Commit ddd193c9 authored by Andrey Filippov's avatar Andrey Filippov

more lwir-related updates

parent e3d871ba
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......@@ -1693,7 +1693,7 @@ if __name__ == "__main__":
VARIANCE_SCALE_DISPARITY = 5.0 #Scale variance if average is above this
NUM_TRAIN_SETS = 32 # 8
FGBGMODE_TEST = 3 # 0 - average, 1 - FG, 2 - BG, 3 - AUX
FGBGMODE_TESTS = [1,3] # 0 - average, 1 - FG, 2 - BG, 3 - AUX
FGBGMODE_TRAIN = 1 # 0 - average, 1 - FG, 2 - BG
RND_AMPLIUDE_TEST = 0.5 # present corr2d rendered +/- this far from the GT
RND_AMPLIUDE_TRAIN_TILE = 0.5 # train with corr2d rendered +/- this far from the GT - independent for each tile component
......@@ -1725,12 +1725,12 @@ if __name__ == "__main__":
''' Prepare full image for testing '''
for model_ml_path in test_sets:
writeTFRecordsFromImageSet(
model_ml_path, # model/version/ml_dir
FGBGMODE_TEST, # 0, # expot_mode, # 0 - GT average, 1 - GT FG, 2 - GT BG, 3 - AUX disparity
RND_AMPLIUDE_TEST, # random_offset, # for modes 0..2 - add random offset of -random_offset to +random_offset, in mode 3 add random to GT average if no AUX data
pathTFR) # TFR directory
for fgbgmode_test in FGBGMODE_TESTS:
writeTFRecordsFromImageSet(
model_ml_path, # model/version/ml_dir
fgbgmode_test, # 0, # expot_mode, # 0 - GT average, 1 - GT FG, 2 - GT BG, 3 - AUX disparity
RND_AMPLIUDE_TEST, # random_offset, # for modes 0..2 - add random offset of -random_offset to +random_offset, in mode 3 add random to GT average if no AUX data
pathTFR) # TFR directory
# disp_bins = 20,
......
......@@ -49,6 +49,7 @@ TILE_SIDE, TILE_LAYERS, TWO_TRAINS, NET_ARCH1, NET_ARCH2 = [None]*5
ABSOLUTE_DISPARITY,SYM8_SUB, WLOSS_LAMBDA, SLOSS_LAMBDA, SLOSS_CLIP = [None]*5
SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE = [None]*5
CLUSTER_RADIUS = None
FGBG_MODE = 1 # 0 - do not filter by single-plane, 1 - remove split plabnes tiles, 2 - remove split planes and neighbors
PARTIALS_WEIGHTS, MAX_IMGS_IN_MEM, MAX_FILES_PER_GROUP, BATCH_WEIGHTS, ONLY_TILE = [None] * 5
USE_CONFIDENCE, WBORDERS_ZERO, EPOCHS_TO_RUN, FILE_UPDATE_EPOCHS = [None] * 4
LR600,LR400,LR200,LR100,LR = [None]*5
......
......@@ -47,8 +47,10 @@ Defined in config file
"""
TILE_SIDE, TILE_LAYERS, TWO_TRAINS, NET_ARCH1, NET_ARCH2 = [None]*5
ABSOLUTE_DISPARITY,SYM8_SUB, WLOSS_LAMBDA, SLOSS_LAMBDA, SLOSS_CLIP = [None]*5
CORR2D_LIMITS = [None, None]
SPREAD_CONVERGENCE, INTER_CONVERGENCE, HOR_FLIP, DISP_DIFF_CAP, DISP_DIFF_SLOPE = [None]*5
CLUSTER_RADIUS = None
FGBG_MODE = 1 # 0 - do not filter by single-plane, 1 - remove split plabnes tiles, 2 - remove split planes and neighbors
PARTIALS_WEIGHTS, MAX_IMGS_IN_MEM, MAX_FILES_PER_GROUP, BATCH_WEIGHTS, ONLY_TILE = [None] * 5
USE_CONFIDENCE, WBORDERS_ZERO, EPOCHS_TO_RUN, FILE_UPDATE_EPOCHS = [None] * 4
LR600,LR400,LR200,LR100,LR = [None]*5
......@@ -64,7 +66,7 @@ globals().update(parameters)
TRAIN_BUFFER_SIZE = TRAIN_BUFFER_GPU * TRAIN_BUFFER_CPU # in merged (quad) batches
qsf.setCorr2Limits(CORR2D_LIMITS) # limit min/max 2d correlation tiles values
#exit(0)
......@@ -91,9 +93,10 @@ NN_LAYOUT2 = qcstereo_network.NN_LAYOUTS[NET_ARCH2]
USE_PARTIALS = not PARTIALS_WEIGHTS is None # False - just a single Siamese net, True - partial outputs that use concentric squares of the first level subnets
# Tiff export slice labels
SLICE_LABELS = ["nn_out_ext","hier_out_ext","gt_disparity","gt_strength",
SLICE_LABELS = ["nn_out_ext","target_disp","gt_disparity","gt_strength",
"cutcorn_cost_nw","cutcorn_cost",
"gt-avg_dist","avg8_disp","gt_disp","out-avg"]
"gt_avg_dist","avg8_disp","gt_disp","out_avg",
"aux_disp","fg_disp","bg_disp","gt_rms","gt_rms_split"]
##############################################################################
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
......@@ -126,6 +129,7 @@ if not USE_PARTIALS:
qsf.evaluateAllResults(result_files = files['result'],
absolute_disparity = ABSOLUTE_DISPARITY,
cluster_radius = CLUSTER_RADIUS,
fgbg_mode= FGBG_MODE,
labels = SLICE_LABELS,
logpath= LOGPATH)
......@@ -390,7 +394,7 @@ with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
merged = tf.summary.merge_all()
merged = tf.compat.v1.summary.merge_all()
tt_writers = []
for p in TT_PATHS:
tt_writers.append(tf.summary.FileWriter(p, sess.graph))
......@@ -614,9 +618,9 @@ with tf.Session() as sess:
files = files,
indx = ntest,
cluster_radius = CLUSTER_RADIUS,
tile_layers = TILE_LAYERS,
tile_side = TILE_SIDE,
width = IMG_WIDTH,
tile_layers = TILE_LAYERS, # 4
tile_side = TILE_SIDE, # 9
width = IMG_WIDTH, #160
replace_nans = True)
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_img['corr2d'],
......@@ -659,24 +663,41 @@ with tf.Session() as sess:
os.makedirs(os.path.dirname(result_file))
except:
pass
extra = dataset_img['t_extra']
if extra is None:
extra = np.array([dataset_img['gtruths'].shape[0],0])
rslt = np.concatenate(
[disp_out.reshape(-1,1),
dataset_img['t_disps'], #t_disps[ntest],
dataset_img['t_disps'], #t_disps[ntest], disp_out.shape[0],BATCH_SIZE
dataset_img['gtruths'], # gtruths[ntest],
dbg_cost_nw.reshape(-1,1),
dbg_cost_w.reshape(-1,1),
dbg_d.reshape(-1,1),
dbg_avg_disparity.reshape(-1,1),
dbg_gt_disparity.reshape(-1,1),
dbg_offs.reshape(-1,1)],1)
np.save(result_file, rslt.reshape(HEIGHT,WIDTH,-1))
rslt = qsf.eval_results(result_file, ABSOLUTE_DISPARITY, radius=CLUSTER_RADIUS, logfile=lf)
img_gain_test0 = rslt[0][0]/rslt[0][1]
img_gain_test9 = rslt[9][0]/rslt[9][1]
dbg_offs.reshape(-1,1),
extra, # len 3..6,
#adding extra data layers
],1)
num_slices = rslt.shape[1]
np.save(
result_file,
rslt.reshape(HEIGHT,WIDTH,-1))
eval_rslt = qsf.eval_results(
result_file,
ABSOLUTE_DISPARITY,
radius=0, # CLUSTER_RADIUS,
last_fgbg_mode = 1,
logfile=lf)
img_gain_test0 = eval_rslt[0][0]/eval_rslt[0][1]
img_gain_test9 = eval_rslt[9][0]/eval_rslt[9][1]
if SAVE_TIFFS:
qsf.result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True,labels=SLICE_LABELS, logfile=lf)
qsf.result_npy_to_tiff(
result_file,
ABSOLUTE_DISPARITY,
fix_nan = True,
labels=SLICE_LABELS[0:num_slices],
logfile=lf)
"""
Remove dataset_img (if it is not [0] to reduce memory footprint
......
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