I am trying to run inference on my model weight modified tensors from the result of training with STDP, however, I encounter this error message even after placing my model on CUDA and if I run on 'cpu', I get NotImplementedError.
bindsnet.pipeline.environment_pipeline, bindsnet.pipeline.dataloader_pipeline, bindsnet.pipeline.action, bindsnet.pipeline, bindsnet.evaluation.evaluation, bindsnet.evaluation, bindsnet.conversion.nodes, bindsnet.conversion.topology, bindsnet.conversion.conversion, bindsnet.conversion, bindsnet, self_models.modified_bindsnet, self_models
Run on time: 2022-09-19 10:13:57.515296
Arguments:
gpu : True
seed : 0
dataset : MNIST
batch_size : 1
n_epochs : 2
n_test : None
update_interval : 500
n_neurons : 900
exc : 22.5
inh : 22.5
theta_plus : 0.05
pattern_time : 250
network : DiehlAndCook2015
dt : 1.0
n_workers : -1
pretrained_snn :
test_acc_every_batch : False
train_acc_batches : 200
devices : 0
test_only : False
pretrained_ann :
lr_interval : 0.00 0.00 0.00
log : False
n_inpt : 784
norm : 78.4
tc_theta_decay : 10000000.0
inpt_shape : None
nu : (0, 0.01)
wmax : 1.0
wmin : 0.0
Traceback (most recent call last):
File Z:\workspace\stdp-nmnist-main\snn_2.py:424 in <module>
model = DiehlAndCook2015(n_inpt = 784, n_neurons = 900, exc = 22.5, inh = 22.5, dt = 1.0, norm = 78.4,
File Z:\workspace\stdp-nmnist-main\bindsnet\models\models.py:198 in __init__
self.add_layer(exc_layer, name="Ae")
File Z:\workspace\stdp-nmnist-main\bindsnet\network\network.py:130 in add_layer
layer.set_batch_size(self.batch_size)
File Z:\workspace\stdp-nmnist-main\bindsnet\network\nodes.py:1144 in set_batch_size
self.v = self.rest * torch.ones(batch_size, *self.shape, device=self.v.device)
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu
