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python3-imagej-tiff
Commit 07c7d46a authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
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display weights

parent a8911582
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nn_ds_inmem4_tmp.py
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#!/usr/bin/env python3 #!/usr/bin/env python3
from numpy import float64 from numpy import float64
from _stat import S_IEXEC
__copyright__ = "Copyright 2018, Elphel, Inc." __copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+" __license__ = "GPL-3.0+"
...@@ -27,23 +28,23 @@ DEBUG_LEVEL= 1 ...@@ -27,23 +28,23 @@ DEBUG_LEVEL= 1
DISP_BATCH_BINS = 20 # Number of batch disparity bins DISP_BATCH_BINS = 20 # Number of batch disparity bins
STR_BATCH_BINS = 10 # Number of batch strength 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) 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 #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_BATCH_FILES = 10 #maximal number of files to use in a batch
MAX_EPOCH = 500 MAX_EPOCH = 500
#LR = 1e-4 # learning rate #LR = 1e-4 # learning rate
LR = 1e-3 # learning rate LR = 1e-3 # learning rate
USE_CONFIDENCE = False USE_CONFIDENCE = False
ABSOLUTE_DISPARITY = False # True # False ABSOLUTE_DISPARITY = True # True # False
DEBUG_PLT_LOSS = True DEBUG_PLT_LOSS = True
FEATURES_PER_TILE = 324 FEATURES_PER_TILE = 324
EPOCHS_TO_RUN = 10000 #0 EPOCHS_TO_RUN = 10000 #0
RUN_TOT_AVG = 100 # last batches to average. Epoch is 307 training batches 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 BATCH_SIZE = 1000 # Each batch of tiles has balanced D/S tiles, shuffled batches but not inside batches
SHUFFLE_EPOCH = True SHUFFLE_EPOCH = True
NET_ARCH = 3 # overwrite with argv? NET_ARCH = 0 # overwrite with argv?
#DEBUG_PACK_TILES = True #DEBUG_PACK_TILES = True
SUFFIX=str(NET_ARCH)+ (["R","A"][ABSOLUTE_DISPARITY]) SUFFIX=str(NET_ARCH)+ (["R","A"][ABSOLUTE_DISPARITY])
MAX_TRAIN_FILES_TFR = 4 MAX_TRAIN_FILES_TFR = 6
#http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python #http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python
class bcolors: class bcolors:
HEADER = '\033[95m' HEADER = '\033[95m'
...@@ -82,7 +83,7 @@ def readTFRewcordsEpoch(train_filename): ...@@ -82,7 +83,7 @@ def readTFRewcordsEpoch(train_filename):
corr2d= np.array(corr2d_list) corr2d= np.array(corr2d_list)
target_disparity = np.array(target_disparity_list) target_disparity = np.array(target_disparity_list)
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
#from http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/ #from http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/
def read_and_decode(filename_queue): def read_and_decode(filename_queue):
...@@ -102,7 +103,7 @@ def read_and_decode(filename_queue): ...@@ -102,7 +103,7 @@ def read_and_decode(filename_queue):
gt_ds = tf.cast(features['gt_ds'], tf.float32) # tf.decode_raw(features['gt_ds'], 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) in_features = tf.concat([corr2d,target_disparity],0)
# still some nan-s in correlation data? # still some nan-s in correlation data?
# in_features_clean = tf.where(tf.is_nan(in_features), tf.zeros_like(in_features), in_features) # 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_clean, target_disparity, gt_ds],
corr2d_out, target_disparity_out, gt_ds_out = tf.train.shuffle_batch( [in_features, 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, batch_size=1000, # 2,
...@@ -124,14 +125,14 @@ except IndexError: ...@@ -124,14 +125,14 @@ except IndexError:
# if the path is a directory # if the path is a directory
if os.path.isdir(train_filenameTFR): if os.path.isdir(train_filenameTFR):
train_filesTFR = glob.glob(train_filenameTFR+"/*train-*.tfrecords") train_filesTFR = glob.glob(train_filenameTFR+"/*train-*.tfrecords")
train_filenameTFR = train_filesTFR[0] train_filenameTFR = train_filesTFR[0]
else: else:
train_filesTFR = [train_filenameTFR] train_filesTFR = [train_filenameTFR]
train_filesTFR.sort() train_filesTFR.sort()
print("Train tfrecords: "+str(train_filesTFR)) print("Train tfrecords: "+str(train_filesTFR))
# tfrecords' paths for testing # tfrecords' paths for testing
try: try:
test_filenameTFR = sys.argv[2] test_filenameTFR = sys.argv[2]
except IndexError: except IndexError:
...@@ -140,13 +141,13 @@ except IndexError: ...@@ -140,13 +141,13 @@ except IndexError:
# if the path is a directory # if the path is a directory
if os.path.isdir(test_filenameTFR): if os.path.isdir(test_filenameTFR):
test_filesTFR = glob.glob(test_filenameTFR+"/test_*.tfrecords") test_filesTFR = glob.glob(test_filenameTFR+"/test_*.tfrecords")
test_filenameTFR = test_filesTFR[0] test_filenameTFR = test_filesTFR[0]
else: else:
test_filesTFR = [test_filenameTFR] test_filesTFR = [test_filenameTFR]
test_filesTFR.sort() test_filesTFR.sort()
print("Test tfrecords: "+str(test_filesTFR)) print("Test tfrecords: "+str(test_filesTFR))
# Now we are left with 2 lists - train and test list # Now we are left with 2 lists - train and test list
n_allowed_train_filesTFR = min(MAX_TRAIN_FILES_TFR,len(train_filesTFR)) n_allowed_train_filesTFR = min(MAX_TRAIN_FILES_TFR,len(train_filesTFR))
...@@ -165,11 +166,11 @@ gt_ds_trains = [None]*n_allowed_train_filesTFR ...@@ -165,11 +166,11 @@ gt_ds_trains = [None]*n_allowed_train_filesTFR
for i in range(n_allowed_train_filesTFR): for i in range(n_allowed_train_filesTFR):
corr2d_trains[i], target_disparity_trains[i], gt_ds_trains[i] = readTFRewcordsEpoch(train_filesTFR[i]) corr2d_trains[i], target_disparity_trains[i], gt_ds_trains[i] = readTFRewcordsEpoch(train_filesTFR[i])
print_time("Parsed "+train_filesTFR[i]) print_time("Parsed "+train_filesTFR[i])
corr2d_train = corr2d_trains[0] corr2d_train = corr2d_trains[0]
target_disparity_train = target_disparity_trains[0] target_disparity_train = target_disparity_trains[0]
gt_ds_train = gt_ds_trains[0] gt_ds_train = gt_ds_trains[0]
print_time(" Done") print_time(" Done")
corr2d_train_placeholder = tf.placeholder(corr2d_train.dtype, (None,324)) # corr2d_train.shape) corr2d_train_placeholder = tf.placeholder(corr2d_train.dtype, (None,324)) # corr2d_train.shape)
...@@ -211,6 +212,9 @@ def lrelu(x): ...@@ -211,6 +212,9 @@ def lrelu(x):
# return tf.nn.relu(x) # return tf.nn.relu(x)
def network_fc_simple(input, arch = 0): def network_fc_simple(input, arch = 0):
global image_summary_op1
layouts = {0:[0, 0, 0, 32, 20, 16], layouts = {0:[0, 0, 0, 32, 20, 16],
1:[0, 0, 0, 256, 128, 64], 1:[0, 0, 0, 256, 128, 64],
2:[0, 128, 32, 32, 32, 16], 2:[0, 128, 32, 32, 32, 16],
...@@ -224,11 +228,88 @@ def network_fc_simple(input, arch = 0): ...@@ -224,11 +228,88 @@ def network_fc_simple(input, arch = 0):
inp = fc[-1] inp = fc[-1]
else: else:
inp = input inp = input
fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu,scope='g_fc'+str(i))) fc.append(slim.fully_connected(inp, num_outs, activation_fn=lrelu,scope='g_fc'+str(i)))
with tf.variable_scope('g_fc'+str(i)+'/fully_connected',reuse=tf.AUTO_REUSE):
#with tf.variable_scope('g_fc'+str(i)+'/fully_connected',reuse=tf.AUTO_REUSE):
with tf.variable_scope('g_fc'+str(i),reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[inp.shape[1],num_outs]) w = tf.get_variable('weights',shape=[inp.shape[1],num_outs])
b = tf.get_variable('weights',shape=[inp.shape[1],num_outs]) #image = tf.get_variable('w_images',shape=[1, inp.shape[1],num_outs,1])
if (i==3):
# red border
grid = tf.constant([255,100,100],dtype=tf.float32,name="GRID")
# (325,32)
wimg_1 = w
# (32,325)
wimg_2 = tf.transpose(wimg_1,[1,0])
# (32,324)
wimg_3 = wimg_2[:,:-1]
# res?
#wimg_res = tf.get_variable('wimg_res',shape=[32*(9+1),(9+1)*4, 3])
# long list
tmp1 = []
for mi in range(32):
tmp2 = []
for mj in range(4):
s_i = mj*81
e_i = (mj+1)*81
tile = tf.reshape(wimg_3[mi,s_i:e_i],shape=(9,9))
tiles = tf.stack([tile]*3,axis=2)
#gtiles1 = tf.concat([tiles, tf.reshape(9*[grid],shape=(1,9,3))],axis=0)
gtiles1 = tf.concat([tiles, tf.expand_dims(9*[grid],0)],axis=0)
gtiles2 = tf.concat([gtiles1,tf.expand_dims(10*[grid],1)],axis=1)
tmp2.append(gtiles2)
ts = tf.concat(tmp2,axis=2)
tmp1.append(ts)
image_summary_op2 = tf.concat(tmp1,axis=0)
#image_summary_op1 = tf.assign(wimg_res,tf.zeros(shape=[32*(9+1),(9+1)*4, 3],dtype=tf.float32))
#wimgo1 = tf.zeros(shape=[32*(9+1),(9+1)*4, 3],dtype=tf.float32)
#tf.summary.image("wimg_res1",tf.reshape(wimg_res,[1,32*(9+1),(9+1)*4, 3]))
#tf.summary.image("wimgo1",tf.reshape(wimgo1,[1,32*(9+1),(9+1)*4, 3]))
#tf.summary.image("wimgo2",tf.reshape(wimgo2,[1,32*(9+1),(9+1)*4, 3]))
tf.summary.image("SWEIGTS",tf.reshape(gtiles2,[1,10,10,3]))
tf.summary.image("WEIGTS",tf.reshape(image_summary_op2,[1,320,40,3]))
# borders
#for mi in range(0,wimg_res.shape[0],10):
# for mj in range(wimg_res.shape[1]):
# wimg_res[mi,mj].assign([255,255,255])
#wimg_res[9::(9+1),:].assign([255,0,0])
#wimg_res[:,9::(9+1)].assign([255,0,0])
#for mi in range(0,wimg_res.shape[0],10):
# print(mi)
#wimg_res = tf.stack([wing_res,])
#wimg_1 = tf.reshape(w,[1,inp.shape[1],num_outs,1])
#wimg_1t = tf.transpose(wimg_1,[0,2,1,3])
# w = w[a,b]
# wt = w[b,a]
# for i in range(b):
# tmp =
#tf.summary.image("wimg_1",wimg_1)
#tf.summary.image("wimg_1t",wimg_1t)
#tf.summary.image("wimg_res1",tf.reshape(wimg_res,[1,32*(9+1),(9+1)*4, 3]))
b = tf.get_variable('biases',shape=[num_outs])
tf.summary.histogram("weights",w) tf.summary.histogram("weights",w)
tf.summary.histogram("biases",b) tf.summary.histogram("biases",b)
""" """
...@@ -241,32 +322,34 @@ def network_fc_simple(input, arch = 0): ...@@ -241,32 +322,34 @@ def network_fc_simple(input, arch = 0):
### fc3 = slim.fully_connected(input, 32, activation_fn=lrelu,scope='g_fc3') ### fc3 = slim.fully_connected(input, 32, activation_fn=lrelu,scope='g_fc3')
### fc4 = slim.fully_connected(fc3, 20, activation_fn=lrelu,scope='g_fc4') ### fc4 = slim.fully_connected(fc3, 20, activation_fn=lrelu,scope='g_fc4')
### fc5 = slim.fully_connected(fc4, 16, activation_fn=lrelu,scope='g_fc5') ### fc5 = slim.fully_connected(fc4, 16, activation_fn=lrelu,scope='g_fc5')
if USE_CONFIDENCE: if USE_CONFIDENCE:
fc_out = slim.fully_connected(fc[-1], 2, activation_fn=lrelu,scope='g_fc_out') fc_out = slim.fully_connected(fc[-1], 2, activation_fn=lrelu,scope='g_fc_out')
with tf.variable_scope('g_fc_out',reuse=tf.AUTO_REUSE): with tf.variable_scope('g_fc_out',reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[fc[-1].shape[1],2]) w = tf.get_variable('weights',shape=[fc[-1].shape[1],2])
b = tf.get_variable('biases',shape=[fc[-1].shape[1],2]) tf.summary.image("wimage",tf.reshape(w,[1,fc[-1].shape[1],2,1]))
b = tf.get_variable('biases',shape=[2])
tf.summary.histogram("weights",w) tf.summary.histogram("weights",w)
tf.summary.histogram("biases",b) tf.summary.histogram("biases",b)
else: else:
fc_out = slim.fully_connected(fc[-1], 1, activation_fn=None,scope='g_fc_out') fc_out = slim.fully_connected(fc[-1], 1, activation_fn=None,scope='g_fc_out')
with tf.variable_scope('g_fc_out',reuse=tf.AUTO_REUSE): with tf.variable_scope('g_fc_out',reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[fc[-1].shape[1],1]) w = tf.get_variable('weights',shape=[fc[-1].shape[1],1])
tf.summary.image("wimage",tf.reshape(w,[1,fc[-1].shape[1],1,1]))
b = tf.get_variable('biases',shape=[1]) b = tf.get_variable('biases',shape=[1])
tf.summary.histogram("weights",w) tf.summary.histogram("weights",w)
tf.summary.histogram("biases",b) tf.summary.histogram("biases",b)
#If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only #If using residual disparity, split last layer into 2 or remove activation and add rectifier to confidence only
return fc_out return fc_out
def batchLoss(out_batch, # [batch_size,(1..2)] tf_result def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
target_disparity_batch, # [batch_size] tf placeholder target_disparity_batch, # [batch_size] tf placeholder
gt_ds_batch, # [batch_size,2] tf placeholder gt_ds_batch, # [batch_size,2] tf placeholder
absolute_disparity = True, #when false there should be no activation on disparity output ! absolute_disparity = True, #when false there should be no activation on disparity output !
use_confidence = True, use_confidence = True,
lambda_conf_avg = 0.01, lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1, lambda_conf_pwr = 0.1,
conf_pwr = 2.0, conf_pwr = 2.0,
...@@ -276,7 +359,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result ...@@ -276,7 +359,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
disp_wmin = 1.0, # minimal disparity to apply weight boosting for small disparities 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 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) use_out = False): # use calculated disparity for disparity weight boosting (False - use target disparity)
with tf.name_scope("BatchLoss"): 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 Here confidence should be after relU. Disparity - may be also if absolute, but no activation if output is residual disparity
...@@ -295,7 +378,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result ...@@ -295,7 +378,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
else: else:
# w_slice = tf.slice(gt_ds_batch,[0,1],[-1,1], name = "w_gt_slice") # 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_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_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.clip_by_value(w_sub, tf_0f,tf_maxw, name = "w_clip")
w_clip = tf.maximum(w_sub, tf_0f, name = "w_clip") w_clip = tf.maximum(w_sub, tf_0f, name = "w_clip")
...@@ -303,7 +386,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result ...@@ -303,7 +386,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
w = w_clip w = w_clip
else: else:
w=tf.pow(w_clip, tf_gt_conf_pwr, name = "w_pow") w=tf.pow(w_clip, tf_gt_conf_pwr, name = "w_pow")
if use_confidence: if use_confidence:
tf_num_tilesf = tf.cast(tf_num_tiles, dtype=tf.float32, name="tf_num_tilesf") 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.slice(out_batch,[0,1],[-1,1], name = "conf_slice")
...@@ -313,7 +396,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result ...@@ -313,7 +396,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
conf_avg1 = tf.subtract(conf_avg, tf_1f, name = "conf_avg1") conf_avg1 = tf.subtract(conf_avg, tf_1f, name = "conf_avg1")
conf_avg2 = tf.square(conf_avg1, name = "conf_avg2") conf_avg2 = tf.square(conf_avg1, name = "conf_avg2")
cost2 = tf.multiply (conf_avg2, tf_lambda_conf_avg, name = "cost2") cost2 = tf.multiply (conf_avg2, tf_lambda_conf_avg, name = "cost2")
iconf_avg = tf.divide(tf_1f, conf_avg, name = "iconf_avg") iconf_avg = tf.divide(tf_1f, conf_avg, name = "iconf_avg")
nconf = tf.multiply (conf_slice, iconf_avg, name = "nconf") #normalized confidence nconf = tf.multiply (conf_slice, iconf_avg, name = "nconf") #normalized confidence
nconf_pwr = tf.pow(nconf, conf_pwr, name = "nconf_pwr") nconf_pwr = tf.pow(nconf, conf_pwr, name = "nconf_pwr")
...@@ -324,17 +407,17 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result ...@@ -324,17 +407,17 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
else: else:
w_all = w w_all = w
# cost2 = 0.0 # cost2 = 0.0
# cost3 = 0.0 # cost3 = 0.0
# normalize weights # normalize weights
w_sum = tf.reduce_sum(w_all, name = "w_sum") w_sum = tf.reduce_sum(w_all, name = "w_sum")
iw_sum = tf.divide(tf_1f, w_sum, name = "iw_sum") iw_sum = tf.divide(tf_1f, w_sum, name = "iw_sum")
w_norm = tf.multiply (w_all, iw_sum, name = "w_norm") w_norm = tf.multiply (w_all, iw_sum, name = "w_norm")
# disp_slice = tf.slice(out_batch,[0,0],[-1,1], name = "disp_slice") # 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") # 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") 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") d_gt_slice = tf.reshape(gt_ds_batch[:,0],[-1], name = "d_gt_slice")
""" """
if absolute_disparity: if absolute_disparity:
out_diff = tf.subtract(disp_slice, d_gt_slice, name = "out_diff") out_diff = tf.subtract(disp_slice, d_gt_slice, name = "out_diff")
...@@ -342,7 +425,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result ...@@ -342,7 +425,7 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat") td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
residual_disp = tf.subtract(d_gt_slice, td_flat, name = "residual_disp") residual_disp = tf.subtract(d_gt_slice, td_flat, name = "residual_disp")
out_diff = tf.subtract(disp_slice, residual_disp, name = "out_diff") out_diff = tf.subtract(disp_slice, residual_disp, name = "out_diff")
""" """
td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat") td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
if absolute_disparity: if absolute_disparity:
adisp = disp_slice adisp = disp_slice
...@@ -350,16 +433,16 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result ...@@ -350,16 +433,16 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
# td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat") # td_flat = tf.reshape(target_disparity_batch,[-1], name = "td_flat")
adisp = tf.add(disp_slice, td_flat, name = "adisp") adisp = tf.add(disp_slice, td_flat, name = "adisp")
out_diff = tf.subtract(adisp, d_gt_slice, name = "out_diff") out_diff = tf.subtract(adisp, d_gt_slice, name = "out_diff")
out_diff2 = tf.square(out_diff, name = "out_diff2") out_diff2 = tf.square(out_diff, name = "out_diff2")
out_wdiff2 = tf.multiply (out_diff2, w_norm, name = "out_wdiff2") out_wdiff2 = tf.multiply (out_diff2, w_norm, name = "out_wdiff2")
cost1 = tf.reduce_sum(out_wdiff2, name = "cost1") cost1 = tf.reduce_sum(out_wdiff2, name = "cost1")
out_diff2_offset = tf.subtract(out_diff2, error2_offset, name = "out_diff2_offset") 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") out_diff2_biased = tf.maximum(out_diff2_offset, 0.0, name = "out_diff2_biased")
# calculate disparity-based weight boost # calculate disparity-based weight boost
if use_out: if use_out:
dispw = tf.clip_by_value(adisp, disp_wmin, disp_wmax, name = "dispw") dispw = tf.clip_by_value(adisp, disp_wmin, disp_wmax, name = "dispw")
...@@ -370,19 +453,19 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result ...@@ -370,19 +453,19 @@ def batchLoss(out_batch, # [batch_size,(1..2)] tf_result
dispw_sum = tf.reduce_sum(dispw_comp, name = "dispw_sum") dispw_sum = tf.reduce_sum(dispw_comp, name = "dispw_sum")
idispw_sum = tf.divide(tf_1f, dispw_sum, name = "idispw_sum") idispw_sum = tf.divide(tf_1f, dispw_sum, name = "idispw_sum")
dispw_norm = tf.multiply (dispw_comp, idispw_sum, name = "dispw_norm") 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, dispw_norm, name = "out_diff2_wbiased")
# out_diff2_wbiased = tf.multiply(out_diff2_biased, w_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") cost1b = tf.reduce_sum(out_diff2_wbiased, name = "cost1b")
if use_confidence: if use_confidence:
cost12 = tf.add(cost1b, cost2, name = "cost12") cost12 = tf.add(cost1b, cost2, name = "cost12")
cost123 = tf.add(cost12, cost3, name = "cost123") cost123 = tf.add(cost12, cost3, name = "cost123")
return cost123, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1 return cost123, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
else: else:
return cost1b, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1 return cost1b, disp_slice, d_gt_slice, out_diff,out_diff2, w_norm, out_wdiff2, cost1
#corr2d325 = tf.concat([corr2d,target_disparity],0) #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'],tf.reshape(next_element_train['target_disparity'],(-1,1))],1)
...@@ -397,7 +480,7 @@ G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _ ...@@ -397,7 +480,7 @@ G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _
target_disparity_batch= next_element_train['target_disparity'], # target_disparity, ### target_d, # [batch_size] tf placeholder 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 gt_ds_batch = next_element_train['gt_ds'], # gt_ds, ### gt, # [batch_size,2] tf placeholder
absolute_disparity = ABSOLUTE_DISPARITY, absolute_disparity = ABSOLUTE_DISPARITY,
use_confidence = USE_CONFIDENCE, # True, use_confidence = USE_CONFIDENCE, # True,
lambda_conf_avg = 0.01, lambda_conf_avg = 0.01,
lambda_conf_pwr = 0.1, lambda_conf_pwr = 0.1,
conf_pwr = 2.0, conf_pwr = 2.0,
...@@ -407,7 +490,7 @@ G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _ ...@@ -407,7 +490,7 @@ G_loss, _disp_slice, _d_gt_slice, _out_diff, _out_diff2, _w_norm, _out_wdiff2, _
disp_wmin = 1.0, # minimal disparity to apply weight boosting for small disparities 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 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) use_out = False) # use calculated disparity for disparity weight boosting (False - use target disparity)
tf_ph_G_loss = tf.placeholder(tf.float32,shape=None,name='G_loss_avg') 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_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
with tf.name_scope('sample'): with tf.name_scope('sample'):
...@@ -446,14 +529,14 @@ def read_new_tfrecord_file(filename,result): ...@@ -446,14 +529,14 @@ def read_new_tfrecord_file(filename,result):
result.append(c) result.append(c)
print("Loaded new tfrecord file: "+str(filename)) print("Loaded new tfrecord file: "+str(filename))
train_record_index_counter = 0 train_record_index_counter = 0
train_file_index = 0 train_file_index = 0
with tf.Session() as sess: with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer()) sess.run(tf.local_variables_initializer())
merged = tf.summary.merge_all() merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph) train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph)
test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph) test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph)
...@@ -461,29 +544,29 @@ with tf.Session() as sess: ...@@ -461,29 +544,29 @@ with tf.Session() as sess:
loss_test_hist= np.empty(dataset_test_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_train_hist= np.empty(dataset_train_size, dtype=np.float32)
loss2_test_hist= np.empty(dataset_test_size, dtype=np.float32) loss2_test_hist= np.empty(dataset_test_size, dtype=np.float32)
train_avg = 0.0 train_avg = 0.0
train2_avg = 0.0 train2_avg = 0.0
test_avg = 0.0 test_avg = 0.0
test2_avg = 0.0 test2_avg = 0.0
for epoch in range(EPOCHS_TO_RUN): for epoch in range(EPOCHS_TO_RUN):
train_file_index = epoch%n_allowed_train_filesTFR train_file_index = epoch%n_allowed_train_filesTFR
print("train_file_index: "+str(train_file_index)) print("train_file_index: "+str(train_file_index))
if epoch%10==0: if epoch%10==0:
# if there are more files than python3 memory allows # if there are more files than python3 memory allows
if (n_allowed_train_filesTFR<len(train_filesTFR)): if (n_allowed_train_filesTFR<len(train_filesTFR)):
# circular loading? # circular loading?
tmp_train_index = (n_allowed_train_filesTFR+train_record_index_counter)%len(train_filesTFR) tmp_train_index = (n_allowed_train_filesTFR+train_record_index_counter)%len(train_filesTFR)
# wait for old thread # wait for old thread
if epoch!=0: if epoch!=0:
if thr.is_alive(): if thr.is_alive():
print_time("Waiting until tfrecord gets loaded") print_time("Waiting until tfrecord gets loaded")
thr.join() thr.join()
# do replacement # do replacement
## remove the first ## remove the first
corr2d_trains.pop(0) corr2d_trains.pop(0)
target_disparity_trains.pop(0) target_disparity_trains.pop(0)
gt_ds_trains.pop(0) gt_ds_trains.pop(0)
...@@ -491,19 +574,21 @@ with tf.Session() as sess: ...@@ -491,19 +574,21 @@ with tf.Session() as sess:
corr2d_trains.append(thr_result[0]) corr2d_trains.append(thr_result[0])
target_disparity_trains.append(thr_result[1]) target_disparity_trains.append(thr_result[1])
gt_ds_trains.append(thr_result[2]) gt_ds_trains.append(thr_result[2])
print_time("Time to begin loading a new tfrecord file") print_time("Time to begin loading a new tfrecord file")
# new thread # new thread
thr_result = [] thr_result = []
thr = Thread(target=read_new_tfrecord_file, args=(train_filesTFR[tmp_train_index],thr_result)) thr = Thread(target=read_new_tfrecord_file, args=(train_filesTFR[tmp_train_index],thr_result))
# start # start
thr.start() thr.start()
train_record_index_counter += 1 train_record_index_counter += 1
# if SHUFFLE_EPOCH: # if SHUFFLE_EPOCH:
# dataset_train = dataset_train.shuffle(buffer_size=10000) # dataset_train = dataset_train.shuffle(buffer_size=10000)
# RUN TRAIN SESSION
sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_trains[train_file_index], sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_trains[train_file_index],
target_disparity_train_placeholder: target_disparity_trains[train_file_index], target_disparity_train_placeholder: target_disparity_trains[train_file_index],
gt_ds_train_placeholder: gt_ds_trains[train_file_index]}) gt_ds_train_placeholder: gt_ds_trains[train_file_index]})
...@@ -524,24 +609,22 @@ with tf.Session() as sess: ...@@ -524,24 +609,22 @@ with tf.Session() as sess:
corr2d325, corr2d325,
], ],
feed_dict={lr:LR,tf_ph_G_loss:train_avg, tf_ph_sq_diff:train2_avg}) # pfrevious value of *_avg feed_dict={lr:LR,tf_ph_G_loss:train_avg, tf_ph_sq_diff:train2_avg}) # pfrevious value of *_avg
# save all for now as a test # save all for now as a test
#train_writer.add_summary(summary, i) #train_writer.add_summary(summary, i)
#train_writer.add_summary(train_summary, i) #train_writer.add_summary(train_summary, i)
loss_train_hist[i] = G_loss_trained loss_train_hist[i] = G_loss_trained
loss2_train_hist[i] = out_cost1 loss2_train_hist[i] = out_cost1
except tf.errors.OutOfRangeError: except tf.errors.OutOfRangeError:
print("train done at step %d"%(i)) print("train done at step %d"%(i))
break break
train_avg = np.average(loss_train_hist).astype(np.float32) train_avg = np.average(loss_train_hist).astype(np.float32)
train2_avg = np.average(loss2_train_hist).astype(np.float32) train2_avg = np.average(loss2_train_hist).astype(np.float32)
#_,_=sess.run([tf_ph_G_loss,tf_ph_sq_diff],feed_dict={tf_ph_G_loss:train_avg, tf_ph_sq_diff:train2_avg})
#tf_ph_G_loss = tf.placeholder(tf.float32,shape=None,name='G_loss_avg') # RUN TEST SESSION
#tf_ph_sq_diff = tf.placeholder(tf.float32,shape=None,name='sq_diff_avg')
sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_test, sess.run(iterator_train.initializer, feed_dict={corr2d_train_placeholder: corr2d_test,
target_disparity_train_placeholder: target_disparity_test, target_disparity_train_placeholder: target_disparity_test,
gt_ds_train_placeholder: gt_ds_test}) gt_ds_train_placeholder: gt_ds_test})
...@@ -566,18 +649,16 @@ with tf.Session() as sess: ...@@ -566,18 +649,16 @@ with tf.Session() as sess:
except tf.errors.OutOfRangeError: except tf.errors.OutOfRangeError:
print("test done at step %d"%(i)) print("test done at step %d"%(i))
break break
# print_time("%d:%d -> %f"%(epoch,i,G_current)) test_avg = np.average(loss_test_hist).astype(np.float32)
test_avg = np.average(loss_test_hist).astype(np.float32)
test2_avg = np.average(loss2_test_hist).astype(np.float32) test2_avg = np.average(loss2_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})
# they include image summaries as well
train_writer.add_summary(train_summary, epoch) train_writer.add_summary(train_summary, epoch)
test_writer.add_summary(test_summary, epoch) test_writer.add_summary(test_summary, epoch)
print_time("%d:%d -> %f %f (%f %f)"%(epoch,i,train_avg, test_avg,train2_avg, test2_avg)) print_time("%d:%d -> %f %f (%f %f)"%(epoch,i,train_avg, test_avg,train2_avg, test2_avg))
# Close writers # Close writers
train_writer.close() train_writer.close()
test_writer.close() test_writer.close()
......
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