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python3-imagej-tiff
Commit f0673b1a authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
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+test,-unused

parent 866cd457
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infer_qcds_02.py → infer_qcds_graph_01_pbtest.py
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#!/usr/bin/env python3
from tensorflow.python.framework.ops import GraphKeys
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "andrey@elphel.com"
#Builds (and saved) inference model from trained by nn_ds_neibs21.py
#Model and weights are used by the inference-only infer_qcds_graph.py
# Just inference, currently uses /data_ssd/data_sets/tf_data_5x5_main_13_heur/inference/
import os
import sys
import numpy as np
import time
import shutil
import qcstereo_network
##import qcstereo_network
import qcstereo_functions as qsf
import tensorflow as tf
from tensorflow.python.ops import resource_variable_ops
tf.ResourceVariable = resource_variable_ops.ResourceVariable
#from tensorflow.python.ops import resource_variable_ops
#tf.ResourceVariable = resource_variable_ops.ResourceVariable
qsf.TIME_START = time.time()
qsf.TIME_LAST = qsf.TIME_START
......@@ -24,6 +21,7 @@ qsf.TIME_LAST = qsf.TIME_START
IMG_WIDTH = 324 # tiles per image row
DEBUG_LEVEL= 1
try:
conf_file = sys.argv[1]
except IndexError:
......@@ -59,10 +57,12 @@ TRAIN_BUFFER_GPU, TRAIN_BUFFER_CPU = [None]*2
TEST_TITLES = None
USE_SPARSE_ONLY = True
LOGFILE="results-infer.txt"
"""
Next gets globals from the config file
"""
globals().update(parameters)
WIDTH = 324
HEIGHT = 242
TILE_SIZE = TILE_SIDE* TILE_SIDE # == 81
......@@ -81,8 +81,8 @@ SUFFIX=(str(NET_ARCH1)+'-'+str(NET_ARCH2)+
('_CP'+str(DISP_DIFF_CAP)) +
('_S'+str(DISP_DIFF_SLOPE))
)
NN_LAYOUT1 = qcstereo_network.NN_LAYOUTS[NET_ARCH1]
NN_LAYOUT2 = qcstereo_network.NN_LAYOUTS[NET_ARCH2]
##NN_LAYOUT1 = qcstereo_network.NN_LAYOUTS[NET_ARCH1]
##NN_LAYOUT2 = qcstereo_network.NN_LAYOUTS[NET_ARCH2]
# Tiff export slice labels
SLICE_LABELS = ["nn_out_ext","hier_out_ext","gt_disparity","gt_strength"]#,
# "cutcorn_cost_nw","cutcorn_cost",
......@@ -94,6 +94,11 @@ qsf.prepareFiles(dirs,
files,
suffix = SUFFIX)
"""
Next is tag for pb (pb == protocol buffer) model
"""
#PB_TAGS = ["model_pb"]
print ("Copying config files to results directory:\n ('%s' -> '%s')"%(conf_file,dirs['result']))
try:
os.makedirs(dirs['result'])
......@@ -103,10 +108,6 @@ except:
shutil.copy2(conf_file,dirs['result'])
LOGPATH = os.path.join(dirs['result'],LOGFILE)
image_data = qsf.initImageData( # just use image_data[0]
files = files,
max_imgs = MAX_IMGS_IN_MEM,
......@@ -118,170 +119,66 @@ image_data = qsf.initImageData( # just use image_data[0]
infer = True,
keep_gt = True) # to generate same output files
ph_corr2d = tf.placeholder(np.float32, (None,FEATURES_PER_TILE), name = 'ph_corr2d')
ph_target_disparity = tf.placeholder(np.float32, (None,1), name = 'ph_target_disparity')
ph_ntile = tf.placeholder(np.int32, (None,), name = 'ph_ntile') #nTile
ph_ntile_out = tf.placeholder(np.int32, (None,), name = 'ph_ntile_out') #which tiles should be calculated in stage2
#corr2d9x325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE]) , tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1])],2)
tf_intile325 = tf.concat([ph_corr2d, ph_target_disparity],axis=1,name="tf_intile325") # [?,325]
pass
"""
target_disparity_cluster = tf.reshape(next_element_tt['target_disparity'], [-1,cluster_size, 1], name="targdisp_cluster")
corr2d_Nx325 = tf.concat([tf.reshape(next_element_tt['corr2d'],[-1,cluster_size,FEATURES_PER_TILE], name="coor2d_cluster"),
target_disparity_cluster], axis=2, name = "corr2d_Nx325")
"""
cluster_radius = CLUSTER_RADIUS
"""
Probably ResourceVariable is not needed here because of the tf.scatter_update()
If collection is not provided, it defaults to [GraphKeys.GLOBAL_VARIABLES], and that in turn fails saver.restore() as this variable was not available in the trained model
"""
#rv_stage1_out = resource_variable_ops.ResourceVariable(
rv_stage1_out = tf.Variable(
np.zeros([HEIGHT * WIDTH, NN_LAYOUT1[-1]]),
## collections = [],
collections = [GraphKeys.LOCAL_VARIABLES],# Works, available with tf.local_variables()
dtype=np.float32,
name = 'rv_stage1_out')
'''
rv_stage1_out = tf.get_variable("rv_stage1_out",
shape=[HEIGHT * WIDTH, NN_LAYOUT1[-1]],
dtype=tf.float32,
initializer=tf.zeros_initializer,
collections = [GraphKeys.LOCAL_VARIABLES], trainable=False)
'''
#rv_stageX_out_init_placeholder = tf.placeholder(tf.float32, shape=[HEIGHT * WIDTH, NN_LAYOUT1[-1]])
#rv_stageX_out_init_op = rv_stageX_out.assign(rv_stageX_out_init_placeholder)
##stage1_tiled = tf.reshape(rv_stage1_out.read_value(),[HEIGHT, WIDTH, -1], name = 'stage1_tiled')
stage1_tiled = tf.reshape(rv_stage1_out, [HEIGHT, WIDTH, -1], name = 'stage1_tiled') # no need to synchronize here?
tf_stage1_exth = tf.concat([stage1_tiled[:,:1,:]]*cluster_radius +
[stage1_tiled] +
[stage1_tiled[:,-1:,:]]*cluster_radius, axis = 1,name = 'stage1_exth')
tf_stage1_ext = tf.concat([tf_stage1_exth[ :1,:,:]]*cluster_radius +
[tf_stage1_exth] +
[tf_stage1_exth[-1:,:,:]]*cluster_radius, axis = 0, name = 'stage1_exth')
tf_stage1_ext4 = tf.expand_dims(tf_stage1_ext, axis = 2, name = 'stage1_ext4')
concat_list = []
cluster_side = 2 * cluster_radius+1
for dy in range(cluster_side):
for dx in range(cluster_side):
# concat_list.append(tf_stage1_ext4[dy: cluster_side-dy, dx: cluster_side-dx,:,:])
concat_list.append(tf.slice(tf_stage1_ext4,[dy,dx,0,0],[HEIGHT, WIDTH,-1,-1]))
pass
tf_stage2_inm = tf.concat(concat_list, axis = 2, name ='stage2_inm') #242, 324, 25, 64
tf_stage2_in = tf.reshape(tf_stage2_inm,[-1,rv_stage1_out.shape[1]*cluster_side*cluster_side], name = 'stage2_in')
tf_stage2_in_sparse = tf.gather(tf_stage2_in, indices= ph_ntile_out, axis=0, name = 'stage2_in_sparse')
#aextv=np.concatenate([a[:,:1,:]]*2 + [a] + [a[:,-1:,:]]*2,axis = 1)
#ext=np.concatenate([aextv[:1,:,:]]*1 + [aextv] + [aextv[-1:,:,:]]*3,axis = 0)
with tf.name_scope("Disparity_net"): # to have the same scope for weight/biases?
ns, _ = qcstereo_network.network_sub(tf_intile325,
input_global = [None,ph_target_disparity][SPREAD_CONVERGENCE], # input_global[:,i,:],
layout= NN_LAYOUT1,
reuse= False,
sym8 = SYM8_SUB,
cluster_radius = 0)
update=tf.scatter_update(ref=rv_stage1_out,
indices = ph_ntile,
updates = ns,
use_locking = False,
name = 'update')
with tf.control_dependencies([update]):
stage1done = tf.constant(1, dtype=tf.int32, name="stage1done")
pass
stage2_out_sparse0 = qcstereo_network.network_inter (
input_tensor = tf_stage2_in_sparse,
input_global = None, # [None, ig][inter_convergence], # optionally feed all convergence values (from each tile of a cluster)
layout = NN_LAYOUT2,
reuse = False,
use_confidence = False)
stage2_out_sparse = tf.identity(stage2_out_sparse0, name = 'stage2_out_sparse')
if not USE_SPARSE_ONLY: #Does it reduce the graph size?
stage2_out_full0 = qcstereo_network.network_inter (
input_tensor = tf_stage2_in,
input_global = None, # [None, ig][inter_convergence], # optionally feed all convergence values (from each tile of a cluster)
layout = NN_LAYOUT2,
reuse = True,
use_confidence = False)
stage2_out_full = tf.identity(stage2_out_full0, name = 'stage2_out_full')
pass
ROOT_PATH = './attic/infer_qcds_graph'+SUFFIX+"/" # for tensorboard
"""
This is needed if ResourceVariable is used - then i/o tensors names somehow disappeared
and were replaced by 'Placeholder_*'
collection_io = 'collection_io'
tf.add_to_collection(collection_io, ph_corr2d)
tf.add_to_collection(collection_io, ph_target_disparity)
tf.add_to_collection(collection_io, ph_ntile)
tf.add_to_collection(collection_io, ph_ntile_out)
tf.add_to_collection(collection_io, stage1done)
tf.add_to_collection(collection_io, stage2_out_sparse)
"""
##saver=tf.train.Saver()
saver =tf.train.Saver(tf.global_variables())
#saver = tf.train.Saver(tf.global_variables()+tf.local_variables())
saver_def = saver.as_saver_def()
pass
"""
saver_def = saver.as_saver_def()
# The name of the tensor you must feed with a filename when saving/restoring.
print ('saver_def.filename_tensor_name=',saver_def.filename_tensor_name)
# The name of the target operation you must run when restoring.
print ('saver_def.restore_op_name=',saver_def.restore_op_name)
# The name of the target operation you must run when saving.
print ('saver_def.save_tensor_name=',saver_def.save_tensor_name)
saver_def.filename_tensor_name= save/Const:0
saver_def.restore_op_name= save/restore_all
saver_def.save_tensor_name= save/control_dependency:0
print(saver.save(sess, files["checkpoints"]))
"""
try:
os.makedirs(os.path.dirname(files['inference']))
print ("Created directory ",os.path.dirname(files['inference']))
except:
pass
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# so, here I need to restore from inference and save to inference_global
#saver.restore(sess, files["checkpoints"])
saver.restore(sess, files["inference"])
# Actually, refresh all the time and have an extra script to restore from it.
# use_Saved_Model = False
#if os.path.isdir(dirs['exportdir']):
# # check if dir contains "Saved Model" model
# use_saved_model = tf.saved_model.loader.maybe_saved_model_directory(dirs['exportdir'])
#if use_saved_model:
# print("Model restore: using Saved_Model model MetaGraph protocol buffer")
# meta_graph_source = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], dirs['exportdir'])
#else:
use_saved_model = tf.saved_model.loader.maybe_saved_model_directory(dirs['exportdir'])
# now add to global
tf.add_to_collection(GraphKeys.GLOBAL_VARIABLES,rv_stage1_out)
if not use_saved_model:
print("ERROR: Saved_Model not found. Run previous script to create it.")
sys.exit()
saver.save(sess, 'data_sets/tf_data_5x5_main_13_heur/inference_global/model')
meta_graph_source = tf.saved_model.loader.load(sess, [tf.saved_model.tag_constants.SERVING], dirs['exportdir'])
infer_saver = tf.train.import_meta_graph(meta_graph_source)
graph=tf.get_default_graph()
ph_corr2d = graph.get_tensor_by_name('ph_corr2d:0')
ph_target_disparity = graph.get_tensor_by_name('ph_target_disparity:0')
ph_ntile = graph.get_tensor_by_name('ph_ntile:0')
ph_ntile_out = graph.get_tensor_by_name('ph_ntile_out:0')
stage1done = graph.get_tensor_by_name('Disparity_net/stage1done:0') #<tf.Operation 'Siam_net/stage1done' type=Const>,
stage2_out_sparse = graph.get_tensor_by_name('Disparity_net/stage2_out_sparse:0')#not found
if not USE_SPARSE_ONLY: #Does it reduce the graph size?
stage2_out_full = graph.get_tensor_by_name('Disparity_net/stage2_out_full:0')
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
#infer_saver.restore(sess, dirs['exportdir'])
#infer_saver.restore(sess, files["inference"])
infer_saver.restore(sess,dirs['exportdir']+'/variables/variables')
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter(ROOT_PATH, sess.graph)
lf = None
if LOGPATH:
lf=open(LOGPATH,"w") #overwrite previous (or make it "a"?
#_ = sess.run([rv_stageX_out_init_op],feed_dict={rv_stageX_out_init_placeholder: np.zeros((HEIGHT * WIDTH, NN_LAYOUT1[-1]))})
for nimg,_ in enumerate(image_data):
dataset_img = qsf.readImageData(
image_data = image_data,
......@@ -294,12 +191,9 @@ with tf.Session() as sess:
replace_nans = True,
infer = True,
keep_gt = True) # to generate same output files
img_corr2d = dataset_img['corr2d'] # [?,324)
img_target = dataset_img['target_disparity'] # [?,324)
img_ntile = dataset_img['ntile'].reshape([-1])
img_corr2d = dataset_img['corr2d'] # (?,324)
img_target = dataset_img['target_disparity'] # (?,1)
img_ntile = dataset_img['ntile'].reshape([-1]) # (?) - 0...78k int32
#run first stage network
qsf.print_time("Running inferred model, stage1", end=" ")
_ = sess.run([stage1done],
......@@ -315,8 +209,7 @@ with tf.Session() as sess:
try:
os.makedirs(os.path.dirname(result_file))
except:
pass
pass
rslt = np.concatenate(
[disp_out.reshape(-1,1),
dataset_img['t_disps'], #t_disps[ntest],
......@@ -326,15 +219,11 @@ with tf.Session() as sess:
rslt = qsf.eval_results(result_file, ABSOLUTE_DISPARITY, radius=CLUSTER_RADIUS, logfile=lf) # (re-loads results). Only uses first 4 layers
if SAVE_TIFFS:
qsf.result_npy_to_tiff(result_file, ABSOLUTE_DISPARITY, fix_nan = True,labels=SLICE_LABELS, logfile=lf)
"""
Remove dataset_img (if it is not [0] to reduce memory footprint
"""
image_data[nimg] = None
meta_graph_def = tf.train.export_meta_graph(files["inference"]+'.meta')
if lf:
lf.close()
writer.close()
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