def initialize(self, device, **kwargs):
super(StochasticPoolingBase, self).initialize(device=device, **kwargs)
if self.uniform is None:
self.uniform = Uniform(self)
if self.uniform.output_bytes < (self.output_size << 1):
if self.uniform.is_initialized:
raise ValueError(
"uniform is already initialized and does not have enough "
"output size")
self.uniform.output_bytes = self.output_size << 1
self.uniform.initialize(self.device)
def initialize(self, device, **kwargs):
super(StochasticPoolingBase, self).initialize(device=device, **kwargs)
if self.uniform is None:
self.uniform = Uniform(self)
if self.uniform.output_bytes < (self.output_size << 1):
if self.uniform.is_initialized:
raise ValueError(
"uniform is already initialized and does not have enough "
"output size")
self.uniform.output_bytes = self.output_size << 1
self.uniform.initialize(self.device)
class StochasticPoolingBase(OffsetPooling):
"""Stochastic pooling forward propagation.
Attributes:
uniform: instance of veles.prng.Uniform.
"""
MAPPING = set()
hide_from_registry = True
def __init__(self, workflow, **kwargs):
super(StochasticPoolingBase, self).__init__(workflow, **kwargs)
self.uniform = kwargs.get("uniform")
def init_unpickled(self):
super(StochasticPoolingBase, self).init_unpickled()
self._rand_set = False
self._rand_arg = 3
self._kernel_name = "stochastic_pooling"
def initialize(self, device, **kwargs):
super(StochasticPoolingBase, self).initialize(device=device, **kwargs)
if self.uniform is None:
self.uniform = Uniform(self)
if self.uniform.output_bytes < (self.output_size << 1):
if self.uniform.is_initialized:
raise ValueError(
"uniform is already initialized and does not have enough "
"output size")
self.uniform.output_bytes = self.output_size << 1
self.uniform.initialize(self.device)
def ocl_init(self):
super(StochasticPoolingBase, self).ocl_init()
self.assign_kernel(self._kernel_name)
self.set_args()
def cuda_init(self):
super(StochasticPoolingBase, self).cuda_init()
self.assign_kernel(self._kernel_name)
self.set_args()
def add_ref(self, unit):
pass
def numpy_run(self):
self.uniform.numpy_fill(self.output_size << 1)
super(StochasticPoolingBase, self).numpy_run()
def ocl_run(self):
if not self._rand_set:
self.set_arg(self._rand_arg, self.uniform.output)
self._rand_set = True
self.uniform.ocl_fill(self.output_size << 1)
super(StochasticPoolingBase, self).ocl_run()
def cuda_run(self):
if not self._rand_set:
self.set_arg(self._rand_arg, self.uniform.output)
self._rand_set = True
self.uniform.cuda_fill(self.output_size << 1)
super(StochasticPoolingBase, self).cuda_run()
def calculate_position_cpu(self, index, vsum):
rnd = self.uniform.output.mem.view(dtype=numpy.uint16)[index]
return rnd * vsum / 65536
def calculate_random_index_cpu(self, cut, index):
rnd = self.uniform.output.mem.view(dtype=numpy.uint16)[index]
return int(rnd * cut.size >> 16)
class StochasticPoolingBase(OffsetPooling):
"""Stochastic pooling forward propagation.
Attributes:
uniform: instance of veles.prng.Uniform.
"""
MAPPING = set()
hide_from_registry = True
def __init__(self, workflow, **kwargs):
super(StochasticPoolingBase, self).__init__(workflow, **kwargs)
self.uniform = kwargs.get("uniform")
def init_unpickled(self):
super(StochasticPoolingBase, self).init_unpickled()
self._rand_set = False
self._rand_arg = 3
self._kernel_name = "stochastic_pooling"
def initialize(self, device, **kwargs):
super(StochasticPoolingBase, self).initialize(device=device, **kwargs)
if self.uniform is None:
self.uniform = Uniform(self)
if self.uniform.output_bytes < (self.output_size << 1):
if self.uniform.is_initialized:
raise ValueError(
"uniform is already initialized and does not have enough "
"output size")
self.uniform.output_bytes = self.output_size << 1
self.uniform.initialize(self.device)
def ocl_init(self):
super(StochasticPoolingBase, self).ocl_init()
self.assign_kernel(self._kernel_name)
self.set_args()
def cuda_init(self):
super(StochasticPoolingBase, self).cuda_init()
self.assign_kernel(self._kernel_name)
self.set_args()
def add_ref(self, unit):
pass
def numpy_run(self):
self.uniform.numpy_fill(self.output_size << 1)
super(StochasticPoolingBase, self).numpy_run()
def ocl_run(self):
if not self._rand_set:
self.set_arg(self._rand_arg, self.uniform.output)
self._rand_set = True
self.uniform.ocl_fill(self.output_size << 1)
super(StochasticPoolingBase, self).ocl_run()
def cuda_run(self):
if not self._rand_set:
self.set_arg(self._rand_arg, self.uniform.output)
self._rand_set = True
self.uniform.cuda_fill(self.output_size << 1)
super(StochasticPoolingBase, self).cuda_run()
def calculate_position_cpu(self, index, vsum):
rnd = self.uniform.output.mem.view(dtype=numpy.uint16)[index]
return rnd * vsum / 65536
def calculate_random_index_cpu(self, cut, index):
rnd = self.uniform.output.mem.view(dtype=numpy.uint16)[index]
return int(rnd * cut.size >> 16)