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python3-imagej-tiff
Commit da662515 authored by Andrey Filippov's avatar Andrey Filippov
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Compared 3 methods of feeding data: 15x, then 20x more

parent be2fa3ca
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explore_data.py
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......@@ -14,7 +14,8 @@ import resource
import timeit
import matplotlib.pyplot as plt
from scipy.ndimage.filters import gaussian_filter
import time
import tensorflow as tf
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
class bcolors:
......@@ -27,21 +28,65 @@ class bcolors:
BOLD = '\033[1m'
BOLDWHITE = '\033[1;37m'
UNDERLINE = '\033[4m'
def print_time():
print(bcolors.BOLDWHITE+"time: "+str(time.time())+bcolors.ENDC)
TIME_START = time.time()
TIME_LAST = TIME_START
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)
TIME_LAST = t
def _dtype_feature(ndarray):
"""match appropriate tf.train.Feature class with dtype of ndarray. """
assert isinstance(ndarray, np.ndarray)
dtype_ = ndarray.dtype
if dtype_ == np.float64 or dtype_ == np.float32:
return lambda array: tf.train.Feature(float_list=tf.train.FloatList(value=array))
elif dtype_ == np.int64:
return lambda array: tf.train.Feature(int64_list=tf.train.Int64List(value=array))
else:
raise ValueError("The input should be numpy ndarray. \
Instead got {}".format(ndarray.dtype))
def readTFRewcordsEpoch(train_filename):
# filenames = [train_filename]
# dataset = tf.data.TFRecordDataset(filenames)
if not '.tfrecords' in train_filename:
train_filename += '.tfrecords'
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))
target_disparity_list.append(np.array(example.features.feature['target_disparity'] .float_list .value[0]))
gt_ds_list.append(np.array(example.features.feature['gt_ds'] .float_list .value))
corr2d= np.array(corr2d_list)
target_disparity = np.array(target_disparity_list)
gt_ds = np.array(gt_ds_list)
return corr2d, target_disparity, gt_ds
class ExploreData:
PATTERN = "*-DSI_COMBO.tiff"
ML_DIR = "ml"
ML_PATTERN = "*-ML_DATA-*.tiff"
def getComboList(self, top_dir):
patt = "*-DSI_COMBO.tiff"
# patt = "*-DSI_COMBO.tiff"
tlist = []
for i in range(5):
pp = top_dir#) ,'**', patt) # works
for j in range (i):
pp = os.path.join(pp,'*')
pp = os.path.join(pp,patt)
pp = os.path.join(pp, ExploreData.PATTERN)
tlist += glob.glob(pp)
print (pp+" "+str(len(tlist)))
if (self.debug_level > 0):
print (pp+" "+str(len(tlist)))
if (self.debug_level > 0):
print("Found "+str(len(tlist))+" combo DSI files in "+top_dir+" :")
if (self.debug_level > 1):
......@@ -65,7 +110,7 @@ class ExploreData:
def getHistogramDSI(
self,
combo_rds,
list_rds,
disparity_bins = 1000,
strength_bins = 100,
disparity_min_drop = -0.1,
......@@ -79,36 +124,46 @@ class ExploreData:
normalize = True,
no_histogram = False
):
good_tiles = np.empty((combo_rds.shape[0], combo_rds.shape[1],combo_rds.shape[2]), dtype=bool)
for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]
ds = combo_rds[ids]
disparity = ds[...,0]
strength = ds[...,1]
good_tiles[ids] = disparity >= disparity_min_drop
good_tiles[ids] &= disparity <= disparity_max_drop
good_tiles[ids] &= strength >= strength_min_drop
good_tiles[ids] &= strength <= strength_max_drop
# if not self.good_tiles is None:
# good_tiles[ids] &= self.good_tiles
disparity = np.nan_to_num(disparity, copy = False) # to be able to multiply by 0.0 in mask | copy=False, then out=disparity all done in-place
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(strength, strength_min_clip, strength_max_clip, out = strength)
if no_histogram:
strength *= good_tiles[ids]
return None # no histogram, just condition data
good_tiles_list=[]
for combo_rds in list_rds:
good_tiles = np.empty((combo_rds.shape[0], combo_rds.shape[1],combo_rds.shape[2]), dtype=bool)
for ids in range (combo_rds.shape[0]): #iterate over all scenes ds[2][rows][cols]
ds = combo_rds[ids]
disparity = ds[...,0]
strength = ds[...,1]
good_tiles[ids] = disparity >= disparity_min_drop
good_tiles[ids] &= disparity <= disparity_max_drop
good_tiles[ids] &= strength >= strength_min_drop
good_tiles[ids] &= strength <= strength_max_drop
disparity = np.nan_to_num(disparity, copy = False) # to be able to multiply by 0.0 in mask | copy=False, then out=disparity all done in-place
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(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)
combo_rds = np.concatenate(list_rds)
hist, xedges, yedges = np.histogram2d( # xedges, yedges - just for debugging
x = combo_rds[...,1].flatten(),
y = combo_rds[...,0].flatten(),
bins= (strength_bins, disparity_bins),
range= ((strength_min_clip,strength_max_clip),(disparity_min_clip,disparity_max_clip)),
normed= normalize,
weights= good_tiles.flatten())
weights= np.concatenate(good_tiles_list).flatten())
for i, combo_rds in enumerate(list_rds):
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]
return hist, xedges, yedges
def __init__(self,
topdir_all,
topdir_train,
topdir_test,
debug_level = 0,
disparity_bins = 1000,
strength_bins = 100,
......@@ -125,9 +180,10 @@ class ExploreData:
):
# file name
self.debug_level = debug_level
self.disparity_bins = disparity_bins
self.strength_bins = strength_bins
self.disparity_min_drop = disparity_min_drop
#self.testImageTiles()
self.disparity_bins = disparity_bins
self.strength_bins = strength_bins
self.disparity_min_drop = disparity_min_drop
self.disparity_min_clip = disparity_min_clip
self.disparity_max_drop = disparity_max_drop
self.disparity_max_clip = disparity_max_clip
......@@ -139,10 +195,15 @@ class ExploreData:
self.hist_cutoff= hist_cutoff # of maximal
self.pre_log_offs = 0.001 # of histogram maximum
self.good_tiles = None
filelist = self.getComboList(topdir_all)
combo_rds = self.loadComboFiles(filelist)
self.files_train = self.getComboList(topdir_train)
self.files_test = self.getComboList(topdir_test)
self.train_ds = self.loadComboFiles(self.files_train)
self.test_ds = self.loadComboFiles(self.files_test)
self.num_tiles = self.train_ds.shape[1]*self.train_ds.shape[2]
self.hist, xedges, yedges = self.getHistogramDSI(
combo_rds = combo_rds,
list_rds = [self.train_ds,self.test_ds], # combo_rds,
disparity_bins = self.disparity_bins,
strength_bins = self.strength_bins,
disparity_min_drop = self.disparity_min_drop,
......@@ -155,7 +216,6 @@ class ExploreData:
strength_max_clip = self.strength_max_clip,
normalize = True,
no_histogram = False
)
log_offset = self.pre_log_offs * self.hist.max()
h_cutoff = hist_cutoff * self.hist.max()
......@@ -165,26 +225,16 @@ class ExploreData:
self.good_tiles = self.blurred_hist >= h_cutoff
self.blurred_hist *= self.good_tiles # set bad ones to zero
def getTrainDS(self, topdir_train):
self.trainlist = self.getComboList(topdir_train)
self.train_ds = self.loadComboFiles(self.trainlist)
self.getHistogramDSI(
combo_rds = self.train_ds, # will condition strength
disparity_bins = self.disparity_bins,
strength_bins = self.strength_bins,
disparity_min_drop = self.disparity_min_drop,
disparity_min_clip = self.disparity_min_clip,
disparity_max_drop = self.disparity_max_drop,
disparity_max_clip = self.disparity_max_clip,
strength_min_drop = self.strength_min_drop,
strength_min_clip = self.strength_min_clip,
strength_max_drop = self.strength_max_drop,
strength_max_clip = self.strength_max_clip,
normalize = True,
no_histogram = True)
pass
def assignBatchBins(self, disp_bins, str_bins):
def assignBatchBins(self,
disp_bins,
str_bins,
files_per_scene = 5, # not used here, will be used when generating batches
min_batch_choices=10, # not used here, will be used when generating batches
max_batch_files = 10): # not used here, will be used when generating batches
self.files_per_scene = files_per_scene
self.min_batch_choices=min_batch_choices
self.max_batch_files = max_batch_files
hist_to_batch = np.zeros((self.blurred_hist.shape[0],self.blurred_hist.shape[1]),dtype=int) #zeros_like?
hist_to_batch_multi = np.ones((self.blurred_hist.shape[0],self.blurred_hist.shape[1]),dtype=int) #zeros_like?
scale_hist= (disp_bins * str_bins)/self.blurred_hist.sum()
......@@ -240,26 +290,260 @@ class ExploreData:
disp_batch += 1
disp_run_tot = disp_run_tot_new
pass
self.hist_to_batch = hist_to_batch
return hist_to_batch
def makeBatchLists(self,
train_ds = None):
if train_ds is None:
train_ds = self.train_ds
hist_to_batch = self.hist_to_batch
files_batch_list = []
disp_step = ( self.disparity_max_clip - self.disparity_min_clip )/ self.disparity_bins
str_step = ( self.strength_max_clip - self.strength_min_clip )/ self.strength_bins
bb = np.empty((train_ds.shape[0],train_ds.shape[1],train_ds.shape[2]),int)
num_batch_tiles = np.empty((train_ds.shape[0],self.hist_to_batch.max()+1),dtype = int)
for findx in range(train_ds.shape[0]):
ds = train_ds[findx]
gt = ds[...,1] > 0.0 # all true - check
db = (((ds[...,0] - self.disparity_min_clip)/disp_step).astype(int))*gt
sb = (((ds[...,1] - self.strength_min_clip)/ str_step).astype(int))*gt
np.clip(db, 0, self.disparity_bins-1, out = db)
np.clip(sb, 0, self.strength_bins-1, out = sb)
bb[findx] = (self.hist_to_batch[sb.reshape(self.num_tiles),db.reshape(self.num_tiles)]).reshape(db.shape[0],db.shape[1]) + (gt -1)
pass
# return bb
list_of_file_lists=[]
for findx in range(train_ds.shape[0]):
foffs = findx * self.num_tiles
lst = []
for i in range (self.hist_to_batch.max()+1):
lst.append([])
# bb1d = bb[findx].reshape(self.num_tiles)
for n, indx in enumerate(bb[findx].reshape(self.num_tiles)):
if indx >= 0:
lst[indx].append(foffs + n)
lst_arr=[]
for i,l in enumerate(lst):
# lst_arr.append(np.array(l,dtype = int))
lst_arr.append(l)
num_batch_tiles[findx,i] = len(l)
list_of_file_lists.append(lst_arr)
self.list_of_file_lists= list_of_file_lists
self.num_batch_tiles = num_batch_tiles
return list_of_file_lists, num_batch_tiles
#todo: only use other files if there are no enough choices in the main file!
def augmentBatchFileIndices(self,
seed_index,
min_choices=None,
max_files = None,
set_ds = None
):
if min_choices is None:
min_choices = self.min_batch_choices
if max_files is None:
max_files = self.max_batch_files
if set_ds is None:
set_ds = self.train_ds
full_num_choices = self.num_batch_tiles[seed_index].copy()
flist = [seed_index]
all_choices = list(range(self.num_batch_tiles.shape[0]))
all_choices.remove(seed_index)
for _ in range (max_files-1):
if full_num_choices.min() >= min_choices:
break
findx = np.random.choice(all_choices)
flist.append(findx)
all_choices.remove(findx)
full_num_choices += self.num_batch_tiles[findx]
file_tiles_sparse = [[] for _ in set_ds] #list of empty lists for each train scene (will be sparse)
for nt in range(self.num_batch_tiles.shape[1]): #number of tiles per batch (not counting ml file variant)
tl = []
nchoices = 0
for findx in flist:
if (len(self.list_of_file_lists[findx][nt])):
tl.append(self.list_of_file_lists[findx][nt])
nchoices+= self.num_batch_tiles[findx][nt]
if nchoices >= min_choices: # use minimum of extra files
break;
tile = np.random.choice(np.concatenate(tl))
# print (nt, tile, tile//self.num_tiles, tile % self.num_tiles)
if not type (tile) is np.int64:
print("tile=",tile)
file_tiles_sparse[tile//self.num_tiles].append(tile % self.num_tiles)
file_tiles = []
for findx in flist:
file_tiles.append(np.sort(np.array(file_tiles_sparse[findx],dtype=int)))
return flist, file_tiles # file indices, list if tile indices for each file
def getMLList(self,flist=None):
if flist is None:
flist = self.files_train # train_list
ml_list = []
for fn in flist:
ml_patt = os.path.join(os.path.dirname(fn), ExploreData.ML_DIR, ExploreData.ML_PATTERN)
ml_list.append(glob.glob(ml_patt))
self.ml_list = ml_list
return ml_list
def getBatchData(
self,
flist,
tiles,
ml_list,
ml_num = None ): # 0 - use all ml files for the scene, >0 select random number
if ml_num is None:
ml_num = self.files_per_scene
ml_all_files = []
for findx in flist:
mli = list(range(len(ml_list[findx])))
if (ml_num > 0) and (ml_num < len(mli)):
mli_left = mli
mli = []
for _ in range(ml_num):
ml = np.random.choice(mli_left)
mli.append(ml)
mli_left.remove(ml)
ml_files = []
for ml_index in mli:
ml_files.append(ml_list[findx][ml_index])
ml_all_files.append(ml_files)
return ml_all_files
def prepareBatchData(self, seed_index, min_choices=None, max_files = None, ml_num = None, test_set = False):
if min_choices is None:
min_choices = self.min_batch_choices
if max_files is None:
max_files = self.max_batch_files
if ml_num is None:
ml_num = self.files_per_scene
set_ds = [self.train_ds, self.test_ds][test_set]
corr_layers = ['hor-pairs', 'vert-pairs','diagm-pair', 'diago-pair']
flist,tiles = self.augmentBatchFileIndices(seed_index, min_choices, max_files, set_ds)
# total number of layers in tiff
ml_all_files = self.getBatchData(flist, tiles, self.ml_list, ml_num) # 0 - use all ml files for the scene, >0 select random number
if self.debug_level > 1:
print ("==============",seed_index, flist)
for i, findx in enumerate(flist):
print(i,"\n".join(ml_all_files[i]))
print(tiles[i])
total_tiles = 0
for i, t in enumerate(tiles):
total_tiles += len(t)*len(ml_all_files[i]) # tiles per scene * offset files per scene
if self.debug_level > 1:
print("Tiles in the batch=",total_tiles)
corr2d_batch = None # np.empty((total_tiles, len(corr_layers),81))
gt_ds_batch = np.empty((total_tiles,2), dtype=float)
target_disparity_batch = np.empty((total_tiles,), dtype=float)
start_tile = 0
for nscene, scene_files in enumerate(ml_all_files):
for path in scene_files:
img = ijt.imagej_tiff(path, corr_layers, tile_list=tiles[nscene])
corr2d = img.corr2d
target_disparity = img.target_disparity
gt_ds = img.gt_ds
end_tile = start_tile + corr2d.shape[0]
if corr2d_batch is None:
corr2d_batch = np.empty((total_tiles, len(corr_layers), corr2d.shape[-1]))
gt_ds_batch [start_tile:end_tile] = gt_ds
target_disparity_batch [start_tile:end_tile] = target_disparity
corr2d_batch [start_tile:end_tile] = corr2d
start_tile = end_tile
"""
Sometimes get bad tile in ML file that was not bad in COMBO-DSI
Need to recover
np.argwhere(np.isnan(target_disparity_batch))
"""
bad_tiles = np.argwhere(np.isnan(target_disparity_batch))
if (len(bad_tiles)>0):
print ("*** Got %d bad tiles in a batch, replacing..."%(len(bad_tiles)), end=" ")
# for now - just repeat some good tile
for ibt in bad_tiles:
while np.isnan(target_disparity_batch[ibt]):
irt = np.random.randint(0,total_tiles)
if not np.isnan(target_disparity_batch[irt]):
target_disparity_batch[ibt] = target_disparity_batch[irt]
corr2d_batch[ibt] = corr2d_batch[irt]
gt_ds_batch[ibt] = gt_ds_batch[irt]
break
print (" done replacing")
self.corr2d_batch = corr2d_batch
self.target_disparity_batch = target_disparity_batch
self.gt_ds_batch = gt_ds_batch
return corr2d_batch, target_disparity_batch, gt_ds_batch
def writeTFRewcordsEpoch(self, tfr_filename, test_set=False):
# train_filename = 'train.tfrecords' # address to save the TFRecords file
# open the TFRecords file
if not '.tfrecords' in tfr_filename:
tfr_filename += '.tfrecords'
writer = tf.python_io.TFRecordWriter(tfr_filename)
files_list = [self.files_train, self.files_test][test_set]
seed_list = np.arange(len(files_list))
np.random.shuffle(seed_list)
for nscene, seed_index in enumerate(seed_list):
corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(seed_index, min_choices=None, max_files = None, ml_num = None, test_set = test_set)
#shuffles tiles in a batch
tiles_in_batch = len(target_disparity_batch)
permut = np.random.permutation(tiles_in_batch)
corr2d_batch_shuffled = corr2d_batch[permut].reshape((corr2d_batch.shape[0], corr2d_batch.shape[1]*corr2d_batch.shape[2]))
target_disparity_batch_shuffled = target_disparity_batch[permut].reshape((tiles_in_batch,1))
gt_ds_batch_shuffled = gt_ds_batch[permut]
if nscene == 0:
dtype_feature_corr2d = _dtype_feature(corr2d_batch_shuffled)
dtype_target_disparity = _dtype_feature(target_disparity_batch_shuffled)
dtype_feature_gt_ds = _dtype_feature(gt_ds_batch_shuffled)
for i in range(tiles_in_batch):
x = corr2d_batch_shuffled[i]
y = target_disparity_batch_shuffled[i]
z = gt_ds_batch_shuffled[i]
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())
if (self.debug_level > 0):
print("Scene %d of %d"%(nscene, len(seed_list)))
writer.close()
sys.stdout.flush()
#MAIN
if __name__ == "__main__":
try:
topdir_all = sys.argv[1]
topdir_train = sys.argv[1]
except IndexError:
topdir_all = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/all"#test" #all/"
topdir_train = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train"#test" #all/"
try:
topdir_train = sys.argv[2]
topdir_test = sys.argv[2]
except IndexError:
topdir_train = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train"#test" #all/"
topdir_test = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/test"#test" #all/"
try:
train_filenameTFR = sys.argv[3]
except IndexError:
train_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/train.tfrecords"
try:
test_filenameTFR = sys.argv[4]
except IndexError:
test_filenameTFR = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/tf_data/test.tfrecords"
# corr2d, target_disparity, gt_ds = readTFRewcordsEpoch(train_filenameTFR)
# print_time("Read %d tiles"%(corr2d.shape[0]))
# exit (0)
ex_data = ExploreData(
topdir_all = topdir_all,
debug_level = 3,
topdir_train = topdir_train,
topdir_test = topdir_test,
debug_level = 1, #3, ##0, #3,
disparity_bins = 200, #1000,
strength_bins = 100,
disparity_min_drop = -0.1,
......@@ -272,16 +556,6 @@ if __name__ == "__main__":
strength_max_clip = 0.9,
hist_sigma = 2.0, # Blur log histogram
hist_cutoff= 0.001) # of maximal
"""
tlist = getComboList(top_dir)
pre_log_offs = 0.001
combo_rds= loadComboFiles(tlist)
hist, xedges, yedges = getHistogramDSI(combo_rds)
lhist = np.log(hist + pre_log_offs)
blurred = gaussian_filter(lhist, sigma=2.0)
"""
mytitle = "Disparity_Strength histogram"
fig = plt.figure()
......@@ -290,34 +564,29 @@ if __name__ == "__main__":
# plt.imshow(lhist,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.imshow(ex_data.blurred_hist, vmin=0, vmax=.1 * ex_data.blurred_hist.max())#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.colorbar(orientation='horizontal') # location='bottom')
bb = ex_data.assignBatchBins(
hist_to_batch = ex_data.assignBatchBins(
disp_bins = 20,
str_bins=10)
bb_display = bb.copy()
"""
bb_display %= 20
bb_mask = (bb > 0)
bb_scale = 1.0 * (bb_mask)
bb_scale = (1.05 * bb_scale)
bb_display = bb_display.astype(float) * bb_scale
"""
# bb_display = ( ((2 * (bb_display % 2) -1) * (2 * ((bb_display % 20)//10) -1) + 2)/2)*(bb > 0) #).astype(float)
bb_display = ( 1+ (bb_display % 2) + 2 * ((bb_display % 20)//10)) * (bb > 0) #).astype(float)
bb_display = hist_to_batch.copy()
bb_display = ( 1+ (bb_display % 2) + 2 * ((bb_display % 20)//10)) * (hist_to_batch > 0) #).astype(float)
fig2 = plt.figure()
fig2.canvas.set_window_title("Batch indices")
fig2.suptitle("Batch index for each disparity/strength cell")
# plt.imshow(lhist,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.imshow(bb_display) #, vmin=0, vmax=.1 * ex_data.blurred_hist.max())#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.colorbar(orientation='horizontal') # location='bottom')
# Get image stats for train data only
plt.ioff()
""" prepare test dataset """
ml_list=ex_data.getMLList(ex_data.files_test)
ex_data.makeBatchLists(train_ds = ex_data.test_ds)
ex_data.writeTFRewcordsEpoch(test_filenameTFR, test_set=True)
""" prepare train dataset """
ml_list=ex_data.getMLList(ex_data.files_train) # train_list)
ex_data.makeBatchLists(train_ds = ex_data.train_ds)
ex_data.writeTFRewcordsEpoch(train_filenameTFR,test_set = False)
plt.show()
ex_data.getTrainDS(topdir_train)
pass
imagej_tiff.py
View file @ da662515
......@@ -102,7 +102,7 @@ class imagej_tiff:
__TIFF_TAG_LABELS_STRINGS = 50839
# init
def __init__(self,filename, layers = None):
def __init__(self,filename, layers = None, tile_list = None):
# file name
self.fname = filename
tif = Image.open(filename)
......@@ -120,6 +120,10 @@ class imagej_tiff:
self.__split_labels(tif.n_frames,tif.tag)
self.__parse_info()
try:
self.nan_bug = self.props['VERSION']== '1.0' # data between min and max is mapped to 0..254 instead of 1.255
except:
self.nan_bug = False # other files, not ML ones
# image layers stacked along depth - (think RGB)
self.image = []
......@@ -139,11 +143,16 @@ class imagej_tiff:
_max = self.data_max
_MIN = 1
_MAX = 255
if (self.nan_bug):
_MIN = 0
_MAX = 254
else:
if self.labels[i]!='other':
a[a==0]=np.nan
a = a.astype(float)
if self.labels[i]!='other':
a[a==0]=np.nan
# a[a==0]=np.nan
a = (_max-_min)*(a-_MIN)/(_MAX-_MIN)+_min
# init
if i==0:
self.image = a
......@@ -151,15 +160,59 @@ class imagej_tiff:
else:
self.image = np.append(self.image,a,axis=2)
else:
indx = 0
for layer in layers:
tif.seek(self.labels.index(layer))
a = np.array(tif)
if not indx:
self.image = np.empty((a.shape[0],a.shape[1],len(layers)),a.dtype)
self.image[...,indx] = a
indx += 1
if tile_list is None:
indx = 0
for layer in layers:
tif.seek(self.labels.index(layer))
a = np.array(tif)
if not indx:
self.image = np.empty((a.shape[0],a.shape[1],len(layers)),a.dtype)
self.image[...,indx] = a
indx += 1
else:
other_label = "other"
# print(tile_list)
num_tiles = len(tile_list)
num_layers = len(layers)
tiles_corr = np.empty((num_tiles,num_layers,self.tileH*self.tileW),dtype=float)
# tiles_other=np.empty((num_tiles,3),dtype=float)
tiles_other=self.gettilesvalues(
tif = tif,
tile_list=tile_list,
label=other_label)
for nl,label in enumerate(layers):
tif.seek(self.labels.index(label))
layer = np.array(tif) # 8 or 32 bits
tilesX = layer.shape[1]//self.tileW
for nt,tl in enumerate(tile_list):
ty = tl // tilesX
tx = tl % tilesX
# tiles_corr[nt,nl] = np.ravel(layer[self.tileH*ty:self.tileH*(ty+1),self.tileW*tx:self.tileW*(tx+1)])
a = np.ravel(layer[self.tileH*ty:self.tileH*(ty+1),self.tileW*tx:self.tileW*(tx+1)])
#convert from int8
if self.bpp==8:
a = a.astype(float)
if np.isnan(tiles_other[nt][0]):
# print("Skipping NaN tile ",tl)
a[...] = np.nan
else:
_min = self.data_min
_max = self.data_max
_MIN = 1
_MAX = 255
if (self.nan_bug):
_MIN = 0
_MAX = 254
else:
a[a==0] = np.nan
a = (_max-_min)*(a-_MIN)/(_MAX-_MIN)+_min
tiles_corr[nt,nl] = a
pass
pass
self.corr2d = tiles_corr
self.target_disparity = tiles_other[...,0]
self.gt_ds = tiles_other[...,1:3]
pass
# init done, close the image
tif.close()
......@@ -193,10 +246,46 @@ class imagej_tiff:
b[b==-256] = np.nan
c = res[:,:,2]
c[c==0] = np.nan
return res
# 3 values per tile: target disparity, GT disparity, GT confidence
def gettilesvalues(self,
tif,
tile_list,
label=""):
res = np.empty((len(tile_list),3),dtype=float)
tif.seek(self.labels.index(label))
layer = np.array(tif) # 8 or 32 bits
tilesX = layer.shape[1]//self.tileW
for i,tl in enumerate(tile_list):
ty = tl // tilesX
tx = tl % tilesX
m = np.ravel(layer[self.tileH*ty:self.tileH*(ty+1),self.tileW*tx:self.tileW*(tx+1)])
if self.bpp==32:
res[i,0] = m[0]
res[i,1] = m[2]
res[i,2] = m[4]
elif self.bpp==8:
res[i,0] = ((m[0]-128)*256+m[1])/128
res[i,1] = ((m[2]-128)*256+m[3])/128
res[i,2] = (m[4]*256+m[5])/65536.0
else:
res[i,0] = np.nan
res[i,1] = np.nan
res[i,2] = np.nan
# NaNize
a = res[...,0]
a[a==-256] = np.nan
b = res[...,1]
b[b==-256] = np.nan
c = res[...,2]
c[c==0] = np.nan
return res
# get ordered stack of images by provided items
# by index or label name
def getstack(self,items=[],shape_as_tiles=False):
......@@ -323,8 +412,9 @@ if __name__ == "__main__":
try:
fname = sys.argv[1]
except IndexError:
fname = "1521849031_093189-ML_DATA-32B-O-OFFS1.0.tiff"
fname = "1521849031_093189-ML_DATA-08B-O-OFFS1.0.tiff"
fname = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train/1527182807_896892/v02/ml/1527182807_896892-ML_DATA-08B-O-FZ0.05-OFFS0.40000.tiff"
# fname = "1521849031_093189-ML_DATA-32B-O-OFFS1.0.tiff"
# fname = "1521849031_093189-ML_DATA-08B-O-OFFS1.0.tiff"
#fname = "1521849031_093189-DISP_MAP-D0.0-46.tif"
#fname = "1526905735_662795-ML_DATA-08B-AIOTD-OFFS2.0.tiff"
......
nn_ds_dataset.py 0 → 100644
View file @ da662515
#!/usr/bin/env python3
from numpy import float64
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
from PIL import Image
import os
import sys
import glob
import pack_tile as pile
import numpy as np
import itertools
import time
import matplotlib.pyplot as plt
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
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 = 3e-3 # learning rate
USE_CONFIDENCE = False
ABSOLUTE_DISPARITY = False
DEBUG_PLT_LOSS = True
FEATURES_PER_TILE = 324
EPOCHS_TO_RUN = 20 #0
RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batches
#DEBUG_PACK_TILES = True
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)
TIME_LAST = t
#reading to memory (testing)
def readTFRewcordsEpoch(train_filename):
# filenames = [train_filename]
# dataset = tf.data.TFRecordDataset(filenames)
if not '.tfrecords' in train_filename:
train_filename += '.tfrecords'
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))
target_disparity_list.append(np.array(example.features.feature['target_disparity'] .float_list .value[0]))
gt_ds_list.append(np.array(example.features.feature['gt_ds'] .float_list .value))
corr2d= np.array(corr2d_list)
target_disparity = np.array(target_disparity_list)
gt_ds = np.array(gt_ds_list)
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([324],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/
#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.tfrecords"
#FILES_PER_SCENE
print_time("Importing TensorCrawl")
import tensorflow as tf
import tensorflow.contrib.slim as slim
print_time("TensorCrawl imported")
result_dir = './result/'
checkpoint_dir = './result/'
save_freq = 500
def lrelu(x):
return tf.maximum(x*0.2,x)
# return tf.nn.relu(x)
def network(input):
# fc1 = slim.fully_connected(input, 512, activation_fn=lrelu,scope='g_fc1')
# fc2 = slim.fully_connected(fc1, 512, activation_fn=lrelu,scope='g_fc2')
fc3 = slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc3')
fc4 = slim.fully_connected(fc3, 128, activation_fn=lrelu,scope='g_fc4')
fc5 = slim.fully_connected(fc4, 64, activation_fn=lrelu,scope='g_fc5')
if USE_CONFIDENCE:
fc6 = slim.fully_connected(fc5, 2, activation_fn=lrelu,scope='g_fc6')
else:
fc6 = slim.fully_connected(fc5, 1, activation_fn=None,scope='g_fc6')
#If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only
return fc6
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):
"""
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")
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.slice(out_batch,[0,0],[-1,1], name = "disp_slice")
# d_gt_slice = tf.slice(gt_ds_batch,[0,0],[-1,1], name = "d_gt_slice")
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")
if absolute_disparity:
out_diff = tf.subtract(disp_slice, d_gt_slice, name = "out_diff")
else:
td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
residual_disp = tf.subtract(d_gt_slice, td_flat, name = "residual_disp")
out_diff = tf.subtract(disp_slice, residual_disp, 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")
if use_confidence:
cost12 = tf.add(cost1, 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 cost1, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
filename_queue = tf.train.string_input_producer(
[train_filenameTFR], num_epochs = EPOCHS_TO_RUN) #0)
# Even when reading in multiple threads, share the filename
# queue.
corr2d325, target_disparity, gt_ds = read_and_decode(filename_queue)
# The op for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
#sess = tf.Session()
"""
in_tile = tf.placeholder(tf.float32,[None,9 * 9 * 4 + 1])
gt = tf.placeholder(tf.float32,[None,2])
target_d = tf.placeholder(tf.float32,[None])
out = network(in_tile)
"""
out = network(corr2d325)
#Try standard loss functions first
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= target_disparity, ### target_d, # [batch_size] tf placeholder
gt_ds_batch = 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 = 1.0)
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()
# ?!!!!!
#merged = tf.summary.merge_all()
#train_writer = tf.summary.FileWriter(result_dir + '/train', sess.graph)
#test_writer = tf.summary.FileWriter(result_dir + '/test')
#http://rtfcode.com/xref/tensorflow-1.4.1/tensorflow/docs_src/api_guides/python/reading_data.md
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# sess.run(init_op) # Was reporting beta1 not initialized in Adam
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
writer = tf.summary.FileWriter('./attic/nn_ds_dataset_graph1', sess.graph)
writer.close()
# for i in range(1000):
loss_hist = np.zeros(RUN_TOT_AVG, dtype=np.float32)
i = 0
try:
while not coord.should_stop():
print_time("%d: Run "%(i), end = "")
_,G_current,output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(
[G_opt,G_loss,out,_disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1, corr2d325, target_disparity, gt_ds],
feed_dict={lr: LR})
# print_time("loss=%f, running average=%f"%(G_current,mean_loss))
loss_hist[i % RUN_TOT_AVG] = G_current
if (i < RUN_TOT_AVG):
loss_avg = np.average(loss_hist[:i])
else:
loss_avg = np.average(loss_hist)
print_time("loss=%f, running average=%f"%(G_current,loss_avg))
# print ("%d: corr2d_out.shape="%(i),corr2d325_out.shape)
## print ("target_disparity_out.shape=",target_disparity_out.shape)
## print ("gt_ds_out.shape=",gt_ds_out.shape)
i += 1
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
coord.join(threads)
#sess.close() ('whith' does that)
'''
ckpt=tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
allfolders = glob.glob('./result/*0')
lastepoch = 0
for folder in allfolders:
lastepoch = np.maximum(lastepoch, int(folder[-4:]))
recorded_loss = []
recorded_mean_loss = []
recorded_gt_d = []
recorded_gt_c = []
recorded_pr_d = []
recorded_pr_c = []
LR = 1e-3
print(bcolors.HEADER+"Last Epoch = "+str(lastepoch)+bcolors.ENDC)
if DEBUG_PLT_LOSS:
plt.ion() # something about plotting
plt.figure(1, figsize=(4,12))
pass
training_tiles = np.array([])
training_values = np.array([])
graph_saved = False
for epoch in range(20): #MAX_EPOCH):
print_time("epoch="+str(epoch))
train_seed_list = np.arange(len(ex_data.files_train))
np.random.shuffle(train_seed_list)
g_loss = np.zeros(len(train_seed_list))
for nscene, seed_index in enumerate(train_seed_list):
corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(seed_index)
num_tiles = corr2d_batch.shape[0] # 1000
num_tile_slices = corr2d_batch.shape[1] # 4
num_cell_in_slice = corr2d_batch.shape[2] # 81
in_data = np.empty((num_tiles, num_tile_slices*num_cell_in_slice + 1), dtype = np.float32)
in_data[...,0:num_tile_slices*num_cell_in_slice] = corr2d_batch.reshape((corr2d_batch.shape[0],corr2d_batch.shape[1]*corr2d_batch.shape[2]))
in_data[...,num_tile_slices*num_cell_in_slice] = target_disparity_batch
st=time.time()
#run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
#run_metadata = tf.RunMetadata()
#_,G_current,output = sess.run([G_opt,G_loss,out],feed_dict={in_tile:input_patch,gt:gt_patch,lr:LR},options=run_options,run_metadata=run_metadata)
print_time("%d:%d Run "%(epoch, nscene), end = "")
_,G_current,output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm = sess.run([G_opt,G_loss,out,_disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm],
feed_dict={in_tile: in_data,
gt: gt_ds_batch,
target_d: target_disparity_batch,
lr: LR})
if not graph_saved:
writer = tf.summary.FileWriter('./attic/nn_ds_single_graph1', sess.graph)
writer.close()
graph_saved = True
# exit(0)
g_loss[nscene]=G_current
mean_loss = np.mean(g_loss[np.where(g_loss)])
print_time("loss=%f, running average=%f"%(G_current,mean_loss))
pass
'''
#if wait_and_show: # wait and show images
# plt.show()
print_time("All done, exiting...")
\ No newline at end of file
nn_ds_inmem.py 0 → 100644
View file @ da662515
#!/usr/bin/env python3
from numpy import float64
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
from PIL import Image
import os
import sys
import glob
import pack_tile as pile
import numpy as np
import itertools
import time
import matplotlib.pyplot as plt
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
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
USE_CONFIDENCE = False
ABSOLUTE_DISPARITY = False
DEBUG_PLT_LOSS = True
FEATURES_PER_TILE = 324
EPOCHS_TO_RUN = 10000 #0
RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batches
BATCH_SIZE = 1000 # Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
SHUFFLE_EPOCH = True
#DEBUG_PACK_TILES = True
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.TFRecordDataset(filenames)
if not '.tfrecords' in train_filename:
train_filename += '.tfrecords'
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))
corr2d= np.array(corr2d_list)
target_disparity = np.array(target_disparity_list)
gt_ds = np.array(gt_ds_list)
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([324],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/
#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.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
print_time("Importing TensorCrawl")
import tensorflow as tf
import tensorflow.contrib.slim as slim
print_time("TensorCrawl imported")
print_time("Importing training data... ", end="")
corr2d_train, target_disparity_train, gt_ds_train = readTFRewcordsEpoch(train_filenameTFR)
print_time(" Done")
dataset_train = tf.data.Dataset.from_tensor_slices({
"corr2d":corr2d_train,
"target_disparity": target_disparity_train,
"gt_ds": gt_ds_train})
dataset_train_size = len(corr2d_train)
print_time("dataset_train.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))
dataset_train = dataset_train.batch(BATCH_SIZE)
dataset_train_size /= BATCH_SIZE
print("dataset_train.output_types "+str(dataset_train.output_types)+", dataset_train.output_shapes "+str(dataset_train.output_shapes)+", number of elements="+str(dataset_train_size))
iterator_train = dataset_train.make_initializable_iterator()
next_element_train = iterator_train.get_next()
'''
print_time("Importing test data... ", end="")
corr2d_test, target_disparity_test, gt_ds_test = readTFRewcordsEpoch(test_filenameTFR)
print_time(" Done")
dataset_test = tf.data.Dataset.from_tensor_slices({
"corr2d":corr2d_test,
"target_disparity": target_disparity_test,
"gt_ds": gt_ds_test})
dataset_test_size = len(corr2d_test)
print_time("dataset_test.output_types "+str(dataset_test.output_types)+", dataset_test.output_shapes "+str(dataset_test.output_shapes)+", number of elements="+str(dataset_test_size))
dataset_test = dataset_test.batch(BATCH_SIZE)
dataset_test_size /= BATCH_SIZE
print("dataset_test.output_types "+str(dataset_test.output_types)+", dataset_test.output_shapes "+str(dataset_test.output_shapes)+", number of elements="+str(dataset_test_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_inmem/'
checkpoint_dir = './attic/result_inmem/'
save_freq = 500
def lrelu(x):
return tf.maximum(x*0.2,x)
# return tf.nn.relu(x)
def network(input):
# fc1 = slim.fully_connected(input, 512, activation_fn=lrelu,scope='g_fc1')
# fc2 = slim.fully_connected(fc1, 512, activation_fn=lrelu,scope='g_fc2')
fc3 = slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc3')
fc4 = slim.fully_connected(fc3, 128, activation_fn=lrelu,scope='g_fc4')
fc5 = slim.fully_connected(fc4, 64, activation_fn=lrelu,scope='g_fc5')
if USE_CONFIDENCE:
fc6 = slim.fully_connected(fc5, 2, activation_fn=lrelu,scope='g_fc6')
else:
fc6 = slim.fully_connected(fc5, 1, activation_fn=None,scope='g_fc6')
#If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only
return fc6
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):
"""
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")
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.slice(out_batch,[0,0],[-1,1], name = "disp_slice")
# d_gt_slice = tf.slice(gt_ds_batch,[0,0],[-1,1], name = "d_gt_slice")
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")
if absolute_disparity:
out_diff = tf.subtract(disp_slice, d_gt_slice, name = "out_diff")
else:
td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
residual_disp = tf.subtract(d_gt_slice, td_flat, name = "residual_disp")
out_diff = tf.subtract(disp_slice, residual_disp, 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")
if use_confidence:
cost12 = tf.add(cost1, 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 cost1, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
#corr2d325 = tf.concat([corr2d,target_disparity],0)
#corr2d325 = tf.concat([next_element_train['corr2d'],tf.reshape(next_element_train['target_disparity'],(-1,1))],1)
corr2d325 = tf.concat([next_element_train['corr2d'], next_element_train['target_disparity']],1)
#next_element_train
# in_features = tf.concat([corr2d,target_disparity],0)
out = network(corr2d325)
#Try standard loss functions first
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_train['target_disparity'], # target_disparity, ### target_d, # [batch_size] tf placeholder
gt_ds_batch = next_element_train['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 = 1.0)
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()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
writer = tf.summary.FileWriter('./attic/nn_ds_inmem_graph1', sess.graph)
writer.close()
for epoch in range(EPOCHS_TO_RUN):
if SHUFFLE_EPOCH:
dataset_train = dataset_train.shuffle(buffer_size=10000)
sess.run(iterator_train.initializer)
i=0
while True:
try:
# _, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(
_, G_current, output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out = sess.run(
[G_opt,
G_loss,
out,
_disp_slice,
_d_gt_slice,
_out_diff,
_out_diff2,
_w_norm,
_out_wdiff2,
_cost1,
corr2d325,
# target_disparity,
# gt_ds
],
feed_dict={lr: LR})
except tf.errors.OutOfRangeError:
# print('Done with epoch training')
break
i+=1
# print_time("%d:%d -> %f"%(epoch,i,G_current))
print_time("%d:%d -> %f"%(epoch,i,G_current))
#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)
filename_queue = tf.train.string_input_producer(
[train_filenameTFR], num_epochs = EPOCHS_TO_RUN) #0)
# Even when reading in multiple threads, share the filename
# queue.
corr2d325, target_disparity, gt_ds = read_and_decode(filename_queue)
# The op for initializing the variables.
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
#sess = tf.Session()
out = network(corr2d325)
#Try standard loss functions first
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= target_disparity, ### target_d, # [batch_size] tf placeholder
gt_ds_batch = 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 = 1.0)
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()
# ?!!!!!
#merged = tf.summary.merge_all()
#train_writer = tf.summary.FileWriter(result_dir + '/train', sess.graph)
#test_writer = tf.summary.FileWriter(result_dir + '/test')
#http://rtfcode.com/xref/tensorflow-1.4.1/tensorflow/docs_src/api_guides/python/reading_data.md
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# sess.run(init_op) # Was reporting beta1 not initialized in Adam
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
writer = tf.summary.FileWriter('./attic/nn_ds_inmem_graph1', sess.graph)
writer.close()
# for i in range(1000):
loss_hist = np.zeros(RUN_TOT_AVG, dtype=np.float32)
i = 0
try:
while not coord.should_stop():
print_time("%d: Run "%(i), end = "")
_,G_current,output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(
[G_opt,G_loss,out,_disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _cost1, corr2d325, target_disparity, gt_ds],
feed_dict={lr: LR})
# print_time("loss=%f, running average=%f"%(G_current,mean_loss))
loss_hist[i % RUN_TOT_AVG] = G_current
if (i < RUN_TOT_AVG):
loss_avg = np.average(loss_hist[:i])
else:
loss_avg = np.average(loss_hist)
print_time("loss=%f, running average=%f"%(G_current,loss_avg))
# print ("%d: corr2d_out.shape="%(i),corr2d325_out.shape)
## print ("target_disparity_out.shape=",target_disparity_out.shape)
## print ("gt_ds_out.shape=",gt_ds_out.shape)
i += 1
except tf.errors.OutOfRangeError:
print('Done training -- epoch limit reached')
finally:
# When done, ask the threads to stop.
coord.request_stop()
coord.join(threads)
#sess.close() ('whith' does that)
'''
ckpt=tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
allfolders = glob.glob('./result/*0')
lastepoch = 0
for folder in allfolders:
lastepoch = np.maximum(lastepoch, int(folder[-4:]))
recorded_loss = []
recorded_mean_loss = []
recorded_gt_d = []
recorded_gt_c = []
recorded_pr_d = []
recorded_pr_c = []
LR = 1e-3
print(bcolors.HEADER+"Last Epoch = "+str(lastepoch)+bcolors.ENDC)
if DEBUG_PLT_LOSS:
plt.ion() # something about plotting
plt.figure(1, figsize=(4,12))
pass
training_tiles = np.array([])
training_values = np.array([])
graph_saved = False
for epoch in range(20): #MAX_EPOCH):
print_time("epoch="+str(epoch))
train_seed_list = np.arange(len(ex_data.files_train))
np.random.shuffle(train_seed_list)
g_loss = np.zeros(len(train_seed_list))
for nscene, seed_index in enumerate(train_seed_list):
corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(seed_index)
num_tiles = corr2d_batch.shape[0] # 1000
num_tile_slices = corr2d_batch.shape[1] # 4
num_cell_in_slice = corr2d_batch.shape[2] # 81
in_data = np.empty((num_tiles, num_tile_slices*num_cell_in_slice + 1), dtype = np.float32)
in_data[...,0:num_tile_slices*num_cell_in_slice] = corr2d_batch.reshape((corr2d_batch.shape[0],corr2d_batch.shape[1]*corr2d_batch.shape[2]))
in_data[...,num_tile_slices*num_cell_in_slice] = target_disparity_batch
st=time.time()
#run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
#run_metadata = tf.RunMetadata()
#_,G_current,output = sess.run([G_opt,G_loss,out],feed_dict={in_tile:input_patch,gt:gt_patch,lr:LR},options=run_options,run_metadata=run_metadata)
print_time("%d:%d Run "%(epoch, nscene), end = "")
_,G_current,output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm = sess.run([G_opt,G_loss,out,_disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm],
feed_dict={in_tile: in_data,
gt: gt_ds_batch,
target_d: target_disparity_batch,
lr: LR})
if not graph_saved:
writer = tf.summary.FileWriter('./attic/nn_ds_single_graph1', sess.graph)
writer.close()
graph_saved = True
# exit(0)
g_loss[nscene]=G_current
mean_loss = np.mean(g_loss[np.where(g_loss)])
print_time("loss=%f, running average=%f"%(G_current,mean_loss))
pass
'''
#if wait_and_show: # wait and show images
# plt.show()
print_time("All done, exiting...")
\ No newline at end of file
nn_ds_single.py 0 → 100644
View file @ da662515
#!/usr/bin/env python3
from numpy import float64
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
from PIL import Image
import os
import sys
import glob
import explore_data as exd
import pack_tile as pile
import numpy as np
import itertools
import time
import matplotlib.pyplot as plt
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
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
USE_CONFIDENCE = False
ABSOLUTE_DISPARITY = False
DEBUG_PLT_LOSS = True
#DEBUG_PACK_TILES = True
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)
TIME_LAST = t
#Main code
try:
topdir_train = sys.argv[1]
except IndexError:
topdir_train = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/train"#test" #all/"
try:
topdir_test = sys.argv[2]
except IndexError:
topdir_test = "/mnt/dde6f983-d149-435e-b4a2-88749245cc6c/home/eyesis/x3d_data/data_sets/test"#test" #all/"
print_time("Exploring dataset (long operation)")
ex_data = exd.ExploreData(
topdir_train = topdir_train,
topdir_test = topdir_test,
debug_level = 0, #DEBUG_LEVEL, # 3, ##0, #3,
disparity_bins = 200, #1000,
strength_bins = 100,
disparity_min_drop = -0.1,
disparity_min_clip = -0.1,
disparity_max_drop = 20.0, #100.0,
disparity_max_clip = 20.0, #100.0,
strength_min_drop = 0.1,
strength_min_clip = 0.1,
strength_max_drop = 1.0,
strength_max_clip = 0.9,
hist_sigma = 2.0, # Blur log histogram
hist_cutoff= 0.001) # of maximal
print_time(("Done exploring dataset, assigning DSI histogram tiles to batch bins (%d disparity bins, %d strength bins, %d disparity offsets: total %d tiles per batch)"%(
DISP_BATCH_BINS,STR_BATCH_BINS, FILES_PER_SCENE, DISP_BATCH_BINS*STR_BATCH_BINS*FILES_PER_SCENE)))
ex_data.assignBatchBins(disp_bins = DISP_BATCH_BINS, # Number of batch disparity bins
str_bins = STR_BATCH_BINS, # Number of batch strength bins
files_per_scene = FILES_PER_SCENE, # not used here, will be used when generating batches
min_batch_choices=MIN_BATCH_CHOICES, # not used here, will be used when generating batches
max_batch_files = MAX_BATCH_FILES) # not used here, will be used when generating batches
#FILES_PER_SCENE
wait_and_show = False
if DEBUG_LEVEL > 0:
mytitle = "Disparity_Strength histogram"
fig = plt.figure()
fig.canvas.set_window_title(mytitle)
fig.suptitle(mytitle)
plt.imshow(ex_data.blurred_hist, vmin=0, vmax=.1 * ex_data.blurred_hist.max())#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
plt.colorbar(orientation='horizontal') # location='bottom')
bb_display = ex_data.hist_to_batch.copy()
bb_display = ( 1+ (bb_display % 2) + 2 * ((bb_display % 20)//10)) * (ex_data.hist_to_batch > 0) #).astype(float)
fig2 = plt.figure()
fig2.canvas.set_window_title("Batch indices")
fig2.suptitle("Batch index for each disparity/strength cell")
plt.imshow(bb_display) #, vmin=0, vmax=.1 * ex_data.blurred_hist.max())#,vmin=-6,vmax=-2) # , vmin=0, vmax=.01)
wait_and_show = True
print_time("Creating lists of available correlation data files for each scene")
ex_data.getMLList(ex_data.files_train) # train_list)
print_time("Creating lists of tiles to fall into each DS bin for each scene (long run).")
ex_data.makeBatchLists(train_ds = ex_data.train_ds)
print_time("Done with lists of tiles.")
print_time("Importing TensorCrawl")
import tensorflow as tf
import tensorflow.contrib.slim as slim
print_time("TensorCrawl imported")
result_dir = './result/'
checkpoint_dir = './result/'
save_freq = 500
def lrelu(x):
#return tf.maximum(x*0.2,x)
return tf.nn.relu(x)
def network(input):
# fc1 = slim.fully_connected(input, 512, activation_fn=lrelu,scope='g_fc1')
# fc2 = slim.fully_connected(fc1, 512, activation_fn=lrelu,scope='g_fc2')
fc3 = slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc3')
fc4 = slim.fully_connected(fc3, 128, activation_fn=lrelu,scope='g_fc4')
fc5 = slim.fully_connected(fc4, 64, activation_fn=lrelu,scope='g_fc5')
if USE_CONFIDENCE:
fc6 = slim.fully_connected(fc5, 2, activation_fn=lrelu,scope='g_fc6')
else:
fc6 = slim.fully_connected(fc5, 1, activation_fn=None,scope='g_fc6')
#If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only
return fc6
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):
"""
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")
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.slice(out_batch,[0,0],[-1,1], name = "disp_slice")
# d_gt_slice = tf.slice(gt_ds_batch,[0,0],[-1,1], name = "d_gt_slice")
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")
if absolute_disparity:
out_diff = tf.subtract(disp_slice, d_gt_slice, name = "out_diff")
else:
residual_disp = tf.subtract(d_gt_slice, target_disparity_batch, name = "residual_disp")
out_diff = tf.subtract(disp_slice, residual_disp, 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")
if use_confidence:
cost12 = tf.add(cost1, cost2, name = "cost12")
cost123 = tf.add(cost12, cost3, name = "cost123")
return cost123, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm
else:
return cost1, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm
sess = tf.Session()
#seems that float64 can feed float32!
in_tile = tf.placeholder(tf.float32,[None,9 * 9 * 4 + 1])
gt = tf.placeholder(tf.float32,[None,2])
target_d = tf.placeholder(tf.float32,[None])
out = network(in_tile)
#Try standard loss functions first
G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm = batchLoss(out_batch = out, # [batch_size,(1..2)] tf_result
target_disparity_batch= target_d, # [batch_size] tf placeholder
gt_ds_batch = 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 = 1.0)
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()
# ?!!!!!
#merged = tf.summary.merge_all()
#train_writer = tf.summary.FileWriter(result_dir + '/train', sess.graph)
#test_writer = tf.summary.FileWriter(result_dir + '/test')
sess.run(tf.global_variables_initializer())
ckpt=tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt:
print('loaded '+ckpt.model_checkpoint_path)
saver.restore(sess,ckpt.model_checkpoint_path)
allfolders = glob.glob('./result/*0')
lastepoch = 0
for folder in allfolders:
lastepoch = np.maximum(lastepoch, int(folder[-4:]))
recorded_loss = []
recorded_mean_loss = []
recorded_gt_d = []
recorded_gt_c = []
recorded_pr_d = []
recorded_pr_c = []
LR = 1e-3
print(bcolors.HEADER+"Last Epoch = "+str(lastepoch)+bcolors.ENDC)
if DEBUG_PLT_LOSS:
plt.ion() # something about plotting
plt.figure(1, figsize=(4,12))
pass
training_tiles = np.array([])
training_values = np.array([])
graph_saved = False
for epoch in range(20): #MAX_EPOCH):
print_time("epoch="+str(epoch))
train_seed_list = np.arange(len(ex_data.files_train))
np.random.shuffle(train_seed_list)
g_loss = np.zeros(len(train_seed_list))
for nscene, seed_index in enumerate(train_seed_list):
corr2d_batch, target_disparity_batch, gt_ds_batch = ex_data.prepareBatchData(seed_index)
num_tiles = corr2d_batch.shape[0] # 1000
num_tile_slices = corr2d_batch.shape[1] # 4
num_cell_in_slice = corr2d_batch.shape[2] # 81
in_data = np.empty((num_tiles, num_tile_slices*num_cell_in_slice + 1), dtype = np.float32)
in_data[...,0:num_tile_slices*num_cell_in_slice] = corr2d_batch.reshape((corr2d_batch.shape[0],corr2d_batch.shape[1]*corr2d_batch.shape[2]))
in_data[...,num_tile_slices*num_cell_in_slice] = target_disparity_batch
st=time.time()
#run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
#run_metadata = tf.RunMetadata()
#_,G_current,output = sess.run([G_opt,G_loss,out],feed_dict={in_tile:input_patch,gt:gt_patch,lr:LR},options=run_options,run_metadata=run_metadata)
print_time("%d:%d Run "%(epoch, nscene), end = "")
_,G_current,output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm = sess.run([G_opt,G_loss,out,_disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm],
feed_dict={in_tile: in_data,
gt: gt_ds_batch,
target_d: target_disparity_batch,
lr: LR})
if not graph_saved:
writer = tf.summary.FileWriter('./attic/nn_ds_single_graph1', sess.graph)
writer.close()
graph_saved = True
# exit(0)
g_loss[nscene]=G_current
mean_loss = np.mean(g_loss[np.where(g_loss)])
print_time("loss=%f, running average=%f"%(G_current,mean_loss))
pass
"""
"""
if wait_and_show: # wait and show images
plt.show()
print_time("All done, exiting...")
\ No newline at end of file
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