def perf_checks():
" Suggest to user what improvements they could do to their setup to speed things up"
# libjpeg_turbo check
from PIL import features, Image
from packaging import version
print("\n*** libjpeg-turbo status")
if version.parse(Image.PILLOW_VERSION) >= version.parse("5.4.0"):
if features.check_feature('libjpeg_turbo'):
print("libjpeg-turbo is on")
else:
print(
"libjpeg-turbo is not on. It's recommended you install libjpeg-turbo to speed up JPEG decoding. See https://docs.fast.ai/performance.html#libjpeg-turbo"
)
else:
print(
f"libjpeg-turbo' status can't be derived - need Pillow(-SIMD)? >= 5.4.0 to tell, current version {Image.PILLOW_VERSION}"
)
print("\n*** Pillow-SIMD status")
if re.search(r'\.post\d+', Image.PILLOW_VERSION):
print("Running Pillow-SIMD")
else:
print(
"Running Pillow; It's recommended you install Pillow-SIMD to speed up image resizing and other operations. See https://docs.fast.ai/performance.html#pillow-simd"
)
def test_check():
# Check the correctness of the convenience function
for module in features.modules:
assert features.check_module(module) == features.check(module)
for codec in features.codecs:
assert features.check_codec(codec) == features.check(codec)
for feature in features.features:
assert features.check_feature(feature) == features.check(feature)
def check_perf():
"Suggest how to improve the setup to speed things up"
from PIL import features, Image
from packaging import version
print("Running performance checks.")
# libjpeg_turbo check
print("\n*** libjpeg-turbo status")
#TODO: do some workarounf for later versions of Pillow
pil_version = pillow_version()
if version.parse(pil_version) >= version.parse("5.3.9"):
if features.check_feature('libjpeg_turbo'):
print("✔ libjpeg-turbo is on")
else:
print("✘ libjpeg-turbo is not on. It's recommended you install libjpeg-turbo to speed up JPEG decoding. See https://docs.fast.ai/performance.html#libjpeg-turbo")
else:
print(f"❓ libjpeg-turbo's status can't be derived - need Pillow(-SIMD)? >= 5.4.0 to tell, current version {Image.PILLOW_VERSION}")
# XXX: remove this check/note once Pillow and Pillow-SIMD 5.4.0 is available
pillow_ver_5_4_is_avail = pypi_module_version_is_available("Pillow", "5.4.0")
if pillow_ver_5_4_is_avail == False:
print("5.4.0 is not yet available, other than the dev version on github, which can be installed via pip from git+https://github.com/python-pillow/Pillow. See https://docs.fast.ai/performance.html#libjpeg-turbo")
# Pillow-SIMD check
#TODO: same as above
print("\n*** Pillow-SIMD status")
if re.search(r'\.post\d+', pil_version):
print(f"✔ Running Pillow-SIMD {pil_version}")
else:
print(f"✘ Running Pillow {pil_version}; It's recommended you install Pillow-SIMD to speed up image resizing and other operations. See https://docs.fast.ai/performance.html#pillow-simd")
# CUDA version check
# compatibility table: k: min nvidia ver is required for v: cuda ver
# note: windows nvidia driver version is slightly higher, see:
# https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
# note: add new entries if pytorch starts supporting new cudaXX
nvidia2cuda = {
"410.00": "10.0",
"384.81": "9.0",
"367.48": "8.0",
}
print("\n*** CUDA status")
if torch.cuda.is_available():
pynvml = load_pynvml_env()
nvidia_ver = (pynvml.nvmlSystemGetDriverVersion().decode('utf-8') if platform.system() != "Darwin" else "Cannot be determined on OSX yet")
cuda_ver = torch.version.cuda
max_cuda = "8.0"
for k in sorted(nvidia2cuda.keys()):
if version.parse(nvidia_ver) > version.parse(k): max_cuda = nvidia2cuda[k]
if version.parse(str(max_cuda)) <= version.parse(cuda_ver):
print(f"✔ Running the latest CUDA {cuda_ver} with NVIDIA driver {nvidia_ver}")
else:
print(f"✘ You are running pytorch built against cuda {cuda_ver}, your NVIDIA driver {nvidia_ver} supports cuda10. See https://pytorch.org/get-started/locally/ to install pytorch built against the faster CUDA version.")
else:
print(f"❓ Running cpu-only torch version, CUDA check is not relevant")
print("\nRefer to https://docs.fast.ai/performance.html to make sense out of these checks and suggestions.")
def check_perf():
"Suggest how to improve the setup to speed things up"
from PIL import features, Image
from packaging import version
print("Running performance checks.")
# libjpeg_turbo check
print("\n*** libjpeg-turbo status")
if version.parse(Image.PILLOW_VERSION) >= version.parse("5.3.9"):
if features.check_feature('libjpeg_turbo'):
print("✔ libjpeg-turbo is on")
else:
print("✘ libjpeg-turbo is not on. It's recommended you install libjpeg-turbo to speed up JPEG decoding. See https://docs.fast.ai/performance.html#libjpeg-turbo")
else:
print(f"❓ libjpeg-turbo's status can't be derived - need Pillow(-SIMD)? >= 5.4.0 to tell, current version {Image.PILLOW_VERSION}")
# XXX: remove this check/note once Pillow and Pillow-SIMD 5.4.0 is available
pillow_ver_5_4_is_avail = pypi_module_version_is_available("Pillow", "5.4.0")
if pillow_ver_5_4_is_avail == False:
print("5.4.0 is not yet available, other than the dev version on github, which can be installed via pip from git+https://github.com/python-pillow/Pillow. See https://docs.fast.ai/performance.html#libjpeg-turbo")
# Pillow-SIMD check
print("\n*** Pillow-SIMD status")
if re.search(r'\.post\d+', Image.PILLOW_VERSION):
print(f"✔ Running Pillow-SIMD {Image.PILLOW_VERSION}")
else:
print(f"✘ Running Pillow {Image.PILLOW_VERSION}; It's recommended you install Pillow-SIMD to speed up image resizing and other operations. See https://docs.fast.ai/performance.html#pillow-simd")
# CUDA version check
# compatibility table: k: min nvidia ver is required for v: cuda ver
# note: windows nvidia driver version is slightly higher, see:
# https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
# note: add new entries if pytorch starts supporting new cudaXX
nvidia2cuda = {
"410.00": "10.0",
"384.81": "9.0",
"367.48": "8.0",
}
print("\n*** CUDA status")
if torch.cuda.is_available():
pynvml = load_pynvml_env()
nvidia_ver = (pynvml.nvmlSystemGetDriverVersion().decode('utf-8') if platform.system() != "Darwin" else "Cannot be determined on OSX yet")
cuda_ver = torch.version.cuda
max_cuda = "8.0"
for k in sorted(nvidia2cuda.keys()):
if version.parse(nvidia_ver) > version.parse(k): max_cuda = nvidia2cuda[k]
if version.parse(str(max_cuda)) <= version.parse(cuda_ver):
print(f"✔ Running the latest CUDA {cuda_ver} with NVIDIA driver {nvidia_ver}")
else:
print(f"✘ You are running pytorch built against cuda {cuda_ver}, your NVIDIA driver {nvidia_ver} supports cuda10. See https://pytorch.org/get-started/locally/ to install pytorch built against the faster CUDA version.")
else:
print(f"❓ Running cpu-only torch version, CUDA check is not relevant")
print("\nRefer to https://docs.fast.ai/performance.html to make sense out of these checks and suggestions.")
def test_check(self):
# Check the correctness of the convenience function
for module in features.modules:
self.assertEqual(features.check_module(module),
features.check(module))
for codec in features.codecs:
self.assertEqual(features.check_codec(codec),
features.check(codec))
for feature in features.features:
self.assertEqual(features.check_feature(feature),
features.check(feature))
def test_check(self):
# Check the correctness of the convenience function
for module in features.modules:
self.assertEqual(features.check_module(module),
features.check(module))
for codec in features.codecs:
self.assertEqual(features.check_codec(codec),
features.check(codec))
for feature in features.features:
self.assertEqual(features.check_feature(feature),
features.check(feature))
def get_quantization_method(self, value):
method = None
if value == "FastOctree":
method = Image.FASTOCTREE
elif value == "MedianCut":
method = Image.MEDIANCUT
elif value == "MaxCoverage":
method = Image.MAXCOVERAGE
elif value == "libimagequant":
if features.check_feature(feature="libimagequant"):
method = Image.LIBIMAGEQUANT
else:
self.log("Quantization method 'libimagequant' is unsupported by your platform.")
self.log("Using {method} quantization method".format(method="default" if method is None else value))
return method
def check_perf():
"Suggest how to improve the setup to speed things up"
from PIL import features, Image
print("Running performance checks:")
# Pillow-SIMD check
try:
pil_version = Image.__version__ # PIL >= 7
except:
pil_version = Image.PILLOW_VERSION # PIL < 7
if re.search(r'\.post\d+', pil_version):
print(f"✔ Running Pillow-SIMD {pil_version}")
else:
print(f"✘ Running Pillow {pil_version}")
# libjpeg_turbo check
if features.check_feature('libjpeg_turbo'):
print("✔ libjpeg-turbo is on")
else:
print("✘ libjpeg-turbo is not on.")
import json
import pickle
from PIL import Image, features
# from torchpie.logging import logger
import logging
logger = logging.getLogger(__name__)
logger.info(f'pillow version: {Image.PILLOW_VERSION}')
logger.info(
'Using jpeg-turbo: {}'.format(features.check_feature('libjpeg_turbo')))
def load_json(filename: str):
with open(filename, 'r') as f:
return json.load(f)
def dump_json(obj, filename: str):
with open(filename, 'w') as f:
json.dump(obj, f)
def load_pickle(filename: str):
with open(filename, 'rb') as f:
return pickle.load(f)
def dump_pickle(obj, filename: str):
with open(filename, 'wb') as f:
pickle.dump(obj, f)
import PIL
from PIL import features
print(features.check_feature('libjpeg_turbo'))
print(PIL.__version__)
