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python3-imagej-tiff
Commit 94636179 authored by Andrey Filippov's avatar Andrey Filippov
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Added zero border conditions for 1-st layer weights, generation of the 

test files same lengtha s train ones
parent 464cacc9
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Showing with 273 additions and 90 deletions
explore_data2.py
View file @ 94636179
......@@ -76,7 +76,7 @@ def writeTFRewcordsImageTiles(img_path, tfr_filename): # test_set=False):
num_tiles = 242*324 # fixme
all_image_tiles = np.array(range(num_tiles))
corr_layers = ['hor-pairs', 'vert-pairs','diagm-pair', 'diago-pair']
img = ijt.imagej_tiff(test_corr, corr_layers, all_image_tiles)
img = ijt.imagej_tiff(img_path, corr_layers, all_image_tiles)
corr2d = img.corr2d.reshape((num_tiles,-1))
target_disparity = img.target_disparity.reshape((num_tiles,-1))
......@@ -493,7 +493,7 @@ class ExploreData:
if nchoices >= min_choices: # use minimum of extra files
break;
while len(tl)==0:
print("** BUG! could not find a single candidate from files ",flist," for cell "+nt)
print("** BUG! could not find a single candidate from files ",flist," for cell ",nt)
print("trying to use some other cell")
nt1 = np.random.randint(0,self.num_batch_tiles.shape[1])
for findx in flist:
......@@ -753,7 +753,7 @@ ValueError: need at least one array to concatenate
def writeTFRewcordsEpoch(self, tfr_filename, ml_list, files_list = None, set_ds= None, radius = 0): # test_set=False):
def writeTFRewcordsEpoch(self, tfr_filename, ml_list, files_list = None, set_ds= None, radius = 0, num_scenes = None): # test_set=False):
# train_filename = 'train.tfrecords' # address to save the TFRecords file
# open the TFRecords file
if not '.tfrecords' in tfr_filename:
......@@ -777,7 +777,10 @@ ValueError: need at least one array to concatenate
return
writer = tf.python_io.TFRecordWriter(tfr_filename)
#$ files_list = [self.files_train, self.files_test][test_set]
seed_list = np.arange(len(files_list))
if num_scenes is None:
num_scenes = len(files_list)
seed_list = np.arange(num_scenes) % len(files_list)
# seed_list = np.arange(len(files_list))
np.random.shuffle(seed_list)
cluster_size = (2 * radius + 1) * (2 * radius + 1)
for nscene, seed_index in enumerate(seed_list):
......@@ -815,7 +818,7 @@ ValueError: need at least one array to concatenate
example = tf.train.Example(features=tf.train.Features(feature=d_feature))
writer.write(example.SerializeToString())
if (self.debug_level > 0):
print_time("Scene %d of %d -> %s"%(nscene, len(seed_list), tfr_filename))
print_time("Scene %d (%d) of %d -> %s"%(nscene, seed_index, len(seed_list), tfr_filename))
writer.close()
sys.stdout.flush()
......@@ -904,7 +907,7 @@ if __name__ == "__main__":
except IndexError:
# pathTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data_3x3b" #no trailing "/"
# pathTFR = "/home/eyesis/x3d_data/data_sets/tf_data_5x5" #no trailing "/"
pathTFR = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_4" #no trailing "/"
pathTFR = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" #no trailing "/"
try:
ml_subdir = sys.argv[4]
......@@ -914,14 +917,20 @@ if __name__ == "__main__":
# pathTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data_3x3b" #no trailing "/"
# test_corr = '/home/eyesis/x3d_data/models/var_main/www/html/x3domlet/models/all-clean/overlook/1527257933_150165/v04/mlr32_18a/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff'
test_corr = '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527256816_150165/v02/mlr32_18a/1527256816_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff'
# test_corr = '/home/eyesis/x3d_data/models/dsi_combo_and_ml_all/state_street/1527256858_150165/v01/mlr32_18a/1527256858_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff'
#Parameters to generate neighbors data. Set radius to 0 to generate single-tile
# test_corr = '/home/eyesis/x3d_data/models/var_main/www/html/x3domlet/models/all-clean/overlook/1527257933_150165/v04/mlr32_18a/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff' # overlook
# test_corr = '/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527256816_150165/v02/mlr32_18a/1527256816_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff' # State Street
# test_corr = '/home/eyesis/x3d_data/models/dsi_combo_and_ml_all/state_street/1527256858_150165/v01/mlr32_18a/1527256858_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff' # State Street
test_corrs = ['/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527182802_096892/v02/mlr32_18a/1527182802_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # near plane"
'/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527182805_096892/v02/mlr32_18a/1527182805_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # medium plane"
'/home/eyesis/x3d_data/data_sets/test_mlr32_18a/1527182810_096892/v02/mlr32_18a/1527182810_096892-ML_DATA-32B-O-FZ0.05-MAIN.tiff', # far plane
]
#Parameters to generate neighbors data. Set radius to 0 to generate single-tile
TEST_SAME_LENGTH_AS_TRAIN = True # make test to have same number of entries as train ones
RADIUS = 2 # 5x5
MIN_NEIBS = (2 * RADIUS + 1) * (2 * RADIUS + 1) # All tiles valid == 9
VARIANCE_THRESHOLD = 1.5
NUM_TRAIN_SETS = 24 # 8
NUM_TRAIN_SETS = 32 # 8
if RADIUS == 0:
BATCH_DISP_BINS = 50 # 1000 * 1
......@@ -980,6 +989,16 @@ if __name__ == "__main__":
# RADIUS = 1
# MIN_NEIBS = (2 * RADIUS + 1) * (2 * RADIUS + 1) # All tiles valid
# VARIANCE_THRESHOLD = 1.5
for test_corr in test_corrs:
scene = os.path.basename(test_corr)[:17]
scene_version= os.path.basename(os.path.dirname(os.path.dirname(test_corr)))
fname =scene+'-'+scene_version
img_filenameTFR = os.path.join(pathTFR,'img',fname)
print_time("Saving test image %s as tiles..."%(img_filenameTFR),end = " ")
writeTFRewcordsImageTiles(test_corr, img_filenameTFR)
print_time("Done")
pass
if (RADIUS > 0):
disp_var_test, num_neibs_test = ex_data.exploreNeibs(ex_data.test_ds, RADIUS)
......@@ -1011,6 +1030,8 @@ if __name__ == "__main__":
ml_list_train=ex_data.getMLList(ml_subdir, ex_data.files_train)
ml_list_test= ex_data.getMLList(ml_subdir, ex_data.files_test)
num_test_scenes = [ex_data.files_test, ex_data.files_train][TEST_SAME_LENGTH_AS_TRAIN]
if RADIUS == 0 :
list_of_file_lists_train, num_batch_tiles_train = ex_data.makeBatchLists( # results are also saved to self.*
......@@ -1034,7 +1055,7 @@ if __name__ == "__main__":
max_var = VARIANCE_THRESHOLD, # Maximal tile variance to include
min_neibs = MIN_NEIBS) # Minimal number of valid tiles to include
fpath = test_filenameTFR # +("-%03d"%(train_var,))
ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_train, files_list = ex_data.files_test, set_ds= ex_data.test_ds)
ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_train, files_list = ex_data.files_test, set_ds= ex_data.test_ds, num_scenes = num_test_scenes)
pass
else: # RADIUS > 0
# train
......@@ -1079,7 +1100,7 @@ if __name__ == "__main__":
print("Number of <= %f disparity variance tiles: %d (est)"%(VARIANCE_THRESHOLD, num_le_test))
fpath = test_filenameTFR +("TEST_R%d_LE%4.1f"%(RADIUS,VARIANCE_THRESHOLD))
ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_test, files_list = ex_data.files_test, set_ds= ex_data.test_ds, radius = RADIUS)
ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_test, files_list = ex_data.files_test, set_ds= ex_data.test_ds, radius = RADIUS, num_scenes = num_test_scenes)
list_of_file_lists_test, num_batch_tiles_test = ex_data.makeBatchLists( # results are also saved to self.*
data_ds = ex_data.test_ds,
......@@ -1092,8 +1113,9 @@ if __name__ == "__main__":
high_fract_test = 1.0 * num_gt_test / (num_le_test + num_gt_test)
print("Number of > %f disparity variance tiles: %d, fraction = %f (test)"%(VARIANCE_THRESHOLD, num_gt_test, high_fract_test))
fpath = test_filenameTFR +("TEST_R%d_GT%4.1f"%(RADIUS,VARIANCE_THRESHOLD))
ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_test, files_list = ex_data.files_test, set_ds= ex_data.test_ds, radius = RADIUS)
ex_data.writeTFRewcordsEpoch(fpath, ml_list = ml_list_test, files_list = ex_data.files_test, set_ds= ex_data.test_ds, radius = RADIUS, num_scenes = num_test_scenes)
plt.show()
"""
scene = os.path.basename(test_corr)[:17]
scene_version= os.path.basename(os.path.dirname(os.path.dirname(test_corr)))
fname =scene+'-'+scene_version
......@@ -1102,6 +1124,6 @@ if __name__ == "__main__":
writeTFRewcordsImageTiles(test_corr, img_filenameTFR)
print_time("Done")
pass
"""
pass
nn_ds_neibs9.py
View file @ 94636179
......@@ -23,6 +23,7 @@ import matplotlib.pyplot as plt
import shutil
import sys
from threading import Thread
TIME_START = time.time()
TIME_LAST = TIME_START
......@@ -56,14 +57,19 @@ BATCH_SIZE = ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles ha
SHUFFLE_EPOCH = True
NET_ARCH1 = 0 # 4 # #4 # 8 # 4 # 0 #3 # 0 # 0 # 0 # 0 # 8 # 0 # 7 # 2 #0 # 6 #0 # 4 # 3 # overwrite with argv?
NET_ARCH2 = 0 # 4 # 0 # 0 # 4 # 0 # 0 # 3 # 0 # 3 # 0 # 3 # 0 # 2 #0 # 6 # 0 # 3 # overwrite with argv?
SYM8_SUB = True #False # True # False # True # False # True # False # enforce inputs from 2d correlation have symmetrical ones (groups of 8)
SYM8_SUB = False # True #False # True # False # True # False # True # False # enforce inputs from 2d correlation have symmetrical ones (groups of 8)
ONLY_TILE = None # 4 # None # 0 # 4# None # (remove all but center tile data), put None here for normal operation)
ZIP_LHVAR = True # combine _lvar and _hvar as odd/even elements
#DEBUG_PACK_TILES = True
# CLUSTER_RADIUS should match input data
CLUSTER_RADIUS = 2 # 1 # 1 - 3x3, 2 - 5x5 tiles
SHUFFLE_FILES = True
WLOSS_LAMBDA = 50.0 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
WLOSS_LAMBDA = 0.0 # 50.0 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
WBORDERS_ZERO = True # Border conditions for first layer weights: False - free, True - tied to 0
MAX_FILES_PER_GROUP = 6 # just to try, normally should be 8
FILE_UPDATE_EPOCHS = 2 # update train files each this many epochs. 0 - do not update
SUFFIX=str(NET_ARCH1)+'-'+str(NET_ARCH2)+ (["R","A"][ABSOLUTE_DISPARITY]) +(["NS","S8"][SYM8_SUB])+"LMBD"+str(WLOSS_LAMBDA)
NN_LAYOUTS = {0:[0, 0, 0, 32, 20, 16],
......@@ -98,6 +104,12 @@ def print_time(txt="",end="\n"):
print(("%s"+bcolors.BOLDWHITE+"at %.4fs (+%.4fs)"+bcolors.ENDC)%(txt,t-TIME_START,t-TIME_LAST), end = end, flush=True)
TIME_LAST = t
#reading to memory (testing)
train_next = [{'file':0, 'slot':0, 'files':0, 'slots':0},
{'file':0, 'slot':0, 'files':0, 'slots':0}]
if TWO_TRAINS:
train_next += [{'file':0, 'slot':0, 'files':0, 'slots':0},
{'file':0, 'slot':0, 'files':0, 'slots':0}]
def readTFRewcordsEpoch(train_filename):
# filenames = [train_filename]
# dataset = tf.data.TFRecorDataset(filenames)
......@@ -141,8 +153,20 @@ def readTFRewcordsEpoch(train_filename):
np.save(file_corr2d, corr2d)
np.save(file_target_disparity, target_disparity)
np.save(file_gt_ds, gt_ds)
return corr2d, target_disparity, gt_ds
return corr2d, target_disparity, gt_ds
def getMoreFiles(fpaths,rslt):
for fpath in fpaths:
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
dataset = {"corr2d": corr2d,
"target_disparity": target_disparity,
"gt_ds": gt_ds}
if FILE_TILE_SIDE > TILE_SIDE:
reduce_tile_size([dataset], TILE_LAYERS, TILE_SIDE)
reformat_to_clusters([dataset])
rslt.append(dataset)
#from http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
def read_and_decode(filename_queue):
......@@ -265,6 +289,20 @@ def shuffle_chunks_in_place(datasets_data, tiles_groups_per_chunk):
ds['target_disparity'] = ds['target_disparity'].reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
ds['gt_ds'] = ds['gt_ds']. reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
def _setFileSlot(train_next,files):
train_next['files'] = files
train_next['slots'] = min(train_next['files'], MAX_FILES_PER_GROUP)
def _nextFileSlot(train_next):
train_next['file'] = (train_next['file'] + 1) % train_next['files']
train_next['slot'] = (train_next['slot'] + 1) % train_next['slots']
def replaceNextDataset(datasets_data, new_dataset, train_next, nset,period):
replaceDataset(datasets_data, new_dataset, nset, period, findx = train_next['slot'])
# _nextFileSlot(train_next[nset])
def replaceDataset(datasets_data, new_dataset, nset, period, findx):
"""
......@@ -391,18 +429,19 @@ except IndexError:
train_filenameTFR1 = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_01.tfrecords"
"""
data_dir = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
data_dir1 = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_2" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
data_dir1 = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_4" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg" #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
img_dir = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
#img_dir = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
img_dir = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_dbg"
dir_train_lvar = data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
dir_train_hvar = data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
dir_train_lvar1 = data_dir1 #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_1" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
dir_train_hvar1 = data_dir1 # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
dir_test_lvar = data_dir # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center/" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
dir_test_hvar = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_3" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
dir_img = os.path.join(img_dir,"img") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/img"
dir_result = os.path.join(data_dir,"result") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/result"
dir_test_lvar = data_dir # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center/" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
dir_test_hvar = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_3" # data_dir #"/home/eyesis/x3d_data/data_sets/tf_data_5x5_center" # data_dir # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main"
dir_img = os.path.join(img_dir,"img") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/img"
dir_result = os.path.join(data_dir,"result") # "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main/result"
files_train_lvar = ["train000_R2_LE_1.5.tfrecords",
"train001_R2_LE_1.5.tfrecords",
......@@ -412,6 +451,30 @@ files_train_lvar = ["train000_R2_LE_1.5.tfrecords",
"train005_R2_LE_1.5.tfrecords",
"train006_R2_LE_1.5.tfrecords",
"train007_R2_LE_1.5.tfrecords",
"train008_R2_LE_1.5.tfrecords",
"train009_R2_LE_1.5.tfrecords",
"train010_R2_LE_1.5.tfrecords",
"train011_R2_LE_1.5.tfrecords",
"train012_R2_LE_1.5.tfrecords",
"train013_R2_LE_1.5.tfrecords",
"train014_R2_LE_1.5.tfrecords",
"train015_R2_LE_1.5.tfrecords",
"train016_R2_LE_1.5.tfrecords",
"train017_R2_LE_1.5.tfrecords",
"train018_R2_LE_1.5.tfrecords",
"train019_R2_LE_1.5.tfrecords",
"train020_R2_LE_1.5.tfrecords",
"train021_R2_LE_1.5.tfrecords",
"train022_R2_LE_1.5.tfrecords",
"train023_R2_LE_1.5.tfrecords",
"train024_R2_LE_1.5.tfrecords",
"train025_R2_LE_1.5.tfrecords",
"train026_R2_LE_1.5.tfrecords",
"train027_R2_LE_1.5.tfrecords",
"train028_R2_LE_1.5.tfrecords",
"train029_R2_LE_1.5.tfrecords",
"train030_R2_LE_1.5.tfrecords",
"train031_R2_LE_1.5.tfrecords",
]
files_train_hvar = ["train000_R2_GT_1.5.tfrecords",
"train001_R2_GT_1.5.tfrecords",
......@@ -421,6 +484,30 @@ files_train_hvar = ["train000_R2_GT_1.5.tfrecords",
"train005_R2_GT_1.5.tfrecords",
"train006_R2_GT_1.5.tfrecords",
"train007_R2_GT_1.5.tfrecords",
"train008_R2_GT_1.5.tfrecords",
"train009_R2_GT_1.5.tfrecords",
"train010_R2_GT_1.5.tfrecords",
"train011_R2_GT_1.5.tfrecords",
"train012_R2_GT_1.5.tfrecords",
"train013_R2_GT_1.5.tfrecords",
"train014_R2_GT_1.5.tfrecords",
"train015_R2_GT_1.5.tfrecords",
"train016_R2_GT_1.5.tfrecords",
"train017_R2_GT_1.5.tfrecords",
"train018_R2_GT_1.5.tfrecords",
"train019_R2_GT_1.5.tfrecords",
"train020_R2_GT_1.5.tfrecords",
"train021_R2_GT_1.5.tfrecords",
"train022_R2_GT_1.5.tfrecords",
"train023_R2_GT_1.5.tfrecords",
"train024_R2_GT_1.5.tfrecords",
"train025_R2_GT_1.5.tfrecords",
"train026_R2_GT_1.5.tfrecords",
"train027_R2_GT_1.5.tfrecords",
"train028_R2_GT_1.5.tfrecords",
"train029_R2_GT_1.5.tfrecords",
"train030_R2_GT_1.5.tfrecords",
"train031_R2_GT_1.5.tfrecords",
]
files_train_lvar1 = ["train000_R2_LE_1.5.tfrecords",
"train001_R2_LE_1.5.tfrecords",
......@@ -430,6 +517,22 @@ files_train_lvar1 = ["train000_R2_LE_1.5.tfrecords",
"train005_R2_LE_1.5.tfrecords",
"train006_R2_LE_1.5.tfrecords",
"train007_R2_LE_1.5.tfrecords",
"train008_R2_LE_1.5.tfrecords",
"train009_R2_LE_1.5.tfrecords",
"train010_R2_LE_1.5.tfrecords",
"train011_R2_LE_1.5.tfrecords",
"train012_R2_LE_1.5.tfrecords",
"train013_R2_LE_1.5.tfrecords",
"train014_R2_LE_1.5.tfrecords",
"train015_R2_LE_1.5.tfrecords",
"train016_R2_LE_1.5.tfrecords",
"train017_R2_LE_1.5.tfrecords",
"train018_R2_LE_1.5.tfrecords",
"train019_R2_LE_1.5.tfrecords",
"train020_R2_LE_1.5.tfrecords",
"train021_R2_LE_1.5.tfrecords",
"train022_R2_LE_1.5.tfrecords",
"train023_R2_LE_1.5.tfrecords",
]
files_train_hvar1 = ["train000_R2_GT_1.5.tfrecords",
"train001_R2_GT_1.5.tfrecords",
......@@ -439,10 +542,24 @@ files_train_hvar1 = ["train000_R2_GT_1.5.tfrecords",
"train005_R2_GT_1.5.tfrecords",
"train006_R2_GT_1.5.tfrecords",
"train007_R2_GT_1.5.tfrecords",
"train008_R2_GT_1.5.tfrecords",
"train009_R2_GT_1.5.tfrecords",
"train010_R2_GT_1.5.tfrecords",
"train011_R2_GT_1.5.tfrecords",
"train012_R2_GT_1.5.tfrecords",
"train013_R2_GT_1.5.tfrecords",
"train014_R2_GT_1.5.tfrecords",
"train015_R2_GT_1.5.tfrecords",
"train016_R2_GT_1.5.tfrecords",
"train017_R2_GT_1.5.tfrecords",
"train018_R2_GT_1.5.tfrecords",
"train019_R2_GT_1.5.tfrecords",
"train020_R2_GT_1.5.tfrecords",
"train021_R2_GT_1.5.tfrecords",
"train022_R2_GT_1.5.tfrecords",
"train023_R2_GT_1.5.tfrecords",
]
"""
Try again - all hvar training, train than different hvar testing.
tensorboard --logdir="attic/nn_ds_neibs6_graph0-0RNS-bothtrain-test-hvar" --port=7069
......@@ -459,8 +576,12 @@ Compare with other (not used) train sets (use 7 of each instead of 8, 8-th as te
files_test_lvar = ["train004_R2_GT_1.5.tfrecords"]# ["train007_R2_LE_1.5.tfrecords"]# "testTEST_R2_LE_1.5.tfrecords"] # testTEST_R2_LE_1.5.tfrecords"]
files_test_hvar = ["train000_R2_GT_1.5.tfrecords"]#"testTEST_R2_GT_1.5.tfrecords"] # "testTEST_R2_GT_1.5.tfrecords"]
#files_img = ['1527257933_150165-v04'] # overlook
#files_img = ['1527256858_150165-v01'] # state Street
files_img = ['1527256816_150165-v02'] # staete Street
#files_img = ['1527256858_150165-v01'] # State Street
#files_img = ['1527256816_150165-v02'] # State Street
#files_img = ['1527182802_096892-v02'] # plane near
files_img = ['1527182805_096892-v02'] # plane midrange
#files_img = ['1527182810_096892-v02'] # plane far
#MAX_FILES_PER_GROUP
for i, path in enumerate(files_train_lvar):
files_train_lvar[i]=os.path.join(dir_train_lvar, path)
......@@ -474,8 +595,6 @@ for i, path in enumerate(files_train_lvar1):
for i, path in enumerate(files_train_hvar1):
files_train_hvar1[i]=os.path.join(dir_train_hvar1, path)
for i, path in enumerate(files_test_lvar):
files_test_lvar[i]=os.path.join(dir_test_lvar, path)
......@@ -488,6 +607,7 @@ for i, path in enumerate(files_img):
files_img[i] = os.path.join(dir_img, path+'.tfrecords')
result_files.append(os.path.join(dir_result, path+"_"+SUFFIX+'.npy'))
files_train = [files_train_lvar,files_train_hvar,files_train_lvar1,files_train_hvar1]
#file_test_hvar= None
weight_hvar = 0.26
......@@ -522,42 +642,28 @@ pass
pass
datasets_train_lvar = []
datasets_train_hvar = []
datasets_train_lvar1 = []
datasets_train_hvar1 = []
for fpath in files_train_lvar:
print_time("Importing train data (low variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_train_lvar.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
for fpath in files_train_hvar:
print_time("Importing train data (high variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_train_hvar.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
if TWO_TRAINS:
for fpath in files_train_lvar1:
print_time("Importing train data1 (low variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_train_lvar1.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
for fpath in files_train_hvar1:
print_time("Importing train data1 (high variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_train_hvar1.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
datasets_train_all = [[],[],[],[]]
#files_train = [files_train_lvar,files_train_hvar,files_train_lvar1,files_train_hvar1]
for n_train, f_train in enumerate(files_train):
if len(f_train) and ((n_train<2) or TWO_TRAINS):
_setFileSlot(train_next[n_train], len(f_train))
for i, fpath in enumerate(f_train):
if i >= MAX_FILES_PER_GROUP:
break
print_time("Importing train data "+(["low variance","high variance", "low variance1","high variance1"][n_train]) +" from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_train_all[n_train].append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
_nextFileSlot(train_next[n_train])
print_time(" Done")
datasets_test_lvar = []
for fpath in files_test_lvar:
......@@ -586,27 +692,18 @@ center_tile_index = 2 * CLUSTER_RADIUS * (CLUSTER_RADIUS + 1)
# Reformat input data
if FILE_TILE_SIDE > TILE_SIDE:
print_time("Reducing correlation tile size from %d to %d"%(FILE_TILE_SIDE, TILE_SIDE), end="")
reduce_tile_size(datasets_train_lvar, TILE_LAYERS, TILE_SIDE)
reduce_tile_size(datasets_train_hvar, TILE_LAYERS, TILE_SIDE)
reduce_tile_size(datasets_train_lvar1, TILE_LAYERS, TILE_SIDE)
reduce_tile_size(datasets_train_hvar1, TILE_LAYERS, TILE_SIDE)
for d_train in datasets_train_all:
reduce_tile_size(d_train, TILE_LAYERS, TILE_SIDE)
reduce_tile_size(datasets_test_lvar, TILE_LAYERS, TILE_SIDE)
reduce_tile_size(datasets_test_hvar, TILE_LAYERS, TILE_SIDE)
print_time(" Done")
pass
print_time("Reshaping train data (low variance)", end="")
reformat_to_clusters(datasets_train_lvar)
print_time(" Done")
print_time("Reshaping train data (high variance)", end="")
reformat_to_clusters(datasets_train_hvar)
print_time(" Done")
print_time("Reshaping train data1 (low variance)", end="")
reformat_to_clusters(datasets_train_lvar1)
print_time(" Done")
print_time("Reshaping train data1 (high variance)", end="")
reformat_to_clusters(datasets_train_hvar1)
print_time(" Done")
# Reformat to 1/9/25 tile clusters
for n_train, d_train in enumerate(datasets_train_all):
print_time("Reshaping train data ("+(["low variance","high variance", "low variance1","high variance1"][n_train])+") ", end="")
reformat_to_clusters(d_train)
print_time(" Done")
print_time("Reshaping test data (low variance)", end="")
reformat_to_clusters(datasets_test_lvar)
......@@ -621,15 +718,9 @@ datasets_train_lvar & datasets_train_hvar ( that will increase batch size and pl
test has to have even original, batches will not zip - just use two batches for one big one
"""
datasets_train_list = [datasets_train_lvar, datasets_train_hvar]
if TWO_TRAINS:
datasets_train_list += [datasets_train_lvar1, datasets_train_hvar1]
if ZIP_LHVAR:
print_time("Zipping together datasets datasets_train_lvar and datasets_train_hvar", end="")
datasets_train = zip_lvar_hvar(datasets_train_list, del_src = True) # no shuffle, delete src
datasets_train = zip_lvar_hvar(datasets_train_all, del_src = True) # no shuffle, delete src
print_time(" Done")
else:
#Alternate lvar/hvar
......@@ -970,11 +1061,19 @@ def weightsLoss(inp_weights): # [batch_size,(1..2)] tf_result
# w_neib = tf.const([[weight_diag, weight_ortho, weight_diag],
# [weight_ortho, -1.0, weight_ortho],
# [weight_diag, weight_ortho, weight_diag]])
#WBORDERS_ZERO
with tf.name_scope("WeightsLoss"):
# Adding 1 tile border
tf_inp = tf.reshape(inp_weights[:TILE_LAYERS * TILE_SIZE,:], [TILE_LAYERS, FILE_TILE_SIDE, FILE_TILE_SIDE, inp_weights.shape[1]], name = "tf_inp")
tf_inp_ext_h = tf.concat([tf_inp [:, :, :1, :], tf_inp, tf_inp [:, :, -1:, :]], axis = 2, name ="tf_inp_ext_h")
tf_inp_ext = tf.concat([tf_inp_ext_h [:, :1, :, :], tf_inp_ext_h, tf_inp_ext_h[:, -1:, :, :]], axis = 1, name ="tf_inp_ext")
if WBORDERS_ZERO:
tf_zero_col = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], tf_inp.shape[1], 1, tf_inp.shape[3]], name = "tf_zero_col")
tf_zero_row = tf.constant(0.0, dtype=tf.float32, shape=[tf_inp.shape[0], 1 , tf_inp.shape[2], tf_inp.shape[3]], name = "tf_zero_row")
tf_inp_ext_h = tf.concat([tf_zero_col, tf_inp, tf_zero_col ], axis = 2, name ="tf_inp_ext_h")
tf_inp_ext = tf.concat([tf_zero_row, tf_inp_ext_h, tf_zero_row ], axis = 1, name ="tf_inp_ext")
else:
tf_inp_ext_h = tf.concat([tf_inp [:, :, :1, :], tf_inp, tf_inp [:, :, -1:, :]], axis = 2, name ="tf_inp_ext_h")
tf_inp_ext = tf.concat([tf_inp_ext_h [:, :1, :, :], tf_inp_ext_h, tf_inp_ext_h[:, -1:, :, :]], axis = 1, name ="tf_inp_ext")
s_ortho = tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:,:] + tf_inp_ext[:,1:-1,:-2,:] + tf_inp_ext[:,1:-1, 2:, :]
s_corn = tf_inp_ext[:, :-2,:-2,:] + tf_inp_ext[:, :-2, 2:,:] + tf_inp_ext[:,2:, :-2,:] + tf_inp_ext[:,2: , 2:, :]
w_diff = tf.subtract(tf_inp, s_ortho * weight_ortho + s_corn * weight_diag, name="w_diff")
......@@ -1020,7 +1119,7 @@ if WLOSS_LAMBDA > 0.0:
GW_loss = tf.add(G_loss, WLOSS_LAMBDA * W_loss, name = "GW_loss")
else:
GW_loss = G_loss
W_loss = 0.0
W_loss = tf.constant(0.0, dtype=tf.float32,name = "W_loss")
#debug
GT_variance = debug_gt_variance(indx = 0, # This tile index (0..8)
center_indx = 4, # center tile index
......@@ -1056,7 +1155,7 @@ G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(GW_loss)
saver=tf.train.Saver()
ROOT_PATH = './attic/nn_ds_neibs8_graph'+SUFFIX+"/"
ROOT_PATH = './attic/nn_ds_neibs9_graph'+SUFFIX+"/"
TRAIN_PATH = ROOT_PATH + 'train'
TEST_PATH = ROOT_PATH + 'test'
TEST_PATH1 = ROOT_PATH + 'test1'
......@@ -1108,7 +1207,69 @@ with tf.Session() as sess:
img_gain_test9 = 1.0
num_train_variants = len(datasets_train)
thr=None;
trains_to_update = [train_next[n_train]['files'] > train_next[n_train]['slots'] for n_train in range(len(train_next))]
for epoch in range (EPOCHS_TO_RUN):
"""
update files after each epoch, all 4.
Convert to threads after testing
"""
if (FILE_UPDATE_EPOCHS > 0) and (epoch % FILE_UPDATE_EPOCHS == 0):
if not thr is None:
if thr.is_alive():
print_time("Waiting until tfrecord gets loaded", end=" ")
else:
print_time("tfrecord is already loaded loaded", end=" ")
thr.join()
print_time("Done")
print_time("Inserting new data", end=" ")
for n_train in range(len(trains_to_update)):
if trains_to_update[n_train]:
replaceNextDataset(datasets_train,
thr_result[n_train],
train_next= train_next[n_train],
nset=n_train,
period=len(train_next))
_nextFileSlot(train_next[n_train])
print_time("Done")
thr_result = []
fpaths = []
for n_train in range(len(train_next)):
if train_next[n_train]['files'] > train_next[n_train]['slots']:
fpaths.append(files_train[n_train][train_next[n_train]['file']])
print_time("Will read in background: "+fpaths[-1])
thr = Thread(target=getMoreFiles, args=(fpaths,thr_result))
thr.start()
"""
for n_train in range(len(train_next)):
if train_next[n_train]['files'] > train_next[n_train]['slots']:
fpath = files_train[n_train][train_next[n_train]['file']]
print_time("Importing train data "+(["low variance","high variance", "low variance1","high variance1"][n_train]) +" from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_train_new = {"corr2d": corr2d,
"target_disparity": target_disparity,
"gt_ds": gt_ds}
print_time(" Done")
if FILE_TILE_SIDE > TILE_SIDE:
print_time("Reducing correlation tile size from %d to %d"%(FILE_TILE_SIDE, TILE_SIDE), end="")
reduce_tile_size([datasets_train_new], TILE_LAYERS, TILE_SIDE)
print_time(" Done")
# Reformat to 1/9/25 tile clusters
print_time("Reshaping train data ("+(["low variance","high variance", "low variance1","high variance1"][n_train])+") ", end="")
reformat_to_clusters([datasets_train_new])
print_time(" Done")
replaceNextDataset(datasets_train,
datasets_train_new,
train_next= train_next[n_train],
nset=n_train,
period=len(train_next))
_nextFileSlot(train_next[n_train])
print_time(" Done")
"""
# file_index = (epoch // 20) % 2
file_index = epoch % num_train_variants
if epoch >=600:
......@@ -1123,7 +1284,7 @@ with tf.Session() as sess:
learning_rate = LR
# print ("sr1",file=sys.stderr,end=" ")
if (file_index == 0) and SHUFFLE_FILES:
num_sets = len(datasets_train_list)
num_sets = len(datasets_train_all)
print_time("Shuffling how datasets datasets_train_lvar and datasets_train_hvar are zipped together", end="")
for i in range(num_sets):
shuffle_in_place (datasets_train, i, num_sets)
......@@ -1160,7 +1321,7 @@ with tf.Session() as sess:
tf_ph_sq_diff: train2_avg,
tf_gtvar_diff: gtvar_train_avg,
tf_img_test0: img_gain_test0,
tf_img_test9:img_gain_test9}) # previous value of *_avg
tf_img_test9: img_gain_test9}) # previous value of *_avg #Fetch argument 0.0 has invalid type <class 'float'>, must be a string or Tensor. (Can not convert a float into a Tensor or Operation.)
loss_gw_train_hist[i] = GW_loss_trained
loss_g_train_hist[i] = G_loss_trained
......
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