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python3-imagej-tiff
Commit 92d4bc4e authored by Andrey Filippov's avatar Andrey Filippov
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XML configuration sample

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<?xml version="1.0" encoding="UTF-8"?>
<properties>
<parameters>
<EPOCHS_TO_RUN> 650 </EPOCHS_TO_RUN> <!-- 752# 3000#0 #0 -->
<NET_ARCH1> 0 </NET_ARCH1> <!--1-st stage network -->
<NET_ARCH2> 9 </NET_ARCH2> <!-- 2-nd stage network -->
<SYM8_SUB> False </SYM8_SUB> <!-- enforce inputs from 2d correlation have symmetrical ones (groups of 8) -->
<SPREAD_CONVERGENCE> False </SPREAD_CONVERGENCE><!-- Input target disparity to all nodes of the 1-st stage -->
<INTER_CONVERGENCE> False </INTER_CONVERGENCE><!-- Input target disparity to all nodes of the 2-nd stage -->
<LR> 3e-4 </LR> <!-- learning rate -->
<LR100> 1e-4 </LR100> <!-- LR # 1e-4 -->
<LR200> 3e-5 </LR200> <!-- LR100 # 3e-5 -->
<LR400> 1e-5 </LR400> <!-- LR200 # 1e-5 -->
<LR600> 3e-6 </LR600> <!-- LR400 # 3e-6 -->
<USE_CONFIDENCE> False </USE_CONFIDENCE>
<ABSOLUTE_DISPARITY> False </ABSOLUTE_DISPARITY> <!-- True # False # True # False -->
<DEBUG_PLT_LOSS> True </DEBUG_PLT_LOSS> <!-- -->
<TILE_LAYERS> 4 </TILE_LAYERS> <!-- -->
<TILE_SIDE> 9 </TILE_SIDE> <!-- 7 -->
<EPOCHS_FULL_TEST> 5 </EPOCHS_FULL_TEST> <!-- 10 # 25# repeat full image test after this number of epochs -->
<TWO_TRAINS> True </TWO_TRAINS> <!-- use 2 train sets -->
<ONLY_TILE> None </ONLY_TILE> <!-- (remove all but center tile data), put None here for normal operation) -->
<CLUSTER_RADIUS> 2 </CLUSTER_RADIUS> <!-- 1 # 1 - 3x3, 2 - 5x5 tiles -->
<SHUFFLE_FILES> True </SHUFFLE_FILES>
<WLOSS_LAMBDA> 3.0 </WLOSS_LAMBDA> <!-- fraction of the W_loss (input layers weight non-uniformity) added to G_loss -->
<SLOSS_LAMBDA> 0.1 </SLOSS_LAMBDA> <!-- weight of loss for smooth fg/bg transitions -->
<SLOSS_CLIP> 0.2 </SLOSS_CLIP> <!-- limit punishment for cutting corners (disparity pix) -->
<WBORDERS_ZERO> True </WBORDERS_ZERO> <!-- Border conditions for first layer weights: False - free, True - tied to 0 -->
<MAX_FILES_PER_GROUP> 4 </MAX_FILES_PER_GROUP> <!-- Reduces memory footprint - file shuffle buffer -->
<MAX_IMGS_IN_MEM> 1 </MAX_IMGS_IN_MEM> <!-- Number of simultaneously loaded images -->
<FILE_UPDATE_EPOCHS> 2 </FILE_UPDATE_EPOCHS> <!-- Update train files each this many epochs. 0 - do not update. Adjust to match speed of HDD/SDD with GPU)-->
<PARTIALS_WEIGHTS> [2.0,0.5,0.2] </PARTIALS_WEIGHTS><!-- weight of full 5x5, center 3x3 and center 1x1. len(PARTIALS_WEIGHTS) == CLUSTER_RADIUS + 1. Set to None -->
<HOR_FLIP> True </HOR_FLIP><!-- randomly flip training data horizontally -->
<SAVE_TIFFS> True </SAVE_TIFFS><!-- save Tiff files after each image evaluation -->
<BATCH_WEIGHTS> [0.9, 1.0, 0.9, 1.0]</BATCH_WEIGHTS> <!-- lvar, hvar, lvar1, hvar1 (increase importance of non-flat clusters -->
<DISP_DIFF_CAP> 0.3 </DISP_DIFF_CAP><!-- cap disparity difference (do not increase loss above)-->
<DISP_DIFF_SLOPE> 0.03 </DISP_DIFF_SLOPE><!-- allow squared error to grow above DISP_DIFF_CAP -->
</parameters>
<directories>
<train_lvar>
"tf_data_5x5_main_1"
</train_lvar>
<train_hvar>
"tf_data_5x5_main_1"
</train_hvar>
<train_lvar1>
"tf_data_5x5_main_6"
</train_lvar1>
<train_hvar1>
"tf_data_5x5_main_6"
</train_hvar1>
<test_lvar>
"tf_data_5x5_main_1"
</test_lvar>
<test_hvar>
"tf_data_5x5_main_5"
</test_hvar>
<images>
"tf_data_5x5_main_6/img"
</images>
<result>
"tf_data_5x5_main_6/result"
</result>
</directories>
<files>
<train_lvar>
["train000_R2_LE_1.5.tfrecords",
"train001_R2_LE_1.5.tfrecords",
"train002_R2_LE_1.5.tfrecords",
"train003_R2_LE_1.5.tfrecords",
"train004_R2_LE_1.5.tfrecords",
"train005_R2_LE_1.5.tfrecords",
"train006_R2_LE_1.5.tfrecords",
"train007_R2_LE_1.5.tfrecords",
"train008_R2_LE_1.5.tfrecords",
"train009_R2_LE_1.5.tfrecords",
"train010_R2_LE_1.5.tfrecords",
"train011_R2_LE_1.5.tfrecords",
"train012_R2_LE_1.5.tfrecords",
"train013_R2_LE_1.5.tfrecords",
"train014_R2_LE_1.5.tfrecords",
"train015_R2_LE_1.5.tfrecords",
"train016_R2_LE_1.5.tfrecords",
"train017_R2_LE_1.5.tfrecords",
"train018_R2_LE_1.5.tfrecords",
"train019_R2_LE_1.5.tfrecords",
"train020_R2_LE_1.5.tfrecords",
"train021_R2_LE_1.5.tfrecords",
"train022_R2_LE_1.5.tfrecords",
"train023_R2_LE_1.5.tfrecords",
"train024_R2_LE_1.5.tfrecords",
"train025_R2_LE_1.5.tfrecords",
"train026_R2_LE_1.5.tfrecords",
"train027_R2_LE_1.5.tfrecords",
"train028_R2_LE_1.5.tfrecords",
"train029_R2_LE_1.5.tfrecords",
"train030_R2_LE_1.5.tfrecords",
"train031_R2_LE_1.5.tfrecords"]
</train_lvar>
<train_hvar>
["train000_R2_GT_1.5.tfrecords",
"train001_R2_GT_1.5.tfrecords",
"train002_R2_GT_1.5.tfrecords",
"train003_R2_GT_1.5.tfrecords",
"train004_R2_GT_1.5.tfrecords",
"train005_R2_GT_1.5.tfrecords",
"train006_R2_GT_1.5.tfrecords",
"train007_R2_GT_1.5.tfrecords",
"train008_R2_GT_1.5.tfrecords",
"train009_R2_GT_1.5.tfrecords",
"train010_R2_GT_1.5.tfrecords",
"train011_R2_GT_1.5.tfrecords",
"train012_R2_GT_1.5.tfrecords",
"train013_R2_GT_1.5.tfrecords",
"train014_R2_GT_1.5.tfrecords",
"train015_R2_GT_1.5.tfrecords",
"train016_R2_GT_1.5.tfrecords",
"train017_R2_GT_1.5.tfrecords",
"train018_R2_GT_1.5.tfrecords",
"train019_R2_GT_1.5.tfrecords",
"train020_R2_GT_1.5.tfrecords",
"train021_R2_GT_1.5.tfrecords",
"train022_R2_GT_1.5.tfrecords",
"train023_R2_GT_1.5.tfrecords",
"train024_R2_GT_1.5.tfrecords",
"train025_R2_GT_1.5.tfrecords",
"train026_R2_GT_1.5.tfrecords",
"train027_R2_GT_1.5.tfrecords",
"train028_R2_GT_1.5.tfrecords",
"train029_R2_GT_1.5.tfrecords",
"train030_R2_GT_1.5.tfrecords",
"train031_R2_GT_1.5.tfrecords"]
</train_hvar>
<train_lvar1>
["train000_R2_LE_0.4.tfrecords",
"train001_R2_LE_0.4.tfrecords",
"train002_R2_LE_0.4.tfrecords",
"train003_R2_LE_0.4.tfrecords",
"train004_R2_LE_0.4.tfrecords",
"train005_R2_LE_0.4.tfrecords",
"train006_R2_LE_0.4.tfrecords",
"train007_R2_LE_0.4.tfrecords",
"train008_R2_LE_0.4.tfrecords",
"train009_R2_LE_0.4.tfrecords",
"train010_R2_LE_0.4.tfrecords",
"train011_R2_LE_0.4.tfrecords",
"train012_R2_LE_0.4.tfrecords",
"train013_R2_LE_0.4.tfrecords",
"train014_R2_LE_0.4.tfrecords",
"train015_R2_LE_0.4.tfrecords",
"train016_R2_LE_0.4.tfrecords",
"train017_R2_LE_0.4.tfrecords",
"train018_R2_LE_0.4.tfrecords",
"train019_R2_LE_0.4.tfrecords",
"train020_R2_LE_0.4.tfrecords",
"train021_R2_LE_0.4.tfrecords",
"train022_R2_LE_0.4.tfrecords",
"train023_R2_LE_0.4.tfrecords",
"train024_R2_LE_0.4.tfrecords",
"train025_R2_LE_0.4.tfrecords",
"train026_R2_LE_0.4.tfrecords",
"train027_R2_LE_0.4.tfrecords",
"train028_R2_LE_0.4.tfrecords",
"train029_R2_LE_0.4.tfrecords",
"train030_R2_LE_0.4.tfrecords",
"train031_R2_LE_0.4.tfrecords"]
</train_lvar1>
<train_hvar1>
["train000_R2_GT_0.4.tfrecords",
"train001_R2_GT_0.4.tfrecords",
"train002_R2_GT_0.4.tfrecords",
"train003_R2_GT_0.4.tfrecords",
"train004_R2_GT_0.4.tfrecords",
"train005_R2_GT_0.4.tfrecords",
"train006_R2_GT_0.4.tfrecords",
"train007_R2_GT_0.4.tfrecords",
"train008_R2_GT_0.4.tfrecords",
"train009_R2_GT_0.4.tfrecords",
"train010_R2_GT_0.4.tfrecords",
"train011_R2_GT_0.4.tfrecords",
"train012_R2_GT_0.4.tfrecords",
"train013_R2_GT_0.4.tfrecords",
"train014_R2_GT_0.4.tfrecords",
"train015_R2_GT_0.4.tfrecords",
"train016_R2_GT_0.4.tfrecords",
"train017_R2_GT_0.4.tfrecords",
"train018_R2_GT_0.4.tfrecords",
"train019_R2_GT_0.4.tfrecords",
"train020_R2_GT_0.4.tfrecords",
"train021_R2_GT_0.4.tfrecords",
"train022_R2_GT_0.4.tfrecords",
"train023_R2_GT_0.4.tfrecords",
"train024_R2_GT_0.4.tfrecords",
"train025_R2_GT_0.4.tfrecords",
"train026_R2_GT_0.4.tfrecords",
"train027_R2_GT_0.4.tfrecords",
"train028_R2_GT_0.4.tfrecords",
"train029_R2_GT_0.4.tfrecords",
"train030_R2_GT_0.4.tfrecords",
"train031_R2_GT_0.4.tfrecords"]
</train_hvar1>
<test_lvar>
["train004_R2_GT_1.5.tfrecords"]
</test_lvar>
<test_hvar>
["testTEST_R2_GT_1.5.tfrecords"]
</test_hvar>
<images>
["1527256858_150165-v01", <!-- State Street -->
"1527257933_150165-v04", <!-- overlook -->
"1527256816_150165-v02", <!-- State Street -->
"1527182802_096892-v02", <!-- plane near plane -->
"1527182805_096892-v02", <!-- plane midrange used up to -49 plane -->
"1527182810_096892-v02"] <!-- plane far -->
</images>
</files>
</properties>
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