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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# The MIT License (MIT)
#
# Copyright (c) 2018-2021 www.open3d.org
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
# ----------------------------------------------------------------------------
import numpy as np
import open3d as o3d
import pytest
from collections import namedtuple
import importlib
from types import SimpleNamespace
import urllib.request
import io
# skip all tests if the ml ops were not built
default_marks = [
pytest.mark.skipif(not (o3d._build_config['BUILD_TENSORFLOW_OPS'] or
o3d._build_config['BUILD_PYTORCH_OPS']),
reason='ml ops not built'),
]
MLModules = namedtuple('MLModules', [
'module', 'ops', 'layers', 'classes', 'device', 'cpu_device',
'device_is_gpu'
])
# define the list of frameworks and devices for running the ops
_ml_modules = {}
try:
# Suppress deprecated imp module warnings caused by tensorflow,
# see https://github.com/tensorflow/tensorflow/issues/31412
import warnings
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
tf = importlib.import_module('tensorflow')
ml3d_ops = importlib.import_module('open3d.ml.tf.ops')
ml3d_layers = importlib.import_module('open3d.ml.tf.layers')
_ml_modules['tf'] = MLModules(tf, ml3d_ops, ml3d_layers, None, 'CPU:0',
'CPU:0', False)
# check for GPUs and set memory growth to prevent tf from allocating all memory
tf_gpu_devices = tf.config.experimental.list_physical_devices('GPU')
for dev in tf_gpu_devices:
tf.config.experimental.set_memory_growth(dev, True)
if tf_gpu_devices and o3d._build_config['BUILD_CUDA_MODULE']:
_ml_modules['tf_gpu'] = MLModules(tf, ml3d_ops, ml3d_layers, None,
'GPU:0', 'CPU:0', True)
except ImportError:
pass
try:
torch = importlib.import_module('torch')
ml3d_ops = importlib.import_module('open3d.ml.torch.ops')
ml3d_layers = importlib.import_module('open3d.ml.torch.layers')
ml3d_classes = importlib.import_module('open3d.ml.torch.classes')
_ml_modules['torch'] = MLModules(torch, ml3d_ops, ml3d_layers, ml3d_classes,
'cpu', 'cpu', False)
if torch.cuda.is_available() and o3d._build_config['BUILD_CUDA_MODULE']:
_ml_modules['torch_cuda'] = MLModules(torch, ml3d_ops, ml3d_layers,
ml3d_classes, 'cuda', 'cpu', True)
except ImportError:
pass
def is_gpu_device_name(name):
return name in ('GPU:0', 'cuda')
def to_numpy(tensor):
if 'torch' in _ml_modules and isinstance(tensor, torch.Tensor):
if tensor.requires_grad:
tensor = tensor.detach()
if tensor.device.type == 'cuda':
tensor = tensor.cpu()
return tensor.numpy()
else:
return tensor.numpy()
def to_torch(x, device):
"""Converts x such that it can be used as input to a pytorch op."""
if isinstance(x, np.ndarray):
return torch.from_numpy(x).contiguous().to(device)
else:
return x
def run_op(ml, device_name, check_device, fn, *args, **kwargs):
"""Runs an op using an ml framework"""
if ml.module.__name__ == 'tensorflow':
with tf.device(device_name):
ans = fn(*args, **kwargs)
if check_device:
# not all returned tensors have to use the device.
# check if there is at least one tensor using device memory
tensor_on_device = False
if isinstance(ans, tf.Tensor):
if device_name in ans.device:
tensor_on_device = True
else:
for x in ans:
if device_name in x.device:
tensor_on_device = True
assert tensor_on_device
elif ml.module.__name__ == 'torch':
_args = [to_torch(x, device_name) for x in args]
_kwargs = {k: to_torch(v, device_name) for k, v in kwargs.items()}
ans = fn(*_args, **_kwargs)
if check_device:
# not all returned tensor have to use the device.
# check if there is at least one tensor using device memory
tensor_on_device = False
if isinstance(ans, torch.Tensor):
if device_name == ans.device.type:
tensor_on_device = True
else:
for x in ans:
if isinstance(
x, torch.Tensor) and device_name == x.device.type:
tensor_on_device = True
assert tensor_on_device
else:
raise ValueError('unsupported ml framework {}'.format(ml.module))
# convert outputs to numpy.
if hasattr(ans, 'numpy'):
new_ans = to_numpy(ans)
else:
# we assume the output is a (named)tuple if there is no numpy() function
return_type = type(ans)
output_as_numpy = [to_numpy(x) for x in ans]
new_ans = return_type(*output_as_numpy)
return new_ans
def run_op_grad(ml, device_name, check_device, fn, x, y_attr_name,
backprop_values, *args, **kwargs):
"""Computes the gradient for input x of an op using an ml framework"""
if ml.module.__name__ == 'tensorflow':
x_var = tf.constant(x)
_args = [x_var if a is x else a for a in args]
_kwargs = {k: x_var if a is x else a for k, a in kwargs.items()}
with tf.device(device_name):
with tf.GradientTape() as tape:
tape.watch(x_var)
ans = fn(*_args, **_kwargs)
if y_attr_name:
y = getattr(ans, y_attr_name)
else:
y = ans
dy_dx = tape.gradient(y, x_var, backprop_values)
if check_device:
# check if the gradient is using device memory
tensor_on_device = False
if device_name in dy_dx.device:
tensor_on_device = True
assert tensor_on_device
elif ml.module.__name__ == 'torch':
x_var = to_torch(x, device_name)
x_var.requires_grad = True
_args = [x_var if a is x else to_torch(a, device_name) for a in args]
_kwargs = {
k: x_var if a is x else to_torch(a, device_name)
for k, a in kwargs.items()
}
ans = fn(*_args, **_kwargs)
if y_attr_name:
y = getattr(ans, y_attr_name)
else:
y = ans
y.backward(to_torch(backprop_values, device_name))
dy_dx = x_var.grad
if check_device:
# check if the gradient is using device memory
tensor_on_device = False
if isinstance(dy_dx,
torch.Tensor) and device_name == dy_dx.device.type:
tensor_on_device = True
assert tensor_on_device
else:
raise ValueError('unsupported ml framework {}'.format(ml.module))
return to_numpy(dy_dx)
class MLTensor:
"""Class for dealing with ml framework specific tensors and rng.
Args:
module: Either the tensorflow or torch module
"""
def __init__(self, module):
self.module = module
def get_dtype(self, dtype_str):
return getattr(self.module, dtype_str)
def set_seed(self, seed):
if self.module.__name__ == 'tensorflow':
self.module.random.set_seed(seed)
elif self.module.__name__ == 'torch':
self.module.manual_seed(seed)
else:
raise Exception('Unsupported ml framework')
def set_deterministic(self, deterministic):
if self.module.__name__ == 'tensorflow':
pass
elif self.module.__name__ == 'torch':
self.module.set_deterministic(deterministic)
else:
raise Exception('Unsupported ml framework')
def random_uniform(self, size, dtype, minval=0, maxval=1):
if isinstance(dtype, str):
dtype = self.get_dtype(dtype)
if self.module.__name__ == 'tensorflow':
return self.module.random.uniform(shape=size,
dtype=dtype,
minval=minval,
maxval=maxval)
elif self.module.__name__ == 'torch':
ans = self.module.empty(size=size, dtype=dtype)
return ans.uniform_(minval, maxval)
else:
raise Exception('Unsupported ml framework')
def empty(self, shape, dtype):
if isinstance(dtype, str):
dtype = self.get_dtype(dtype)
if self.module.__name__ == 'tensorflow':
return self.module.zeros(shape=shape, dtype=dtype)
elif self.module.__name__ == 'torch':
return self.module.empty(size=shape, dtype=dtype)
else:
raise Exception('Unsupported ml framework')
def zeros(self, shape, dtype):
if isinstance(dtype, str):
dtype = self.get_dtype(dtype)
if self.module.__name__ == 'tensorflow':
return self.module.zeros(shape=shape, dtype=dtype)
elif self.module.__name__ == 'torch':
return self.module.zeros(size=shape, dtype=dtype)
else:
raise Exception('Unsupported ml framework')
# add parameterizations for the ml module and the device
parametrize = SimpleNamespace(
ml=pytest.mark.parametrize('ml', _ml_modules.values()),
ml_cpu_only=pytest.mark.parametrize(
'ml', [v for k, v in _ml_modules.items() if not v.device_is_gpu]),
ml_gpu_only=pytest.mark.parametrize(
'ml', [v for k, v in _ml_modules.items() if v.device_is_gpu]),
ml_torch_only=pytest.mark.parametrize(
'ml',
[v for k, v in _ml_modules.items() if v.module.__name__ == 'torch']),
ml_tf_only=pytest.mark.parametrize('ml', [
v for k, v in _ml_modules.items() if v.module.__name__ == 'tensorflow'
]),
)
def fetch_numpy(url):
# prevents security issue
if url.lower().startswith('http'):
req = urllib.request.Request(url)
else:
raise ValueError from None
with urllib.request.urlopen(req) as response: #nosec
np_file = response.read()
return np.load(io.BytesIO(np_file))
return None
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