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David Rotermund 2023-02-06 09:55:30 +01:00 committed by GitHub
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5 changed files with 206 additions and 87 deletions
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146
network/HDynamicLayer.py
View file

@ -21,6 +21,8 @@ class HDynamicLayer(torch.nn.Module):
device: torch.device device: torch.device
default_dtype: torch.dtype default_dtype: torch.dtype
_force_forward_h_dynamic_on_cpu: bool
def __init__( def __init__(
self, self,
output_size: list[int], output_size: list[int],
@ -32,6 +34,7 @@ class HDynamicLayer(torch.nn.Module):
device: torch.device | None = None, device: torch.device | None = None,
default_dtype: torch.dtype | None = None, default_dtype: torch.dtype | None = None,
gpu_tuning_factor: int = 5, gpu_tuning_factor: int = 5,
force_forward_h_dynamic_on_cpu: bool = False,
) -> None: ) -> None:
super().__init__() super().__init__()
@ -46,11 +49,14 @@ class HDynamicLayer(torch.nn.Module):
self._output_size = output_size self._output_size = output_size
self._output_layer = bool(output_layer) self._output_layer = bool(output_layer)
self._local_learning = bool(local_learning) self._local_learning = bool(local_learning)
self._force_forward_h_dynamic_on_cpu = force_forward_h_dynamic_on_cpu
global_sbs_gpu_setting.append(torch.tensor([0])) global_sbs_gpu_setting.append(torch.tensor([0]))
global_sbs_size.append(torch.tensor([0, 0, 0, 0])) global_sbs_size.append(torch.tensor([0, 0, 0, 0]))
if device == torch.device("cpu"): if (device == torch.device("cpu")) or (
self._force_forward_h_dynamic_on_cpu is True
):
global_sbs_hdynamic_cpp.append(HDynamicCNNCPU()) global_sbs_hdynamic_cpp.append(HDynamicCNNCPU())
else: else:
global_sbs_hdynamic_cpp.append(HDynamicCNNGPU()) global_sbs_hdynamic_cpp.append(HDynamicCNNGPU())
@ -146,11 +152,6 @@ class FunctionalSbS(torch.autograd.Function):
number_of_spikes: int = int(spikes.shape[1]) number_of_spikes: int = int(spikes.shape[1])
if input.device == torch.device("cpu"):
hdyn_number_of_cpu_processes: int = int(parameter_list[0])
else:
hdyn_number_of_cpu_processes = -1
output_size_0: int = int(parameter_list[1]) output_size_0: int = int(parameter_list[1])
output_size_1: int = int(parameter_list[2]) output_size_1: int = int(parameter_list[2])
gpu_tuning_factor: int = int(parameter_list[3]) gpu_tuning_factor: int = int(parameter_list[3])
@ -158,6 +159,30 @@ class FunctionalSbS(torch.autograd.Function):
sbs_gpu_setting_position = int(parameter_list[4]) sbs_gpu_setting_position = int(parameter_list[4])
sbs_hdynamic_cpp_position = int(parameter_list[5]) sbs_hdynamic_cpp_position = int(parameter_list[5])
if (
isinstance(
global_sbs_hdynamic_cpp[sbs_hdynamic_cpp_position], HDynamicCNNCPU
)
is True
):
are_we_on_a_cpu: bool = True
work_device: torch.device = torch.device("cpu")
else:
are_we_on_a_cpu = False
work_device = input.device
target_device: torch.device = input.device
if target_device == work_device:
data_is_on_the_same_device: bool = True
else:
data_is_on_the_same_device = False
if are_we_on_a_cpu is True:
hdyn_number_of_cpu_processes: int = int(parameter_list[0])
else:
hdyn_number_of_cpu_processes = -1
# ########################################################### # ###########################################################
# H dynamic # H dynamic
# ########################################################### # ###########################################################
@ -169,7 +194,7 @@ class FunctionalSbS(torch.autograd.Function):
# Make space for the results # Make space for the results
# ############################################ # ############################################
output = torch.empty( output_work: torch.Tensor = torch.empty(
( (
int(input.shape[0]), int(input.shape[0]),
int(weights.shape[1]), int(weights.shape[1]),
@ -177,17 +202,43 @@ class FunctionalSbS(torch.autograd.Function):
output_size_1, output_size_1,
), ),
dtype=input.dtype, dtype=input.dtype,
device=input.device, device=work_device,
) )
assert output.is_contiguous() is True assert output_work.is_contiguous() is True
if epsilon_xy is not None: if epsilon_xy is not None:
assert epsilon_xy.is_contiguous() is True assert epsilon_xy.is_contiguous() is True
assert epsilon_xy.ndim == 3 assert epsilon_xy.ndim == 3
if data_is_on_the_same_device is False:
epsilon_xy_work = epsilon_xy.to(work_device)
else:
epsilon_xy_work = epsilon_xy
else:
epsilon_xy_work = None
assert epsilon_t_0.is_contiguous() is True assert epsilon_t_0.is_contiguous() is True
if data_is_on_the_same_device is False:
epsilon_t_0_work = epsilon_t_0.to(work_device)
else:
epsilon_t_0_work = epsilon_t_0
assert weights.is_contiguous() is True assert weights.is_contiguous() is True
if data_is_on_the_same_device is False:
weights_work = weights.to(work_device)
else:
weights_work = weights
assert spikes.is_contiguous() is True assert spikes.is_contiguous() is True
if data_is_on_the_same_device is False:
spikes_work = spikes.to(work_device)
else:
spikes_work = spikes
assert h_initial.is_contiguous() is True assert h_initial.is_contiguous() is True
if data_is_on_the_same_device is False:
h_initial_work = h_initial.to(work_device)
else:
h_initial_work = h_initial
assert weights.ndim == 2 assert weights.ndim == 2
assert h_initial.ndim == 1 assert h_initial.ndim == 1
@ -196,32 +247,32 @@ class FunctionalSbS(torch.autograd.Function):
sbs_size = global_sbs_size[sbs_gpu_setting_position].clone() sbs_size = global_sbs_size[sbs_gpu_setting_position].clone()
if input.device != torch.device("cpu"): if are_we_on_a_cpu is False:
if ( if (
(sbs_profile.numel() == 1) (sbs_profile.numel() == 1)
or (sbs_size[0] != int(output.shape[0])) or (sbs_size[0] != int(output_work.shape[0]))
or (sbs_size[1] != int(output.shape[1])) or (sbs_size[1] != int(output_work.shape[1]))
or (sbs_size[2] != int(output.shape[2])) or (sbs_size[2] != int(output_work.shape[2]))
or (sbs_size[3] != int(output.shape[3])) or (sbs_size[3] != int(output_work.shape[3]))
): ):
sbs_profile = torch.zeros( sbs_profile = torch.zeros(
(14, 7), dtype=torch.int64, device=torch.device("cpu") (14, 7), dtype=torch.int64, device=torch.device("cpu")
) )
global_sbs_hdynamic_cpp[sbs_hdynamic_cpp_position].gpu_occupancy_export( global_sbs_hdynamic_cpp[sbs_hdynamic_cpp_position].gpu_occupancy_export(
int(output.shape[2]), int(output_work.shape[2]),
int(output.shape[3]), int(output_work.shape[3]),
int(output.shape[0]), int(output_work.shape[0]),
int(output.shape[1]), int(output_work.shape[1]),
sbs_profile.data_ptr(), sbs_profile.data_ptr(),
int(sbs_profile.shape[0]), int(sbs_profile.shape[0]),
int(sbs_profile.shape[1]), int(sbs_profile.shape[1]),
) )
global_sbs_gpu_setting[sbs_gpu_setting_position] = sbs_profile.clone() global_sbs_gpu_setting[sbs_gpu_setting_position] = sbs_profile.clone()
sbs_size[0] = int(output.shape[0]) sbs_size[0] = int(output_work.shape[0])
sbs_size[1] = int(output.shape[1]) sbs_size[1] = int(output_work.shape[1])
sbs_size[2] = int(output.shape[2]) sbs_size[2] = int(output_work.shape[2])
sbs_size[3] = int(output.shape[3]) sbs_size[3] = int(output_work.shape[3])
global_sbs_size[sbs_gpu_setting_position] = sbs_size.clone() global_sbs_size[sbs_gpu_setting_position] = sbs_size.clone()
else: else:
@ -232,32 +283,41 @@ class FunctionalSbS(torch.autograd.Function):
) )
global_sbs_hdynamic_cpp[sbs_hdynamic_cpp_position].update( global_sbs_hdynamic_cpp[sbs_hdynamic_cpp_position].update(
output.data_ptr(), output_work.data_ptr(),
int(output.shape[0]), int(output_work.shape[0]),
int(output.shape[1]), int(output_work.shape[1]),
int(output.shape[2]), int(output_work.shape[2]),
int(output.shape[3]), int(output_work.shape[3]),
epsilon_xy.data_ptr() if epsilon_xy is not None else int(0), epsilon_xy_work.data_ptr() if epsilon_xy_work is not None else int(0),
int(epsilon_xy.shape[0]) if epsilon_xy is not None else int(0), int(epsilon_xy_work.shape[0]) if epsilon_xy_work is not None else int(0),
int(epsilon_xy.shape[1]) if epsilon_xy is not None else int(0), int(epsilon_xy_work.shape[1]) if epsilon_xy_work is not None else int(0),
int(epsilon_xy.shape[2]) if epsilon_xy is not None else int(0), int(epsilon_xy_work.shape[2]) if epsilon_xy_work is not None else int(0),
epsilon_t_0.data_ptr(), epsilon_t_0_work.data_ptr(),
int(epsilon_t_0.shape[0]), int(epsilon_t_0_work.shape[0]),
weights.data_ptr(), weights_work.data_ptr(),
int(weights.shape[0]), int(weights_work.shape[0]),
int(weights.shape[1]), int(weights_work.shape[1]),
spikes.data_ptr(), spikes_work.data_ptr(),
int(spikes.shape[0]), int(spikes_work.shape[0]),
int(spikes.shape[1]), int(spikes_work.shape[1]),
int(spikes.shape[2]), int(spikes_work.shape[2]),
int(spikes.shape[3]), int(spikes_work.shape[3]),
h_initial.data_ptr(), h_initial_work.data_ptr(),
int(h_initial.shape[0]), int(h_initial_work.shape[0]),
hdyn_number_of_cpu_processes, hdyn_number_of_cpu_processes,
float(forgetting_offset.cpu().item()), float(forgetting_offset.cpu().item()),
int(gpu_tuning_factor), int(gpu_tuning_factor),
) )
if data_is_on_the_same_device is False:
output = output_work.to(target_device)
else:
output = output_work
# print(output)
# print(output.sum(dim=1))
# print(output.sum(dim=1).shape)
# exit()
# ########################################################### # ###########################################################
# Save the necessary data for the backward pass # Save the necessary data for the backward pass
# ########################################################### # ###########################################################

3
network/Parameter.py
View file

@ -142,6 +142,9 @@ class Config:
epsilon_0: float = field(default=1.0) epsilon_0: float = field(default=1.0)
forgetting_offset: float = field(default=-1.0) forgetting_offset: float = field(default=-1.0)
force_forward_h_dynamic_on_cpu: bool = field(default=True)
spike_full_layer_input_distribution: list[bool] = field(default_factory=list)
def __post_init__(self) -> None: def __post_init__(self) -> None:
"""Post init determines the number of cores. """Post init determines the number of cores.
Creates the required directory and gives us an optimized Creates the required directory and gives us an optimized

23
network/SbSLayer.py
View file

@ -52,7 +52,9 @@ class SbSLayer(torch.nn.Module):
_reduction_cooldown: float = 1.0 _reduction_cooldown: float = 1.0
_layer_id: int = -1 _layer_id: int = -1
spike_full_layer_input_distribution: bool = False _spike_full_layer_input_distribution: bool
_force_forward_h_dynamic_on_cpu: bool
def __init__( def __init__(
self, self,
@ -81,6 +83,10 @@ class SbSLayer(torch.nn.Module):
layer_id: int = -1, layer_id: int = -1,
cooldown_after_number_of_spikes: int = -1, cooldown_after_number_of_spikes: int = -1,
reduction_cooldown: float = 1.0, reduction_cooldown: float = 1.0,
force_forward_h_dynamic_on_cpu: bool = True,
spike_full_layer_input_distribution: bool = False,
force_forward_spike_on_cpu: bool = False,
force_forward_spike_output_on_cpu: bool = False,
) -> None: ) -> None:
super().__init__() super().__init__()
@ -109,6 +115,8 @@ class SbSLayer(torch.nn.Module):
self.reduction_cooldown = float(reduction_cooldown) self.reduction_cooldown = float(reduction_cooldown)
self._layer_id = layer_id self._layer_id = layer_id
self._epsilon_xy_use = epsilon_xy_use self._epsilon_xy_use = epsilon_xy_use
self._force_forward_h_dynamic_on_cpu = force_forward_h_dynamic_on_cpu
self._spike_full_layer_input_distribution = spike_full_layer_input_distribution
assert len(input_size) == 2 assert len(input_size) == 2
self._input_size = input_size self._input_size = input_size
@ -140,6 +148,8 @@ class SbSLayer(torch.nn.Module):
number_of_spikes=self._number_of_spikes, number_of_spikes=self._number_of_spikes,
number_of_cpu_processes=self._number_of_cpu_processes, number_of_cpu_processes=self._number_of_cpu_processes,
device=self.device, device=self.device,
force_forward_spike_on_cpu=force_forward_spike_on_cpu,
force_forward_spike_output_on_cpu=force_forward_spike_output_on_cpu,
) )
self.h_dynamic = HDynamicLayer( self.h_dynamic = HDynamicLayer(
@ -152,6 +162,7 @@ class SbSLayer(torch.nn.Module):
device=device, device=device,
default_dtype=self.default_dtype, default_dtype=self.default_dtype,
gpu_tuning_factor=gpu_tuning_factor, gpu_tuning_factor=gpu_tuning_factor,
force_forward_h_dynamic_on_cpu=self._force_forward_h_dynamic_on_cpu,
) )
assert len(input_size) >= 2 assert len(input_size) >= 2
@ -169,10 +180,6 @@ class SbSLayer(torch.nn.Module):
number_of_cpu_processes=number_of_cpu_processes, number_of_cpu_processes=number_of_cpu_processes,
) )
# TODO: TEST
if layer_id == 0:
self.spike_full_layer_input_distribution = True
# ############################################################### # ###############################################################
# Initialize the weights # Initialize the weights
# ############################################################### # ###############################################################
@ -438,7 +445,7 @@ class SbSLayer(torch.nn.Module):
else: else:
assert self._epsilon_xy is None assert self._epsilon_xy is None
if self.spike_full_layer_input_distribution is False: if self._spike_full_layer_input_distribution is False:
spike = self.spike_generator(input_convolved, int(self._number_of_spikes)) spike = self.spike_generator(input_convolved, int(self._number_of_spikes))
else: else:
input_shape = input.shape input_shape = input.shape
@ -457,7 +464,9 @@ class SbSLayer(torch.nn.Module):
(input_shape[0], input_shape[1], input_shape[2], input_shape[3]) (input_shape[0], input_shape[1], input_shape[2], input_shape[3])
) )
) )
spike = self.spikes_sorter(spike_unsorted).to(device=input_convolved.device) spike = self.spikes_sorter(spike_unsorted)
if self._force_forward_h_dynamic_on_cpu is False:
spike = spike.to(device=input_convolved.device)
output = self.h_dynamic( output = self.h_dynamic(
input=input_convolved, input=input_convolved,

108
network/SpikeLayer.py
View file

@ -15,12 +15,16 @@ class SpikeLayer(torch.nn.Module):
_number_of_cpu_processes: int _number_of_cpu_processes: int
_number_of_spikes: int _number_of_spikes: int
device: torch.device device: torch.device
_force_forward_spike_on_cpu: bool
_force_forward_spike_output_on_cpu: bool
def __init__( def __init__(
self, self,
number_of_spikes: int = -1, number_of_spikes: int = -1,
number_of_cpu_processes: int = 1, number_of_cpu_processes: int = 1,
device: torch.device | None = None, device: torch.device | None = None,
force_forward_spike_on_cpu: bool = False,
force_forward_spike_output_on_cpu: bool = False,
) -> None: ) -> None:
super().__init__() super().__init__()
@ -29,11 +33,15 @@ class SpikeLayer(torch.nn.Module):
self._number_of_cpu_processes = number_of_cpu_processes self._number_of_cpu_processes = number_of_cpu_processes
self._number_of_spikes = number_of_spikes self._number_of_spikes = number_of_spikes
self._force_forward_spike_on_cpu = force_forward_spike_on_cpu
self._force_forward_spike_output_on_cpu = force_forward_spike_output_on_cpu
global_spike_generation_gpu_setting.append(torch.tensor([0])) global_spike_generation_gpu_setting.append(torch.tensor([0]))
global_spike_size.append(torch.tensor([0, 0, 0, 0])) global_spike_size.append(torch.tensor([0, 0, 0, 0]))
if device == torch.device("cpu"): if (device == torch.device("cpu")) or (
self._force_forward_spike_on_cpu is True
):
global_spike_generation_cpp.append(SpikeGenerationCPU()) global_spike_generation_cpp.append(SpikeGenerationCPU())
else: else:
global_spike_generation_cpp.append(SpikeGenerationGPU()) global_spike_generation_cpp.append(SpikeGenerationGPU())
@ -66,6 +74,7 @@ class SpikeLayer(torch.nn.Module):
int(self._spike_generation_cpp_position), # 1 int(self._spike_generation_cpp_position), # 1
int(self._spike_generation_gpu_setting_position), # 2 int(self._spike_generation_gpu_setting_position), # 2
int(number_of_spikes), # 3 int(number_of_spikes), # 3
int(self._force_forward_spike_output_on_cpu), # 4
], ],
dtype=torch.int64, dtype=torch.int64,
) )
@ -83,14 +92,35 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
assert input.dim() == 4 assert input.dim() == 4
if input.device == torch.device("cpu"):
spike_number_of_cpu_processes: int = int(parameter_list[0])
else:
spike_number_of_cpu_processes = -1
spike_generation_cpp_position = int(parameter_list[1]) spike_generation_cpp_position = int(parameter_list[1])
spike_generation_gpu_setting_position = int(parameter_list[2]) spike_generation_gpu_setting_position = int(parameter_list[2])
number_of_spikes: int = int(parameter_list[3]) number_of_spikes: int = int(parameter_list[3])
force_forward_spike_output_on_cpu: bool = bool(parameter_list[4])
if (
isinstance(
global_spike_generation_cpp[spike_generation_cpp_position],
SpikeGenerationCPU,
)
is True
):
are_we_on_a_cpu: bool = True
work_device: torch.device = torch.device("cpu")
else:
are_we_on_a_cpu = False
work_device = input.device
target_device: torch.device = input.device
if target_device == work_device:
data_is_on_the_same_device: bool = True
else:
data_is_on_the_same_device = False
if are_we_on_a_cpu is True:
spike_number_of_cpu_processes: int = int(parameter_list[0])
else:
spike_number_of_cpu_processes = -1
# ########################################################### # ###########################################################
# Spike generation # Spike generation
@ -100,7 +130,12 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
# Normalized cumsum # Normalized cumsum
# (beware of the pytorch bug! Thus .clone()!) # (beware of the pytorch bug! Thus .clone()!)
# ############################################ # ############################################
input_cumsum: torch.Tensor = torch.cumsum(input, dim=1, dtype=input.dtype) if data_is_on_the_same_device is False:
input_work = input.to(work_device)
else:
input_work = input
# input_work = input
input_cumsum: torch.Tensor = torch.cumsum(input_work, dim=1, dtype=input.dtype)
input_cumsum_last: torch.Tensor = input_cumsum[:, -1, :, :].unsqueeze(1).clone() input_cumsum_last: torch.Tensor = input_cumsum[:, -1, :, :].unsqueeze(1).clone()
input_cumsum /= input_cumsum_last input_cumsum /= input_cumsum_last
@ -115,17 +150,19 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
input_cumsum.shape[3], input_cumsum.shape[3],
], ],
dtype=input.dtype, dtype=input.dtype,
device=input.device, device=work_device,
) )
# ############################################ # ############################################
# Make space for the results # Make space for the results
# ############################################ # ############################################
spikes = torch.empty_like(random_values, dtype=torch.int64, device=input.device) spikes_work = torch.empty_like(
random_values, dtype=torch.int64, device=work_device
)
assert input_cumsum.is_contiguous() is True assert input_cumsum.is_contiguous() is True
assert random_values.is_contiguous() is True assert random_values.is_contiguous() is True
assert spikes.is_contiguous() is True assert spikes_work.is_contiguous() is True
# time_start: float = time.perf_counter() # time_start: float = time.perf_counter()
spike_generation_profile = global_spike_generation_gpu_setting[ spike_generation_profile = global_spike_generation_gpu_setting[
@ -136,19 +173,13 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
spike_generation_gpu_setting_position spike_generation_gpu_setting_position
].clone() ].clone()
if ( if are_we_on_a_cpu is False:
isinstance(
global_spike_generation_cpp[spike_generation_cpp_position],
SpikeGenerationGPU,
)
is True
):
if ( if (
(spike_generation_profile.numel() == 1) (spike_generation_profile.numel() == 1)
or (spike_generation_size[0] != int(spikes.shape[0])) or (spike_generation_size[0] != int(spikes_work.shape[0]))
or (spike_generation_size[1] != int(spikes.shape[1])) or (spike_generation_size[1] != int(spikes_work.shape[1]))
or (spike_generation_size[2] != int(spikes.shape[2])) or (spike_generation_size[2] != int(spikes_work.shape[2]))
or (spike_generation_size[3] != int(spikes.shape[3])) or (spike_generation_size[3] != int(spikes_work.shape[3]))
): ):
spike_generation_profile = torch.zeros( spike_generation_profile = torch.zeros(
@ -157,10 +188,10 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
global_spike_generation_cpp[ global_spike_generation_cpp[
spike_generation_cpp_position spike_generation_cpp_position
].gpu_occupancy_export( ].gpu_occupancy_export(
int(spikes.shape[2]), int(spikes_work.shape[2]),
int(spikes.shape[3]), int(spikes_work.shape[3]),
int(spikes.shape[0]), int(spikes_work.shape[0]),
int(spikes.shape[1]), int(spikes_work.shape[1]),
spike_generation_profile.data_ptr(), spike_generation_profile.data_ptr(),
int(spike_generation_profile.shape[0]), int(spike_generation_profile.shape[0]),
int(spike_generation_profile.shape[1]), int(spike_generation_profile.shape[1]),
@ -169,10 +200,10 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
spike_generation_gpu_setting_position spike_generation_gpu_setting_position
] = spike_generation_profile.clone() ] = spike_generation_profile.clone()
spike_generation_size[0] = int(spikes.shape[0]) spike_generation_size[0] = int(spikes_work.shape[0])
spike_generation_size[1] = int(spikes.shape[1]) spike_generation_size[1] = int(spikes_work.shape[1])
spike_generation_size[2] = int(spikes.shape[2]) spike_generation_size[2] = int(spikes_work.shape[2])
spike_generation_size[3] = int(spikes.shape[3]) spike_generation_size[3] = int(spikes_work.shape[3])
global_spike_size[ global_spike_size[
spike_generation_gpu_setting_position spike_generation_gpu_setting_position
] = spike_generation_size.clone() ] = spike_generation_size.clone()
@ -197,15 +228,20 @@ class FunctionalSpikeGeneration(torch.autograd.Function):
int(random_values.shape[1]), int(random_values.shape[1]),
int(random_values.shape[2]), int(random_values.shape[2]),
int(random_values.shape[3]), int(random_values.shape[3]),
spikes.data_ptr(), spikes_work.data_ptr(),
int(spikes.shape[0]), int(spikes_work.shape[0]),
int(spikes.shape[1]), int(spikes_work.shape[1]),
int(spikes.shape[2]), int(spikes_work.shape[2]),
int(spikes.shape[3]), int(spikes_work.shape[3]),
int(spike_number_of_cpu_processes), int(spike_number_of_cpu_processes),
) )
del random_values
del input_cumsum if (force_forward_spike_output_on_cpu is True) and (are_we_on_a_cpu is True):
spikes = spikes_work
elif data_is_on_the_same_device is False:
spikes = spikes_work.to(target_device)
else:
spikes = spikes_work
return spikes return spikes

11
network/build_network.py
View file

@ -120,6 +120,12 @@ def build_network(
cfg.learning_parameters.sbs_skip_gradient_calculation[0] cfg.learning_parameters.sbs_skip_gradient_calculation[0]
) )
spike_full_layer_input_distribution: bool = False
if len(cfg.spike_full_layer_input_distribution) > layer_id:
spike_full_layer_input_distribution = (
cfg.spike_full_layer_input_distribution[layer_id]
)
# ############################################################# # #############################################################
# SbS layer: # SbS layer:
# ############################################################# # #############################################################
@ -138,7 +144,10 @@ def build_network(
assert number_of_spikes > 0 assert number_of_spikes > 0
logging.info( logging.info(
(
f"Layer: {layer_id} -> SbS Layer with {number_of_spikes} spikes " f"Layer: {layer_id} -> SbS Layer with {number_of_spikes} spikes "
f"-- draw spike from full layer: {spike_full_layer_input_distribution}"
)
) )
is_pooling_layer: bool = False is_pooling_layer: bool = False
if cfg.network_structure.layer_type[layer_id].upper().find("POOLING") != -1: if cfg.network_structure.layer_type[layer_id].upper().find("POOLING") != -1:
@ -169,6 +178,8 @@ def build_network(
layer_id=layer_id, layer_id=layer_id,
cooldown_after_number_of_spikes=cfg.cooldown_after_number_of_spikes, cooldown_after_number_of_spikes=cfg.cooldown_after_number_of_spikes,
reduction_cooldown=cfg.reduction_cooldown, reduction_cooldown=cfg.reduction_cooldown,
force_forward_h_dynamic_on_cpu=cfg.force_forward_h_dynamic_on_cpu,
spike_full_layer_input_distribution=spike_full_layer_input_distribution,
) )
) )
# Adding the x,y output dimensions # Adding the x,y output dimensions