Issue with Dataset caching
phisanti opened this issue · comments
Describe the bug
I am training a model for image segmentation. Using a custom transform pipeline, I found that clearing the background using a ratio with a Gaussian blurred image and histogram equalisation helps model convergence. However, when I used Monai compose to do the same, I ran into the issue that the transforms of one image seemed to affect other images. This is how I write the Compose:
img_transforms = Compose(
[
TIFFLoader(keys=["image"]),
TIFFLoader(
keys=["label"], add_channel_dim=configs.trainer_params["binary_segmentation"]
), # If running on 2D images, change to True
ComposeInspect(keys=["image"], step='before clear '),
ClearBackgroundTransform(
keys=["image"], sigma_r=25, method="divide", convert_32=True
),
ComposeInspect(keys=["image"], step='before eq '),
HistEq(keys=["image"]),
ComposeInspect(keys=["image"], step='after eq '),
]
)And these are the individual compose:
class ComposeInspect(object):
def __init__(self, keys=("image", "label"), step=""):
self.keys = keys
self.step = step
def __call__(self, data):
for key in self.keys:
if isinstance(data[key], torch.Tensor):
tensor_debugger(data[key], key + self.step)
elif isinstance(data[key], np.ndarray):
x = torch.from_numpy(data[key].copy().astype(np.float32))
tensor_debugger(x, key + self.step)
else:
print(f"Loading key: {data[key]}")
return data
class TIFFLoader(object):
def __init__(self, keys=("image", "label"), add_channel_dim="auto"):
self.keys = keys
self.add_channel_dim = add_channel_dim
def __call__(self, data):
for key in self.keys:
if isinstance(data[key], str):
data[key] = tifffile.imread(data[key])
# Add an extra channel dimension
if self.add_channel_dim == "auto" and data[key].ndim == 2:
data[key] = np.expand_dims(data[key], axis=0)
elif self.add_channel_dim:
data[key] = np.expand_dims(data[key], axis=0)
else:
pass
elif isinstance(data[key], np.ndarray):
# The data is already an array, so no need to load it
pass
else:
print(f"Loading key: {data[key]}")
raise ValueError(
f"Unsupported data type for key '{key}': {type(data[key])}"
)
return data
class HistEq(object):
def __init__(self, keys):
self.keys = keys
def __call__(self, data):
for key in self.keys:
img = data[key].copy()
img=self.to8bit(img)
if img.ndim == 2:
equalized = cv2.equalizeHist(img)
equalized=equalized.astype(np.float32)
equalized= (equalized - equalized.mean())/equalized.std()
elif img.ndim == 3:
equalized=np.zeros_like(img).astype(np.float32)
for ch in range(img.shape[0]):
equalized_ch = cv2.equalizeHist(img.astype(np.uint8))
equalized_ch=equalized_ch.astype(np.float32)
equalized_ch= (equalized_ch - equalized_ch.mean())/equalized_ch.std()
equalized[ch]=equalized_ch
else:
raise NotImplementedError(
"Equalisation for 3D images or batches is not implemented yet."
)
data[key] = equalized
return data
def to8bit(self, image):
normalized_image = (image - np.min(image)) / (np.max(image) - np.min(image))
scaled_image = normalized_image * 255
uint8_image = scaled_image.astype(np.uint8)
return uint8_image
class ClearBackgroundTransform(object):
def __init__(self, keys, sigma_r=25, method="divide", convert_32=True):
self.keys = keys
self.sigma_r = sigma_r
self.method = method
self.convert_32 = convert_32
def __call__(self, data):
for key in self.keys:
img = data[key].copy()
if self.convert_32:
img = img.astype(np.float32)
if img.ndim == 2:
img = self.backgroud_remove_2d(img, self.sigma_r, self.method)
elif img.ndim == 3:
for i in range(img.shape[0]):
img[i] = self.backgroud_remove_2d(img[i], self.sigma_r, self.method)
else:
raise NotImplementedError(
"Background removal for 3D images is not implemented yet."
)
data[key] = img
return data
def round_to_odd(self, number):
return int(number) if number % 2 == 1 else int(number) + 1
def backgroud_remove_2d(self, img, sigma_r, method):
# Gaussian blur
sigma_r = self.round_to_odd(self.sigma_r)
gaussian_blur = cv2.GaussianBlur(img, (sigma_r, sigma_r), 0)
# Background remove
if self.method == "subtract":
background_removed = cv2.subtract(img, gaussian_blur)
elif self.method == "divide":
background_removed = cv2.divide(img, gaussian_blur)
else:
raise ValueError("Invalid method. Choose either 'subtract' or 'divide'")
return background_removedNote that the ComposeInspect is there for debugging purposes. With this code, what I see is something like:
nans imagebefore clear max: False
inf imagebefore clear max: False
imagebefore clear max: 4095.0
imagebefore clear min: 973.0
imagebefore eq shape: torch.Size([1, 2400, 2400])
nans imagebefore eq max: False
inf imagebefore eq max: False
imagebefore eq max: 1.871726632118225
imagebefore eq min: 0.46691009402275085
imageafter eq shape: torch.Size([1, 2400, 2400])
nans imageafter eq max: False
inf imageafter eq max: False
imageafter eq max: 3.4114506244659424
imageafter eq min: -1.236985206604004
imagebefore clear shape: torch.Size([1, 2400, 2400])
nans imagebefore clear max: False
inf imagebefore clear max: False
imagebefore clear max: 3.4114506244659424
imagebefore clear min: -1.236985206604004
imagebefore eq shape: torch.Size([1, 2400, 2400])
nans imagebefore eq max: False
inf imagebefore eq max: False
imagebefore eq max: 4184.20849609375
imagebefore eq min: -1909.0018310546875
imageafter eq shape: torch.Size([1, 2400, 2400])
nans imageafter eq max: False
inf imageafter eq max: False
imageafter eq max: 2.551238775253296
imageafter eq min: -0.6024657487869263
Which is odd, because it seems that the transformations on one image, also happen on other image and then the values seems to accumulate and get out of hand.
To Reproduce
Steps to reproduce the behavior:
- Go to '...'
- Install '....'
- Run commands '....'
Expected behavior
I would like the transformations to be calculated everytime de novo or to be calculated once and not to accumulate spilling over other images.
Environment
Ensuring you use the relevant python executable, please paste the output of:
# packages in environment at /Users/xxx/miniconda3/envs/DeepLearning:
#
# Name Version Build Channel
absl-py 2.1.0 pypi_0 pypi
annotated-types 0.6.0 pyhd8ed1ab_0 conda-forge
aom 3.8.2 h078ce10_0 conda-forge
appnope 0.1.4 pyhd8ed1ab_0 conda-forge
asttokens 2.4.1 pyhd8ed1ab_0 conda-forge
astunparse 1.6.3 pypi_0 pypi
atomai 0.7.8 pypi_0 pypi
aws-c-auth 0.7.17 h382b9c6_2 conda-forge
aws-c-cal 0.6.11 hd34e5fa_0 conda-forge
aws-c-common 0.9.15 h93a5062_0 conda-forge
aws-c-compression 0.2.18 hd34e5fa_3 conda-forge
aws-c-event-stream 0.4.2 h247c08a_8 conda-forge
aws-c-http 0.8.1 hf9e830b_10 conda-forge
aws-c-io 0.14.7 h33d81b3_6 conda-forge
aws-c-mqtt 0.10.3 h5f4abda_4 conda-forge
aws-c-s3 0.5.7 h606a3d2_1 conda-forge
aws-c-sdkutils 0.1.15 hd34e5fa_3 conda-forge
aws-checksums 0.1.18 hd34e5fa_3 conda-forge
aws-crt-cpp 0.26.6 h13f0230_4 conda-forge
aws-sdk-cpp 1.11.267 h134aaec_6 conda-forge
blosc 1.21.5 hc338f07_0 conda-forge
bokeh 3.4.1 pyhd8ed1ab_0 conda-forge
brotli 1.1.0 hb547adb_1 conda-forge
brotli-bin 1.1.0 hb547adb_1 conda-forge
brotli-python 1.1.0 py39hb198ff7_1 conda-forge
brunsli 0.1 h9f76cd9_0 conda-forge
bzip2 1.0.8 h93a5062_5 conda-forge
c-ares 1.28.1 h93a5062_0 conda-forge
c-blosc2 2.12.0 ha57e6be_0 conda-forge
ca-certificates 2024.2.2 hf0a4a13_0 conda-forge
certifi 2024.2.2 pyhd8ed1ab_0 conda-forge
charls 2.4.2 h13dd4ca_0 conda-forge
charset-normalizer 3.3.2 pyhd8ed1ab_0 conda-forge
click 8.1.7 unix_pyh707e725_0 conda-forge
click-default-group 1.2.4 pyhd8ed1ab_0 conda-forge
cloudpickle 3.0.0 pyhd8ed1ab_0 conda-forge
colorama 0.4.6 pyhd8ed1ab_0 conda-forge
comm 0.2.2 pyhd8ed1ab_0 conda-forge
contourpy 1.2.1 py39h48c5dd5_0 conda-forge
cycler 0.12.1 pypi_0 pypi
cytoolz 0.12.3 py39h17cfd9d_0 conda-forge
dask 2024.4.2 pyhd8ed1ab_0 conda-forge
dask-core 2024.4.2 pyhd8ed1ab_0 conda-forge
dask-expr 1.0.14 pyhd8ed1ab_0 conda-forge
dav1d 1.2.1 hb547adb_0 conda-forge
debugpy 1.8.1 py39hf3050f2_0 conda-forge
decorator 5.1.1 pyhd8ed1ab_0 conda-forge
distributed 2024.4.2 pyhd8ed1ab_0 conda-forge
docformatter 1.7.5 pyhd8ed1ab_0 conda-forge
dpcore 0.9.0 pypi_0 pypi
einops 0.8.0 pypi_0 pypi
exceptiongroup 1.2.0 pyhd8ed1ab_2 conda-forge
executing 2.0.1 pyhd8ed1ab_0 conda-forge
fastremap 1.14.1 pypi_0 pypi
filelock 3.14.0 pyhd8ed1ab_0 conda-forge
flatbuffers 24.3.25 pypi_0 pypi
fonttools 4.51.0 pypi_0 pypi
freetype 2.12.1 hadb7bae_2 conda-forge
fsspec 2024.3.1 pyhca7485f_0 conda-forge
gast 0.5.4 pypi_0 pypi
gflags 2.2.2 hc88da5d_1004 conda-forge
giflib 5.2.2 h93a5062_0 conda-forge
glog 0.7.0 hc6770e3_0 conda-forge
gmp 6.3.0 hebf3989_1 conda-forge
gmpy2 2.1.5 py39hd40a46f_0 conda-forge
google-pasta 0.2.0 pypi_0 pypi
gpytorch 1.11 pypi_0 pypi
grpcio 1.63.0 pypi_0 pypi
h5py 3.11.0 pypi_0 pypi
icu 73.2 hc8870d7_0 conda-forge
idna 3.7 pyhd8ed1ab_0 conda-forge
imagecodecs 2023.9.18 py39ha02826a_2 conda-forge
imageio 2.34.1 pypi_0 pypi
importlib-metadata 7.1.0 pyha770c72_0 conda-forge
importlib-resources 6.4.0 pypi_0 pypi
importlib_metadata 7.1.0 hd8ed1ab_0 conda-forge
ipykernel 6.29.3 pyh3cd1d5f_0 conda-forge
ipython 8.18.1 pyh707e725_3 conda-forge
jaxtyping 0.2.28 pypi_0 pypi
jedi 0.19.1 pyhd8ed1ab_0 conda-forge
jetraw-tools 0.3.6 pypi_0 pypi
jinja2 3.1.3 pyhd8ed1ab_0 conda-forge
joblib 1.4.2 pypi_0 pypi
jupyter_client 8.6.1 pyhd8ed1ab_0 conda-forge
jupyter_core 5.7.2 py39h2804cbe_0 conda-forge
jxrlib 1.1 h93a5062_3 conda-forge
keras 3.3.3 pypi_0 pypi
kiwisolver 1.4.5 pypi_0 pypi
krb5 1.21.2 h92f50d5_0 conda-forge
lazy-loader 0.4 pypi_0 pypi
lcms2 2.16 ha0e7c42_0 conda-forge
lerc 4.0.0 h9a09cb3_0 conda-forge
libabseil 20240116.2 cxx17_hebf3989_0 conda-forge
libaec 1.1.3 hebf3989_0 conda-forge
libarrow 15.0.2 h0fcf22f_2_cpu conda-forge
libarrow-acero 15.0.2 h3f3aa29_2_cpu conda-forge
libarrow-dataset 15.0.2 h3f3aa29_2_cpu conda-forge
libarrow-flight 15.0.2 h224147a_2_cpu conda-forge
libarrow-flight-sql 15.0.2 hb630850_2_cpu conda-forge
libarrow-gandiva 15.0.2 h5fa1bb3_2_cpu conda-forge
libarrow-substrait 15.0.2 hd92e347_2_cpu conda-forge
libavif16 1.0.4 hff135a0_2 conda-forge
libblas 3.9.0 19_osxarm64_openblas conda-forge
libbrotlicommon 1.1.0 hb547adb_1 conda-forge
libbrotlidec 1.1.0 hb547adb_1 conda-forge
libbrotlienc 1.1.0 hb547adb_1 conda-forge
libcblas 3.9.0 19_osxarm64_openblas conda-forge
libclang 18.1.1 pypi_0 pypi
libcrc32c 1.1.2 hbdafb3b_0 conda-forge
libcurl 8.7.1 h2d989ff_0 conda-forge
libcxx 16.0.6 h4653b0c_0 conda-forge
libdeflate 1.19 hb547adb_0 conda-forge
libedit 3.1.20191231 hc8eb9b7_2 conda-forge
libev 4.33 h93a5062_2 conda-forge
libevent 2.1.12 h2757513_1 conda-forge
libffi 3.4.2 h3422bc3_5 conda-forge
libgfortran 5.0.0 13_2_0_hd922786_3 conda-forge
libgfortran5 13.2.0 hf226fd6_3 conda-forge
libgoogle-cloud 2.22.0 hbebe991_1 conda-forge
libgoogle-cloud-storage 2.22.0 h8a76758_1 conda-forge
libgrpc 1.62.2 h9c18a4f_0 conda-forge
libhwloc 2.10.0 default_h52d8fe8_1000 conda-forge
libiconv 1.17 h0d3ecfb_2 conda-forge
libjpeg-turbo 3.0.0 hb547adb_1 conda-forge
liblapack 3.9.0 19_osxarm64_openblas conda-forge
libllvm14 14.0.6 hd1a9a77_4 conda-forge
libllvm16 16.0.6 haab561b_3 conda-forge
libnghttp2 1.58.0 ha4dd798_1 conda-forge
libopenblas 0.3.24 openmp_hd76b1f2_0 conda-forge
libparquet 15.0.2 h5304c63_2_cpu conda-forge
libpng 1.6.43 h091b4b1_0 conda-forge
libprotobuf 4.25.3 hbfab5d5_0 conda-forge
libre2-11 2023.09.01 h7b2c953_2 conda-forge
libsodium 1.0.18 h27ca646_1 conda-forge
libsqlite 3.45.3 h091b4b1_0 conda-forge
libssh2 1.11.0 h7a5bd25_0 conda-forge
libthrift 0.19.0 h026a170_1 conda-forge
libtiff 4.6.0 ha8a6c65_2 conda-forge
libutf8proc 2.8.0 h1a8c8d9_0 conda-forge
libwebp-base 1.4.0 h93a5062_0 conda-forge
libxcb 1.15 hf346824_0 conda-forge
libxml2 2.12.6 h0d0cfa8_2 conda-forge
libxslt 1.1.39 h223e5b9_0 conda-forge
libzlib 1.2.13 h53f4e23_5 conda-forge
libzopfli 1.0.3 h9f76cd9_0 conda-forge
linear-operator 0.5.2 pypi_0 pypi
llvm-openmp 15.0.7 h7cfbb63_0 conda-forge
llvmlite 0.42.0 py39h047a24b_1 conda-forge
locket 1.0.0 pyhd8ed1ab_0 conda-forge
lxml 5.1.1 py39h8557d04_0 conda-forge
lz4 4.3.3 py39hf99b9d6_0 conda-forge
lz4-c 1.9.4 hb7217d7_0 conda-forge
markdown 3.6 pypi_0 pypi
markdown-it-py 3.0.0 pypi_0 pypi
markupsafe 2.1.5 py39h17cfd9d_0 conda-forge
matplotlib 3.8.4 pypi_0 pypi
matplotlib-inline 0.1.7 pyhd8ed1ab_0 conda-forge
mdurl 0.1.2 pypi_0 pypi
mendeleev 0.6.1 pypi_0 pypi
ml-dtypes 0.3.2 pypi_0 pypi
monai 1.3.0 pypi_0 pypi
mpc 1.3.1 h91ba8db_0 conda-forge
mpfr 4.2.1 h41d338b_1 conda-forge
mpmath 1.3.0 pyhd8ed1ab_0 conda-forge
msgpack-python 1.0.7 py39he9de807_0 conda-forge
namex 0.0.8 pypi_0 pypi
ncurses 6.4.20240210 h078ce10_0 conda-forge
nd2 0.10.1 pyhd8ed1ab_0 conda-forge
nest-asyncio 1.6.0 pyhd8ed1ab_0 conda-forge
networkx 3.2.1 pyhd8ed1ab_0 conda-forge
numba 0.59.1 py39h313beb8_0
numpy 1.26.4 py39h7aa2656_0 conda-forge
ome-types 0.5.1.post1 pyhd8ed1ab_0 conda-forge
opencv-python 4.9.0.80 pypi_0 pypi
openjpeg 2.5.2 h9f1df11_0 conda-forge
openssl 3.3.0 h0d3ecfb_0 conda-forge
opt-einsum 3.3.0 pypi_0 pypi
optree 0.11.0 pypi_0 pypi
orc 2.0.0 h3d3088e_0 conda-forge
packaging 24.0 pyhd8ed1ab_0 conda-forge
pandas 2.2.2 py39h47e51b9_0 conda-forge
parso 0.8.4 pyhd8ed1ab_0 conda-forge
partd 1.4.1 pyhd8ed1ab_0 conda-forge
pexpect 4.9.0 pyhd8ed1ab_0 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pillow 10.3.0 py39h3352c98_0 conda-forge
pint 0.23 pyhd8ed1ab_0 conda-forge
pip 24.0 pyhd8ed1ab_0 conda-forge
platformdirs 4.2.1 pyhd8ed1ab_0 conda-forge
progressbar2 4.4.2 pypi_0 pypi
prompt-toolkit 3.0.42 pyha770c72_0 conda-forge
protobuf 4.25.3 pypi_0 pypi
psutil 5.9.8 py39h17cfd9d_0 conda-forge
pthread-stubs 0.4 h27ca646_1001 conda-forge
ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge
pure_eval 0.2.2 pyhd8ed1ab_0 conda-forge
pyarrow 15.0.2 py39hcacd782_2_cpu conda-forge
pyarrow-hotfix 0.6 pyhd8ed1ab_0 conda-forge
pydantic 2.7.1 pyhd8ed1ab_0 conda-forge
pydantic-compat 0.1.2 pyhd8ed1ab_0 conda-forge
pydantic-core 2.18.2 py39h8081647_0 conda-forge
pyfiglet 1.0.2 pypi_0 pypi
pygments 2.17.2 pyhd8ed1ab_0 conda-forge
pyparsing 3.1.2 pypi_0 pypi
pysocks 1.7.1 pyha2e5f31_6 conda-forge
python 3.9.19 hd7ebdb9_0_cpython conda-forge
python-dateutil 2.9.0 pyhd8ed1ab_0 conda-forge
python-tzdata 2024.1 pyhd8ed1ab_0 conda-forge
python-utils 3.8.2 pypi_0 pypi
python_abi 3.9 4_cp39 conda-forge
pytorch 2.4.0.dev20240506 py3.9_0 pytorch-nightly
pytz 2024.1 pyhd8ed1ab_0 conda-forge
pyyaml 6.0.1 py39h0f82c59_1 conda-forge
pyzmq 26.0.2 py39hf4e74ac_0 conda-forge
rav1e 0.6.6 h69fbcac_2 conda-forge
re2 2023.09.01 h4cba328_2 conda-forge
readline 8.2 h92ec313_1 conda-forge
requests 2.31.0 pyhd8ed1ab_0 conda-forge
resource_backed_dask_array 0.1.0 pyhd8ed1ab_1 conda-forge
rich 13.7.1 pypi_0 pypi
ruff 0.4.2 py39h0a9ff32_0 conda-forge
scikit-image 0.22.0 pypi_0 pypi
scikit-learn 1.4.2 pypi_0 pypi
scipy 1.13.0 pypi_0 pypi
seaborn 0.13.2 pypi_0 pypi
setuptools 69.5.1 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
snappy 1.1.10 hd04f947_1 conda-forge
sortedcontainers 2.4.0 pyhd8ed1ab_0 conda-forge
sqlalchemy 2.0.29 pypi_0 pypi
stack_data 0.6.2 pyhd8ed1ab_0 conda-forge
svt-av1 2.0.0 h078ce10_0 conda-forge
sympy 1.12 pypyh9d50eac_103 conda-forge
tbb 2021.12.0 h420ef59_1 conda-forge
tblib 3.0.0 pyhd8ed1ab_0 conda-forge
tensorboard 2.16.2 pypi_0 pypi
tensorboard-data-server 0.7.2 pypi_0 pypi
tensorflow 2.16.1 pypi_0 pypi
tensorflow-io-gcs-filesystem 0.37.0 pypi_0 pypi
termcolor 2.4.0 pypi_0 pypi
threadpoolctl 3.5.0 pypi_0 pypi
tifffile 2024.4.24 pyhd8ed1ab_0 conda-forge
tk 8.6.13 h5083fa2_1 conda-forge
toolz 0.12.1 pyhd8ed1ab_0 conda-forge
toposort 1.10 pyhd8ed1ab_0 conda-forge
torchvision 0.19.0.dev20240506 py39_cpu pytorch-nightly
tornado 6.4 py39h17cfd9d_0 conda-forge
tqdm 4.66.4 pyhd8ed1ab_0 conda-forge
traitlets 5.14.3 pyhd8ed1ab_0 conda-forge
typeguard 2.13.3 pypi_0 pypi
typing-extensions 4.11.0 hd8ed1ab_0 conda-forge
typing_extensions 4.11.0 pyha770c72_0 conda-forge
tzdata 2024a h0c530f3_0 conda-forge
untokenize 0.1.1 py_0 conda-forge
urllib3 2.2.1 pyhd8ed1ab_0 conda-forge
wcwidth 0.2.13 pyhd8ed1ab_0 conda-forge
werkzeug 3.0.2 pypi_0 pypi
wheel 0.43.0 pyhd8ed1ab_1 conda-forge
wrapt 1.16.0 pypi_0 pypi
xorg-libxau 1.0.11 hb547adb_0 conda-forge
xorg-libxdmcp 1.1.3 h27ca646_0 conda-forge
xsdata 24.3.1 pyhd8ed1ab_0 conda-forge
xyzservices 2024.4.0 pyhd8ed1ab_0 conda-forge
xz 5.2.6 h57fd34a_0 conda-forge
yaml 0.2.5 h3422bc3_2 conda-forge
zeromq 4.3.5 h5119023_3 conda-forge
zfp 1.0.0 h82938aa_4 conda-forge
zict 3.0.0 pyhd8ed1ab_0 conda-forge
zipp 3.17.0 pyhd8ed1ab_0 conda-forge
zlib-ng 2.0.7 h1a8c8d9_0 conda-forge
zstd 1.5.6 hb46c0d2_0 conda-forge
python -c "import monai; monai.config.print_debug_info()"
================================
Printing MONAI config...
================================
MONAI version: 1.3.0
Numpy version: 1.26.4
Pytorch version: 2.4.0.dev20240506
MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False
MONAI rev id: 865972f7a791bf7b42efbcd87c8402bd865b329e
MONAI __file__: /Users/<username>/miniconda3/envs/DeepLearning/lib/python3.9/site-packages/monai/__init__.py
Optional dependencies:
Pytorch Ignite version: NOT INSTALLED or UNKNOWN VERSION.
ITK version: NOT INSTALLED or UNKNOWN VERSION.
Nibabel version: NOT INSTALLED or UNKNOWN VERSION.
scikit-image version: 0.22.0
scipy version: 1.13.0
Pillow version: 10.3.0
Tensorboard version: 2.16.2
gdown version: NOT INSTALLED or UNKNOWN VERSION.
TorchVision version: 0.19.0.dev20240506
tqdm version: 4.66.4
lmdb version: NOT INSTALLED or UNKNOWN VERSION.
psutil version: 5.9.8
pandas version: 2.2.2
einops version: 0.8.0
transformers version: NOT INSTALLED or UNKNOWN VERSION.
mlflow version: NOT INSTALLED or UNKNOWN VERSION.
pynrrd version: NOT INSTALLED or UNKNOWN VERSION.
clearml version: NOT INSTALLED or UNKNOWN VERSION.
For details about installing the optional dependencies, please visit:
https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies
================================
Printing system config...
================================
System: Darwin
Mac version: 14.4.1
Platform: macOS-14.4.1-arm64-arm-64bit
Processor: arm
Machine: arm64
Python version: 3.9.19
Process name: python3.9
Command: ['python', '-c', 'import monai; monai.config.print_debug_info()']
Open files: []
Num physical CPUs: 8
Num logical CPUs: 8
Num usable CPUs: UNKNOWN for given OS
CPU usage (%): [32.5, 30.6, 26.7, 23.5, 42.0, 36.3, 8.6, 7.4]
CPU freq. (MHz): 3504
Load avg. in last 1, 5, 15 mins (%): [50.1, 53.7, 52.9]
Disk usage (%): 77.7
Avg. sensor temp. (Celsius): UNKNOWN for given OS
Total physical memory (GB): 24.0
Available memory (GB): 13.7
Used memory (GB): 8.8
================================
Printing GPU config...
================================
Num GPUs: 0
Has CUDA: False
cuDNN enabled: False
NVIDIA_TF32_OVERRIDE: None
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE: None
Additional context
Not required
Any help will be more than welcome.