1.0 * pw / sum(PARTIALS_WEIGHTS) for pw in PARTIALS_WEIGHTS
]
if not USE_PARTIALS:
partials = partials[0:1]
PARTIALS_WEIGHTS = [1.0]
qsf.evaluateAllResults(result_files=files['result'],
absolute_disparity=ABSOLUTE_DISPARITY,
cluster_radius=CLUSTER_RADIUS,
labels=SLICE_LABELS,
logpath=LOGPATH)
image_data = qsf.initImageData(files=files,
max_imgs=MAX_IMGS_IN_MEM,
cluster_radius=CLUSTER_RADIUS,
tile_layers=TILE_LAYERS,
tile_side=TILE_SIDE,
width=IMG_WIDTH,
replace_nans=True)
corr2d_len, target_disparity_len, _ = qsf.get_lengths(CLUSTER_RADIUS,
TILE_LAYERS, TILE_SIDE)
train_next, dataset_train, datasets_test = qsf.initTrainTestData(
files=files,
cluster_radius=CLUSTER_RADIUS,
buffer_size=TRAIN_BUFFER_SIZE * BATCH_SIZE, # number of clusters per train
test_titles=TEST_TITLES)
corr2d_train_placeholder = tf.placeholder(
dataset_train.dtype,
#PB_TAGS = ["model_pb"]
print ("Copying config files to results directory:\n ('%s' -> '%s')"%(conf_file,dirs['result']))
try:
os.makedirs(dirs['result'])
except:
pass
shutil.copy2(conf_file,dirs['result'])
LOGPATH = os.path.join(dirs['result'],LOGFILE)
image_data = qsf.initImageData( # just use image_data[0]
files = files,
max_imgs = MAX_IMGS_IN_MEM,
cluster_radius = 0, # CLUSTER_RADIUS,
tile_layers = TILE_LAYERS,
tile_side = TILE_SIDE,
width = IMG_WIDTH,
replace_nans = True,
infer = True,
keep_gt = True) # to generate same output files
cluster_radius = CLUSTER_RADIUS
ROOT_PATH = './attic/infer_qcds_graph'+SUFFIX+"/" # for tensorboard
try:
os.makedirs(os.path.dirname(files['inference']))
print ("Created directory ",os.path.dirname(files['inference']))
except:
pass