diff --git a/demos/module_examples/FullCSVReaderDemo.ipynb b/demos/module_examples/FullCSVReaderDemo.ipynb
index fb33cc00..0b2bc739 100644
--- a/demos/module_examples/FullCSVReaderDemo.ipynb
+++ b/demos/module_examples/FullCSVReaderDemo.ipynb
@@ -2,22 +2,9 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 14,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:tensorflow:TensorFlow version 1.10.0\n",
- "CRITICAL:tensorflow:Optional Python module SimpleITK not found, please install SimpleITK and retry if the application fails.\n",
- "INFO:tensorflow:Available Image Loaders:\n",
- "['nibabel', 'opencv', 'skimage', 'pillow', 'dummy'].\n",
- "\u001b[1mINFO:niftynet:\u001b[0m Optional Python module SimpleITK not found, please install SimpleITK and retry if the application fails.\n",
- "\u001b[1mINFO:niftynet:\u001b[0m Optional Python module SimpleITK version None not found, please install SimpleITK-None and retry if the application fails.\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"import sys\n",
"import os\n",
@@ -40,7 +27,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
@@ -63,7 +50,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -199,7 +186,7 @@
"14 CRI CRI"
]
},
- "execution_count": 3,
+ "execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@@ -215,7 +202,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -276,7 +263,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -326,212 +313,212 @@
"
\n",
" 0 | \n",
" RAN | \n",
- " 0.321972 | \n",
- " 0.652648 | \n",
- " 0.113185 | \n",
- " -0.722681 | \n",
- " -0.593930 | \n",
- " -0.900718 | \n",
- " -0.808985 | \n",
- " -0.368086 | \n",
- " -1.262447 | \n",
- " -2.299978 | \n",
+ " -2.018905 | \n",
+ " 0.016700 | \n",
+ " 0.753500 | \n",
+ " 0.225668 | \n",
+ " -0.453137 | \n",
+ " 0.007740 | \n",
+ " 0.405050 | \n",
+ " -0.038025 | \n",
+ " -1.144459 | \n",
+ " -0.437604 | \n",
"
\n",
" \n",
" 1 | \n",
" HAL | \n",
- " -0.439402 | \n",
- " 0.675593 | \n",
- " -0.657466 | \n",
- " 0.768496 | \n",
- " -0.382948 | \n",
- " 0.474596 | \n",
- " -0.276786 | \n",
- " -0.392421 | \n",
- " -0.119055 | \n",
- " 1.252547 | \n",
+ " 0.750608 | \n",
+ " 1.339236 | \n",
+ " 0.393103 | \n",
+ " 0.403952 | \n",
+ " 0.034992 | \n",
+ " 1.743924 | \n",
+ " 0.222566 | \n",
+ " 0.411477 | \n",
+ " -0.557221 | \n",
+ " 0.407324 | \n",
"
\n",
" \n",
" 2 | \n",
" MIL | \n",
- " 1.085595 | \n",
- " 0.076346 | \n",
- " 0.345417 | \n",
- " -0.196474 | \n",
- " 0.716523 | \n",
- " -0.548726 | \n",
- " 0.408472 | \n",
- " 0.018024 | \n",
- " -0.583368 | \n",
- " -0.780949 | \n",
+ " -0.581589 | \n",
+ " -0.988355 | \n",
+ " -1.536794 | \n",
+ " 0.344513 | \n",
+ " -0.161370 | \n",
+ " -0.118697 | \n",
+ " -0.322209 | \n",
+ " 0.063637 | \n",
+ " -1.028091 | \n",
+ " -0.392706 | \n",
"
\n",
" \n",
" 3 | \n",
" CHA | \n",
- " -2.210596 | \n",
- " -0.137564 | \n",
- " 0.323351 | \n",
- " 0.891198 | \n",
- " -0.778046 | \n",
- " 0.031523 | \n",
- " -0.463217 | \n",
- " -0.685250 | \n",
- " -0.172702 | \n",
- " 0.011653 | \n",
+ " 2.234084 | \n",
+ " -0.264239 | \n",
+ " -0.923163 | \n",
+ " -0.817617 | \n",
+ " 0.388325 | \n",
+ " 0.560078 | \n",
+ " -0.477164 | \n",
+ " 1.966860 | \n",
+ " -0.018510 | \n",
+ " 0.442490 | \n",
"
\n",
" \n",
" 4 | \n",
" GRA | \n",
- " 0.829021 | \n",
- " -0.344907 | \n",
- " -1.552366 | \n",
- " -2.156463 | \n",
- " -0.772904 | \n",
- " -0.023208 | \n",
- " 1.357851 | \n",
- " 0.409369 | \n",
- " 0.667640 | \n",
- " -0.151443 | \n",
+ " 0.653114 | \n",
+ " 0.477493 | \n",
+ " 0.650812 | \n",
+ " 1.353049 | \n",
+ " 0.734319 | \n",
+ " -1.539332 | \n",
+ " 0.653350 | \n",
+ " -0.752011 | \n",
+ " -0.634769 | \n",
+ " -0.231503 | \n",
"
\n",
" \n",
" 5 | \n",
" PLA | \n",
- " 0.410349 | \n",
- " 1.233560 | \n",
- " 0.108877 | \n",
- " -0.946675 | \n",
- " 0.379490 | \n",
- " -0.195135 | \n",
- " -0.299669 | \n",
- " -0.072078 | \n",
- " 0.060394 | \n",
- " 0.123195 | \n",
+ " -0.983351 | \n",
+ " -1.600570 | \n",
+ " 0.223609 | \n",
+ " -1.100547 | \n",
+ " -0.430255 | \n",
+ " 1.315413 | \n",
+ " -0.702730 | \n",
+ " -1.838355 | \n",
+ " -0.810046 | \n",
+ " 2.419473 | \n",
"
\n",
" \n",
" 6 | \n",
" NAR | \n",
- " 0.836927 | \n",
- " 0.857035 | \n",
- " 0.874829 | \n",
- " 0.686557 | \n",
- " -0.891095 | \n",
- " -0.223142 | \n",
- " 0.021994 | \n",
- " 1.012295 | \n",
- " 1.178720 | \n",
- " -1.403227 | \n",
+ " 0.810422 | \n",
+ " -2.304446 | \n",
+ " -0.079281 | \n",
+ " 0.456476 | \n",
+ " 0.643375 | \n",
+ " 0.268823 | \n",
+ " 0.366755 | \n",
+ " 0.530736 | \n",
+ " 1.345358 | \n",
+ " 0.515989 | \n",
"
\n",
" \n",
" 7 | \n",
" WEB | \n",
- " 1.330076 | \n",
- " -1.140574 | \n",
- " 2.268951 | \n",
- " -1.947345 | \n",
- " -0.734009 | \n",
- " 0.002782 | \n",
- " -0.182076 | \n",
- " -0.303120 | \n",
- " 1.700154 | \n",
- " 0.836683 | \n",
+ " 0.710464 | \n",
+ " -0.665521 | \n",
+ " -1.130775 | \n",
+ " -0.437872 | \n",
+ " -0.359845 | \n",
+ " -0.128057 | \n",
+ " -0.472871 | \n",
+ " -0.060746 | \n",
+ " 1.754929 | \n",
+ " -1.009953 | \n",
"
\n",
" \n",
" 8 | \n",
" PAR | \n",
- " -0.861265 | \n",
- " 0.076940 | \n",
- " 1.981806 | \n",
- " -0.018995 | \n",
- " -0.362837 | \n",
- " -1.420218 | \n",
- " 0.347479 | \n",
- " -0.656022 | \n",
- " 1.957113 | \n",
- " -0.974995 | \n",
+ " 1.380408 | \n",
+ " -0.825570 | \n",
+ " -0.630116 | \n",
+ " -2.312509 | \n",
+ " -0.335770 | \n",
+ " -1.176994 | \n",
+ " -0.455428 | \n",
+ " -0.757432 | \n",
+ " -0.171152 | \n",
+ " 0.803127 | \n",
"
\n",
" \n",
" 9 | \n",
" HON | \n",
- " -0.133184 | \n",
- " -0.681829 | \n",
- " 0.238010 | \n",
- " 1.288651 | \n",
- " -0.573452 | \n",
- " 2.361199 | \n",
- " -0.795531 | \n",
- " -1.134068 | \n",
- " -0.439827 | \n",
- " -0.498937 | \n",
+ " -0.312656 | \n",
+ " -0.707139 | \n",
+ " 0.571808 | \n",
+ " 0.682854 | \n",
+ " 0.907817 | \n",
+ " 0.067288 | \n",
+ " -0.384104 | \n",
+ " -1.030702 | \n",
+ " 0.015677 | \n",
+ " -0.808565 | \n",
"
\n",
" \n",
" 10 | \n",
" HAF | \n",
- " 0.684381 | \n",
- " -1.145172 | \n",
- " -0.115175 | \n",
- " 0.446454 | \n",
- " -0.649224 | \n",
- " -0.882938 | \n",
- " -0.983248 | \n",
- " 0.571107 | \n",
- " -0.390658 | \n",
- " 0.067865 | \n",
+ " 2.413476 | \n",
+ " -0.258832 | \n",
+ " 0.351502 | \n",
+ " -1.447595 | \n",
+ " -0.861127 | \n",
+ " 1.074561 | \n",
+ " -0.550000 | \n",
+ " 1.082476 | \n",
+ " 0.487512 | \n",
+ " -0.926261 | \n",
"
\n",
" \n",
" 11 | \n",
" LEW | \n",
- " 1.150170 | \n",
- " -0.793785 | \n",
- " 0.590165 | \n",
- " -0.126870 | \n",
- " -0.887013 | \n",
- " 0.495526 | \n",
- " -0.395386 | \n",
- " 0.448884 | \n",
- " -1.646706 | \n",
- " 0.523825 | \n",
+ " 0.466895 | \n",
+ " -0.375061 | \n",
+ " 0.657998 | \n",
+ " 1.203485 | \n",
+ " 1.347131 | \n",
+ " 0.552526 | \n",
+ " -0.705073 | \n",
+ " 1.992426 | \n",
+ " -0.816416 | \n",
+ " 0.532992 | \n",
"
\n",
" \n",
" 12 | \n",
" SOU | \n",
- " 0.792047 | \n",
- " -1.516437 | \n",
- " 1.087191 | \n",
- " -3.077545 | \n",
- " 0.286219 | \n",
- " 0.756354 | \n",
- " -0.795725 | \n",
- " -1.012792 | \n",
- " 0.194806 | \n",
- " -0.874716 | \n",
+ " -0.009581 | \n",
+ " -0.436214 | \n",
+ " -0.600287 | \n",
+ " 2.147111 | \n",
+ " 0.839317 | \n",
+ " -0.444572 | \n",
+ " 0.197550 | \n",
+ " 0.548611 | \n",
+ " 0.334053 | \n",
+ " -1.498843 | \n",
"
\n",
" \n",
" 13 | \n",
" SPE | \n",
- " -1.675722 | \n",
- " -1.730294 | \n",
- " 1.013535 | \n",
- " 0.354879 | \n",
- " -1.044123 | \n",
- " 0.656389 | \n",
- " 0.549337 | \n",
- " -1.416101 | \n",
- " -1.320003 | \n",
- " -1.398806 | \n",
+ " 0.238420 | \n",
+ " -0.966039 | \n",
+ " -0.073183 | \n",
+ " 1.595601 | \n",
+ " -0.093269 | \n",
+ " -1.048532 | \n",
+ " -0.657099 | \n",
+ " -1.178905 | \n",
+ " 0.795620 | \n",
+ " -0.974147 | \n",
"
\n",
" \n",
" 14 | \n",
" CRI | \n",
- " -1.193647 | \n",
- " 1.017665 | \n",
- " -1.156596 | \n",
- " 0.947748 | \n",
- " -2.377250 | \n",
- " -0.177449 | \n",
- " 0.893184 | \n",
- " 1.063577 | \n",
- " -0.580513 | \n",
- " -0.128269 | \n",
+ " -0.188791 | \n",
+ " 1.611999 | \n",
+ " 0.423356 | \n",
+ " -1.644961 | \n",
+ " -0.961844 | \n",
+ " -1.446989 | \n",
+ " 0.869527 | \n",
+ " -1.843371 | \n",
+ " -2.446698 | \n",
+ " -1.428567 | \n",
"
\n",
" \n",
"\n",
@@ -539,41 +526,41 @@
],
"text/plain": [
" subject_id 0 1 2 3 4 5 \\\n",
- "0 RAN 0.321972 0.652648 0.113185 -0.722681 -0.593930 -0.900718 \n",
- "1 HAL -0.439402 0.675593 -0.657466 0.768496 -0.382948 0.474596 \n",
- "2 MIL 1.085595 0.076346 0.345417 -0.196474 0.716523 -0.548726 \n",
- "3 CHA -2.210596 -0.137564 0.323351 0.891198 -0.778046 0.031523 \n",
- "4 GRA 0.829021 -0.344907 -1.552366 -2.156463 -0.772904 -0.023208 \n",
- "5 PLA 0.410349 1.233560 0.108877 -0.946675 0.379490 -0.195135 \n",
- "6 NAR 0.836927 0.857035 0.874829 0.686557 -0.891095 -0.223142 \n",
- "7 WEB 1.330076 -1.140574 2.268951 -1.947345 -0.734009 0.002782 \n",
- "8 PAR -0.861265 0.076940 1.981806 -0.018995 -0.362837 -1.420218 \n",
- "9 HON -0.133184 -0.681829 0.238010 1.288651 -0.573452 2.361199 \n",
- "10 HAF 0.684381 -1.145172 -0.115175 0.446454 -0.649224 -0.882938 \n",
- "11 LEW 1.150170 -0.793785 0.590165 -0.126870 -0.887013 0.495526 \n",
- "12 SOU 0.792047 -1.516437 1.087191 -3.077545 0.286219 0.756354 \n",
- "13 SPE -1.675722 -1.730294 1.013535 0.354879 -1.044123 0.656389 \n",
- "14 CRI -1.193647 1.017665 -1.156596 0.947748 -2.377250 -0.177449 \n",
+ "0 RAN -2.018905 0.016700 0.753500 0.225668 -0.453137 0.007740 \n",
+ "1 HAL 0.750608 1.339236 0.393103 0.403952 0.034992 1.743924 \n",
+ "2 MIL -0.581589 -0.988355 -1.536794 0.344513 -0.161370 -0.118697 \n",
+ "3 CHA 2.234084 -0.264239 -0.923163 -0.817617 0.388325 0.560078 \n",
+ "4 GRA 0.653114 0.477493 0.650812 1.353049 0.734319 -1.539332 \n",
+ "5 PLA -0.983351 -1.600570 0.223609 -1.100547 -0.430255 1.315413 \n",
+ "6 NAR 0.810422 -2.304446 -0.079281 0.456476 0.643375 0.268823 \n",
+ "7 WEB 0.710464 -0.665521 -1.130775 -0.437872 -0.359845 -0.128057 \n",
+ "8 PAR 1.380408 -0.825570 -0.630116 -2.312509 -0.335770 -1.176994 \n",
+ "9 HON -0.312656 -0.707139 0.571808 0.682854 0.907817 0.067288 \n",
+ "10 HAF 2.413476 -0.258832 0.351502 -1.447595 -0.861127 1.074561 \n",
+ "11 LEW 0.466895 -0.375061 0.657998 1.203485 1.347131 0.552526 \n",
+ "12 SOU -0.009581 -0.436214 -0.600287 2.147111 0.839317 -0.444572 \n",
+ "13 SPE 0.238420 -0.966039 -0.073183 1.595601 -0.093269 -1.048532 \n",
+ "14 CRI -0.188791 1.611999 0.423356 -1.644961 -0.961844 -1.446989 \n",
"\n",
" 6 7 8 9 \n",
- "0 -0.808985 -0.368086 -1.262447 -2.299978 \n",
- "1 -0.276786 -0.392421 -0.119055 1.252547 \n",
- "2 0.408472 0.018024 -0.583368 -0.780949 \n",
- "3 -0.463217 -0.685250 -0.172702 0.011653 \n",
- "4 1.357851 0.409369 0.667640 -0.151443 \n",
- "5 -0.299669 -0.072078 0.060394 0.123195 \n",
- "6 0.021994 1.012295 1.178720 -1.403227 \n",
- "7 -0.182076 -0.303120 1.700154 0.836683 \n",
- "8 0.347479 -0.656022 1.957113 -0.974995 \n",
- "9 -0.795531 -1.134068 -0.439827 -0.498937 \n",
- "10 -0.983248 0.571107 -0.390658 0.067865 \n",
- "11 -0.395386 0.448884 -1.646706 0.523825 \n",
- "12 -0.795725 -1.012792 0.194806 -0.874716 \n",
- "13 0.549337 -1.416101 -1.320003 -1.398806 \n",
- "14 0.893184 1.063577 -0.580513 -0.128269 "
+ "0 0.405050 -0.038025 -1.144459 -0.437604 \n",
+ "1 0.222566 0.411477 -0.557221 0.407324 \n",
+ "2 -0.322209 0.063637 -1.028091 -0.392706 \n",
+ "3 -0.477164 1.966860 -0.018510 0.442490 \n",
+ "4 0.653350 -0.752011 -0.634769 -0.231503 \n",
+ "5 -0.702730 -1.838355 -0.810046 2.419473 \n",
+ "6 0.366755 0.530736 1.345358 0.515989 \n",
+ "7 -0.472871 -0.060746 1.754929 -1.009953 \n",
+ "8 -0.455428 -0.757432 -0.171152 0.803127 \n",
+ "9 -0.384104 -1.030702 0.015677 -0.808565 \n",
+ "10 -0.550000 1.082476 0.487512 -0.926261 \n",
+ "11 -0.705073 1.992426 -0.816416 0.532992 \n",
+ "12 0.197550 0.548611 0.334053 -1.498843 \n",
+ "13 -0.657099 -1.178905 0.795620 -0.974147 \n",
+ "14 0.869527 -1.843371 -2.446698 -1.428567 "
]
},
- "execution_count": 5,
+ "execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
@@ -590,7 +577,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -604,8 +591,8 @@
"\n",
"\u001b[1mINFO:niftynet:\u001b[0m Image reader: loading 15 subjects from sections ['CT'] as input [image]\n",
"\u001b[1mWARNING:niftynet:\u001b[0m This method will read your entire csv into memory\n",
- "One sample from the csv_reader: [-1.67572209 -1.73029411 1.01353478 0.35487928 -1.0441231 0.65638927\n",
- " 0.54933651 -1.41610099 -1.32000264 -1.39880624]\n",
+ "One sample from the csv_reader: [ 0.23841972 -0.96603888 -0.07318273 1.59560139 -0.09326917 -1.04853203\n",
+ " -0.65709902 -1.17890471 0.7956195 -0.97414747]\n",
"\u001b[1mINFO:niftynet:\u001b[0m reading size of preprocessed images\n",
"\u001b[1mWARNING:niftynet:\u001b[0m sampler queue_length should be larger than batch_size, defaulting to batch_size * 5.0 (10).\n",
"(1, 100, 100, 1, 1, 1)\n",
@@ -651,14 +638,13 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[42m[Layer]\u001b[0m csv_reader_6 \u001b[46m(input undecided)\u001b[0m\n",
"\u001b[1mINFO:niftynet:\u001b[0m \n",
"\n",
"Number of subjects 15, input section names: ['subject_id', 'CT']\n",
@@ -711,6 +697,21 @@
"print(sample['features'].shape)\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "modalities = ['t1ce.', 't1.', 'flair.', 't2.']\n",
+ "def get_modality(string):\n",
+ " return modalities[[True if mod in string else False for mod in modalities].index(True)][:-1]\n",
+ " \n",
+ "files = [(file.replace('.nii.gz', ''), get_modality(file)) \\\n",
+ " for file in os.listdir('/home/tom/data/BRATS_18_SPLITS/train') if 'seg' not in file]\n",
+ "pd.DataFrame(data=files, columns=['subject_id', 'label']).to_csv('/home/tom/phd/NiftyNet-Generator-PR/NiftyNet/modality_labels.csv', index=None)\n"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
diff --git a/doc/source/config_spec.md b/doc/source/config_spec.md
index 2c290952..57efe0dc 100644
--- a/doc/source/config_spec.md
+++ b/doc/source/config_spec.md
@@ -119,7 +119,7 @@ within each section.
Name | Type | Example | Default
---- | ---- | ------- | -------
-[csv_file](#csv-file) | `string` | `csv_file=file_list.csv` | `''`
+[csv_path_file](#csv-path-file) | `string` | `csv_path_file=file_list.csv` | `''`
[path_to_search](#path-to-search) | `string` | `path_to_search=my_data/fold_1` | NiftyNet home folder
[filename_contains](#filename-contains) | `string` or `string array` | `filename_contains=foo, bar` | `''`
[filename_not_contains](#filename-not-contains) | `string` or `string array` | `filename_not_contains=foo` | `''`
@@ -130,7 +130,7 @@ within each section.
[spatial_window_size](#spatial-window-size) | `integer array` | `spatial_window_size=64, 64, 64` | `''`
[loader](#loader) | `string` | `loader=simpleitk` | `None`
-###### `csv_file`
+###### `csv_path_file`
A file path to a list of input images. If the file exists, input image name
list will be loaded from the file; the filename based input image search will
be disabled; [path_to_search](#path-to-search),
@@ -221,8 +221,8 @@ with an interpolation order of `3`.
A CSV file with the matched filenames and extracted subject names will be
generated to `T1Image.csv` in [`model_dir`](#model-dir) (by default; the CSV
-file location can be specified by setting [csv_file](#csv-file)). To exclude
-particular images, the [csv_file](#csv-file) can be edited manually.
+file location can be specified by setting [csv_path_file](#csv-path-file)). To exclude
+particular images, the [csv_path_file](#csv-path-file) can be edited manually.
This input source can be used alone, as a `T1` MRI input to an application.
It can also be used along with other modalities, a multi-modality example
diff --git a/niftynet/contrib/csv_reader/classification_application.py b/niftynet/contrib/csv_reader/classification_application.py
index 173b9874..848fd7c3 100755
--- a/niftynet/contrib/csv_reader/classification_application.py
+++ b/niftynet/contrib/csv_reader/classification_application.py
@@ -269,7 +269,6 @@ def switch_sampler(for_training):
data_dict = switch_sampler(for_training=True)
image = tf.cast(data_dict['image'], tf.float32)
- print(self.sampler[0][0]()['label'])
net_args = {'is_training': self.is_training,
'keep_prob': self.net_param.keep_prob}
net_out = self.net(image, **net_args)
diff --git a/niftynet/io/image_reader.py b/niftynet/io/image_reader.py
index e92ab90b..42cc344f 100755
--- a/niftynet/io/image_reader.py
+++ b/niftynet/io/image_reader.py
@@ -410,7 +410,7 @@ def _filename_to_image_list(file_list, mod_dict, data_param):
if not volume_list:
tf.logging.fatal(
"Empty filename lists, please check the csv "
- "files. (removing csv_file keyword if it is in the config file "
+ "files. (removing csv_path_file keyword if it is in the config file "
"to automatically search folders and generate new csv "
"files again)\n\n"
"Please note in the matched file names, each subject id are "
diff --git a/niftynet/layer/bn.py b/niftynet/layer/bn.py
index fee050fd..01838504 100755
--- a/niftynet/layer/bn.py
+++ b/niftynet/layer/bn.py
@@ -135,5 +135,5 @@ def layer_op(self, inputs):
variables_collections=None,
outputs_collections=None,
trainable=True,
- data_format='NHWC',
+ data_format='NWC',
scope=None)
diff --git a/niftynet/layer/loss_classification.py b/niftynet/layer/loss_classification.py
index 2deaedc5..80b8f7cc 100755
--- a/niftynet/layer/loss_classification.py
+++ b/niftynet/layer/loss_classification.py
@@ -79,6 +79,6 @@ def cross_entropy(prediction,
:return: the loss
"""
ground_truth = tf.to_int64(ground_truth)
- loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=ground_truth)
+ loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=prediction, labels=ground_truth)
return loss
diff --git a/niftynet/layer/spatial_transformer.py b/niftynet/layer/spatial_transformer.py
index 60c21a48..158f4a02 100755
--- a/niftynet/layer/spatial_transformer.py
+++ b/niftynet/layer/spatial_transformer.py
@@ -78,7 +78,6 @@ def layer_op(self,field):
for d in [0, 1, 2]]
resampled=tf.stack(resampled_list,5)
permuted_shape=[batch_size]+[f-3 for f in self._coeff_shape]+self._knot_spacing+[spatial_rank]
- print(permuted_shape)
permuted=tf.transpose(tf.reshape(resampled,permuted_shape),[0,1,4,2,5,3,6,7])
valid_size=[(f-3)*k for f,k in zip(self._coeff_shape,self._knot_spacing)]
reshaped=tf.reshape(permuted,[batch_size]+valid_size+[spatial_rank])
diff --git a/tests/image_reader_test.py b/tests/image_reader_test.py
index 1610c0f5..3d2cb99b 100755
--- a/tests/image_reader_test.py
+++ b/tests/image_reader_test.py
@@ -18,7 +18,7 @@
# test multiple modalities
MULTI_MOD_DATA = {
'T1': ParserNamespace(
- csv_file=os.path.join('testing_data', 'T1reader.csv'),
+ csv_path_file=os.path.join('testing_data', 'T1reader.csv'),
path_to_search='testing_data',
filename_contains=('_o_T1_time',),
filename_not_contains=('Parcellation',),
@@ -28,7 +28,7 @@
loader=None
),
'FLAIR': ParserNamespace(
- csv_file=os.path.join('testing_data', 'FLAIRreader.csv'),
+ csv_path_file=os.path.join('testing_data', 'FLAIRreader.csv'),
path_to_search='testing_data',
filename_contains=('FLAIR_',),
filename_not_contains=('Parcellation',),
@@ -43,7 +43,7 @@
# test single modalities
SINGLE_MOD_DATA = {
'lesion': ParserNamespace(
- csv_file=os.path.join('testing_data', 'lesion.csv'),
+ csv_path_file=os.path.join('testing_data', 'lesion.csv'),
path_to_search='testing_data',
filename_contains=('Lesion',),
filename_not_contains=('Parcellation',),
@@ -57,7 +57,7 @@
EXISTING_DATA = {
'lesion': ParserNamespace(
- csv_file=os.path.join('testing_data', 'lesion.csv'),
+ csv_path_file=os.path.join('testing_data', 'lesion.csv'),
interp_order=3,
pixdim=None,
axcodes=None,
@@ -68,7 +68,7 @@
# test labels
LABEL_DATA = {
'parcellation': ParserNamespace(
- csv_file=os.path.join('testing_data', 'labels.csv'),
+ csv_path_file=os.path.join('testing_data', 'labels.csv'),
path_to_search='testing_data',
filename_contains=('Parcellation',),
filename_not_contains=('Lesion',),
@@ -82,7 +82,7 @@
BAD_DATA = {
'lesion': ParserNamespace(
- csv_file=os.path.join('testing_data', 'lesion.csv'),
+ csv_path_file=os.path.join('testing_data', 'lesion.csv'),
path_to_search='testing_data',
filename_contains=('Lesion',),
filename_not_contains=('Parcellation',),
@@ -96,7 +96,7 @@
IMAGE_2D_DATA = {
'color_images': ParserNamespace(
- csv_file=os.path.join('testing_data', 'images_2d_u.csv'),
+ csv_path_file=os.path.join('testing_data', 'images_2d_u.csv'),
path_to_search=os.path.join('testing_data', 'images2d'),
filename_contains=('_u.png',),
interp_order=1,
@@ -105,7 +105,7 @@
loader=None
),
'gray_images': ParserNamespace(
- csv_file=os.path.join('testing_data', 'images_2d_g.csv'),
+ csv_path_file=os.path.join('testing_data', 'images_2d_g.csv'),
path_to_search=os.path.join('testing_data', 'images2d'),
filename_contains=('_g.png',),
interp_order=1,
@@ -114,7 +114,7 @@
loader=None
),
'seg_masks': ParserNamespace(
- csv_file=os.path.join('testing_data', 'images_2d_m.csv'),
+ csv_path_file=os.path.join('testing_data', 'images_2d_m.csv'),
path_to_search=os.path.join('testing_data', 'images2d'),
filename_contains=('_m.png',),
interp_order=0,
diff --git a/tests/reader_modular_test.py b/tests/reader_modular_test.py
index baf4a23b..de8f4841 100755
--- a/tests/reader_modular_test.py
+++ b/tests/reader_modular_test.py
@@ -138,7 +138,7 @@ def test_reader_field(self):
def test_input_properties(self):
data_param = {'mr': {'path_to_search': IMAGE_PATH_2D,
- 'csv_file': '2d_test.csv'}}
+ 'csv_path_file': '2d_test.csv'}}
reader = ImageReader().initialise(data_param)
self.default_property_asserts(reader)
idx, data, interp = reader()
@@ -151,7 +151,7 @@ def test_input_properties(self):
def test_no_2d_resampling_properties(self):
data_param = {'mr': {'path_to_search': IMAGE_PATH_2D,
- 'csv_file': '2d_test.csv',
+ 'csv_path_file': '2d_test.csv',
'pixdim': (2, 2, 2),
'axcodes': 'RAS'}}
reader = ImageReader().initialise(data_param)
@@ -199,7 +199,7 @@ class Read2D_1DTest(tf.test.TestCase):
# loading 2d images of rank 3: [x, y, 1]
def test_no_2d_resampling_properties(self):
data_param = {'mr': {'path_to_search': IMAGE_PATH_2D_1,
- 'csv_file': '2d_test.csv',
+ 'csv_path_file': '2d_test.csv',
'filename_contains': '_img',
'pixdim': (2, 2, 2),
'axcodes': 'RAS'}}
@@ -282,7 +282,7 @@ class Read2D_colorTest(tf.test.TestCase):
# loading 2d images of rank 3: [x, y, 3] or [x, y, 4]
def test_no_2d_resampling_properties(self):
data_param = {'mr': {'path_to_search': IMAGE_PATH_2D,
- 'csv_file': '2d_test.csv',
+ 'csv_path_file': '2d_test.csv',
'filename_contains': '_u',
'pixdim': (2, 2, 2),
'axcodes': 'RAS'}}