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
print(blCol.readMetadata()
) # now inria collection has 4 channels, the last one is GT with (0, 1)
# extract patches
extrObj = uab_DataHandlerFunctions.uabPatchExtrRand(
[0, 1, 2, 4], # extract all 4 channels
cSize=chip_size, # patch size as 572*572
numPerTile=121, # 121 images per tile
extSave=['jpg', 'jpg', 'jpg',
'png'], # save rgb files as jpg and gt as png
isTrain=True,
gtInd=3,
pad=model.get_overlap(),
name='Rand{}'.format(RunId)) # pad around the tiles
patchDir = extrObj.run(blCol)
# make data reader
chipFiles = os.path.join(patchDir, 'fileList.txt')
# 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(
def main(flags):
# 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_DeepLabV2.DeeplabV3({'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('spca')
blCol.readMetadata()
img_mean = blCol.getChannelMeans([1, 2, 3]) # get mean of rgb info
# extract patches
extrObj = uab_DataHandlerFunctions.uabPatchExtrRand([0, 1, 2, 3], # extract all 4 channels
cSize=flags.input_size, # patch size as 572*572
numPerTile=256, # overlap as 92
extSave=['png', 'jpg', 'jpg', 'jpg'],
# save rgb files as jpg and gt as png
isTrain=True,
gtInd=0,
pad=model.get_overlap(),
name='Rand{}'.format(flags.run_id)) # pad around the tiles
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(0, 250)])
file_list_valid = uabCrossValMaker.make_file_list_by_key(idx, file_list, [i for i in range(250, 500)])
with tf.name_scope('image_loader'):
# GT has no mean to subtract, append a 0 for block mean
dataReader_train = uabDataReader.ImageLabelReader([0], [1, 2, 3], patchDir, file_list_train, flags.input_size,
flags.tile_size,
flags.batch_size, dataAug='flip,rotate',
block_mean=np.append([0], img_mean))
# no augmentation needed for validation
dataReader_valid = uabDataReader.ImageLabelReader([0], [1, 2, 3], patchDir, file_list_valid, flags.input_size,
flags.tile_size,
flags.batch_size, dataAug=' ', block_mean=np.append([0], img_mean))
# train
start_time = time.time()
model.train_config('X', 'Y', flags.n_train, flags.n_valid, flags.input_size, uabRepoPaths.modelPath,
loss_type='xent')
model.run(train_reader=dataReader_train,
valid_reader=dataReader_valid,
pretrained_model_dir=flags.res_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))
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
print(blCol.readMetadata()
) # now inria collection has 4 channels, the last one is GT with (0, 1)
# extract patches
extrObj = uab_DataHandlerFunctions.uabPatchExtrRand(
[0, 1, 2, 4], # extract all 4 channels
cSize=chip_size, # patch size as 572*572
numPerTile=100, # 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
chipFiles = os.path.join(patchDir, 'fileList.txt')
# 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(16, 143)])
file_list_valid = uabCrossValMaker.make_file_list_by_key(