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nn_ds_single.py
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nn_ds_single.py
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#!/usr/bin/env python3
from numpy import float64
__copyright__ = "Copyright 2018, Elphel, Inc."
__license__ = "GPL-3.0+"
__email__ = "[email protected]"
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...")