Commit 758c8a5d authored by Andrey Filippov's avatar Andrey Filippov

Trying next versions with partial second stage - 5x5+3x3+1x1

parent 17e64c60
......@@ -1030,7 +1030,7 @@ 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]
num_test_scenes = len([ex_data.files_test, ex_data.files_train][TEST_SAME_LENGTH_AS_TRAIN])
if RADIUS == 0 :
......
#!/usr/bin/env python3
from numpy import float64
from tensorflow.contrib.losses.python.metric_learning.metric_loss_ops import npairs_loss
from debian.deb822 import PdiffIndex
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
from PIL import Image
import os
import sys
import glob
import numpy as np
import itertools
import time
import matplotlib.pyplot as plt
import shutil
import sys
from threading import Thread
TIME_START = time.time()
TIME_LAST = TIME_START
DEBUG_LEVEL= 1
DISP_BATCH_BINS = 20 # Number of batch disparity bins
STR_BATCH_BINS = 10 # Number of batch strength bins
FILES_PER_SCENE = 5 # number of random offset files for the scene to select from (0 - use all available)
#MIN_BATCH_CHOICES = 10 # minimal number of tiles in a file for each bin to select from
#MAX_BATCH_FILES = 10 #maximal number of files to use in a batch
#MAX_EPOCH = 500
LR = 1e-3 # learning rate
LR100 = 3e-4 #LR # 1e-4
LR200 = 1e-4 #LR100 # 3e-5
LR400 = 3e-5 #LR200 # 1e-5
LR600 = 1e-5 #LR400 # 3e-6
USE_CONFIDENCE = False
ABSOLUTE_DISPARITY = False # True # False # True # False
DEBUG_PLT_LOSS = True
TILE_LAYERS = 4
FILE_TILE_SIDE = 9
TILE_SIDE = 9 # 7
TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
FEATURES_PER_TILE = TILE_LAYERS * TILE_SIZE# == 324
EPOCHS_TO_RUN = 3000#0 #0
EPOCHS_FULL_TEST = 5 # 10 # 25# repeat full image test after this number of epochs
#EPOCHS_SAME_FILE = 20
RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batches
#BATCH_SIZE = 2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
TWO_TRAINS = True # use 2 train sets
BATCH_SIZE = ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
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 = 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 = 0.5 #3.0 # 1.0 # 0.3 # 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
PARTIALS_WEIGHTS = [1.0,1.0,1.0] # weight of full 5x5, center 3x3 and center 1x1. len(PARTIALS_WEIGHTS) == CLUSTER_RADIUS + 1. Set to None
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],
1:[0, 0, 0, 256, 128, 64],
2:[0, 128, 32, 32, 32, 16],
3:[0, 0, 40, 32, 20, 16],
4:[0, 0, 0, 0, 16, 16],
5:[0, 0, 64, 32, 32, 16],
6:[0, 0, 32, 16, 16, 16],
7:[0, 0, 64, 16, 16, 16],
8:[0, 0, 0, 64, 20, 16],
}
NN_LAYOUT1 = NN_LAYOUTS[NET_ARCH1]
NN_LAYOUT2 = 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
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[38;5;214m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
BOLDWHITE = '\033[1;37m'
UNDERLINE = '\033[4m'
def print_time(txt="",end="\n"):
global TIME_LAST
t = time.time()
if txt:
txt +=" "
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)
if not '.tfrecords' in train_filename:
train_filename += '.tfrecords'
npy_dir_name = "npy"
dirname = os.path.dirname(train_filename)
npy_dir = os.path.join(dirname, npy_dir_name)
filebasename, file_extension = os.path.splitext(train_filename)
filebasename = os.path.basename(filebasename)
file_corr2d = os.path.join(npy_dir,filebasename + '_corr2d.npy')
file_target_disparity = os.path.join(npy_dir,filebasename + '_target_disparity.npy')
file_gt_ds = os.path.join(npy_dir,filebasename + '_gt_ds.npy')
if (os.path.exists(file_corr2d) and
os.path.exists(file_target_disparity) and
os.path.exists(file_gt_ds)):
corr2d= np.load (file_corr2d)
target_disparity = np.load(file_target_disparity)
gt_ds = np.load(file_gt_ds)
pass
else:
record_iterator = tf.python_io.tf_record_iterator(path=train_filename)
corr2d_list=[]
target_disparity_list=[]
gt_ds_list = []
for string_record in record_iterator:
example = tf.train.Example()
example.ParseFromString(string_record)
corr2d_list.append (np.array(example.features.feature['corr2d'].float_list.value, dtype=np.float32))
target_disparity_list.append (np.array(example.features.feature['target_disparity'].float_list.value, dtype=np.float32))
gt_ds_list.append (np.array(example.features.feature['gt_ds'].float_list.value, dtype= np.float32))
pass
corr2d= np.array(corr2d_list)
target_disparity = np.array(target_disparity_list)
gt_ds = np.array(gt_ds_list)
try:
os.makedirs(os.path.dirname(file_corr2d))
except:
pass
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
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):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'corr2d': tf.FixedLenFeature([FEATURES_PER_TILE],tf.float32), #string),
'target_disparity': tf.FixedLenFeature([1], tf.float32), #.string),
'gt_ds': tf.FixedLenFeature([2], tf.float32) #.string)
})
corr2d = features['corr2d'] # tf.decode_raw(features['corr2d'], tf.float32)
target_disparity = features['target_disparity'] # tf.decode_raw(features['target_disparity'], tf.float32)
gt_ds = tf.cast(features['gt_ds'], tf.float32) # tf.decode_raw(features['gt_ds'], tf.float32)
in_features = tf.concat([corr2d,target_disparity],0)
# still some nan-s in correlation data?
# in_features_clean = tf.where(tf.is_nan(in_features), tf.zeros_like(in_features), in_features)
# corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features_clean, target_disparity, gt_ds],
corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features, target_disparity, gt_ds],
batch_size=1000, # 2,
capacity=30,
num_threads=2,
min_after_dequeue=10)
return corr2d_out, target_disparity_out, gt_ds_out
#http://adventuresinmachinelearning.com/introduction-tensorflow-queuing/
def add_margins(npa,radius, val = np.nan):
npa_ext = np.empty((npa.shape[0]+2*radius, npa.shape[1]+2*radius, npa.shape[2]), dtype = npa.dtype)
npa_ext[radius:radius + npa.shape[0],radius:radius + npa.shape[1]] = npa
npa_ext[0:radius,:,:] = val
npa_ext[radius + npa.shape[0]:,:,:] = val
npa_ext[:,0:radius,:] = val
npa_ext[:, radius + npa.shape[1]:,:] = val
return npa_ext
def add_neibs(npa_ext,radius):
height = npa_ext.shape[0]-2*radius
width = npa_ext.shape[1]-2*radius
side = 2 * radius + 1
size = side * side
npa_neib = np.empty((height, width, side, side, npa_ext.shape[2]), dtype = npa_ext.dtype)
for dy in range (side):
for dx in range (side):
npa_neib[:,:,dy, dx,:]= npa_ext[dy:dy+height, dx:dx+width]
return npa_neib.reshape(height, width, -1)
def extend_img_to_clusters(datasets_img,radius):
side = 2 * radius + 1
size = side * side
width = 324
if len(datasets_img) ==0:
return
num_tiles = datasets_img[0]['corr2d'].shape[0]
height = num_tiles // width
for rec in datasets_img:
rec['corr2d'] = add_neibs(add_margins(rec['corr2d'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1))
rec['target_disparity'] = add_neibs(add_margins(rec['target_disparity'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1))
rec['gt_ds'] = add_neibs(add_margins(rec['gt_ds'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1))
pass
def reformat_to_clusters(datasets_data):
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
# Reformat input data
for rec in datasets_data:
rec['corr2d'] = rec['corr2d'].reshape( (rec['corr2d'].shape[0]//cluster_size, rec['corr2d'].shape[1] * cluster_size))
rec['target_disparity'] = rec['target_disparity'].reshape((rec['target_disparity'].shape[0]//cluster_size, rec['target_disparity'].shape[1] * cluster_size))
rec['gt_ds'] = rec['gt_ds'].reshape( (rec['gt_ds'].shape[0]//cluster_size, rec['gt_ds'].shape[1] * cluster_size))
def replace_nan(datasets_data):
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
# Reformat input data
for rec in datasets_data:
np.nan_to_num(rec['corr2d'], copy = False)
np.nan_to_num(rec['target_disparity'], copy = False)
np.nan_to_num(rec['gt_ds'], copy = False)
def permute_to_swaps(perm):
pairs = []
for i in range(len(perm)):
w = np.where(perm == i)[0][0]
if w != i:
pairs.append([i,w])
perm[w] = perm[i]
perm[i] = i
return pairs
def shuffle_in_place(datasets_data, indx, period):
swaps = permute_to_swaps(np.random.permutation(len(datasets_data)))
num_entries = datasets_data[0]['corr2d'].shape[0] // period
for swp in swaps:
ds0 = datasets_data[swp[0]]
ds1 = datasets_data[swp[1]]
tmp = ds0['corr2d'][indx::period].copy()
ds0['corr2d'][indx::period] = ds1['corr2d'][indx::period]
ds1['corr2d'][indx::period] = tmp
tmp = ds0['target_disparity'][indx::period].copy()
ds0['target_disparity'][indx::period] = ds1['target_disparity'][indx::period]
ds1['target_disparity'][indx::period] = tmp
tmp = ds0['gt_ds'][indx::period].copy()
ds0['gt_ds'][indx::period] = ds1['gt_ds'][indx::period]
ds1['gt_ds'][indx::period] = tmp
def shuffle_chunks_in_place(datasets_data, tiles_groups_per_chunk):
"""
Improve shuffling by preserving indices inside batches (0 <->0, ... 39 <->39 for 40 tile group batches)
"""
num_files = len(datasets_data)
#chunks_per_file = datasets_data[0]['target_disparity']
for nf, ds in enumerate(datasets_data):
groups_per_file = ds['corr2d'].shape[0]
chunks_per_file = groups_per_file//tiles_groups_per_chunk
permut = np.random.permutation(chunks_per_file)
ds['corr2d'] = ds['corr2d']. reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
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):
"""
Replace one file in the dataset
"""
datasets_data[findx]['corr2d'] [nset::period] = new_dataset['corr2d']
datasets_data[findx]['target_disparity'][nset::period] = new_dataset['target_disparity']
datasets_data[findx]['gt_ds'] [nset::period] = new_dataset['gt_ds']
def zip_lvar_hvar(datasets_all_data, del_src = True):
# cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
# Reformat input data
num_sets_to_combine = len(datasets_all_data)
datasets_data = []
if num_sets_to_combine:
for nrec in range(len(datasets_all_data[0])):
recs = [[] for _ in range(num_sets_to_combine)]
for nset, datasets in enumerate(datasets_all_data):
recs[nset] = datasets[nrec]
rec = {'corr2d': np.empty((recs[0]['corr2d'].shape[0]*num_sets_to_combine, recs[0]['corr2d'].shape[1]),dtype=np.float32),
'target_disparity': np.empty((recs[0]['target_disparity'].shape[0]*num_sets_to_combine,recs[0]['target_disparity'].shape[1]),dtype=np.float32),
'gt_ds': np.empty((recs[0]['gt_ds'].shape[0]*num_sets_to_combine, recs[0]['gt_ds'].shape[1]),dtype=np.float32)}
for nset, reci in enumerate(recs):
rec['corr2d'] [nset::num_sets_to_combine] = recs[nset]['corr2d']
rec['target_disparity'][nset::num_sets_to_combine] = recs[nset]['target_disparity']
rec['gt_ds'] [nset::num_sets_to_combine] = recs[nset]['gt_ds']
if del_src:
for nset in range(num_sets_to_combine):
datasets_all_data[nset][nrec] = None
datasets_data.append(rec)
return datasets_data
# list of dictionaries
def reduce_tile_size(datasets_data, num_tile_layers, reduced_tile_side):
if (not datasets_data is None) and (len (datasets_data) > 0):
tsz = (datasets_data[0]['corr2d'].shape[1])// num_tile_layers # 81 # list index out of range
tss = int(np.sqrt(tsz)+0.5)
offs = (tss - reduced_tile_side) // 2
for rec in datasets_data:
rec['corr2d'] = (rec['corr2d'].reshape((-1, num_tile_layers, tss, tss))
[..., offs:offs+reduced_tile_side, offs:offs+reduced_tile_side].
reshape(-1,num_tile_layers*reduced_tile_side*reduced_tile_side))
def eval_results(rslt_path, absolute,
min_disp = -0.1, #minimal GT disparity
max_disp = 20.0, # maximal GT disparity
max_ofst_target = 1.0,
max_ofst_result = 1.0,
str_pow = 2.0,
radius = 0):
# for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow in [
variants = [[ -0.1, 5.0, 0.5, 0.5, 1.0],
[ -0.1, 5.0, 0.5, 0.5, 2.0],
[ -0.1, 5.0, 0.2, 0.2, 1.0],
[ -0.1, 5.0, 0.2, 0.2, 2.0],
[ -0.1, 20.0, 0.5, 0.5, 1.0],
[ -0.1, 20.0, 0.5, 0.5, 2.0],
[ -0.1, 20.0, 0.2, 0.2, 1.0],
[ -0.1, 20.0, 0.2, 0.2, 2.0],
[ -0.1, 20.0, 1.0, 1.0, 1.0],
[min_disp, max_disp, max_ofst_target, max_ofst_result, str_pow]]
rslt = np.load(result_file)
not_nan = ~np.isnan(rslt[...,0])
not_nan &= ~np.isnan(rslt[...,1])
not_nan &= ~np.isnan(rslt[...,2])
not_nan &= ~np.isnan(rslt[...,3])
not_nan_ext = np.zeros((rslt.shape[0] + 2*radius,rslt.shape[1] + 2 * radius),dtype=np.bool)
not_nan_ext[radius:-radius,radius:-radius] = not_nan
for dy in range(2*radius+1):
for dx in range(2*radius+1):
not_nan_ext[dy:dy+not_nan.shape[0], dx:dx+not_nan.shape[1]] &= not_nan
not_nan = not_nan_ext[radius:-radius,radius:-radius]
if not absolute:
rslt[...,0] += rslt[...,1]
nn_disparity = np.nan_to_num(rslt[...,0], copy = False)
target_disparity = np.nan_to_num(rslt[...,1], copy = False)
gt_disparity = np.nan_to_num(rslt[...,2], copy = False)
gt_strength = np.nan_to_num(rslt[...,3], copy = False)
rslt = []
for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow in variants:
good_tiles = not_nan.copy();
good_tiles &= (gt_disparity >= min_disparity)
good_tiles &= (gt_disparity <= max_disparity)
good_tiles &= (target_disparity != gt_disparity)
good_tiles &= (np.abs(target_disparity - gt_disparity) <= max_offset_target)
good_tiles &= (np.abs(target_disparity - nn_disparity) <= max_offset_result)
gt_w = gt_strength * good_tiles
gt_w = np.power(gt_w,strength_pow)
sw = gt_w.sum()
diff0 = target_disparity - gt_disparity
diff1 = nn_disparity - gt_disparity
diff0_2w = gt_w*diff0*diff0
diff1_2w = gt_w*diff1*diff1
rms0 = np.sqrt(diff0_2w.sum()/sw)
rms1 = np.sqrt(diff1_2w.sum()/sw)
print ("%7.3f<disp<%7.3f, offs_tgt<%5.2f, offs_rslt<%5.2f pwr=%05.3f, rms0=%7.4f, rms1=%7.4f (gain=%7.4f) num good tiles = %5d"%(
min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow, rms0, rms1, rms0/rms1, good_tiles.sum() ))
rslt.append([rms0,rms1])
return rslt
def concentricSquares(radius):
side = 2 * radius + 1
return [[((i // side) >= var) and
((i // side) < (side - var)) and
((i % side) >= var) and
((i % side) < (side - var)) for i in range (side*side) ] for var in range(radius+1)]
"""
Start of the main code
"""
"""
try:
train_filenameTFR = sys.argv[1]
except IndexError:
train_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_00.tfrecords"
try:
test_filenameTFR = sys.argv[2]
except IndexError:
test_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/test.tfrecords"
#FILES_PER_SCENE
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_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_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_test_hvar = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # 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",
"train002_R2_LE_1.5.tfrecords",
"train003_R2_LE_1.5.tfrecords",
"train004_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",
"train002_R2_GT_1.5.tfrecords",
"train003_R2_GT_1.5.tfrecords",
"train004_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",
"train002_R2_LE_1.5.tfrecords",
"train003_R2_LE_1.5.tfrecords",
"train004_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",
"train002_R2_GT_1.5.tfrecords",
"train003_R2_GT_1.5.tfrecords",
"train004_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
Seems that even different (but used) hvar perfectly match each other, but training for both lvar and hvar never get
match for either of hvar/lvar, even those that were used for training
tensorboard --logdir="attic/nn_ds_neibs6_graph0-0RNS-19-tested_with_same_hvar_lvar_as_trained" --port=7070
try same with higher LR - will they eventually converge?
Compare with other (not used) train sets (use 7 of each instead of 8, 8-th as test)
"""
#just testing:
#files_train_lvar = files_train_hvar
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 = ["testTEST_R2_GT_1.5.tfrecords"] # Now same size as train! # ["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'] # 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)
for i, path in enumerate(files_train_hvar):
files_train_hvar[i]=os.path.join(dir_train_hvar, path)
# Second set of files
for i, path in enumerate(files_train_lvar1):
files_train_lvar1[i]=os.path.join(dir_train_lvar1, path)
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)
for i, path in enumerate(files_test_hvar):
files_test_hvar[i]=os.path.join(dir_test_hvar, path)
result_files=[]
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
weight_lvar = 1.0 - weight_hvar
partials = None
partials = concentricSquares(CLUSTER_RADIUS)
PARTIALS_WEIGHTS = [1.0*pw/sum(PARTIALS_WEIGHTS) for pw in PARTIALS_WEIGHTS]
if not USE_PARTIALS:
partials = partials[0:1]
PARTIALS_WEIGHTS = [1.0]
import tensorflow as tf
import tensorflow.contrib.slim as slim
for result_file in result_files:
try:
eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)
#exit(0)
except:
pass
datasets_img = []
for fpath in files_img:
print_time("Importing test image data from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_img.append( {"corr2d": corr2d,
"target_disparity": target_disparity,
"gt_ds": gt_ds})
print_time(" Done")
gtruth = datasets_img[0]['gt_ds'].copy()
t_disp = datasets_img[0]['target_disparity'].reshape([-1,1]).copy()
extend_img_to_clusters(datasets_img, radius = CLUSTER_RADIUS)
#reformat_to_clusters(datasets_img) already this format
replace_nan(datasets_img)
pass
pass
datasets_train_lvar = []
datasets_train_hvar = []
datasets_train_lvar1 = []
datasets_train_hvar1 = []
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:
print_time("Importing test data (low variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_test_lvar.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
datasets_test_hvar = []
for fpath in files_test_hvar:
print_time("Importing test data (high variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_test_hvar.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
#CLUSTER_RADIUS
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
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="")
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
# 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)
print_time(" Done")
print_time("Reshaping test data (high variance)", end="")
reformat_to_clusters(datasets_test_hvar)
print_time(" Done")
pass
"""
datasets_train_lvar & datasets_train_hvar ( that will increase batch size and placeholders twice
test has to have even original, batches will not zip - just use two batches for one big one
"""
if ZIP_LHVAR:
print_time("Zipping together datasets datasets_train_lvar and datasets_train_hvar", end="")
datasets_train = zip_lvar_hvar(datasets_train_all, del_src = True) # no shuffle, delete src
print_time(" Done")
else:
#Alternate lvar/hvar
datasets_train = []
datasets_weights_train = []
for indx in range(max(len(datasets_train_lvar),len(datasets_train_hvar))):
if (indx < len(datasets_train_lvar)):
datasets_train.append(datasets_train_lvar[indx])
datasets_weights_train.append(weight_lvar)
if (indx < len(datasets_train_hvar)):
datasets_train.append(datasets_train_hvar[indx])
datasets_weights_train.append(weight_hvar)
datasets_test = []
datasets_weights_test = []
for dataset_test_lvar in datasets_test_lvar:
datasets_test.append(dataset_test_lvar)
datasets_weights_test.append(weight_lvar)
for dataset_test_hvar in datasets_test_hvar:
datasets_test.append(dataset_test_hvar)
datasets_weights_test.append(weight_hvar)
corr2d_train_placeholder = tf.placeholder(datasets_train[0]['corr2d'].dtype, (None,FEATURES_PER_TILE * cluster_size)) # corr2d_train.shape)
target_disparity_train_placeholder = tf.placeholder(datasets_train[0]['target_disparity'].dtype, (None,1 * cluster_size)) #target_disparity_train.shape)
gt_ds_train_placeholder = tf.placeholder(datasets_train[0]['gt_ds'].dtype, (None,2 * cluster_size)) #gt_ds_train.shape)
#dataset_tt TensorSliceDataset: <TensorSliceDataset shapes: {gt_ds: (50,), corr2d: (8100,), target_disparity: (25,)}, types: {gt_ds: tf.float32, corr2d: tf.float32, target_disparity: tf.float32}>
dataset_tt = tf.data.Dataset.from_tensor_slices({
"corr2d":corr2d_train_placeholder,
"target_disparity": target_disparity_train_placeholder,
"gt_ds": gt_ds_train_placeholder})
#dataset_train_size = len(corr2d_train)
#dataset_train_size = len(datasets_train_lvar[0]['corr2d'])
dataset_train_size = len(datasets_train[0]['corr2d'])
dataset_train_size //= BATCH_SIZE
dataset_test_size = len(datasets_test_lvar[0]['corr2d'])
dataset_test_size //= BATCH_SIZE
dataset_img_size = len(datasets_img[0]['corr2d'])
dataset_img_size //= BATCH_SIZE
#print_time("dataset_tt.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))
dataset_tt = dataset_tt.batch(BATCH_SIZE)
dataset_tt = dataset_tt.prefetch(BATCH_SIZE)
iterator_tt = dataset_tt.make_initializable_iterator()
next_element_tt = iterator_tt.get_next()
#print("dataset_tt.output_types "+str(dataset_tt.output_types)+", dataset_tt.output_shapes "+str(dataset_tt.output_shapes)+", number of elements="+str(dataset_train_size))
#BatchDataset: <BatchDataset shapes: {gt_ds: (?, 50), corr2d: (?, 8100), target_disparity: (?, 25)}, types: {gt_ds: tf.float32, corr2d: tf.float32, target_disparity: tf.float32}>
"""
next_element_tt dict: {'gt_ds': <tf.Tensor 'IteratorGetNext:1' shape=(?, 50) dtype=float32>, 'corr2d': <tf.Tensor 'IteratorGetNext:0' shape=(?, 8100) dtype=float32>, 'target_disparity': <tf.Tensor 'IteratorGetNext:2' shape=(?, 25) dtype=float32>}
'corr2d' (140405473715624) Tensor: Tensor("IteratorGetNext:0", shape=(?, 8100), dtype=float32)
'gt_ds' (140405473715680) Tensor: Tensor("IteratorGetNext:1", shape=(?, 50), dtype=float32)
'target_disparity' (140405501995888) Tensor: Tensor("IteratorGetNext:2", shape=(?, 25), dtype=float32)
"""
#https://www.tensorflow.org/versions/r1.5/programmers_guide/datasets
result_dir = './attic/result_neibs_'+ SUFFIX+'/'
checkpoint_dir = './attic/result_neibs_'+ SUFFIX+'/'
save_freq = 500
def lrelu(x):
return tf.maximum(x*0.2,x)
# return tf.nn.relu(x)
def sym_inputs8(inp):
"""
get input vector [?:4*9*9+1] (last being target_disparity) and reorder for horizontal flip,
vertical flip and transpose (8 variants, mode + 1 - hor, +2 - vert, +4 - transpose)
return same lengh, reordered
"""
with tf.name_scope("sym_inputs8"):
td = inp[:,-1:] # tf.reshape(inp,[-1], name = "td")[-1]
inp_corr = tf.reshape(inp[:,:-1],[-1,4,TILE_SIDE,TILE_SIDE], name = "inp_corr")
inp_corr_h = tf.stack([-inp_corr [:,0,:,-1::-1], inp_corr [:,1,:,-1::-1], -inp_corr [:,3,:,-1::-1], -inp_corr [:,2,:,-1::-1]], axis=1, name = "inp_corr_h")
inp_corr_v = tf.stack([ inp_corr [:,0,-1::-1,:],-inp_corr [:,1,-1::-1,:], inp_corr [:,3,-1::-1,:], inp_corr [:,2,-1::-1,:]], axis=1, name = "inp_corr_v")
inp_corr_hv = tf.stack([ inp_corr_h[:,0,-1::-1,:],-inp_corr_h[:,1,-1::-1,:], inp_corr_h[:,3,-1::-1,:], inp_corr_h[:,2,-1::-1,:]], axis=1, name = "inp_corr_hv")
inp_corr_t = tf.stack([tf.transpose(inp_corr [:,1], perm=[0,2,1]),
tf.transpose(inp_corr [:,0], perm=[0,2,1]),
tf.transpose(inp_corr [:,2], perm=[0,2,1]),
-tf.transpose(inp_corr [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_t")
inp_corr_ht = tf.stack([tf.transpose(inp_corr_h [:,1], perm=[0,2,1]),
tf.transpose(inp_corr_h [:,0], perm=[0,2,1]),
tf.transpose(inp_corr_h [:,2], perm=[0,2,1]),
-tf.transpose(inp_corr_h [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_ht")
inp_corr_vt = tf.stack([tf.transpose(inp_corr_v [:,1], perm=[0,2,1]),
tf.transpose(inp_corr_v [:,0], perm=[0,2,1]),
tf.transpose(inp_corr_v [:,2], perm=[0,2,1]),
-tf.transpose(inp_corr_v [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_vt")
inp_corr_hvt = tf.stack([tf.transpose(inp_corr_hv[:,1], perm=[0,2,1]),
tf.transpose(inp_corr_hv[:,0], perm=[0,2,1]),
tf.transpose(inp_corr_hv[:,2], perm=[0,2,1]),
-tf.transpose(inp_corr_hv[:,3], perm=[0,2,1])], axis=1, name = "inp_corr_hvt")
# return td, [inp_corr, inp_corr_h, inp_corr_v, inp_corr_hv, inp_corr_t, inp_corr_ht, inp_corr_vt, inp_corr_hvt]
"""
return [tf.concat([tf.reshape(inp_corr, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr"),
tf.concat([tf.reshape(inp_corr_h, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_h"),
tf.concat([tf.reshape(inp_corr_v, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_v"),
tf.concat([tf.reshape(inp_corr_hv, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_hv"),
tf.concat([tf.reshape(inp_corr_t, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_t"),
tf.concat([tf.reshape(inp_corr_ht, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_ht"),
tf.concat([tf.reshape(inp_corr_vt, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_vt"),
tf.concat([tf.reshape(inp_corr_hvt,[inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_hvt")]
"""
cl = 4 * TILE_SIDE * TILE_SIDE
return [tf.concat([tf.reshape(inp_corr, [-1,cl]),td], axis=1,name = "out_corr"),
tf.concat([tf.reshape(inp_corr_h, [-1,cl]),td], axis=1,name = "out_corr_h"),
tf.concat([tf.reshape(inp_corr_v, [-1,cl]),td], axis=1,name = "out_corr_v"),
tf.concat([tf.reshape(inp_corr_hv, [-1,cl]),td], axis=1,name = "out_corr_hv"),
tf.concat([tf.reshape(inp_corr_t, [-1,cl]),td], axis=1,name = "out_corr_t"),
tf.concat([tf.reshape(inp_corr_ht, [-1,cl]),td], axis=1,name = "out_corr_ht"),
tf.concat([tf.reshape(inp_corr_vt, [-1,cl]),td], axis=1,name = "out_corr_vt"),
tf.concat([tf.reshape(inp_corr_hvt,[-1,cl]),td], axis=1,name = "out_corr_hvt")]
# inp_corr_h, inp_corr_v, inp_corr_hv, inp_corr_t, inp_corr_ht, inp_corr_vt, inp_corr_hvt]
def network_sub(input, layout, reuse, sym8 = False):
last_indx = None;
fc = []
inp_weights = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
inp = fc[-1]
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope='g_fc_sub'+str(i), reuse = reuse))
else:
inp = input
if sym8:
inp8 = sym_inputs8(inp)
num_non_sum = num_outs % len(inp8) # if number of first layer outputs is not multiple of 8
num_sym8 = num_outs // len(inp8) # number of symmetrical groups
fc_sym = []
for j in range (len(inp8)): # ==8
reuse_this = reuse | (j > 0)
scp = 'g_fc_sub'+str(i)
fc_sym.append(slim.fully_connected(inp8[j], num_sym8, activation_fn=lrelu, scope= scp, reuse = reuse_this))
if not reuse_this:
with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
if num_non_sum > 0:
reuse_this = reuse
scp = 'g_fc_sub'+str(i)+"r"
fc_sym.append(slim.fully_connected(inp, num_non_sum, activation_fn=lrelu, scope=scp, reuse = reuse_this))
if not reuse_this:
with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
fc.append(tf.concat(fc_sym, 1, name='sym_input_layer'))
else:
scp = 'g_fc_sub'+str(i)
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope= scp, reuse = reuse))
if not reuse:
with tf.variable_scope(scp, reuse=True) : # tf.AUTO_REUSE):
inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
return fc[-1], inp_weights
def network_inter(input, layout, reuse=False):
last_indx = None;
fc = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
inp = fc[-1]
else:
inp = input
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope='g_fc_inter'+str(i), reuse = reuse))
if USE_CONFIDENCE:
fc_out = slim.fully_connected(fc[-1], 2, activation_fn=lrelu, scope='g_fc_inter_out', reuse = reuse)
else:
fc_out = slim.fully_connected(fc[-1], 1, activation_fn=None, scope='g_fc_inter_out', reuse = reuse)
#If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only
return fc_out
def networks_siam(input, # now [?,9,325]-> [?,25,325]
layout1,
layout2,
sym8 = False,
only_tile = None, # just for debugging - feed only data from the center sub-network
partials = None):
with tf.name_scope("Siam_net"):
inp_weights = []
num_legs = input.shape[1] # == 25
if partials is None:
partials = [[True] * num_legs]
# inter_list = []
inter_lists = [[] for _ in partials]
reuse = False
for i in range (num_legs):
if ((only_tile is None) or (i == only_tile)) and any([p[i] for p in partials]) :
ns, ns_weights = network_sub(input[:,i,:],
layout= layout1,
reuse= reuse,
sym8 = sym8)
for n, partial in enumerate(partials):
if partial[i]:
inter_lists[n].append(ns)
else:
# inter_lists[n].append(tf.constant(0.0,dtype=tf.float32, shape=ns.shape))
# inter_lists[n].append(ns * 0.0)
inter_lists[n].append(tf.zeros_like(ns))
# inter_list.append(ns)
inp_weights += ns_weights
reuse = True
outs = []
for n, _ in enumerate(partials):
# outs.append(network_inter (tf.concat(inter_list, 1, name='inter_tensor'+str(n)), layout2, reuse = (n > 0)))
outs.append(network_inter (tf.concat(inter_lists[n], 1, name='inter_tensor'+str(n)), layout2, reuse = (n > 0)))
# inter_tensors.append(tf.concat(inter_list, 1, name='inter_tensor'+str(n)))
# inter_tensor = tf.concat(inter_list, 1, name='inter_tensor')
# return network_inter (inter_tensor, layout2), inp_weights
return outs, inp_weights
def debug_gt_variance(
indx, # This tile index (0..8)
center_indx, # center tile index
gt_ds_batch # [?:9:2]
):
with tf.name_scope("Debug_GT_Variance"):
tf_num_tiles = tf.shape(gt_ds_batch)[0]
d_gt_this = tf.reshape(gt_ds_batch[:,2 * indx],[-1], name = "d_this")
d_gt_center = tf.reshape(gt_ds_batch[:,2 * center_indx],[-1], name = "d_center")
d_gt_diff = tf.subtract(d_gt_this, d_gt_center, name = "d_diff")
d_gt_diff2 = tf.multiply(d_gt_diff, d_gt_diff, name = "d_diff2")
d_gt_var = tf.reduce_mean(d_gt_diff2, name = "d_gt_var")
return d_gt_var
def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
target_disparity_batch, # [batch_size] tf placeholder
gt_ds_batch, # [batch_size,2] tf placeholder
absolute_disparity = True, #when false there should be no activation on disparity output !
use_confidence = True,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 1.0,
error2_offset = 0.0025, # 0.0, # 0.0025, # (0.05^2) ~= coring
disp_wmin = 1.0, # minimal disparity to apply weight boosting for small disparities
disp_wmax = 8.0, # maximal disparity to apply weight boosting for small disparities
use_out = False): # use calculated disparity for disparity weight boosting (False - use target disparity)
with tf.name_scope("BatchLoss"):
"""
Here confidence should be after relU. Disparity - may be also if absolute, but no activation if output is residual disparity
"""
tf_lambda_conf_avg = tf.constant(lambda_conf_avg, dtype=tf.float32, name="tf_lambda_conf_avg")
tf_lambda_conf_pwr = tf.constant(lambda_conf_pwr, dtype=tf.float32, name="tf_lambda_conf_pwr")
tf_conf_pwr = tf.constant(conf_pwr, dtype=tf.float32, name="tf_conf_pwr")
tf_gt_conf_offset = tf.constant(gt_conf_offset, dtype=tf.float32, name="tf_gt_conf_offset")
tf_gt_conf_pwr = tf.constant(gt_conf_pwr, dtype=tf.float32, name="tf_gt_conf_pwr")
tf_num_tiles = tf.shape(gt_ds_batch)[0]
tf_0f = tf.constant(0.0, dtype=tf.float32, name="tf_0f")
tf_1f = tf.constant(1.0, dtype=tf.float32, name="tf_1f")
tf_maxw = tf.constant(1.0, dtype=tf.float32, name="tf_maxw")
if gt_conf_pwr == 0:
w = tf.ones((out_batch.shape[0]), dtype=tf.float32,name="w_ones")
else:
# w_slice = tf.slice(gt_ds_batch,[0,1],[-1,1], name = "w_gt_slice")
w_slice = tf.reshape(gt_ds_batch[:,1],[-1], name = "w_gt_slice")
w_sub = tf.subtract (w_slice, tf_gt_conf_offset, name = "w_sub")
# w_clip = tf.clip_by_value(w_sub, tf_0f,tf_maxw, name = "w_clip")
w_clip = tf.maximum(w_sub, tf_0f, name = "w_clip")
if gt_conf_pwr == 1.0:
w = w_clip
else:
w=tf.pow(w_clip, tf_gt_conf_pwr, name = "w_pow")
if use_confidence:
tf_num_tilesf = tf.cast(tf_num_tiles, dtype=tf.float32, name="tf_num_tilesf")
# conf_slice = tf.slice(out_batch,[0,1],[-1,1], name = "conf_slice")
conf_slice = tf.reshape(out_batch[:,1],[-1], name = "conf_slice")
conf_sum = tf.reduce_sum(conf_slice, name = "conf_sum")
conf_avg = tf.divide(conf_sum, tf_num_tilesf, name = "conf_avg")
conf_avg1 = tf.subtract(conf_avg, tf_1f, name = "conf_avg1")
conf_avg2 = tf.square(conf_avg1, name = "conf_avg2")
cost2 = tf.multiply (conf_avg2, tf_lambda_conf_avg, name = "cost2")
iconf_avg = tf.divide(tf_1f, conf_avg, name = "iconf_avg")
nconf = tf.multiply (conf_slice, iconf_avg, name = "nconf") #normalized confidence
nconf_pwr = tf.pow(nconf, conf_pwr, name = "nconf_pwr")
nconf_pwr_sum = tf.reduce_sum(nconf_pwr, name = "nconf_pwr_sum")
nconf_pwr_offs = tf.subtract(nconf_pwr_sum, tf_1f, name = "nconf_pwr_offs")
cost3 = tf.multiply (conf_avg2, nconf_pwr_offs, name = "cost3")
w_all = tf.multiply (w, nconf, name = "w_all")
else:
w_all = w
# cost2 = 0.0
# cost3 = 0.0
# normalize weights
w_sum = tf.reduce_sum(w_all, name = "w_sum")
iw_sum = tf.divide(tf_1f, w_sum, name = "iw_sum")
w_norm = tf.multiply (w_all, iw_sum, name = "w_norm")
disp_slice = tf.reshape(out_batch[:,0],[-1], name = "disp_slice")
d_gt_slice = tf.reshape(gt_ds_batch[:,0],[-1], name = "d_gt_slice")
td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
if absolute_disparity:
adisp = disp_slice
else:
adisp = tf.add(disp_slice, td_flat, name = "adisp")
out_diff = tf.subtract(adisp, d_gt_slice, name = "out_diff")
out_diff2 = tf.square(out_diff, name = "out_diff2")
out_wdiff2 = tf.multiply (out_diff2, w_norm, name = "out_wdiff2")
cost1 = tf.reduce_sum(out_wdiff2, name = "cost1")
out_diff2_offset = tf.subtract(out_diff2, error2_offset, name = "out_diff2_offset")
out_diff2_biased = tf.maximum(out_diff2_offset, 0.0, name = "out_diff2_biased")
# calculate disparity-based weight boost
if use_out:
dispw = tf.clip_by_value(adisp, disp_wmin, disp_wmax, name = "dispw")
else:
dispw = tf.clip_by_value(td_flat, disp_wmin, disp_wmax, name = "dispw")
dispw_boost = tf.divide(disp_wmax, dispw, name = "dispw_boost")
dispw_comp = tf.multiply (dispw_boost, w_norm, name = "dispw_comp") #HERE??
# dispw_comp = tf.multiply (w_norm, w_norm, name = "dispw_comp") #HERE??
dispw_sum = tf.reduce_sum(dispw_comp, name = "dispw_sum")
idispw_sum = tf.divide(tf_1f, dispw_sum, name = "idispw_sum")
dispw_norm = tf.multiply (dispw_comp, idispw_sum, name = "dispw_norm")
out_diff2_wbiased = tf.multiply(out_diff2_biased, dispw_norm, name = "out_diff2_wbiased")
# out_diff2_wbiased = tf.multiply(out_diff2_biased, w_norm, name = "out_diff2_wbiased")
cost1b = tf.reduce_sum(out_diff2_wbiased, name = "cost1b")
if use_confidence:
cost12 = tf.add(cost1b, cost2, name = "cost12")
cost123 = tf.add(cost12, cost3, name = "cost123")
return cost123, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
else:
return cost1b, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
def weightsLoss(inp_weights): # [batch_size,(1..2)] tf_result
# weights_lambdas): # single lambda or same length as inp_weights.shape[1]
"""
Enforcing 'smooth' weights for the input 2d correlation tiles
@return mean squared difference for each weight and average of 8 neighbors divided by mean squared weights
"""
weight_ortho = 1.0
weight_diag = 0.7
sw = 4.0 * (weight_ortho + weight_diag)
weight_ortho /= sw
weight_diag /= sw
# 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")
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] + 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")
w_diff2 = tf.multiply(w_diff, w_diff, name="w_diff2")
w_var = tf.reduce_mean(w_diff2, name="w_var")
w2_mean = tf.reduce_mean(inp_weights * inp_weights, name="w2_mean")
w_rel = tf.divide(w_var, w2_mean, name= "w_rel")
return w_rel # scalar, cost for weights non-smoothness in 2d
corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")], axis=2, name = "corr2d_Nx325")
"""
out, inp_weights = network_siam(input=corr2d_Nx325,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE) #Remove/put None for normal operation
"""
outs, inp_weights = networks_siam(input=corr2d_Nx325,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE, #Remove/put None for normal operation
partials = partials)
# w_slice = tf.reshape(gt_ds_batch[:,1],[-1], name = "w_gt_slice")
# Extract target disparity and GT corresponding to the center tile (reshape - just to name)
#target_disparity_batch_center = tf.reshape(next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1] , [-1,1], name = "target_center")
#gt_ds_batch_center = tf.reshape(next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * center_tile_index+1], [-1,2], name = "gt_ds_center")
tf_partial_weights = tf.constant(PARTIALS_WEIGHTS,dtype=tf.float32,name="partial_weights")
G_losses = [0.0]*len(partials)
target_disparity_batch= next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1]
gt_ds_batch = next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)]
G_losses[0], _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1 = batchLoss(out_batch = outs[0], # [batch_size,(1..2)] tf_result
target_disparity_batch= target_disparity_batch, # next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1], # target_disparity_batch_center, # next_element_tt['target_disparity'], # target_disparity, ### target_d, # [batch_size] tf placeholder
gt_ds_batch = gt_ds_batch, # next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)], # gt_ds_batch_center, ## next_element_tt['gt_ds'], # gt_ds, ### gt, # [batch_size,2] tf placeholder
absolute_disparity = ABSOLUTE_DISPARITY,
use_confidence = USE_CONFIDENCE, # True,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 2.0,
error2_offset = 0, # 0.0025, # (0.05^2)
disp_wmin = 1.0, # minimal disparity to apply weight boosting for small disparities
disp_wmax = 8.0, # maximal disparity to apply weight boosting for small disparities
use_out = False) # use calculated disparity for disparity weight boosting (False - use target disparity)
G_loss = G_losses[0]
for n in range (1,len(partials)):
G_losses[n], _, _, _, _, _, _, _ = batchLoss(out_batch = outs[n], # [batch_size,(1..2)] tf_result
target_disparity_batch= target_disparity_batch, #next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1], # target_disparity_batch_center, # next_element_tt['target_disparity'], # target_disparity, ### target_d, # [batch_size] tf placeholder
gt_ds_batch = gt_ds_batch, # next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)], # gt_ds_batch_center, ## next_element_tt['gt_ds'], # gt_ds, ### gt, # [batch_size,2] tf placeholder
absolute_disparity = ABSOLUTE_DISPARITY,
use_confidence = USE_CONFIDENCE, # True,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 2.0,
error2_offset = 0, # 0.0025, # (0.05^2)
disp_wmin = 1.0, # minimal disparity to apply weight boosting for small disparities
disp_wmax = 8.0, # maximal disparity to apply weight boosting for small disparities
use_out = False) # use calculated disparity for disparity weight boosting (False - use target disparity)
# G_loss += Glosses[n]*PARTIALS_WEIGHTS[n]
#tf_partial_weights
tf_wlosses = tf.multiply(G_losses, tf_partial_weights, name = "tf_wlosses")
G_losses_sum = tf.reduce_sum(tf_wlosses, name = "G_losses_sum")
if WLOSS_LAMBDA > 0.0:
W_loss = weightsLoss(inp_weights[0]) # inp_weights - list of tensors, currently - just [0]
# GW_loss = tf.add(G_loss, WLOSS_LAMBDA * W_loss, name = "GW_loss")
GW_loss = tf.add(G_losses_sum, WLOSS_LAMBDA * W_loss, name = "GW_loss")
else:
GW_loss = G_losses_sum # G_loss
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
gt_ds_batch = next_element_tt['gt_ds'])# [?:18]
tf_ph_G_loss = tf.placeholder(tf.float32,shape=None,name='G_loss_avg')
tf_ph_G_losses = tf.placeholder(tf.float32,shape=[len(partials)],name='G_losses_avg')
tf_ph_W_loss = tf.placeholder(tf.float32,shape=None,name='W_loss_avg')
tf_ph_GW_loss = tf.placeholder(tf.float32,shape=None,name='GW_loss_avg')
tf_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
tf_gtvar_diff = tf.placeholder(tf.float32,shape=None,name='gtvar_diff')
tf_img_test0 = tf.placeholder(tf.float32,shape=None,name='img_test0')
tf_img_test9 = tf.placeholder(tf.float32,shape=None,name='img_test9')
with tf.name_scope('sample'):
tf.summary.scalar("GW_loss", GW_loss)
tf.summary.scalar("G_loss", G_loss)
tf.summary.scalar("W_loss", W_loss)
tf.summary.scalar("sq_diff", _cost1)
tf.summary.scalar("gtvar_diff", GT_variance)
with tf.name_scope('epoch_average'):
# for i, tl in enumerate(tf_ph_G_losses):
# tf.summary.scalar("GW_loss_epoch_"+str(i), tl)
for i in range(tf_ph_G_losses.shape[0]):
tf.summary.scalar("G_loss_epoch_"+str(i), tf_ph_G_losses[i])
tf.summary.scalar("GW_loss_epoch", tf_ph_GW_loss)
tf.summary.scalar("G_loss_epoch", tf_ph_G_loss)
tf.summary.scalar("W_loss_epoch", tf_ph_W_loss)
tf.summary.scalar("sq_diff_epoch", tf_ph_sq_diff)
tf.summary.scalar("gtvar_diff", tf_gtvar_diff)
tf.summary.scalar("img_test0", tf_img_test0)
tf.summary.scalar("img_test9", tf_img_test9)
t_vars=tf.trainable_variables()
lr=tf.placeholder(tf.float32)
G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(GW_loss)
saver=tf.train.Saver()
ROOT_PATH = './attic/nn_ds_neibs10_graph'+SUFFIX+"/"
TRAIN_PATH = ROOT_PATH + 'train'
TEST_PATH = ROOT_PATH + 'test'
TEST_PATH1 = ROOT_PATH + 'test1'
# CLEAN OLD STAFF
shutil.rmtree(TRAIN_PATH, ignore_errors=True)
shutil.rmtree(TEST_PATH, ignore_errors=True)
shutil.rmtree(TEST_PATH1, ignore_errors=True)
WIDTH=324
HEIGHT=242
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph)
test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph)
test_writer1 = tf.summary.FileWriter(TEST_PATH1, sess.graph)
loss_gw_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss_g_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss_g_train_hists= [np.empty(dataset_train_size, dtype=np.float32) for p in partials]
loss_w_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss_gw_test_hist= np.empty(dataset_test_size, dtype=np.float32)
loss_g_test_hist= np.empty(dataset_test_size, dtype=np.float32)
loss_g_test_hists= [np.empty(dataset_test_size, dtype=np.float32) for p in partials]
loss_w_test_hist= np.empty(dataset_test_size, dtype=np.float32)
loss2_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss2_test_hist= np.empty(dataset_test_size, dtype=np.float32)
train_gw_avg = 0.0
train_g_avg = 0.0
train_g_avgs = [0.0]*len(partials)
train_w_avg = 0.0
test_gw_avg = 0.0
test_g_avg = 0.0
test_g_avgs = [0.0]*len(partials)
test_w_avg = 0.0
train2_avg = 0.0
test2_avg = 0.0
gtvar_train_hist= np.empty(dataset_train_size, dtype=np.float32)
gtvar_test_hist= np.empty(dataset_test_size, dtype=np.float32)
gtvar_train = 0.0
gtvar_test = 0.0
gtvar_train_avg = 0.0
gtvar_test_avg = 0.0
img_gain_test0 = 1.0
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()
file_index = epoch % num_train_variants
if epoch >=600:
learning_rate = LR600
elif epoch >=400:
learning_rate = LR400
elif epoch >=200:
learning_rate = LR200
elif epoch >=100:
learning_rate = LR100
else:
learning_rate = LR
# print ("sr1",file=sys.stderr,end=" ")
if (file_index == 0) and SHUFFLE_FILES:
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)
print_time(" Done")
print_time("Shuffling tile chunks ", end="")
shuffle_chunks_in_place (datasets_train, 1)
print_time(" Done")
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: datasets_train[file_index]['corr2d'],
target_disparity_train_placeholder: datasets_train[file_index]['target_disparity'],
gt_ds_train_placeholder: datasets_train[file_index]['gt_ds']})
for i in range(dataset_train_size):
try:
# train_summary,_, GW_loss_trained, G_loss_trained, W_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
train_summary,_, GW_loss_trained, G_losses_trained, W_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
[ merged,
G_opt,
GW_loss,
# G_loss,
G_losses,
W_loss,
outs[0],
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={lr: learning_rate,
tf_ph_GW_loss: train_gw_avg,
tf_ph_G_loss: train_g_avgs[0], #train_g_avg,
tf_ph_G_losses: train_g_avgs,
tf_ph_W_loss: train_w_avg,
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 #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
for nn, gl in enumerate(G_losses_trained):
loss_g_train_hists[nn][i] = gl
loss_w_train_hist[i] = W_loss_trained
loss2_train_hist[i] = out_cost1
gtvar_train_hist[i] = gt_variance
except tf.errors.OutOfRangeError:
print("train done at step %d"%(i))
break
train_gw_avg = np.average(loss_gw_train_hist).astype(np.float32)
train_g_avg = np.average(loss_g_train_hist).astype(np.float32)
for nn, lgth in enumerate(loss_g_train_hists):
train_g_avgs[nn] = np.average(lgth).astype(np.float32)
###############
train_w_avg = np.average(loss_w_train_hist).astype(np.float32)
train2_avg = np.average(loss2_train_hist).astype(np.float32)
gtvar_train_avg = np.average(gtvar_train_hist).astype(np.float32)
test_summaries = [0.0]*len(datasets_test)
tst_avg = [0.0]*len(datasets_test)
tst2_avg = [0.0]*len(datasets_test)
for ntest,dataset_test in enumerate(datasets_test):
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_test['corr2d'],
target_disparity_train_placeholder: dataset_test['target_disparity'],
gt_ds_train_placeholder: dataset_test['gt_ds']})
for i in range(dataset_test_size):
try:
test_summaries[ntest], GW_loss_tested, G_loss_tested, W_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
[merged,
GW_loss,
G_loss,
W_loss,
outs[0],
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={lr: learning_rate,
tf_ph_GW_loss: test_gw_avg,
tf_ph_G_loss: test_g_avg,
tf_ph_G_losses: train_g_avgs, # temporary, there is o data fro test
tf_ph_W_loss: test_w_avg,
tf_ph_sq_diff: test2_avg,
tf_gtvar_diff: gtvar_test_avg,
tf_img_test0: img_gain_test0,
tf_img_test9: img_gain_test9}) # previous value of *_avg
loss_gw_test_hist[i] = GW_loss_tested
loss_g_test_hist[i] = G_loss_tested
loss_w_test_hist[i] = W_loss_tested
loss2_test_hist[i] = out_cost1
gtvar_test_hist[i] = gt_variance
except tf.errors.OutOfRangeError:
print("test done at step %d"%(i))
break
test_gw_avg = np.average(loss_gw_test_hist).astype(np.float32)
test_g_avg = np.average(loss_g_test_hist).astype(np.float32)
test_w_avg = np.average(loss_w_test_hist).astype(np.float32)
tst_avg[ntest] = test_gw_avg
test2_avg = np.average(loss2_test_hist).astype(np.float32)
tst2_avg[ntest] = test2_avg
gtvar_test_avg = np.average(gtvar_test_hist).astype(np.float32)
train_writer.add_summary(train_summary, epoch)
test_writer.add_summary(test_summaries[0], epoch)
test_writer1.add_summary(test_summaries[1], epoch)
print_time("%d:%d -> %f %f %f (%f %f %f) dbg:%f %f"%(epoch,i,train_gw_avg, tst_avg[0], tst_avg[1], train2_avg, tst2_avg[0], tst2_avg[1], gtvar_train_avg, gtvar_test_avg))
if ((epoch + 1) == EPOCHS_TO_RUN) or (((epoch + 1) % EPOCHS_FULL_TEST) == 0):
###################################################
# Read the full image
###################################################
test_summaries_img = [0.0]*len(datasets_img)
# disp_out= np.empty((dataset_img_size * BATCH_SIZE), dtype=np.float32)
disp_out= np.empty((WIDTH*HEIGHT), dtype=np.float32)
for ntest,dataset_img in enumerate(datasets_img):
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_img['corr2d'],
target_disparity_train_placeholder: dataset_img['target_disparity'],
gt_ds_train_placeholder: dataset_img['gt_ds']})
for start_offs in range(0,disp_out.shape[0],BATCH_SIZE):
end_offs = min(start_offs+BATCH_SIZE,disp_out.shape[0])
try:
test_summaries_img[ntest], G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
[merged,
G_loss,
outs[0],
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={lr: learning_rate,
tf_ph_GW_loss: test_gw_avg,
tf_ph_G_loss: test_g_avg,
tf_ph_G_losses: train_g_avgs, # temporary, there is o data fro test
tf_ph_W_loss: test_w_avg,
tf_ph_sq_diff: test2_avg,
tf_gtvar_diff: gtvar_test_avg,
tf_img_test0: img_gain_test0,
tf_img_test9: img_gain_test9}) # previous value of *_avg
except tf.errors.OutOfRangeError:
print("test done at step %d"%(i))
break
try:
disp_out[start_offs:end_offs] = output.flatten()
except ValueError:
print("dataset_img_size= %d, i=%d, output.shape[0]=%d "%(dataset_img_size, i, output.shape[0]))
break;
pass
result_file = result_files[ntest]
try:
os.makedirs(os.path.dirname(result_file))
except:
pass
rslt = np.concatenate([disp_out.reshape(-1,1), t_disp, gtruth],1)
np.save(result_file, rslt.reshape(HEIGHT,WIDTH,-1))
rslt = eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)
img_gain_test0 = rslt[0][0]/rslt[0][1]
img_gain_test9 = rslt[9][0]/rslt[9][1]
# Close writers
train_writer.close()
test_writer.close()
test_writer1.close()
#reports error: Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x7efc5f720ef0>> if there is no print before exit()
print("All done")
exit (0)
#!/usr/bin/env python3
from numpy import float64
from tensorflow.contrib.losses.python.metric_learning.metric_loss_ops import npairs_loss
from debian.deb822 import PdiffIndex
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
from PIL import Image
import os
import sys
import glob
import numpy as np
import itertools
import time
import matplotlib.pyplot as plt
import shutil
import sys
from threading import Thread
TIME_START = time.time()
TIME_LAST = TIME_START
DEBUG_LEVEL= 1
DISP_BATCH_BINS = 20 # Number of batch disparity bins
STR_BATCH_BINS = 10 # Number of batch strength bins
FILES_PER_SCENE = 5 # number of random offset files for the scene to select from (0 - use all available)
#MIN_BATCH_CHOICES = 10 # minimal number of tiles in a file for each bin to select from
#MAX_BATCH_FILES = 10 #maximal number of files to use in a batch
#MAX_EPOCH = 500
LR = 1e-3 # learning rate
LR100 = 3e-4 #LR # 1e-4
LR200 = 1e-4 #LR100 # 3e-5
LR400 = 3e-5 #LR200 # 1e-5
LR600 = 1e-5 #LR400 # 3e-6
USE_CONFIDENCE = False
ABSOLUTE_DISPARITY = False # True # False # True # False
DEBUG_PLT_LOSS = True
TILE_LAYERS = 4
FILE_TILE_SIDE = 9
TILE_SIDE = 9 # 7
TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
FEATURES_PER_TILE = TILE_LAYERS * TILE_SIZE# == 324
EPOCHS_TO_RUN = 3000#0 #0
EPOCHS_FULL_TEST = 5 # 10 # 25# repeat full image test after this number of epochs
#EPOCHS_SAME_FILE = 20
RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batches
#BATCH_SIZE = 2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
TWO_TRAINS = True # use 2 train sets
BATCH_SIZE = ([1,2][TWO_TRAINS])*2*1000//25 # == 80 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
SHUFFLE_EPOCH = True
NET_ARCH1 = 8 # 0 # 0 # 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 = 3 # 10 # 9 # 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 # 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 = 5.0 # 10.0 # 5.0 # 2.0 # 1.0 # 0.5 #3.0 # 1.0 # 0.3 # 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
PARTIALS_WEIGHTS = [1.0,1.0,1.0] # weight of full 5x5, center 3x3 and center 1x1. len(PARTIALS_WEIGHTS) == CLUSTER_RADIUS + 1. Set to None
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],
1:[0, 0, 0, 256, 128, 64],
2:[0, 128, 32, 32, 32, 16],
3:[0, 0, 40, 32, 20, 16],
4:[0, 0, 0, 0, 16, 16],
5:[0, 0, 64, 32, 32, 16],
6:[0, 0, 32, 16, 16, 16],
7:[0, 0, 64, 16, 16, 16],
8:[0, 0, 0, 64, 20, 16],
9:[0, 0, 256, 64, 32, 16],
10:[0, 256, 128, 64, 32, 16],
}
NN_LAYOUT1 = NN_LAYOUTS[NET_ARCH1]
NN_LAYOUT2 = 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
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKGREEN = '\033[92m'
WARNING = '\033[38;5;214m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
BOLDWHITE = '\033[1;37m'
UNDERLINE = '\033[4m'
def print_time(txt="",end="\n"):
global TIME_LAST
t = time.time()
if txt:
txt +=" "
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)
if not '.tfrecords' in train_filename:
train_filename += '.tfrecords'
npy_dir_name = "npy"
dirname = os.path.dirname(train_filename)
npy_dir = os.path.join(dirname, npy_dir_name)
filebasename, file_extension = os.path.splitext(train_filename)
filebasename = os.path.basename(filebasename)
file_corr2d = os.path.join(npy_dir,filebasename + '_corr2d.npy')
file_target_disparity = os.path.join(npy_dir,filebasename + '_target_disparity.npy')
file_gt_ds = os.path.join(npy_dir,filebasename + '_gt_ds.npy')
if (os.path.exists(file_corr2d) and
os.path.exists(file_target_disparity) and
os.path.exists(file_gt_ds)):
corr2d= np.load (file_corr2d)
target_disparity = np.load(file_target_disparity)
gt_ds = np.load(file_gt_ds)
pass
else:
record_iterator = tf.python_io.tf_record_iterator(path=train_filename)
corr2d_list=[]
target_disparity_list=[]
gt_ds_list = []
for string_record in record_iterator:
example = tf.train.Example()
example.ParseFromString(string_record)
corr2d_list.append (np.array(example.features.feature['corr2d'].float_list.value, dtype=np.float32))
target_disparity_list.append (np.array(example.features.feature['target_disparity'].float_list.value, dtype=np.float32))
gt_ds_list.append (np.array(example.features.feature['gt_ds'].float_list.value, dtype= np.float32))
pass
corr2d= np.array(corr2d_list)
target_disparity = np.array(target_disparity_list)
gt_ds = np.array(gt_ds_list)
try:
os.makedirs(os.path.dirname(file_corr2d))
except:
pass
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
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):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'corr2d': tf.FixedLenFeature([FEATURES_PER_TILE],tf.float32), #string),
'target_disparity': tf.FixedLenFeature([1], tf.float32), #.string),
'gt_ds': tf.FixedLenFeature([2], tf.float32) #.string)
})
corr2d = features['corr2d'] # tf.decode_raw(features['corr2d'], tf.float32)
target_disparity = features['target_disparity'] # tf.decode_raw(features['target_disparity'], tf.float32)
gt_ds = tf.cast(features['gt_ds'], tf.float32) # tf.decode_raw(features['gt_ds'], tf.float32)
in_features = tf.concat([corr2d,target_disparity],0)
# still some nan-s in correlation data?
# in_features_clean = tf.where(tf.is_nan(in_features), tf.zeros_like(in_features), in_features)
# corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features_clean, target_disparity, gt_ds],
corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features, target_disparity, gt_ds],
batch_size=1000, # 2,
capacity=30,
num_threads=2,
min_after_dequeue=10)
return corr2d_out, target_disparity_out, gt_ds_out
#http://adventuresinmachinelearning.com/introduction-tensorflow-queuing/
def add_margins(npa,radius, val = np.nan):
npa_ext = np.empty((npa.shape[0]+2*radius, npa.shape[1]+2*radius, npa.shape[2]), dtype = npa.dtype)
npa_ext[radius:radius + npa.shape[0],radius:radius + npa.shape[1]] = npa
npa_ext[0:radius,:,:] = val
npa_ext[radius + npa.shape[0]:,:,:] = val
npa_ext[:,0:radius,:] = val
npa_ext[:, radius + npa.shape[1]:,:] = val
return npa_ext
def add_neibs(npa_ext,radius):
height = npa_ext.shape[0]-2*radius
width = npa_ext.shape[1]-2*radius
side = 2 * radius + 1
size = side * side
npa_neib = np.empty((height, width, side, side, npa_ext.shape[2]), dtype = npa_ext.dtype)
for dy in range (side):
for dx in range (side):
npa_neib[:,:,dy, dx,:]= npa_ext[dy:dy+height, dx:dx+width]
return npa_neib.reshape(height, width, -1)
def extend_img_to_clusters(datasets_img,radius):
side = 2 * radius + 1
size = side * side
width = 324
if len(datasets_img) ==0:
return
num_tiles = datasets_img[0]['corr2d'].shape[0]
height = num_tiles // width
for rec in datasets_img:
rec['corr2d'] = add_neibs(add_margins(rec['corr2d'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1))
rec['target_disparity'] = add_neibs(add_margins(rec['target_disparity'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1))
rec['gt_ds'] = add_neibs(add_margins(rec['gt_ds'].reshape((height,width,-1)), radius, np.nan), radius).reshape((num_tiles,-1))
pass
def reformat_to_clusters(datasets_data):
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
# Reformat input data
for rec in datasets_data:
rec['corr2d'] = rec['corr2d'].reshape( (rec['corr2d'].shape[0]//cluster_size, rec['corr2d'].shape[1] * cluster_size))
rec['target_disparity'] = rec['target_disparity'].reshape((rec['target_disparity'].shape[0]//cluster_size, rec['target_disparity'].shape[1] * cluster_size))
rec['gt_ds'] = rec['gt_ds'].reshape( (rec['gt_ds'].shape[0]//cluster_size, rec['gt_ds'].shape[1] * cluster_size))
def replace_nan(datasets_data):
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
# Reformat input data
for rec in datasets_data:
np.nan_to_num(rec['corr2d'], copy = False)
np.nan_to_num(rec['target_disparity'], copy = False)
np.nan_to_num(rec['gt_ds'], copy = False)
def permute_to_swaps(perm):
pairs = []
for i in range(len(perm)):
w = np.where(perm == i)[0][0]
if w != i:
pairs.append([i,w])
perm[w] = perm[i]
perm[i] = i
return pairs
def shuffle_in_place(datasets_data, indx, period):
swaps = permute_to_swaps(np.random.permutation(len(datasets_data)))
num_entries = datasets_data[0]['corr2d'].shape[0] // period
for swp in swaps:
ds0 = datasets_data[swp[0]]
ds1 = datasets_data[swp[1]]
tmp = ds0['corr2d'][indx::period].copy()
ds0['corr2d'][indx::period] = ds1['corr2d'][indx::period]
ds1['corr2d'][indx::period] = tmp
tmp = ds0['target_disparity'][indx::period].copy()
ds0['target_disparity'][indx::period] = ds1['target_disparity'][indx::period]
ds1['target_disparity'][indx::period] = tmp
tmp = ds0['gt_ds'][indx::period].copy()
ds0['gt_ds'][indx::period] = ds1['gt_ds'][indx::period]
ds1['gt_ds'][indx::period] = tmp
def shuffle_chunks_in_place(datasets_data, tiles_groups_per_chunk):
"""
Improve shuffling by preserving indices inside batches (0 <->0, ... 39 <->39 for 40 tile group batches)
"""
num_files = len(datasets_data)
#chunks_per_file = datasets_data[0]['target_disparity']
for nf, ds in enumerate(datasets_data):
groups_per_file = ds['corr2d'].shape[0]
chunks_per_file = groups_per_file//tiles_groups_per_chunk
permut = np.random.permutation(chunks_per_file)
ds['corr2d'] = ds['corr2d']. reshape((chunks_per_file,-1))[permut].reshape((groups_per_file,-1))
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):
"""
Replace one file in the dataset
"""
datasets_data[findx]['corr2d'] [nset::period] = new_dataset['corr2d']
datasets_data[findx]['target_disparity'][nset::period] = new_dataset['target_disparity']
datasets_data[findx]['gt_ds'] [nset::period] = new_dataset['gt_ds']
def zip_lvar_hvar(datasets_all_data, del_src = True):
# cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
# Reformat input data
num_sets_to_combine = len(datasets_all_data)
datasets_data = []
if num_sets_to_combine:
for nrec in range(len(datasets_all_data[0])):
recs = [[] for _ in range(num_sets_to_combine)]
for nset, datasets in enumerate(datasets_all_data):
recs[nset] = datasets[nrec]
rec = {'corr2d': np.empty((recs[0]['corr2d'].shape[0]*num_sets_to_combine, recs[0]['corr2d'].shape[1]),dtype=np.float32),
'target_disparity': np.empty((recs[0]['target_disparity'].shape[0]*num_sets_to_combine,recs[0]['target_disparity'].shape[1]),dtype=np.float32),
'gt_ds': np.empty((recs[0]['gt_ds'].shape[0]*num_sets_to_combine, recs[0]['gt_ds'].shape[1]),dtype=np.float32)}
for nset, reci in enumerate(recs):
rec['corr2d'] [nset::num_sets_to_combine] = recs[nset]['corr2d']
rec['target_disparity'][nset::num_sets_to_combine] = recs[nset]['target_disparity']
rec['gt_ds'] [nset::num_sets_to_combine] = recs[nset]['gt_ds']
if del_src:
for nset in range(num_sets_to_combine):
datasets_all_data[nset][nrec] = None
datasets_data.append(rec)
return datasets_data
# list of dictionaries
def reduce_tile_size(datasets_data, num_tile_layers, reduced_tile_side):
if (not datasets_data is None) and (len (datasets_data) > 0):
tsz = (datasets_data[0]['corr2d'].shape[1])// num_tile_layers # 81 # list index out of range
tss = int(np.sqrt(tsz)+0.5)
offs = (tss - reduced_tile_side) // 2
for rec in datasets_data:
rec['corr2d'] = (rec['corr2d'].reshape((-1, num_tile_layers, tss, tss))
[..., offs:offs+reduced_tile_side, offs:offs+reduced_tile_side].
reshape(-1,num_tile_layers*reduced_tile_side*reduced_tile_side))
def eval_results(rslt_path, absolute,
min_disp = -0.1, #minimal GT disparity
max_disp = 20.0, # maximal GT disparity
max_ofst_target = 1.0,
max_ofst_result = 1.0,
str_pow = 2.0,
radius = 0):
# for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow in [
variants = [[ -0.1, 5.0, 0.5, 0.5, 1.0],
[ -0.1, 5.0, 0.5, 0.5, 2.0],
[ -0.1, 5.0, 0.2, 0.2, 1.0],
[ -0.1, 5.0, 0.2, 0.2, 2.0],
[ -0.1, 20.0, 0.5, 0.5, 1.0],
[ -0.1, 20.0, 0.5, 0.5, 2.0],
[ -0.1, 20.0, 0.2, 0.2, 1.0],
[ -0.1, 20.0, 0.2, 0.2, 2.0],
[ -0.1, 20.0, 1.0, 1.0, 1.0],
[min_disp, max_disp, max_ofst_target, max_ofst_result, str_pow]]
rslt = np.load(result_file)
not_nan = ~np.isnan(rslt[...,0])
not_nan &= ~np.isnan(rslt[...,1])
not_nan &= ~np.isnan(rslt[...,2])
not_nan &= ~np.isnan(rslt[...,3])
not_nan_ext = np.zeros((rslt.shape[0] + 2*radius,rslt.shape[1] + 2 * radius),dtype=np.bool)
not_nan_ext[radius:-radius,radius:-radius] = not_nan
for dy in range(2*radius+1):
for dx in range(2*radius+1):
not_nan_ext[dy:dy+not_nan.shape[0], dx:dx+not_nan.shape[1]] &= not_nan
not_nan = not_nan_ext[radius:-radius,radius:-radius]
if not absolute:
rslt[...,0] += rslt[...,1]
nn_disparity = np.nan_to_num(rslt[...,0], copy = False)
target_disparity = np.nan_to_num(rslt[...,1], copy = False)
gt_disparity = np.nan_to_num(rslt[...,2], copy = False)
gt_strength = np.nan_to_num(rslt[...,3], copy = False)
rslt = []
for min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow in variants:
good_tiles = not_nan.copy();
good_tiles &= (gt_disparity >= min_disparity)
good_tiles &= (gt_disparity <= max_disparity)
good_tiles &= (target_disparity != gt_disparity)
good_tiles &= (np.abs(target_disparity - gt_disparity) <= max_offset_target)
good_tiles &= (np.abs(target_disparity - nn_disparity) <= max_offset_result)
gt_w = gt_strength * good_tiles
gt_w = np.power(gt_w,strength_pow)
sw = gt_w.sum()
diff0 = target_disparity - gt_disparity
diff1 = nn_disparity - gt_disparity
diff0_2w = gt_w*diff0*diff0
diff1_2w = gt_w*diff1*diff1
rms0 = np.sqrt(diff0_2w.sum()/sw)
rms1 = np.sqrt(diff1_2w.sum()/sw)
print ("%7.3f<disp<%7.3f, offs_tgt<%5.2f, offs_rslt<%5.2f pwr=%05.3f, rms0=%7.4f, rms1=%7.4f (gain=%7.4f) num good tiles = %5d"%(
min_disparity, max_disparity, max_offset_target, max_offset_result, strength_pow, rms0, rms1, rms0/rms1, good_tiles.sum() ))
rslt.append([rms0,rms1])
return rslt
def concentricSquares(radius):
side = 2 * radius + 1
return [[((i // side) >= var) and
((i // side) < (side - var)) and
((i % side) >= var) and
((i % side) < (side - var)) for i in range (side*side) ] for var in range(radius+1)]
"""
Start of the main code
"""
"""
try:
train_filenameTFR = sys.argv[1]
except IndexError:
train_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train_00.tfrecords"
try:
test_filenameTFR = sys.argv[2]
except IndexError:
test_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/test.tfrecords"
#FILES_PER_SCENE
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_4" # "/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_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_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_test_hvar = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # 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",
"train002_R2_LE_1.5.tfrecords",
"train003_R2_LE_1.5.tfrecords",
"train004_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",
"train002_R2_GT_1.5.tfrecords",
"train003_R2_GT_1.5.tfrecords",
"train004_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",
"train002_R2_LE_1.5.tfrecords",
"train003_R2_LE_1.5.tfrecords",
"train004_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",
"train002_R2_GT_1.5.tfrecords",
"train003_R2_GT_1.5.tfrecords",
"train004_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",
]
"""
files_train_lvar1 = ["train000_R2_LE_1.5.tfrecords",
"train001_R2_LE_1.5.tfrecords",
"train002_R2_LE_1.5.tfrecords",
"train003_R2_LE_1.5.tfrecords",
"train004_R2_LE_1.5.tfrecords",
"train005_R2_LE_1.5.tfrecords",
"train006_R2_LE_1.5.tfrecords",
"train007_R2_LE_1.5.tfrecords",
]
files_train_hvar1 = ["train000_R2_GT_1.5.tfrecords",
"train001_R2_GT_1.5.tfrecords",
"train002_R2_GT_1.5.tfrecords",
"train003_R2_GT_1.5.tfrecords",
"train004_R2_GT_1.5.tfrecords",
"train005_R2_GT_1.5.tfrecords",
"train006_R2_GT_1.5.tfrecords",
"train007_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
Seems that even different (but used) hvar perfectly match each other, but training for both lvar and hvar never get
match for either of hvar/lvar, even those that were used for training
tensorboard --logdir="attic/nn_ds_neibs6_graph0-0RNS-19-tested_with_same_hvar_lvar_as_trained" --port=7070
try same with higher LR - will they eventually converge?
Compare with other (not used) train sets (use 7 of each instead of 8, 8-th as test)
"""
#just testing:
#files_train_lvar = files_train_hvar
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 = ["testTEST_R2_GT_1.5.tfrecords"] # Now same size as train! # ["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'] # State Street
#files_img = ['1527182802_096892-v02'] # plane near
##files_img = ['1527182805_096892-v02'] # plane midrange used up to -49
#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)
for i, path in enumerate(files_train_hvar):
files_train_hvar[i]=os.path.join(dir_train_hvar, path)
# Second set of files
for i, path in enumerate(files_train_lvar1):
files_train_lvar1[i]=os.path.join(dir_train_lvar1, path)
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)
for i, path in enumerate(files_test_hvar):
files_test_hvar[i]=os.path.join(dir_test_hvar, path)
result_files=[]
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
weight_lvar = 1.0 - weight_hvar
partials = None
partials = concentricSquares(CLUSTER_RADIUS)
PARTIALS_WEIGHTS = [1.0*pw/sum(PARTIALS_WEIGHTS) for pw in PARTIALS_WEIGHTS]
if not USE_PARTIALS:
partials = partials[0:1]
PARTIALS_WEIGHTS = [1.0]
import tensorflow as tf
import tensorflow.contrib.slim as slim
for result_file in result_files:
try:
eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)
#exit(0)
except:
pass
datasets_img = []
for fpath in files_img:
print_time("Importing test image data from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_img.append( {"corr2d": corr2d,
"target_disparity": target_disparity,
"gt_ds": gt_ds})
print_time(" Done")
gtruth = datasets_img[0]['gt_ds'].copy()
t_disp = datasets_img[0]['target_disparity'].reshape([-1,1]).copy()
extend_img_to_clusters(datasets_img, radius = CLUSTER_RADIUS)
#reformat_to_clusters(datasets_img) already this format
replace_nan(datasets_img)
pass
pass
datasets_train_lvar = []
datasets_train_hvar = []
datasets_train_lvar1 = []
datasets_train_hvar1 = []
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:
print_time("Importing test data (low variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_test_lvar.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
datasets_test_hvar = []
for fpath in files_test_hvar:
print_time("Importing test data (high variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_test_hvar.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
#CLUSTER_RADIUS
cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
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="")
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
# 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)
print_time(" Done")
print_time("Reshaping test data (high variance)", end="")
reformat_to_clusters(datasets_test_hvar)
print_time(" Done")
pass
"""
datasets_train_lvar & datasets_train_hvar ( that will increase batch size and placeholders twice
test has to have even original, batches will not zip - just use two batches for one big one
"""
if ZIP_LHVAR:
print_time("Zipping together datasets datasets_train_lvar and datasets_train_hvar", end="")
datasets_train = zip_lvar_hvar(datasets_train_all, del_src = True) # no shuffle, delete src
print_time(" Done")
else:
#Alternate lvar/hvar
datasets_train = []
datasets_weights_train = []
for indx in range(max(len(datasets_train_lvar),len(datasets_train_hvar))):
if (indx < len(datasets_train_lvar)):
datasets_train.append(datasets_train_lvar[indx])
datasets_weights_train.append(weight_lvar)
if (indx < len(datasets_train_hvar)):
datasets_train.append(datasets_train_hvar[indx])
datasets_weights_train.append(weight_hvar)
datasets_test = []
datasets_weights_test = []
for dataset_test_lvar in datasets_test_lvar:
datasets_test.append(dataset_test_lvar)
datasets_weights_test.append(weight_lvar)
for dataset_test_hvar in datasets_test_hvar:
datasets_test.append(dataset_test_hvar)
datasets_weights_test.append(weight_hvar)
corr2d_train_placeholder = tf.placeholder(datasets_train[0]['corr2d'].dtype, (None,FEATURES_PER_TILE * cluster_size)) # corr2d_train.shape)
target_disparity_train_placeholder = tf.placeholder(datasets_train[0]['target_disparity'].dtype, (None,1 * cluster_size)) #target_disparity_train.shape)
gt_ds_train_placeholder = tf.placeholder(datasets_train[0]['gt_ds'].dtype, (None,2 * cluster_size)) #gt_ds_train.shape)
#dataset_tt TensorSliceDataset: <TensorSliceDataset shapes: {gt_ds: (50,), corr2d: (8100,), target_disparity: (25,)}, types: {gt_ds: tf.float32, corr2d: tf.float32, target_disparity: tf.float32}>
dataset_tt = tf.data.Dataset.from_tensor_slices({
"corr2d":corr2d_train_placeholder,
"target_disparity": target_disparity_train_placeholder,
"gt_ds": gt_ds_train_placeholder})
#dataset_train_size = len(corr2d_train)
#dataset_train_size = len(datasets_train_lvar[0]['corr2d'])
dataset_train_size = len(datasets_train[0]['corr2d'])
dataset_train_size //= BATCH_SIZE
dataset_test_size = len(datasets_test_lvar[0]['corr2d'])
dataset_test_size //= BATCH_SIZE
dataset_img_size = len(datasets_img[0]['corr2d'])
dataset_img_size //= BATCH_SIZE
#print_time("dataset_tt.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))
dataset_tt = dataset_tt.batch(BATCH_SIZE)
dataset_tt = dataset_tt.prefetch(BATCH_SIZE)
iterator_tt = dataset_tt.make_initializable_iterator()
next_element_tt = iterator_tt.get_next()
#print("dataset_tt.output_types "+str(dataset_tt.output_types)+", dataset_tt.output_shapes "+str(dataset_tt.output_shapes)+", number of elements="+str(dataset_train_size))
#BatchDataset: <BatchDataset shapes: {gt_ds: (?, 50), corr2d: (?, 8100), target_disparity: (?, 25)}, types: {gt_ds: tf.float32, corr2d: tf.float32, target_disparity: tf.float32}>
"""
next_element_tt dict: {'gt_ds': <tf.Tensor 'IteratorGetNext:1' shape=(?, 50) dtype=float32>, 'corr2d': <tf.Tensor 'IteratorGetNext:0' shape=(?, 8100) dtype=float32>, 'target_disparity': <tf.Tensor 'IteratorGetNext:2' shape=(?, 25) dtype=float32>}
'corr2d' (140405473715624) Tensor: Tensor("IteratorGetNext:0", shape=(?, 8100), dtype=float32)
'gt_ds' (140405473715680) Tensor: Tensor("IteratorGetNext:1", shape=(?, 50), dtype=float32)
'target_disparity' (140405501995888) Tensor: Tensor("IteratorGetNext:2", shape=(?, 25), dtype=float32)
"""
#https://www.tensorflow.org/versions/r1.5/programmers_guide/datasets
result_dir = './attic/result_neibs_'+ SUFFIX+'/'
checkpoint_dir = './attic/result_neibs_'+ SUFFIX+'/'
save_freq = 500
def lrelu(x):
return tf.maximum(x*0.2,x)
# return tf.nn.relu(x)
def sym_inputs8(inp):
"""
get input vector [?:4*9*9+1] (last being target_disparity) and reorder for horizontal flip,
vertical flip and transpose (8 variants, mode + 1 - hor, +2 - vert, +4 - transpose)
return same lengh, reordered
"""
with tf.name_scope("sym_inputs8"):
td = inp[:,-1:] # tf.reshape(inp,[-1], name = "td")[-1]
inp_corr = tf.reshape(inp[:,:-1],[-1,4,TILE_SIDE,TILE_SIDE], name = "inp_corr")
inp_corr_h = tf.stack([-inp_corr [:,0,:,-1::-1], inp_corr [:,1,:,-1::-1], -inp_corr [:,3,:,-1::-1], -inp_corr [:,2,:,-1::-1]], axis=1, name = "inp_corr_h")
inp_corr_v = tf.stack([ inp_corr [:,0,-1::-1,:],-inp_corr [:,1,-1::-1,:], inp_corr [:,3,-1::-1,:], inp_corr [:,2,-1::-1,:]], axis=1, name = "inp_corr_v")
inp_corr_hv = tf.stack([ inp_corr_h[:,0,-1::-1,:],-inp_corr_h[:,1,-1::-1,:], inp_corr_h[:,3,-1::-1,:], inp_corr_h[:,2,-1::-1,:]], axis=1, name = "inp_corr_hv")
inp_corr_t = tf.stack([tf.transpose(inp_corr [:,1], perm=[0,2,1]),
tf.transpose(inp_corr [:,0], perm=[0,2,1]),
tf.transpose(inp_corr [:,2], perm=[0,2,1]),
-tf.transpose(inp_corr [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_t")
inp_corr_ht = tf.stack([tf.transpose(inp_corr_h [:,1], perm=[0,2,1]),
tf.transpose(inp_corr_h [:,0], perm=[0,2,1]),
tf.transpose(inp_corr_h [:,2], perm=[0,2,1]),
-tf.transpose(inp_corr_h [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_ht")
inp_corr_vt = tf.stack([tf.transpose(inp_corr_v [:,1], perm=[0,2,1]),
tf.transpose(inp_corr_v [:,0], perm=[0,2,1]),
tf.transpose(inp_corr_v [:,2], perm=[0,2,1]),
-tf.transpose(inp_corr_v [:,3], perm=[0,2,1])], axis=1, name = "inp_corr_vt")
inp_corr_hvt = tf.stack([tf.transpose(inp_corr_hv[:,1], perm=[0,2,1]),
tf.transpose(inp_corr_hv[:,0], perm=[0,2,1]),
tf.transpose(inp_corr_hv[:,2], perm=[0,2,1]),
-tf.transpose(inp_corr_hv[:,3], perm=[0,2,1])], axis=1, name = "inp_corr_hvt")
# return td, [inp_corr, inp_corr_h, inp_corr_v, inp_corr_hv, inp_corr_t, inp_corr_ht, inp_corr_vt, inp_corr_hvt]
"""
return [tf.concat([tf.reshape(inp_corr, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr"),
tf.concat([tf.reshape(inp_corr_h, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_h"),
tf.concat([tf.reshape(inp_corr_v, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_v"),
tf.concat([tf.reshape(inp_corr_hv, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_hv"),
tf.concat([tf.reshape(inp_corr_t, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_t"),
tf.concat([tf.reshape(inp_corr_ht, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_ht"),
tf.concat([tf.reshape(inp_corr_vt, [inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_vt"),
tf.concat([tf.reshape(inp_corr_hvt,[inp_corr.shape[0],-1]),td], axis=1,name = "out_corr_hvt")]
"""
cl = 4 * TILE_SIDE * TILE_SIDE
return [tf.concat([tf.reshape(inp_corr, [-1,cl]),td], axis=1,name = "out_corr"),
tf.concat([tf.reshape(inp_corr_h, [-1,cl]),td], axis=1,name = "out_corr_h"),
tf.concat([tf.reshape(inp_corr_v, [-1,cl]),td], axis=1,name = "out_corr_v"),
tf.concat([tf.reshape(inp_corr_hv, [-1,cl]),td], axis=1,name = "out_corr_hv"),
tf.concat([tf.reshape(inp_corr_t, [-1,cl]),td], axis=1,name = "out_corr_t"),
tf.concat([tf.reshape(inp_corr_ht, [-1,cl]),td], axis=1,name = "out_corr_ht"),
tf.concat([tf.reshape(inp_corr_vt, [-1,cl]),td], axis=1,name = "out_corr_vt"),
tf.concat([tf.reshape(inp_corr_hvt,[-1,cl]),td], axis=1,name = "out_corr_hvt")]
# inp_corr_h, inp_corr_v, inp_corr_hv, inp_corr_t, inp_corr_ht, inp_corr_vt, inp_corr_hvt]
def network_sub(input, layout, reuse, sym8 = False):
last_indx = None;
fc = []
inp_weights = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
inp = fc[-1]
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope='g_fc_sub'+str(i), reuse = reuse))
else:
inp = input
if sym8:
inp8 = sym_inputs8(inp)
num_non_sum = num_outs % len(inp8) # if number of first layer outputs is not multiple of 8
num_sym8 = num_outs // len(inp8) # number of symmetrical groups
fc_sym = []
for j in range (len(inp8)): # ==8
reuse_this = reuse | (j > 0)
scp = 'g_fc_sub'+str(i)
fc_sym.append(slim.fully_connected(inp8[j], num_sym8, activation_fn=lrelu, scope= scp, reuse = reuse_this))
if not reuse_this:
with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
if num_non_sum > 0:
reuse_this = reuse
scp = 'g_fc_sub'+str(i)+"r"
fc_sym.append(slim.fully_connected(inp, num_non_sum, activation_fn=lrelu, scope=scp, reuse = reuse_this))
if not reuse_this:
with tf.variable_scope(scp,reuse=True) : # tf.AUTO_REUSE):
inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
fc.append(tf.concat(fc_sym, 1, name='sym_input_layer'))
else:
scp = 'g_fc_sub'+str(i)
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope= scp, reuse = reuse))
if not reuse:
with tf.variable_scope(scp, reuse=True) : # tf.AUTO_REUSE):
inp_weights.append(tf.get_variable('weights')) # ,shape=[inp.shape[1],num_outs]))
return fc[-1], inp_weights
def network_inter(input, layout, reuse=False):
last_indx = None;
fc = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
inp = fc[-1]
else:
inp = input
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope='g_fc_inter'+str(i), reuse = reuse))
if USE_CONFIDENCE:
fc_out = slim.fully_connected(fc[-1], 2, activation_fn=lrelu, scope='g_fc_inter_out', reuse = reuse)
else:
fc_out = slim.fully_connected(fc[-1], 1, activation_fn=None, scope='g_fc_inter_out', reuse = reuse)
#If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only
return fc_out
def networks_siam(input, # now [?,9,325]-> [?,25,325]
layout1,
layout2,
sym8 = False,
only_tile = None, # just for debugging - feed only data from the center sub-network
partials = None):
with tf.name_scope("Siam_net"):
inp_weights = []
num_legs = input.shape[1] # == 25
if partials is None:
partials = [[True] * num_legs]
# inter_list = []
inter_lists = [[] for _ in partials]
reuse = False
for i in range (num_legs):
if ((only_tile is None) or (i == only_tile)) and any([p[i] for p in partials]) :
ns, ns_weights = network_sub(input[:,i,:],
layout= layout1,
reuse= reuse,
sym8 = sym8)
for n, partial in enumerate(partials):
if partial[i]:
inter_lists[n].append(ns)
else:
# inter_lists[n].append(tf.constant(0.0,dtype=tf.float32, shape=ns.shape))
# inter_lists[n].append(ns * 0.0)
inter_lists[n].append(tf.zeros_like(ns))
# inter_list.append(ns)
inp_weights += ns_weights
reuse = True
outs = []
for n, _ in enumerate(partials):
# outs.append(network_inter (tf.concat(inter_list, 1, name='inter_tensor'+str(n)), layout2, reuse = (n > 0)))
outs.append(network_inter (tf.concat(inter_lists[n], 1, name='inter_tensor'+str(n)), layout2, reuse = (n > 0)))
# inter_tensors.append(tf.concat(inter_list, 1, name='inter_tensor'+str(n)))
# inter_tensor = tf.concat(inter_list, 1, name='inter_tensor')
# return network_inter (inter_tensor, layout2), inp_weights
return outs, inp_weights
def debug_gt_variance(
indx, # This tile index (0..8)
center_indx, # center tile index
gt_ds_batch # [?:9:2]
):
with tf.name_scope("Debug_GT_Variance"):
tf_num_tiles = tf.shape(gt_ds_batch)[0]
d_gt_this = tf.reshape(gt_ds_batch[:,2 * indx],[-1], name = "d_this")
d_gt_center = tf.reshape(gt_ds_batch[:,2 * center_indx],[-1], name = "d_center")
d_gt_diff = tf.subtract(d_gt_this, d_gt_center, name = "d_diff")
d_gt_diff2 = tf.multiply(d_gt_diff, d_gt_diff, name = "d_diff2")
d_gt_var = tf.reduce_mean(d_gt_diff2, name = "d_gt_var")
return d_gt_var
def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
target_disparity_batch, # [batch_size] tf placeholder
gt_ds_batch, # [batch_size,2] tf placeholder
absolute_disparity = True, #when false there should be no activation on disparity output !
use_confidence = True,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 1.0,
error2_offset = 0.0025, # 0.0, # 0.0025, # (0.05^2) ~= coring
disp_wmin = 1.0, # minimal disparity to apply weight boosting for small disparities
disp_wmax = 8.0, # maximal disparity to apply weight boosting for small disparities
use_out = False): # use calculated disparity for disparity weight boosting (False - use target disparity)
with tf.name_scope("BatchLoss"):
"""
Here confidence should be after relU. Disparity - may be also if absolute, but no activation if output is residual disparity
"""
tf_lambda_conf_avg = tf.constant(lambda_conf_avg, dtype=tf.float32, name="tf_lambda_conf_avg")
tf_lambda_conf_pwr = tf.constant(lambda_conf_pwr, dtype=tf.float32, name="tf_lambda_conf_pwr")
tf_conf_pwr = tf.constant(conf_pwr, dtype=tf.float32, name="tf_conf_pwr")
tf_gt_conf_offset = tf.constant(gt_conf_offset, dtype=tf.float32, name="tf_gt_conf_offset")
tf_gt_conf_pwr = tf.constant(gt_conf_pwr, dtype=tf.float32, name="tf_gt_conf_pwr")
tf_num_tiles = tf.shape(gt_ds_batch)[0]
tf_0f = tf.constant(0.0, dtype=tf.float32, name="tf_0f")
tf_1f = tf.constant(1.0, dtype=tf.float32, name="tf_1f")
tf_maxw = tf.constant(1.0, dtype=tf.float32, name="tf_maxw")
if gt_conf_pwr == 0:
w = tf.ones((out_batch.shape[0]), dtype=tf.float32,name="w_ones")
else:
# w_slice = tf.slice(gt_ds_batch,[0,1],[-1,1], name = "w_gt_slice")
w_slice = tf.reshape(gt_ds_batch[:,1],[-1], name = "w_gt_slice")
w_sub = tf.subtract (w_slice, tf_gt_conf_offset, name = "w_sub")
# w_clip = tf.clip_by_value(w_sub, tf_0f,tf_maxw, name = "w_clip")
w_clip = tf.maximum(w_sub, tf_0f, name = "w_clip")
if gt_conf_pwr == 1.0:
w = w_clip
else:
w=tf.pow(w_clip, tf_gt_conf_pwr, name = "w_pow")
if use_confidence:
tf_num_tilesf = tf.cast(tf_num_tiles, dtype=tf.float32, name="tf_num_tilesf")
# conf_slice = tf.slice(out_batch,[0,1],[-1,1], name = "conf_slice")
conf_slice = tf.reshape(out_batch[:,1],[-1], name = "conf_slice")
conf_sum = tf.reduce_sum(conf_slice, name = "conf_sum")
conf_avg = tf.divide(conf_sum, tf_num_tilesf, name = "conf_avg")
conf_avg1 = tf.subtract(conf_avg, tf_1f, name = "conf_avg1")
conf_avg2 = tf.square(conf_avg1, name = "conf_avg2")
cost2 = tf.multiply (conf_avg2, tf_lambda_conf_avg, name = "cost2")
iconf_avg = tf.divide(tf_1f, conf_avg, name = "iconf_avg")
nconf = tf.multiply (conf_slice, iconf_avg, name = "nconf") #normalized confidence
nconf_pwr = tf.pow(nconf, conf_pwr, name = "nconf_pwr")
nconf_pwr_sum = tf.reduce_sum(nconf_pwr, name = "nconf_pwr_sum")
nconf_pwr_offs = tf.subtract(nconf_pwr_sum, tf_1f, name = "nconf_pwr_offs")
cost3 = tf.multiply (conf_avg2, nconf_pwr_offs, name = "cost3")
w_all = tf.multiply (w, nconf, name = "w_all")
else:
w_all = w
# cost2 = 0.0
# cost3 = 0.0
# normalize weights
w_sum = tf.reduce_sum(w_all, name = "w_sum")
iw_sum = tf.divide(tf_1f, w_sum, name = "iw_sum")
w_norm = tf.multiply (w_all, iw_sum, name = "w_norm")
disp_slice = tf.reshape(out_batch[:,0],[-1], name = "disp_slice")
d_gt_slice = tf.reshape(gt_ds_batch[:,0],[-1], name = "d_gt_slice")
td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
if absolute_disparity:
adisp = disp_slice
else:
adisp = tf.add(disp_slice, td_flat, name = "adisp")
out_diff = tf.subtract(adisp, d_gt_slice, name = "out_diff")
out_diff2 = tf.square(out_diff, name = "out_diff2")
out_wdiff2 = tf.multiply (out_diff2, w_norm, name = "out_wdiff2")
cost1 = tf.reduce_sum(out_wdiff2, name = "cost1")
out_diff2_offset = tf.subtract(out_diff2, error2_offset, name = "out_diff2_offset")
out_diff2_biased = tf.maximum(out_diff2_offset, 0.0, name = "out_diff2_biased")
# calculate disparity-based weight boost
if use_out:
dispw = tf.clip_by_value(adisp, disp_wmin, disp_wmax, name = "dispw")
else:
dispw = tf.clip_by_value(td_flat, disp_wmin, disp_wmax, name = "dispw")
dispw_boost = tf.divide(disp_wmax, dispw, name = "dispw_boost")
dispw_comp = tf.multiply (dispw_boost, w_norm, name = "dispw_comp") #HERE??
# dispw_comp = tf.multiply (w_norm, w_norm, name = "dispw_comp") #HERE??
dispw_sum = tf.reduce_sum(dispw_comp, name = "dispw_sum")
idispw_sum = tf.divide(tf_1f, dispw_sum, name = "idispw_sum")
dispw_norm = tf.multiply (dispw_comp, idispw_sum, name = "dispw_norm")
out_diff2_wbiased = tf.multiply(out_diff2_biased, dispw_norm, name = "out_diff2_wbiased")
# out_diff2_wbiased = tf.multiply(out_diff2_biased, w_norm, name = "out_diff2_wbiased")
cost1b = tf.reduce_sum(out_diff2_wbiased, name = "cost1b")
if use_confidence:
cost12 = tf.add(cost1b, cost2, name = "cost12")
cost123 = tf.add(cost12, cost3, name = "cost123")
return cost123, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
else:
return cost1b, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
def weightsLoss(inp_weights): # [batch_size,(1..2)] tf_result
# weights_lambdas): # single lambda or same length as inp_weights.shape[1]
"""
Enforcing 'smooth' weights for the input 2d correlation tiles
@return mean squared difference for each weight and average of 8 neighbors divided by mean squared weights
"""
weight_ortho = 1.0
weight_diag = 0.7
sw = 4.0 * (weight_ortho + weight_diag)
weight_ortho /= sw
weight_diag /= sw
# 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")
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] + 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")
w_diff2 = tf.multiply(w_diff, w_diff, name="w_diff2")
w_var = tf.reduce_mean(w_diff2, name="w_var")
w2_mean = tf.reduce_mean(inp_weights * inp_weights, name="w2_mean")
w_rel = tf.divide(w_var, w2_mean, name= "w_rel")
return w_rel # scalar, cost for weights non-smoothness in 2d
corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")], axis=2, name = "corr2d_Nx325")
"""
out, inp_weights = network_siam(input=corr2d_Nx325,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE) #Remove/put None for normal operation
"""
outs, inp_weights = networks_siam(input=corr2d_Nx325,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE, #Remove/put None for normal operation
partials = partials)
# w_slice = tf.reshape(gt_ds_batch[:,1],[-1], name = "w_gt_slice")
# Extract target disparity and GT corresponding to the center tile (reshape - just to name)
#target_disparity_batch_center = tf.reshape(next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1] , [-1,1], name = "target_center")
#gt_ds_batch_center = tf.reshape(next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * center_tile_index+1], [-1,2], name = "gt_ds_center")
tf_partial_weights = tf.constant(PARTIALS_WEIGHTS,dtype=tf.float32,name="partial_weights")
G_losses = [0.0]*len(partials)
target_disparity_batch= next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1]
gt_ds_batch = next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)]
G_losses[0], _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1 = batchLoss(out_batch = outs[0], # [batch_size,(1..2)] tf_result
target_disparity_batch= target_disparity_batch, # next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1], # target_disparity_batch_center, # next_element_tt['target_disparity'], # target_disparity, ### target_d, # [batch_size] tf placeholder
gt_ds_batch = gt_ds_batch, # next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)], # gt_ds_batch_center, ## next_element_tt['gt_ds'], # gt_ds, ### gt, # [batch_size,2] tf placeholder
absolute_disparity = ABSOLUTE_DISPARITY,
use_confidence = USE_CONFIDENCE, # True,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 2.0,
error2_offset = 0, # 0.0025, # (0.05^2)
disp_wmin = 1.0, # minimal disparity to apply weight boosting for small disparities
disp_wmax = 8.0, # maximal disparity to apply weight boosting for small disparities
use_out = False) # use calculated disparity for disparity weight boosting (False - use target disparity)
G_loss = G_losses[0]
for n in range (1,len(partials)):
G_losses[n], _, _, _, _, _, _, _ = batchLoss(out_batch = outs[n], # [batch_size,(1..2)] tf_result
target_disparity_batch= target_disparity_batch, #next_element_tt['target_disparity'][:,center_tile_index:center_tile_index+1], # target_disparity_batch_center, # next_element_tt['target_disparity'], # target_disparity, ### target_d, # [batch_size] tf placeholder
gt_ds_batch = gt_ds_batch, # next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)], # gt_ds_batch_center, ## next_element_tt['gt_ds'], # gt_ds, ### gt, # [batch_size,2] tf placeholder
absolute_disparity = ABSOLUTE_DISPARITY,
use_confidence = USE_CONFIDENCE, # True,
lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1,
conf_pwr = 2.0,
gt_conf_offset = 0.08,
gt_conf_pwr = 2.0,
error2_offset = 0, # 0.0025, # (0.05^2)
disp_wmin = 1.0, # minimal disparity to apply weight boosting for small disparities
disp_wmax = 8.0, # maximal disparity to apply weight boosting for small disparities
use_out = False) # use calculated disparity for disparity weight boosting (False - use target disparity)
# G_loss += Glosses[n]*PARTIALS_WEIGHTS[n]
#tf_partial_weights
tf_wlosses = tf.multiply(G_losses, tf_partial_weights, name = "tf_wlosses")
G_losses_sum = tf.reduce_sum(tf_wlosses, name = "G_losses_sum")
if WLOSS_LAMBDA > 0.0:
W_loss = weightsLoss(inp_weights[0]) # inp_weights - list of tensors, currently - just [0]
# GW_loss = tf.add(G_loss, WLOSS_LAMBDA * W_loss, name = "GW_loss")
GW_loss = tf.add(G_losses_sum, WLOSS_LAMBDA * W_loss, name = "GW_loss")
else:
GW_loss = G_losses_sum # G_loss
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
gt_ds_batch = next_element_tt['gt_ds'])# [?:18]
tf_ph_G_loss = tf.placeholder(tf.float32,shape=None,name='G_loss_avg')
tf_ph_G_losses = tf.placeholder(tf.float32,shape=[len(partials)],name='G_losses_avg')
tf_ph_W_loss = tf.placeholder(tf.float32,shape=None,name='W_loss_avg')
tf_ph_GW_loss = tf.placeholder(tf.float32,shape=None,name='GW_loss_avg')
tf_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
tf_gtvar_diff = tf.placeholder(tf.float32,shape=None,name='gtvar_diff')
tf_img_test0 = tf.placeholder(tf.float32,shape=None,name='img_test0')
tf_img_test9 = tf.placeholder(tf.float32,shape=None,name='img_test9')
with tf.name_scope('sample'):
tf.summary.scalar("GW_loss", GW_loss)
tf.summary.scalar("G_loss", G_loss)
tf.summary.scalar("W_loss", W_loss)
tf.summary.scalar("sq_diff", _cost1)
tf.summary.scalar("gtvar_diff", GT_variance)
with tf.name_scope('epoch_average'):
# for i, tl in enumerate(tf_ph_G_losses):
# tf.summary.scalar("GW_loss_epoch_"+str(i), tl)
for i in range(tf_ph_G_losses.shape[0]):
tf.summary.scalar("G_loss_epoch_"+str(i), tf_ph_G_losses[i])
tf.summary.scalar("GW_loss_epoch", tf_ph_GW_loss)
tf.summary.scalar("G_loss_epoch", tf_ph_G_loss)
tf.summary.scalar("W_loss_epoch", tf_ph_W_loss)
tf.summary.scalar("sq_diff_epoch", tf_ph_sq_diff)
tf.summary.scalar("gtvar_diff", tf_gtvar_diff)
tf.summary.scalar("img_test0", tf_img_test0)
tf.summary.scalar("img_test9", tf_img_test9)
t_vars=tf.trainable_variables()
lr=tf.placeholder(tf.float32)
G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(GW_loss)
ROOT_PATH = './attic/nn_ds_neibs11_graph'+SUFFIX+"/"
TRAIN_PATH = ROOT_PATH + 'train'
TEST_PATH = ROOT_PATH + 'test'
TEST_PATH1 = ROOT_PATH + 'test1'
# CLEAN OLD STAFF
shutil.rmtree(TRAIN_PATH, ignore_errors=True)
shutil.rmtree(TEST_PATH, ignore_errors=True)
shutil.rmtree(TEST_PATH1, ignore_errors=True)
WIDTH=324
HEIGHT=242
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph)
test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph)
test_writer1 = tf.summary.FileWriter(TEST_PATH1, sess.graph)
loss_gw_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss_g_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss_g_train_hists= [np.empty(dataset_train_size, dtype=np.float32) for p in partials]
loss_w_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss_gw_test_hist= np.empty(dataset_test_size, dtype=np.float32)
# loss_g_test_hist= np.empty(dataset_test_size, dtype=np.float32)
loss_g_test_hists= [np.empty(dataset_test_size, dtype=np.float32) for p in partials]
loss_w_test_hist= np.empty(dataset_test_size, dtype=np.float32)
loss2_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss2_test_hist= np.empty(dataset_test_size, dtype=np.float32)
train_gw_avg = 0.0
train_g_avg = 0.0
train_g_avgs = [0.0]*len(partials)
train_w_avg = 0.0
test_gw_avg = 0.0
test_g_avg = 0.0
test_g_avgs = [0.0]*len(partials)
test_w_avg = 0.0
train2_avg = 0.0
test2_avg = 0.0
gtvar_train_hist= np.empty(dataset_train_size, dtype=np.float32)
gtvar_test_hist= np.empty(dataset_test_size, dtype=np.float32)
gtvar_train = 0.0
gtvar_test = 0.0
gtvar_train_avg = 0.0
gtvar_test_avg = 0.0
img_gain_test0 = 1.0
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()
file_index = epoch % num_train_variants
if epoch >=600:
learning_rate = LR600
elif epoch >=400:
learning_rate = LR400
elif epoch >=200:
learning_rate = LR200
elif epoch >=100:
learning_rate = LR100
else:
learning_rate = LR
# print ("sr1",file=sys.stderr,end=" ")
if (file_index == 0) and SHUFFLE_FILES:
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)
print_time(" Done")
print_time("Shuffling tile chunks ", end="")
shuffle_chunks_in_place (datasets_train, 1)
print_time(" Done")
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: datasets_train[file_index]['corr2d'],
target_disparity_train_placeholder: datasets_train[file_index]['target_disparity'],
gt_ds_train_placeholder: datasets_train[file_index]['gt_ds']})
for i in range(dataset_train_size):
try:
# train_summary,_, GW_loss_trained, G_loss_trained, W_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
train_summary,_, GW_loss_trained, G_losses_trained, W_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
[ merged,
G_opt,
GW_loss,
# G_loss,
G_losses,
W_loss,
outs[0],
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={lr: learning_rate,
tf_ph_GW_loss: train_gw_avg,
tf_ph_G_loss: train_g_avgs[0], #train_g_avg,
tf_ph_G_losses: train_g_avgs,
tf_ph_W_loss: train_w_avg,
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 #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
for nn, gl in enumerate(G_losses_trained):
loss_g_train_hists[nn][i] = gl
loss_w_train_hist[i] = W_loss_trained
loss2_train_hist[i] = out_cost1
gtvar_train_hist[i] = gt_variance
except tf.errors.OutOfRangeError:
print("train done at step %d"%(i))
break
train_gw_avg = np.average(loss_gw_train_hist).astype(np.float32)
train_g_avg = np.average(loss_g_train_hist).astype(np.float32)
for nn, lgth in enumerate(loss_g_train_hists):
train_g_avgs[nn] = np.average(lgth).astype(np.float32)
###############
train_w_avg = np.average(loss_w_train_hist).astype(np.float32)
train2_avg = np.average(loss2_train_hist).astype(np.float32)
gtvar_train_avg = np.average(gtvar_train_hist).astype(np.float32)
test_summaries = [0.0]*len(datasets_test)
tst_avg = [0.0]*len(datasets_test)
tst2_avg = [0.0]*len(datasets_test)
for ntest,dataset_test in enumerate(datasets_test):
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_test['corr2d'],
target_disparity_train_placeholder: dataset_test['target_disparity'],
gt_ds_train_placeholder: dataset_test['gt_ds']})
for i in range(dataset_test_size):
try:
test_summaries[ntest], GW_loss_tested, G_losses_tested, W_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
[merged,
GW_loss,
G_losses,
W_loss,
outs[0],
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={lr: learning_rate,
tf_ph_GW_loss: test_gw_avg,
tf_ph_G_loss: test_g_avg,
tf_ph_G_losses: test_g_avgs, # train_g_avgs, # temporary, there is o data fro test
tf_ph_W_loss: test_w_avg,
tf_ph_sq_diff: test2_avg,
tf_gtvar_diff: gtvar_test_avg,
tf_img_test0: img_gain_test0,
tf_img_test9: img_gain_test9}) # previous value of *_avg
loss_gw_test_hist[i] = GW_loss_tested
# loss_g_test_hist[i] = G_loss_tested
for nn, gl in enumerate(G_losses_tested):
loss_g_test_hists[nn][i] = gl
loss_w_test_hist[i] = W_loss_tested
loss2_test_hist[i] = out_cost1
gtvar_test_hist[i] = gt_variance
except tf.errors.OutOfRangeError:
print("test done at step %d"%(i))
break
test_gw_avg = np.average(loss_gw_test_hist).astype(np.float32)
# test_g_avg = np.average(loss_g_test_hist).astype(np.float32)
for nn, lgth in enumerate(loss_g_test_hists):
test_g_avgs[nn] = np.average(lgth).astype(np.float32)
test_w_avg = np.average(loss_w_test_hist).astype(np.float32)
tst_avg[ntest] = test_gw_avg
test2_avg = np.average(loss2_test_hist).astype(np.float32)
tst2_avg[ntest] = test2_avg
gtvar_test_avg = np.average(gtvar_test_hist).astype(np.float32)
train_writer.add_summary(train_summary, epoch)
test_writer.add_summary(test_summaries[0], epoch)
test_writer1.add_summary(test_summaries[1], epoch)
print_time("%d:%d -> %f %f %f (%f %f %f) dbg:%f %f"%(epoch,i,train_gw_avg, tst_avg[0], tst_avg[1], train2_avg, tst2_avg[0], tst2_avg[1], gtvar_train_avg, gtvar_test_avg))
if ((epoch + 1) == EPOCHS_TO_RUN) or (((epoch + 1) % EPOCHS_FULL_TEST) == 0):
###################################################
# Read the full image
###################################################
test_summaries_img = [0.0]*len(datasets_img)
# disp_out= np.empty((dataset_img_size * BATCH_SIZE), dtype=np.float32)
disp_out= np.empty((WIDTH*HEIGHT), dtype=np.float32)
for ntest,dataset_img in enumerate(datasets_img):
sess.run(iterator_tt.initializer, feed_dict={corr2d_train_placeholder: dataset_img['corr2d'],
target_disparity_train_placeholder: dataset_img['target_disparity'],
gt_ds_train_placeholder: dataset_img['gt_ds']})
for start_offs in range(0,disp_out.shape[0],BATCH_SIZE):
end_offs = min(start_offs+BATCH_SIZE,disp_out.shape[0])
try:
test_summaries_img[ntest], G_loss_tested, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, gt_variance = sess.run(
[merged,
G_loss,
outs[0],
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={lr: learning_rate,
tf_ph_GW_loss: test_gw_avg,
tf_ph_G_loss: test_g_avg,
tf_ph_G_losses: train_g_avgs, # temporary, there is o data fro test
tf_ph_W_loss: test_w_avg,
tf_ph_sq_diff: test2_avg,
tf_gtvar_diff: gtvar_test_avg,
tf_img_test0: img_gain_test0,
tf_img_test9: img_gain_test9}) # previous value of *_avg
except tf.errors.OutOfRangeError:
print("test done at step %d"%(i))
break
try:
disp_out[start_offs:end_offs] = output.flatten()
except ValueError:
print("dataset_img_size= %d, i=%d, output.shape[0]=%d "%(dataset_img_size, i, output.shape[0]))
break;
pass
result_file = result_files[ntest]
try:
os.makedirs(os.path.dirname(result_file))
except:
pass
rslt = np.concatenate([disp_out.reshape(-1,1), t_disp, gtruth],1)
np.save(result_file, rslt.reshape(HEIGHT,WIDTH,-1))
rslt = eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)
img_gain_test0 = rslt[0][0]/rslt[0][1]
img_gain_test9 = rslt[9][0]/rslt[9][1]
# Close writers
train_writer.close()
test_writer.close()
test_writer1.close()
#reports error: Exception ignored in: <bound method BaseSession.__del__ of <tensorflow.python.client.session.Session object at 0x7efc5f720ef0>> if there is no print before exit()
print("All done")
exit (0)
......@@ -58,13 +58,13 @@ 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 = 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)
ONLY_TILE = 12 # 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 = 0.0 # 50.0 # 5.0 # 1.0 # fraction of the W_loss (input layers weight non-uniformity) added to G_loss
WLOSS_LAMBDA = 2.0 #3.0 # 1.0 # 0.3 # 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
......@@ -439,7 +439,8 @@ 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_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_test_hvar = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # 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"
......@@ -574,7 +575,7 @@ Compare with other (not used) train sets (use 7 of each instead of 8, 8-th as te
#just testing:
#files_train_lvar = files_train_hvar
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_test_hvar = ["testTEST_R2_GT_1.5.tfrecords"] # Now same size as train! # ["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'] # State Street
......@@ -1066,8 +1067,8 @@ def weightsLoss(inp_weights): # [batch_size,(1..2)] tf_result
# 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")
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_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] + 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:
......
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