def main(flags):
city_list = ['austin', 'chicago', 'kitsap', 'tyrol-w', 'vienna']
flags.llh_file_dir = flags.llh_file_dir.format(flags.finetune_city)
weight = np.load(flags.llh_file_dir)
# make network
# define place holder
X = tf.placeholder(tf.float32, shape=[None, flags.input_size[0], flags.input_size[1], 3], name='X')
y = tf.placeholder(tf.int32, shape=[None, flags.input_size[0], flags.input_size[1], 1], name='y')
mode = tf.placeholder(tf.bool, name='mode')
model = uabMakeNetwork_UNet.UnetModelCrop({'X': X, 'Y': y},
trainable=mode,
model_name=flags.model_name,
input_size=flags.input_size,
batch_size=flags.batch_size,
learn_rate=flags.learning_rate,
decay_step=flags.decay_step,
decay_rate=flags.decay_rate,
epochs=flags.epochs,
start_filter_num=flags.sfn)
model.create_graph('X', class_num=flags.num_classes)
# create collection
# the original file is in /ei-edl01/data/uab_datasets/inria
blCol = uab_collectionFunctions.uabCollection('inria')
opDetObj = bPreproc.uabOperTileDivide(255) # inria GT has value 0 and 255, we map it back to 0 and 1
# [3] is the channel id of GT
rescObj = uabPreprocClasses.uabPreprocMultChanOp([], 'GT_Divide.tif', 'Map GT to (0, 1)', [3], opDetObj)
rescObj.run(blCol)
img_mean = blCol.getChannelMeans([0, 1, 2]) # get mean of rgb info
# extract patches
extrObj = uab_DataHandlerFunctions.uabPatchExtr([0, 1, 2, 4], # extract all 4 channels
cSize=flags.input_size, # patch size as 572*572
numPixOverlap=int(model.get_overlap()/2), # overlap as 92
extSave=['jpg', 'jpg', 'jpg', 'png'], # save rgb files as jpg and gt as png
isTrain=True,
gtInd=3,
pad=model.get_overlap()) # pad around the tiles
patchDir = extrObj.run(blCol)
# make data reader
# use uabCrossValMaker to get fileLists for training and validation
idx_city, file_list = uabCrossValMaker.uabUtilGetFolds(patchDir, 'fileList.txt', 'city')
idx_tile, _ = uabCrossValMaker.uabUtilGetFolds(patchDir, 'fileList.txt', 'force_tile')
idx = [j * 10 + i for i, j in zip(idx_city, idx_tile)]
# use first city for validation
filter_train = []
filter_valid = []
for i in range(5):
for j in range(1, 37):
if i != flags.finetune_city and j > 5:
filter_train.append(j * 10 + i)
elif i == flags.finetune_city and j <= 5:
filter_valid.append(j * 10 + i)
# use first city for validation
file_list_train = uabCrossValMaker.make_file_list_by_key(idx, file_list, filter_train)
file_list_valid = uabCrossValMaker.make_file_list_by_key(idx, file_list, filter_valid)
dataReader_train = uabDataReader.ImageLabelReaderPatchSampleControl(
[3], [0, 1, 2], patchDir, file_list_train, flags.input_size, flags.batch_size,
weight, dataAug='flip,rotate', block_mean=np.append([0], img_mean))
# no augmentation needed for validation
dataReader_valid = uabDataReader.ImageLabelReader([3], [0, 1, 2], patchDir, file_list_valid, flags.input_size,
flags.batch_size, dataAug=' ',
block_mean=np.append([0], img_mean), batch_code=0)
# train
start_time = time.time()
model.train_config('X', 'Y', flags.n_train, flags.n_valid, flags.input_size, uabRepoPaths.modelPath,
loss_type='xent', par_dir='Inria_Domain_Selection')
model.run(train_reader=dataReader_train,
valid_reader=dataReader_valid,
pretrained_model_dir=flags.pred_model_dir.format(flags.finetune_city),
isTrain=True,
img_mean=img_mean,
verb_step=100, # print a message every 100 step(sample)
save_epoch=5, # save the model every 5 epochs
gpu=GPU,
tile_size=flags.tile_size,
patch_size=flags.input_size
)
duration = time.time() - start_time
print('duration {:.2f} hours'.format(duration/60/60))
def main(flags):
flags.llh_file_dir = flags.llh_file_dir.format(flags.finetune_city)
weight = np.load(flags.llh_file_dir)
# make network
# define place holder
X = tf.placeholder(
tf.float32,
shape=[None, flags.input_size[0], flags.input_size[1], 3],
name='X')
y = tf.placeholder(
tf.int32,
shape=[None, flags.input_size[0], flags.input_size[1], 1],
name='y')
mode = tf.placeholder(tf.bool, name='mode')
model = uabMakeNetwork_UNet.UnetModelGAN_V3Shrink(
{
'X': X,
'Y': y
},
trainable=mode,
model_name=flags.model_name,
input_size=flags.input_size,
batch_size=flags.batch_size,
learn_rate=flags.learning_rate,
decay_step=flags.decay_step,
decay_rate=flags.decay_rate,
epochs=flags.epochs,
start_filter_num=flags.sfn,
pad=flags.pad,
)
model.create_graph(['X', 'Y'], class_num=flags.num_classes)
# create collection
# the original file is in /ei-edl01/data/uab_datasets/inria
blCol = uab_collectionFunctions.uabCollection('inria')
opDetObj = bPreproc.uabOperTileDivide(255)
# [3] is the channel id of GT
rescObj = uabPreprocClasses.uabPreprocMultChanOp([], 'GT_Divide.tif',
'Map GT to (0, 1)', [3],
opDetObj)
rescObj.run(blCol)
img_mean = blCol.getChannelMeans([0, 1, 2])
# extract patches
extrObj = uab_DataHandlerFunctions.uabPatchExtr(
[0, 1, 2, 4],
cSize=flags.input_size,
numPixOverlap=int(model.get_overlap()),
extSave=['jpg', 'jpg', 'jpg', 'png'],
isTrain=True,
gtInd=3,
pad=model.get_overlap() // 2)
patchDir = extrObj.run(blCol)
# make data reader
# use uabCrossValMaker to get fileLists for training and validation
idx_city, file_list = uabCrossValMaker.uabUtilGetFolds(
patchDir, 'fileList.txt', 'city')
idx_tile, _ = uabCrossValMaker.uabUtilGetFolds(patchDir, 'fileList.txt',
'force_tile')
idx = [j * 10 + i for i, j in zip(idx_city, idx_tile)]
# use first city for validation
filter_train = []
filter_train_target = []
filter_valid = []
for i in range(5):
for j in range(1, 37):
if i != flags.finetune_city and j > 5:
filter_train.append(j * 10 + i)
elif i == flags.finetune_city and j > 5:
filter_train_target.append(j * 10 + i)
elif i == flags.finetune_city and j <= 5:
filter_valid.append(j * 10 + i)
# use first city for validation
file_list_train = uabCrossValMaker.make_file_list_by_key(
idx, file_list, filter_train)
filter_list_train_valid = uabCrossValMaker.make_file_list_by_key(
idx, file_list, filter_train_target)
file_list_valid = uabCrossValMaker.make_file_list_by_key(
idx, file_list, filter_valid)
dataReader_train = uabDataReader.ImageLabelReader([3], [0, 1, 2],
patchDir,
file_list_train,
flags.input_size,
flags.batch_size,
dataAug='flip,rotate',
block_mean=np.append(
[0], img_mean),
batch_code=0)
dataReader_train_source = uabDataReader.ImageLabelReaderPatchSampleControl(
[3], [0, 1, 2],
patchDir,
file_list_train,
flags.input_size,
flags.batch_size,
weight,
dataAug='flip,rotate',
block_mean=np.append([0], img_mean))
dataReader_train_target = uabDataReader.ImageLabelReader(
[3], [0, 1, 2],
patchDir,
filter_list_train_valid,
flags.input_size,
flags.batch_size,
dataAug='flip,rotate',
block_mean=np.append([0], img_mean),
batch_code=0)
# no augmentation needed for validation
dataReader_valid = uabDataReader.ImageLabelReader([3], [0, 1, 2],
patchDir,
file_list_valid,
flags.input_size,
flags.batch_size,
dataAug=' ',
block_mean=np.append(
[0], img_mean),
batch_code=0)
# train
start_time = time.time()
model.load_weights(flags.pred_model_dir.format(flags.finetune_city),
layers2load='1,2,3,4,5,6,7,8,9',
load_final_layer=True)
model.train_config('X',
'Y',
flags.n_train,
flags.n_valid,
flags.input_size,
uabRepoPaths.modelPath,
loss_type='xent',
par_dir='Inria_GAN/V3LOO')
model.run(
train_reader=dataReader_train,
train_reader_source=dataReader_train_source,
train_reader_target=dataReader_train_target,
valid_reader=dataReader_valid,
pretrained_model_dir=
None, # train from scratch, no need to load pre-trained model
isTrain=True,
img_mean=img_mean,
verb_step=100, # print a message every 100 step(sample)
save_epoch=flags.save_epoch, # save the model every 5 epochs
gpu=GPU,
tile_size=flags.tile_size,
patch_size=flags.input_size)
duration = time.time() - start_time
print('duration {:.2f} hours'.format(duration / 60 / 60))
'force_tile')
# use first 5 tiles for validation
file_list_train = uabCrossValMaker.make_file_list_by_key(
idx, file_list, [i for i in range(6, 37)])
file_list_valid = uabCrossValMaker.make_file_list_by_key(
idx, file_list, [i for i in range(0, 6)])
patch_id_dict = {}
for cnt, item in enumerate(file_list_train):
p_name = '_'.join(item[0].split('_')[:2])
patch_id_dict[p_name] = cnt
dataReader_train = uabDataReader.ImageLabelReaderPatchSampleControl(
[3], [0, 1, 2],
patchDir,
file_list_train, (321, 321),
100,
patch_prob,
patch_name=True,
block_mean=np.append([0], img_mean))
patch_cnt = np.zeros(len(file_list_train), dtype=np.uint64)
c_cnt = np.zeros(5)
city_dict = {'aus': 0, 'chi': 1, 'kit': 2, 'tyr': 3, 'vie': 4}
for reader_cnt in tqdm(range(100000)):
idx_batch = np.random.choice(len(file_list_train), 100, p=patch_prob)
for i in idx_batch:
row = file_list_train[i]
p_name = '_'.join(row[0].split('_')[:2])
c_cnt[city_dict[p_name[:3]]] += 1
patch_cnt[patch_id_dict[p_name]] += 1
plt.bar(np.arange(5), c_cnt)
def main(flags):
patch_prior = np.load(
os.path.join(flags.group_file_dir,
'unet_{}_patch_prior.npy'.format(flags.train_city)))
# make network
# define place holder
X = tf.placeholder(
tf.float32,
shape=[None, flags.input_size[0], flags.input_size[1], 3],
name='X')
y = tf.placeholder(
tf.int32,
shape=[None, flags.input_size[0], flags.input_size[1], 1],
name='y')
mode = tf.placeholder(tf.bool, name='mode')
model = uabMakeNetwork_UNet.UnetModelCrop({
'X': X,
'Y': y
},
trainable=mode,
model_name=flags.model_name,
input_size=flags.input_size,
batch_size=flags.batch_size,
learn_rate=flags.learning_rate,
decay_step=flags.decay_step,
decay_rate=flags.decay_rate,
epochs=flags.epochs,
start_filter_num=flags.sfn)
model.create_graph('X', class_num=flags.num_classes)
# create collection
# the original file is in /ei-edl01/data/uab_datasets/inria
blCol = uab_collectionFunctions.uabCollection('inria')
opDetObj = bPreproc.uabOperTileDivide(
255) # inria GT has value 0 and 255, we map it back to 0 and 1
# [3] is the channel id of GT
rescObj = uabPreprocClasses.uabPreprocMultChanOp([], 'GT_Divide.tif',
'Map GT to (0, 1)', [3],
opDetObj)
rescObj.run(blCol)
img_mean = blCol.getChannelMeans([0, 1, 2]) # get mean of rgb info
# extract patches
extrObj = uab_DataHandlerFunctions.uabPatchExtr(
[0, 1, 2, 4],
cSize=flags.input_size,
numPixOverlap=int(model.get_overlap()),
extSave=['jpg', 'jpg', 'jpg', 'png'],
isTrain=True,
gtInd=3,
pad=model.get_overlap() / 2)
patchDir = extrObj.run(blCol)
# make data reader
# use uabCrossValMaker to get fileLists for training and validation
idx, file_list = uabCrossValMaker.uabUtilGetFolds(patchDir, 'fileList.txt',
'force_tile')
# use first 5 tiles for validation
file_list_train = uabCrossValMaker.make_file_list_by_key(
idx, file_list, [i for i in range(6, 37)])
file_list_valid = uabCrossValMaker.make_file_list_by_key(
idx, file_list, [i for i in range(0, 6)])
dataReader_train = uabDataReader.ImageLabelReaderPatchSampleControl(
[3], [0, 1, 2],
patchDir,
file_list_train,
flags.input_size,
flags.batch_size,
patch_prior,
dataAug='flip,rotate',
block_mean=np.append([0], img_mean))
# no augmentation needed for validation
dataReader_valid = uabDataReader.ImageLabelReader([3], [0, 1, 2],
patchDir,
file_list_valid,
flags.input_size,
flags.batch_size,
dataAug=' ',
block_mean=np.append(
[0], img_mean),
batch_code=2)
# train
start_time = time.time()
model.train_config('X',
'Y',
flags.n_train,
flags.n_valid,
flags.input_size,
uabRepoPaths.modelPath,
loss_type='xent',
par_dir='Inria_Domain_LOO')
model.run(
train_reader=dataReader_train,
valid_reader=dataReader_valid,
pretrained_model_dir=PRED_DIR,
isTrain=True,
img_mean=img_mean,
verb_step=100, # print a message every 100 step(sample)
save_epoch=5, # save the model every 5 epochs
gpu=GPU,
tile_size=flags.tile_size,
patch_size=flags.input_size,
)
duration = time.time() - start_time
print('duration {:.2f} hours'.format(duration / 60 / 60))
