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python3-imagej-tiff
Commit 03a86da7 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
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Merge branch 'master' of git.elphel.com:Elphel/python3-imagej-tiff

parents f4b16af0 825bd240
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Showing with 1008 additions and 6 deletions
explore_data.py
View file @ 03a86da7
...@@ -70,6 +70,45 @@ def readTFRewcordsEpoch(train_filename): ...@@ -70,6 +70,45 @@ def readTFRewcordsEpoch(train_filename):
gt_ds = np.array(gt_ds_list) gt_ds = np.array(gt_ds_list)
return corr2d, target_disparity, gt_ds return corr2d, target_disparity, gt_ds
def writeTFRewcordsImageTiles(img_path, tfr_filename): # test_set=False):
# train_filename = 'train.tfrecords' # address to save the TFRecords file
# open the TFRecords file
num_tiles = 242*324 # fixme
all_image_tiles = np.array(range(num_tiles))
corr_layers = ['hor-pairs', 'vert-pairs','diagm-pair', 'diago-pair']
img = ijt.imagej_tiff(test_corr, corr_layers, all_image_tiles)
corr2d = img.corr2d.reshape((num_tiles,-1))
target_disparity = img.target_disparity.reshape((num_tiles,-1))
gt_ds = img.gt_ds.reshape((num_tiles,-1))
if not '.tfrecords' in tfr_filename:
tfr_filename += '.tfrecords'
tfr_filename=tfr_filename.replace(' ','_')
try:
os.makedirs(os.path.dirname(tfr_filename))
except:
pass
writer = tf.python_io.TFRecordWriter(tfr_filename)
dtype_feature_corr2d = _dtype_feature(corr2d)
dtype_target_disparity = _dtype_feature(target_disparity)
dtype_feature_gt_ds = _dtype_feature(gt_ds)
for i in range(num_tiles):
x = corr2d[i].astype(np.float32)
y = target_disparity[i].astype(np.float32)
z = gt_ds[i].astype(np.float32)
d_feature = {'corr2d': dtype_feature_corr2d(x),
'target_disparity':dtype_target_disparity(y),
'gt_ds': dtype_feature_gt_ds(z)}
example = tf.train.Example(features=tf.train.Features(feature=d_feature))
writer.write(example.SerializeToString())
pass
writer.close()
sys.stdout.flush()
class ExploreData: class ExploreData:
PATTERN = "*-DSI_COMBO.tiff" PATTERN = "*-DSI_COMBO.tiff"
...@@ -142,10 +181,6 @@ class ExploreData: ...@@ -142,10 +181,6 @@ class ExploreData:
strength = np.nan_to_num(strength, copy = False) # likely should never happen strength = np.nan_to_num(strength, copy = False) # likely should never happen
np.clip(disparity, disparity_min_clip, disparity_max_clip, out = disparity) np.clip(disparity, disparity_min_clip, disparity_max_clip, out = disparity)
np.clip(strength, strength_min_clip, strength_max_clip, out = strength) np.clip(strength, strength_min_clip, strength_max_clip, out = strength)
# if no_histogram:
# strength *= good_tiles[ids]
# if no_histogram:
# return None # no histogram, just condition data
good_tiles_list.append(good_tiles) good_tiles_list.append(good_tiles)
combo_rds = np.concatenate(list_rds) combo_rds = np.concatenate(list_rds)
hist, xedges, yedges = np.histogram2d( # xedges, yedges - just for debugging hist, xedges, yedges = np.histogram2d( # xedges, yedges - just for debugging
...@@ -157,8 +192,6 @@ class ExploreData: ...@@ -157,8 +192,6 @@ class ExploreData:
weights= np.concatenate(good_tiles_list).flatten()) weights= np.concatenate(good_tiles_list).flatten())
for i, combo_rds in enumerate(list_rds): for i, combo_rds in enumerate(list_rds):
for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols] for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]
# strength = combo_rds[ids][...,1]
# strength *= good_tiles_list[i][ids]
combo_rds[ids][...,1]*= good_tiles_list[i][ids] combo_rds[ids][...,1]*= good_tiles_list[i][ids]
return hist, xedges, yedges return hist, xedges, yedges
...@@ -636,6 +669,9 @@ class ExploreData: ...@@ -636,6 +669,9 @@ class ExploreData:
print("Scene %d of %d -> %s"%(nscene, len(seed_list), tfr_filename)) print("Scene %d of %d -> %s"%(nscene, len(seed_list), tfr_filename))
writer.close() writer.close()
sys.stdout.flush() sys.stdout.flush()
def showVariance(self, def showVariance(self,
rds_list, # list of disparity/strength files, suchas training, testing rds_list, # list of disparity/strength files, suchas training, testing
...@@ -721,6 +757,20 @@ if __name__ == "__main__": ...@@ -721,6 +757,20 @@ if __name__ == "__main__":
ml_subdir = sys.argv[4] ml_subdir = sys.argv[4]
except IndexError: except IndexError:
ml_subdir = "ml" ml_subdir = "ml"
# pathTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data_3x3b" #no trailing "/"
test_corr = '/home/eyesis/x3d_data/models/var_main/www/html/x3domlet/models/all-clean/overlook/1527257933_150165/v04/mlr32_18a/1527257933_150165-ML_DATA-32B-O-FZ0.05-MAIN.tiff'
scene = os.path.basename(test_corr)[:17]
scene_version= os.path.basename(os.path.dirname(os.path.dirname(test_corr)))
fname =scene+'-'+scene_version
img_filenameTFR = os.path.join(pathTFR,'img',fname)
writeTFRewcordsImageTiles(test_corr, img_filenameTFR)
pass
exit(0)
#Parameters to generate neighbors data. Set radius to 0 to generate single-tile #Parameters to generate neighbors data. Set radius to 0 to generate single-tile
RADIUS = 1 RADIUS = 1
......
nn_ds_neibs2.py 0 → 100644
View file @ 03a86da7
#!/usr/bin/env python3
from numpy import float64
from tensorflow.contrib.losses.python.metric_learning.metric_loss_ops import npairs_loss
__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
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-4 # learning rate
LR100 = 1e-4
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 = 1000#0 #0
EPOCHS_SAME_FILE = 20
RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batches
#BATCH_SIZE = 1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
BATCH_SIZE = 2*1080//9 # == 120 Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
SHUFFLE_EPOCH = True
NET_ARCH1 = 0 # 6 #0 # 4 # 3 # overwrite with argv?
NET_ARCH2 = 0 # 6 # 0 # 3 # overwrite with argv?
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
SUFFIX=str(NET_ARCH1)+'-'+str(NET_ARCH2)+ (["R","A"][ABSOLUTE_DISPARITY])
# CLUSTER_RADIUS should match input data
CLUSTER_RADIUS = 1 # 1 - 3x3, 2 - 5x5 tiles
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],
}
NN_LAYOUT1 = NN_LAYOUTS[NET_ARCH1]
NN_LAYOUT2 = NN_LAYOUTS[NET_ARCH2]
#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)
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[0], 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
#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 zip_lvar_hvar(datasets_lvar_data, datasets_hvar_data, del_src = True):
# cluster_size = (2 * CLUSTER_RADIUS + 1) * (2 * CLUSTER_RADIUS + 1)
# Reformat input data
datasets_data = []
# for rec1, rec2 in zip(datasets_lvar_data, datasets_hvar_data):
for nrec in range(len(datasets_lvar_data)):
rec1 = datasets_lvar_data[nrec]
rec2 = datasets_hvar_data[nrec]
rec = {'corr2d': np.empty((rec1['corr2d'].shape[0]*2,rec1['corr2d'].shape[1]),dtype=np.float32),
'target_disparity': np.empty((rec1['target_disparity'].shape[0]*2,rec1['target_disparity'].shape[1]),dtype=np.float32),
'gt_ds': np.empty((rec1['gt_ds'].shape[0]*2,rec1['gt_ds'].shape[1]),dtype=np.float32)}
rec['corr2d'][0::2] = rec1['corr2d']
rec['corr2d'][1::2] = rec2['corr2d']
rec['target_disparity'][0::2] = rec1['target_disparity']
rec['target_disparity'][1::2] = rec2['target_disparity']
rec['gt_ds'][0::2] = rec1['gt_ds']
rec['gt_ds'][1::2] = rec2['gt_ds']
# if del_src:
# rec1['corr2d'] = None
# rec1['target_disparity'] = None
# rec1['gt_ds'] = None
# rec2['corr2d'] = None
# rec2['target_disparity'] = None
# rec2['gt_ds'] = None
if del_src:
datasets_lvar_data[nrec] = None
datasets_hvar_data[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):
# 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])
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)
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() ))
return rms0, rms1
"""
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"
"""
files_img = ['/home/eyesis/x3d_data/data_sets/tf_data_3x3b/img/1527257933_150165-v04.tfrecords']
result_file = '/home/eyesis/x3d_data/data_sets/tf_data_3x3b/rslt/1527257933_150165-v04R.npy'
files_train_lvar = ["/home/eyesis/x3d_data/data_sets/tf_data_rand2/train000_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train001_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train002_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train003_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train004_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train005_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train006_R1_LE_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train007_R1_LE_1.5.tfrecords",
]
files_train_hvar = ["/home/eyesis/x3d_data/data_sets/tf_data_rand2/train000_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train001_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train002_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train003_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train004_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train005_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train006_R1_GT_1.5.tfrecords",
"/home/eyesis/x3d_data/data_sets/tf_data_rand2/train007_R1_GT_1.5.tfrecords",
]
#files_train_hvar = []
#file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_3x3a/train000_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
file_test_lvar= "/home/eyesis/x3d_data/data_sets/tf_data_rand2/testTEST_R1_LE_1.5.tfrecords"
file_test_hvar= "/home/eyesis/x3d_data/data_sets/tf_data_rand2/testTEST_R1_GT_1.5.tfrecords" # None # "/home/eyesis/x3d_data/data_sets/tf_data_3x3/train-002_R1_LE_1.5.tfrecords" # "/home/eyesis/x3d_data/data_sets/train-000_R1_LE_1.5.tfrecords"
#file_test_hvar= None
weight_hvar = 0.13
weight_lvar = 1.0 - weight_hvar
import tensorflow as tf
import tensorflow.contrib.slim as slim
#try:
eval_results(result_file, ABSOLUTE_DISPARITY)
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 = []
for fpath in files_train_lvar:
print_time("Importing train data (low variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_train_lvar.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
datasets_train_hvar = []
for fpath in files_train_hvar:
print_time("Importing train data (high variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(fpath)
datasets_train_hvar.append({"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds})
print_time(" Done")
if (file_test_lvar):
print_time("Importing test data (low variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(file_test_lvar)
dataset_test_lvar = {"corr2d":corr2d,
"target_disparity":target_disparity,
"gt_ds":gt_ds}
print_time(" Done")
if (file_test_hvar):
print_time("Importing test data (high variance) from "+fpath, end="")
corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(file_test_hvar)
dataset_test_hvar = {"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="")
reduce_tile_size(datasets_train_lvar, TILE_LAYERS, TILE_SIDE)
reduce_tile_size(datasets_train_hvar, TILE_LAYERS, TILE_SIDE)
if (file_test_lvar):
reduce_tile_size([dataset_test_lvar], TILE_LAYERS, TILE_SIDE)
if (file_test_hvar):
reduce_tile_size([dataset_test_hvar], TILE_LAYERS, TILE_SIDE)
print_time(" Done")
pass
pass
print_time("Reshaping train data (low variance)", end="")
reformat_to_clusters(datasets_train_lvar)
print_time(" Done")
print_time("Reshaping train data (high variance)", end="")
reformat_to_clusters(datasets_train_hvar)
print_time(" Done")
if (file_test_lvar):
print_time("Reshaping test data (low variance)", end="")
reformat_to_clusters([dataset_test_lvar])
print_time(" Done")
if (file_test_hvar):
print_time("Reshaping test data (high variance)", end="")
reformat_to_clusters([dataset_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:
datasets_train = zip_lvar_hvar(datasets_train_lvar, datasets_train_hvar)
pass
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 = []
if (file_test_lvar):
datasets_test.append(dataset_test_lvar)
datasets_weights_test.append(weight_lvar)
if (file_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 = 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(dataset_test_lvar['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))
"""
iterator_test = dataset_test.make_initializable_iterator()
next_element_test = iterator_test.get_next()
"""
#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 network_sub(input, layout, reuse):
last_indx = None;
fc = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
inp = fc[-1]
else:
inp = input
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope='g_fc_sub'+str(i), reuse = reuse))
return fc[-1]
def network_inter(input, layout):
last_indx = None;
fc = []
for i, num_outs in enumerate (layout):
if num_outs:
if fc:
inp = fc[-1]
else:
inp = input
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu, scope='g_fc_inter'+str(i)))
if USE_CONFIDENCE:
fc_out = slim.fully_connected(fc[-1], 2, activation_fn=lrelu,scope='g_fc_inter_out')
else:
fc_out = slim.fully_connected(fc[-1], 1, activation_fn=None,scope='g_fc_inter_out')
#If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only
return fc_out
def network_siam(input, # now [?:9,325]
layout1,
layout2,
only_tile=None): # just for debugging - feed only data from the center sub-network
with tf.name_scope("Siam_net"):
num_legs = input.shape[1] # == 9
inter_list = []
reuse = False
for i in range (num_legs):
if (only_tile is None) or (i == only_tile):
inter_list.append(network_sub(input[:,i,:],
layout= layout1,
reuse= reuse))
reuse = True
inter_tensor = tf.concat(inter_list, 1, name='inter_tensor')
return network_inter (inter_tensor, layout2)
#corr2d9x325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[None,cluster_size,FEATURES_PER_TILE]) , tf.reshape(next_element_tt['target_disparity'], [None,cluster_size, 1])],2)
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, # (0.05^2)
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)
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
#In GPU - reformat inputs
##corr2d325 = tf.concat([next_element_tt['corr2d'], next_element_tt['target_disparity']],1)
#Should have shape (?,9,325)
corr2d9x325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE]) , tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1])],2)
#corr2d9x324 = tf.reshape( next_element_tt['corr2d'], [-1, cluster_size, FEATURES_PER_TILE], name = 'corr2d9x324')
#td9x1 = tf.reshape(next_element_tt['target_disparity'], [-1, cluster_size, 1], name = 'td9x1')
#corr2d9x325 = tf.concat([corr2d9x324 , td9x1],2, name = 'corr2d9x325')
# in_features = tf.concat([corr2d,target_disparity],0)
#out = network_fc_simple(input=corr2d325, arch = NET_ARCH1)
out = network_siam(input=corr2d9x325,
layout1 = NN_LAYOUT1,
layout2 = NN_LAYOUT2,
only_tile = ONLY_TILE) #Remove/put None for normal operation
# 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")
G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1 = batchLoss(out_batch = out, # [batch_size,(1..2)] tf_result
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 = 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 = 0.0025, # (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)
#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_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
tf_gtvar_diff = tf.placeholder(tf.float32,shape=None,name='gtvar_diff')
with tf.name_scope('sample'):
tf.summary.scalar("G_loss", G_loss)
tf.summary.scalar("sq_diff", _cost1)
tf.summary.scalar("gtvar_diff", GT_variance)
with tf.name_scope('epoch_average'):
tf.summary.scalar("G_loss_epoch", tf_ph_G_loss)
tf.summary.scalar("sq_diff_epoch",tf_ph_sq_diff)
tf.summary.scalar("gtvar_diff", tf_gtvar_diff)
t_vars=tf.trainable_variables()
lr=tf.placeholder(tf.float32)
G_opt=tf.train.AdamOptimizer(learning_rate=lr).minimize(G_loss)
saver=tf.train.Saver()
ROOT_PATH = './attic/nn_ds_neibs2_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_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss_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_avg = 0.0
train2_avg = 0.0
test_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
# num_train_variants = len(files_train_lvar)
num_train_variants = len(datasets_train)
for epoch in range (EPOCHS_TO_RUN):
# file_index = (epoch // 20) % 2
file_index = epoch % num_train_variants
learning_rate = [LR,LR100][epoch >=100]
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']})
# disp_out= np.empty((dataset_train_size * BATCH_SIZE), dtype=np.float32)
for i in range(dataset_train_size):
try:
# train_summary,_, G_loss_trained, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run(
train_summary,_, G_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,
G_loss,
out,
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
# corr2d325,
],
feed_dict={lr:learning_rate,tf_ph_G_loss:train_avg, tf_ph_sq_diff:train2_avg, tf_gtvar_diff:gtvar_train_avg}) # previous value of *_avg
# save all for now as a test
#train_writer.add_summary(summary, i)
#train_writer.add_summary(train_summary, i)
# disp_out[BATCH_SIZE*i:BATCH_SIZE*(i+1)] = output.flatten()
loss_train_hist[i] = G_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_avg = np.average(loss_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)
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_summary, 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[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,
out,
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={lr:learning_rate,tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg, tf_gtvar_diff:gtvar_test_avg}) # previous value of *_avg
loss_test_hist[i] = G_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_avg = np.average(loss_test_hist).astype(np.float32)
test2_avg = np.average(loss2_test_hist).astype(np.float32)
gtvar_test_avg = np.average(gtvar_test_hist).astype(np.float32)
# _,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg})
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) dbg:%f %f"%(epoch,i,train_avg, test_avg,train2_avg, test2_avg, gtvar_train_avg, gtvar_test_avg))
###################################################
# 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']})
# start_offs=0
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,
out,
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
GT_variance
],
feed_dict={lr:learning_rate,tf_ph_G_loss:test_avg, tf_ph_sq_diff:test2_avg, tf_gtvar_diff:gtvar_test_avg}) # previous value of *_avg
# loss_test_hist[i] = G_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
try:
# disp_out[BATCH_SIZE*i:BATCH_SIZE*(i+1)] = output.flatten()
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
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))
eval_results(result_file, ABSOLUTE_DISPARITY)
# 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)
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