357.9139706489,
name='sm')
D_real = tf.reduce_mean(tf.nn.softplus(H1), name='dr')
D_fake = tf.reduce_mean(softminus(H2), name='df')
loss_adversary = tf.identity(1.0 - D_fake, name='ad')
loss_G = tf.identity(loss_perceptual * FLAGS.perceptual_weight +
loss_smooth * FLAGS.smoothness_weight +
loss_adversary * FLAGS.adversary_weight,
name='lg')
loss_D = tf.identity(D_fake - D_real, name='ld')
phases = []
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "G")
phases.append(('generate',
optimizer.minimize(loss_G,
global_step=global_step,
var_list=var_list),
[loss_G, loss_perceptual, loss_smooth,
loss_adversary], [GTCOLOR, COLOR]))
var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "D")
phases.append(
('discriminate',
optimizer.minimize(loss_D, global_step=global_step,
var_list=var_list), [loss_D, D_real, D_fake], []))
metric_names = []
for _, _, metrics, _ in phases:
metric_names.extend([x.name[:-2] for x in metrics])
init = tf.global_variables_initializer()
saver = tf.train.Saver(max_to_keep=FLAGS.max_to_keep)
summaries = tf.summary.merge_all()
if FLAGS.resume is None:
if FLAGS.log[0] != '/':
subprocess.check_call("rm -rf %s" % FLAGS.log, shell=True)
log = tf.summary.FileWriter(FLAGS.log,
tf.get_default_graph(),
flush_secs=20)
tf.get_default_graph().finalize()
picpac_config = dict(
seed=2016,
cache=False,
max_size=200,
min_size=192,
crop_width=176,
crop_height=176,
shuffle=True,
reshuffle=True,
batch=FLAGS.batch,
round_div=stride,
channels=3,
stratify=False,
pert_min_scale=0.97, #0.92,
pert_max_scale=1.5,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
stream = picpac.ImageStream(FLAGS.db,
perturb=True,
loop=True,
**picpac_config)
sess_config = tf.ConfigProto()
with tf.Session(config=sess_config) as sess:
coord = tf.train.Coordinator()
sess.run(init)
if FLAGS.resume:
saver.restore(sess, FLAGS.resume)
def encode_sample():
while not coord.should_stop():
images, _, _ = stream.next()
gray, color, w = _pic2pic.encode_bgr(images, downsize)
#print("GRAY:", gray.shape)
#print("COLOR:", color.shape)
sess.run([enc],
feed_dict={
enc_GRAY: gray,
enc_GTCOLOR: color,
enc_W: w
})
pass
# create encoding threads
threads = [
threading.Thread(target=encode_sample, args=())
for _ in range(FLAGS.encoding_threads)
]
for t in threads:
t.start()
step = 0
epoch = 0
global_start_time = time.time()
while step < FLAGS.max_steps:
start_time = time.time()
avg = np.array([0] * len(metric_names), dtype=np.float32)
for _ in tqdm(range(FLAGS.epoch_steps), leave=False):
forward_dict = {}
m_off = 0
for _, train, metrics, forward in phases:
feed_dict = {}
feed_dict.update(forward_dict)
_, m, f = sess.run([train, metrics, forward],
feed_dict=feed_dict)
forward_dict = dict(zip(forward, f))
m_off_n = m_off + len(metrics)
avg[m_off:m_off_n] += m
m_off = m_off_n
pass
step += 1
pass
images, _, _ = stream.next()
images = images[:5, :, :, :]
gray, gtcolor, _ = _pic2pic.encode_bgr(images, downsize)
color = sess.run(COLOR, feed_dict={GRAY: gray})
full = np.zeros(images.shape, dtype=np.float32)
_, H, W, _ = images.shape
color /= 255.0
gray /= 255.0
for i in range(images.shape[0]):
lab = cv2.cvtColor(cv2.cvtColor(gray[i], cv2.COLOR_GRAY2BGR),
cv2.COLOR_BGR2LAB)
full[i, :, :, :1] = lab[:, :, :1]
one = cv2.resize(color[i], (W, H))
lab = cv2.cvtColor(one, cv2.COLOR_BGR2LAB)
full[i, :, :, 1:] = lab[:, :, 1:]
cv2.cvtColor(full[i], cv2.COLOR_LAB2BGR, full[i])
pass
s, = sess.run([summaries],
feed_dict={
COLOR: full,
GTCOLOR: images
})
log.add_summary(s, step)
avg /= FLAGS.epoch_steps
stop_time = time.time()
epoch += 1
saved = ''
if epoch % FLAGS.ckpt_epochs == 0:
saver.save(sess, '%s/%d' % (FLAGS.model, step))
saved = ' saved'
txt = ', '.join(
['%s=%.4f' % (a, b) for a, b in zip(metric_names, list(avg))])
print('step=%d elapsed=%.4f/%.4f %s%s' %
(step, (stop_time - start_time),
(stop_time - global_start_time), txt, saved))
pass
pass
coord.request_stop()
coord.join(threads)
log.close()
pass
def main(_):
logging.basicConfig(level=FLAGS.verbose)
try:
os.makedirs(FLAGS.model)
except:
pass
assert FLAGS.A and os.path.exists(FLAGS.A)
assert FLAGS.B and os.path.exists(FLAGS.B)
A = tf.placeholder(tf.float32,
shape=(None, None, None, FLAGS.channels),
name="A")
B = tf.placeholder(tf.float32,
shape=(None, None, None, FLAGS.channels),
name="B")
rate = tf.constant(FLAGS.learning_rate)
if FLAGS.opt == 'adam':
rate /= 100
global_step = tf.Variable(0, name='global_step', trainable=False)
if FLAGS.decay:
rate = tf.train.exponential_decay(rate,
global_step,
FLAGS.decay_steps,
FLAGS.decay_rate,
staircase=True)
tf.summary.scalar('learning_rate', rate)
if FLAGS.opt == 'adam':
optimizer = tf.train.AdamOptimizer(rate)
elif FLAGS.opt == 'mom':
optimizer = tf.train.MomentumOptimizer(rate, FLAGS.momentum)
else:
optimizer = tf.train.GradientDescentOptimizer(rate)
pass
phases = build_graph(A, B, optimizer, optimizer, global_step)
metric_names = []
for _, _, metrics, _, _ in phases:
metric_names.extend([x.name[:-2] for x in metrics])
summaries = tf.constant(1)
summary_writer = None
if FLAGS.log:
summaries = tf.summary.merge_all()
assert not summaries is None
summary_writer = tf.summary.FileWriter(FLAGS.log,
tf.get_default_graph(),
flush_secs=20)
saver = tf.train.Saver(max_to_keep=FLAGS.max_to_keep)
init = tf.global_variables_initializer()
tf.get_default_graph().finalize()
graph = tf.get_default_graph()
graph.finalize()
graph_def = graph.as_graph_def()
for node in graph_def.node:
if node.name[0] == 'D':
#print(node.name)
pass
picpac_config = dict(
seed=2016,
shuffle=True,
reshuffle=True,
batch=1,
split=1,
split_fold=0,
round_div=FLAGS.stride,
channels=FLAGS.channels,
stratify=True,
pert_color1=20,
pert_angle=20,
pert_min_scale=0.9,
pert_max_scale=1.5,
pert_hflip=True,
pert_vflip=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
streamA = picpac.ImageStream(FLAGS.A,
perturb=True,
loop=True,
**picpac_config)
streamB = picpac.ImageStream(FLAGS.B,
perturb=True,
loop=True,
**picpac_config)
sess_config = tf.ConfigProto()
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
sess.run(init)
if FLAGS.resume:
saver.restore(sess, FLAGS.resume)
step = 0
epoch = 0
global_start_time = time.time()
while step < FLAGS.max_steps:
start_time = time.time()
avg = np.array([0] * len(metric_names), dtype=np.float32)
for _ in tqdm(range(FLAGS.epoch_steps), leave=False):
a, _, _ = streamA.next()
b, _, _ = streamB.next()
forward_dict = {}
m_off = 0
for _, train, metrics, forward, show in phases:
feed_dict = {A: a, B: b}
feed_dict.update(forward_dict)
_, m, f, ss = sess.run([train, metrics, forward, show],
feed_dict=feed_dict)
forward_dict = dict(zip(forward, f))
m_off_n = m_off + len(metrics)
avg[m_off:m_off_n] += m
m_off = m_off_n
pass
step += 1
pass
avg /= FLAGS.epoch_steps
stop_time = time.time()
txt = ', '.join(
['%s=%.4f' % (a, b) for a, b in zip(metric_names, list(avg))])
print('step %d: elapsed=%.4f time=%.4f %s' %
(step, (stop_time - global_start_time),
(stop_time - start_time), txt))
if summary_writer:
s, = sess.run([summaries], feed_dict=feed_dict)
summary_writer.add_summary(s, step)
epoch += 1
if epoch and (epoch % FLAGS.ckpt_epochs == 0):
ckpt_path = '%s/%d' % (FLAGS.model, step)
start_time = time.time()
saver.save(sess, ckpt_path)
stop_time = time.time()
print('epoch %d step %d, saving to %s in %.4fs.' %
(epoch, step, ckpt_path, stop_time - start_time))
pass
pass
if summary_writer:
summary_writer.close()
pass
def run_training(start_time):
picpac_config = dict(
seed=2016,
#loop=True,
shuffle=True,
reshuffle=True,
# max_size = 400,
resize_width=FLAGS.img_size,
resize_height=FLAGS.img_size,
batch=FLAGS.batch_size,
pert_angle=5,
pert_hflip=True,
pert_vflip=False,
channels=3,
# mixin = None
# mixin_group_delta=1,
# stratify=True,
#pad=False,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
if FLAGS.train_db:
tr_stream = picpac.ImageStream(FLAGS.train_db,
perturb=False,
loop=True,
**picpac_config)
tr_val_stream = picpac.ImageStream(FLAGS.train_db,
perturb=False,
loop=False,
**picpac_config)
else:
return
if FLAGS.test_db:
te_stream = picpac.ImageStream(FLAGS.test_db,
perturb=False,
loop=False,
**picpac_config)
# use VGG model
slim = tf.contrib.slim
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
# Input images and labels.
images_placeholder = tf.placeholder(tf.float32,
shape=(FLAGS.batch_size,
FLAGS.img_size,
FLAGS.img_size, 3),
name='images')
labels_placeholder = tf.placeholder(tf.float32,
shape=(FLAGS.batch_size, ),
name='labels')
# model inference, loss and accuracy
predictions, _ = inference(images_placeholder, FLAGS.num_classes,
FLAGS.net)
loss, accuracy = fcn_loss(predictions, labels_placeholder)
# Prediction results in a batch.
top_k_op = tf.nn.in_top_k(predictions,
tf.to_int32(labels_placeholder),
1,
name="accuracy")
tf.summary.scalar('xentropy_mean', loss)
tf.summary.scalar('accuracy_mean', accuracy)
# Specify the optimization scheme:
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=FLAGS.learning_rate)
# create_train_op that ensures that when we evaluate it to get the loss,
# the update_ops are done and the gradient updates are computed.
train_op = optimizer.minimize(loss)
tf.summary.scalar('learning_rate', FLAGS.learning_rate)
tf.summary.scalar('batch_size', FLAGS.batch_size)
# tf.summary only track the changes of a tensor.
# Our accuracy is given by a scaler number.
# So create two tf.Variable objects to summarize evaluation results
# initial evaluation accuracy to 0.5
eval_tr = tf.Variable(0.5, name="eval_validationdata")
eval_te = tf.Variable(0.5, name="eval_trainingdata")
eval_s1 = tf.summary.scalar('eval_validationdata', eval_te)
eval_s2 = tf.summary.scalar('eval_trainingdata', eval_tr)
# The op for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
# Create a saver for writing training checkpoints.
saver = tf.train.Saver(max_to_keep=1000)
# Build the summary Tensor based on the TF collection of Summaries
summary_op = tf.summary.merge_all()
# Instantiate a SummaryWriter to output summaries and the Graph
summary_writer = tf.summary.FileWriter(FLAGS.log_dir,
tf.get_default_graph())
config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.3
loss_sum = 0
acc_sum = 0
batch_sum = 0
with tf.Session(config=config) as sess:
sess.run(init_op)
# Start input enqueue threads.
# coord = tf.train.Coordinator()
# threads = tf.train.start_queue_runners(coord=coord)
for step in xrange(FLAGS.max_steps + 1):
# Get value of a batch of training images and labels
images, labels, pad = tr_stream.next()
feed_dict = {
images_placeholder: images,
labels_placeholder: labels
}
# start training
_, loss_value, acc_value = sess.run([train_op, loss, accuracy],
feed_dict=feed_dict)
loss_sum += loss_value * FLAGS.batch_size
acc_sum += acc_value * FLAGS.batch_size
batch_sum += FLAGS.batch_size
# Print an overview fairly often.
if step % 100 == 0:
duration = time.time() - start_time
print('Step %d: loss = %.4f, accuracy = %.4f (%.3f sec)' %
(step, loss_sum / batch_sum, acc_sum / batch_sum,
duration))
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
loss_sum = 0
acc_sum = 0
batch_sum = 0
# Save a checkpoint and evaluate the model periodically
if step != 0:
if step % FLAGS.save_steps == 0 or step == FLAGS.max_steps:
checkpoint_file = os.path.join(FLAGS.log_dir,
'model.ckpt')
saver.save(sess, checkpoint_file, global_step=step)
# Evaluate against the training set
tr_val_stream.reset()
batch_sum2 = 0
acc_sum2 = 0
loss_sum2 = 0
for images, labels, pad in tr_val_stream:
bs = FLAGS.batch_size - pad
if pad > 0:
np.resize(images, (bs, ) + images.shape[1:])
np.resize(labels, (bs, ))
feed_dict = {
images_placeholder: images,
labels_placeholder: labels
}
loss_value, acc_value = sess.run(
[loss, accuracy], feed_dict=feed_dict)
batch_sum2 += bs
loss_sum2 += loss_value * bs
acc_sum2 += acc_value * bs
pass
print(
'Step %d: training loss = %.4f, accuracy = %.4f (%.f sec)'
% (step, loss_sum2 / batch_sum2, acc_sum2 /
batch_sum2, time.time() - start_time))
# Evaluate against the validation set
te_stream.reset()
batch_sum3 = 0
acc_sum3 = 0
loss_sum3 = 0
for images, labels, pad in te_stream:
bs = FLAGS.batch_size - pad
if pad > 0:
np.resize(images, (bs, ) + images.shape[1:])
np.resize(labels, (bs, ))
feed_dict = {
images_placeholder: images,
labels_placeholder: labels
}
loss_value, acc_value = sess.run(
[loss, accuracy], feed_dict=feed_dict)
batch_sum3 += bs
loss_sum3 += loss_value * bs
acc_sum3 += acc_value * bs
pass
print(
'Step %d: validation loss = %.4f, accuracy = %.4f (%.f sec)'
% (step, loss_sum3 / batch_sum3, acc_sum3 /
batch_sum3, time.time() - start_time))
# update validation results
sess.run(eval_tr.assign(acc_sum2 / batch_sum2))
sess.run(eval_te.assign(acc_sum3 / batch_sum3))
# print(eval_tr.eval())
# print(eval_te.eval())
merged_str = sess.run(tf.summary.merge(
[eval_s1, eval_s2]),
feed_dict=feed_dict)
summary_writer.add_summary(merged_str, step)
summary_writer.flush()
print('Done training for %d steps.' % (FLAGS.max_steps))
def main(_):
X = tf.placeholder(tf.float32,
shape=(None, None, None, FLAGS.channels),
name="images")
model = Model(X, FLAGS.model, 'xxx')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
model.loader(sess)
gal = Gallery('output', cols=2, ext='.jpg')
CC = 0
if FLAGS.list:
with open(FLAGS.list, 'r') as f:
for path in f:
if CC > FLAGS.max:
break
path = path.strip()
print(path)
if FLAGS.channels == 3:
image = cv2.imread(path, cv2.IMREAD_COLOR)
elif FLAGS.channels == 1:
image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
image = np.expand_dims(image, axis=3)
else:
assert False
H, W = image.shape[:2]
if max(H, W) > FLAGS.max_size:
f = FLAGS.max_size / max(H, W)
image = cv2.resize(image, None, fx=f, fy=f)
H, W = image.shape[:2]
'''BEGIN INFERENCE'''
# clip edge
H = H // FLAGS.clip_stride * FLAGS.clip_stride
W = W // FLAGS.clip_stride * FLAGS.clip_stride
image = image[:H, :W].astype(np.float32)
# change from BGR to RGB
if FLAGS.channels == 3 and FLAGS.colorspace == 'RGB':
image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
batch = np.expand_dims(image_rgb, axis=0)
else:
batch = np.expand_dims(image, axis=0)
prob = sess.run(model.prob, feed_dict={X: batch})
'''END INFERENCE'''
save_prediction_image(gal, image, prob[0])
CC += 1
if FLAGS.db:
stream = picpac.ImageStream({
'db':
FLAGS.db,
'loop':
False,
'channels':
FLAGS.channels,
'colorspace':
FLAGS.colorspace,
'threads':
1,
'shuffle':
False,
'transforms': [{
"type": "resize",
"max_size": FLAGS.max_size
}, {
"type": "clip",
"round": FLAGS.clip_stride
}]
})
for meta, batch in stream:
if CC > FLAGS.max:
break
print(meta.ids)
image = batch[0]
if FLAGS.channels == 3 and FLAGS.colorspace == 'RGB':
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
prob = sess.run(model.prob, feed_dict={X: batch})
'''END INFERENCE'''
save_prediction_image(gal, image, prob[0])
CC += 1
gal.flush()
pass
def eval():
picpac_config = dict(
seed=2016,
shuffle=False,
reshuffle=False,
# max_size = 400,
# resize_width=FLAGS.img_size,
# resize_height=FLAGS.img_size,
batch=1,
pert_angle=5,
pert_hflip=True,
pert_vflip=False,
channels=3,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
te_stream = picpac.ImageStream(FLAGS.test_db,
perturb=False,
loop=False,
**picpac_config)
with tf.Graph().as_default():
images_placeholder = tf.placeholder(tf.float32,
shape=(1, None, None, 3),
name='images')
labels_placeholder = tf.placeholder(tf.int32,
shape=(1, ),
name='labels')
resized_images = tf.image.resize_images(images_placeholder,
size=(FLAGS.img_size,
FLAGS.img_size))
# inference model
logits, _ = inference(resized_images,
FLAGS.num_classes,
FLAGS.net,
is_training=False)
scores = tf.nn.softmax(logits, dim=-1, name=None)
# Calculate predictions
top_k_op = tf.nn.in_top_k(predictions=logits,
targets=labels_placeholder,
k=FLAGS.num_classes - 1,
name="accuracy")
checkpoint_file = os.path.join(FLAGS.checkpoint_dir,
'model.ckpt-' + FLAGS.ckpt_step)
saver = tf.train.Saver()
with tf.Session() as sess:
saver.restore(sess, checkpoint_file)
htmlf = open(os.path.join(FLAGS.outdir, FLAGS.html), 'w')
num = 0
true_count = 0
for images, labels, pad in te_stream:
predictions, correct = sess.run([scores, top_k_op],
feed_dict={
images_placeholder: images,
labels_placeholder: labels
})
print('image %s score: %s, correct: %s' %
(num, predictions, np.sum(correct)))
# save images
path = os.path.join(FLAGS.outdir, FLAGS.imagedir,
str(num) + '.jpg')
cv2.imwrite(path, images[0])
# wirte to html
htmlf.write(
"<div style='float:left; width:400;height:350;'><img src={} width='300'><br>score={}<br>groundtruth={}<br>prediction={}</div>\n"
.format(os.path.join(FLAGS.imagedir,
str(num) + '.jpg'), predictions[0][1],
labels[0], correct[0]))
num += 1
true_count += np.sum(correct)
# Compute precisions
precision = true_count / num
print('precision = {}'.format(precision))
htmlf.write('<br>total accuracy = {0:.5}\n'.format(precision))
htmlf.close()
def main(_):
X = tf.placeholder(tf.float32, shape=(None, None, None, 3), name="images")
anchor_th = tf.constant(FLAGS.anchor_th, tf.float32)
nms_max = tf.constant(FLAGS.nms_max, tf.int32)
nms_th = tf.constant(FLAGS.nms_th, tf.float32)
model = Model(X, anchor_th, nms_max, nms_th, FLAGS.model, 'xxx')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model.loader(sess)
if FLAGS.input:
assert os.path.exists(FLAGS.input)
image = cv2.imread(FLAGS.input, cv2.IMREAD_COLOR)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
batch = np.expand_dims(image, axis=0).astype(dtype=np.float32)
preds = sess.run(model.predictions, feed_dict={X: batch})
save_prediction_image(FLAGS.input + '.prob.png', image, preds)
if FLAGS.input_db:
assert os.path.exists(FLAGS.input_db)
import picpac
from gallery import Gallery
picpac_config = {
"db":
FLAGS.input_db,
"loop":
False,
"shuffle":
False,
"reshuffle":
False,
"annotate":
False,
"channels":
3,
"stratify":
False,
"dtype":
"float32",
"colorspace":
"RGB",
"batch":
1,
"transforms": [
{
"type": "resize",
"max_size": FLAGS.max_size,
"min_size": FLAGS.min_size
},
{
"type": "clip",
"round": FLAGS.stride
},
]
}
stream = picpac.ImageStream(picpac_config)
gal = Gallery('output')
C = 0
for _, images in stream:
preds = sess.run(model.predictions, feed_dict={X: images})
save_prediction_image(gal.next(), images[0], preds)
C += 1
if FLAGS.max and C >= FLAGS.max:
break
pass
pass
gal.flush()
pass
def main(_):
logging.basicConfig(level=FLAGS.verbose)
try:
os.makedirs(FLAGS.model)
except:
pass
assert FLAGS.db and os.path.exists(FLAGS.db)
X = tf.placeholder(tf.float32,
shape=(None, None, None, FLAGS.channels),
name="images")
Y = tf.placeholder(tf.float32, shape=(None, None, None, 1), name="labels")
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose, slim.max_pool2d],
padding='SAME'):
logits, stride = getattr(nets, FLAGS.net)(X, num_classes=FLAGS.classes)
loss, metrics = fcn_loss(logits, Y)
#tf.summary.scalar("loss", loss)
metric_names = [x.name[:-2] for x in metrics]
for x in metrics:
tf.summary.scalar(x.name.replace(':', '_'), x)
rate = FLAGS.learning_rate
if FLAGS.opt == 'adam':
rate /= 100
global_step = tf.Variable(0, name='global_step', trainable=False)
if FLAGS.decay:
rate = tf.train.exponential_decay(rate,
global_step,
FLAGS.decay_steps,
FLAGS.decay_rate,
staircase=True)
tf.summary.scalar('learning_rate', rate)
if FLAGS.opt == 'adam':
optimizer = tf.train.AdamOptimizer(rate)
elif FLAGS.opt == 'mom':
optimizer = tf.train.MomentumOptimizer(rate, FLAGS.momentum)
else:
optimizer = tf.train.GradientDescentOptimizer(rate)
pass
train_op = optimizer.minimize(loss, global_step=global_step)
summary_writer = None
train_summaries = tf.constant(1)
#val_summaries = tf.constant(1)
if FLAGS.log:
train_summaries = tf.summary.merge_all()
assert not train_summaries is None
if not train_summaries is None:
summary_writer = tf.summary.FileWriter(FLAGS.log,
tf.get_default_graph(),
flush_secs=20)
#assert train_summaries
#val_summaries = tf.summary.merge_all(key='val_summaries')
picpac_config = dict(
seed=2016,
shuffle=True,
reshuffle=True,
batch=1,
split=1,
split_fold=0,
round_div=stride,
annotate='json',
channels=FLAGS.channels,
stratify=True,
pert_color1=20,
pert_angle=180,
pert_min_scale=0.8,
pert_max_scale=1.2,
pert_hflip=True,
pert_vflip=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
if not FLAGS.mixin is None:
picpac_config['mixin'] = FLAGS.mixin
picpac_config['mixin_group_delta'] = 1
tr_stream = picpac.ImageStream(FLAGS.db,
perturb=True,
loop=True,
**picpac_config)
val_stream = None
if FLAGS.val:
assert os.path.exists(FLAGS.val)
val_stream = picpac.ImageStream(FLAGS.val,
perturb=False,
loop=False,
**picpac_config)
init = tf.global_variables_initializer()
saver = tf.train.Saver(max_to_keep=FLAGS.max_to_keep)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# check if padding have been properly setup
# this should be ensured if all layers are added via slim
graph = tf.get_default_graph()
graph.finalize()
graph_def = graph.as_graph_def()
with tf.Session(config=config) as sess:
sess.run(init)
if FLAGS.resume:
saver.restore(sess, FLAGS.resume)
step = 0
epoch = 0
global_start_time = time.time()
while step < FLAGS.max_steps:
start_time = time.time()
avg = np.array([0] * len(metrics), dtype=np.float32)
for _ in tqdm(range(FLAGS.epoch_steps), leave=False):
images, labels, _ = tr_stream.next()
feed_dict = {X: images, Y: labels}
mm, _, summaries = sess.run(
[metrics, train_op, train_summaries], feed_dict=feed_dict)
avg += np.array(mm)
step += 1
pass
avg /= FLAGS.epoch_steps
stop_time = time.time()
txt = ', '.join(
['%s=%.4f' % (a, b) for a, b in zip(metric_names, list(avg))])
print('step %d: elapsed=%.4f time=%.4f, %s' %
(step, (stop_time - global_start_time),
(stop_time - start_time), txt))
if summary_writer:
summary_writer.add_summary(summaries, step)
epoch += 1
if epoch and (epoch % FLAGS.ckpt_epochs == 0):
ckpt_path = '%s/%d' % (FLAGS.model, step)
start_time = time.time()
saver.save(sess, ckpt_path)
stop_time = time.time()
print('epoch %d step %d, saving to %s in %.4fs.' %
(epoch, step, ckpt_path, stop_time - start_time))
if epoch and (epoch % FLAGS.val_epochs == 0) and val_stream:
val_stream.reset()
avg = np.array([0] * len(metrics), dtype=np.float32)
cc = 0
for images, labels, _ in val_stream:
_, H, W, _ = images.shape
if FLAGS.max_size:
if max(H, W) > FLAGS.max_size:
continue
if FLAGS.padding == 'SAME' and FLAGS.clip:
images = clip(images, stride)
labels = clip(labels, stride)
feed_dict = {X: images, Y: labels}
#print("XXX", images.shape)
mm, = sess.run([metrics], feed_dict=feed_dict)
avg += np.array(mm)
cc += 1
avg /= cc
txt = ', '.join([
'%s=%.4f' % (a, b) for a, b in zip(metric_names, list(avg))
])
print('epoch %d step %d, validation %s' % (epoch, step, txt))
pass
pass
if summary_writer:
summary_writer.close()
pass
def main (_):
try:
os.makedirs(FLAGS.model)
except:
pass
X = tf.placeholder(tf.float32, shape=(None, None, None, FLAGS.channels), name="images")
# ground truth labels
Y = tf.placeholder(tf.float32, shape=(None, ), name="labels")
is_training = tf.placeholder(tf.bool, name="is_training")
# load network
logits = getattr(nets, FLAGS.net)(X, is_training, FLAGS.classes)
loss, metrics = fcn_loss(logits, Y)
metric_names = [x.name[:-2] for x in metrics]
global_step = tf.Variable(0, name='global_step', trainable=False)
rate = FLAGS.learning_rate
rate = tf.train.exponential_decay(rate, global_step, FLAGS.decay_steps, FLAGS.decay_rate, staircase=True)
optimizer = tf.train.AdamOptimizer(rate)
train_op = optimizer.minimize(loss, global_step=global_step)
init = tf.global_variables_initializer()
saver = tf.train.Saver(max_to_keep=FLAGS.max_to_keep)
picpac_config = dict(seed=2017,
cache=True, # set to False for large datasets
shuffle=True,
reshuffle=True,
batch=FLAGS.batch,
channels=FLAGS.channels, # 3 by default
stratify=True,
pert_colorspace='SAME', # do not change colorspace
# which typically means BGR
# or HSV or Lab
pert_color1=20,
pert_color2=20,
pert_color3=20,
pert_angle=20,
pert_min_scale=0.9,
pert_max_scale=1.2,
pert_hflip=True,
pert_vflip=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
if not FLAGS.mixin is None:
assert os.path.exists(FLAGS.mixin)
picpac_config['mixin'] = FLAGS.mixin
picpac_config['mixin_group_delta'] = 1
pass
# do we want to apply below to validation images?
if not FLAGS.resize_width is None:
config['resize_width'] = FLAGS.resize_width
if not FLAGS.resize_height is None:
config['resize_height'] = FLAGS.resize_height
if not FLAGS.max_size is None:
config['max_size'] = FLAGS.max_size
# load training db
assert FLAGS.db and os.path.exists(FLAGS.db)
stream = picpac.ImageStream(FLAGS.db, perturb=True, loop=True, **picpac_config)
# load validation db
val_stream = None
if FLAGS.val_db:
assert os.path.exists(FLAGS.val_db)
val_stream = picpac.ImageStream(FLAGS.val_db, perturb=False, loop=False, **picpac_config)
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
with tf.Session(config=config) as sess:
sess.run(init)
if FLAGS.resume:
saver.restore(sess, FLAGS.resume)
step = 0
epoch = 0
global_start_time = time.time()
while step < FLAGS.max_steps:
start_time = time.time()
avg = np.array([0] * len(metrics), dtype=np.float32)
for _ in tqdm(range(FLAGS.epoch_steps), leave=False):
images, labels, _ = stream.next()
feed_dict = {X: images, Y: labels, is_training: True}
mm, _, = sess.run([metrics, train_op, ], feed_dict=feed_dict)
avg += np.array(mm)
pass
step += FLAGS.epoch_steps
avg /= FLAGS.epoch_steps
stop_time = time.time()
txt = ', '.join(['%s=%.4f' % (a, b) for a, b in zip(metric_names, list(avg))])
print('step %d: elapsed=%.4f time=%.4f, %s'
% (step, (stop_time - global_start_time), (stop_time - start_time), txt))
epoch += 1
# validation
if epoch and (epoch % FLAGS.val_epochs == 0) and not val_stream is None:
# evaluation
val_stream.reset()
avg = np.array([0] * len(metrics), dtype=np.float32)
C = 0
for images, labels, _ in val_stream:
feed_dict = {X: images, Y: labels, is_training: False}
mm = sess.run(metrics, feed_dict=feed_dict)
avg += np.array(mm)
C += 1
pass
avg /= C
txt = ', '.join(['%s=%.4f' % (a, b) for a, b in zip(metric_names, list(avg))])
print('step %d, validation: %s' % (step, txt))
# model saving
if epoch and (epoch % FLAGS.ckpt_epochs == 0):
ckpt_path = '%s/%d' % (FLAGS.model, step)
saver.save(sess, ckpt_path)
print('step %d, saving to %s.' % (step, ckpt_path))
pass
pass
pass
def main(_):
X = tf.placeholder(tf.float32,
shape=(None, None, None, FLAGS.channels),
name="images")
is_training = tf.placeholder(tf.bool, name="is_training")
model = Model(X, is_training, FLAGS.model, 'xxx')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
model.loader(sess)
if FLAGS.input:
assert False
'''
assert os.path.exists(FLAGS.input)
image = cv2.imread(FLAGS.input, cv2.IMREAD_COLOR)
batch = np.expand_dims(image, axis=0).astype(dtype=np.float32)
boxes, probs = sess.run([model.boxes, model.probs], feed_dict={X: batch, is_training: False})
save_prediction_image(FLAGS.input + '.prob.png', image, boxes, probs)
'''
if FLAGS.input_db:
assert os.path.exists(FLAGS.input_db)
from gallery import Gallery
picpac_config = {
"db":
FLAGS.input_db,
"loop":
False,
"shuffle":
False,
"reshuffle":
False,
"annotate":
False,
"channels":
FLAGS.channels,
"colorspace":
"RGB",
"stratify":
False,
"dtype":
"float32",
"batch":
1,
"annotate": [1],
"transforms": [
{
"type": "resize",
"max_size": FLAGS.max_size
},
{
"type": "clip",
"round": FLAGS.backbone_stride
},
{
"type": "keypoints.basic",
'downsize': 1,
'classes': 1,
'radius': 25
},
{
"type": "drop"
}, # remove original annotation
]
}
stream = picpac.ImageStream(picpac_config)
gal = Gallery('out')
C = 0
for meta, images, _, label, _ in stream:
shape = list(images.shape)
shape[1] //= FLAGS.stride
shape[2] //= FLAGS.stride
shape[3] = 1
prob, offsets = sess.run([model.prob, model.offsets],
feed_dict={
X: images,
is_training: False
})
kp = cpp.predict_basic_keypoints(prob[0], offsets[0],
FLAGS.stride, 0.1)
print(images.shape, prob.shape, offsets.shape, kp)
save_prediction_image(gal.next(), images[0], kp,
label[0, :, :, 0], prob[0, :, :, 0])
C += 1
if FLAGS.max and C >= FLAGS.max:
break
pass
pass
gal.flush()
pass
def create_picpac_stream(db_path, is_training):
assert os.path.exists(db_path)
augments = []
if is_training and FLAGS.augments:
with open(FLAGS.augments, 'r') as f:
augments = json.loads(f.read())
print("Using augments:")
print(json.dumps(augments))
pass
print("CACHE:", FLAGS.cache)
statinfo = os.stat(db_path)
if statinfo.st_size > 0x40000000 and FLAGS.cache:
print_red(
"DB is probably too big too be cached, consider adding --cache 0")
picpac_config = {
"db":
db_path,
"loop":
is_training,
"shuffle":
is_training,
"reshuffle":
is_training,
"annotate": [1],
"channels":
FLAGS.channels,
"stratify":
is_training,
"dtype":
"float32",
"batch":
FLAGS.batch,
"colorspace":
"RGB",
"cache":
FLAGS.cache,
"transforms":
augments + [
{
"type": "resize",
"max_size": FLAGS.max_size
},
{
"type": "clip",
"round": FLAGS.backbone_stride
},
{
"type": "anchors.dense.box",
'downsize': FLAGS.anchor_stride
},
{
"type": "box_feature"
},
{
"type": "rasterize"
},
]
}
if is_training and not FLAGS.mixin is None:
#print("mixin support is incomplete in new picpac.")
assert os.path.exists(FLAGS.mixin)
picpac_config['mixin'] = FLAGS.mixin
picpac_config['mixin_group_reset'] = 0
picpac_config['mixin_group_delta'] = 1
return picpac.ImageStream(picpac_config)
def run_training():
try:
os.makedirs(FLAGS.model)
except:
pass
config = dict(
seed=1996,
shuffle=True,
reshuffle=True,
#resize_width=FLAGS.resize,
#resize_height=FLAGS.resize,
batch=FLAGS.batch,
split=FLAGS.split,
split_fold=FLAGS.split_fold,
channels=FLAGS.channels,
stratify=True,
#mixin="db0",
#mixin_group_delta=0,
pert_color1=10,
pert_color2=10,
pert_color3=10,
#pert_angle=10,
pert_min_scale=0.8,
pert_max_scale=1.2,
#pad=False,
#pert_hflip=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
# training stream
tr_stream = picpac.ImageStream(FLAGS.db,
split_negate=False,
perturb=False,
loop=True,
**config)
te_stream = None
if FLAGS.test_steps > 0:
# testing stream, "negate" inverts the image selection specified by split & split_fold
# so different images are used for training and testing
if FLAGS.test_db:
if FLAGS.split > 1:
print(
"Cannot use cross-validation & evaluation db at the same time"
)
print("If --test-db is specified, do not set --split")
raise Exception("bad parameters")
te_stream = picpac.ImageStream(FLAGS.test_db,
perturb=False,
loop=False,
**config)
elif FLAGS.split > 1:
te_stream = picpac.ImageStream(FLAGS.db,
split_negate=True,
perturb=False,
loop=False,
**config)
pass
pass
with tf.Graph().as_default():
X = tf.placeholder(tf.float32,
shape=(FLAGS.batch, None, None, FLAGS.channels),
name="images")
Y_ = tf.placeholder(tf.float32, shape=(FLAGS.batch, ), name="labels")
logits = inference(X, FLAGS.classes)
loss, accuracy = fcn_loss(logits, Y_)
train_op = training(loss, FLAGS.learning_rate)
#summary_op = tf.merge_all_summaries()
#summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, tf.get_default_graph())
init = tf.global_variables_initializer()
#graph_txt = tf.get_default_graph().as_graph_def().SerializeToString()
#with open(os.path.join(FLAGS.train_dir, "graph"), "w") as f:
# f.write(graph_txt)
# pass
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
#run_metadata = tf.RunMetadata()
loss_sum = 0
accuracy_sum = 0
batch_sum = 0
with tf.Session(config=config) as sess:
sess.run(init)
for step in xrange(FLAGS.max_steps):
images, labels, pad = tr_stream.next()
#print(images.shape, labels.shape)
feed_dict = {X: images, Y_: labels}
#l_v, s_v = sess.run([logits, score], feed_dict=feed_dict)
#print(images.shape, s_v.shape, l_v.shape)
#_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict, options=run_options, run_metadata=run_metadata)
_, loss_value, accuracy_value, ll = sess.run(
[train_op, loss, accuracy, logits], feed_dict=feed_dict)
#print('XXX', labels[0], accuracy_value, ll[0])
loss_sum += loss_value * FLAGS.batch
accuracy_sum += accuracy_value * FLAGS.batch
batch_sum += FLAGS.batch
if (step + 1) % 1000 == 0:
#tl = timeline.Timeline(run_metadata.step_stats)
#ctf = tl.generate_chrome_trace_format()
#with open('timeline.json', 'w') as f:
# f.write(ctf)
print(datetime.datetime.now())
print('step %d: loss = %.4f, accuracy = %.4f' %
(step + 1, loss_sum / batch_sum,
accuracy_sum / batch_sum))
loss_sum = 0
accuracy_sum = 0
batch_sum = 0
#summary_str = sess.run(summary_op, feed_dict=feed_dict)
#summary_writer.add_summary(summary_str, step)
#summary_writer.flush()
if te_stream and (step + 1) % FLAGS.test_steps == 0:
# evaluation
te_stream.reset()
cmatrix = np.zeros((FLAGS.classes, FLAGS.classes))
total = 0
batch_sum2 = 0
loss_sum2 = 0
accuracy_sum2 = 0
for images, labels, pad in te_stream:
bs = FLAGS.batch - pad
total += 1
if pad > 0:
numpy.resize(images, (bs, ) + images.shape[1:])
numpy.resize(labels, (bs, ))
feed_dict = {X: images, Y_: labels}
loss_value, accuracy_value, ll = sess.run(
[loss, accuracy, logits], feed_dict=feed_dict)
batch_sum2 += bs
cmatrix[int(labels),
np.where(ll == np.max(ll))[1]] += np.divide(
float(1),
np.size(np.where(ll == np.max(ll))[1]))
loss_sum2 += loss_value * bs
#print(int(labels))
#print(cmatrix)
#print(np.size(np.where(ll == np.max(ll))[1]))
#print(ll)
accuracy_sum2 += accuracy_value * bs
pass
rowsum = np.sum(cmatrix, axis=1)
colsum = np.sum(cmatrix, axis=0)
#print(total)
#print(np.tile(rowsum,(FLAGS.classes,1)).transpose())
#print(np.tile(colsum,(FLAGS.classes,1)))
print('row---label; colum ---predict')
print(total)
print('evaluation: loss = %.4f, accuracy = %.4f' %
(loss_sum2 / batch_sum2, accuracy_sum2 / batch_sum2))
print('accuracy from confusion matrix = %.4f' %
np.divide(np.trace(cmatrix), float(total)))
print('absolute confusion matrix:')
print(cmatrix)
#print('confusion matrix divided by total:')
#print(np.divide(cmatrix,float(total)))
print('confusion matrix divided by col sum:')
print(np.divide(cmatrix, np.tile(colsum, (5, 1))))
print('confusion matrix divided by row sum:')
print(
np.divide(cmatrix,
np.tile(rowsum, (5, 1)).transpose()))
print('Weighted kappa quadratic scores:')
print(quadratic_kappa(cmatrix))
if (step + 1) % FLAGS.save_steps == 0 or (
step + 1) == FLAGS.max_steps:
ckpt_path = '%s/%d' % (FLAGS.model, (step + 1))
saver.save(sess, ckpt_path)
pass
pass
pass
pass
def main(_):
X = tf.placeholder(tf.float32, shape=(None, None, None, FLAGS.channels))
model = Model(X, FLAGS.model, 'xxx')
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
stream = picpac.ImageStream({
'db':
'scratch/val.db',
'cache':
False,
'loop':
False,
'channels':
FLAGS.channels,
'shuffle':
False,
'batch':
1,
'raw': [1],
'colorspace':
'RGB',
'transforms': [
{
"type": "resize",
"max_size": FLAGS.max_size
},
{
"type": "clip",
"width": FLAGS.fix_size,
"height": FLAGS.fix_size
},
]
})
with tf.Session(config=config) as sess:
model.loader(sess)
lookup = {}
C = 0
for meta, batch in tqdm(stream, total=stream.size()):
path = meta.raw[0][0].decode('ascii')
probs = sess.run(model.probs, feed_dict={X: batch})
case = '/'.join(path.split('/')[:-1]) + '/'
if not case in lookup:
lookup[case] = [0, np.zeros((14, ), dtype=np.float32)]
pass
case = lookup[case]
case[0] = case[0] + 1
case[1] += probs[0]
C += 1
if FLAGS.max > 0 and C >= FLAGS.max:
break
pass
with open('predict.csv', 'w') as f, \
open('predict.csv.full', 'w') as f2:
for k, v in lookup.items():
probs = np.reshape(v[1] / v[0], (7, 2))
prob = np.sum(probs[:, 1])
if prob > 0.5:
l = 1
else:
l = 0
f.write('%s,%d\n' % (k, l))
f2.write('%s,%g\n' % (k, prob))
pass
pass
def run_training ():
seed = 1996
config = dict(seed=seed,
loop=True,
shuffle=True,
reshuffle=True,
#resize_width=256,
#resize_height=256,
batch=1,
split=1,
split_fold=0,
annotate='json',
channels=FLAGS.channels,
stratify=False,
#mixin="db0",
#mixin_group_delta=0,
#pert_color1=10,
#pert_angle=5,
#pert_min_scale=0.8,
#pert_max_scale=1.2,
#pad=False,
#pert_hflip=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
db='db'
tr_stream = picpac.ImageStream(db, negate=False, perturb=True, **config)
with tf.Graph().as_default():
X = tf.placeholder(tf.float32, shape=(BATCH, None, None, FLAGS.channels), name="images")
Y_ = tf.placeholder(tf.int32, shape=(BATCH, None, None, 1), name="labels")
logits, score, params = inference(X)
loss = fcn_loss(logits, Y_)
train_op = training(loss, FLAGS.learning_rate)
summary_op = tf.merge_all_summaries()
summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, tf.get_default_graph())
init = tf.initialize_all_variables()
graph_txt = tf.get_default_graph().as_graph_def().SerializeToString()
with open(os.path.join(FLAGS.train_dir, "graph"), "w") as f:
f.write(graph_txt)
pass
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(init)
for step in xrange(FLAGS.max_steps):
images, labels, pad = tr_stream.next()
#print(images.shape, labels.shape)
feed_dict = {X: images,
Y_: labels}
#l_v, s_v = sess.run([logits, score], feed_dict=feed_dict)
#print(images.shape, s_v.shape, l_v.shape)
_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)
if step % 100 == 0:
print('step %d: loss = %.4f' % (step, loss_value))
summary_str = sess.run(summary_op, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
summary_writer.flush()
if (step + 1) % 1000 == 0 or (step + 1) == FLAGS.max_steps:
saver.save(sess, os.path.join(FLAGS.train_dir, "model"), global_step=step)
pass
pass
pass
pass
def run_training():
config = dict(
seed=1996,
shuffle=True,
reshuffle=True,
resize_width=FLAGS.resize,
resize_height=FLAGS.resize,
batch=FLAGS.batch,
split=FLAGS.split,
split_fold=FLAGS.split_fold,
channels=FLAGS.channels,
stratify=True,
pert_color1=10,
pert_color2=10,
pert_color3=10,
pert_angle=10,
pert_min_scale=0.8,
pert_max_scale=1.2,
#pad=False,
#pert_hflip=True,
channel_first=False # this is tensorflow specific
# Caffe's dimension order is different.
)
# training stream
tr_stream = picpac.ImageStream(FLAGS.db,
split_negate=False,
perturb=True,
loop=True,
**config)
te_stream = None
if FLAGS.test_steps > 0:
# testing stream, "negate" inverts the image selection specified by split & split_fold
# so different images are used for training and testing
if FLAGS.test_db:
if FLAGS.split > 1:
print(
"Cannot use cross-validation & evaluation db at the same time"
)
print("If --test-db is specified, do not set --split")
raise Exception("bad parameters")
te_stream = picpac.ImageStream(FLAGS.test_db,
perturb=False,
loop=False,
**config)
elif FLAGS.split > 1:
te_stream = picpac.ImageStream(FLAGS.db,
split_negate=True,
perturb=False,
loop=False,
**config)
pass
pass
with tf.Graph().as_default():
shape = (FLAGS.batch, FLAGS.resize, FLAGS.resize / FLAGS.branches,
FLAGS.channels)
Xs = [
tf.placeholder(tf.float32, shape=shape, name="input%d" % i)
for i in range(FLAGS.branches)
]
Y = tf.placeholder(tf.float32, shape=(FLAGS.batch, ), name="labels")
if FLAGS.disable:
logits = multipod(
[X for i, X in enumerate(Xs) if i != FLAGS.disable],
FLAGS.classes)
else:
logits = multipod(Xs, FLAGS.classes)
loss, accuracy = fcn_loss(logits, Y)
train_op = training(loss, FLAGS.learning_rate)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
loss_sum = 0
accuracy_sum = 0
batch_sum = 0
with tf.Session(config=config) as sess:
sess.run(init)
for step in xrange(FLAGS.max_steps):
images, labels, pad = tr_stream.next()
#print(images.shape, labels.shape)
feed_dict = make_feed_dict(Xs, Y, images, labels)
_, loss_value, accuracy_value = sess.run(
[train_op, loss, accuracy], feed_dict=feed_dict)
loss_sum += loss_value * FLAGS.batch
accuracy_sum += accuracy_value * FLAGS.batch
batch_sum += FLAGS.batch
if step % 100 == 0:
#tl = timeline.Timeline(run_metadata.step_stats)
#ctf = tl.generate_chrome_trace_format()
#with open('timeline.json', 'w') as f:
# f.write(ctf)
print(datetime.datetime.now())
print(
'step %d: loss = %.4f, accuracy = %.4f' %
(step, loss_sum / batch_sum, accuracy_sum / batch_sum))
loss_sum = 0
accuracy_sum = 0
batch_sum = 0
if te_stream and step % FLAGS.test_steps == 0:
# evaluation
te_stream.reset()
batch_sum2 = 0
loss_sum2 = 0
accuracy_sum2 = 0
for images, labels, pad in te_stream:
bs = FLAGS.batch - pad
if pad > 0:
numpy.resize(images, (bs, ) + images.shape[1:])
numpy.resize(labels, (bs, ))
feed_dict = make_feed_dict(Xs, Y, images, labels)
_, loss_value, accuracy_value = sess.run(
[train_op, loss, accuracy], feed_dict=feed_dict)
batch_sum2 += bs
loss_sum2 += loss_value * bs
accuracy_sum2 += accuracy_value * bs
pass
print('evaluation: loss = %.4f, accuracy = %.4f' %
(loss_sum2 / batch_sum2, accuracy_sum2 / batch_sum2))
if (step + 1) % FLAGS.save_steps == 0 or (
step + 1) == FLAGS.max_steps:
saver.save(sess,
os.path.join(FLAGS.train_dir, "model"),
global_step=step)
pass
pass
pass
pass
