I'm trying to prepare training data with this detail :
ArcGIS Pro 2.4.2
inRaster = "E:\Work\ArcDL\Base\MANADO_FIX.tif" (raster 8bit unsigned, thematic, with nodata=0)
out_folder = "E:\Work\ArcDL\gtg6"
in_training = "E:\Work\ArcDL\Experiment_BuildFoot_2\MyProject\Shape\House.shp" (shp that has Classvalue, Classname, RED, GREEN, BLUE field)
image_chip_format = "TIFF"
tile_size_x = "256"
tile_size_y = "256"
stride_x="128"
stride_y="128"
output_nofeature_tiles="ONLY_TILES_WITH_FEATURES"
metadata_format="Labeled_Tiles"
start_index = 0
classvalue_field = "Classvalue"
buffer_radius = 1
in_mask_polygons = "E:\Work\ArcDL\Experiment_BuildFoot_2\MyProject\Shape\Mask.shp"
rotation_angle = 0
I run my prepare data syntax just like this :
data = prepare_data('gtg6')
But I always ended up in this kind of error for labeled_tiles metadata format, there's no further explanation and I cant use the data for training since data.show_batch() will result in OSError: -2
Any solution?
Thanks in advance.
AFAIK it's installed by using "conda install -c pytorch -c fastai fastai=1.0.39 pytorch=1.0.0 torchvision" but to be sured I created new environment again and installed required moduls using above command as sugested and test prepare_data() command again and stil it resulted in error.
Here's my environment clone from default environment of ArcGIS pro before running above command :
arcgis==1.6.1
asn1crypto==0.24.0
atomicwrites==1.3.0
attrs==19.1.0
backcall==0.1.0
bleach==3.1.0
certifi==2019.3.9
cffi==1.12.2
cftime==1.0.0b1
chardet==3.0.4
colorama==0.4.1
cryptography==2.6.1
cycler==0.10.0
decorator==4.4.0
defusedxml==0.5.0
despatch==0.1.0
entrypoints==0.3
et-xmlfile==1.0.1
fastcache==1.0.2
future==0.17.1
h5py==2.9.0
html5lib==1.0.1
idna==2.8
ipykernel==5.1.0
ipython==7.4.0
ipython-genutils==0.2.0
ipywidgets==7.4.2
jdcal==1.4
jedi==0.13.3
Jinja2==2.10.1
jsonschema==3.0.1
jupyter-client==5.2.4
jupyter-console==6.0.0
jupyter-core==4.4.0
jupyterlab==0.35.4
jupyterlab-server==0.2.0
keyring==19.0.1
kiwisolver==1.0.1
MarkupSafe==1.1.1
matplotlib==3.0.3
mistune==0.8.4
mkl-fft==1.0.10
mkl-random==1.0.2
more-itertools==6.0.0
mpmath==1.1.0
nbconvert==5.4.1
nbformat==4.4.0
netCDF4==1.5.0.1
nose==1.3.7
notebook==5.7.8
numexpr==2.6.9
numpy==1.16.2
openpyxl==2.6.1
pandas==0.24.2
pandocfilters==1.4.2
parso==0.3.4
pickleshare==0.7.5
pluggy==0.9.0
prometheus-client==0.6.0
prompt-toolkit==2.0.9
py==1.8.0
pycparser==2.19
Pygments==2.3.1
pyOpenSSL==19.0.0
pyparsing==2.4.0
pyrsistent==0.14.11
pyshp==1.2.12
PySocks==1.6.8
pytest==4.4.0
python-dateutil==2.8.0
pytz==2018.9
pywin32-ctypes==0.2.0
pywinpty==0.5
pyzmq==18.0.0
requests==2.21.0
scipy==1.2.1
Send2Trash==1.5.0
simplegeneric==0.8.1
six==1.12.0
sympy==1.3
terminado==0.8.1
testpath==0.4.2
tornado==6.0.2
traitlets==4.3.2
urllib3==1.24.1
wcwidth==0.1.7
webencodings==0.5.1
widgetsnbextension==3.4.2
win-inet-pton==1.1.0
wincertstore==0.2
winkerberos==0.7.0
x86cpu==0.4
xlrd==1.2.0
xlwt==1.3.0
and here's my environment after running above command :
arcgis==1.6.2
asn1crypto==1.2.0
atomicwrites==1.3.0
attrs==19.3.0
backcall==0.1.0
bleach==3.1.0
blis==0.2.4
Bottleneck==1.3.1
certifi==2019.11.28
cffi==1.13.2
cftime==1.0.0b1
chardet==3.0.4
colorama==0.4.1
cryptography==2.8
cycler==0.10.0
cymem==2.0.2
dataclasses==0.6
decorator==4.4.1
defusedxml==0.6.0
despatch==0.1.0
entrypoints==0.3
et-xmlfile==1.0.1
fastai==1.0.39
fastcache==1.1.0
fastprogress==0.1.22
future==0.18.2
h5py==2.9.0
html5lib==1.0.1
idna==2.8
importlib-metadata==1.1.0
ipykernel==5.1.3
ipython==7.9.0
ipython-genutils==0.2.0
ipywidgets==7.5.1
jdcal==1.4.1
jedi==0.15.1
Jinja2==2.10.3
json5==0.8.5
jsonschema==3.2.0
jupyter-client==5.3.4
jupyter-console==5.2.0
jupyter-core==4.6.1
jupyterlab==1.2.3
jupyterlab-server==1.0.6
keyring==19.2.0
kiwisolver==1.1.0
MarkupSafe==1.1.1
matplotlib==3.0.3
mistune==0.8.4
mkl-fft==1.0.12
mkl-random==1.0.2
more-itertools==7.2.0
mpmath==1.1.0
murmurhash==1.0.2
nb-conda==2.2.1
nb-conda-kernels==2.2.2
nbconvert==5.6.1
nbformat==4.4.0
netCDF4==1.5.0.1
nose==1.3.7
notebook==6.0.2
numexpr==2.6.9
numpy==1.16.2
olefile==0.46
openpyxl==3.0.2
packaging==19.2
pandas==0.25.3
pandocfilters==1.4.2
parso==0.5.1
pickleshare==0.7.5
Pillow==6.2.1
plac==0.9.6
pluggy==0.13.1
preshed==2.0.1
prometheus-client==0.6.0
prompt-toolkit==3.0.2
py==1.8.0
pycparser==2.19
Pygments==2.5.2
pyOpenSSL==19.1.0
pyparsing==2.4.5
pyrsistent==0.15.6
pyshp==1.2.12
PySocks==1.7.1
pytest==5.3.1
python-dateutil==2.8.1
pytz==2019.3
pywin32==223
pywin32-ctypes==0.2.0
pywinpty==0.5.5
PyYAML==3.12
pyzmq==18.1.0
requests==2.22.0
scipy==1.2.1
Send2Trash==1.5.0
simplegeneric==0.8.1
six==1.13.0
spacy==2.1.8
srsly==0.1.0
sympy==1.4
terminado==0.8.3
testpath==0.4.4
thinc==7.0.8
torch==1.0.0
torchvision==0.2.2
tornado==6.0.3
tqdm==4.40.0
traitlets==4.3.3
typing==3.6.4
urllib3==1.24.2
wasabi==0.2.2
wcwidth==0.1.7
webencodings==0.5.1
widgetsnbextension==3.5.1
win-inet-pton==1.1.0
wincertstore==0.2
winkerberos==0.7.0
x86cpu==0.4
xlrd==1.2.0
xlwt==1.3.0
zipp==0.6.0
it seems the pillow were installed by command above using conda install, and this is what changed :
anything wrong with my setup or I missed something?
Thanks in advance.
To verify your pillow installation try to load an exported image chip directly using the following code
from PIL import Image
Image.open(image_path)
it should give a similar output as below
Now it seems to make sense, it resulted in an error :
so what should I do ? in another side I need this conda install -c pytorch -c fastai fastai=1.0.39 pytorch=1.0.0 torchvision command to prepare_data()
One more thing, it can read normal image png, but it won't work from image created from export training data
Okay, now I can read the Image from export training data but it leaves some error and I'm afraid it will cause something wrong later :
image successfully read :
should I run :
conda install -c fastai nvidia-ml-py3since I'm using GPU for this