Skip to content

P
python3-imagej-tiff
Commit a1e5cb79 authored by Andrey Filippov's avatar Andrey Filippov
Browse files
Options

Split ns_ds_* program, added more costs

parent 0797a429
Show whitespace changes
Inline Side-by-side
Showing with 6842 additions and 0 deletions
nn_ds_neibs12.py 0 → 100644
View file @ a1e5cb79
#!/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 = 1 # 11 # 0 # 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 = 9 # 9 # 9 # 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 = False # 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
SPREAD_CONVERGENCE = True # Input target disparity to all nodes of the 1-st stage
INTER_CONVERGENCE = False# Input target disparity to all nodes of the 2-ndt stage
SUFFIX=(str(NET_ARCH1)+'-'+str(NET_ARCH2)+
(["R","A"][ABSOLUTE_DISPARITY]) +
(["NS","S8"][SYM8_SUB])+
"LMBD"+str(WLOSS_LAMBDA)+
(['_nG','_G'][SPREAD_CONVERGENCE])+
(['_nI','_I'][INTER_CONVERGENCE])
)
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],
11:[0, 0, 0, 0, 64, 32],
}
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,
input_global, #add to all layers (but first) if not None
layout,
reuse,
sym8 = False):
last_indx = None;
fc = []
inp_weights = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
if input_global is None:
inp = fc[-1]
else:
inp = tf.concat([fc[-1], input_global], axis = 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,
input_global, #add to all layers (but first) if not None
layout,
reuse=False):
last_indx = None;
fc = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
if input_global is None:
inp = fc[-1]
else:
inp = tf.concat([fc[-1], input_global], axis = 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]
input_global, # add to all layers (but first) if not None
layout1,
layout2,
inter_convergence,
sym8 = False,
only_tile = None, # just for debugging - feed only data from the center sub-network
partials = None):
center_index = (input.shape[1] - 1) // 2
with tf.name_scope("Siam_net"):
inp_weights = []
num_legs = input.shape[1] # == 25
if partials is None:
partials = [[True] * num_legs]
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,:],
input_global[:,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.zeros_like(ns))
inp_weights += ns_weights
reuse = True
outs = []
for n, _ in enumerate(partials):
outs.append(network_inter (tf.concat(inter_lists[n],
axis=1,
name='inter_tensor'+str(n)),
[None, input_global[:,center_index,:]][inter_convergence], # optionally feed all convergence values (from each tile of a cluster)
layout2,
reuse = (n > 0)))
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
target_disparity_cluster = tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")
corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
target_disparity_cluster], axis=2, name = "corr2d_Nx325")
if SPREAD_CONVERGENCE:
outs, inp_weights = networks_siam(input=corr2d_Nx325,
input_global = target_disparity_cluster,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
inter_convergence = INTER_CONVERGENCE,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE, #Remove/put None for normal operation
partials = partials)
else:
outs, inp_weights = networks_siam(input= corr2d_Nx325,
input_global = None,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
inter_convergence = None,
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_neibs12_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)
nn_ds_neibs13.py 0 → 100644
View file @ a1e5cb79
#!/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
import imagej_tiffwriter
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#3 # learning rate
LR50 = 3e-4
LR100 = 1e-4#4 #LR # 1e-4
LR200 = 3e-5#4 #LR100 # 3e-5
LR400 = 1e-5#5 #LR200 # 1e-5
LR600 = 3e-6#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 = 752# 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 # 1 # 0 # 0 # 0 # 1 # 11 # 0 # 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 = 10 # 10 # 9 # 10 # 9 # 9 # 9 # 9 # 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 = False # 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 = 4 # 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
SPREAD_CONVERGENCE = False # True # Input target disparity to all nodes of the 1-st stage
INTER_CONVERGENCE = False# Input target disparity to all nodes of the 2-nd stage
HOR_FLIP = True # randomly flip training data horizontally
SAVE_TIFFS = True # save Tiff files after each image evaluation
SUFFIX=(str(NET_ARCH1)+'-'+str(NET_ARCH2)+
(["R","A"][ABSOLUTE_DISPARITY]) +
(["NS","S8"][SYM8_SUB])+
"LMBD"+str(WLOSS_LAMBDA)+
(['_nG','_G'][SPREAD_CONVERGENCE])+
(['_nI','_I'][INTER_CONVERGENCE]) +
(['_nHF',"_HF"][HOR_FLIP])
)
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],
11:[0, 0, 0, 0, 64, 32],
}
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])
if HOR_FLIP:
if np.random.randint(2):
print_time("Performing horizontal flip", end=" ")
flip_horizontal([dataset])
print_time("Done")
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 flip_horizontal(datasets_data):
cluster_side = 2 * CLUSTER_RADIUS + 1
cluster_size = cluster_side * cluster_side
"""
TILE_LAYERS = 4
TILE_SIDE = 9 # 7
TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
"""
for rec in datasets_data:
corr2d = rec['corr2d'].reshape( (rec['corr2d'].shape[0], cluster_side, cluster_side, TILE_LAYERS, TILE_SIDE,TILE_SIDE))
target_disparity = rec['target_disparity'].reshape((rec['corr2d'].shape[0], cluster_side, cluster_side, -1))
gt_ds = rec['gt_ds'].reshape( (rec['corr2d'].shape[0], cluster_side, cluster_side, -1))
"""
Horizontal flip of tiles
"""
corr2d = corr2d[:,:,::-1,...]
target_disparity = target_disparity[:,:,::-1,...]
gt_ds = gt_ds[:,:,::-1,...]
corr2d[:,:,:,0,:,:] = corr2d[:,:,:,0,::-1,:] # flip vertical layer0 (hor)
corr2d[:,:,:,1,:,:] = corr2d[:,:,:,1,:,::-1] # flip horizontal layer1 (vert)
corr2d_2 = corr2d[:,:,:,3,::-1,:].copy() # flip vertical layer3 (diago)
corr2d[:,:,:,3,:,:] = corr2d[:,:,:,2,::-1,:] # flip vertical layer2 (diago)
corr2d[:,:,:,2,:,:] = corr2d_2
rec['corr2d'] = corr2d.reshape((corr2d.shape[0],-1))
rec['target_disparity'] = target_disparity.reshape((target_disparity.shape[0],-1))
rec['gt_ds'] = gt_ds.reshape((gt_ds.shape[0],-1))
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 result_npy_to_tiff(npy_path, absolute, fix_nan):
"""
@param npy_path full path to the npy file with 4-layer data (242,324,4) - nn_disparity(offset), target_disparity, gt disparity, gt strength
data will be written as 4-layer tiff, extension '.npy' replaced with '.tiff'
@param absolute - True - the first layer contains absolute disparity, False - difference from target_disparity
@param fix_nan - replace nan in target_disparity with 0 to apply offset, target_disparity will still contain nan
"""
tiff_path = npy_path.replace('.npy','.tiff')
data = np.load(npy_path) #(324,242,4) [nn_disp, target_disp,gt_disp, gt_conf]
if not absolute:
if fix_nan:
data[...,0] += np.nan_to_num(data[...,1], copy=True)
else:
data[...,0] += data[...,1]
data = data.transpose(2,0,1)
imagej_tiffwriter.save(tiff_path,data[...,np.newaxis])
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 = "/home/eyesis/x3d_data/data_sets/tf_data_5x5_main_5" # 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_lvar1 = ["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_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 used up to -49
#files_img = ['1527182810_096892-v02'] # plane far
files_img = ['1527256858_150165-v01',# State Street
'1527257933_150165-v04', # overlook
'1527256816_150165-v02', # State Street
'1527182802_096892-v02', # plane near plane
'1527182805_096892-v02', # plane midrange used up to -49 plane
'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:
print_time("Reading resuts from "+result_file, end=" ")
eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)
print_time("Done")
print_time("Saving resuts to tiff", end=" ")
result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True)
print_time("Done")
except:
print_time(" - does not exist")
pass
datasets_img = []
gtruths = []
t_disps = []
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")
gtruths.append(datasets_img[-1]['gt_ds'].copy())
t_disps.append(datasets_img[-1]['target_disparity'].reshape([-1,1]).copy())
#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,
input_global, #add to all layers (but first) if not None
layout,
reuse,
sym8 = False):
last_indx = None;
fc = []
inp_weights = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
if input_global is None:
inp = fc[-1]
else:
inp = tf.concat([fc[-1], input_global], axis = 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,
input_global, #add to all layers (but first) if not None
layout,
reuse=False):
last_indx = None;
fc = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
if input_global is None:
inp = fc[-1]
else:
inp = tf.concat([fc[-1], input_global], axis = 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]
input_global, # add to all layers (but first) if not None
layout1,
layout2,
inter_convergence,
sym8 = False,
only_tile = None, # just for debugging - feed only data from the center sub-network
partials = None):
center_index = (input.shape[1] - 1) // 2
with tf.name_scope("Siam_net"):
inp_weights = []
num_legs = input.shape[1] # == 25
if partials is None:
partials = [[True] * num_legs]
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]) :
if input_global is None:
ig = None
else:
ig =input_global[:,i,:]
ns, ns_weights = network_sub(input[:,i,:],
ig, # input_global[:,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.zeros_like(ns))
inp_weights += ns_weights
reuse = True
outs = []
for n, _ in enumerate(partials):
if input_global is None:
ig = None
else:
ig =input_global[:,center_index,:]
outs.append(network_inter (tf.concat(inter_lists[n],
axis=1,
name='inter_tensor'+str(n)),
[None, ig][inter_convergence], # optionally feed all convergence values (from each tile of a cluster)
layout2,
reuse = (n > 0)))
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
target_disparity_cluster = tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")
corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
target_disparity_cluster], axis=2, name = "corr2d_Nx325")
if SPREAD_CONVERGENCE:
outs, inp_weights = networks_siam(input=corr2d_Nx325,
input_global = target_disparity_cluster,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
inter_convergence = INTER_CONVERGENCE,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE, #Remove/put None for normal operation
partials = partials)
else:
outs, inp_weights = networks_siam(input= corr2d_Nx325,
input_global = None,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
inter_convergence = False,
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_neibs13_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
elif epoch >=50:
learning_rate = LR50
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)) and (len(datasets_img) > 0) :
last_epoch = (epoch + 1) == EPOCHS_TO_RUN
d_img = [datasets_img[0]]
if last_epoch:
d_img = datasets_img
###################################################
# Read the full image
###################################################
test_summaries_img = [0.0]*len(d_img) # 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(d_img): # 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)
rslt = np.concatenate([disp_out.reshape(-1,1), t_disps[ntest], gtruths[ntest]],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]
if SAVE_TIFFS:
result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True)
# 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)
nn_ds_neibs14.py 0 → 100644
View file @ a1e5cb79
#!/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
import imagej_tiffwriter
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 = 752# 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 # 0 # 0 # 1 # 11 # 0 # 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 = 9 # 10 # 9 # 9 # 9 # 9 # 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 = False # 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 = 4 # 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
SPREAD_CONVERGENCE = False # True # Input target disparity to all nodes of the 1-st stage
INTER_CONVERGENCE = False# Input target disparity to all nodes of the 2-nd stage
HOR_FLIP = True # randomly flip training data horizontally
SAVE_TIFFS = True # save Tiff files after each image evaluation
BATCH_WEIGHTS= [0.2, 0.8, 0.2, 0.8] # lvar, hvar, lvar1, hvar1 (increase importance of non-flat clusters
DISP_DIFF_CAP= 0.3 # cap disparity difference (do not increase loss above)
SUFFIX=(str(NET_ARCH1)+'-'+str(NET_ARCH2)+
(["R","A"][ABSOLUTE_DISPARITY]) +
(["NS","S8"][SYM8_SUB])+
"LMBD"+str(WLOSS_LAMBDA)+
(['_nG','_G'][SPREAD_CONVERGENCE])+
(['_nI','_I'][INTER_CONVERGENCE]) +
(['_nHF',"_HF"][HOR_FLIP]) +
('_CP'+str(DISP_DIFF_CAP))
)
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],
11:[0, 0, 0, 0, 64, 32],
}
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])
if HOR_FLIP:
if np.random.randint(2):
print_time("Performing horizontal flip", end=" ")
flip_horizontal([dataset])
print_time("Done")
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 flip_horizontal(datasets_data):
cluster_side = 2 * CLUSTER_RADIUS + 1
cluster_size = cluster_side * cluster_side
"""
TILE_LAYERS = 4
TILE_SIDE = 9 # 7
TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
"""
for rec in datasets_data:
corr2d = rec['corr2d'].reshape( (rec['corr2d'].shape[0], cluster_side, cluster_side, TILE_LAYERS, TILE_SIDE,TILE_SIDE))
target_disparity = rec['target_disparity'].reshape((rec['corr2d'].shape[0], cluster_side, cluster_side, -1))
gt_ds = rec['gt_ds'].reshape( (rec['corr2d'].shape[0], cluster_side, cluster_side, -1))
"""
Horizontal flip of tiles
"""
corr2d = corr2d[:,:,::-1,...]
target_disparity = target_disparity[:,:,::-1,...]
gt_ds = gt_ds[:,:,::-1,...]
corr2d[:,:,:,0,:,:] = corr2d[:,:,:,0,::-1,:] # flip vertical layer0 (hor)
corr2d[:,:,:,1,:,:] = corr2d[:,:,:,1,:,::-1] # flip horizontal layer1 (vert)
corr2d_2 = corr2d[:,:,:,3,::-1,:].copy() # flip vertical layer3 (diago)
corr2d[:,:,:,3,:,:] = corr2d[:,:,:,2,::-1,:] # flip vertical layer2 (diago)
corr2d[:,:,:,2,:,:] = corr2d_2
rec['corr2d'] = corr2d.reshape((corr2d.shape[0],-1))
rec['target_disparity'] = target_disparity.reshape((target_disparity.shape[0],-1))
rec['gt_ds'] = gt_ds.reshape((gt_ds.shape[0],-1))
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 result_npy_to_tiff(npy_path, absolute, fix_nan):
"""
@param npy_path full path to the npy file with 4-layer data (242,324,4) - nn_disparity(offset), target_disparity, gt disparity, gt strength
data will be written as 4-layer tiff, extension '.npy' replaced with '.tiff'
@param absolute - True - the first layer contains absolute disparity, False - difference from target_disparity
@param fix_nan - replace nan in target_disparity with 0 to apply offset, target_disparity will still contain nan
"""
tiff_path = npy_path.replace('.npy','.tiff')
data = np.load(npy_path) #(324,242,4) [nn_disp, target_disp,gt_disp, gt_conf]
if not absolute:
if fix_nan:
data[...,0] += np.nan_to_num(data[...,1], copy=True)
else:
data[...,0] += data[...,1]
data = data.transpose(2,0,1)
imagej_tiffwriter.save(tiff_path,data[...,np.newaxis])
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_lvar1 = ["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_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 used up to -49
#files_img = ['1527182810_096892-v02'] # plane far
files_img = ['1527256858_150165-v01',# State Street - overlook???
'1527257933_150165-v04', # overlook
'1527256816_150165-v02', # State Street - overlook?
'1527182802_096892-v02', # plane near plane+overlook
'1527182805_096892-v02', # plane midrange used up to -49 plane+overlook
'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:
print_time("Reading resuts from "+result_file, end=" ")
eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)
print_time("Done")
print_time("Saving resuts to tiff", end=" ")
result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True)
print_time("Done")
except:
print_time(" - does not exist")
pass
datasets_img = []
gtruths = []
t_disps = []
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")
gtruths.append(datasets_img[-1]['gt_ds'].copy())
t_disps.append(datasets_img[-1]['target_disparity'].reshape([-1,1]).copy())
#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})
tf_batch_weights = tf.placeholder(shape=(None,), dtype=tf.float32, name = "batch_weights") # way to increase importance of the high variance clusters
feed_batch_weights = np.array(BATCH_WEIGHTS*(BATCH_SIZE//len(BATCH_WEIGHTS)), dtype=np.float32)
feed_batch_weight_1 = np.array([1.0], dtype=np.float32)
#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,
input_global, #add to all layers (but first) if not None
layout,
reuse,
sym8 = False):
last_indx = None;
fc = []
inp_weights = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
if input_global is None:
inp = fc[-1]
else:
inp = tf.concat([fc[-1], input_global], axis = 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,
input_global, #add to all layers (but first) if not None
layout,
reuse=False):
last_indx = None;
fc = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
if input_global is None:
inp = fc[-1]
else:
inp = tf.concat([fc[-1], input_global], axis = 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]
input_global, # add to all layers (but first) if not None
layout1,
layout2,
inter_convergence,
sym8 = False,
only_tile = None, # just for debugging - feed only data from the center sub-network
partials = None):
center_index = (input.shape[1] - 1) // 2
with tf.name_scope("Siam_net"):
inp_weights = []
num_legs = input.shape[1] # == 25
if partials is None:
partials = [[True] * num_legs]
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]) :
if input_global is None:
ig = None
else:
ig =input_global[:,i,:]
ns, ns_weights = network_sub(input[:,i,:],
ig, # input_global[:,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.zeros_like(ns))
inp_weights += ns_weights
reuse = True
outs = []
for n, _ in enumerate(partials):
if input_global is None:
ig = None
else:
ig =input_global[:,center_index,:]
outs.append(network_inter (tf.concat(inter_lists[n],
axis=1,
name='inter_tensor'+str(n)),
[None, ig][inter_convergence], # optionally feed all convergence values (from each tile of a cluster)
layout2,
reuse = (n > 0)))
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
batch_weights, # [batch_size] now batch index % 4 - different sources, even - low variance, odd - high variance
disp_diff_cap = 10.0, # cap disparity difference to this value (give up on large errors)
absolute_disparity = True, #when false there should be no activation on disparity output !
use_confidence = False,
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")
tf_disp_diff_cap2= tf.constant(disp_diff_cap*disp_diff_cap, dtype=tf.float32, name="disp_diff_cap2")
if gt_conf_pwr == 0:
w = tf.ones((out_batch.shape[0]), dtype=tf.float32,name="w_ones")
else:
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.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.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_diff2_capped = tf.minimum(out_diff2, tf_disp_diff_cap2, name = "out_diff2_capped")
out_wdiff2 = tf.multiply (out_diff2_capped, 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??
if batch_weights.shape[0] > 1:
dispw_batch = tf.multiply (dispw_comp, batch_weights, name = "dispw_batch")# apply weights for high/low variance and sources
else:
dispw_batch = tf.multiply (dispw_comp, tf_1f, name = "dispw_batch")# apply weights for high/low variance and sources
dispw_sum = tf.reduce_sum(dispw_batch, name = "dispw_sum")
idispw_sum = tf.divide(tf_1f, dispw_sum, name = "idispw_sum")
dispw_norm = tf.multiply (dispw_batch, 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
target_disparity_cluster = tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")
corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
target_disparity_cluster], axis=2, name = "corr2d_Nx325")
if SPREAD_CONVERGENCE:
outs, inp_weights = networks_siam(input=corr2d_Nx325,
input_global = target_disparity_cluster,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
inter_convergence = INTER_CONVERGENCE,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE, #Remove/put None for normal operation
partials = partials)
else:
outs, inp_weights = networks_siam(input= corr2d_Nx325,
input_global = None,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
inter_convergence = False,
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
batch_weights = tf_batch_weights,
disp_diff_cap = DISP_DIFF_CAP,
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
batch_weights = tf_batch_weights,
disp_diff_cap = DISP_DIFF_CAP,
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_neibs14_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={tf_batch_weights: feed_batch_weights,
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={tf_batch_weights: feed_batch_weight_1 , # feed_batch_weights,
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)) and (len(datasets_img) > 0) :
last_epoch = (epoch + 1) == EPOCHS_TO_RUN
d_img = [datasets_img[0]]
if last_epoch:
d_img = datasets_img
###################################################
# Read the full image
###################################################
test_summaries_img = [0.0]*len(d_img) # 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(d_img): # 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(
## test_summaries_img[ntest], G_loss_tested, output = sess.run(
test_summaries_img[ntest],output = 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={
tf_batch_weights: feed_batch_weight_1, # feed_batch_weights,
# 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)
rslt = np.concatenate([disp_out.reshape(-1,1), t_disps[ntest], gtruths[ntest]],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]
if SAVE_TIFFS:
result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True)
# 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)
nn_ds_neibs15.py 0 → 100644
View file @ a1e5cb79
#!/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
import imagej_tiffwriter
import qcstereo_network
import qcstereo_losses
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 = 1600 # 752# 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 # 0 # 0 # 0 # 1 # 11 # 0 # 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 = 10 # 9 # 10 # 9 # 9 # 9 # 9 # 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 = False # 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
SLOSS_LAMBDA = 0.3 # 0.1 #0.01 # 0.3 # date train files each this many epochs. 0 - do not update
WBORDERS_ZERO = True # Border conditions for first layer weights: False - free, True - tied to 0
MAX_FILES_PER_GROUP = 3 #4 # 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
SPREAD_CONVERGENCE = False # True # Input target disparity to all nodes of the 1-st stage
INTER_CONVERGENCE = False# Input target disparity to all nodes of the 2-nd stage
HOR_FLIP = True # randomly flip training data horizontally
SAVE_TIFFS = True # save Tiff files after each image evaluation
BATCH_WEIGHTS= [0.2, 0.8, 0.2, 0.8] # lvar, hvar, lvar1, hvar1 (increase importance of non-flat clusters
DISP_DIFF_CAP= 0.3 # cap disparity difference (do not increase loss above)
DISP_DIFF_SLOPE= 0.03 # allow squared error to grow above DISP_DIFF_CAP
SUFFIX=(str(NET_ARCH1)+'-'+str(NET_ARCH2)+
(["R","A"][ABSOLUTE_DISPARITY]) +
(["NS","S8"][SYM8_SUB])+
"WLAM"+str(WLOSS_LAMBDA)+
"SLAM"+str(SLOSS_LAMBDA)+
(['_nG','_G'][SPREAD_CONVERGENCE])+
(['_nI','_I'][INTER_CONVERGENCE]) +
(['_nHF',"_HF"][HOR_FLIP]) +
('_CP'+str(DISP_DIFF_CAP)) +
('_S'+str(DISP_DIFF_SLOPE))
)
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],
11:[0, 0, 0, 0, 64, 32],
}
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])
if HOR_FLIP:
if np.random.randint(2):
print_time("Performing horizontal flip", end=" ")
flip_horizontal([dataset])
print_time("Done")
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 flip_horizontal(datasets_data):
cluster_side = 2 * CLUSTER_RADIUS + 1
cluster_size = cluster_side * cluster_side
"""
TILE_LAYERS = 4
TILE_SIDE = 9 # 7
TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
"""
for rec in datasets_data:
corr2d = rec['corr2d'].reshape( (rec['corr2d'].shape[0], cluster_side, cluster_side, TILE_LAYERS, TILE_SIDE,TILE_SIDE))
target_disparity = rec['target_disparity'].reshape((rec['corr2d'].shape[0], cluster_side, cluster_side, -1))
gt_ds = rec['gt_ds'].reshape( (rec['corr2d'].shape[0], cluster_side, cluster_side, -1))
"""
Horizontal flip of tiles
"""
corr2d = corr2d[:,:,::-1,...]
target_disparity = target_disparity[:,:,::-1,...]
gt_ds = gt_ds[:,:,::-1,...]
corr2d[:,:,:,0,:,:] = corr2d[:,:,:,0,::-1,:] # flip vertical layer0 (hor)
corr2d[:,:,:,1,:,:] = corr2d[:,:,:,1,:,::-1] # flip horizontal layer1 (vert)
corr2d_2 = corr2d[:,:,:,3,::-1,:].copy() # flip vertical layer3 (diago)
corr2d[:,:,:,3,:,:] = corr2d[:,:,:,2,::-1,:] # flip vertical layer2 (diago)
corr2d[:,:,:,2,:,:] = corr2d_2
rec['corr2d'] = corr2d.reshape((corr2d.shape[0],-1))
rec['target_disparity'] = target_disparity.reshape((target_disparity.shape[0],-1))
rec['gt_ds'] = gt_ds.reshape((gt_ds.shape[0],-1))
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 result_npy_to_tiff(npy_path, absolute, fix_nan):
"""
@param npy_path full path to the npy file with 4-layer data (242,324,4) - nn_disparity(offset), target_disparity, gt disparity, gt strength
data will be written as 4-layer tiff, extension '.npy' replaced with '.tiff'
@param absolute - True - the first layer contains absolute disparity, False - difference from target_disparity
@param fix_nan - replace nan in target_disparity with 0 to apply offset, target_disparity will still contain nan
"""
tiff_path = npy_path.replace('.npy','.tiff')
data = np.load(npy_path) #(324,242,4) [nn_disp, target_disp,gt_disp, gt_conf]
if not absolute:
if fix_nan:
data[...,0] += np.nan_to_num(data[...,1], copy=True)
else:
data[...,0] += data[...,1]
data = data.transpose(2,0,1)
imagej_tiffwriter.save(tiff_path,data[...,np.newaxis])
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_lvar1 = ["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_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 used up to -49
#files_img = ['1527182810_096892-v02'] # plane far
files_img = ['1527256858_150165-v01',# State Street - overlook???
'1527257933_150165-v04', # overlook
'1527256816_150165-v02', # State Street - overlook?
'1527182802_096892-v02', # plane near plane+overlook
'1527182805_096892-v02', # plane midrange used up to -49 plane+overlook
'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:
print_time("Reading resuts from "+result_file, end=" ")
eval_results(result_file, ABSOLUTE_DISPARITY,radius=CLUSTER_RADIUS)
print_time("Done")
print_time("Saving resuts to tiff", end=" ")
result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True)
print_time("Done")
except:
print_time(" - does not exist")
pass
datasets_img = []
gtruths = []
t_disps = []
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")
gtruths.append(datasets_img[-1]['gt_ds'].copy())
t_disps.append(datasets_img[-1]['target_disparity'].reshape([-1,1]).copy())
#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})
tf_batch_weights = tf.placeholder(shape=(None,), dtype=tf.float32, name = "batch_weights") # way to increase importance of the high variance clusters
feed_batch_weights = np.array(BATCH_WEIGHTS*(BATCH_SIZE//len(BATCH_WEIGHTS)), dtype=np.float32)
feed_batch_weight_1 = np.array([1.0], dtype=np.float32)
#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)
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
target_disparity_cluster = tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")
corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
target_disparity_cluster], axis=2, name = "corr2d_Nx325")
if SPREAD_CONVERGENCE:
outs, inp_weights = qcstereo_network.networks_siam(
input = corr2d_Nx325,
input_global = target_disparity_cluster,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
inter_convergence = INTER_CONVERGENCE,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE, #Remove/put None for normal operation
partials = partials,
use_confidence= USE_CONFIDENCE)
else:
outs, inp_weights = qcstereo_network.networks_siam(
input= corr2d_Nx325,
input_global = None,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
inter_convergence = False,
sym8 = SYM8_SUB,
only_tile = ONLY_TILE, #Remove/put None for normal operation
partials = partials,
use_confidence= USE_CONFIDENCE)
# 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_clust = next_element_tt['gt_ds']
#gt_ds_batch = next_element_tt['gt_ds'][:,2 * center_tile_index: 2 * (center_tile_index +1)]
gt_ds_batch = gt_ds_batch_clust[:,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 = qcstereo_losses.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
batch_weights = tf_batch_weights,
disp_diff_cap = DISP_DIFF_CAP,
disp_diff_slope= DISP_DIFF_SLOPE,
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], _, _, _, _, _, _, _ = qcstereo_losses.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
batch_weights = tf_batch_weights,
disp_diff_cap = DISP_DIFF_CAP,
disp_diff_slope= DISP_DIFF_SLOPE,
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)
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 SLOSS_LAMBDA > 0:
S_loss, rslt_cost_nw, rslt_cost_w, rslt_d , rslt_avg_disparity, rslt_gt_disparity, rslt_offs = qcstereo_losses.smoothLoss(
out_batch = outs[0], # [batch_size,(1..2)] tf_result
target_disparity_batch = target_disparity_batch, # [batch_size] tf placeholder
gt_ds_batch_clust = gt_ds_batch_clust, # [batch_size,25,2] tf placeholder
absolute_disparity = ABSOLUTE_DISPARITY, #when false there should be no activation on disparity output !
cluster_radius = CLUSTER_RADIUS)
GS_loss = tf.add(G_losses_sum, SLOSS_LAMBDA * S_loss, name = "GS_loss")
else:
S_loss = tf.constant(0.0, dtype=tf.float32,name = "S_loss")
GS_loss = G_losses_sum # G_loss
# G_loss += Glosses[n]*PARTIALS_WEIGHTS[n]
#tf_partial_weights
if WLOSS_LAMBDA > 0.0:
W_loss = qcstereo_losses.weightsLoss(
inp_weights = inp_weights[0], # inp_weights - list of tensors, currently - just [0]
tile_layers= TILE_LAYERS, # 4
tile_side = TILE_SIDE, # 9
wborders_zero = WBORDERS_ZERO)
# GW_loss = tf.add(G_loss, WLOSS_LAMBDA * W_loss, name = "GW_loss")
GW_loss = tf.add(GS_loss, WLOSS_LAMBDA * W_loss, name = "GW_loss")
else:
GW_loss = GS_loss # 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_S_loss = tf.placeholder(tf.float32,shape=None,name='S_loss_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("S_loss", S_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("S_loss_epoch", tf_ph_S_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_neibs15_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_s_train_hist= np.empty(dataset_train_size, dtype=np.float32)
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_s_test_hist= np.empty(dataset_test_size, dtype=np.float32)
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
train_s_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
test_s_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, S_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(
[ merged,
G_opt,
GW_loss,
# G_loss,
G_losses,
S_loss,
W_loss,
outs[0],
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={tf_batch_weights: feed_batch_weights,
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_S_loss: train_s_avg,
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_s_train_hist[i] = S_loss_trained
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_s_avg = np.average(loss_s_train_hist).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, S_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_losses,
S_loss,
W_loss,
outs[0],
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={tf_batch_weights: feed_batch_weight_1 , # feed_batch_weights,
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_S_loss: test_s_avg,
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_s_test_hist[i] = S_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)
for nn, lgth in enumerate(loss_g_test_hists):
test_g_avgs[nn] = np.average(lgth).astype(np.float32)
test_s_avg = np.average(loss_s_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)) and (len(datasets_img) > 0) :
last_epoch = (epoch + 1) == EPOCHS_TO_RUN
d_img = [datasets_img[0]]
if last_epoch:
d_img = datasets_img
###################################################
# Read the full image
###################################################
test_summaries_img = [0.0]*len(d_img) # datasets_img)
# disp_out= np.empty((dataset_img_size * BATCH_SIZE), dtype=np.float32)
disp_out= np.empty((WIDTH*HEIGHT), dtype=np.float32)
dbg_cost_nw= np.empty((WIDTH*HEIGHT), dtype=np.float32)
dbg_cost_w= np.empty((WIDTH*HEIGHT), dtype=np.float32)
dbg_d= np.empty((WIDTH*HEIGHT), dtype=np.float32)
dbg_avg_disparity = np.empty((WIDTH*HEIGHT), dtype=np.float32)
dbg_gt_disparity = np.empty((WIDTH*HEIGHT), dtype=np.float32)
dbg_offs = np.empty((WIDTH*HEIGHT), dtype=np.float32)
for ntest,dataset_img in enumerate(d_img): # 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(
## test_summaries_img[ntest], G_loss_tested, output = sess.run(
test_summaries_img[ntest],output, cost_nw, cost_w, dd, avg_disparity, gt_disparity, offs = sess.run(
[merged,
## G_loss,
outs[0], # {?,1]
rslt_cost_nw, #[?,]
rslt_cost_w, #[?,]
rslt_d, #[?,]
rslt_avg_disparity,
rslt_gt_disparity,
rslt_offs
# _disp_slice,
# _d_gt_slice,
# _out_diff,
# _out_diff2,
# _w_norm,
# _out_wdiff2,
# _cost1,
# GT_variance
],
feed_dict={
tf_batch_weights: feed_batch_weight_1, # feed_batch_weights,
# 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 for test
tf_ph_S_loss: test_s_avg,
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()
dbg_cost_nw[start_offs:end_offs] = cost_nw.flatten()
dbg_cost_w [start_offs:end_offs] = cost_w.flatten()
dbg_d[start_offs:end_offs] = dd.flatten()
dbg_avg_disparity[start_offs:end_offs] = avg_disparity.flatten()
dbg_gt_disparity[start_offs:end_offs] = gt_disparity.flatten()
dbg_offs[start_offs:end_offs] = offs.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)
rslt = np.concatenate(
[disp_out.reshape(-1,1),
t_disps[ntest],
gtruths[ntest],
dbg_cost_nw.reshape(-1,1),
dbg_cost_w.reshape(-1,1),
dbg_d.reshape(-1,1),
dbg_avg_disparity.reshape(-1,1),
dbg_gt_disparity.reshape(-1,1),
dbg_offs.reshape(-1,1)],1)
np.save(result_file, rslt.reshape(HEIGHT,WIDTH,-1))
rslt = 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]
if SAVE_TIFFS:
result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True)
# 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)
qcstereo_losses.py 0 → 100644
View file @ a1e5cb79
#!/usr/bin/env python3
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
#from numpy import float64
import numpy as np
import tensorflow as tf
def smoothLoss(out_batch, # [batch_size,(1..2)] tf_result
target_disparity_batch, # [batch_size] tf placeholder
gt_ds_batch_clust, # [batch_size,25,2] tf placeholder
absolute_disparity = False, #when false there should be no activation on disparity output !
cluster_radius = 2):
with tf.name_scope("SmoothLoss"):
center_tile_index = 2 * cluster_radius * (cluster_radius + 1)
cluster_side = 2 * cluster_radius + 1
cluster_size = cluster_side * cluster_side
w_corner = 0.7
w8 = [w_corner,1.0,w_corner,1.0,1.0,w_corner,1.0,w_corner]
w8 = [w/sum(w8) for w in w8]
tf_w8=tf.reshape(tf.constant(w8, dtype=tf.float32, name="w8_"), shape=[1,-1], name="w8")
i8 = []
for dy in [-1,0,1]:
for dx in [-1,0,1]:
if (dy != 0) or (dx != 0):
i8.append(center_tile_index+(dy*cluster_side)+dx)
tf_gt_ds_all = tf.reshape(gt_ds_batch_clust,[-1,cluster_size,gt_ds_batch_clust.shape[1]//cluster_size], name = "gt_ds_all")
tf_neibs8 = tf.gather(tf_gt_ds_all, indices = i8, axis = 1, name = "neibs8")
tf_gt_disparity8 = tf.reshape(tf_neibs8[:,:,0], [-1,8], name = "gt8_disparity") # (?,8)
tf_gt_strength8 = tf.reshape(tf_neibs8[:,:,1], [-1,8], name = "gt8_strength") # (?,8)
tf_w = tf.multiply(tf_gt_strength8, tf_w8, name = "w")
tf_dw = tf.multiply(tf_gt_disparity8, tf_w, name = "dw")
tf_sum_w = tf.reduce_sum(tf_w, axis = 1, name = "sum_w")
tf_sum_dw = tf.reduce_sum(tf_dw, axis = 1, name = "sum_dw")
tf_avg_disparity = tf.divide(tf_sum_dw, tf_sum_w, name = "avg_disparity") # (?,)
tf_gt_disparity = tf.reshape(tf_gt_ds_all[:,center_tile_index,0], [-1], name = "gt_disparity") # (?,)
"""
It is good to limit tf_gt_disparityby min/max (+margin) tf.reduce_min(tf_gt_disparity8, axis=1,...) but there could be zeros caused by undefined GT for the tile
"""
tf_gt_strength = tf.reshape(tf_gt_ds_all[:,center_tile_index,1], [-1], name = "gt_strength") # (?,)
tf_d0 = tf.abs(tf_gt_disparity - tf_avg_disparity, name = "tf_d0")
tf_d = tf.maximum(tf_d0, 0.001, name = "tf_d")
tf_d2 = tf.multiply(tf_d, tf_d, name = "tf_d2")
tf_out = tf.reshape(out_batch[:,0],[-1], name = "tf_out")
if absolute_disparity:
tf_out_disparity = tf_out
else:
tf_out_disparity = tf.add(tf_out, tf.reshape(target_disparity_batch,[-1]),name = "out_disparity")
tf_offs = tf.subtract(tf_out_disparity, tf_avg_disparity, name = "offs")
tf_offs2 = tf.multiply(tf_offs, tf_offs, name = "offs2")
# tf_parab = tf.divide(tf_offs2, tf_d, name = "parab")
# tf_cost_nlim = tf.subtract(tf_d2, tf_offs2, name = "cost_nlim")
# tf_cost_nw = tf.maximum(tf_d - tf_parab, 0.0, name = "cost_nw")
tf_cost_nw = tf.maximum(tf_d2 - tf_offs2, 0.0, name = "cost_nw")
tf_cost_w = tf.multiply(tf_cost_nw, tf_gt_strength, name = "cost_w")
tf_sum_wc = tf.reduce_sum(tf_gt_strength, name = "sum_wc")
tf_sum_costw = tf.reduce_sum(tf_cost_w, name = "sum_costw")
tf_cost = tf.divide(tf_sum_costw, tf_sum_wc, name = "cost")
return tf_cost, tf_cost_nw, tf_cost_w, tf_d , tf_avg_disparity, tf_gt_disparity, tf_offs
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
batch_weights, # [batch_size] now batch index % 4 - different sources, even - low variance, odd - high variance
disp_diff_cap = 10.0, # cap disparity difference to this value (give up on large errors)
disp_diff_slope= 0.0, #allow squared error to grow above disp_diff_cap
absolute_disparity = False, #when false there should be no activation on disparity output !
use_confidence = False,
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")
tf_disp_diff_cap2= tf.constant(disp_diff_cap*disp_diff_cap, dtype=tf.float32, name="disp_diff_cap2")
tf_disp_diff_slope= tf.constant(disp_diff_slope, dtype=tf.float32, name="disp_diff_slope")
if gt_conf_pwr == 0:
w = tf.ones((out_batch.shape[0]), dtype=tf.float32,name="w_ones")
else:
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.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.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")
pre_cap0 = tf.abs(out_diff, name = "pre_cap0")
pre_cap = tf.multiply(pre_cap0, tf_disp_diff_slope, name = "pre_cap")
diff_cap = tf.add(pre_cap, tf_disp_diff_cap2, name = "diff_cap")
out_diff2_capped = tf.minimum(out_diff2, diff_cap, name = "out_diff2_capped")
out_wdiff2 = tf.multiply (out_diff2_capped, 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??
if batch_weights.shape[0] > 1:
dispw_batch = tf.multiply (dispw_comp, batch_weights, name = "dispw_batch")# apply weights for high/low variance and sources
else:
dispw_batch = tf.multiply (dispw_comp, tf_1f, name = "dispw_batch")# apply weights for high/low variance and sources
dispw_sum = tf.reduce_sum(dispw_batch, name = "dispw_sum")
idispw_sum = tf.divide(tf_1f, dispw_sum, name = "idispw_sum")
dispw_norm = tf.multiply (dispw_batch, 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,
tile_layers,
tile_side,
wborders_zero):
# [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")
tf_inp = tf.reshape(inp_weights[:tile_layers * tile_side * tile_side,:], [tile_layers, tile_side, 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
qcstereo_network.py 0 → 100644
View file @ a1e5cb79
#!/usr/bin/env python3
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
#from numpy import float64
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
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,
input_global, #add to all layers (but first) if not None
layout,
reuse,
sym8 = False):
last_indx = None;
fc = []
inp_weights = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
if input_global is None:
inp = fc[-1]
else:
inp = tf.concat([fc[-1], input_global], axis = 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,
input_global, #add to all layers (but first) if not None
layout,
reuse=False,
use_confidence=False):
last_indx = None;
fc = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
if input_global is None:
inp = fc[-1]
else:
inp = tf.concat([fc[-1], input_global], axis = 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]
input_global, # add to all layers (but first) if not None
layout1,
layout2,
inter_convergence,
sym8 = False,
only_tile = None, # just for debugging - feed only data from the center sub-network
partials = None,
use_confidence=False):
center_index = (input.shape[1] - 1) // 2
with tf.name_scope("Siam_net"):
inp_weights = []
num_legs = input.shape[1] # == 25
if partials is None:
partials = [[True] * num_legs]
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]) :
if input_global is None:
ig = None
else:
ig =input_global[:,i,:]
ns, ns_weights = network_sub(input[:,i,:],
ig, # input_global[:,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.zeros_like(ns))
inp_weights += ns_weights
reuse = True
outs = []
for n, _ in enumerate(partials):
if input_global is None:
ig = None
else:
ig =input_global[:,center_index,:]
outs.append(network_inter (input = tf.concat(inter_lists[n],
axis=1,
name='inter_tensor'+str(n)),
input_global = [None, ig][inter_convergence], # optionally feed all convergence values (from each tile of a cluster)
layout = layout2,
reuse = (n > 0),
use_confidence = use_confidence))
return outs, inp_weights
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment